pCO2 product synopsis 2023

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Last updated: 2024-09-20

Checks: 7 0

Knit directory: heatwave_co2_flux_2023/analysis/

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---

center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)

worldmap <- ne_countries(scale = 'small',
                         type = 'map_units',
                         returnclass = 'sf')

worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans)

coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans)


bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)

bbox_graticules <- st_graticule(
  x = bbox_trans,
  crs = st_crs(bbox_trans),
  datum = st_crs(bbox_trans),
  lon = c(20, 20.001),
  lat = c(-78,65),
  ndiscr = 1e3,
  margin = 0.001
)

bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans)

lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans, linewidth = 1) +
#   coord_sf(crs = target_crs,
#            ylim = lat_lim,
#            xlim = lon_lim,
#            expand = FALSE) +
#   theme(
#     panel.border = element_blank(),
#     axis.text = element_blank(),
#     axis.ticks = element_blank()
#   )

latitude_graticules <- st_graticule(
  x = bbox_graticules,
  crs = st_crs(bbox_graticules),
  datum = st_crs(bbox_graticules),
  lon = c(20, 20.001),
  lat = c(-60,-30,0,30,60),
  ndiscr = 1e3,
  margin = 0.001
)

latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)

latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
                 lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))

latitude_labels <- st_as_sf(x = latitude_labels,
               coords = c("lon", "lat"),
               crs = "+proj=longlat")

latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans) +
#   geom_sf(data = latitude_graticules_trans,
#           col = "grey60",
#           linewidth = 0.2) +
#   geom_sf_text(data = latitude_labels_trans,
#                aes(label = lat_label),
#                size = 3,
#                col = "grey60")

pCO2_product_synopsis <-
  knitr::knit_expand(
    file = here::here("analysis/child/pCO2_product_synopsis.Rmd"),
    year_anom = 2023
  )

Read data

map <-
  read_rds(here::here("data/map.rds"))

key_biomes <-
  read_rds(here::here("data/key_biomes.rds"))

key_biomes <- 
key_biomes[!str_detect(key_biomes, "NP")]


biome_mask <-
  read_rds(here::here("data/biome_mask.rds"))

biome_mask_print <-
  biome_mask %>%
  filter(!str_detect(biome, "SO-SPSS|SO-ICE|Arctic")) %>%
  select(lon, lat)

region_biomes <-
  read_rds(here::here("data/region_biomes.rds"))

nino_sst <- read_table(here::here("data/nino34sst.txt"))

nino_sst <-
  nino_sst %>%
  select(year = YR,
         month = MON,
         resid = ANOM_3)

name_core <- c("fgco2", "fgco2_int", "fgco2_hov",
               # "sfco2", "atm_fco2", 
               "dfco2",
               # "kw_sol", 
               "temperature", 
               # "salinity",
               # "dissic", "talk", "sdissic", "stalk", "cstar", 
               "sdissic_stalk",
               "no3", "o2",
               "mld", "thetao", 
               # "so",
               "intpp", "chl",
               "sfco2_therm","sfco2_nontherm","sfco2_total",
               "resid_fgco2_dfco2", "resid_fgco2_kw_sol", "resid_fgco2_dfco2_kw_sol")


all_product_list <- c("OceanSODAv2",
                      "SOM-FFN",
                      "fCO2-Residual",
                      "CMEMS",
                      "ETHZ-CESM",
                      "FESOM-REcoM")

gobm_product_list <- c("ETHZ-CESM",
                       "FESOM-REcoM")


pco2_product_list <- c("OceanSODAv2",
                      "SOM-FFN",
                      "fCO2-Residual",
                      "CMEMS"
                      )

color_products <- c(
  "OceanSODAv2" = "#672933",
  "SOM-FFN" = "#d1495b",
  "fCO2-Residual" = "#edae49",
  "CMEMS" = "#AD8E55",
  "ETHZ-CESM" = "#66a182",
  "FESOM-REcoM" = "#00798c"
)

warm_color <- "#c33c57"
cold_color <- "#3f6fb3"
trend_color <- "#66a182"


warm_cool_gradient <- 
rev(c(
  "#61195a",
  "#6f185f",
  "#8d1e62",
  "#aa2960",
  "#c33c57",
  "#da5351",
  "#e77155",
  "#f09264",
  "#f09264",
  "#fbd297",
  "#fefefe",
  "#c6e8ea",
  "#97d4db",
  "#79bcd0",
  "#5ca2c6",
  "#4a88bc",
  "#3f6fb3",
  "#3e56a2",
  "#3c3f82",
  "#2f2c5a",
  "#272648"
))

# cmocean("balance")(100)

files <- list.files(here::here("data/"),
                    pattern = "FESOM-REcoM")

file_types <- str_remove(files, paste0("FESOM-REcoM_",2023,"_"))
file_types <- str_remove(file_types, ".csv")

for(i_file_type in file_types) {
  
  # print(i_file_type)
  # i_file_type <- file_types[1]
  
  files <- list.files(here::here("data/"),
                      pattern = paste(2023, i_file_type, sep = "_"),
                      full.names = TRUE)
  

  pco2_product <-
    read_csv(files, id = "product")
  
  pco2_product <-
    pco2_product %>%
    mutate(
      product = str_extract(
        product,
        "OceanSODAv2|SOM-FFN|CMEMS|fCO2-Residual|ETHZ-CESM|FESOM-REcoM"
      )
    )
  
  if (!str_detect(files[1], "slope")) {
    pco2_product <-
      pco2_product %>%
      mutate(
        name = factor(name, levels = name_core),
        product = factor(product, levels = all_product_list)
      ) %>%
      filter(!is.na(name))
  } else {
    pco2_product <-
      pco2_product %>%
      mutate(product = factor(product, levels = all_product_list))
  }
  
  assign(paste("pco2_product", i_file_type, sep = "_"), pco2_product)

}

Define labels and breaks

labels_breaks <- function(i_name) {
  if (i_name == "dco2") {
    i_legend_title <- "ΔpCO<sub>2</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "dfco2") {
    i_legend_title <- "ΔfCO<sub>2</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-12, 12, 3), Inf)
  }
  
  if (i_name == "atm_co2") {
    i_legend_title <- "pCO<sub>2,atm</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "atm_fco2") {
    i_legend_title <- "fCO<sub>2,atm</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-2, 2, 0.5), Inf)
  }
  
  if (i_name == "sol") {
    i_legend_title <- "K<sub>0</sub> anom.<br>(mol m<sup>-3</sup> µatm<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "kw") {
    i_legend_title <- "k<sub>w</sub> anom.<br>(m yr<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "kw_sol") {
    i_legend_title <- "k<sub>w</sub> K<sub>0</sub> anom.<br>(mol yr<sup>-1</sup> m<sup>-2</sup> µatm<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.015, 0.015, 0.003), Inf)
  }
  
  if (i_name == "spco2") {
    i_legend_title <- "pCO<sub>2,ocean</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-12, 12, 3), Inf)
  }
  
  if (i_name == "sfco2") {
    i_legend_title <- "fCO<sub>2,ocean</sub> anom.<br>(µatm)"
    i_breaks <- c(-Inf, seq(-12, 12, 3), Inf)
  }
  
  if (i_name == "intpp") {
    i_legend_title <- "NPP<sub>int</sub> anom.<br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-3, 3, 0.5), Inf)
  }
  
  if (i_name == "no3") {
    i_legend_title <- "NO<sub>3</sub> anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-1.5, 1.5, 0.3), Inf)
  }
  
  if (i_name == "o2") {
    i_legend_title <- "O<sub>2</sub> anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "dissic") {
    i_legend_title <- "DIC anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-15, 15, 3), Inf)
  }
  
  if (i_name == "sdissic") {
    i_legend_title <- "sDIC anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-15, 15, 3), Inf)
  }
  
  if (i_name == "cstar") {
    i_legend_title <- "C* anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "talk") {
    i_legend_title <- "TA anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-15, 15, 3), Inf)
  }
  
  if (i_name == "stalk") {
    i_legend_title <- "sTA anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-15, 15, 3), Inf)
  }
  
  if (i_name == "sdissic_stalk") {
    i_legend_title <- "sDIC - sTA anom.<br>(μmol kg<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-15, 15, 3), Inf)
  }
  
  if (i_name == "sfco2_total") {
    i_legend_title <- "total"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "sfco2_therm") {
    i_legend_title <- "thermal"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "sfco2_nontherm") {
    i_legend_title <- "non-thermal"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "fgco2") {
    i_legend_title <- "FCO<sub>2</sub> anom.<br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "fgco2_hov") {
    i_legend_title <- "FCO<sub>2</sub> anom.<br>(PgC deg<sup>-1</sup> yr<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "fgco2_int") {
    i_legend_title <- "FCO<sub>2</sub> anom.<br>(PgC yr<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "thetao") {
    i_legend_title <- "Temp. anom.<br>(°C)"
    i_breaks <- c(-Inf, seq(-1.6, 1.6, 0.4), Inf)
  }
  
  if (i_name == "temperature") {
    i_legend_title <- "SST anom.<br>(°C)"
    i_breaks <- c(-Inf, seq(-1.6, 1.6, 0.4), Inf)
  }
  
  if (i_name == "salinity") {
    i_legend_title <- "SSS anom."
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "so") {
    i_legend_title <- "Salinity anom."
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "chl") {
    i_legend_title <- "lg(Chl-a) anom.<br>(lg(mg m<sup>-3</sup>))"
    i_breaks <- c(-Inf, seq(-0.2, 0.2, 0.05), Inf)
  }
  
  if (i_name == "mld") {
    i_legend_title <- "MLD anom.<br>(m)"
    i_breaks <- c(-Inf, seq(-40, 40, 10), Inf)
  }
  
  if (i_name == "press") {
    i_legend_title <- "pressure<sub>atm</sub> anom.<br>(Pa)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "wind") {
    i_legend_title <- "Wind anom.<br>(m sec<sup>-1</sup>)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "SSH") {
    i_legend_title <- "SSH anom.<br>(m)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "fice") {
    i_legend_title <- "Sea ice anom.<br>(%)"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  
  if (i_name == "resid_fgco2") {
    i_legend_title <-
      "Observed"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "resid_fgco2_dfco2") {
    i_legend_title <-
      "ΔfCO<sub>2</sub> contr."
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "resid_fgco2_kw_sol") {
    i_legend_title <-
      "k<sub>w</sub> K<sub>0</sub> contr."
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "resid_fgco2_dfco2_kw_sol") {
    i_legend_title <-
      "ΔfCO<sub>2</sub> ⨯ k<sub>w</sub> K<sub>0</sub> contr."
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "resid_fgco2_sum") {
    i_legend_title <-
      "∑"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  if (i_name == "resid_fgco2_offset") {
    i_legend_title <-
      "Obs. - ∑"
    i_breaks <- c(-Inf, seq(-0.5, 0.5, 0.1), Inf)
  }
  
  all_labels_breaks <- lst(i_legend_title, i_breaks)
  
  return(all_labels_breaks)
  
}

x_axis_labels <-
  c(
    "dco2" = labels_breaks("dco2")$i_legend_title,
    "dfco2" = labels_breaks("dfco2")$i_legend_title,
    "atm_co2" = labels_breaks("atm_co2")$i_legend_title,
    "atm_fco2" = labels_breaks("atm_fco2")$i_legend_title,
    "sol" = labels_breaks("sol")$i_legend_title,
    "kw" = labels_breaks("kw")$i_legend_title,
    "kw_sol" = labels_breaks("kw_sol")$i_legend_title,
    "intpp" = labels_breaks("intpp")$i_legend_title,
    "no3" = labels_breaks("no3")$i_legend_title,
    "o2" = labels_breaks("o2")$i_legend_title,
    "dissic" = labels_breaks("dissic")$i_legend_title,
    "sdissic" = labels_breaks("sdissic")$i_legend_title,
    "cstar" = labels_breaks("cstar")$i_legend_title,
    "talk" = labels_breaks("talk")$i_legend_title,
    "stalk" = labels_breaks("stalk")$i_legend_title,
    "sdissic_stalk" = labels_breaks("sdissic_stalk")$i_legend_title,
    "spco2" = labels_breaks("spco2")$i_legend_title,
    "sfco2" = labels_breaks("sfco2")$i_legend_title,
    "sfco2_total" = labels_breaks("sfco2_total")$i_legend_title,
    "sfco2_therm" = labels_breaks("sfco2_therm")$i_legend_title,
    "sfco2_nontherm" = labels_breaks("sfco2_nontherm")$i_legend_title,
    "fgco2" = labels_breaks("fgco2")$i_legend_title,
    "fgco2_hov" = labels_breaks("fgco2_hov")$i_legend_title,
    "fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
    "thetao" = labels_breaks("thetao")$i_legend_title,
    "temperature" = labels_breaks("temperature")$i_legend_title,
    "salinity" = labels_breaks("salinity")$i_legend_title,
    "so" = labels_breaks("so")$i_legend_title,
    "chl" = labels_breaks("chl")$i_legend_title,
    "mld" = labels_breaks("mld")$i_legend_title,
    "press" = labels_breaks("press")$i_legend_title,
    "wind" = labels_breaks("wind")$i_legend_title,
    "SSH" = labels_breaks("SSH")$i_legend_title,
    "fice" = labels_breaks("fice")$i_legend_title,
    "resid_fgco2" = labels_breaks("resid_fgco2")$i_legend_title,
    "resid_fgco2_dfco2" = labels_breaks("resid_fgco2_dfco2")$i_legend_title,
    "resid_fgco2_kw_sol" = labels_breaks("resid_fgco2_kw_sol")$i_legend_title,
    "resid_fgco2_dfco2_kw_sol" = labels_breaks("resid_fgco2_dfco2_kw_sol")$i_legend_title,
    "resid_fgco2_sum" = labels_breaks("resid_fgco2_sum")$i_legend_title,
    "resid_fgco2_offset" = labels_breaks("resid_fgco2_offset")$i_legend_title
  )

# create axis labels for absolute values by removing anom.
x_axis_labels_abs <- x_axis_labels
x_axis_labels_abs <- str_replace_all(x_axis_labels_abs, " anom.", "") 
names(x_axis_labels_abs) <- names(x_axis_labels)

Functions

Seasonality plots

p_season <- function(df, 
                     dim_row = "name", 
                     dim_col = "product", 
                     title = NULL, 
                     var = "resid",
                     scales = "free_y") {
  
  p <- ggplot(data = df,
              aes(month, !!ensym(var)))
  
  if(var == "resid"){
      p <- p +
        geom_hline(yintercept = 0, linewidth =0.5)
    
  }
  
  
  
  p <- p +
      geom_path(data = . %>% filter(year != 2023),
                aes(group = as.factor(year),
                    col = as.factor(paste(min(year), max(year), sep = "-"))), 
                alpha = 0.5)+
      geom_path(data = . %>% 
                  filter(year != 2023) %>% 
                  group_by_at(vars(month, dim_col, dim_row)) %>% 
                  summarise(!!ensym(var) := mean(!!ensym(var))),
                aes(col = "Climatological\nmean"), 
                linewidth = 0.7) +
    scale_color_manual(values = c("grey60", "grey10"),
                       guide = guide_legend(order = 2,
                                            reverse = TRUE)) +
    new_scale_color()+
    geom_path(data = . %>% filter(year == 2023),
                aes(col = as.factor(year)),
                linewidth = 1.2) +
      scale_color_manual(
        values = warm_color,
        guide = guide_legend(order = 1)
      ) +
      scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
      labs(title = title,
           x = "Month")
  
    if(df %>% filter(name == "fgco2") %>% nrow() > 0 & "value" %in% names(df)){
    
    df_sink <- df %>% 
      filter(year == 2023,
             name == "fgco2")
    
      p <- p +
          geom_point(data = df_sink %>% filter(value < 0),
             aes(shape = "Sink"), fill = "white") +
          geom_point(data = df_sink %>% filter(value >= 0),
             aes(shape = "Source"), fill = "white") +
        scale_shape_manual(values = c(25,24))
    
  }
  
  
  if (!(is.null(dim_col))) {
    p <- p +
      facet_grid2(
        as.formula(paste(dim_row, "~", dim_col)),
        scales = scales,
        # independent = "y",
        labeller = labeller(name = x_axis_labels),
        switch = "y"
      )
    
    
  } else {
    p <- p +
      facet_grid(
        as.formula(paste(dim_row, "~ .")),
        scales = scales,
        # independent = "y",
        labeller = labeller(name = x_axis_labels),
        switch = "y"
      )
  }
  
  p <- p +
    theme(
      strip.text.y.left = element_markdown(),
      strip.placement = "outside",
      strip.background.y = element_blank(),
      axis.title.y = element_blank(),
      legend.title = element_blank(),
      axis.text.y.right = element_blank()
    ) 
    # scale_y_continuous(sec.axis = dup_axis())
  
  p
  
}

fCO2 decomposition

fco2_decomposition <- function(df, ...) {
  
  group_by <- quos(...)
  # group_by <- quos(lon, lat, month)
  # group_by <- quos(biome, year, month)
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    df %>%
    filter(name %in% c("temperature", "sfco2"))
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    inner_join(
      pco2_product_biome_monthly_fCO2_decomposition %>%
        filter(name == "temperature") %>%
        select(-c(value, fit)) %>%
        pivot_wider(values_from = resid),
      pco2_product_biome_monthly_fCO2_decomposition %>%
        filter(name == "sfco2") %>%
        select(-c(value, resid)) %>%
        pivot_wider(values_from = fit)
    )
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    pco2_product_biome_monthly_fCO2_decomposition %>%
    mutate(sfco2_therm = (sfco2 * exp(0.0423 * temperature)) - sfco2)
  
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    inner_join(
      pco2_product_biome_monthly_fCO2_decomposition,
      df %>%
        filter(name %in% c("sfco2")) %>%
        select(-c(value, fit, name)) %>%
        rename(sfco2_total = resid)
    )
  
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    pco2_product_biome_monthly_fCO2_decomposition %>%
    mutate(sfco2_nontherm = sfco2_total - sfco2_therm)
  
  pco2_product_biome_monthly_fCO2_decomposition <-
    pco2_product_biome_monthly_fCO2_decomposition %>%
    select(-c(temperature, sfco2)) %>%
    pivot_longer(starts_with("sfco2"),
                 values_to = "resid")
  
}

Flux attribution

flux_attribution <- function(df, ...) {
  
  group_by <- quos(...)
  # group_by <- quos(lon, lat, month)
  
  pco2_product_flux_attribution <-
    df %>%
    filter(name %in% c("dfco2", "kw_sol", "fgco2"))
  
  
  pco2_product_flux_attribution <-
    inner_join(
      pco2_product_flux_attribution %>%
        select(-c(value, fit)) %>%
        pivot_wider(values_from = resid,
                    names_prefix = "resid_"),
      pco2_product_flux_attribution %>%
        select(-c(value, resid)) %>%
        filter(name != "fgco2") %>%
        pivot_wider(values_from = fit)
    )
  
    pco2_product_flux_attribution <-
    pco2_product_flux_attribution %>%
    mutate(
      resid_fgco2_dfco2 = resid_dfco2 * kw_sol,
      resid_fgco2_kw_sol = resid_kw_sol * dfco2,
      resid_fgco2_dfco2_kw_sol = resid_dfco2 * resid_kw_sol
      # resid_fgco2_sum = resid_fgco2_dfco2 + resid_fgco2_kw_sol + resid_fgco2_dfco2_kw_sol
    )
  
  # pco2_product_flux_attribution <-
  #   pco2_product_flux_attribution %>%
  #   mutate(resid_fgco2_offset = resid_fgco2 - resid_fgco2_sum)
  
  pco2_product_flux_attribution <-
    pco2_product_flux_attribution %>%
    select(product, !!!group_by, starts_with("resid_fgco2")) %>%
    pivot_longer(starts_with("resid_"),
                 values_to = "resid")
  
  
  pco2_product_flux_attribution <-
    pco2_product_flux_attribution %>%
    filter(str_detect(name, "dfco2|kw_sol")) %>% 
    mutate(name = factor(
      name,
      levels = c(
        "resid_fgco2",
        "resid_fgco2_dfco2",
        "resid_fgco2_kw_sol",
        "resid_fgco2_dfco2_kw_sol",
        "resid_fgco2_sum",
        "resid_fgco2_offset"
      )
    ))
  
}

Robinson map

bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 76, ymin = -54), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)

bbox_graticules <- st_graticule(
  x = bbox_trans,
  crs = st_crs(bbox_trans),
  datum = st_crs(bbox_trans),
  lon = c(20, 20.001),
  lat = c(-54,76),
  ndiscr = 1e3,
  margin = 0.001
)

bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(bbox, bbox_trans)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans)

lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005


p_map_mdim_robinson <-
  function(df,
           df_uncertainty = NULL,
           dim_row = NULL,
           dim_col = NULL,
           dim_wrap = NULL,
           n_col = NULL,
           var,
           legend_title = NULL,
           breaks = NULL,
           n_labels = 2,
           target_crs = "+proj=robin +over +lon_0=-160",
           col = "divergent",
           col_scale = "warm_cold",
           plot_latitudes = FALSE,
           legend_position = "top") {
    
    if (is.null(dim_col) & is.null(dim_row) & is.null(dim_wrap)) {
      df_raster <- df %>%
        select(lon, lat, all_of(var)) %>% 
        rast(crs = "+proj=longlat")
      
      df_raster <-
        project(df_raster, target_crs)
      
      df_tibble <-
        df_raster %>%
        as.data.frame(xy = TRUE, na.rm = FALSE) %>%
        as_tibble() %>%
        rename(lon = x, lat = y) %>%
        drop_na()
      
      
    } else {
      
      # if (!is.null(dim_col) & !is.null(dim_row) & !is.null(dim_wrap)) {
      #   names_sep <- ";"
      # } else {
      #   names_sep <- NULL
      # }
      
      names_sep <- ";"

      df_raster <- df %>%
        select(lon, lat,
               all_of(c(dim_row, dim_col, dim_wrap)),
               all_of(var)) %>%
        pivot_wider(names_from = all_of(c(dim_row, dim_col, dim_wrap)), 
                    values_from = all_of(var),
                    names_sep = names_sep) %>%
        rast(crs = "+proj=longlat")
      
      
      df_raster <-
        project(df_raster, target_crs)

           
            
      if (length(c(dim_row, dim_col, dim_wrap)) <= 1) {
        names_sep <- NULL
      }

      df_tibble <-
        df_raster %>%
        as.data.frame(xy = TRUE, na.rm = FALSE) %>%
        as_tibble() %>%
        rename(lon = x, lat = y) %>%
        pivot_longer(
          -c(lon, lat),
          names_sep = names_sep,
          names_to = c(dim_row, dim_col, dim_wrap),
          values_to = var
        ) %>%
        drop_na()
      
