CMEMS_GCB

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Last updated: 2025-03-07

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Knit directory: heatwave_co2_flux_2023/analysis/

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File	Version	Author	Date	Message
html	36eac45	jens-daniel-mueller	2025-03-01	Build site.
Rmd	8b4b79f	jens-daniel-mueller	2025-02-28	rerun all analysis
html	bdc4da6	jens-daniel-mueller	2025-02-28	Build site.
Rmd	4d8eb67	jens-daniel-mueller	2025-02-28	ingest with time shifted fgco2 data
html	ca52e03	jens-daniel-mueller	2025-02-27	Build site.
Rmd	a32dc55	jens-daniel-mueller	2025-02-27	unit correction
html	e30251a	jens-daniel-mueller	2025-02-27	Build site.
Rmd	4fc29e2	jens-daniel-mueller	2025-02-27	ingest GCB files

---

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")

Read data

path_pCO2_products <-
  "/nfs/kryo/work/datasets/gridded/ocean/2d/observation/pco2/gcb_2024_pco2_products/"

library(ncdf4)
nc <-
  nc_open(paste0(
    path_pCO2_products,
    "GCB-2024_dataprod_CMEMS-LSCE-FFNN_1990-2023_r100_v6.nc"
  ))

time <- ncvar_get(nc,"time")
time

print(nc)
ncmeta::nc_extended(nc)

pco2_product_nc <-
  tidync(
    paste0(
      path_pCO2_products,
      "GCB-2024_dataprod_CMEMS-LSCE-FFNN_1990-2023_r100_v6.nc"
    )
  )

for (i in 1:468) {
  
  i_pco2_product <-
    pco2_product_nc %>%
    hyper_filter(time = index == i) %>%
    hyper_tibble(select_var = c("fco2atm", "fgco2", "kw", "alpha", "sfco2", "tos")) %>%
    mutate(time = ymd("1990-01-15") %m+% months(i-1))
  
  if (exists("pco2_product")) {
    pco2_product <-
      bind_rows(pco2_product, i_pco2_product)
  }
  
  if (!exists("pco2_product")) {
    pco2_product <- i_pco2_product
  }
  
  # print(i)
  
}

pco2_product_fgco2 <-
  pco2_product %>% 
  select(lon, lat, time, fgco2) %>% 
  mutate(time = time - years(5))

# pco2_product_fgco2 %>% 
#   distinct(time) %>% 
#   pull()
# 
# pco2_product %>% 
#   distinct(time) %>% 
#   pull()

pco2_product <-
  full_join(pco2_product %>% select(-fgco2) %>% filter(time < ymd("2024-01-01")),
            pco2_product_fgco2 %>% filter(time > ymd("1990-01-01")))

pco2_product <-
  pco2_product %>%
  rename(temperature = tos,
         atm_fco2 = fco2atm,
         sol = alpha)

pco2_product <-
  pco2_product %>%
  mutate(lon = as.numeric(lon),
         lat = as.numeric(lat),
         area = earth_surf(lat, lon),
         year = year(time),
         month = month(time))

pco2_product <-
  pco2_product %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon))

pco2_product <-
  pco2_product %>%
  mutate(dfco2 = sfco2 - atm_fco2)

pco2_product <-
  pco2_product %>% 
  mutate(fgco2 = -fgco2 * 60 * 60 * 24 * 365,
         kw_sol = kw * sol * 1e-2 * 24 * 365 * 1.025e-3)

pco2_product <-
  pco2_product %>%
  select(-c(kw, sol))

pCO2_product_preprocessing <-
  knitr::knit_expand(
    file = here::here("analysis/child/pCO2_product_preprocessing.Rmd"),
    product_name = "CMEMS_GCB"
  )

Preprocessing

# model <- TRUE
model <- str_detect('CMEMS_GCB', "FESOM-REcoM|ETHZ-CESM")

Load masks

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

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

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

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

Define labels and breaks

labels_breaks <- function(i_name) {
  
  if (i_name == "dco2") {
    i_legend_title <- "ΔpCO<sub>2</sub><br>(µatm)"
  }
  
  if (i_name == "dfco2") {
    i_legend_title <- "ΔfCO<sub>2</sub><br>(µatm)"
  }
  
  if (i_name == "atm_co2") {
    i_legend_title <- "pCO<sub>2,atm</sub><br>(µatm)"
  }
  
  if (i_name == "atm_fco2") {
    i_legend_title <- "fCO<sub>2,atm</sub><br>(µatm)"
  }
  
  if (i_name == "sol") {
    i_legend_title <- "K<sub>0</sub><br>(mol m<sup>-3</sup> µatm<sup>-1</sup>)"
  }
  
  if (i_name == "kw") {
    i_legend_title <- "k<sub>w</sub><br>(m yr<sup>-1</sup>)"
  }
  
  if (i_name == "kw_sol") {
    i_legend_title <- "k<sub>w</sub> K<sub>0</sub><br>(mol yr<sup>-1</sup> m<sup>-2</sup> µatm<sup>-1</sup>)"
  }
  
  if (i_name == "spco2") {
    i_legend_title <- "pCO<sub>2,ocean</sub><br>(µatm)"
  }
  
  if (i_name == "sfco2") {
    i_legend_title <- "fCO<sub>2,ocean</sub><br>(µatm)"
  }
  
  if (i_name == "intpp") {
    i_legend_title <- "NPP<sub>int</sub><br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
  }

  if (i_name == "no3") {
    i_legend_title <- "NO<sub>3</sub><br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "o2") {
    i_legend_title <- "O<sub>2</sub><br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "dissic") {
    i_legend_title <- "DIC<br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "sdissic") {
    i_legend_title <- "sDIC<br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "cstar") {
    i_legend_title <- "C*<br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "talk") {
    i_legend_title <- "TA<br>(μmol kg<sup>-1</sup>)"
  }

  if (i_name == "stalk") {
    i_legend_title <- "sTA<br>(μmol kg<sup>-1</sup>)"
  }
  
  
  if (i_name == "sdissic_stalk") {
    i_legend_title <- "sDIC-sTA<br>(μmol kg<sup>-1</sup>)"
  }
  
  if (i_name == "sfco2_total") {
    i_legend_title <- "total"
  }
  
  if (i_name == "sfco2_therm") {
    i_legend_title <- "thermal"
  }
  
  if (i_name == "sfco2_nontherm") {
    i_legend_title <- "non-thermal"
  }
  
  if (i_name == "fgco2") {
    i_legend_title <- "FCO<sub>2</sub><br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
  }
  
  if (i_name == "fgco2_predict") {
    i_legend_title <- "FCO<sub>2</sub> pred.<br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
  }
  
  if (i_name == "fgco2_hov") {
    i_legend_title <- "FCO<sub>2</sub><br>(PgC deg<sup>-1</sup> yr<sup>-1</sup>)"
  }
  
  if (i_name == "fgco2_int") {
    i_legend_title <- "FCO<sub>2</sub><br>(PgC yr<sup>-1</sup>)"
  }
  
  if (i_name == "fgco2_predict_int") {
    i_legend_title <- "FCO<sub>2</sub> pred.<br>(PgC yr<sup>-1</sup>)"
  }
  
  if (i_name == "thetao") {
    i_legend_title <- "Temp.<br>(°C)"
  }
  
  if (i_name == "temperature") {
    i_legend_title <- "SST<br>(°C)"
  }
  
  if (i_name == "salinity") {
    i_legend_title <- "SSS"
  }
  
  if (i_name == "so") {
    i_legend_title <- "salinity"
  }
  
  if (i_name == "chl") {
    i_legend_title <- "lg(Chl-a)<br>(lg(mg m<sup>-3</sup>))"
  }
  
  if (i_name == "mld") {
    i_legend_title <- "MLD<br>(m)"
  }
  
  if (i_name == "press") {
    i_legend_title <- "pressure<sub>atm</sub><br>(Pa)"
  }
  
  if (i_name == "wind") {
    i_legend_title <- "Wind <br>(m sec<sup>-1</sup>)"
  }
  
  if (i_name == "SSH") {
    i_legend_title <- "SSH <br>(m)"
  }
  
  if (i_name == "fice") {
    i_legend_title <- "Sea ice <br>(%)"
  }
  
    
  if (i_name == "resid_fgco2") {
    i_legend_title <-
      "Observed"
  }
    
  if (i_name == "resid_fgco2_dfco2") {
    i_legend_title <-
      "ΔfCO<sub>2</sub>"
  }
    
  if (i_name == "resid_fgco2_kw_sol") {
    i_legend_title <-
      "k<sub>w</sub> K<sub>0</sub>"
  }
    
  if (i_name == "resid_fgco2_dfco2_kw_sol") {
    i_legend_title <-
      "k<sub>w</sub> K<sub>0</sub> X ΔfCO<sub>2</sub>"
  }
    
  if (i_name == "resid_fgco2_sum") {
    i_legend_title <-
      "∑"
  }
    
  if (i_name == "resid_fgco2_offset") {
    i_legend_title <-
      "Obs. - ∑"
  }
  
  all_labels_breaks <- lst(i_legend_title)
  