      
    }
    
    
    if (is.null(legend_title)) {
      legend_title <- var
    }
    
    var <- sym(var)
    
    p_map <- ggplot() +
      geom_raster(data = df_tibble, aes(
        x = lon,
        y = lat,
        fill = cut(!!var, breaks, include.lowest = TRUE)
      ))
    
    
    p_map <- p_map +
      geom_sf(data = worldmap_trans %>% select(-name),
              fill = "grey90",
              col = "grey90") +
      geom_sf(data = coastline_trans, linewidth = 0.3) +
      geom_sf(data = bbox_graticules_trans, linewidth = 0.5)
    
    if (plot_latitudes) {
      p_map <- p_map +
        geom_sf(data = latitude_graticules_trans,
                col = "grey60",
                linewidth = 0.2) +
        geom_sf_text(
          data = latitude_labels_trans,
          aes(label = lat_label),
          size = 3,
          col = "grey60"
        )
    }
    
    if (!is.null(df_uncertainty)) {
      p_map <- p_map +
        geom_sf(
          data = df_uncertainty %>% filter(signif_single == 0),
          col = "grey60",
          size = 0.05
        )
    }
    
    p_map <- p_map +
      coord_sf(
        crs = target_crs,
        ylim = lat_lim,
        xlim = lon_lim,
        expand = FALSE
      )
    
    if (legend_position == "top") {
      p_map <- p_map +
        guides(
          fill = guide_colorsteps(
            barheight = unit(0.3, "cm"),
            barwidth = unit(8, "cm"),
            ticks = TRUE,
            ticks.colour = "grey20",
            frame.colour = "grey20",
            label.position = "top",
            direction = "horizontal"
          )
        ) +
        theme_void() +
        theme(
          legend.margin=margin(t = .1, b = .1, unit='cm'),
          plot.margin = margin(.1,.1,.1,.1,"cm"),
          panel.spacing = unit(.1,"cm"),
          legend.position = "top",
          legend.title.align = 1,
          legend.box.spacing = unit(0.1, "cm"),
          legend.title = element_markdown(halign = 1, lineheight = 1.5)
        )
    }
    
    if (legend_position == "bottom") {
      p_map <- p_map +
        guides(
          fill = guide_colorsteps(
            barheight = unit(0.3, "cm"),
            barwidth = unit(8, "cm"),
            ticks = TRUE,
            ticks.colour = "grey20",
            frame.colour = "grey20",
            label.position = "bottom",
            direction = "horizontal"
          )
        ) +
        theme_void() +
        theme(
          legend.margin=margin(t = .1, b = .1, unit='cm'),
          plot.margin = margin(.1,.1,.1,.1,"cm"),
          panel.spacing = unit(.1,"cm"),
          legend.position = "bottom",
          legend.title.align = 1,
          legend.box.spacing = unit(0.1, "cm"),
          legend.title = element_markdown(halign = 1, lineheight = 1.5)
        )
    }
    
    if (legend_position == "right") {
      p_map <- p_map +
        guides(
          fill = guide_colorsteps(
            barheight = unit(6, "cm"),
            barwidth = unit(0.3, "cm"),
            ticks = TRUE,
            ticks.colour = "grey20",
            frame.colour = "grey20",
            label.position = "right",
            direction = "vertical"
          )
        ) +
        theme_void() +
        theme(
          legend.position = "right",
          legend.title.align = 0,
          legend.box.spacing = unit(0.1, "cm"),
          legend.title = element_markdown(halign = 0, lineheight = 1.5)
        )
    }
    
    if (legend_position == "left") {
      p_map <- p_map +
        guides(
          fill = guide_colorsteps(
            barheight = unit(6, "cm"),
            barwidth = unit(0.3, "cm"),
            ticks = TRUE,
            ticks.colour = "grey20",
            frame.colour = "grey20",
            label.position = "left",
            direction = "vertical"
          )
        ) +
        theme_void() +
        theme(
          legend.position = "left",
          legend.title.align = 0,
          legend.box.spacing = unit(0.1, "cm"),
          legend.title = element_markdown(halign = 0, lineheight = 1.5)
        )
    }
    
    if (col == "sequential") {
      breaks_test <- breaks[!breaks == Inf]
      breaks_test <- breaks_test[!breaks_test == -Inf]
      breaks_reverse <-
        abs(first(breaks_test)) < abs(last(breaks_test))
      
      if (breaks_reverse == TRUE) {
        direction_value = 1
        reverse_value = TRUE
      } else{
        direction_value = -1
        reverse_value = FALSE
      }
      
      if (n_labels == 1) {
        labels <- breaks_test
      } else {
        breaks_test[seq_along(breaks_test) %% 2 == 0] <- ""
        labels <- breaks_test
      }
      
      if (col_scale %in% c("viridis", "plasma", "cividis")) {
        p_map <- p_map +
          scale_fill_viridis_d(
            drop = FALSE,
            name = legend_title,
            direction = direction_value,
            option = col_scale,
            labels = unname(labels)
          )
      }
      
    } else {
      
      breaks_test <- breaks[!breaks == Inf]
      breaks_test <- breaks_test[!breaks_test == -Inf]
      
      if (n_labels == 1) {
        labels <- breaks_test
      } else {
        breaks_test[seq_along(breaks_test) %% 2 == 0] <- ""
        labels <- breaks_test
      }
      
      p_map <- p_map +
        scale_fill_gradientn(
          colours = warm_cool_gradient,
          # rescaler = ~ scales::rescale_mid(.x, mid = 0),
          super = ScaleDiscretised,
          name = legend_title,
          labels = unname(labels)
        )
        # colorspace::scale_fill_discrete_divergingx(
        #   palette = "RdBu",
        #   drop = FALSE,
        #   rev = TRUE,
        #   name = legend_title,
        #   labels = unname(labels)
        # )
    }
    
    
    
    if (!(is.null(dim_row) & is.null(dim_col))) {
      if (is.null(dim_col)) {
        dim_col <- "."
      }
      
      if (is.null(dim_row)) {
        dim_row <- "."
      }
      
      p_map <- p_map +
        facet_grid(as.formula(paste(dim_row, "~", dim_col)),
                   labeller = labeller(name = x_axis_labels),
                   switch = "y") +
        theme(strip.text.x.top = element_markdown(),
              strip.text.y.left = element_markdown())
      
    }
    
    if (!is.null(dim_wrap) & is.null(n_col)) {

      p_map <- p_map +
        facet_wrap(as.formula(paste("~", dim_wrap)))
    }
    

    if (!(is.null(dim_wrap) & is.null(n_col))) {
      if (dim_wrap == "name") {
        p_map <- p_map +
          facet_wrap(as.formula(paste("~", dim_wrap)),
                     labeller = labeller(name = x_axis_labels),
                     ncol = n_col) +
          theme(strip.text.x.top = element_markdown())
      } else{
        p_map <- p_map +
          facet_wrap(as.formula(paste("~", dim_wrap)), ncol = n_col) +
          theme(strip.text.x.top = element_markdown())
      }
    }
    
    p_map
    
  }

Maps

The following maps show the anomalies of each variable in 2023 as provided through the fCO2 product. Anomalies are determined based on the predicted value of a linear regression model fit to the available data from 1990 to 2022.

Maps are first presented as annual means, and than as monthly means. Note that the 2023 predictions for the monthly maps are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.

Note: The increase the computational speed, I regridded all maps to 5X5° grid.

Annual means

2023 anomaly

pco2_product_map_annual_anomaly <-
  inner_join(
    biome_mask_print,
    pco2_product_map_annual_anomaly
  )

pco2_product_map_annual_anomaly %>%
  filter(year == 2023) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      var = "resid",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks,
      dim_wrap = "product",
      n_col = 2
    )
  )

[[1]]

Past versions of anomaly_year_annual_mean_maps-1.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[2]]

Past versions of anomaly_year_annual_mean_maps-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[3]]

Past versions of anomaly_year_annual_mean_maps-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[4]]

Past versions of anomaly_year_annual_mean_maps-4.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[5]]

Past versions of anomaly_year_annual_mean_maps-5.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[6]]

Past versions of anomaly_year_annual_mean_maps-6.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
60abdac	jens-daniel-mueller	2024-04-23
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[7]]

Past versions of anomaly_year_annual_mean_maps-7.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b754e95	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
97eff6a	jens-daniel-mueller	2024-05-25
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[8]]

Past versions of anomaly_year_annual_mean_maps-8.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
a58162a	jens-daniel-mueller	2024-07-11
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7c448f7	jens-daniel-mueller	2024-05-31
bf01e6c	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b754e95	jens-daniel-mueller	2024-05-28
fbba0a0	jens-daniel-mueller	2024-05-28
e1e0ccb	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
e44a62b	jens-daniel-mueller	2024-04-23
6709afa	jens-daniel-mueller	2024-04-12
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

# plot_list <- 
# pco2_product_map_annual_anomaly %>%
#   filter(year == 2023,
#          product == "ETHZ-CESM",
#          name %in% c(
#            "fgco2",
#            "dfco2",
#            "kw_sol",
#            "temperature",
#            "salinity",
#            "sdissic",
#            "stalk",
#            "no3",
#            "mld",
#            "intpp",
#            "chl"
#          )) %>%
#   group_split(name) %>% 
#   # head(1) %>%
#   map(
#     ~ map +
#       geom_tile(data = .x,
#                 aes(lon, lat, fill = resid)) +
#       scale_fill_gradientn(
#         colours = warm_cool_gradient,
#         rescaler = ~ scales::rescale_mid(.x, mid = 0),
#         name = labels_breaks(.x %>% distinct(name))$i_legend_title,
#         limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
#         oob = squish
#       ) +
#       theme(legend.title = element_markdown(),
#             legend.justification = "left")
#   )


plot_list <- 
pco2_product_map_annual_anomaly %>%
  filter(year == 2023,
         product == "ETHZ-CESM",
         name %in% c(
           "fgco2",
           "dfco2",
           "kw_sol",
           "temperature",
           "salinity",
           "sdissic",
           "stalk",
           "sdissic_stalk",
           "no3",
           "mld",
           "intpp",
           "chl"
         )) %>%
  group_split(name) %>% 
  # head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      var = "resid",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks
    )
  )


ggsave(plot = wrap_plots(plot_list,
                         ncol = 3,
                         byrow = FALSE),
       width = 14,
       height = 11,
       filename = "../output/map_anomaly_ETHZ-CESM.jpg")

plot_list <- 
pco2_product_map_annual_anomaly %>%
  filter(year == 2023,
         product == "FESOM-REcoM",
         name %in% c(
           "fgco2",
           "dfco2",
           "kw_sol",
           "temperature",
           "salinity",
           "sdissic",
           "stalk",
           "sdissic_stalk",
           "no3",
           "mld",
           "intpp",
           "chl"
         )) %>%
  group_split(name) %>% 
  # head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      var = "resid",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks
    )
  )


ggsave(plot = wrap_plots(plot_list,
                         ncol = 3,
                         byrow = FALSE),
       width = 14,
       height = 11,
       filename = "../output/map_anomaly_FESOM-REcoM.jpg")

rm(plot_list)

pco2_product_map_annual_anomaly_ensemble <-
  pco2_product_map_annual_anomaly %>% 
  filter(year == 2023,
         product %in% pco2_product_list) %>%
  fgroup_by(name, lon, lat) %>%
  fsummarise(
    resid_sd = fsd(resid),
    resid_mean = fmean(resid),
    value_sd = fsd(value),
    value_mean = fmean(value),
    n = fnobs(resid)
  ) %>%
  filter(n == length(pco2_product_list)) %>% 
  select(-n)

pco2_product_map_annual_anomaly_ensemble_coarse <-
  m_grid_horizontal_coarse(pco2_product_map_annual_anomaly_ensemble) %>%
  fgroup_by(name, lon_grid, lat_grid) %>%
  fsummarise(
    resid_sd_coarse = fmean(resid_sd, na.rm = TRUE),
    resid_mean_coarse = fmean(resid_mean, na.rm = TRUE),
    value_sd_coarse = fmean(value_sd, na.rm = TRUE),
    value_mean_coarse = fmean(value_mean, na.rm = TRUE)
  ) %>% 
  rename(lon = lon_grid, lat = lat_grid)

pco2_product_map_annual_anomaly_ensemble_uncertainty <-
  pco2_product_map_annual_anomaly_ensemble_coarse %>%
  mutate(signif_single = if_else(abs(resid_mean_coarse) < resid_sd_coarse, 0, 1)) %>% 
  select(lon, lat, name, signif_single) %>% 
  st_as_sf(coords = c("lon", "lat"), crs = "+proj=longlat")


pco2_product_map_annual_anomaly_ensemble %>%
  mutate(product = "Ensemble mean") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      df_uncertainty = pco2_product_map_annual_anomaly_ensemble_uncertainty %>% 
        filter(name == .x %>% distinct(name) %>% pull()),
      var = "resid_mean",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks,
      n_labels = 2
    )
  )

[[1]]

Past versions of anomaly_annual_mean_maps_ensemble_mean-1.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

[[2]]

Past versions of anomaly_annual_mean_maps_ensemble_mean-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

[[3]]

Past versions of anomaly_annual_mean_maps_ensemble_mean-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

[[4]]

Past versions of anomaly_annual_mean_maps_ensemble_mean-4.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

plot_list <- pco2_product_map_annual_anomaly_ensemble %>%
  mutate(product = "Ensemble mean") %>%
  filter(name %in% c("fgco2", "temperature")) %>% 
  group_split(name) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      df_uncertainty = pco2_product_map_annual_anomaly_ensemble_uncertainty %>% 
        filter(name == .x %>% distinct(name) %>% pull()),
      var = "resid_mean",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      legend_position = "bottom",
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks,
      n_labels = 2
    )
  )

ggsave(plot = wrap_plots(plot_list,
                         ncol = 2,
                         byrow = FALSE),
       width = 10,
       height = 3,
       filename = "../output/map_anomaly_ensemble_mean_pco2_products.jpg")


pco2_product_map_annual_anomaly_ensemble_uncertainty <-
  pco2_product_map_annual_anomaly_ensemble_coarse %>%
  mutate(signif_single = if_else(abs(value_mean_coarse) < value_sd_coarse, 0, 1)) %>% 
  select(lon, lat, name, signif_single) %>% 
  st_as_sf(coords = c("lon", "lat"), crs = "+proj=longlat")

pco2_product_map_annual_anomaly_ensemble %>%
  mutate(product = "Ensemble mean") %>%
  filter(name %in% c("fgco2")) %>% 
  group_split(name) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      df_uncertainty = pco2_product_map_annual_anomaly_ensemble_uncertainty %>%
        filter(name == .x %>% distinct(name) %>% pull()),
      var = "value_mean",
      legend_title = str_remove(
        labels_breaks(.x %>% distinct(name))$i_legend_title,
        " anom."),
      breaks = c(-Inf, seq(-4,4,1), Inf),
      n_labels = 2
    )
  )

[[1]]

Past versions of anomaly_annual_mean_maps_ensemble_mean-5.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
6a96e1f	jens-daniel-mueller	2024-08-26
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

ggsave(width = 5,
       height = 3,
       filename = "../output/map_absolute_ensemble_mean_pco2_products.jpg")




rm(pco2_product_map_annual_anomaly_ensemble_uncertainty)

pco2_product_map_annual_anomaly_ensemble_offset <-
left_join(
    pco2_product_map_annual_anomaly_ensemble,
    pco2_product_map_annual_anomaly %>% 
      filter(year == 2023,
             product %in% pco2_product_list)
  ) %>%
  mutate(`Anomaly offset` = resid - resid_mean) %>% 
  select(name, lon, lat, product, `Anomaly offset`)

pco2_product_map_annual_anomaly_ensemble_baseline <-
  pco2_product_map_annual_anomaly %>% 
  filter(year == 2023,
         product %in% pco2_product_list) %>%
  group_by(name, lon, lat) %>%
  summarize(
    fit_mean = mean(fit),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n == length(pco2_product_list)) %>% 
  select(-n)

pco2_product_map_annual_anomaly_ensemble_baseline <-
left_join(
    pco2_product_map_annual_anomaly_ensemble_baseline,
    pco2_product_map_annual_anomaly %>% 
      filter(year == 2023,
             product %in% pco2_product_list)
  ) %>%
  mutate(`Baseline offset` = fit - fit_mean) %>% 
  select(name, lon, lat, product, `Baseline offset`)

full_join(
  pco2_product_map_annual_anomaly_ensemble_offset,
  pco2_product_map_annual_anomaly_ensemble_baseline
) %>%
  pivot_longer(contains("offset"),
               names_to = "offset") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = value)) +
      labs(title =  paste(2023, "offset from ensemble mean")) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name))$i_legend_title,
        limits = c(quantile(.x$value, .01), quantile(.x$value, .99)),
        oob = squish
      ) +
      facet_grid(product ~ offset) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(legend.title = element_markdown(), legend.position = "top")
  )

rm(pco2_product_map_annual_anomaly_ensemble_offset,
   pco2_product_map_annual_anomaly_ensemble_baseline)

gc()

pco2_product_map_annual_anomaly_ensemble_gobm <-
  pco2_product_map_annual_anomaly %>% 
  filter(year == 2023,
         product %in% gobm_product_list) %>%
  group_by(name, lon, lat) %>%
  summarize(
    resid_sd = sd(resid),
    resid_range = max(resid) - min(resid),
    resid_mean = mean(resid),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n == length(gobm_product_list)) %>% 
  select(-n)


plot_list <- 
pco2_product_map_annual_anomaly_ensemble_gobm %>%
  filter(name %in% c(
           "fgco2",
           "dfco2",
           "kw_sol",
           "temperature",
           "salinity",
           "sdissic",
           "stalk",
           "sdissic_stalk",
           "no3",
           "mld",
           "intpp",
           "chl"
         )) %>%
  group_split(name) %>% 
  # head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      var = "resid_mean",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks
    )
  )


ggsave(plot = wrap_plots(plot_list,
                         ncol = 2,
                         byrow = FALSE),
       width = 10,
       height = 16,
       filename = "../output/map_anomaly_ensemble_mean_gobm.jpg")

rm(plot_list,
   pco2_product_map_annual_anomaly_ensemble_gobm)

gc()

            used   (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   3229036  172.5    7821332  417.8   7821332  417.8
Vcells 292269403 2229.9  601733777 4590.9 601704281 4590.7

Bivariate anomaly

bivariate_map <-
  pco2_product_map_annual_anomaly %>%
  filter(year == 2023, name %in% c("fgco2", "temperature")) %>%
  select(product, name, lon, lat, resid) %>%
  pivot_wider(names_from = name, values_from = resid) %>%
  drop_na()

dim_set <- 3


bivariate_map <-
  bivariate_map %>%
  mutate(
    temperature = cut(
      temperature,
      breaks = c(
        min(bivariate_map$temperature),
        0,
        0.3,
        max(bivariate_map$temperature)
      ),
      include.lowest = TRUE
    ),
    fgco2 = cut(
      fgco2,
      breaks = c(
        min(bivariate_map$fgco2),
        0,
        0.1,
        max(bivariate_map$fgco2)
      ),
      include.lowest = TRUE
    )
  )


bivariate_map <-
  bi_class(
    bivariate_map,
    x = temperature,
    y = fgco2,
    dim = dim_set,
    style = "quantile"
  )

bi_breaks <-
  bi_class_breaks(
    bivariate_map,
    x = temperature,
    y = fgco2,
    dim = dim_set,
    style = "quantile",
    dig_lab = 1,
    split = TRUE
  )

bivariate_map_raster <-
bivariate_map %>%
    relocate(lon, lat) %>%
    select(lon, lat, product, bi_class) %>%
    mutate(bi_class_numeric = as.character(as.numeric(as.factor(bi_class))))


bivariate_map_raster_values <- 
bivariate_map_raster %>% 
  distinct(bi_class, bi_class_numeric)

bivariate_map_raster <- rast(
  bivariate_map_raster %>%
    select(-bi_class) %>% 
    pivot_wider(names_from = product,
                values_from = bi_class_numeric),
    crs = "+proj=longlat"
)


bivariate_map_raster <- project(bivariate_map_raster, target_crs, method = "near")

bivariate_map_tibble <- bivariate_map_raster %>%
  as.data.frame(xy = TRUE, na.rm = FALSE) %>%
  as_tibble() %>%
  rename(lon = x, lat = y) %>%
  pivot_longer(-c(lon, lat),
               names_to = "product",
               values_to = "bi_class_numeric") %>% 
  drop_na()

bivariate_map_tibble <-
  right_join(
    bivariate_map_tibble,
    bivariate_map_raster_values %>%
      mutate(bi_class_numeric = as.numeric(bi_class_numeric))
  )


ggplot() +
  geom_raster(data = bivariate_map_tibble,
            aes(x = lon, y = lat, fill = bi_class)) +
  bi_scale_fill(pal = "DkBlue2", dim = dim_set, flip_axes = TRUE) +
  geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
  geom_sf(data = coastline_trans, linewidth = 0.3) +
  geom_sf(data = bbox_graticules_trans, linewidth = 0.5) +
  coord_sf(
    crs = target_crs,
    ylim = lat_lim,
    xlim = lon_lim,
    expand = FALSE
  ) +
  theme(
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.border = element_rect(colour = "transparent"),
    strip.background = element_blank(),
    legend.position = "none"
  ) +
  facet_wrap( ~ product, ncol = 2)

Past versions of bivariate_anomaly_year_annual_mean_maps-1.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
c50054d	jens-daniel-mueller	2024-08-29
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
67956dd	jens-daniel-mueller	2024-07-08
8cdfed7	jens-daniel-mueller	2024-06-21
478e699	jens-daniel-mueller	2024-06-14
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
6709afa	jens-daniel-mueller	2024-04-12

ggsave(
  width = 6,
  height = 5,
  dpi = 600,
  filename = "../output/map_anomaly_bivariate_all_products.jpg"
)


bi_breaks$bi_x <- bi_breaks$bi_x[-1]
bi_breaks$bi_x[1] <- paste0("-", bi_breaks$bi_x[1])

bi_breaks$bi_y <- bi_breaks$bi_y[-1]
bi_breaks$bi_y[1] <- paste0("-", bi_breaks$bi_y[1])


bi_legend(
  pal = "DkBlue2",
  xlab = labels_breaks("temperature")$i_legend_title,
  ylab = labels_breaks("fgco2")$i_legend_title,
  dim = dim_set,
  pad_width = 2,
  breaks = bi_breaks,
  arrows = FALSE,
  flip_axes = TRUE
) +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    axis.ticks = element_blank(),
    axis.text = element_text(size = 10)
  )