  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_predict" = labels_breaks("fgco2_predict")$i_legend_title,
    "fgco2_hov" = labels_breaks("fgco2_hov")$i_legend_title,
    "fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
    "fgco2_predict_int" = labels_breaks("fgco2_int")$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
  )

Analysis settings

name_quadratic_fit <- c("atm_co2", "atm_fco2", "spco2", "sfco2")
# name_quadratic_fit <- c(name_quadratic_fit, "dfco2", "fgco2", "fgco2_int", "temperature")

start_year <- 1990

name_divergent <- c("dco2", "dfco2", "fgco2", "fgco2_hov", "fgco2_int")

Data preprocessing

pco2_product <-
  pco2_product %>%
  filter(year >= start_year)

pco2_product_interior <-
  pco2_product_interior %>%
  filter(time >= ymd(paste0(start_year, "-01-01")))

biome_mask <- biome_mask %>% 
  mutate(area = earth_surf(lat, lon))

pco2_product <-
  full_join(pco2_product,
            biome_mask)

# set all values outside biome mask to NA

pco2_product <-
  pco2_product %>%
  mutate(across(-c(lat, lon, time, area, year, month, biome), 
                ~ if_else(is.na(biome), NA, .)))

Compuations

Maps

Biome means

pco2_product_biome_monthly_global <-
  pco2_product %>%
  filter(!is.na(fgco2)) %>%
  mutate(fgco2_int = fgco2) %>%
  mutate(biome = case_when(str_detect(biome, "SO-SPSS|SO-ICE|Arctic") ~ "Polar",
                           TRUE ~ "Global non-polar")) %>%
  filter(biome == "Global non-polar") %>%
  select(-c(lon, lat, year, month)) %>%
  group_by(time, biome) %>%
  summarise(across(-c(fgco2_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_int,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_biome_monthly_biome <-
  pco2_product %>%
  filter(!is.na(fgco2)) %>% 
  mutate(fgco2_int = fgco2) %>% 
  select(-c(lon, lat, year, month)) %>% 
  group_by(time, biome) %>%
  summarise(across(-c(fgco2_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_int,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()


pco2_product_biome_monthly <-
  bind_rows(pco2_product_biome_monthly_global,
            pco2_product_biome_monthly_biome)

rm(
  pco2_product_biome_monthly_global,
  pco2_product_biome_monthly_biome
)


pco2_product_biome_monthly <-
  pco2_product_biome_monthly %>% 
  filter(!is.na(biome))

pco2_product_biome_monthly <-
  pco2_product_biome_monthly %>%
  mutate(year = year(time),
         month = month(time),
         .after = time)

pco2_product_biome_monthly <-
  pco2_product_biome_monthly %>%
  pivot_longer(-c(time, year, month, biome))


pco2_product_biome_annual <-
  pco2_product_biome_monthly %>%
  group_by(year, biome, name) %>%
  summarise(value = mean(value)) %>%
  ungroup()

Profiles

pco2_product_interior <- 
  left_join(
    biome_mask,
    pco2_product_interior
  )

pco2_product_profiles <- pco2_product_interior %>%
  fselect(-c(lat, lon)) %>%
  fgroup_by(biome, depth, time) %>% {
    add_vars(fgroup_vars(., "unique"),
             fmean(.,
                   w = area,
                   keep.w = FALSE,
                   keep.group_vars = FALSE))
  }

pco2_product_profiles <-
  pco2_product_profiles %>%
  mutate(
    year = year(time),
    month = month(time)
  )

gc()

Zonal mean sections

pco2_product_interior <- 
  left_join(
    region_mask,
    pco2_product_interior %>% select(-c(biome, area))
  )

pco2_product_zonal_mean <- pco2_product_interior %>%
  fselect(-c(lon)) %>%
  fgroup_by(region, depth, lat, time) %>% {
    add_vars(fgroup_vars(., "unique"),
             fmean(.,
                   keep.group_vars = FALSE))
  }

pco2_product_zonal_mean <-
  pco2_product_zonal_mean %>%
  mutate(
    year = year(time),
    month = month(time)
  )

gc()

rm(pco2_product_interior)
gc()

Absolute values

Hovmoeller plots

The following Hovmoeller plots show the value of each variable as provided through the pCO2 product. Hovmoeller plots are first presented as annual means, and than as monthly means.

Annual means

pco2_product_hovmoeller_annual <-
  pco2_product %>%
  mutate(fgco2_int = fgco2) %>% 
  select(-c(lon, time, month, biome)) %>%
  group_by(year, lat) %>%
  summarise(across(-c(fgco2_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_int,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup() %>%
  rename(fgco2_hov = fgco2_int) %>% 
  filter(fgco2_hov != 0)

pco2_product_hovmoeller_annual <-
  pco2_product_hovmoeller_annual %>%
  pivot_longer(-c(year, lat)) %>% 
  drop_na()

# pco2_product_hovmoeller_annual %>%
#   filter(!(name %in% name_divergent)) %>% 
#   group_split(name) %>%
#   # tail(5) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(year, lat, fill = value)) +
#       geom_raster() +
#       scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
#       theme(legend.title = element_markdown()) +
#       coord_cartesian(expand = 0) +
#       labs(title = "Annual means",
#            y = "Latitude") +
#       theme(axis.title.x = element_blank())
#   )
# 
# pco2_product_hovmoeller_annual %>%
#   filter(name %in% name_divergent) %>% 
#   group_split(name) %>%
#   # head(1) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(year, lat, fill = value)) +
#       geom_raster() +
#       scale_fill_gradientn(
#         colours = cmocean("curl")(100),
#         rescaler = ~ scales::rescale_mid(.x, mid = 0),
#         name = labels_breaks(.x %>% distinct(name)),
#         limits = c(quantile(.x$value, .01), quantile(.x$value, .99)),
#         oob = squish
#       ) +
#       theme(legend.title = element_markdown()) +
#       coord_cartesian(expand = 0) +
#       labs(title = "Annual means",
#            y = "Latitude") +
#       theme(axis.title.x = element_blank())
#   )

Monthly means

pco2_product_hovmoeller_monthly <-
  pco2_product %>%
  mutate(fgco2_int = fgco2) %>% 
  select(-c(lon, time, biome)) %>%
  group_by(year, month, lat) %>%
  summarise(across(-c(fgco2_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_int,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup() %>%
  rename(fgco2_hov = fgco2_int) %>% 
  filter(fgco2_hov != 0)


pco2_product_hovmoeller_monthly <-
  pco2_product_hovmoeller_monthly %>%
  pivot_longer(-c(year, month, lat)) %>% 
  drop_na()

pco2_product_hovmoeller_monthly <-
  pco2_product_hovmoeller_monthly %>% 
  mutate(decimal = year + (month-1) / 12)

# pco2_product_hovmoeller_monthly %>%
#   filter(!(name %in% name_divergent)) %>%
#   group_split(name) %>%
#   # head(1) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(decimal, lat, fill = value)) +
#       geom_raster() +
#       scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
#       theme(legend.title = element_markdown()) +
#       labs(title = "Monthly means",
#            y = "Latitude") +
#       coord_cartesian(expand = 0) +
#       theme(axis.title.x = element_blank())
#   )
# 
# pco2_product_hovmoeller_monthly %>%
#   filter(name %in% name_divergent) %>%
#   group_split(name) %>%
#   # head(1) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(decimal, lat, fill = value)) +
#       geom_raster() +
#       scale_fill_gradientn(
#         colours = cmocean("curl")(100),
#         rescaler = ~ scales::rescale_mid(.x, mid = 0),
#         name = labels_breaks(.x %>% distinct(name)),
#         limits = c(quantile(.x$value, .01), quantile(.x$value, .99)),
#         oob = squish
#       )+
#       theme(legend.title = element_markdown()) +
#       labs(title = "Monthly means",
#            y = "Latitude") +
#       coord_cartesian(expand = 0) +
#       theme(axis.title.x = element_blank())
#   )

rm(pco2_product)

gc()

pCO2_product_analysis_2023 <-
  knitr::knit_expand(
    file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
    product_name = "CMEMS_GCB",
    year_anom = 2023
  )

2023 anomalies

Functions

Anomaly detection

For the detection of anomalies at any point in time and space, we fit regression models and compare the fitted to the actual value.

We use linear regression models for all parameters, except for , which are approximated with quadratic fits.