Past versions of bivariate_anomaly_year_annual_mean_maps-2.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
c50054d	jens-daniel-mueller	2024-08-29
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
ba4aaac	jens-daniel-mueller	2024-07-08
8cdfed7	jens-daniel-mueller	2024-06-21
478e699	jens-daniel-mueller	2024-06-14
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
960912c	jens-daniel-mueller	2024-05-16
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
6709afa	jens-daniel-mueller	2024-04-12

ggsave(
  width = 4,
  height = 3,
  dpi = 600,
  filename = "../output/map_anomaly_bivariate_all_products_legend.jpg"
)

bivariate_map <- 
pco2_product_map_annual_anomaly_ensemble %>%
  filter(name %in% c("fgco2", "temperature")) %>%
  select(name, lon, lat, resid_mean) %>% 
  pivot_wider(names_from = name,
              values_from = resid_mean) %>% 
  drop_na()


dim_set <- 3

bivariate_map <-
  bivariate_map %>%
  mutate(
    temperature = cut(
      temperature,
      breaks = c(
        min(bivariate_map$temperature),
        0,
        0.3,
        max(bivariate_map$temperature)
      ),
      include.lowest = TRUE
    ),
    fgco2 = cut(
      fgco2,
      breaks = c(
        max(bivariate_map$fgco2),
        0.1,
        0,
        min(bivariate_map$fgco2)
      ),
      include.lowest = TRUE
    )
  )

bivariate_map <-
  bi_class(
    bivariate_map,
    x = temperature,
    y = fgco2,
    dim = dim_set,
    style = "quantile"
  )

bi_breaks <-
  bi_class_breaks(
    bivariate_map,
    x = temperature,
    y = fgco2,
    dim = dim_set,
    style = "quantile",
    dig_lab = 1,
    split = TRUE
  )

bivariate_map_raster <-
bivariate_map %>%
    relocate(lon, lat) %>%
    select(lon, lat, bi_class) %>%
    mutate(bi_class_numeric = as.character(as.numeric(as.factor(bi_class))))


bivariate_map_raster_values <- 
bivariate_map_raster %>% 
  distinct(bi_class, bi_class_numeric)

bivariate_map_raster <- rast(
  bivariate_map_raster %>%
    select(-bi_class),
    crs = "+proj=longlat"
)


bivariate_map_raster <- project(bivariate_map_raster, target_crs, method = "near")

bivariate_map_tibble <- bivariate_map_raster %>%
  as.data.frame(xy = TRUE, na.rm = FALSE) %>%
  as_tibble() %>%
  rename(lon = x, lat = y) %>%
  drop_na()

bivariate_map_tibble <-
  right_join(
    bivariate_map_tibble,
    bivariate_map_raster_values %>%
      mutate(bi_class_numeric = as.numeric(bi_class_numeric))
  )


ggplot() +
  geom_raster(data = bivariate_map_tibble,
            aes(x = lon, y = lat, fill = bi_class)) +
  bi_scale_fill(pal = "DkBlue2", dim = dim_set, flip_axes = TRUE) +
  geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
  geom_sf(data = coastline_trans, linewidth = 0.3) +
  geom_sf(data = bbox_graticules_trans, linewidth = 0.5) +
  coord_sf(
    crs = target_crs,
    ylim = lat_lim,
    xlim = lon_lim,
    expand = FALSE
  ) +
  theme(
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.border = element_rect(colour = "transparent"),
    strip.background = element_blank(),
    legend.position = "none"
  )

Past versions of bivariate_anomaly_year_annual_mean_maps_ensemble_mean-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
478e699	jens-daniel-mueller	2024-06-14

ggsave(width = 5,
       height = 2.5,
       dpi = 600,
       filename = "../output/map_anomaly_bivariate_ensemble_mean_pco2_products.jpg")

bi_breaks$bi_x <- bi_breaks$bi_x[-1]
bi_breaks$bi_x[1] <- paste0("-", bi_breaks$bi_x[1])

bi_breaks$bi_y <- bi_breaks$bi_y[-1]
bi_breaks$bi_y[1] <- paste0("-", bi_breaks$bi_y[1])


bi_legend(
  pal = "DkBlue2",
  xlab = labels_breaks("temperature")$i_legend_title,
  ylab = labels_breaks("fgco2")$i_legend_title,
  dim = dim_set,
  pad_width = 2,
  breaks = bi_breaks,
  arrows = FALSE,
  flip_axes = TRUE
) +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    axis.ticks = element_blank(),
    axis.text = element_text(size = 10)
  )

Past versions of bivariate_anomaly_year_annual_mean_maps_ensemble_mean-2.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
c50054d	jens-daniel-mueller	2024-08-29
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
ba4aaac	jens-daniel-mueller	2024-07-08
aeca619	jens-daniel-mueller	2024-06-19
478e699	jens-daniel-mueller	2024-06-14

ggsave(width = 4,
       height = 3,
       dpi = 600,
       filename = "../output/map_anomaly_bivariate_ensemble_mean_pco2_products_legend.jpg")

pco2_product_zonal_annual_anomaly <-
pco2_product_hovmoeller_monthly_anomaly %>%
  filter(year == 2023) %>%
  group_by(product, name, lat) %>%
  summarise(resid = mean(resid)) %>%
  ungroup() 


pco2_product_zonal_annual_anomaly %>%
  ggplot(aes(resid, lat, col = product)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  geom_path() +
  scale_color_manual(values = color_products) +
  facet_wrap( ~ name, scales = "free_x", ncol = 4)

Past versions of zonal_means-1.png

Version	Author	Date
3bb8433	jens-daniel-mueller	2024-09-03

pco2_product_zonal_annual_anomaly_ensemble <- 
pco2_product_zonal_annual_anomaly %>%
  filter(product %in% pco2_product_list) %>% 
  group_by(lat, name) %>% 
  fsummarise(
    resid_sd = fsd(resid),
    resid_mean = fmean(resid)
  )

pco2_product_zonal_annual_anomaly_ensemble %>%
  filter(name %in% c("fgco2_hov", "temperature")) %>%
  ggplot(aes(resid_mean, lat)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  # geom_ribbon(aes(xmin = resid_mean - resid_sd, xmax = resid_mean + resid_sd),
  #             alpha = 0.5) +
  geom_ribbon(aes(xmin = 0, xmax = pmax(0, resid_mean), fill = "Positive"),
              alpha = 0.5) +
  geom_ribbon(aes(xmax = 0, xmin = pmin(0, resid_mean), fill = "Negative"),
              alpha = 0.5) +
  scale_fill_manual(values = c(cold_color, warm_color)) +
  geom_path() +
  facet_grid(. ~ name,
             labeller = labeller(name = x_axis_labels),
             scales = "free_x",
             switch = "x") +
  scale_y_continuous(breaks = seq(-60,60,30),
                     name = "Lat (°N)",
                     limits = c(-54,76),
                     expand = c(0,0)) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.x = element_blank(),
    legend.position = "none"
  )

Past versions of zonal_means-2.png

Version	Author	Date
3bb8433	jens-daniel-mueller	2024-09-03

bi_pal("DkBlue2", preview = FALSE)

      1-1       2-1       3-1       1-2       2-2       3-2       1-3       2-3 
"#d3d3d3" "#97c5c5" "#52b6b6" "#c098b9" "#898ead" "#4a839f" "#ad5b9c" "#7c5592" 
      3-3 
"#434e87" 

# "#d3d3d3" "#97c5c5" "#52b6b6" "#c098b9" "#898ead" "#4a839f" "#ad5b9c" "#7c5592" "#434e87"

p_zonal_fgco2 <- 
pco2_product_zonal_annual_anomaly_ensemble %>%
  filter(name %in% c("fgco2_hov")) %>%
  mutate(resid_mean = resid_mean * 1000) %>% 
  ggplot(aes(resid_mean, lat)) +
  geom_vline(xintercept = 0) +
  geom_ribbon(aes(xmin = 0, xmax = pmax(0, resid_mean), fill = "Positive"),
              alpha = 0.9) +
  geom_ribbon(aes(xmax = 0, xmin = pmin(0, resid_mean), fill = "Negative"),
              alpha = 0.9) +
  scale_fill_manual(values = c("#d3d3d3", "#52b6b6")) +
  geom_path() +
  scale_y_continuous(breaks = seq(-60,60,30),
                     name = "Lat (°N)",
                     limits = c(-54,76),
                     expand = c(0,0)) +
  scale_x_continuous(breaks = seq(-5,5,5),
                     name = str_replace(
                       labels_breaks("fgco2_hov")$i_legend_title,
                     "PgC", "TgC"
                     )) +
  theme_classic() +
  theme(
    legend.position = "none",
    axis.title.x = element_markdown(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    axis.line.y = element_blank()
  )


p_zonal_temperature <- 
pco2_product_zonal_annual_anomaly_ensemble %>%
  filter(name %in% c("temperature")) %>%
  ggplot(aes(resid_mean, lat)) +
  geom_vline(xintercept = 0) +
  geom_ribbon(aes(xmin = 0, xmax = pmax(0, resid_mean), fill = "Positive"),
              alpha = 0.9) +
  geom_ribbon(aes(xmax = 0, xmin = pmin(0, resid_mean), fill = "Negative"),
              alpha = 0.9) +
  scale_fill_manual(values = c("#d3d3d3", "#ad5b9c")) +
  geom_path() +
  scale_y_continuous(breaks = seq(-60,60,30),
                     name = "Lat (°N)",
                     limits = c(-54,76),
                     expand = c(0,0)) +
  scale_x_continuous(breaks = seq(-0.6,0.6,0.3),
                     name = labels_breaks("temperature")$i_legend_title) +
  theme_classic() +
  theme(
    legend.position = "none",
    axis.title.x = element_markdown(),
    axis.title.y = element_text(angle = 0)
  )

p_zonal_temperature | p_zonal_fgco2

Past versions of zonal_means-3.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
3bb8433	jens-daniel-mueller	2024-09-03

ggsave(width = 2.8,
       height = 4.5,
       filename = "../output/zonal_mean_anomaly_pco2_product_ensemble_mean.jpg")

pco2_product_map_annual_slope %>%
  p_map_mdim_robinson(
    var = "slope",
    legend_title = "Slope FCO<sub>2</sub> anom. / SST anom.<br>(mol m<sup>-2</sup> yr<sup>-1</sup> °C<sup>-1</sup>)",
    breaks = c(-Inf, seq(-1, 1, 0.25), Inf),
    dim_wrap = "product",
    n_col = 2
  )

Past versions of anomaly_correlation_map-1.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
aeca619	jens-daniel-mueller	2024-06-19

ggsave(width = 7,
       height = 6,
       dpi = 600,
       filename = "../output/map_anomaly_correlation_all_products.jpg")

# map +
#   geom_tile(data = pco2_product_map_annual_slope, aes(lon, lat, fill = slope)) +
#   scale_fill_gradientn(
#     colours = warm_cool_gradient,
#     rescaler = ~ scales::rescale_mid(.x, mid = 0),
#     limits = c(
#       quantile(pco2_product_map_annual_slope$slope, .01),
#       quantile(pco2_product_map_annual_slope$slope, .99)
#     ),
#     oob = squish
#   ) +
#   facet_wrap( ~ product) +
#   guides(
#     fill = guide_colorbar(
#       barheight = unit(0.3, "cm"),
#       barwidth = unit(6, "cm"),
#       ticks = TRUE,
#       ticks.colour = "grey20",
#       frame.colour = "grey20",
#       label.position = "top",
#       direction = "horizontal"
#     )
#   ) +
#   theme(legend.title = element_markdown(), legend.position = "top") +
#   labs(title = "Correlation of historic annual flux and SST anomalies")
# 
# pco2_product_map_annual_slope_ensemble <-
#   pco2_product_map_annual_slope %>% 
#   filter(product %in% pco2_product_list) %>%
#   fgroup_by(lon, lat) %>%
#   fsummarise(
#     slope_sd = fsd(slope),
#     slope_mean = fmean(slope),
#     n = fnobs(slope)
#   ) %>%
#   filter(n == length(pco2_product_list)) %>% 
#   select(-n)
# 
# pco2_product_map_annual_slope_ensemble_coarse <-
#   m_grid_horizontal_coarse(pco2_product_map_annual_slope_ensemble) %>%
#   fgroup_by(lon_grid, lat_grid) %>%
#   fsummarise(
#     slope_sd_coarse = fmean(slope_sd, na.rm = TRUE),
#     slope_mean_coarse = fmean(slope_mean, na.rm = TRUE)
#   ) %>% 
#   rename(lon = lon_grid, lat = lat_grid)
# 
# pco2_product_map_annual_slope_ensemble <-
#   left_join(
#     pco2_product_map_annual_slope_ensemble,
#     pco2_product_map_annual_slope_ensemble_coarse
#   )
# 
# 
# map +
#   geom_tile(data = pco2_product_map_annual_slope_ensemble, 
#             aes(lon, lat, fill = slope_mean)) +
#   geom_point(
#     data = pco2_product_map_annual_slope_ensemble %>%
#       filter(abs(slope_mean_coarse) < slope_sd_coarse),
#     aes(lon, lat, shape = "Ensemble mean\n< StDev"),
#     col = "grey80",
#     size = 1
#   )+
#   scale_fill_gradientn(
#     colours = warm_cool_gradient,
#     rescaler = ~ scales::rescale_mid(.x, mid = 0),
#     limits = c(
#       quantile(pco2_product_map_annual_slope$slope,.01),
#       quantile(pco2_product_map_annual_slope$slope, .99)),
#     oob = squish,
#     name = paste0("Slope<br><br>",
#                   labels_breaks("fgco2"),
#                   " / <br><br>",
#                   labels_breaks("temperature"))
#   ) +
#   scale_shape(name = "") +
#   labs(title = "Correlation of historic annual flux and SST anomalies", 
#        subtitle = "fCO2 product ensemble mean") +
#   guides(
#     fill = guide_colorbar(
#       barheight = unit(0.3, "cm"),
#       barwidth = unit(6, "cm"),
#       ticks = TRUE,
#       ticks.colour = "grey20",
#       frame.colour = "grey20",
#       label.position = "top",
#       direction = "horizontal"
#     )
#   ) +
#   theme(legend.title = element_markdown(), 
#         legend.position = "top")

Monthly means

2023 anomaly

pco2_product_map_monthly_anomaly <-
  inner_join(
    biome_mask_print,
    pco2_product_map_monthly_anomaly
  )

pco2_product_map_monthly_anomaly %>%
  filter(name %in% name_core,
         year == 2023) %>%
  group_split(name) %>%
  head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid)) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name))$i_legend_title,
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      theme(legend.title = element_markdown()) +
      facet_grid(month ~ product) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(legend.title = element_markdown(),
            legend.position = "top")
  )

pco2_product_map_monthly_anomaly_ensemble <-
  pco2_product_map_monthly_anomaly %>%
  filter(year == 2023,
         product %in% pco2_product_list) %>%
  fgroup_by(name, lon, lat, month) %>%
  fsummarise(
    resid_sd = fsd(resid),
    resid_mean = fmean(resid),
    n = fnobs(resid)
  ) %>%
  filter(n == length(pco2_product_list)) %>%
  select(-n)

pco2_product_map_monthly_anomaly_ensemble_coarse <-
  m_grid_horizontal_coarse(pco2_product_map_monthly_anomaly_ensemble) %>%
  fgroup_by(name, month, lon_grid, lat_grid) %>%
  fsummarise(resid_sd_coarse = fmean(resid_sd, na.rm = TRUE),
             resid_mean_coarse = fmean(resid_mean, na.rm = TRUE)) %>%
  rename(lon = lon_grid, lat = lat_grid)

pco2_product_map_monthly_anomaly_ensemble <-
  left_join(
    pco2_product_map_monthly_anomaly_ensemble,
    pco2_product_map_monthly_anomaly_ensemble_coarse
  )


pco2_product_map_monthly_anomaly_ensemble %>%
  filter(name %in% name_core) %>%
  mutate(month = as.character(month),
         month = fct_inorder(month)) %>% 
  group_split(name) %>%
  head(1) %>%
  map(
    ~ p_map_mdim_robinson(
      df = .x,
      var = "resid_mean",
      dim_wrap = "month",
      legend_title = labels_breaks(.x %>% distinct(name))$i_legend_title,
      breaks = labels_breaks(.x %>% distinct(name))$i_breaks
    )
  )

[[1]]

Past versions of anomaly_2023_monthly_mean_maps_ensemble-1.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14

rm(
  pco2_product_map_monthly_anomaly_ensemble,
  pco2_product_map_monthly_anomaly_ensemble_coarse
)

gc()

            used   (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   3305953  176.6    7821332  417.8   7821332  417.8
Vcells 268966959 2052.1  601733777 4590.9 601733337 4590.9

fCO2 decomposition

pco2_product_map_monthly_fCO2_decomposition <-
  inner_join(pco2_product_map_monthly_fCO2_decomposition,
             biome_mask_print)

pco2_product_map_monthly_fCO2_decomposition %>%
  filter(year == 2023) %>% 
  group_split(product) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid)) +
      labs(title = .x$product) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks("sfco2"),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      facet_grid(month ~ name,
                 labeller = labeller(name = x_axis_labels)) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(legend.title = element_markdown(),
            legend.position = "top")
  )

pco2_product_map_monthly_fCO2_decomposition %>%
  filter(year == 2023,
         product %in% pco2_product_list) %>%
  group_by(name, lon, lat, month) %>%
  summarize(
    resid_sd = sd(resid),
    resid_mean = mean(resid),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n == length(pco2_product_list)) %>% 
  select(-n) %>% 
  mutate(product = "Ensemble mean") %>% 
  group_split(product) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid_mean)) +
      # geom_point(
      #   data = .x %>% filter(abs(resid_mean) < resid_sd),
      #   aes(lon, lat, shape = "Ensemble mean\n< StDev"),
      #   col = "grey"
      # ) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks("sfco2"),,
        limits = c(quantile(.x$resid_mean, .01), quantile(.x$resid_mean, .99)),
        oob = squish
      ) +
      scale_shape_manual(values = 46, name = "") +
      facet_grid(month ~ name,
                 labeller = labeller(name = x_axis_labels)) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(legend.title = element_markdown(),
            legend.position = "top")
  )

pco2_product_map_annual_fCO2_decomposition <-
  pco2_product_map_monthly_fCO2_decomposition %>% 
  select(product, year, lat, lon, name, resid) %>% 
  fgroup_by(product, year, lat, lon, name) %>% 
  fmean()

gc()

            used   (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells   3270864  174.7    7821332  417.8   7821332  417.8
Vcells 243554743 1858.2  601733777 4590.9 601733337 4590.9

pco2_product_map_annual_fCO2_decomposition %>%
  filter(year == 2023) %>%
  select(-year) %>% 
  relocate(lon, lat) %>% 
  # mutate(name = str_remove(name, "sfco2_")) %>%
  p_map_mdim_robinson(
    var = "resid",
    dim_col = "name",
    dim_row = "product",
    legend_title = labels_breaks("sfco2")$i_legend_title,
    breaks = 2 * (labels_breaks("sfco2")$i_breaks),
    n_labels = 2
  )

Past versions of fCO2_decomposition_annual_maps-1.png

Version	Author	Date
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
a29d870	jens-daniel-mueller	2024-05-16

pco2_product_map_annual_fCO2_decomposition_ensemble <-
  pco2_product_map_annual_fCO2_decomposition %>%
  filter(product %in% pco2_product_list, year == 2023) %>%
  group_by(name, lon, lat) %>%
  summarize(resid_sd = sd(resid),
            resid_mean = mean(resid),
            n = n()) %>%
  ungroup() %>%
  filter(n == length(pco2_product_list)) %>%
  select(-n)


pco2_product_map_annual_fCO2_decomposition_ensemble_coarse <-
  m_grid_horizontal_coarse(pco2_product_map_annual_fCO2_decomposition_ensemble) %>%
  fgroup_by(name, lon_grid, lat_grid) %>%
  fsummarise(resid_sd_coarse = fmean(resid_sd, na.rm = TRUE),
             resid_mean_coarse = fmean(resid_mean, na.rm = TRUE)) %>%
  rename(lon = lon_grid, lat = lat_grid)



pco2_product_map_annual_fCO2_decomposition_ensemble_uncertainty <-
  pco2_product_map_annual_fCO2_decomposition_ensemble_coarse %>%
  mutate(signif_single = if_else(abs(resid_mean_coarse) < resid_sd_coarse, 0, 1)) %>% 
  select(lon, lat, name, signif_single) %>% 
  st_as_sf(coords = c("lon", "lat"), crs = "+proj=longlat")


pco2_product_map_annual_fCO2_decomposition_ensemble %>%
  select(lon, lat, name, resid_mean) %>% 
  mutate(name = fct_relevel(name,
                            c("sfco2_therm", "sfco2_nontherm"))) %>% 
  p_map_mdim_robinson(
    df_uncertainty = pco2_product_map_annual_fCO2_decomposition_ensemble_uncertainty,
    var = "resid_mean",
    legend_title = labels_breaks("sfco2")$i_legend_title,
    breaks = 2*(labels_breaks("sfco2")$i_breaks),
    dim_wrap = "name",
    n_col = 1,
    n_labels = 2
  )

Past versions of fCO2_decomposition_annual_maps-2.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
1eefab2	jens-daniel-mueller	2024-05-21
a29d870	jens-daniel-mueller	2024-05-16

ggsave(width = 5,
       height = 7,
       dpi = 600,
       filename = "../output/map_anomaly_fco2_decomposition_ensemble_mean_pco2_products.jpg")

Flux attribution

pco2_product_map_monthly_flux_attribution <-
  inner_join(pco2_product_map_monthly_flux_attribution, biome_mask_print)