The regression models are fitted to all data since , except 2023.

anomaly_determination <- function(df,...) {
  
  group_by <- quos(...)
  # group_by <- quos(lon, lat)
  # group_by <- quos(biome)
  # df <- pco2_product_map_annual
  # Linear regression models

  df_lm <-
    df %>%
    filter(year != 2023,
           !(name %in% name_quadratic_fit)) %>%
    drop_na() %>%
    nest(data = -c(name, !!!group_by)) %>%
    mutate(fit = map(data, ~ flm(
      formula = value ~ year, data = .x
    )))
  
  df_lm <-
    left_join(
      df_lm %>%
        unnest_wider(fit) %>%
        select(name, !!!group_by,
               intercept = `(Intercept)`,  slope = year) %>%
        mutate(intercept = as.vector(intercept),
               slope = as.vector(slope)),
      df
    ) %>%
    mutate(fit = intercept + year * slope) %>%
    select(name, !!!group_by, year, fit, value) %>%
    mutate(resid = value - fit)

  # df_lm <-
  #   df %>%
  #   filter(year != 2023,
  #          !(name %in% name_quadratic_fit)) %>%
  #   drop_na() %>% 
  #   nest(data = -c(name, !!!group_by)) %>%
  #   mutate(
  #     fit = map(data, ~ lm(value ~ year, data = .x)),
  #     tidied = map(fit, tidy),
  #     augmented = map(fit, augment)
  #   )
  # 
  # 
  # df_lm_year_anom <-
  #   full_join(
  #     df_lm %>%
  #       unnest(tidied) %>%
  #       select(name, !!!group_by, term, estimate) %>%
  #       pivot_wider(names_from = term,
  #                   values_from = estimate) %>%
  #       mutate(fit = `(Intercept)` + year * 2023) %>%
  #       select(name, !!!group_by, fit) %>%
  #       mutate(year = 2023),
  #     df %>%
  #       filter(year == 2023,
  #              !(name %in% name_quadratic_fit))
  #   ) %>%
  #   mutate(resid = value - fit)
  # 
  # 
  # df_lm <-
  #   bind_rows(
  #     df_lm %>%
  #       unnest(augmented) %>%
  #       select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
  #     df_lm_year_anom
  #   )
  # 
  # rm(df_lm_year_anom)
  
  # Quadratic regression models
  
  if(any(df %>% distinct(name) %>% pull() %in% name_quadratic_fit)){
  
  df_quadratic <-
    df %>%
    filter(year != 2023,
           name %in% name_quadratic_fit) %>%
    drop_na() %>% 
    mutate(year = year - 2000) %>% 
    nest(data = -c(name, !!!group_by)) %>%
    mutate(
      fit = map(data, ~ flm(
        # formula = value ~ year + I(year ^ 2), data = .x))
        # formula = value ~ year + I(year ^ 2) + I(year ^ 3), data = .x))
        formula = value ~ poly(year, 2, raw = TRUE), data = .x))
    )
  
  df_quadratic <-
    left_join(
      df_quadratic %>%
        unnest_wider(fit) %>%
        select(name, !!!group_by,
               intercept = `(Intercept)`,
               slope = `1`, 
               slope_squared = `2`
               # slope_cube = `3`
               ) %>%
        mutate(intercept = as.vector(intercept),
               slope = as.vector(slope),
               slope_squared = as.vector(slope_squared)
               # slope_cube = as.vector(slope_cube)
               ),
      df %>% 
        mutate(year = year - 2000)
    ) %>%
    mutate(fit = intercept + year * slope + year^2 * slope_squared) %>%
    select(name, !!!group_by, year, fit, value) %>%
    mutate(resid = value - fit,
           year = year + 2000)
  
  
  # df_quadratic <-
  #   df %>%
  #   filter(year != 2023,
  #          name %in% name_quadratic_fit) %>%
  #   nest(data = -c(name, !!!group_by)) %>%
  #   mutate(
  #     fit = map(data, ~ lm(value ~ year + I(year ^ 2), data = .x)),
  #     tidied = map(fit, tidy),
  #     augmented = map(fit, augment)
  #   )
  # 
  # df_quadratic_year_anom <-
  #   full_join(
  #     df_quadratic %>%
  #       unnest(tidied) %>%
  #       select(name, !!!group_by, term, estimate) %>%
  #       pivot_wider(names_from = term,
  #                   values_from = estimate) %>%
  #       mutate(fit = `(Intercept)` + year * 2023 + `I(year^2)` * 2023 ^ 2) %>%
  #       select(name, !!!group_by, fit) %>%
  #       mutate(year = 2023),
  #     df %>%
  #       filter(year == 2023,
  #              name %in% name_quadratic_fit)
  #   ) %>%
  #   mutate(resid = value - fit)
  # 
  # 
  # df_quadratic <-
  #   bind_rows(
  #     df_quadratic %>%
  #       unnest(augmented) %>%
  #       select(name, !!!group_by, year, value, fit = .fitted, resid = .resid),
  #     df_quadratic_year_anom
  #   )
  # 
  # rm(df_quadratic_year_anom)
  
  # Join linear and quadratic regression results
  
  df_anomaly <-
    bind_rows(df_lm,
              df_quadratic)
  
  rm(df_lm,
     df_quadratic)
  
  } else{
    
    df_anomaly <- df_lm
    
    rm(df_lm)
  }
  
  df_anomaly <-
    df_anomaly %>%
    arrange(year)
  
  
  return(df_anomaly)
  
}

Seasonality plots

warm_color <- "#B84A60FF"
cold_color <- "#16877CFF"

p_season <- function(df, 
                     dim_row = "name", 
                     dim_col = "biome", 
                     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 = 1) +
    scale_color_manual(values = c("grey", "black"),
                       guide = guide_legend(order = 2,
                                            reverse = TRUE)) +
    new_scale_color()+
    geom_path(data = . %>% filter(year == 2023),
                aes(col = as.factor(year)),
                linewidth = 1) +
      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_grid(
        as.formula(paste(dim_row, "~", dim_col)),
        scales = scales,
        labeller = labeller(name = x_axis_labels),
        switch = "y"
      )
    
    
  } else {
    p <- p +
      facet_grid(
        as.formula(paste(dim_row, "~ .")),
        scales = "free_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()
    )
  
  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")
  
}

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,
      sfco2_nontherm = (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(!!!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"
      )
    ))
  
}

Maps

The following maps show the absolute state of each variable in 2023 as provided through the pCO2 product, the change in that variable from 1990 to 2023, as well es the anomalies in 2023. Changes and anomalies are determined based on the predicted value of a linear regression model fit to the 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 absolute

pco2_product_map_annual_anomaly <-
  pco2_product_map_annual %>%
  drop_na() %>% 
  anomaly_determination(lon, lat)

pco2_product_map_annual_anomaly <-
  pco2_product_map_annual_anomaly %>%
  drop_na()

pco2_product_map_annual_anomaly %>%
  filter(year == 2023,
         !(name %in% name_divergent)) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = value)) +
      labs(title = paste("Annual mean", 2023)) +
      scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
      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")
  )

Past versions of 2023_map_annual_absolute-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_absolute-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_absolute-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_absolute-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_map_annual_anomaly %>%
  filter(year == 2023,
         name %in% name_divergent) %>%
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = value)) +
         labs(title = paste("Annual mean", 2023)) +
         scale_fill_gradientn(
           colours = cmocean("curl")(100),
           rescaler = ~ scales::rescale_mid(.x, mid = 0),
           name = labels_breaks(.x %>% distinct(name)),
           limits = c(quantile(.x$value, .01), quantile(.x$value, .99)),
           oob = squish
         ) +
      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")
  )

Past versions of 2023_map_annual_absolute-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_absolute-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Trends

pco2_product_map_annual_anomaly %>%
  group_by(name) %>%
  filter(year %in% c(min(year), max(year), 2023)) %>%
  ungroup() %>%
  select(-c(value, resid)) %>%
  filter(year %in% c(min(year), max(year))) %>%
  arrange(year) %>% 
  group_by(lon, lat, name) %>%
  mutate(change = fit - lag(fit),
         period = paste(lag(year), year, sep = "-")) %>%
  ungroup() %>%
  filter(!is.na(change)) %>%
  group_split(name) %>% 
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = change)) +
      labs(title =  paste("Change: ",.x$period)) +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$change, .01), quantile(.x$change, .99)),
        oob = squish
      ) +
      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")
  )

Past versions of 2023_map_annual_change-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_change-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_change-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_change-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_change-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_change-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