# pco2_product_map_monthly_flux_attribution <-
#   flux_attribution(pco2_product_map_monthly_anomaly,
#                    year, month, lon, lat)

pco2_product_map_monthly_flux_attribution %>%
  filter(year == 2023) %>% 
  drop_na() %>% 
  group_split(product) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid)) +
      labs(subtitle = .x$product) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks("fgco2"),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      theme(legend.title = element_markdown(), 
            legend.position = "bottom") +
      facet_grid(month ~ name,
                 labeller = labeller(name = x_axis_labels)) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(legend.title = element_markdown(),
            legend.position = "top",
            strip.text.x.top = element_markdown())
  )



pco2_product_map_monthly_flux_attribution %>%
  filter(year == 2023) %>% 
  drop_na() %>% 
  filter(product %in% pco2_product_list) %>%
  group_by(name, lon, lat, month) %>%
  summarize(
    resid_sd = sd(resid),
    resid_mean = mean(resid),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n == length(pco2_product_list)) %>% 
  select(-n) %>% 
  mutate(product = "Ensemble mean") %>% 
  drop_na() %>% 
  group_split(product) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid_mean)) +
      # geom_point(data = .x %>% filter(abs(resid_mean) < resid_sd),
      #            aes(lon, lat, shape = "Ensemble mean\n< StDev"))+
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks("fgco2"),
        limits = c(quantile(.x$resid_mean, .01), quantile(.x$resid_mean, .99)),
        oob = squish
      )+
      scale_shape_manual(values = 46, name = "") +
      theme(legend.title = element_markdown(),
            legend.position = "bottom") +
      facet_grid(month ~ name,
                 labeller = labeller(name = x_axis_labels)) +
      guides(
        fill = guide_colorbar(
          barheight = unit(0.3, "cm"),
          barwidth = unit(6, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.title = element_markdown(),
        legend.position = "top",
        strip.text.x.top = element_markdown()
      )
  )

pco2_product_map_annual_flux_attribution <-
  pco2_product_map_monthly_flux_attribution %>% 
  group_by(product, year, lat, lon, name) %>% 
  summarise(resid = mean(resid, na.rm = TRUE)) %>% 
  ungroup()

pco2_product_map_annual_flux_attribution %>%
  filter(year == 2023) %>%
  select(-year) %>% 
  relocate(lon, lat) %>% 
  # mutate(name = str_remove_all(name, "_")) %>%
  p_map_mdim_robinson(
    var = "resid",
    dim_row = "product",
    dim_col = "name",
    legend_title = labels_breaks("fgco2")$i_legend_title,
    breaks = labels_breaks("fgco2")$i_breaks,
    n_labels = 2
  )

Past versions of flux_attribution_annual_maps-1.png

Version	Author	Date
681629f	jens-daniel-mueller	2024-08-26
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

pco2_product_map_annual_flux_attribution_ensemble <-
pco2_product_map_annual_flux_attribution %>%
  filter(year == 2023,
         product %in% pco2_product_list) %>%
  group_by(name, lon, lat) %>%
  summarize(
    resid_sd = sd(resid),
    resid_mean = mean(resid),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n == length(pco2_product_list)) %>% 
  select(-n) %>% 
  drop_na()

pco2_product_map_annual_flux_attribution_ensemble_coarse <-
  m_grid_horizontal_coarse(pco2_product_map_annual_flux_attribution_ensemble) %>%
  fgroup_by(name, lon_grid, lat_grid) %>%
  fsummarise(resid_sd_coarse = fmean(resid_sd, na.rm = TRUE),
             resid_mean_coarse = fmean(resid_mean, na.rm = TRUE)) %>%
  rename(lon = lon_grid, lat = lat_grid)



pco2_product_map_annual_flux_attribution_ensemble_uncertainty <-
  pco2_product_map_annual_flux_attribution_ensemble_coarse %>%
  mutate(signif_single = if_else(abs(resid_mean_coarse) < resid_sd_coarse, 0, 1)) %>% 
  select(lon, lat, name, signif_single) %>% 
  st_as_sf(coords = c("lon", "lat"), crs = "+proj=longlat")


pco2_product_map_annual_flux_attribution_ensemble %>%
  select(lon, lat, name, resid_mean) %>% 
  p_map_mdim_robinson(
    df_uncertainty = pco2_product_map_annual_flux_attribution_ensemble_uncertainty,
    var = "resid_mean",
    legend_title = labels_breaks("fgco2")$i_legend_title,
    breaks = labels_breaks("fgco2")$i_breaks,
    dim_wrap = "name",
    n_col = 1,
    n_labels = 2
  )

Past versions of flux_attribution_annual_maps-2.png

Version	Author	Date
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4a437fb	jens-daniel-mueller	2024-07-09
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
1eefab2	jens-daniel-mueller	2024-05-21
a29d870	jens-daniel-mueller	2024-05-16
dbc1fc6	jens-daniel-mueller	2024-05-16

ggsave(width = 5,
       height = 7,
       dpi = 600,
       filename = "../output/map_anomaly_flux_attribution_ensemble_mean_pco2_products.jpg")

Hovmoeller plots

The following Hovmoeller plots show the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.

Hovmoeller plots are presented as monthly means. Note that the predictions for the monthly Hovmoeller plots are done individually for each month, such the mean seasonal anomaly from the annual mean is removed.

Monthly means

Anomalies

pco2_product_hovmoeller_monthly_anomaly %>%
  filter(name %in% name_core) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name))$i_legend_title,
        limits = c(quantile(.x$resid,.01),quantile(.x$resid,.99)),
        oob = squish
      ) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank()) +
      facet_wrap(~ product, ncol = 1)
  )

pco2_product_hovmoeller_monthly_anomaly_ensemble <-
  pco2_product_hovmoeller_monthly_anomaly %>% 
  group_by(name, decimal, lat) %>%
  summarize(
    resid_range = max(resid) - min(resid),
    resid_mean = mean(resid),
    n = n()
  ) %>%
  ungroup() %>%
  filter(n > 1)
  

pco2_product_hovmoeller_monthly_anomaly_ensemble %>%
  mutate(product = "Ensemble mean") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid_mean)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name))$i_legend_title,
        limits = c(quantile(.x$resid_mean, .01), quantile(.x$resid_mean, .99)),
        oob = squish
      ) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank()) +
      facet_wrap( ~ product, ncol = 1)
  )

left_join(
    pco2_product_hovmoeller_monthly_anomaly_ensemble,
    pco2_product_hovmoeller_monthly_anomaly
  ) %>%
  mutate(resid_offset = resid - resid_mean) %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid_offset)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name))$i_legend_title,
        limits = c(quantile(.x$resid_mean, .01), quantile(.x$resid_mean, .99)),
        oob = squish
      ) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0)+
      labs(title = "Monthly offset from ensemble mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank()) +
      facet_wrap( ~ product, ncol = 1)
  )

Regional means and integrals

The following plots show biome-, super biome- or global- averaged/integrated values of each variable as provided through the fCO2 product, represented here as the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.

Anomalies are presented relative to the predicted annual mean of each year, hence preserving the seasonality.

Annual anomalies

pco2_product_biome_annual_anomaly_ensemble <-
  pco2_product_biome_annual_anomaly %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, name, biome) %>%
  summarise(resid_sd = sd(resid),
            resid = mean(resid),
            value = mean(value),
            fit = mean(fit)) %>%
  ungroup()


lm_fgco2_sst <- pco2_product_biome_annual_anomaly %>%
  filter(
    name %in% c("fgco2_int", "temperature"),
    biome == "Global non-polar",
    year != 2023,
    product %in% pco2_product_list
  ) %>%
  select(year, product, name, resid) %>%
  pivot_wider(values_from = resid) %>%
  nest(data = -product) %>%
  mutate(fit = map(data, ~ flm(
    formula = fgco2_int ~ temperature, data = .x
  )))

lm_fgco2_sst <-
  left_join(
    lm_fgco2_sst %>%
      unnest_wider(fit) %>%
      select(product, intercept = `(Intercept)`, slope = temperature) %>%
      mutate(intercept = as.vector(intercept), slope = as.vector(slope)),
    pco2_product_biome_annual_anomaly %>%
      filter(
        name %in% c("temperature"),
        biome == "Global non-polar",
        year == 2023,
        product %in% pco2_product_list
      ) %>%
      select(product, name, resid) %>%
      pivot_wider(values_from = resid)
  ) %>%
  mutate(resid = intercept + temperature * slope)


lm_fgco2_sst

# A tibble: 4 × 5
  product       intercept  slope temperature   resid
  <fct>             <dbl>  <dbl>       <dbl>   <dbl>
1 CMEMS         -1.27e-14 -0.612       0.191 -0.117 
2 fCO2-Residual  1.90e-15 -0.739       0.203 -0.150 
3 OceanSODAv2   -4.62e-15 -0.385       0.225 -0.0866
4 SOM-FFN        1.75e-16 -0.432       0.233 -0.101 

lm_fgco2_sst %>%
  mutate(across(c(slope, temperature, resid), ~ round(.x, 2)),
         across(c(intercept), ~ signif(.x, 2))) %>%
  write_csv("../output/lm_fgco2_sst.csv")

lm_fgco2_sst <-
lm_fgco2_sst %>% 
  summarise(resid_sd = sd(resid),
            resid_mean = mean(resid),
            temperature_sd = sd(temperature),
            temperature_mean = mean(temperature))



pco2_product_biome_annual_anomaly_ensemble_lm_fgco2_sst <-
  bind_cols(
    lm_fgco2_sst,
    pco2_product_biome_annual_anomaly_ensemble %>%
      filter(name %in% c("fgco2_int"), biome == "Global non-polar",
             year == 2023) %>%
      select(year, name, fit)
  ) %>%
  mutate(fgco2_predict = resid_mean + fit) %>%
  select(-fit)

nino_sst %>% 
  filter(year >= 1990) %>% 
  ggplot(aes(year + month/12, resid)) +
  geom_hline(yintercept = 0.5) +
  geom_path() +
  geom_path(data = . %>% 
              group_by(year) %>% 
              mutate(resid = mean(resid)) %>% 
              ungroup())

Past versions of absolute_integrals_trends-1.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
12f6571	jens-daniel-mueller	2024-05-22
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
1eefab2	jens-daniel-mueller	2024-05-21

bind_rows(
  pco2_product_biome_annual_anomaly_ensemble,
  pco2_product_biome_annual_anomaly_ensemble %>%
    filter(year == max(year)) %>%
    mutate(year = year + 1) %>%
    select(-c(resid, resid_sd))
) %>%
  filter(name %in% c("fgco2_int", "temperature"), biome == "Global non-polar") %>%
  mutate(name = fct_rev(as.factor(name))) %>%
  ggplot() +
  geom_path(
    data = pco2_product_biome_monthly_anomaly %>%
      filter(
        product %in% pco2_product_list,
        name %in% c("fgco2_int", "temperature"),
        biome == "Global non-polar"
      ) %>%
      group_by(year, month, name, biome) %>%
      summarise(value = mean(value)) %>%
      ungroup(),
    aes(year + month / 12, value),
    col = "grey90"
  ) +

  geom_rect(
    data = pco2_product_biome_annual_anomaly_ensemble_lm_fgco2_sst %>%
      filter(year %in% c(2023)),
    aes(xmin = year, xmax = year + 1, ymin = fgco2_predict - resid_sd,
        ymax = fgco2_predict + resid_sd),
    fill = trend_color, col = trend_color
  ) +
  geom_text(
    data = pco2_product_biome_annual_anomaly_ensemble_lm_fgco2_sst %>%
      filter(year %in% c(2023)),
    aes(x = year + 1, y = fgco2_predict - 0.2, label = "Expected 2023 anomaly"),
    hjust = 1,
    fontface = "bold",
    col = trend_color
  ) +
  geom_text(
    data = . %>%
      filter(year == 1991, name == "temperature"),
    aes(x = year, y = 21.95, label = "Warm"),
    hjust = 0,
    fontface = "bold",
    col = warm_color
  ) +
  geom_text(
    data = . %>%
      filter(year == 1991, name == "temperature"),
    aes(x = year, y = 21.45, label = "Cold"),
    hjust = 0,
    fontface = "bold",
    col = cold_color
  ) +
  geom_text(
    data = . %>%
      filter(year == 1991, name == "fgco2_int"),
    aes(x = year, y = -0.85, label = "Weak carbon sink"),
    hjust = 0,
    fontface = "bold",
    col = warm_color
  ) +
  geom_text(
    data = . %>%
      filter(year == 1991, name == "fgco2_int"),
    aes(x = year, y = -2.1, label = "Strong carbon sink"),
    hjust = 0,
    fontface = "bold",
    col = cold_color
  ) +
  geom_text(
    data = . %>%
      filter(year %in% c(1997, 2015, 2023), name == "fgco2_int"),
    aes(
      x = year + 0.5,
      y = -2.6,
      label = "EN"
    ), size = 3, fontface = "italic", col = "grey20") +
  geom_text(
    data = . %>%
      filter(year %in% c(1997, 2015, 2023), name == "temperature"),
    aes(
      x = year + 0.5,
      y = 21.45,
      label = "EN"
    ), size = 3, fontface = "italic", col = "grey20") +
  geom_rect(
    data = . %>% filter(year != max(year)),
    aes(
      xmin = year,
      xmax = year + 1,
      ymin = fit,
      ymax = value,
      fill = as.factor(sign(-resid))
    ),
    alpha = 0.7
  ) +
  geom_step(aes(year, fit, col = "Baseline")) +
  geom_step(aes(year, value, col = "Observed")) +
  geom_linerange(aes(
    x = year + 0.5,
    ymin = value - resid_sd,
    ymax = value + resid_sd,
    linetype = "Product SD"
  )) +
  scale_color_manual(values = c("grey40", "grey10"), name = "Annual means") +
  scale_fill_manual(
    values = c(warm_color, cold_color),
    labels = c("positive", "negative"),
    name = "Anomalies"
  ) +
  scale_linetype(name = "Anomaly uncertainty") +
  guides(
    color = guide_legend(order = 1),
    fill = guide_legend(order = 2),
    linetype = guide_legend(order = 3)
  ) +
  scale_x_continuous(limits = c(1989.5, 2024.8), expand = c(0, 0)) +
  facet_wrap(
    . ~ name,
    scales = "free_y",
    strip.position = "left",
    labeller = labeller(name = x_axis_labels_abs)
    # switch = "y"
  )+
  labs(x = "Year") +
  theme(
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    legend.position = "none",
    legend.direction = "vertical"
  )

Past versions of absolute_integrals_trends-2.png

Version	Author	Date
713d232	jens-daniel-mueller	2024-09-12
4acb1fc	jens-daniel-mueller	2024-09-05
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
ba4aaac	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
ac8cc7e	jens-daniel-mueller	2024-05-24

ggsave(width = 10,
       height = 2,
       dpi = 600,
       filename = "../output/timeseries_ensemble_mean_pco2_products.jpg")

bind_rows(
  pco2_product_biome_annual_anomaly,
  pco2_product_biome_annual_anomaly %>%
    filter(year == max(year)) %>%
    mutate(year = year + 1) %>% 
    select(-c(resid))
) %>% 
  filter(name %in% c("fgco2_int", "temperature"),
         biome == "Global non-polar") %>%
  ggplot() +
  geom_path(
    data = pco2_product_biome_monthly_anomaly %>%
      filter(name %in% c("fgco2_int", "temperature"),
             biome == "Global non-polar"),
    aes(year + month / 12, value),
    col = "grey90"
  )+
  geom_rect(
    data = . %>% filter(year != max(year)),
    aes(
      xmin = year,
      xmax = year + 1,
      ymin = fit,
      ymax = value,
      fill = as.factor(sign(-resid))
    ),
    alpha = 0.5
  ) +
  geom_step(aes(year, fit, col = "Baseline")) +
  geom_step(aes(year, value, col = "Observed")) +
  scale_color_manual(values = c("grey40", "grey10"),
                     name = "Annual means") +
  scale_fill_manual(
    values = c(warm_color, cold_color),
    labels = c("positive", "negative"),
    name = "Anomalies"
  ) +
  guides(
    color = guide_legend(order = 1),
    fill = guide_legend(order = 2)
  ) +
  scale_x_continuous(limits = c(1989.5,2024.5), expand = c(0,0),
                     breaks = c(1990,2010)) +
  facet_grid(
    name ~ product,
    scales = "free_y",
    labeller = labeller(name = x_axis_labels),
    switch = "y"
  ) +
  theme(
    axis.title = element_blank(),
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    legend.position = "none",
    legend.direction = "vertical"
  )

Past versions of absolute_integrals_trends-3.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
ac8cc7e	jens-daniel-mueller	2024-05-24

ggsave(width = 8,
       height = 3,
       dpi = 600,
       filename = "../output/timeseries_all_products.jpg")


bind_rows(
  pco2_product_biome_monthly_anomaly,
  pco2_product_biome_monthly_anomaly %>%
    filter(year == max(year),
           month == 12) %>%
    mutate(month = month + 1)
) %>%
  mutate(year = year + month/12) %>% 
  filter(name %in% c("fgco2_int", "temperature"),
         product == "OceanSODAv2",
         biome == "Global non-polar",
         year >= 2010) %>%
  ggplot() +
  geom_rect(
    data = . %>% filter(year != max(year)),
    aes(
      xmin = year,
      xmax = year + 1/12,
      ymin = fit,
      ymax = value,
      fill = as.factor(sign(-resid))
    ),
    alpha = 0.5
  ) +
  geom_step(aes(year, fit, col = "Baseline")) +
  scale_color_manual(values = c("grey40", "grey10"),
                     name = "Annual means") +
  scale_fill_manual(
    values = c(warm_color, cold_color),
    labels = c("positive", "negative"),
    name = "Anomalies"
  ) +
  guides(color = guide_legend(order = 1),
         fill = guide_legend(order = 2))+
  facet_grid(
    name ~ .,
    scales = "free_y",
    labeller = labeller(name = x_axis_labels),
    switch = "y"
  ) +
  coord_cartesian(expand = 0) +
  theme(
    axis.title = element_blank(),
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    legend.position = "top",
    legend.direction = "vertical"
  )

Past versions of absolute_integrals_trends-4.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
ba4aaac	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
bf01e6c	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
ac8cc7e	jens-daniel-mueller	2024-05-24

pco2_product_biome_annual_anomaly %>%
  filter(year == 2023,
         name %in% c("fgco2", "fgco2_int", "dfco2",
                     "kw_sol", "temperature",
                     "no3", "mld", "intpp", "chl")) %>%
  mutate(region = case_when(biome == "Global non-polar" ~ "Global non-polar",
                            # biome %in% super_biomes ~ "Super biomes",
                            TRUE ~ "Biomes"),
         region = factor(region, levels = c("Global non-polar", "Biomes"))) %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_col(aes(biome, value, fill = product),
                 position = "dodge2") +
      scale_fill_manual(values = color_products) +
      geom_col(aes(biome, fit, group = product, col = paste0(2023,"\nlinear\nprediction")),
               position = "dodge2",
               fill = "transparent") +
      labs(y = labels_breaks(unique(.x$name))$i_legend_title,
           title = "Absolute") +
      scale_color_grey() +
      facet_grid(.~region, scales = "free_x", space = "free_x") +
      theme(legend.title = element_blank(),
            axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
            axis.title.x = element_blank(),
            axis.title.y = element_markdown(),
            strip.background = element_blank(),
            legend.position = "top")
  )

[[1]]

Past versions of absolute_integrals-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[2]]

Past versions of absolute_integrals-2.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[3]]

Past versions of absolute_integrals-3.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[4]]

Past versions of absolute_integrals-4.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[5]]

Past versions of absolute_integrals-5.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[6]]

Past versions of absolute_integrals-6.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
60abdac	jens-daniel-mueller	2024-04-23
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[7]]

Past versions of absolute_integrals-7.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
231f7cd	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[8]]

Past versions of absolute_integrals-8.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
2a28b07	jens-daniel-mueller	2024-07-22
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
7b6f27c	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
7f9c687	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

full_join(
  pco2_product_biome_annual_anomaly %>%
    filter(year != 2023,
           name %in% name_core) %>%
    group_by(product, name, biome) %>% 
    summarise(resid_sd = sd(resid)) %>% 
    ungroup(),
  pco2_product_biome_annual_anomaly %>%
    filter(year == 2023,
           name %in% name_core)) %>%
  mutate(
    region = case_when(
      biome == "Global non-polar" ~ "Global non-polar",
      TRUE ~ "Biomes"
    ),
    region = factor(region, levels = c("Global non-polar", "Biomes"))
  ) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_col(aes(biome, value - fit, fill = product),
                 position = "dodge2") +
      scale_fill_manual(values = color_products) +
      geom_col(aes(biome, resid_sd * sign(value - fit), 
                   group = product, col = paste0("Anomaly SD\nexcl.",2023)),
               position = "dodge2",
               fill = "transparent") +
      labs(y = labels_breaks(unique(.x$name))$i_legend_title,
           title = "Anomalies") +
      scale_color_grey() +
      facet_grid(.~region, scales = "free_x", space = "free_x") +
      theme(legend.title = element_blank(),
            axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
            axis.title.x = element_blank(),
            axis.title.y = element_markdown(),
            strip.background = element_blank(),
            legend.position = "top")
  )

[[1]]

Past versions of absolute_anomaly_integrals-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[2]]

Past versions of absolute_anomaly_integrals-2.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[3]]

Past versions of absolute_anomaly_integrals-3.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[4]]

Past versions of absolute_anomaly_integrals-4.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[5]]

Past versions of absolute_anomaly_integrals-5.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[6]]

Past versions of absolute_anomaly_integrals-6.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
60abdac	jens-daniel-mueller	2024-04-23
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[7]]

Past versions of absolute_anomaly_integrals-7.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
231f7cd	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[8]]

Past versions of absolute_anomaly_integrals-8.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
4d3ccb2	jens-daniel-mueller	2024-05-29
7b6f27c	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
7f9c687	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

[[9]]

Past versions of absolute_anomaly_integrals-9.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
4d3ccb2	jens-daniel-mueller	2024-05-29
7b6f27c	jens-daniel-mueller	2024-05-27
e1e0ccb	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
e44a62b	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05