2023 anomaly

pco2_product_map_annual_anomaly %>%
  filter(year == 2023) %>%
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = resid)) +
         labs(title =  paste(2023,"anomaly")) +
         scale_fill_gradientn(
           colours = cmocean("curl")(100),
           rescaler = ~ scales::rescale_mid(.x, mid = 0),
           name = labels_breaks(.x %>% distinct(name)),
           limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
           oob = squish
         )+
      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")
  )

Past versions of 2023_map_annual_anomaly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_anomaly-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_anomaly-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_anomaly-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_anomaly-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_annual_anomaly-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

SST flux slope

pco2_product_map_annual_slope <-
pco2_product_map_annual_anomaly %>%
  filter(year != 2023) %>% 
  select(year, lon, lat, resid, name) %>% 
  pivot_wider(values_from = resid) %>%
  select(lon, lat, fgco2, temperature) %>%
  drop_na() %>% 
  nest(data = -c(lon, lat)) %>%
  mutate(fit = map(data, ~ flm(
    formula = fgco2 ~ temperature, data = .x
  )))
  
pco2_product_map_annual_slope <-
  pco2_product_map_annual_slope %>%
  unnest_wider(fit) %>%
  select(lon, lat, slope = temperature) %>%
  mutate(slope = as.vector(slope))

map +
  geom_tile(data = pco2_product_map_annual_slope, 
            aes(lon, lat, fill = slope)) +
  scale_fill_gradientn(
    colours = cmocean("curl")(100),
    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
  ) +
  labs(title = "Correlation of historic annual flux and SST anomalies") +
  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")

Past versions of map_annual_anomaly_slope-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_map_annual_anomaly_temperature_predict <-
  inner_join(
    pco2_product_map_annual_slope,
    pco2_product_map_annual_anomaly %>%
      filter(name %in% c("temperature", "fgco2")) %>% 
      select(lon, lat, name, resid, year) %>% 
      drop_na() %>% 
      arrange(desc(name)) %>% 
      pivot_wider(values_from = "resid")
  )

pco2_product_map_annual_anomaly_temperature_predict <-
  pco2_product_map_annual_anomaly_temperature_predict %>%
  mutate(fgco2_predict = slope * temperature)

pco2_product_map_annual_anomaly_temperature_predict %>%
  filter(year == 2023) %>%
  select(-slope) %>% 
  pivot_longer(c(fgco2, temperature, fgco2_predict),
               values_to = "resid") %>%
  drop_na() %>% 
  # filter(name == "fgco2_predict") %>% 
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = resid)) +
         labs(title =  paste(2023,"anomaly")) +
         scale_fill_gradientn(
           colours = cmocean("curl")(100),
           rescaler = ~ scales::rescale_mid(.x, mid = 0),
           name = labels_breaks(.x %>% distinct(name)),
           limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
           oob = squish
         ) +
      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")
  )

[[1]]

Past versions of map_annual_anomaly_slope-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

[[2]]

Past versions of map_annual_anomaly_slope-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

[[3]]

Past versions of map_annual_anomaly_slope-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly_temperature_predict <-
  full_join(
    pco2_product_map_annual_anomaly_temperature_predict %>% 
      select(lon, lat, year, temperature, fgco2, fgco2_predict),
    biome_mask %>%
      mutate(area = earth_surf(lat, lon))
  )


pco2_product_biome_annual_anomaly_temperature_predict_global <-
  pco2_product_biome_annual_anomaly_temperature_predict %>%
  drop_na() %>%
  mutate(fgco2_int = fgco2,
         fgco2_predict_int = fgco2_predict) %>%
  mutate(biome = case_when(str_detect(biome, "SO-SPSS|SO-ICE|Arctic") ~ "Polar",
                           TRUE ~ "Global non-polar")) %>%
  filter(biome == "Global non-polar") %>%
  select(-c(lon, lat)) %>%
  group_by(year, biome) %>%
  summarise(across(-c(fgco2_int, fgco2_predict_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(c(fgco2_int, fgco2_predict_int),
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_biome_annual_anomaly_temperature_predict <-
  pco2_product_biome_annual_anomaly_temperature_predict %>%
  drop_na() %>%
  mutate(fgco2_int = fgco2,
         fgco2_predict_int = fgco2_predict) %>%
  select(-c(lon, lat)) %>%
  group_by(year, biome) %>%
  summarise(across(-c(fgco2_int, fgco2_predict_int, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(c(fgco2_int, fgco2_predict_int),
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_biome_annual_anomaly_temperature_predict <-
  bind_rows(pco2_product_biome_annual_anomaly_temperature_predict_global,
            pco2_product_biome_annual_anomaly_temperature_predict)

rm(pco2_product_biome_annual_anomaly_temperature_predict_global)

pco2_product_biome_annual_anomaly_temperature_predict <-
  pco2_product_biome_annual_anomaly_temperature_predict %>% 
  filter(!is.na(biome))

pco2_product_biome_annual_anomaly_temperature_predict %>% 
  select(year, biome, fgco2_int, fgco2_predict_int) %>% 
  filter(biome == "Global non-polar") %>% 
  pivot_longer(starts_with("fgco2"),
               values_to = "resid") %>% 
  ggplot(aes(year, resid, col = name)) +
  geom_path() +
  geom_point()

Past versions of compute_global_means_and_integrals_predicted_fluxes-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly_temperature_predict %>%
  ggplot(aes(fgco2_int, fgco2_predict_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")$i_legend_title,
       x = labels_breaks("fgco2_predict_int")$i_legend_title) +
  facet_wrap(~ biome, scales = "free") +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    legend.title = element_blank(),
    legend.position = "top"
  )

Past versions of compute_global_means_and_integrals_predicted_fluxes-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly_temperature_predict %>%
  ggplot(aes(fgco2_int, fgco2_predict_int)) +
  geom_smooth(
    data = . %>% filter(year != 2023),
    method = "lm",
    fill = "grey",
    col = "grey40",
    fullrange = TRUE,
    level = 0.68
  ) +
  geom_point(aes(fill = temperature),
             shape = 21) +
  scale_fill_divergent() +
  labs(y = labels_breaks("fgco2_int")$i_legend_title,
       x = labels_breaks("fgco2_predict_int")$i_legend_title) +
  facet_wrap(~ biome, scales = "free") +
  theme(
    axis.title.x = element_markdown(),
    axis.title.y = element_markdown(),
    legend.title = element_blank(),
    legend.position = "top"
  )

Past versions of compute_global_means_and_integrals_predicted_fluxes-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_map_annual_anomaly %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_annual_anomaly.csv"
    )
  )

pco2_product_map_annual_anomaly_temperature_predict %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_annual_anomaly_temperature_predict.csv"
    )
  )

pco2_product_biome_annual_anomaly_temperature_predict %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_annual_anomaly_temperature_predict.csv"
    )
  )

rm(pco2_product_map_annual_anomaly,
   pco2_product_map_annual_slope,
   pco2_product_map_annual_anomaly_temperature_predict,
   pco2_product_biome_annual_anomaly_temperature_predict)
gc()

Monthly means

2023 absolute

pco2_product_map_monthly_anomaly <-
  pco2_product_map_monthly %>%
  drop_na() %>% 
  anomaly_determination(lon, lat, month)

pco2_product_map_monthly_anomaly <-
  pco2_product_map_monthly_anomaly %>% 
  drop_na()



pco2_product_map_monthly_anomaly %>%
  filter(year == 2023, !(name %in% name_divergent)) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(lon, lat, fill = value)) +
      labs(title = paste("Monthly means", 2023)) +
      scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
      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") +
      facet_wrap(~ month, ncol = 2)
  )

Past versions of 2023_map_monthly_absolute-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_absolute-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_absolute-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_absolute-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_map_monthly_anomaly %>%
  filter(year == 2023, name %in% name_divergent) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(lon, lat, fill = value)) +
      labs(title = paste("Monthly means", 2023)) +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$value, .01), quantile(.x$value, .99)),
        oob = squish
      ) +
      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") +
      facet_wrap( ~ month, ncol = 2)
  )

Past versions of 2023_map_monthly_absolute-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_absolute-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Trends

pco2_product_map_monthly_anomaly %>%
  group_by(name) %>%
  filter(year %in% c(min(year), max(year))) %>%
  ungroup() %>%
  select(-c(value, resid)) %>%
  arrange(year) %>%
  group_by(lon, lat, name, month) %>%
  mutate(change = fit - lag(fit),
         period = paste(lag(year), year, sep = "-")) %>%
  ungroup() %>%
  filter(!is.na(change)) %>%
  group_split(name) %>%
  head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(lon, lat, fill = change)) +
      labs(title =  paste("Change: ", .x$period)) +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$change, .01), quantile(.x$change, .99)),
        oob = squish
      ) +
      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") +
      facet_wrap(~ month, ncol = 2)
  )

2023 anomaly

pco2_product_map_monthly_anomaly %>%
  filter(year == 2023) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x, aes(lon, lat, fill = resid)) +
      labs(title = paste(2023, "anomaly")) +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      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") +
      facet_wrap( ~ month, ncol = 2)
  )

Past versions of 2023_map_monthly_anomaly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_anomaly-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_anomaly-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_anomaly-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_anomaly-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_map_monthly_anomaly-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

fCO2 decomposition

pco2_product_map_monthly_fCO2_decomposition <-
  fco2_decomposition(pco2_product_map_monthly_anomaly,
                     year, month, lon, lat)


# pco2_product_map_monthly_fCO2_decomposition %>%
#   filter(year == 2023) %>%
#   mutate(product == "pco2 product") %>%
#   group_split(product) %>%
#   head(1) %>%
#   map(
#     ~ map +
#       geom_tile(data = .x,
#                 aes(lon, lat, fill = resid)) +
#       labs(title = .x$product) +
#       scale_fill_gradientn(
#         colours = cmocean("curl")(100),
#         rescaler = ~ scales::rescale_mid(.x, mid = 0),
#         name = labels_breaks("sfco2"),
#         limits = c(quantile(.x$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_annual_fCO2_decomposition <-
  pco2_product_map_monthly_fCO2_decomposition %>% 
  select(year, lat, lon, name, resid) %>% 
  fgroup_by(year, lat, lon, name) %>% 
  fmean()

gc()

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    4278597   228.6   41237837  2202.4  125847888  6721.1
Vcells 1682804038 12838.8 4138972802 31577.9 4138965342 31577.8

map +
  geom_tile(data = pco2_product_map_annual_fCO2_decomposition %>%
              filter(year == 2023), aes(lon, lat, fill = resid)) +
  scale_fill_gradientn(
    colours = cmocean("curl")(100),
    rescaler = ~ scales::rescale_mid(.x, mid = 0),
    name = labels_breaks("sfco2"),
    limits = c(
      quantile(pco2_product_map_annual_fCO2_decomposition$resid, .01),
      quantile(pco2_product_map_annual_fCO2_decomposition$resid, .99)
    ),
    oob = squish
  ) +
  facet_wrap(~ name,
             ncol = 2,
             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")

Past versions of fCO2_decomposition_annual_maps-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Flux attribution

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() %>%
#   mutate(product == "pco2 product") %>%
#   group_split(product) %>%
#   head(1) %>%
#   map(
#     ~ map +
#       geom_tile(data = .x,
#                 aes(lon, lat, fill = resid)) +
#       labs(subtitle = .x$product) +
#       scale_fill_gradientn(
#         colours = cmocean("curl")(100),
#         rescaler = ~ scales::rescale_mid(.x, mid = 0),
#         name = labels_breaks("fgco2"),
#         limits = c(quantile(.x$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_annual_flux_attribution <-
  pco2_product_map_monthly_flux_attribution %>% 
  group_by(year, lat, lon, name) %>% 
  summarise(resid = mean(resid, na.rm = TRUE)) %>% 
  ungroup()

map +
  geom_tile(data = pco2_product_map_annual_flux_attribution %>%
              filter(year == 2023), aes(lon, lat, fill = resid)) +
  scale_fill_gradientn(
    colours = cmocean("curl")(100),
    rescaler = ~ scales::rescale_mid(.x, mid = 0),
    name = labels_breaks("fgco2"),
    limits = c(
      quantile(pco2_product_map_annual_flux_attribution$resid, .01, na.rm = TRUE),
      quantile(pco2_product_map_annual_flux_attribution$resid, .99, na.rm = TRUE)
    ),
    oob = squish
  ) +
  theme(legend.title = element_markdown(), legend.position = "bottom") +
  facet_wrap(~ name,
             ncol = 2,
             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()
    
  )

Past versions of flux_attribution_annual_maps-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

gc()

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells    4296525   229.5   32990270  1761.9  125847888  6721.1
Vcells 1928041834 14709.8 4138972802 31577.9 4138965342 31577.8

pco2_product_map_monthly_anomaly %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_monthly_anomaly.csv"
    )
  )

pco2_product_map_annual_flux_attribution %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_annual_flux_attribution.csv"
    )
  )

pco2_product_map_annual_fCO2_decomposition %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_annual_fCO2_decomposition.csv"
    )
  )

pco2_product_map_monthly_flux_attribution %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_monthly_flux_attribution.csv"
    )
  )

pco2_product_map_monthly_fCO2_decomposition %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_map_monthly_fCO2_decomposition.csv"
    )
  )

rm(pco2_product_map_annual_flux_attribution,
   pco2_product_map_annual_fCO2_decomposition)

gc()

Hovmoeller plots

The following Hovmoeller plots show the anomalies from the prediction of the linear/quadratic fits.

Hovmoeller plots are first presented as annual means, and than 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.

2023 annual anomalies

pco2_product_hovmoeller_annual_anomaly <-
  pco2_product_hovmoeller_annual %>%
  anomaly_determination(lat) %>% 
  filter(!is.na(resid))

  
pco2_product_hovmoeller_annual_anomaly %>%
  # filter(name == "mld") %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x, aes(year, lat, fill = resid)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      coord_cartesian(expand = 0) +
      labs(title = "Annual mean anomalies", y = "Latitude") +
      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")
  )

Past versions of 2023_hovmoeller_annual_anomaly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_annual_anomaly-7.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

2023 monthly anomalies

pco2_product_hovmoeller_monthly_anomaly <-
  pco2_product_hovmoeller_monthly %>%
  select(-c(decimal)) %>% 
  anomaly_determination(lat, month) %>% 
  filter(!is.na(resid))

  
pco2_product_hovmoeller_monthly_anomaly <-
  pco2_product_hovmoeller_monthly_anomaly %>%
  mutate(decimal = year + (month - 1) / 12)
  
pco2_product_hovmoeller_monthly_anomaly %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      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")
  )

Past versions of 2023_hovmoeller_monthly_anomalies-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies-7.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Three years prior 2023

pco2_product_hovmoeller_monthly_anomaly %>%
  filter(between(year, 2023-2, 2023)) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
      geom_raster() +
      scale_fill_gradientn(
        colours = cmocean("curl")(100),
        rescaler = ~ scales::rescale_mid(.x, mid = 0),
        name = labels_breaks(.x %>% distinct(name)),
        limits = c(quantile(.x$resid, .01), quantile(.x$resid, .99)),
        oob = squish
      ) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      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")
  )

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-5.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-6.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-7.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_hovmoeller_monthly_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_hovmoeller_monthly_anomaly.csv"
    )
  )

rm(
  pco2_product_hovmoeller_annual,
  pco2_product_hovmoeller_monthly,
  pco2_product_hovmoeller_annual_anomaly,
  pco2_product_hovmoeller_monthly_anomaly
)

gc()

Regional means and integrals

The following plots show regionally averaged (or integrated) values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit.

Anomalies are first presented relative to the predicted annual mean of each year, hence preserving the seasonality. Furthermore, anomalies are presented relative to the predicted monthly mean values, such that the mean seasonality is removed.

2023 absolute values

Global non-polar

fig.height <- pco2_product_biome_monthly %>% 
  distinct(name) %>% 
  nrow()

fig.height <- (fig.height + 2) * 0.1

pco2_product_biome_monthly %>%
  filter(biome %in% "Global non-polar") %>%
  ggplot(aes(month, value, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Absolute values | Global non-polar") +
  facet_wrap(name ~ .,
             scales = "free_y",
             labeller = labeller(name = x_axis_labels),
             strip.position = "left",
             ncol = 2) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    legend.title = element_blank(),
    axis.title.y = element_blank()
  )

Past versions of 2023_absolute_means_and_integrals_global-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Key biomes

pco2_product_biome_monthly %>%
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, value, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Absolute values | Selected biomes") +
  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(),
    legend.title = element_blank(),
    axis.title.y = element_blank()
  )

Past versions of 2023_absolute_means_and_integrals_overview_biomes-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly %>%
  filter(biome %in% key_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, value, group = as.factor(year))) +
      geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(between(year, 2023-1, 2023)),
        aes(col = as.factor(year)),
        linewidth = 1
      ) +
      scale_color_manual(
        values = c("orange", "red"),
        guide = guide_legend(reverse = TRUE,
                             order = 1)
      ) +
      scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = paste("Absolute values |", .x$biome)) +
  facet_wrap(name ~ .,
             scales = "free_y",
             labeller = labeller(name = x_axis_labels),
             strip.position = "left",
             ncol = 2) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    legend.title = element_blank(),
    axis.title.y = element_blank()
  )
  )