Super regions

pco2_product_biome_annual_anomaly_super_regions <-
  full_join(
    pco2_product_biome_annual_anomaly %>% 
      filter(biome != "Global non-polar"),
    biome_mask %>%
      mutate(area = earth_surf(lat, lon)) %>%
      group_by(biome) %>%
      summarise(area = sum(area)) %>%
      ungroup()
  ) %>% 
  pivot_longer(c(value,resid,fit),
               names_to = "estimate") %>% 
  pivot_wider()

pco2_product_biome_annual_anomaly_super_regions <-
bind_rows(
  pco2_product_biome_annual_anomaly_super_regions %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "Global"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(!str_detect(biome, "SO-ICE|SO-SPSS|Arctic")) %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "Global non-polar"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(str_detect(biome, "NA-|NP-")) %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "NH extratropics"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(str_detect(biome, "NA-")) %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "North Atlantic"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(str_detect(biome, "NP-")) %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "North Pacific"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(str_detect(biome, "PEQU|AEQU|Equ")) %>%
    select(-biome) %>% 
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int,
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "Tropics"),
  pco2_product_biome_annual_anomaly_super_regions %>%
    filter(str_detect(biome, "SA-|SP-|Southern|SO-STSS")) %>%
    select(-biome) %>%
    group_by(product, estimate, year) %>%
    summarise(across(-c(fgco2_int, area),
                     ~ weighted.mean(., area, na.rm = TRUE)),
              across(fgco2_int, 
                     ~ sum(., na.rm = TRUE))) %>%
    ungroup() %>%
    mutate(region = "SH extratropics")) %>%
  mutate(region = fct_inorder(region)) %>% 
  pivot_longer(-c(product, year, region, estimate)) %>% 
  pivot_wider(names_from = estimate)

pco2_product_biome_annual_anomaly_super_regions %>% 
  filter(year == 2023,
         name %in% c("fgco2", "fgco2_int", "dfco2", "temperature")) %>%    
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_hline(yintercept = 0) +
      geom_col(aes(region, value, fill = product),
                 position = "dodge2") +
      scale_fill_manual(values = color_products) +
      geom_col(aes(region, fit, group = product, col = paste0(2023,"\nlinear\nprediction")),
               position = "dodge2",
               fill = "transparent") +
      labs(y = str_remove(labels_breaks(unique(.x$name))$i_legend_title, " anom.")) +
      scale_color_grey() +
      facet_grid(.~region, scales = "free_x", space = "free_x") +
      theme(legend.title = element_blank(),
            axis.text.x = element_blank(),
            axis.title.x = element_blank(),
            axis.ticks.x = element_blank(),
            axis.title.y = element_markdown(),
            strip.background = element_blank(),
            legend.position = "top")
  )

[[1]]

Past versions of absolute_integrals_global_definitions-1.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
6a96e1f	jens-daniel-mueller	2024-08-26
c62d92d	jens-daniel-mueller	2024-08-23
4897f6e	jens-daniel-mueller	2024-07-08
ba4aaac	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
dd97823	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

[[2]]

Past versions of absolute_integrals_global_definitions-2.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

[[3]]

Past versions of absolute_integrals_global_definitions-3.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
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[[4]]

Past versions of absolute_integrals_global_definitions-4.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

full_join(pco2_product_biome_annual_anomaly_super_regions %>%
  group_by(product, name, region) %>%
  summarise(resid_sd = sd(resid, na.rm = TRUE)) %>%
  ungroup(),
pco2_product_biome_annual_anomaly_super_regions %>%  
  filter(year == 2023)) %>% 
  filter(name %in% c("fgco2", "fgco2_int", "dfco2", "temperature")) %>%    
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_hline(yintercept = 0) +
      geom_col(aes(region, resid, fill = product),
               position = "dodge2") +
      scale_fill_manual(values = color_products) +
      geom_col(aes(region, resid_sd * sign(value - fit), 
                   group = product, col = paste0("Anomaly SD\nexcl.",2023)),
               position = "dodge2",
               fill = "transparent") +
      labs(y = labels_breaks(unique(.x$name))$i_legend_title) +
      scale_color_grey() +
      facet_grid(. ~ region, scales = "free_x", space = "free_x") +
      theme(legend.title = element_blank(),
            axis.text.x = element_blank(),
            axis.title.x = element_blank(),
            axis.ticks.x = element_blank(),
            axis.title.y = element_markdown(),
            strip.background = element_blank(),
            legend.position = "top")
  )

[[1]]

Past versions of absolute_integrals_global_definitions-5.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

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Past versions of absolute_integrals_global_definitions-6.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

[[3]]

Past versions of absolute_integrals_global_definitions-7.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

[[4]]

Past versions of absolute_integrals_global_definitions-8.png

Version	Author	Date
0f5b472	jens-daniel-mueller	2024-08-27
08ca8c7	jens-daniel-mueller	2024-08-27
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
7ad8576	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
4be90dd	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27

pco2_product_biome_annual_anomaly_super_regions <-
  bind_rows(
    pco2_product_biome_annual_anomaly_super_regions %>%
      rename(biome = region),
    pco2_product_biome_annual_anomaly %>% 
      filter(biome != "Global non-polar")
  ) %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, name, biome) %>%
  summarise(
    resid_sd = sd(resid),
    resid = mean(resid),
    value_sd = sd(value),
    value = mean(value)
  ) %>%
  ungroup()


pco2_product_biome_annual_anomaly_super_regions <-
  pco2_product_biome_annual_anomaly_super_regions %>%
  filter(name %in% c("temperature", "fgco2", "fgco2_int"))

pco2_product_biome_annual_anomaly_super_regions %>%
  filter(year == 2023) %>%
  mutate(
    resid = paste(ifelse(
      resid > 0, paste0("+", round(resid, 2)), round(resid, 2)
    ), round(resid_sd, 2), sep = "±"),
    value = paste(ifelse(
      value > 0, paste0("+", round(value, 2)), round(value, 2)
    ), round(value_sd, 2), sep = "±")
  ) %>%
  select(-c(contains("_sd"), year)) %>%
  pivot_wider(values_from = c(resid, value)) %>%
  relocate(
    biome,
    value_temperature,
    resid_temperature,
    value_fgco2_int,
    resid_fgco2_int,
    value_fgco2,
    resid_fgco2
  ) %>%
  arrange(match(
    biome,
    c(
      "NA-SPSS",
      "NA-STPS",
      "NA-STSS",
            "North Atlantic",
            "NP-SPSS",
      "NP-STPS",
      "NP-STSS",
      "North Pacific",
      "NH extratropics",
      "PEQU-E",
      "PEQU-W",
      "AEQU",
      "Equatorial Indian",
      "Tropics",
      "SA-STPS",
      "SP-STPS",
      "Southern Indian",
      "SO-STSS",
      "SH extratropics",
      "Global non-polar",
      "SO-SPSS",
      "SO-ICE",
      "Arctic",
      "Global"
    )
  )) %>% 
  write_csv("../output/biome_anomaly_ensemble_mean_pco2_products.csv")

pco2_product_biome_annual_anomaly_merged <-
full_join(region_biomes,
          pco2_product_biome_annual_anomaly) %>%
  mutate(region = case_when(biome == "Global non-polar" ~ "Global\nnon-polar",
                            region == "atlantic" ~ "Atlantic",
                            region == "pacific" ~ "Pacific",
                            region == "indian" ~ "Indian Ocean",
                            TRUE ~ region),
         region = fct_rev(fct_inorder(region))) %>% 
  mutate(
    latitude = case_when(
      biome == "Global non-polar" ~ "Global\nnon-polar",
      biome %in% c(
        "NA-SPSS",
        "NA-STSS",
        "NA-STPS",
        "NP-SPSS",
        "NP-STSS",
        "NP-STPS"
      ) ~ "NH extratropics",
      biome %in% c(
        "Equatorial Indian",
        "PEQU-W",
        "PEQU-E",
        "AEQU"
      ) ~ "Tropics",
      biome %in% c("SA-STPS", "SP-STPS", "Southern Indian", "SO-STSS") ~ "SH extratropics",
      biome %in% c("SO-SPSS", "SO-ICE") ~ "SH polar",
      biome %in% c("Arctic") ~ "NH polar",
      TRUE ~ "other"
    ),
    latitude = fct_relevel(latitude, c("Global\nnon-polar",
                                       "NH polar",
                                       "NH extratropics",
                                       "Tropics",
                                       "SH extratropics",
                                       "SH polar"))) %>% 
  mutate(basin = case_when(
    biome == "Global non-polar" ~ "",
    str_detect(biome, "NA-|SA-|AEQU") ~ "Atlantic",
    str_detect(biome, "NP-|SP-") ~ "Pacific",
    str_detect(biome, "Indian") ~ "Indian",
    str_detect(biome, "SO-") ~ "Southern\nOcean",
    str_detect(biome, "Arctic") ~ "Arctic",
    biome == "PEQU-E" ~ "Pacific-E",
    biome == "PEQU-W" ~ "Pacific-W",
    TRUE ~ "other")) %>% 
  mutate(biome_class = case_when(
    str_detect(biome, "SPSS") ~ "Subpolar\nseasonally\nstratified\n(SPSS)",
    str_detect(biome, "STSS") ~ "Subtropical\nseasonally\nstratified\n(STSS)",
    str_detect(biome, "STPS|Southern Indian") ~ "Subtropical\npermanently\nstratified\n(STPS)",
    str_detect(biome, "Arctic|ICE") ~ "Ice",
    TRUE ~ ""),
    biome_class = fct_relevel(biome_class, 
                              "Subtropical\nseasonally\nstratified\n(STSS)", 
                              after = 2)) %>% 
  filter(year == 2023,
         name %in% c("temperature", "fgco2", "fgco2_int"))

pco2_product_biome_annual_anomaly_merged_ensemble <- 
pco2_product_biome_annual_anomaly_merged %>% 
  filter(product %in% pco2_product_list) %>% 
  group_by(name, biome, basin, region, latitude, biome_class) %>%
  summarise(resid_sd = sd(resid),
            resid = mean(resid))

pco2_product_biome_annual_anomaly_merged_ensemble %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

name	biome	basin	region	latitude	biome_class	resid_sd	resid
fgco2	AEQU	Atlantic	Atlantic	Tropics		0.1277725	-0.1073872
fgco2	Arctic	Arctic	arctic	NH polar	Ice	0.1218973	0.2860942
fgco2	Equatorial Indian	Indian	Indian Ocean	Tropics		0.0811651	-0.0242599
fgco2	Global non-polar		Global non-polar	Global non-polar		0.0810832	0.0465589
fgco2	NA-SPSS	Atlantic	Atlantic	NH extratropics	Subpolar seasonally stratified (SPSS)	0.0820582	0.1823648
fgco2	NA-STPS	Atlantic	Atlantic	NH extratropics	Subtropical permanently stratified (STPS)	0.0306792	0.1359820
fgco2	NA-STSS	Atlantic	Atlantic	NH extratropics	Subtropical seasonally stratified (STSS)	0.1038162	0.1227338
fgco2	NP-SPSS	Pacific	Pacific	NH extratropics	Subpolar seasonally stratified (SPSS)	0.2998148	0.2031217
fgco2	NP-STPS	Pacific	Pacific	NH extratropics	Subtropical permanently stratified (STPS)	0.1258440	0.0990390
fgco2	NP-STSS	Pacific	Pacific	NH extratropics	Subtropical seasonally stratified (STSS)	0.1121846	0.2206666
fgco2	PEQU-E	Pacific-E	Pacific	Tropics		0.1349176	-0.3132277
fgco2	PEQU-W	Pacific-W	Pacific	Tropics		0.0335235	0.0539867
fgco2	SA-STPS	Atlantic	Atlantic	SH extratropics	Subtropical permanently stratified (STPS)	0.0616408	-0.0603114
fgco2	SO-ICE	Southern Ocean	southern	SH polar	Ice	0.1971660	-0.0503321
fgco2	SO-SPSS	Southern Ocean	southern	SH polar	Subpolar seasonally stratified (SPSS)	0.3781589	-0.1670718
fgco2	SO-STSS	Southern Ocean	southern	SH extratropics	Subtropical seasonally stratified (STSS)	0.1416620	0.0176299
fgco2	SP-STPS	Pacific	Pacific	SH extratropics	Subtropical permanently stratified (STPS)	0.1410510	0.0566787
fgco2	Southern Indian	Indian	Indian Ocean	SH extratropics	Subtropical permanently stratified (STPS)	0.1052116	0.1269398
fgco2_int	AEQU	Atlantic	Atlantic	Tropics		0.0128727	-0.0108902
fgco2_int	Arctic	Arctic	arctic	NH polar	Ice	0.0183644	0.0328425
fgco2_int	Equatorial Indian	Indian	Indian Ocean	Tropics		0.0262191	-0.0076427
fgco2_int	Global non-polar		Global non-polar	Global non-polar		0.2753267	0.1593786
fgco2_int	NA-SPSS	Atlantic	Atlantic	NH extratropics	Subpolar seasonally stratified (SPSS)	0.0085618	0.0200128
fgco2_int	NA-STPS	Atlantic	Atlantic	NH extratropics	Subtropical permanently stratified (STPS)	0.0086391	0.0366133
fgco2_int	NA-STSS	Atlantic	Atlantic	NH extratropics	Subtropical seasonally stratified (STSS)	0.0076904	0.0089896
fgco2_int	NP-SPSS	Pacific	Pacific	NH extratropics	Subpolar seasonally stratified (SPSS)	0.0480354	0.0329081
fgco2_int	NP-STPS	Pacific	Pacific	NH extratropics	Subtropical permanently stratified (STPS)	0.0648094	0.0513067
fgco2_int	NP-STSS	Pacific	Pacific	NH extratropics	Subtropical seasonally stratified (STSS)	0.0104441	0.0211142
fgco2_int	PEQU-E	Pacific-E	Pacific	Tropics		0.0244429	-0.0568594
fgco2_int	PEQU-W	Pacific-W	Pacific	Tropics		0.0051078	0.0084888
fgco2_int	SA-STPS	Atlantic	Atlantic	SH extratropics	Subtropical permanently stratified (STPS)	0.0142388	-0.0140380
fgco2_int	SO-ICE	Southern Ocean	southern	SH polar	Ice	0.0429179	-0.0113393
fgco2_int	SO-SPSS	Southern Ocean	southern	SH polar	Subpolar seasonally stratified (SPSS)	0.1401301	-0.0620249
fgco2_int	SO-STSS	Southern Ocean	southern	SH extratropics	Subtropical seasonally stratified (STSS)	0.0493971	0.0060158
fgco2_int	SP-STPS	Pacific	Pacific	SH extratropics	Subtropical permanently stratified (STPS)	0.0926202	0.0372483
fgco2_int	Southern Indian	Indian	Indian Ocean	SH extratropics	Subtropical permanently stratified (STPS)	0.0213909	0.0261113
temperature	AEQU	Atlantic	Atlantic	Tropics		0.0757248	0.2335304
temperature	Arctic	Arctic	arctic	NH polar	Ice	0.0856725	-0.0715219
temperature	Equatorial Indian	Indian	Indian Ocean	Tropics		0.0572257	0.0140388
temperature	Global non-polar		Global non-polar	Global non-polar		0.0194881	0.2131626
temperature	NA-SPSS	Atlantic	Atlantic	NH extratropics	Subpolar seasonally stratified (SPSS)	0.0468549	0.1262277
temperature	NA-STPS	Atlantic	Atlantic	NH extratropics	Subtropical permanently stratified (STPS)	0.0451547	0.4822180
temperature	NA-STSS	Atlantic	Atlantic	NH extratropics	Subtropical seasonally stratified (STSS)	0.0287486	0.2492396
temperature	NP-SPSS	Pacific	Pacific	NH extratropics	Subpolar seasonally stratified (SPSS)	0.0349335	0.3692734
temperature	NP-STPS	Pacific	Pacific	NH extratropics	Subtropical permanently stratified (STPS)	0.0280210	-0.0030638
temperature	NP-STSS	Pacific	Pacific	NH extratropics	Subtropical seasonally stratified (STSS)	0.0549194	0.4250527
temperature	PEQU-E	Pacific-E	Pacific	Tropics		0.0823128	1.2148275
temperature	PEQU-W	Pacific-W	Pacific	Tropics		0.0315601	0.0408612
temperature	SA-STPS	Atlantic	Atlantic	SH extratropics	Subtropical permanently stratified (STPS)	0.0380908	0.1071125
temperature	SO-ICE	Southern Ocean	southern	SH polar	Ice	0.0389170	0.0888580
temperature	SO-SPSS	Southern Ocean	southern	SH polar	Subpolar seasonally stratified (SPSS)	0.0358043	0.1312189
temperature	SO-STSS	Southern Ocean	southern	SH extratropics	Subtropical seasonally stratified (STSS)	0.0652036	0.2751257
temperature	SP-STPS	Pacific	Pacific	SH extratropics	Subtropical permanently stratified (STPS)	0.0281054	0.1133666
temperature	Southern Indian	Indian	Indian Ocean	SH extratropics	Subtropical permanently stratified (STPS)	0.0905071	0.1065200

pco2_product_biome_annual_anomaly_merged_ensemble %>%
  filter(name != "fgco2_int", !str_detect(biome, "SO-SPSS|SO-ICE|Arctic")) %>%
  ggplot(aes(x = basin, y = resid)) +
  geom_hline(yintercept = 0) +
  geom_col(aes(fill = "fCO2 product\nensemble mean"), col = "grey20") +
  geom_linerange(aes(
    ymin = resid - resid_sd,
    ymax = resid + resid_sd,
    col = "fCO2 product\nensemble SD"
  )) +
  scale_color_manual(values = "grey20", name = "") +
  scale_fill_manual(values = "grey90", name = "") +
  new_scale_color() +
  geom_point(
    data = pco2_product_biome_annual_anomaly_merged %>%
      filter(
        name != "fgco2_int",
        product %in% pco2_product_list,
        !str_detect(biome, "SO-SPSS|SO-ICE|Arctic")
      ),
    aes(col = product, shape = product)
  ) +
  scale_color_manual(values = color_products, name = "fCO2 products") +
  scale_shape_manual(values = 21:24, name = "fCO2 products") +
  new_scale_color() +
  new_scale("shape") +
  geom_point(
    data = pco2_product_biome_annual_anomaly_merged %>%
      filter(
        name != "fgco2_int",
        product %in% gobm_product_list,
        !str_detect(biome, "SO-SPSS|SO-ICE|Arctic")
      ),
    aes(col = product, shape = product),
    position = position_nudge(x = 0.2)
  ) +
  scale_color_manual(values = color_products, name = "GOBMs") +
  scale_shape_manual(values = 21:22, name = "GOBMs") +
  facet_nested(
    name ~ latitude + biome_class,
    scales = "free",
    space = "free_x",
    labeller = labeller(name = x_axis_labels),
    switch = "y",
    nest_line = element_line(linewidth = 0.8),
    solo_line = TRUE,
    strip = strip_nested(
      text_x = list(
        element_text(face = "bold"),
        element_text(face = "bold"),
        element_text(face = "bold"),
        element_text(face = "bold"),
        elem_list_text(),
        elem_list_text(),
        elem_list_text(),
        elem_list_text(),
        elem_list_text(),
        elem_list_text(),
        elem_list_text()
      )
    )
  ) +
  theme(
    axis.text.x = element_text(
      angle = 90,
      vjust = 0.5,
      hjust = 1
    ),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    strip.background.x = element_blank()
  )

Past versions of absolute_anomaly_integrals_merged-1.png

Version	Author	Date
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
c6f967e	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12

ggsave(width = 10,
       height = 6,
       dpi = 600,
       filename = "../output/biome_anomaly_ensemble_mean_pco2_products.jpg")


p_global <- pco2_product_biome_annual_anomaly_merged_ensemble %>% 
  filter(biome == "Global non-polar") %>% 
  ggplot(aes(basin, resid)) +
  geom_hline(yintercept = 0) +
  geom_col(aes(fill = "fCO2 product\nensemble mean"), col = "grey20") +
  geom_linerange(aes(ymin = resid - resid_sd,
                     ymax = resid + resid_sd,
                     col = "fCO2 product\nensemble SD")) +
  scale_color_manual(values = "grey20", name = "") +
  scale_fill_manual(values = "grey90", name = "") +
  new_scale_color()+
  geom_point(
    data = pco2_product_biome_annual_anomaly_merged %>%
      filter(biome == "Global non-polar",
             product %in% pco2_product_list),
    aes(col = product),
    # position = position_nudge(x = -0.15),
    shape = 21
  ) +
  scale_color_manual(values = color_products,
                     name = "fCO2 products") +
  new_scale_color()+
  geom_point(data = pco2_product_biome_annual_anomaly_merged %>% 
               filter(biome == "Global",
                      product %in% gobm_product_list),
             aes(col = product),
             position = position_nudge(x = 0.2),
             shape = 21) +
  scale_color_manual(values = color_products,
                     name = "GOBMs") +
  facet_nested(name ~ latitude + biome_class, 
             scales = "free", space = "free_x",
             labeller = labeller(name = x_axis_labels),
             switch = "y",
             nest_line = element_line(),
             solo_line = TRUE) +
  theme(
    axis.text.x = element_text(
      angle = 90,
      vjust = 0.5,
      hjust = 1
    ),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    strip.background.x = element_blank(),
    legend.position = "none"
  )
  
p_biome <- pco2_product_biome_annual_anomaly_merged_ensemble %>% 
  filter(biome != "Global non-polar") %>% 
  ggplot(aes(basin, resid)) +
  geom_hline(yintercept = 0) +
  geom_col(aes(fill = "fCO2 product\nensemble mean"), col = "grey20") +
  geom_linerange(aes(ymin = resid - resid_sd,
                     ymax = resid + resid_sd,
                     col = "fCO2 product\nensemble SD")) +
  scale_color_manual(values = "grey20", name = "") +
  scale_fill_manual(values = "grey90", name = "") +
  new_scale_color()+
  geom_point(
    data = pco2_product_biome_annual_anomaly_merged %>%
      filter(biome != "Global non-polar",
             product %in% pco2_product_list),
    aes(col = product),
    # position = position_nudge(x = -0.15),
    shape = 21
  ) +
  scale_color_manual(values = color_products,
                     name = "fCO2 products") +
  new_scale_color()+
  geom_point(data = pco2_product_biome_annual_anomaly_merged %>% 
               filter(biome != "Global non-polar",
                      product %in% gobm_product_list),
             aes(col = product),
             position = position_nudge(x = 0.2),
             shape = 21) +
  scale_color_manual(values = color_products,
                     name = "GOBMs") +
  facet_nested(name ~ latitude + biome_class, 
             scales = "free", space = "free_x",
             labeller = labeller(name = ""),
             # switch = "y",
             nest_line = element_line(),
             solo_line = TRUE
             ) +
  theme(
    axis.text.x = element_text(
      angle = 90,
      vjust = 0.5,
      hjust = 1
    ),
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    strip.text.y.right = element_text(colour = "transparent",
                                      size = 0),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    strip.background.x = element_blank(),
    legend.position = "bottom",
    legend.direction = "vertical"
  )


ggsave(cowplot::plot_grid(p_global, p_biome,
                   align = "hv",
                   axis = "tb",
                   rel_widths = c(1,7)),
       width = 12,
       height = 8,
       dpi = 600,
       filename = "../output/biome_anomaly_ensemble_mean_pco2_products_with_integrated_flux_and_SO.jpg")