Past versions of 2023_absolute_means_and_integrals_key_biomes-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_absolute_means_and_integrals_key_biomes-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_absolute_means_and_integrals_key_biomes-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_absolute_means_and_integrals_key_biomes-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

2023 anomalies

Annual mean trends

pco2_product_biome_annual %>% 
  filter(biome %in% "Global non-polar") %>%
  ggplot(aes(year, value)) +
  geom_path() +
  geom_point(data = . %>% filter(year != 2023)) +
  geom_point(data = . %>% filter(year == 2023),
             shape = 1) +
  geom_smooth(data = . %>% filter(year != 2023,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              aes(col = "linear"),
              se = FALSE) +
  geom_smooth(data = . %>% filter(year != 2023,
                                  name %in% name_quadratic_fit),
              method = "lm",
              fullrange = TRUE,
              formula = y ~ poly(x,2),
              aes(col = "poly2"),
              se = FALSE) +
  scale_color_brewer(
    palette = "Set1",
    name = paste("Regression fit\nexcl.", 2023)) +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Annual mean trends | Global non-polar") +
  facet_wrap(name ~ .,
             scales = "free_y",
             labeller = labeller(name = x_axis_labels),
             strip.position = "left",
             ncol = 2) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    axis.title = element_blank()
  )

Past versions of 2023_annual_mean_trends-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(year, value)) +
  geom_path() +
  geom_point(data = . %>% filter(year != 2023),
             size = 0.2) +
  geom_point(data = . %>% filter(year == 2023),
             shape = 1) +
  geom_smooth(data = . %>% filter(year != 2023,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              aes(col = "linear"),
              se = FALSE) +
  geom_smooth(data = . %>% filter(year != 2023,
                                  name %in% name_quadratic_fit),
              method = "lm",
              fullrange = TRUE,
              formula = y ~ poly(x,2),
              aes(col = "poly2"),
              se = FALSE) +
  scale_color_brewer(
    palette = "Set1",
    name = paste("Regression fit\nexcl.", 2023)) +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Annual mean trends | Selected biomes") +
  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 = element_blank()
  )

Past versions of 2023_annual_mean_trends-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly <-
  pco2_product_biome_annual %>%
  anomaly_determination(biome)
  

pco2_product_biome_annual_anomaly %>%
  filter(biome %in% "Global non-polar") %>%
  ggplot(aes(year, resid)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
             aes(fill = year),
             shape = 21) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
             aes(fill = as.factor(year)),
             shape = 21, size = 2)  +
  scale_fill_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Residual from fit to annual means (actual - predicted) | Global non-polar") +
  facet_wrap(name ~ .,
             scales = "free_y",
             labeller = labeller(name = x_axis_labels),
             strip.position = "left",
             ncol = 2) +
  theme(
    strip.text.y.left = element_markdown(),
    strip.placement = "outside",
    strip.background.y = element_blank(),
    axis.title = element_blank(),
    legend.title = element_blank()
  )

Past versions of 2023_annual_mean_trends-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly %>%
  filter(biome %in% key_biomes) %>%
  ggplot(aes(year, resid)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
             aes(fill = year),
             shape = 21) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
             aes(fill = as.factor(year)),
             shape = 21, size = 2)  +
  scale_fill_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Residual from fit to annual means (actual - predicted) | Selected biomes") +
  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 = element_blank(),
    legend.title = element_blank()
  )

Past versions of 2023_annual_mean_trends-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_annual_anomaly.csv"
    )
  )

pco2_product_biome_annual_detrended <-
  full_join(pco2_product_biome_monthly,
            pco2_product_biome_annual_anomaly %>% select(-c(value, resid))) %>%
  mutate(resid = value - fit)

pco2_product_biome_annual_detrended %>% 
  filter(biome %in% "Global non-polar") %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Anomalies from predicted annual mean | Global non-polar") +
  facet_wrap(name ~ .,
             scales = "free_y",
             labeller = labeller(name = x_axis_labels),
             strip.position = "left",
             ncol = 2) +
  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 2023_anomalies_from_annual_mean_trends_overview-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_detrended %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Anomalies from predicted annual mean | Selected biomes") +
  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(),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
  )

Past versions of 2023_anomalies_from_annual_mean_trends_overview-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_detrended %>%
  filter(biome %in% key_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, resid, group = as.factor(year))) +
      geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(between(year, 2023-1, 2023)),
        aes(col = as.factor(year)),
        linewidth = 1
      ) +
      scale_color_manual(
        values = c("orange", "red"),
        guide = guide_legend(reverse = TRUE,
                             order = 1)
      ) +
      scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
      labs(title = paste("Anomalies from predicted annual mean |", .x$biome)) +
      facet_wrap(
        name ~ .,
        scales = "free_y",
        labeller = labeller(name = x_axis_labels),
        strip.position = "left",
        ncol = 2
      ) +
      theme(
        strip.text.y.left = element_markdown(),
        strip.placement = "outside",
        strip.background.y = element_blank(),
        axis.title = element_blank(),
        legend.title = element_blank()
      )
  )

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_annual_detrended %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_annual_detrended.csv"
    )
  )

Monthly mean trends

# pco2_product_biome_monthly %>% 
#   filter(biome %in% "Global non-polar") %>%
#   mutate(month = as.factor(month)) %>% 
#   ggplot(aes(year, value, col = month)) +
#   # geom_point() +
#   geom_smooth(data = . %>% filter(year != 2023,
#                                   !(name %in% name_quadratic_fit)),
#               method = "lm",
#               se = FALSE,
#               fullrange = TRUE) +
#   geom_smooth(
#     data = . %>% filter(year != 2023,
#                         name %in% name_quadratic_fit),
#     method = "lm",
#     fullrange = TRUE,
#     formula = y ~ x + I(x ^ 2),
#     se = FALSE
#   ) +
#   scale_color_scico_d(palette = "romaO", 
#     name = paste("Regression fit\nexcl.", 2023)) +
#   scale_x_continuous(breaks = seq(1980, 2020, 20)) +
#   labs(title = "Monthly mean trends | Global non-polar") +
#   facet_wrap(name ~ .,
#              scales = "free_y",
#              labeller = labeller(name = x_axis_labels),
#              strip.position = "left",
#              ncol = 2) +
#   theme(
#     strip.text.y.left = element_markdown(),
#     strip.placement = "outside",
#     strip.background.y = element_blank(),
#     axis.title = element_blank()
#   )
# 
# pco2_product_biome_monthly %>% 
#   filter(biome %in% key_biomes) %>%
#   mutate(month = as.factor(month)) %>% 
#   ggplot(aes(year, value, col = month)) +
#   # geom_point() +
#   geom_smooth(data = . %>% filter(year != 2023,
#                                   !(name %in% name_quadratic_fit)),
#               method = "lm",
#               fullrange = TRUE,
#               se = FALSE) +
#   geom_smooth(
#     data = . %>% filter(year != 2023,
#                         name %in% name_quadratic_fit),
#     method = "lm",
#     fullrange = TRUE,
#     formula = y ~ x + I(x ^ 2),
#     se = FALSE
#   ) +
#   scale_color_scico_d(palette = "romaO",
#     name = paste("Regression fit\nexcl.", 2023)) +
#   scale_x_continuous(breaks = seq(1980, 2020, 20)) +
#   labs(title = "Monthly mean trends | Selected biomes") +
#   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 = element_blank()
#   )

pco2_product_biome_monthly_anomaly <-
  pco2_product_biome_monthly %>%
  anomaly_determination(biome, month)