Seasonal anomalies

Annual mean trends

pco2_product_biome_monthly_detrended %>%
  filter(biome %in% "NA-STPS",
         name %in% c("temperature"),
         product %in% pco2_product_list) %>%
  select(-c(time, product)) %>% 
  group_by(year, month, biome, name) %>% 
  summarise(across(where(is.numeric), mean)) %>% 
  ungroup()

# A tibble: 408 × 7
    year month biome   name        value   fit   resid
   <dbl> <dbl> <chr>   <fct>       <dbl> <dbl>   <dbl>
 1  1990     1 NA-STPS temperature  23.3  23.4 -0.0403
 2  1990     2 NA-STPS temperature  22.9  22.9  0.0672
 3  1990     3 NA-STPS temperature  22.7  22.8 -0.0665
 4  1990     4 NA-STPS temperature  23.2  23.2 -0.0419
 5  1990     5 NA-STPS temperature  24.3  24.2  0.111 
 6  1990     6 NA-STPS temperature  25.3  25.3 -0.0603
 7  1990     7 NA-STPS temperature  26.3  26.3 -0.0180
 8  1990     8 NA-STPS temperature  27.0  27.0  0.0671
 9  1990     9 NA-STPS temperature  27.3  27.2  0.131 
10  1990    10 NA-STPS temperature  26.8  26.7  0.175 
# ℹ 398 more rows

pco2_product_biome_monthly_detrended %>%
  filter(biome %in% "Global non-polar",
         name %in% c("temperature"),
         product %in% pco2_product_list) %>%
  select(-c(time, product)) %>% 
  group_by(year, month, biome, name) %>% 
  summarise(across(where(is.numeric), mean)) %>% 
  ungroup() %>% 
  # mutate(product = "Ensemble mean") %>% 
  p_season(title = NULL,
           dim_col = NULL) +
  theme(legend.position = "none")

Past versions of anomalies_from_annual_mean_trends_overview_OceanSODAv2-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20

ggsave(width = 5,
       height = 2,
       dpi = 600,
       filename = "../output/global_seasonality_sst_ensemble_mean_pco2_products.jpg")

pco2_product_biome_monthly_detrended %>%
  filter(biome %in% "Global non-polar",
         name %in% c("fgco2_int"),
         product %in% pco2_product_list) %>%
  select(-c(time, product)) %>% 
  group_by(year, month, biome, name) %>% 
  summarise(across(where(is.numeric), mean)) %>% 
  ungroup() %>% 
  # mutate(product = "Ensemble mean") %>% 
  p_season(title = NULL,
           dim_col = NULL) +
  theme(legend.position = "none")

Past versions of anomalies_from_annual_mean_trends_overview_OceanSODAv2-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20

ggsave(width = 5,
       height = 2,
       dpi = 600,
       filename = "../output/global_seasonality_fgco2_ensemble_mean_pco2_products.jpg")

pco2_product_biome_annual_detrended %>% 
  filter(biome %in% "Global non-polar",
         name %in% c("fgco2", "temperature")) %>%
  p_season(title = NULL) +
  theme(legend.position = "top")

Past versions of anomalies_from_annual_mean_trends_overview_pCO2_products-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7919445	jens-daniel-mueller	2024-05-31
6fc213f	jens-daniel-mueller	2024-05-31
7c448f7	jens-daniel-mueller	2024-05-31
b754e95	jens-daniel-mueller	2024-05-28
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
1eefab2	jens-daniel-mueller	2024-05-21

ggsave(width = 10,
       height = 4,
       dpi = 600,
       filename = "../output/global_seasonality_sst_all_products.jpg")

pco2_product_biome_annual_detrended %>%
  filter(biome %in% "Global non-polar", name %in% name_core) %>%
  p_season(title = "Anomalies from predicted annual mean | Global non-polar")

Past versions of anomalies_from_annual_mean_trends_overview-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
2a28b07	jens-daniel-mueller	2024-07-22
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7919445	jens-daniel-mueller	2024-05-31
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
e1e0ccb	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
47f8868	jens-daniel-mueller	2024-05-15
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
e44a62b	jens-daniel-mueller	2024-04-23
7f9c687	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
238d229	jens-daniel-mueller	2024-04-11
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
a83c8fc	jens-daniel-mueller	2024-04-03

pco2_product_biome_annual_detrended %>%
  filter(biome %in% key_biomes,
         name %in% name_core) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(~ p_season(
    df = .x,
    title = paste("Anomalies from predicted annual mean |", .x$biome)
  ))

[[1]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2a28b07	jens-daniel-mueller	2024-07-22
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
e1e0ccb	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
47f8868	jens-daniel-mueller	2024-05-15
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
e44a62b	jens-daniel-mueller	2024-04-23
7f9c687	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
238d229	jens-daniel-mueller	2024-04-11
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
a83c8fc	jens-daniel-mueller	2024-04-03

[[2]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2a28b07	jens-daniel-mueller	2024-07-22
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7919445	jens-daniel-mueller	2024-05-31
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
e1e0ccb	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
47f8868	jens-daniel-mueller	2024-05-15
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
6709afa	jens-daniel-mueller	2024-04-12
238d229	jens-daniel-mueller	2024-04-11
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
a83c8fc	jens-daniel-mueller	2024-04-03

[[3]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2a28b07	jens-daniel-mueller	2024-07-22
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
7919445	jens-daniel-mueller	2024-05-31
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
e1e0ccb	jens-daniel-mueller	2024-05-27
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
47f8868	jens-daniel-mueller	2024-05-15
589243f	jens-daniel-mueller	2024-05-15
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14
6709afa	jens-daniel-mueller	2024-04-12
238d229	jens-daniel-mueller	2024-04-11
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
a83c8fc	jens-daniel-mueller	2024-04-03

Monthly mean trends

grDevices::cairo_pdf("../output/biome_seasonality_all_products.pdf",
    width = 9,
    height = 10,
    onefile = TRUE)
pco2_product_biome_monthly_detrended %>%
  filter(biome %in% c("Global non-polar", key_biomes),
         name %in% name_core) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(~ p_season(
    df = .x,
    title = paste("Anomalies from predicted monthly mean |", .x$biome)
  ))

[[1]]

[[2]]

[[3]]

[[4]]

dev.off()

png 
  2 

Flux anomaly correlation

The following plots aim to unravel the correlation between biome-, super-biome- or globally- integrated monthly flux anomalies and the corresponding anomalies of the means/integrals of each other variable.

Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot the globally and biome-integrated fluxes separately. Secondly, we normalize the anomalies to the monthly spread (expressed as standard deviation) of the anomalies from 1990 to 2021.

Annual anomalies

Absolute

pco2_product_biome_annual_anomaly %>%
  filter(biome %in% c("Global non-polar", key_biomes),
         name %in% name_core) %>%
  mutate(biome = if_else(biome == "Global non-polar", "Global non-polar", biome)) %>% 
  select(-c(value, fit)) %>%
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, biome, fgco2_int))  %>%
  filter(name == "temperature") %>% 
  group_split(name) %>%
  # tail(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_smooth(
        data = . %>% filter(year != 2023),
        method = "lm",
        fill = "grey",
        col = "grey40",
        fullrange = TRUE,
        level = 0.68
      ) +
      geom_point(
        data = . %>% filter(!year %in% c(2023, 1997, 2015)),
        aes(fill = "1990-2022"),
        shape = 21
      ) +
      scale_color_manual(values = "grey60", name = "X") +
      scale_fill_manual(values = "grey60", name = "X") +
      new_scale_fill() +
      new_scale_color() +
      geom_point(
        data = . %>% filter(year %in% c(2023, 1997, 2015)),
        aes(fill = as.factor(year)),
        shape = 21,
        size = 3
      )  +
      scale_fill_manual(
        values = rev(warm_cool_gradient[c(17,13,20)]),
        guide = guide_legend(reverse = TRUE,
                             order = 2)
      ) +
      scale_color_manual(
        values = rev(warm_cool_gradient[c(17,13,20)]),
        guide = guide_legend(reverse = TRUE,
                             order = 2)
      ) +
      labs(y = labels_breaks("fgco2_int")$i_legend_title,
           x = labels_breaks(unique(.x$name))$i_legend_title) +
      facet_grid2(
        product ~ biome,
        scales = "free",
        independent = "y"
      ) +
      theme(
        axis.title.x = element_markdown(),
        axis.title.y = element_markdown(),
        legend.title = element_blank(),
        legend.position = "top"
      )
  )

[[1]]

Past versions of anomalies_correlation_absolute_annual_biome_key-1.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
0493049	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

ggsave(width = 8,
       height = 10,
       dpi = 600,
       filename = "../output/biome_anomaly_correlation_all_pco2_products.jpg")


pco2_product_biome_annual_anomaly_ensemble <-
  pco2_product_biome_annual_anomaly %>%
  filter(name %in% name_core, product %in% pco2_product_list) %>%
  select(-c(value, fit, product)) %>%
  fgroup_by(name, biome, year) %>%
  fsummarise(sd = fsd(resid),
             mean = fmean(resid))

pco2_product_biome_annual_anomaly_ensemble <-
  full_join(
    pco2_product_biome_annual_anomaly_ensemble %>%
      filter(name == "fgco2_int") %>%
      pivot_wider(values_from = c(sd, mean)),
    pco2_product_biome_annual_anomaly_ensemble %>%
      filter(name != "fgco2_int")
  )



pco2_product_biome_annual_anomaly_super_regions %>%
  filter(name %in% c("fgco2_int", "temperature")) %>%
  select(-contains("value")) %>%
  pivot_wider(values_from = contains("resid")) %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot(aes(resid_temperature, resid_fgco2_int)) +
  # geom_vline(xintercept = 0) +
  # geom_hline(yintercept = 0) +
  geom_smooth(
    data = . %>% filter(year != 2023),
    method = "lm",
    fill = "grey",
    col = "grey40",
    fullrange = TRUE,
    level = 0.68
  )+
  geom_linerange(
    data = . %>% filter(!year %in% c(2023, 1997, 2015)),
    aes(
      ymin = resid_fgco2_int - resid_sd_fgco2_int,
      ymax = resid_fgco2_int + resid_sd_fgco2_int,
      col = "1990-2022"
    )
  ) +
  geom_linerange(
    data = . %>% filter(!year %in% c(2023, 1997, 2015)),
    aes(
      xmin = resid_temperature - resid_sd_temperature,
      xmax = resid_temperature + resid_sd_temperature,
      col = "1990-2022"
    )
  ) +
  geom_point(data = . %>% filter(!year %in% c(2023, 1997, 2015)),
             aes(fill = "1990-2022"),
             shape = 21) +
  scale_color_manual(values = "grey60", name = "X") +
  scale_fill_manual(values = "grey60", name = "X") +
  new_scale_fill() +
  new_scale_color() +
  geom_linerange(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(
      ymin = resid_fgco2_int - resid_sd_fgco2_int,
      ymax = resid_fgco2_int + resid_sd_fgco2_int,
      col = as.factor(year)
    ),
    linewidth = 1
  ) +
  geom_linerange(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(
      xmin = resid_temperature - resid_sd_temperature,
      xmax = resid_temperature + resid_sd_temperature,
      col = as.factor(year)
    ),
    linewidth = 1
  ) +
  geom_point(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(fill = as.factor(year)),
    shape = 21,
    size = 3
  )  +
  scale_fill_manual(values = rev(warm_cool_gradient[c(17, 13, 20)]),
                    guide = guide_legend(reverse = TRUE, order = 2)) +
  scale_color_manual(values = rev(warm_cool_gradient[c(17, 13, 20)]),
                     guide = guide_legend(reverse = TRUE, order = 2)) +
  labs(y = labels_breaks("fgco2_int")$i_legend_title,
       x = labels_breaks(unique("temperature"))$i_legend_title) +
  facet_wrap(~ biome, scales = "free") +
  # theme_classic() +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    legend.title = element_blank()
    # strip.background = element_blank()
  )

Past versions of anomalies_correlation_absolute_annual_biome_key-2.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
ba4aaac	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
dd97823	jens-daniel-mueller	2024-06-28
c6f967e	jens-daniel-mueller	2024-06-28
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
0493049	jens-daniel-mueller	2024-05-29
7013182	jens-daniel-mueller	2024-05-27
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

ggsave(width = 8,
       height = 6,
       dpi = 600,
       filename = "../output/biome_anomaly_correlation_ensemble_mean_pco2_products.jpg")


pco2_product_biome_annual_anomaly_super_regions %>%
  filter(name %in% c("fgco2_int", "temperature")) %>%
  select(-contains("value")) %>%
  pivot_wider(values_from = contains("resid")) %>%
  ggplot(aes(resid_temperature, resid_fgco2_int)) +
  # geom_vline(xintercept = 0) +
  # geom_hline(yintercept = 0) +
  geom_smooth(
    data = . %>% filter(year != 2023),
    method = "lm",
    fill = "grey",
    col = "grey40",
    fullrange = TRUE,
        level = 0.68
  ) +
  geom_linerange(
    data = . %>% filter(!year %in% c(2023, 1997, 2015)),
    aes(
      ymin = resid_fgco2_int - resid_sd_fgco2_int,
      ymax = resid_fgco2_int + resid_sd_fgco2_int,
      col = "1990-2022"
    )
  ) +
  geom_linerange(
    data = . %>% filter(!year %in% c(2023, 1997, 2015)),
    aes(
      xmin = resid_temperature - resid_sd_temperature,
      xmax = resid_temperature + resid_sd_temperature,
      col = "1990-2022"
    )
  ) +
  geom_point(data = . %>% filter(!year %in% c(2023, 1997, 2015)),
             aes(fill = "1990-2022"),
             shape = 21) +
  scale_color_manual(values = "grey60", name = "X") +
  scale_fill_manual(values = "grey60", name = "X") +
  new_scale_fill() +
  new_scale_color() +
  geom_linerange(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(
      ymin = resid_fgco2_int - resid_sd_fgco2_int,
      ymax = resid_fgco2_int + resid_sd_fgco2_int,
      col = as.factor(year)
    ),
    linewidth = 1
  ) +
  geom_linerange(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(
      xmin = resid_temperature - resid_sd_temperature,
      xmax = resid_temperature + resid_sd_temperature,
      col = as.factor(year)
    ),
    linewidth = 1
  ) +
  geom_point(
    data = . %>% filter(year %in% c(2023, 1997, 2015)),
    aes(fill = as.factor(year)),
    shape = 21,
    size = 3
  )  +
  scale_fill_manual(values = rev(warm_cool_gradient[c(17, 13, 20)]),
                    guide = guide_legend(reverse = TRUE, order = 2)) +
  scale_color_manual(values = rev(warm_cool_gradient[c(17, 13, 20)]),
                     guide = guide_legend(reverse = TRUE, order = 2)) +
  labs(y = labels_breaks("fgco2_int")$i_legend_title,
       x = labels_breaks(unique("temperature"))$i_legend_title) +
  facet_wrap(~ biome, scales = "free",
             ncol = 4) +
  # theme_classic() +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    legend.title = element_blank(),
    legend.position = "top"
  )

Past versions of anomalies_correlation_absolute_annual_biome_key-3.png

Version	Author	Date
4acb1fc	jens-daniel-mueller	2024-09-05
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
0493049	jens-daniel-mueller	2024-05-29
b99b329	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

ggsave(width = 9,
       height = 12,
       dpi = 600,
       filename = "../output/biome_anomaly_correlation_ensemble_mean_pco2_products_all_biomes.jpg")


pco2_product_biome_annual_anomaly %>%
  filter(
    biome %in% c("Global non-polar", key_biomes),
    name %in% c(
      "fgco2_int",
      "chl",
      "dfco2",
      "sfco2",
      "atm_fco2",
      "temperature",
      "sdissic",
      "no3",
      "int_pp",
      "mld",
      "kw_sol"
    )
  ) %>% 
  select(-c(value, fit)) %>%
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, biome, fgco2_int)) %>% 
  group_by(product, name, biome) %>% 
  summarise(correlation = cor(fgco2_int, value)) %>% 
  ungroup() %>% 
  group_by(name) %>% 
  mutate(correlation_mean = mean(abs(correlation), na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(name = fct_reorder(name, correlation_mean)) %>% 
  ggplot(aes(product,name,fill=correlation)) +
  geom_tile() +
  scale_fill_divergent() +
  facet_wrap(~ biome) +
  labs(title = "Correlation with FCO2 on a annual mean basis") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        axis.title = element_blank(),
        legend.position = c(0.85,0.1),
        legend.direction = "horizontal")

Past versions of anomalies_correlation_absolute_annual_biome_key-4.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
0493049	jens-daniel-mueller	2024-05-29
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

pco2_product_biome_monthly_anomaly %>%
filter(
    biome %in% c("Global non-polar", key_biomes),
    name %in% c(
      "fgco2_int",
      "chl",
      "dfco2",
      "sfco2",
      "atm_fco2",
      "temperature",
      "sdissic",
      "no3",
      "int_pp",
      "mld",
      "kw_sol"
    )
  ) %>% 
  select(-c(value, fit)) %>%
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, month, biome, fgco2_int)) %>% 
  group_by(product, name, biome) %>% 
  summarise(correlation = cor(fgco2_int, value)) %>% 
  ungroup() %>% 
  group_by(name) %>% 
  mutate(correlation_mean = mean(abs(correlation), na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(name = fct_reorder(name, correlation_mean)) %>% 
  ggplot(aes(product,name,fill=correlation)) +
  geom_tile() +
  scale_fill_divergent() +
  facet_wrap(~ biome) +
  labs(title = "Correlation with FCO2 on a monthly mean basis") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        axis.title = element_blank(),
        legend.position = c(0.85,0.1),
        legend.direction = "horizontal")

Past versions of anomalies_correlation_absolute_annual_biome_key-5.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
478e699	jens-daniel-mueller	2024-06-14
0493049	jens-daniel-mueller	2024-05-29
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

pco2_product_biome_monthly_anomaly %>%
filter(
    biome %in% c("Global non-polar", key_biomes),
    name %in% c(
      "fgco2_int",
      "chl",
      "dfco2",
      "sfco2",
      "atm_fco2",
      "temperature",
      "sdissic",
      "no3",
      "int_pp",
      "mld",
      "kw_sol"
    )
  ) %>% 
  select(-c(value, fit)) %>%
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, month, biome, fgco2_int)) %>% 
  group_by(product, name, biome, month) %>% 
  summarise(correlation = cor(fgco2_int, value)) %>% 
  ungroup() %>% 
  group_by(name) %>% 
  mutate(correlation_mean = mean(abs(correlation), na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(name = fct_reorder(name, correlation_mean)) %>% 
  ggplot(aes(month, correlation, col = name)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  facet_grid(product ~ biome) +
  labs(title = "Correlation with FCO2 on a monthly mean basis")

Past versions of anomalies_correlation_absolute_annual_biome_key-6.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
0493049	jens-daniel-mueller	2024-05-29
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
29e0ec4	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
589243f	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
3fea035	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
dfcf790	jens-daniel-mueller	2024-04-11
d5075c5	jens-daniel-mueller	2024-04-11
19f40c9	jens-daniel-mueller	2024-04-05
a83c8fc	jens-daniel-mueller	2024-04-03

Monthly anomalies

Absolute

pco2_product_biome_monthly_detrended %>%
  filter(biome == "Global non-polar") %>%
  select(-c(time, fit, value)) %>% 
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, month, biome, fgco2_int))  %>%
  filter(name == "temperature") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_hline(yintercept = 0) +
      geom_point(
        data = . %>% filter(year != 2023),
        aes(col = paste(min(year), max(year), sep = "-")),
        alpha = 0.2
      ) +
      geom_smooth(
        data = . %>% filter(year != 2023),
        aes(col = paste(min(year), max(year), sep = "-")),
        method = "lm",
        se = FALSE,
        fullrange = TRUE
      )  +
      scale_color_grey(name = "") +
      new_scale_color() +
      geom_path(data = . %>% filter(year == 2023),
      aes(col = as.factor(month), group = 1))  +
      geom_point(
        data = . %>% filter(year == 2023),
        aes(fill =  as.factor(month)),
        shape = 21,
        size = 3
      )  +
      scale_color_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = paste("Month\nof", 2023)
      ) +
      scale_fill_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = paste("Month\nof", 2023)
      ) +
      labs(
        y = labels_breaks("fgco2_int")$i_legend_title,
        x = labels_breaks(unique(.x$name))$i_legend_title
      ) +
      facet_grid(biome ~ product,
                 scales = "free_y") +
      theme(
        axis.title.x = element_markdown(),
        axis.title.y = element_markdown()
      )
  )

[[1]]

Past versions of anomalies_correlation_absolute_monthly_global-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
d82bd91	jens-daniel-mueller	2024-08-27
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
6709afa	jens-daniel-mueller	2024-04-12
58e3680	jens-daniel-mueller	2024-04-11

pco2_product_biome_monthly_detrended %>%
  filter(biome %in% key_biomes) %>%
  select(-c(time, fit, value)) %>% 
  pivot_wider(values_from = resid) %>%
  pivot_longer(-c(product, year, month, biome, fgco2_int))  %>%
  filter(name == "temperature") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_hline(yintercept = 0) +
      geom_point(
        data = . %>% filter(year != 2023),
        aes(col = paste(min(year), max(year), sep = "-")),
        alpha = 0.2
      ) +
      geom_smooth(
        data = . %>% filter(year != 2023),
        aes(col = paste(min(year), max(year), sep = "-")),
        method = "lm",
        se = FALSE,
        fullrange = TRUE
      )  +
      scale_color_grey(name = "") +
      new_scale_color() +
      geom_path(data = . %>% filter(year == 2023),
      aes(col = as.factor(month), group = 1))  +
      geom_point(
        data = . %>% filter(year == 2023),
        aes(fill =  as.factor(month)),
        shape = 21,
        size = 3
      )  +
      scale_color_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = paste("Month\nof", 2023)
      ) +
      scale_fill_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = paste("Month\nof", 2023)
      ) +
      labs(
        y = labels_breaks("fgco2_int")$i_legend_title,
        x = labels_breaks(unique(.x$name))$i_legend_title
      ) +
      facet_grid(biome ~ product,
                 scales = "free_y") +
      theme(
        axis.title.x = element_markdown(),
        axis.title.y = element_markdown()
      )
  )

[[1]]