# pco2_product_biome_monthly_anomaly %>%
#   filter(biome %in% "Global non-polar") %>%
#   group_split(month) %>%
#   head(1) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(year, resid)) +
#       geom_hline(yintercept = 0) +
#       geom_path() +
#       geom_point(
#         data = . %>% filter(!between(year, 2023-1, 2023)),
#         aes(fill = year),
#         shape = 21
#       ) +
#       scale_fill_grayC() +
#       new_scale_fill() +
#       geom_point(
#         data = . %>% filter(between(year, 2023-1, 2023)),
#         aes(fill = as.factor(year)),
#         shape = 21,
#         size = 2
#       )  +
#       scale_fill_manual(
#         values = c("orange", "red"),
#         guide = guide_legend(reverse = TRUE,
#                              order = 1)
#       ) +
#       scale_x_continuous(breaks = seq(1980, 2020, 20)) +
#       labs(title = "Residual from fit to monthly means (actual - predicted) | Global non-polar",
#            subtitle = paste("Month:", .x$month)) +
#       facet_wrap(
#         name ~ .,
#         scales = "free_y",
#         labeller = labeller(name = x_axis_labels),
#         strip.position = "left",
#         ncol = 2
#       ) +
#       theme(
#         strip.text.y.left = element_markdown(),
#         strip.placement = "outside",
#         strip.background.y = element_blank(),
#         axis.title = element_blank(),
#         legend.title = element_blank()
#       )
#   )
# 
# pco2_product_biome_monthly_anomaly %>%
#   filter(biome %in% key_biomes) %>%
#   group_split(month) %>%
#   head(1) %>%
#   map(
#     ~ ggplot(data = .x,
#              aes(year, resid)) +
#       geom_hline(yintercept = 0) +
#       geom_path() +
#       geom_point(
#         data = . %>% filter(!between(year, 2023-1, 2023)),
#         aes(fill = year),
#         shape = 21
#       ) +
#       scale_fill_grayC() +
#       new_scale_fill() +
#       geom_point(
#         data = . %>% filter(between(year, 2023-1, 2023)),
#         aes(fill = as.factor(year)),
#         shape = 21,
#         size = 2
#       )  +
#       scale_fill_manual(
#         values = c("orange", "red"),
#         guide = guide_legend(reverse = TRUE,
#                              order = 1)
#       ) +
#       scale_x_continuous(breaks = seq(1980, 2020, 20)) +
#       labs(title = "Residual from fit to monthly means (actual - predicted) | Selected biomes",
#            subtitle = paste("Month:", .x$month)) +
#       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 = element_blank(),
#         legend.title = element_blank()
#       )
#   )

pco2_product_biome_monthly_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_monthly_anomaly.csv"
    )
  )

Global non-polar

pco2_product_biome_monthly_detrended <-
  full_join(pco2_product_biome_monthly,
            pco2_product_biome_monthly_anomaly %>% select(-c(value, resid))) %>%
  mutate(resid = value - fit)

pco2_product_biome_monthly_detrended %>% 
  filter(biome %in% "Global non-polar") %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Anomalies from predicted monthly mean | Global non-polar") +
  facet_wrap(
    name ~ .,
    scales = "free_y",
    labeller = labeller(name = x_axis_labels),
    strip.position = "left",
    ncol = 2
  ) +
  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 2023_anomalies_from_monthly_mean_trends_global-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly_detrended %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
  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 2023_anomalies_from_monthly_mean_trends_global-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Key biomes

pco2_product_biome_monthly_detrended %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(between(year, 2023-1, 2023)),
            aes(col = as.factor(year)),
            linewidth = 1) +
  scale_color_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
  labs(title = "Anomalies from predicted monthly mean | Selected biomes") +
  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 2023_anomalies_from_monthly_mean_trends__key_biomes_overview-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly_detrended %>%
  filter(biome %in% key_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, resid, group = as.factor(year))) +
      geom_path(data = . %>% filter(!between(year, 2023-1, 2023)),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(between(year, 2023-1, 2023)),
        aes(col = as.factor(year)),
        linewidth = 1
      ) +
      scale_color_manual(
        values = c("orange", "red"),
        guide = guide_legend(reverse = TRUE,
                             order = 1)
      ) +
      scale_x_continuous(breaks = seq(1, 12, 3), expand = c(0, 0)) +
      labs(title = paste("Anomalies from predicted monthly mean |", .x$biome)) +
      facet_wrap(
        name ~ .,
        scales = "free_y",
        labeller = labeller(name = x_axis_labels),
        strip.position = "left",
        ncol = 2
      ) +
      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 2023_anomalies_from_monthly_mean_trends_key_biomes-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-2.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-3.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-4.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly_detrended %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_monthly_detrended.csv"
    )
  )

2023 anomaly correlation

The following plots aim to unravel the correlation between regionally 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 integrated fluxes separately for each region. Secondly, we normalize the monthly anomalies to the spread (expressed as standard deviation) of the residuals from the fit.

Annual anomalies

Absolute

pco2_product_biome_annual_anomaly %>%
  filter(biome == "Global non-polar") %>%
  select(-c(value, fit)) %>% 
  pivot_wider(values_from = resid) %>% 
  pivot_longer(-c(year, biome, fgco2_int))  %>%
  ggplot(aes(value, fgco2_int)) +
  geom_hline(yintercept = 0) +
  geom_point(data = . %>% filter(!between(year, 2023-1, 2023)),
             aes(fill = year),
             shape = 21) +
  geom_smooth(
    data = . %>% filter(!between(year, 2023-1, 2023)),
    method = "lm",
    se = FALSE,
    fullrange = TRUE,
    aes(col = paste("Regression fit\nexcl.", 2023))
  ) +
  scale_color_grey() +
  scale_fill_grayC()+
  new_scale_fill() +
  geom_point(data = . %>% filter(between(year, 2023-1, 2023)),
             aes(fill = as.factor(year)),
             shape = 21, size = 2)  +
  scale_fill_manual(values = c("orange", "red"),
                     guide = guide_legend(reverse = TRUE,
                                          order = 1)) +
  labs(title = "Global non-polar integrated fluxes",
       y = labels_breaks("fgco2_int")$i_legend_title) +
  facet_wrap(
    ~ name,
    scales = "free_x",
    labeller = labeller(name = x_axis_labels),
    strip.position = "bottom",
    ncol = 2
  ) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.y = element_markdown(),
    axis.title.x = element_blank(),
    legend.title = element_blank()
  )

Past versions of 2023_anomalies_correlation_absolute_global_annual-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

Monthly anomalies

Absolute

pco2_product_biome_monthly_detrended_anomaly <-
  pco2_product_biome_monthly_detrended %>%
  select(year, month, biome, name, resid) %>%
  pivot_wider(names_from = name,
              values_from = resid)


pco2_product_biome_monthly_detrended_anomaly %>%
  filter(biome == "Global non-polar") %>%
  pivot_longer(-c(year, month, biome, fgco2_int))  %>%
  ggplot(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(title = "Global non-polar integrated fluxes",
       y = labels_breaks("fgco2_int")$i_legend_title) +
  facet_wrap(
    ~ name,
    scales = "free_x",
    labeller = labeller(name = x_axis_labels),
    strip.position = "bottom",
    ncol = 2
  ) +
  theme(
    strip.text.x.bottom = element_markdown(),
    strip.placement = "outside",
    strip.background.x = element_blank(),
    axis.title.y = element_markdown(),
    axis.title.x = element_blank()
  )

Past versions of 2023_anomalies_correlation_absolute_global_monthly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly_detrended_anomaly %>%
  filter(!(biome %in% c(key_biomes, "Global non-polar"))) %>%
  pivot_longer(-c(year, month, biome, fgco2_int))  %>%
  filter(name %in% c("temperature", "chl", "dfco2", "kw_sol")) %>% 
  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)
      ) +
      facet_wrap( ~ biome, ncol = 3, scales = "free") +
      labs(
        title = "Biome integrated fluxes",
        y = labels_breaks("fgco2_int")$i_legend_title,
        x = labels_breaks(.x %>% distinct(name))$i_legend_title
      ) +
      theme(axis.title.x = element_markdown(),
            axis.title.y = element_markdown())
  )

Past versions of 2023_anomalies_correlation_absolute_biome_monthly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Relative to spread

pco2_product_biome_monthly_detrended_anomaly_spread <-
  pco2_product_biome_monthly_detrended_anomaly %>%
  pivot_longer(-c(month, biome, year)) %>%
  filter(year != 2023) %>%
  group_by(month, biome, name) %>%
  summarise(spread = sd(value, na.rm = TRUE)) %>%
  ungroup()



pco2_product_biome_monthly_detrended_anomaly_relative <-
  full_join(
    pco2_product_biome_monthly_detrended_anomaly_spread,
    pco2_product_biome_monthly_detrended_anomaly %>%
      pivot_longer(-c(month, biome, year))
  )

pco2_product_biome_monthly_detrended_anomaly_relative <-
  pco2_product_biome_monthly_detrended_anomaly_relative %>%
  mutate(value = value / spread) %>%
  select(-spread) %>%
  pivot_wider() %>%
  pivot_longer(-c(month, biome, year, fgco2_int))



pco2_product_biome_monthly_detrended_anomaly_relative %>%
  filter(name %in% c("temperature", "chl", "dfco2", "kw_sol")) %>% 
  group_split(name) %>%
  head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_vline(xintercept = 0) +
      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)
      ) +
      facet_wrap( ~ biome, ncol = 3) +
      coord_fixed() +
      labs(
        title = "Biome integrated fluxes normalized to spread",
        y = str_split_i(labels_breaks("fgco2_int")$i_legend_title, "<br>", i = 1),
        x = str_split_i(labels_breaks(.x %>% distinct(name))$i_legend_title, "<br>", i = 1)
      ) +
      theme(axis.title.x = element_markdown(),
            axis.title.y = element_markdown())
  )