Past versions of anomalies_correlation_absolute_monthly_biomes_key-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
d82bd91	jens-daniel-mueller	2024-08-27
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
b99b329	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
009791f	jens-daniel-mueller	2024-05-14
8c96de4	jens-daniel-mueller	2024-05-08
4b81eaf	jens-daniel-mueller	2024-05-07
60abdac	jens-daniel-mueller	2024-04-23
1ff6eb0	jens-daniel-mueller	2024-04-22
9ecd92e	jens-daniel-mueller	2024-04-22
231f7cd	jens-daniel-mueller	2024-04-17
a5911f0	jens-daniel-mueller	2024-04-17
6709afa	jens-daniel-mueller	2024-04-12

fCO2 decomposition

pco2_product_biome_monthly_fCO2_decomposition %>%
  filter(biome %in% c("Global non-polar",key_biomes)) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ p_season(df = .x,
               title  = paste("Anomalies from predicted monthly mean |", .x$biome))
  )

[[1]]

Past versions of pCO2_decomposition-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14

[[2]]

Past versions of pCO2_decomposition-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14

[[3]]

Past versions of pCO2_decomposition-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14

[[4]]

Past versions of pCO2_decomposition-4.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
5af03d1	jens-daniel-mueller	2024-05-17
dbc1fc6	jens-daniel-mueller	2024-05-16
1e4c153	jens-daniel-mueller	2024-05-14
009791f	jens-daniel-mueller	2024-05-14

pco2_product_biome_annual_fCO2_decomposition <-
  pco2_product_biome_monthly_fCO2_decomposition %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, name, biome, product) %>%
  summarise(resid = mean(resid)) %>%
  ungroup() %>%
  group_by(year, name, biome) %>%
  summarise(resid_sd = sd(resid), resid = mean(resid)) %>%
  ungroup()

pco2_product_biome_annual_fCO2_decomposition %>%
  ggplot(aes(year, resid, colour = name)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  facet_wrap( ~ biome)

Past versions of pCO2_decomposition_ensemble-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29

pco2_product_biome_annual_fCO2_decomposition %>%
  pivot_wider(values_from = contains("resid")) %>% 
  ggplot(aes(resid_sfco2_therm, resid_sfco2_nontherm, col = "observed")) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 0) +
  geom_abline(slope = -1, intercept = 0) +
  geom_smooth(method = "lm", se = FALSE) +
  geom_point(shape = 21) +
  scale_color_muted() +
  facet_wrap( ~ biome, scales = "free")

Past versions of pCO2_decomposition_ensemble-2.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29

pco2_product_biome_annual_fCO2_decomposition %>%
  filter(year == 2023) %>%
  ggplot(aes(name, resid, fill = name)) +
  geom_hline(yintercept = 0) +
  geom_col(col = "grey20") +
  scale_fill_manual(values = c(warm_color, cold_color, "grey80")) +
  labs(y = labels_breaks("sfco2")$i_legend_title) +
  facet_wrap(~ biome, scales = "free_y") +
  theme(
    legend.title = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown(),
    legend.position = c(0.9, 0.1)
  )

Past versions of pCO2_decomposition_ensemble-3.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29

pco2_product_biome_annual_fCO2_decomposition %>%
  filter(year == 2023, biome %in% c("PEQU-E", "NA-STPS")) %>%
  mutate(name = case_when(
    name == "sfco2_therm" ~ "thermal",
    name == "sfco2_nontherm" ~ "non-thermal",
    name == "sfco2_total" ~ "total"
  ),
  name = fct_inorder(name)) %>% 
  ggplot(aes(name, resid, fill = name)) +
  geom_hline(yintercept = 0) +
  geom_col(col = "grey20") +
  geom_text(
    data = . %>% filter(biome == "NA-STPS"),aes(
    label = name,
    col = name,
    hjust = if_else(sign(resid) > 0, 0, 1),
    y = resid + if_else(sign(resid) > 0, 1, -1)
  ),
  angle = 90,
  fontface = "bold") +
  scale_color_manual(values = c(warm_color, cold_color, "grey20")) +
  scale_fill_manual(values = c(warm_color, cold_color, "grey20")) +
  labs(y = labels_breaks("sfco2")$i_legend_title) +
  scale_y_continuous(breaks = seq(-20, 20, 20)) +
  facet_grid(. ~ fct_rev(biome)) +
  theme_classic() +
  theme(
    legend.title = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown(),
    strip.background = element_blank(),
    strip.text = element_text(face = "bold", size = 16),
    axis.line.x = element_blank(),
    legend.position = "none"
  )

Past versions of pCO2_decomposition_ensemble-4.png

Version	Author	Date
3bb8433	jens-daniel-mueller	2024-09-03
c50054d	jens-daniel-mueller	2024-08-29

# ggsave(width = 6,
#        height = 3,
#        dpi = 600,
#        filename = "../output/biome_annual_fco2_decomposition.jpg")

pco2_product_biome_annual_fCO2_decomposition %>%
  filter(year == 2023) %>%
  mutate(name = case_when(
    name == "sfco2_therm" ~ "thermal",
    name == "sfco2_nontherm" ~ "non-thermal",
    name == "sfco2_total" ~ "total"
  ),
  name = fct_inorder(name)) %>% 
  ggplot(aes(name, resid, fill = name)) +
  geom_hline(yintercept = 0) +
  geom_col(col = "grey20") +
    geom_linerange(aes(
    name,
    ymin = resid - resid_sd,
    ymax = resid + resid_sd
  ), col = "grey20") +
  scale_color_manual(values = c(warm_color, cold_color, "grey20")) +
  scale_fill_manual(values = c(warm_color, cold_color, "grey20")) +
  labs(y = labels_breaks("sfco2")$i_legend_title) +
  facet_wrap(. ~ biome, scales = "free_y", ncol = 4) +
  theme(
    legend.title = element_blank(),
    legend.position = c(0.9,0.1),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown()
  )

Past versions of pCO2_decomposition_ensemble-5.png

Version	Author	Date
713d232	jens-daniel-mueller	2024-09-12
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29

ggsave(width = 7,
       height = 7,
       dpi = 600,
       filename = "../output/biome_annual_fco2_decomposition_all_biomes.jpg")

Flux attribution

Seasonal

pco2_product_biome_annual_flux_attribution_ensemble <- 
pco2_product_biome_annual_flux_attribution %>%
      filter(product %in% pco2_product_list) %>% 
      group_by(biome, name) %>% 
      summarise(
        resid_sd = sd(resid),
        resid = mean(resid)) %>% 
      ungroup()



pco2_product_biome_annual_flux_attribution_ensemble %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_col(aes("", resid), fill = "grey90", col = "grey20") +
  geom_point(
    data = pco2_product_biome_annual_flux_attribution %>%
      filter(biome %in% c("Global non-polar", key_biomes)),
    aes("", resid, fill = product),
    shape = 21
  ) +
  scale_fill_manual(values = color_products) +
  scale_y_continuous(breaks = seq(-10, 10, 0.1)) +
  labs(y = labels_breaks(unique("fgco2"))$i_legend_title) +
  facet_grid(
    biome ~ name,
    labeller = labeller(name = x_axis_labels),
    scales = "free_y",
    space = "free_y",
    switch = "x"
  ) +
  theme(
    legend.title = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown(),
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    legend.position = "top"
  )

Past versions of flux_attribution-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
563345f	jens-daniel-mueller	2024-05-21
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15
00ad9d5	jens-daniel-mueller	2024-05-15
47f8868	jens-daniel-mueller	2024-05-15

pco2_product_biome_annual_flux_attribution_ensemble %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_col(aes(name, resid, fill = name), col = "grey20") +
  geom_linerange(aes(
    name,
    ymin = resid - resid_sd,
    ymax = resid + resid_sd
  ), col = "grey20") +
  scale_fill_bright(labels = x_axis_labels) +
  labs(y = labels_breaks(unique("fgco2"))$i_legend_title) +
  facet_wrap( ~ biome, scales = "free_y", ncol = 4) +
  theme(
    legend.title = element_blank(),
    legend.text = element_markdown(),
    legend.position = c(0.8,0.1),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown()
  )

Past versions of flux_attribution-2.png

Version	Author	Date
713d232	jens-daniel-mueller	2024-09-12
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
563345f	jens-daniel-mueller	2024-05-21
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15
00ad9d5	jens-daniel-mueller	2024-05-15
47f8868	jens-daniel-mueller	2024-05-15

ggsave(width = 7,
       height = 7,
       dpi = 600,
       filename = "../output/biome_annual_flux_attribution_all_biomes.jpg")


ggplot() +
  geom_hline(yintercept = 0) +
  geom_col(
    data = pco2_product_biome_annual_flux_attribution %>%
      filter(biome %in% c("Global non-polar", key_biomes)),
    aes("", resid, fill = product),
    position = position_dodge(width = 1),
    alpha = 0.5, col = "grey30"
  ) +
  geom_point(
    data = pco2_product_biome_monthly_flux_attribution %>%
      filter(year == 2023,
             biome %in% c("Global non-polar", key_biomes)),
    aes("", resid, fill = product),
    position = position_dodge(width = 1),
    shape = 21, alpha = 0.5, col = "grey30"
  ) +
  scale_fill_manual(values = color_products) +
  # scale_color_manual(values = color_products) +
  scale_y_continuous(breaks = seq(-10,10,0.2)) +
  labs(y = labels_breaks(unique("fgco2"))$i_legend_title) +
  facet_grid(biome ~ name,
             labeller = labeller(name = x_axis_labels),
                          scales = "free_y",
             space = "free_y",
             switch = "x") +
  theme(
    legend.title = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_markdown(),
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    legend.position = "top"
  )

Past versions of flux_attribution-3.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
571e2f8	jens-daniel-mueller	2024-05-22
563345f	jens-daniel-mueller	2024-05-21
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
b7d0689	jens-daniel-mueller	2024-05-15
00ad9d5	jens-daniel-mueller	2024-05-15
47f8868	jens-daniel-mueller	2024-05-15

pco2_product_biome_monthly_flux_attribution %>%
  filter(year == 2023,
         biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_path(
    aes(month, resid, col = product)
  ) +
  geom_point(
    aes(month, resid, fill = product),
    shape = 21,
    alpha = 0.5,
    col = "grey30"
  ) +
  scale_fill_manual(values = color_products) +
  scale_color_manual(values = color_products) +
  scale_y_continuous(breaks = seq(-10,10,0.2)) +
  scale_x_continuous(position = "top", breaks = seq(1,12,3)) +
  labs(y = labels_breaks(unique("fgco2"))$i_legend_title) +
  facet_grid(biome ~ name,
             labeller = labeller(name = x_axis_labels),
             scales = "free_y",
             space = "free_y", 
             switch = "x") +
  theme(
    legend.title = element_blank(),
    axis.title.y = element_markdown(),
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    legend.position = "top"
  )

Past versions of flux_attribution-4.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
b7d0689	jens-daniel-mueller	2024-05-15
00ad9d5	jens-daniel-mueller	2024-05-15
47f8868	jens-daniel-mueller	2024-05-15

pco2_product_biome_monthly_flux_attribution %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>% 
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ p_season(
      df = .x,
      title  = paste("Anomalies from predicted monthly mean |", .x$biome)
    ) +
      facet_grid(
        name ~ product,
        labeller = labeller(name = x_axis_labels),
        switch = "y"
      ) +
      theme(
        strip.text.y.left = element_markdown(),
        strip.placement = "outside",
        strip.background.y = element_blank(),
        axis.title.y = element_blank(),
        legend.title = element_blank()
      )
  )

[[1]]

Past versions of flux_attribution_seasonal-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[2]]

Past versions of flux_attribution_seasonal-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[3]]

Past versions of flux_attribution_seasonal-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[4]]

Past versions of flux_attribution_seasonal-4.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

Annual

# pco2_product_biome_annual_flux_attribution <-
# full_join(
# pco2_product_biome_annual_flux_attribution %>% 
#   filter(year == 2023) %>% 
#   select(-year),
# pco2_product_biome_annual_flux_attribution %>% 
#   filter(year != 2023) %>% 
#   group_by(product, biome, name) %>% 
#   summarise(resid_mean = mean(abs(resid))) %>% 
#   ungroup())

pco2_product_biome_annual_flux_attribution %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>% 
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_col(aes("x", resid, fill = product),
               position = "dodge2") +
      scale_fill_manual(values = color_products) +
      geom_col(
        aes(
          "x",
          resid_mean * sign(resid),
          group = product,
          col = paste0("Mean\nexcl.",2023)
        ),
        position = "dodge2",
        fill = "transparent"
      ) +
      labs(y = labels_breaks(unique("fgco2"))$i_legend_title,
           title = .x$biome) +
      facet_grid(
        .~name,
        labeller = labeller(name = x_axis_labels),
        switch = "x"
      ) +
      scale_color_grey() +
      theme(
        legend.title = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.x = element_blank(),
        axis.title.y = element_markdown(),
        strip.text.x.bottom = element_markdown(),
        strip.placement = "outside",
        strip.background.x = element_blank(),
        legend.position = "top"
      )
  )

[[1]]

Past versions of flux_attribution_annual-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[2]]

Past versions of flux_attribution_annual-2.png

Version	Author	Date
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[3]]

Past versions of flux_attribution_annual-3.png

Version	Author	Date
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

[[4]]

Past versions of flux_attribution_annual-4.png

Version	Author	Date
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
acaac5f	jens-daniel-mueller	2024-05-28
b99b329	jens-daniel-mueller	2024-05-28
7b6f27c	jens-daniel-mueller	2024-05-27
29e0ec4	jens-daniel-mueller	2024-05-21
7c08e1c	jens-daniel-mueller	2024-05-21
dbc1fc6	jens-daniel-mueller	2024-05-16
aea0b99	jens-daniel-mueller	2024-05-16
3310cf6	jens-daniel-mueller	2024-05-16
b7d0689	jens-daniel-mueller	2024-05-15

Merged seasonality plots

pco2_product_biome_monthly_detrended %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, month, biome, name) %>%
  summarise(across(where(is.numeric), mean)) %>%
  ungroup() %>%
  filter(name %in% c("temperature", "fgco2"), biome %in% key_biomes,
         year != 2023) %>%
  group_by(month, biome, name) %>% 
  summarise(resid_sd = sd(resid)) %>% 
  ungroup() %>% 
  ggplot(aes(month, resid_sd)) +
  geom_path() +
  facet_grid(name ~ biome, scales = "free_y")

Past versions of merged_seasonality_ensemble_mean-1.png

Version	Author	Date
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
aeca619	jens-daniel-mueller	2024-06-19
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28

pco2_product_biome_monthly_detrended %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, month, biome, name) %>%
  summarise(across(where(is.numeric), mean)) %>%
  ungroup() %>%
  filter(name %in% c("temperature", "fgco2"), biome %in% key_biomes) %>%
  p_season(dim_col = "biome", 
           title = "Ensemble mean anomalies from predicted monthly mean") +
  theme(axis.title.x = element_blank(), axis.text.x = element_blank()) +
  new_scale_color() +
  scale_color_manual(values = warm_cool_gradient[15]) +
  geom_path(
    data = pco2_product_biome_monthly_detrended %>%
      filter(
        product %in% gobm_product_list,
        year == 2023,
        name %in% c("temperature", "fgco2"),
        biome %in% key_biomes
      ) %>%
      group_by(year, month, biome, name) %>%
      summarise(across(where(is.numeric), mean)) %>%
      ungroup(),
    aes(month, resid, col = "2023\nGOBM mean")
  )

Past versions of merged_seasonality_ensemble_mean-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
ba4aaac	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
aeca619	jens-daniel-mueller	2024-06-19
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
acaac5f	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28

ggsave(width = 9,
       height = 4,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fgco2_sst_ensemble_mean_pco2_products.jpg")

pco2_product_biome_monthly_flux_attribution %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, month, biome, name) %>%
  summarise(across(where(is.numeric), mean)) %>%
  ungroup() %>%
  filter(name %in% c("resid_fgco2_dfco2", "resid_fgco2_kw_sol"),
         biome %in% key_biomes) %>%
  p_season(dim_col = "biome",
           title = "Ensemble mean drivers of flux anomaly",
           scales = "fixed") +
  new_scale_color() +
  scale_color_manual(values = warm_cool_gradient[15]) +
  geom_path(
    data = pco2_product_biome_monthly_flux_attribution %>%
      filter(
        product %in% gobm_product_list,
        year == 2023,
        name %in% c("resid_fgco2_dfco2", "resid_fgco2_kw_sol"),
        biome %in% key_biomes
      ) %>%
      group_by(year, month, biome, name) %>%
      summarise(across(where(is.numeric), mean)) %>%
      ungroup(),
    aes(month, resid, col = "2023\nGOBM mean")
  )

Past versions of merged_seasonality_ensemble_mean-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
2f165ec	jens-daniel-mueller	2024-07-23
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
b18b0e5	jens-daniel-mueller	2024-06-28
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
aeca619	jens-daniel-mueller	2024-06-19
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
b754e95	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28

ggsave(width = 9,
       height = 4,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fgco2_attribution_ensemble_mean_pco2_products.jpg")

pco2_product_biome_monthly_fCO2_decomposition %>%
  filter(product %in% pco2_product_list) %>%
  group_by(year, month, biome, name) %>%
  summarise(across(where(is.numeric), mean)) %>%
  ungroup() %>%
  filter(name %in% c("sfco2_nontherm", "sfco2_therm", "sfco2_total"),
         biome %in% c("Global non-polar", key_biomes)) %>%
  p_season(dim_col = "biome",
           title = "Ensemble mean decomposition of fCO2 anomaly")  

Past versions of merged_seasonality_ensemble_mean-4.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c50054d	jens-daniel-mueller	2024-08-29
aecb187	jens-daniel-mueller	2024-08-28
c62d92d	jens-daniel-mueller	2024-08-23
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02

ggsave(width = 9,
       height = 4,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fco2_decomposition_ensemble_mean_pco2_products.jpg")

pco2_product_biome_monthly_detrended %>% 
  filter(year == 2023,
         name %in% c("temperature", "fgco2"),
         biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot(aes(month, resid)) +
  geom_hline(yintercept = 0, linewidth = 0.5) +
  geom_path(aes(col = product)) +
  scale_color_manual(values = color_products) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(x = "Month",
       title = "Anomalies from predicted monthly mean") +
  facet_grid(
    name ~ biome,
    scales = "free_y",
    labeller = labeller(name = x_axis_labels),
    switch = "y"
  ) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    axis.title.y = element_blank(),
    legend.title = element_blank()
  )

Past versions of merged_seasonality_all_products-1.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
aeca619	jens-daniel-mueller	2024-06-19
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
7868a54	jens-daniel-mueller	2024-05-22

ggsave(width = 9,
       height = 3,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fgco2_sst_all_products.jpg")

pco2_product_biome_monthly_flux_attribution %>%
  filter(year == 2023,
         name %in% c("resid_fgco2_dfco2", "resid_fgco2_kw_sol"),
         biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot(aes(month, resid)) +
  geom_hline(yintercept = 0, linewidth = 0.5) +
  geom_path(aes(col = product)) +
  scale_color_manual(values = color_products) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(x = "Month",
       title = "Drivers of flux anomaly") +
  facet_grid(
    name ~ biome,
    scales = "fixed",
    labeller = labeller(name = x_axis_labels),
    switch = "y"
  ) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    axis.title.y = element_blank(),
    legend.title = element_blank()
  )

Past versions of merged_seasonality_all_products-2.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
2f165ec	jens-daniel-mueller	2024-07-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
197dac4	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
aeca619	jens-daniel-mueller	2024-06-19
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
acaac5f	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
7868a54	jens-daniel-mueller	2024-05-22

ggsave(width = 9,
       height = 3,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fgco2_attribution_all_products.jpg")

pco2_product_biome_monthly_fCO2_decomposition %>% 
  filter(year == 2023,
         name %in% c("sfco2_nontherm", "sfco2_therm", "sfco2_total"),
         biome %in% c("Global non-polar", key_biomes)) %>%
  ggplot(aes(month, resid)) +
  geom_hline(yintercept = 0, linewidth = 0.5) +
  geom_path(aes(col = product)) +
  scale_color_manual(values = color_products) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(x = "Month",
       title = "Decomposition of fCO2 anomaly") +
  facet_grid(
    name ~ biome,
    scales = "free_y",
    labeller = labeller(name = x_axis_labels),
    switch = "y"
  ) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    axis.title.y = element_blank(),
    legend.title = element_blank()
  )

Past versions of merged_seasonality_all_products-3.png

Version	Author	Date
878c674	jens-daniel-mueller	2024-09-10
c62d92d	jens-daniel-mueller	2024-08-23
4ef742b	jens-daniel-mueller	2024-07-20
a58162a	jens-daniel-mueller	2024-07-11
4a437fb	jens-daniel-mueller	2024-07-09
ba4aaac	jens-daniel-mueller	2024-07-08
67956dd	jens-daniel-mueller	2024-07-08
b7806ad	jens-daniel-mueller	2024-07-02
197dac4	jens-daniel-mueller	2024-06-27
9589349	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
aeca619	jens-daniel-mueller	2024-06-19
d2a80a9	jens-daniel-mueller	2024-06-14
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
9cfceb9	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
b99b329	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
fe97ed3	jens-daniel-mueller	2024-05-25
7868a54	jens-daniel-mueller	2024-05-22

ggsave(width = 9,
       height = 4,
       dpi = 600,
       filename = "../output/biome_seasonal_anomaly_fco2_decomposition_all_products.jpg")

Biome profiles

The following analysis is available for GOBMs only.

Annual means

2023 anomaly

pco2_product_profiles_annual %>%
  filter(biome %in% key_biomes,
         name %in% name_core) %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_vline(xintercept = 0) +
      geom_path(aes(resid, depth, group = year), col = "grey30", alpha = 0.3) +
      geom_path(data = .x %>% filter(year == 2023),
                aes(resid, depth, col = as.factor(year)),
                linewidth = 1) +
      scale_color_brewer(palette = "Set1") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(50,100,200,400)) +
      coord_cartesian(expand = 0) +
      facet_grid2(biome ~ product,
                  scales = "free_x", independent = "x") +
      labs(y = "Depth (m)",
           x = labels_breaks(.x %>% distinct(name))$i_legend_title) +
      theme(legend.title = element_blank(),
            axis.title.x = element_markdown())
  )

[[1]]

Past versions of 2023_anomaly_profiles_annual-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
d533f68	jens-daniel-mueller	2024-05-28
97eff6a	jens-daniel-mueller	2024-05-25

[[2]]

Past versions of 2023_anomaly_profiles_annual-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
d533f68	jens-daniel-mueller	2024-05-28
97eff6a	jens-daniel-mueller	2024-05-25

[[3]]

Past versions of 2023_anomaly_profiles_annual-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
d533f68	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25

Monthly means

2023 anomaly

pco2_product_profiles_monthly %>%
  filter(year == 2023,
         biome %in% key_biomes,
         name %in% name_core) %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_vline(xintercept = 0) +
      geom_path(aes(resid, depth, col = as.factor(month)),
                linewidth = 1) +
      scale_color_viridis_d(option = "magma", end = .8) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(50,100,200,400)) +
      coord_cartesian(expand = 0) +
      facet_grid2(biome ~ product,
                  scales = "free_x", independent = "x") +
      labs(y = "Depth (m)",
           x = labels_breaks(.x %>% distinct(name))$i_legend_title) +
      theme(legend.title = element_blank(),
            axis.title.x = element_markdown())
  )

[[1]]

Past versions of 2023_anomaly_profiles_monthly-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
fbba0a0	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
97eff6a	jens-daniel-mueller	2024-05-25

[[2]]

Past versions of 2023_anomaly_profiles_monthly-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
fbba0a0	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
97eff6a	jens-daniel-mueller	2024-05-25

[[3]]

Past versions of 2023_anomaly_profiles_monthly-3.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
4d3ccb2	jens-daniel-mueller	2024-05-29
fbba0a0	jens-daniel-mueller	2024-05-28
d533f68	jens-daniel-mueller	2024-05-28
7013182	jens-daniel-mueller	2024-05-27
97eff6a	jens-daniel-mueller	2024-05-25

pco2_product_profiles_monthly %>%
  filter(year == 2023,
         biome %in% key_biomes,
         product == "ETHZ-CESM",
         name %in% name_core) %>% 
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_path(aes(resid, depth, col = as.factor(month)),
            linewidth = 1) +
  scale_color_viridis_d(option = "magma", end = .8,
                        name = paste("Month of\n", 2023)) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(50, 100, 200, 400)) +
  coord_cartesian(expand = 0) +
  facet_grid2(
    biome ~ name,
    scales = "free_x",
    independent = "x",
    labeller = labeller(name = x_axis_labels),
    switch = "x"
  ) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.x = element_blank()
  ) +
  labs(y = "Depth (m)",
       title = "Anomalies from monthly baseline (deseasonalized)")

Past versions of 2023_anomaly_profiles_monthly_CESM-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
8cdfed7	jens-daniel-mueller	2024-06-21
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
e83b65a	jens-daniel-mueller	2024-05-31
6fc213f	jens-daniel-mueller	2024-05-31
fc1b92d	jens-daniel-mueller	2024-05-30
4d3ccb2	jens-daniel-mueller	2024-05-29
acaac5f	jens-daniel-mueller	2024-05-28

# ggsave(width = 10,
#        height = 8,
#        dpi = 600,
#        filename = "../output/CESM_2023_anomaly_profiles.jpg")

pco2_product_profiles_monthly %>%
  filter(year == 2023,
         biome %in% key_biomes,
         product == "ETHZ-CESM",
         name %in% name_core) %>%
  arrange(month) %>% 
  group_by(biome, name, depth) %>% 
  mutate(resid = resid - first(resid)) %>% 
  ungroup() %>% 
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_path(aes(resid, depth, col = as.factor(month)),
            linewidth = 1) +
  scale_color_viridis_d(option = "magma", end = .8,
                        name = paste("Month of\n", 2023)) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(50, 100, 200, 400)) +
  coord_cartesian(expand = 0) +
  facet_grid2(
    biome ~ name,
    scales = "free_x",
    independent = "x",
    labeller = labeller(name = x_axis_labels),
    switch = "x"
  ) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.x = element_blank()
  ) +
  labs(y = "Depth (m)",
       title = "Monthly anomaly evolution relative to January 2023")

Past versions of 2023_anomaly_profiles_monthly_CESM-2.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
2a28b07	jens-daniel-mueller	2024-07-22
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
6fc213f	jens-daniel-mueller	2024-05-31
fc1b92d	jens-daniel-mueller	2024-05-30

pco2_product_profiles_monthly %>%
  filter(year == 2023,
         biome %in% key_biomes,
         product == "FESOM-REcoM",
         name %in% name_core) %>% 
  ggplot() +
  geom_vline(xintercept = 0) +
  geom_path(aes(resid, depth, col = as.factor(month)),
            linewidth = 1) +
  scale_color_viridis_d(option = "magma", end = .8,
                        name = paste("Month of\n", 2023)) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(50, 100, 200, 400)) +
  coord_cartesian(expand = 0) +
  facet_grid2(
    biome ~ name,
    scales = "free_x",
    independent = "x",
    labeller = labeller(name = x_axis_labels),
    switch = "x"
  ) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.x = element_blank()
  ) +
  labs(y = "Depth (m)",
       title = "Anomalies from monthly baseline (deseasonalized)")

Past versions of 2023_anomaly_profiles_monthly_FESOM-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29
ba4aaac	jens-daniel-mueller	2024-07-08
197dac4	jens-daniel-mueller	2024-06-27
f03b1d8	jens-daniel-mueller	2024-06-12
de65385	jens-daniel-mueller	2024-06-12
34b4fe2	jens-daniel-mueller	2024-06-12
0a7394b	jens-daniel-mueller	2024-06-11
54af933	jens-daniel-mueller	2024-06-03

# ggsave(width = 10,
#        height = 8,
#        dpi = 600,
#        filename = "../output/FESOM_2023_anomaly_profiles.jpg")

Hovmoeller

plot_list <-
  full_join(
    pco2_product_profiles_monthly %>%
      filter(
        year == 2023,
        biome %in% key_biomes,
        name %in% c("sdissic_stalk", "thetao")
      ),
    pco2_product_biome_monthly_detrended %>%
      filter(
        biome %in% key_biomes,
        name %in% "mld",
        year == 2023,
        product %in% gobm_product_list
      ) %>%
      select(product, month, biome, mld = value)
  ) %>%
  group_split(name, biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(month, depth, z = resid)) +
      geom_line(aes(month, mld))+
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        super = ScaleDiscretised,
        name = labels_breaks(.x %>% distinct(name))$i_legend_title
      )+
      scale_y_continuous(trans = trans_reverser("sqrt"), breaks = c(20, 50, 100, 200, 400)) +
      coord_cartesian(expand = 0,
                      ylim = c(300,NA)) +
      facet_grid(product ~ biome) +
      guides(
        fill = guide_colorsteps(
          barheight = unit(0.3, "cm"),
          barwidth = unit(10, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.position = "top",
        legend.title.align = 1,
        legend.box.spacing = unit(0.1, "cm"),
        legend.title = element_markdown(halign = 1,
                                        lineheight = 1.5)
      )
  )

plot_list

[[1]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-1.png

Version	Author	Date
2a28b07	jens-daniel-mueller	2024-07-22
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

[[2]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-2.png

Version	Author	Date
2a28b07	jens-daniel-mueller	2024-07-22
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

[[3]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-3.png

Version	Author	Date
2a28b07	jens-daniel-mueller	2024-07-22
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

[[4]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-4.png

Version	Author	Date
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

[[5]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-5.png

Version	Author	Date
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

[[6]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller-6.png

Version	Author	Date
a58162a	jens-daniel-mueller	2024-07-11
ba4aaac	jens-daniel-mueller	2024-07-08

ggsave(plot = wrap_plots(plot_list,
                         ncol = 3),
       width = 18,
       height = 12,
       dpi = 600,
       filename = "../output/profiles_hovmoeller_all_gobm.jpg")

plot_list <-
  full_join(
    pco2_product_profiles_monthly %>%
      filter(
        year == 2023,
        biome %in% key_biomes,
        name %in% c("sdissic_stalk", "thetao")
      ) %>% 
      arrange(month) %>% 
      group_by(product, name, biome, depth) %>% 
      mutate(resid = if_else(name == "sdissic_stalk",
                             resid - first(resid),
                             resid)) %>% 
      ungroup(),
    pco2_product_biome_monthly_detrended %>%
      filter(
        biome %in% key_biomes,
        name %in% "mld",
        year == 2023,
        product %in% gobm_product_list
      ) %>%
      select(product, month, biome, mld = value)
  ) %>%
  group_split(name, biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(month, depth, z = resid)) +
      geom_line(aes(month, mld))+
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        super = ScaleDiscretised,
        name = labels_breaks(.x %>% distinct(name))$i_legend_title
      )+
      scale_y_continuous(trans = trans_reverser("sqrt"), breaks = c(20, 50, 100, 200, 400)) +
      coord_cartesian(expand = 0,
                      ylim = c(300,NA)) +
      facet_grid(product ~ biome) +
      guides(
        fill = guide_colorsteps(
          barheight = unit(0.3, "cm"),
          barwidth = unit(10, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.position = "top",
        legend.title.align = 1,
        legend.box.spacing = unit(0.1, "cm"),
        legend.title = element_markdown(halign = 1,
                                        lineheight = 1.5)
      )
  )

ggsave(plot = wrap_plots(plot_list,
                         ncol = 3),
       width = 18,
       height = 12,
       dpi = 600,
       filename = "../output/profiles_hovmoeller_all_gobm_evolution.jpg")

CESM_depth_grid <- pco2_product_profiles_monthly %>%
  filter(year == 2023, 
         product == "ETHZ-CESM",
         biome %in% key_biomes,
         name %in% c("sdissic_stalk", "thetao")) %>%
  distinct(name, biome, month, depth)

pco2_product_profiles_monthly_FESOM_regrid <-
full_join(
  pco2_product_profiles_monthly %>%
    filter(
      year == 2023,
      product == "FESOM-REcoM",
      biome %in% key_biomes,
      name %in% c("sdissic_stalk", "thetao")
    ),
  CESM_depth_grid %>% mutate(product = "FESOM-REcoM")
)

pco2_product_profiles_monthly_FESOM_regrid <-
pco2_product_profiles_monthly_FESOM_regrid %>%
  arrange(product, name, biome, month, depth)
  
  
pco2_product_profiles_monthly_FESOM_regrid <-
pco2_product_profiles_monthly_FESOM_regrid %>%
  arrange(depth) %>%
  group_by(product, name, biome, month) %>%
  mutate(resid = spline(
    depth,
    resid,
    method = "natural",
    xout = depth
  )$y) %>%
  ungroup()

CESM_depth <- 
  CESM_depth_grid %>% distinct(depth) %>% pull()

pco2_product_profiles_monthly_FESOM_regrid <-
  pco2_product_profiles_monthly_FESOM_regrid %>%
  filter(depth %in% CESM_depth)


pco2_product_profiles_monthly_merged <-
  bind_rows(
    pco2_product_profiles_monthly_FESOM_regrid,
    pco2_product_profiles_monthly %>%
      filter(
        year == 2023,
        product == "ETHZ-CESM",
        biome %in% key_biomes,
        name %in% c("sdissic_stalk", "thetao")
      )
  )


pco2_product_profiles_monthly_ensemble <-
  pco2_product_profiles_monthly_merged %>%
  group_by(name, biome, month, depth) %>%
  summarise(resid = mean(resid)) %>%
  ungroup()


pco2_product_profiles_monthly_ensemble <-
  full_join(
    pco2_product_profiles_monthly_ensemble %>%
      filter(
        biome %in% key_biomes,
        name %in% c("sdissic_stalk", "thetao")
      ),
    pco2_product_biome_monthly_detrended %>%
      filter(
        biome %in% key_biomes,
        name %in% "mld",
        year == 2023,
        product %in% gobm_product_list
      ) %>%
      group_by(month, biome) %>% 
      summarise(mld = mean(value)) %>% 
      ungroup()
  ) 



# plot_list <-
  pco2_product_profiles_monthly_ensemble %>%
  group_split(name, biome) %>% 
  head(1) %>% 
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(month, depth, z = resid)) +
      geom_line(aes(month, mld)) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        super = ScaleDiscretised,
        name = labels_breaks(.x %>% distinct(name))$i_legend_title
      ) +
      scale_y_continuous(trans = trans_reverser("sqrt"), breaks = c(50, 100, 200, 400)) +
      coord_cartesian(expand = 0, ylim = c(300, NA)) +
      facet_wrap(~ biome) +
      guides(
        fill = guide_colorsteps(
          barheight = unit(0.3, "cm"),
          barwidth = unit(10, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.position = "top",
        legend.title.align = 1,
        legend.box.spacing = unit(0.1, "cm"),
        legend.title = element_markdown(halign = 1, lineheight = 1.5)
      )
  )

[[1]]

Past versions of 2023_anomaly_profiles_monthly_hovmoeller_ensemble-1.png

Version	Author	Date
c50054d	jens-daniel-mueller	2024-08-29

ggsave(plot = wrap_plots(plot_list,
                         ncol = 3),
       width = 18,
       height = 8,
       dpi = 600,
       filename = "../output/profiles_hovmoeller_ensemble_mean_gobm.jpg")

labels_breaks_hov <- function(i_name, i_biome) {
  
  if (i_name == "sdissic_stalk") {
    i_legend_title <- "sDIC - sTA<br>anom.<br>(μmol kg<sup>-1</sup>)"
  }
  
  if (i_name == "thetao") {
    i_legend_title <- "Temp.<br>anom.<br>(°C)"
  }
  
  if (i_name == "sdissic_stalk" & i_biome == "NA-SPSS") {
    i_breaks <- c(-Inf, seq(-2, 2, 0.5), Inf)
  }
  
  if (i_name == "thetao" & i_biome == "NA-SPSS") {
    i_breaks <- c(-Inf, seq(-0.4, 0.4, 0.1), Inf)
  }
  
  if (i_name == "sdissic_stalk" & i_biome == "NA-STPS") {
    i_breaks <- c(-Inf, seq(-2.4, 2.4, 0.6), Inf)
  }
  
  if (i_name == "thetao" & i_biome == "NA-STPS") {
    i_breaks <- c(-Inf, seq(-0.6, 0.6, 0.15), Inf)
  }
  
  if (i_name == "sdissic_stalk" & i_biome == "PEQU-E") {
    i_breaks <- c(-Inf, seq(-32, 32, 8), Inf)
  }
  
  if (i_name == "thetao" & i_biome == "PEQU-E") {
    i_breaks <- c(-Inf, seq(-2, 2, 0.5), Inf)
  }
  
  i_breaks_labels <- i_breaks[!i_breaks == Inf]
  i_breaks_labels <- i_breaks_labels[!i_breaks_labels == -Inf]
  i_breaks_labels[seq_along(i_breaks_labels) %% 2 == 0] <- ""
  
  all_labels_breaks <- lst(i_legend_title, i_breaks, i_breaks_labels)
  
  return(all_labels_breaks)
  
}

labels_breaks_hov("sdissic_stalk", "NA-SPSS")

$i_legend_title
[1] "sDIC - sTA<br>anom.<br>(μmol kg<sup>-1</sup>)"

$i_breaks
 [1] -Inf -2.0 -1.5 -1.0 -0.5  0.0  0.5  1.0  1.5  2.0  Inf

$i_breaks_labels
[1] "-2" ""   "-1" ""   "0"  ""   "1"  ""   "2" 

plot_list_left <-
  pco2_product_profiles_monthly_ensemble %>%
  arrange(month) %>%
  group_by(name, biome, depth) %>%
  mutate(resid = if_else(name == "sdissic_stalk", resid - first(resid), resid)) %>%
  ungroup() %>%
  group_split(biome, name) %>%
  head(2) %>%
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(month, depth, z = resid),
                          breaks = labels_breaks_hov(.x %>% distinct(name),
                                                     .x %>% distinct(biome))$i_breaks) +
      geom_line(aes(month, mld)) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        super = ScaleDiscretised,
        name = labels_breaks_hov(.x %>% distinct(name),
                                   .x %>% distinct(biome))$i_legend_title,
        labels = labels_breaks_hov(.x %>% distinct(name),
                                   .x %>% distinct(biome))$i_breaks_labels
      ) +
      # scale_fill_gradientn(
      #   colours = warm_cool_gradient,
      #   rescaler = ~ scales::rescale_mid(.x, mid = 0),
      #   super = ScaleDiscretised,
      #   name = labels_breaks(.x %>% distinct(name))$i_legend_title
      # ) +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(20, 50, 100, 200, 400)
      ) +
      coord_cartesian(expand = 0, ylim = c(300, NA)) +
      labs(y = "Depth (m)",
           x = "Month") +
      facet_wrap(~ biome) +
      guides(
        fill = guide_colorsteps(
          barheight = unit(0.3, "cm"),
          barwidth = unit(5, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.position = "top",
        legend.box.spacing = unit(0.1, "cm"),
        legend.title = element_markdown(hjust = 1,
                                        lineheight = 1.5)
      )
  )

plot_list_right <-
  pco2_product_profiles_monthly_ensemble %>%
  arrange(month) %>%
  group_by(name, biome, depth) %>%
  mutate(resid = if_else(name == "sdissic_stalk", resid - first(resid), resid)) %>%
  ungroup() %>%
  group_split(biome, name) %>%
  tail(4) %>%
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(month, depth, z = resid),
                          breaks = labels_breaks_hov(.x %>% distinct(name),
                                                     .x %>% distinct(biome))$i_breaks) +
      geom_line(aes(month, mld)) +
      scale_fill_gradientn(
        colours = warm_cool_gradient,
        super = ScaleDiscretised,
        name = labels_breaks_hov(.x %>% distinct(name),
                                   .x %>% distinct(biome))$i_legend_title,
        labels = labels_breaks_hov(.x %>% distinct(name),
                                   .x %>% distinct(biome))$i_breaks_labels
      ) +
      scale_y_continuous(
        trans = trans_reverser("sqrt"),
        breaks = c(20, 50, 100, 200, 400)
      ) +
      coord_cartesian(expand = 0, ylim = c(300, NA)) +
      labs(y = "Depth (m)", x = "Month")+
      facet_wrap(~ biome) +
      guides(
        fill = guide_colorsteps(
          barheight = unit(0.3, "cm"),
          barwidth = unit(5, "cm"),
          ticks = TRUE,
          ticks.colour = "grey20",
          frame.colour = "grey20",
          label.position = "top",
          direction = "horizontal"
        )
      ) +
      theme(
        legend.position = "top",
        # legend.margin = margin(0, 0, 0, 0),
        # legend.justification = "left",
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        legend.title.align = 1,
        legend.box.spacing = unit(0.1, "cm"),
        legend.title = element_blank()
      )
  )

plot_list <- c(plot_list_left, plot_list_right)

ggsave(plot = wrap_plots(plot_list,
                         ncol = 3,
                         byrow = FALSE),
       width = 10,
       height = 6,
       dpi = 600,
       filename = "../output/profiles_hovmoeller_ensemble_mean_gobm_evolution.jpg")

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kableExtra_1.3.4    cmocean_0.3-1       ggh4x_0.2.8        
 [4] scales_1.2.1        biscale_1.0.0       ggtext_0.1.2       
 [7] khroma_1.9.0        ggnewscale_0.4.8    terra_1.7-65       
[10] sf_1.0-9            rnaturalearth_0.1.0 geomtextpath_0.1.1 
[13] colorspace_2.0-3    marelac_2.1.10      shape_1.4.6        
[16] ggforce_0.4.1       metR_0.13.0         scico_1.3.1        
[19] patchwork_1.1.2     collapse_1.8.9      forcats_0.5.2      
[22] stringr_1.5.0       dplyr_1.1.3         purrr_1.0.2        
[25] readr_2.1.3         tidyr_1.3.0         tibble_3.2.1       
[28] ggplot2_3.4.4       tidyverse_1.3.2     workflowr_1.7.0    

loaded via a namespace (and not attached):
  [1] readxl_1.4.1            backports_1.4.1         systemfonts_1.0.4      
  [4] sp_1.5-1                splines_4.2.2           digest_0.6.30          
  [7] htmltools_0.5.3         fansi_1.0.3             magrittr_2.0.3         
 [10] checkmate_2.1.0         memoise_2.0.1           googlesheets4_1.0.1    
 [13] tzdb_0.3.0              modelr_0.1.10           vroom_1.6.0            
 [16] svglite_2.1.0           timechange_0.1.1        rvest_1.0.3            
 [19] textshaping_0.3.6       haven_2.5.1             xfun_0.35              
 [22] callr_3.7.3             crayon_1.5.2            jsonlite_1.8.3         
 [25] glue_1.6.2              polyclip_1.10-4         gtable_0.3.1           
 [28] gargle_1.2.1            webshot_0.5.4           DBI_1.1.3              
 [31] Rcpp_1.0.11             isoband_0.2.6           viridisLite_0.4.1      
 [34] gridtext_0.1.5          units_0.8-0             bit_4.0.5              
 [37] proxy_0.4-27            httr_1.4.4              seacarb_3.3.1          
 [40] RColorBrewer_1.1-3      ellipsis_0.3.2          pkgconfig_2.0.3        
 [43] farver_2.1.1            sass_0.4.4              dbplyr_2.2.1           
 [46] utf8_1.2.2              here_1.0.1              tidyselect_1.2.0       
 [49] labeling_0.4.2          rlang_1.1.1             later_1.3.0            
 [52] munsell_0.5.0           cellranger_1.1.0        tools_4.2.2            
 [55] cachem_1.0.6            cli_3.6.1               generics_0.1.3         
 [58] broom_1.0.5             evaluate_0.18           fastmap_1.1.0          
 [61] yaml_2.3.6              ragg_1.2.4              oce_1.7-10             
 [64] processx_3.8.0          knitr_1.41              bit64_4.0.5            
 [67] fs_1.5.2                nlme_3.1-160            whisker_0.4            
 [70] xml2_1.3.3              compiler_4.2.2          rstudioapi_0.15.0      
 [73] e1071_1.7-12            reprex_2.0.2            tweenr_2.0.2           
 [76] bslib_0.4.1             stringi_1.7.8           highr_0.9              
 [79] ps_1.7.2                lattice_0.20-45         Matrix_1.5-3           
 [82] classInt_0.4-8          commonmark_1.8.1        markdown_1.4           
 [85] vctrs_0.6.4             pillar_1.9.0            lifecycle_1.0.3        
 [88] jquerylib_0.1.4         gsw_1.1-1               data.table_1.14.6      
 [91] cowplot_1.1.1           httpuv_1.6.6            R6_2.5.1               
 [94] promises_1.2.0.1        KernSmooth_2.23-20      codetools_0.2-18       
 [97] MASS_7.3-58.1           assertthat_0.2.1        rprojroot_2.0.3        
[100] withr_2.5.0             SolveSAPHE_2.1.0        mgcv_1.8-41            
[103] parallel_4.2.2          hms_1.1.2               grid_4.2.2             
[106] rnaturalearthdata_0.1.0 class_7.3-20            rmarkdown_2.18         
[109] googledrive_2.0.0       git2r_0.30.1            getPass_0.2-2          
[112] lubridate_1.9.0