Past versions of 2023_anomalies_correlation_realtive_to_spread_monthly-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

fCO2 decomposition

biome_mask <-
  bind_rows(
    biome_mask,
    biome_mask %>% 
      filter(!str_detect(biome, "SO-SPSS|SO-ICE|Arctic")) %>% 
      mutate(biome = "Global non-polar")
  )

pco2_product_biome_monthly_fCO2_decomposition <-
  full_join(pco2_product_map_monthly_fCO2_decomposition,
            biome_mask,
            relationship = "many-to-many") %>% 
  group_by(year, month, biome, name) %>% 
  summarise(resid = mean(resid, na.rm = TRUE)) %>% 
  ungroup() %>% 
  drop_na()


pco2_product_biome_annual_fCO2_decomposition <-
  pco2_product_biome_monthly_fCO2_decomposition %>%
  group_by(year, biome, name) %>%
  summarise(resid = mean(resid)) %>%
  ungroup()


pco2_product_biome_monthly_fCO2_decomposition %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>%
  p_season(title  = paste("Anomalies from predicted monthly mean"))

Past versions of pCO2_decomposition-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
e30251a	jens-daniel-mueller	2025-02-27

Flux attribution

Seasonal

pco2_product_biome_monthly_flux_attribution <-
  full_join(pco2_product_map_monthly_flux_attribution,
            biome_mask,
            relationship = "many-to-many") %>% 
  group_by(year, month, biome, name) %>% 
  summarise(resid = mean(resid, na.rm = TRUE)) %>% 
  ungroup() %>% 
  drop_na()

pco2_product_biome_monthly_flux_attribution_total <-
  full_join(pco2_product_map_monthly_anomaly %>% 
              filter(name == "fgco2") %>% 
              mutate(name = "resid_fgco2"),
            biome_mask,
            relationship = "many-to-many") %>% 
  group_by(year, month, biome, name) %>% 
  summarise(resid = mean(resid, na.rm = TRUE)) %>% 
  ungroup() %>% 
  drop_na()

pco2_product_biome_monthly_flux_attribution <-
  bind_rows(
    pco2_product_biome_monthly_flux_attribution,
    pco2_product_biome_monthly_flux_attribution_total
  )


pco2_product_biome_annual_flux_attribution <-
  pco2_product_biome_monthly_flux_attribution %>%
  group_by(year, biome, name) %>%
  summarise(resid = mean(resid)) %>%
  ungroup()


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)
  ) +
  geom_point(
    aes(month, resid),
    shape = 21,
    alpha = 0.5,
    col = "grey30"
  ) +
  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-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

pco2_product_biome_monthly_flux_attribution %>%
  filter(biome %in% c("Global non-polar", key_biomes)) %>%
  p_season(title  = paste("Anomalies from predicted monthly mean"))

Past versions of flux_attribution_seasonal-1.png

Version	Author	Date
bdc4da6	jens-daniel-mueller	2025-02-28
ca52e03	jens-daniel-mueller	2025-02-27
e30251a	jens-daniel-mueller	2025-02-27

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(biome, name) %>%
      summarise(resid_mean = mean(abs(resid))) %>%
      ungroup()
  )

# pco2_product_biome_annual_flux_attribution %>%
#   filter(biome %in% c("Global non-polar", key_biomes)) %>%
#   mutate(product == "pco2 product") %>%
#   group_split(product) %>%
#   # head(1) %>%
#   map(
#     ~ ggplot(data = .x) +
#       geom_col(aes("x", resid),
#                position = "dodge2") +
#       geom_col(
#         aes(
#           "x",
#           resid_mean * sign(resid),
#           col = paste0("Mean\nexcl.",2023)
#         ),
#         position = "dodge2",
#         fill = "transparent"
#       ) +
#       labs(y = labels_breaks(unique("fgco2"))$i_legend_title,
#            title = .x$biome) +
#       facet_grid(
#         biome~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"
#       )
#   )

pco2_product_biome_annual_flux_attribution %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_annual_flux_attribution.csv"
    )
  )

pco2_product_biome_monthly_flux_attribution %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_monthly_flux_attribution.csv"
    )
  )

pco2_product_biome_annual_fCO2_decomposition %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_annual_fCO2_decomposition.csv"
    )
  )

pco2_product_biome_monthly_fCO2_decomposition %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_biome_monthly_fCO2_decomposition.csv"
    )
  )

rm(
  pco2_product_biome_annual_flux_attribution,
  pco2_product_biome_monthly_flux_attribution,
  pco2_product_biome_annual_fCO2_decomposition,
  pco2_product_biome_monthly_fCO2_decomposition
)

gc()

Zonal mean sections

The following analysis is available for GOBMs only.

Annual means

2023 anomaly

pco2_product_zonal_mean_annual <-   pco2_product_zonal_mean %>%
  pivot_longer(-c(region, depth, lat, time, year, month)) %>%
  group_by(region, lat, depth, year, name) %>%
  summarise(value = mean(value)) %>%
  ungroup() %>%
  drop_na() %>%
  mutate(region = str_to_title(region))

pco2_product_zonal_mean_annual_anomaly <-
  pco2_product_zonal_mean_annual %>% 
  anomaly_determination(region, lat, depth)

pco2_product_zonal_mean_annual_anomaly %>%
  filter(year == 2023) %>%
  group_split(name) %>%
  # head(3) %>%
  map(
    ~ ggplot(data = .x) +
      geom_contour_filled(aes(lat, depth, z = resid)) +
      scale_fill_discrete_divergingx(name = labels_breaks(.x %>% distinct(name))$i_legend_title) +
      guides(fill = guide_colorsteps(
        barheight = unit(8, "cm"),
        show.limits = TRUE
      )) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(50,100,200,400)) +
      scale_x_continuous(breaks = seq(-100, 100, 20)) +
      coord_cartesian(expand = 0) +
      facet_wrap( ~ region, ncol = 1) +
      labs(y = "Depth (m)") +
      theme(legend.title = element_markdown())
  )

pco2_product_zonal_mean_annual_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_zonal_mean_sections_annual.csv"
    )
  )

Biome profiles

The following analysis is available for GOBMs only.

Annual means

2023 anomaly

pco2_product_profiles_annual <-   pco2_product_profiles %>%
  pivot_longer(-c(biome, depth, time, year, month)) %>%
  group_by(biome, depth, year, name) %>%
  summarise(value = mean(value)) %>%
  ungroup() %>%
  drop_na()

pco2_product_profiles_annual_anomaly <-
  pco2_product_profiles_annual %>% 
  anomaly_determination(biome, depth)

pco2_product_profiles_annual_anomaly %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x) +
      geom_path(aes(resid, depth, group = year), col = "grey30", alpha = 0.5) +
      geom_path(data = .x %>% filter(year == 2023),
                aes(resid, depth, col = as.factor(year)),
                linewidth = 1) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(50,100,200,400)) +
      facet_wrap( ~ biome) +
      labs(y = "Depth (m)",
           x = labels_breaks(.x %>% distinct(name))$i_legend_title) +
      theme(legend.title = element_blank(),
            axis.title.x = element_markdown())
  )

pco2_product_profiles_annual_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_profiles_annual.csv"
    )
  )

Monthly means

2023 anomaly

pco2_product_profiles_monthly <-   pco2_product_profiles %>%
  pivot_longer(-c(biome, depth, time, year, month)) %>%
  group_by(biome, depth, year, month, name) %>%
  summarise(value = mean(value)) %>%
  ungroup() %>%
  drop_na()

pco2_product_profiles_monthly_anomaly <-
  pco2_product_profiles_monthly %>% 
  anomaly_determination(biome, depth, month)

pco2_product_profiles_monthly_anomaly %>%
  filter(year == 2023) %>% 
  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_scico_d(palette = "hawaii") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(50, 100, 200, 400)) +
      facet_wrap(~ biome,
                 scales = "free_x") +
      labs(y = "Depth (m)",
           x = labels_breaks(.x %>% distinct(name))$i_legend_title) +
      theme(legend.title = element_blank(),
            axis.title.x = element_markdown())
  )

pco2_product_profiles_monthly_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "CMEMS_GCB",
      "_",
      "2023",
      "_profiles_monthly.csv"
    )
  )

Session information

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: openSUSE Leap 15.6

Matrix products: default
BLAS/LAPACK: /usr/local/OpenBLAS-0.3.28/lib/libopenblas_haswellp-r0.3.28.so;  LAPACK version 3.12.0

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       

time zone: Europe/Zurich
tzcode source: system (glibc)

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

other attached packages:
 [1] ggh4x_0.3.0         scales_1.3.0        cmocean_0.3-2      
 [4] ggtext_0.1.2        broom_1.0.7         khroma_1.14.0      
 [7] ggnewscale_0.5.0    tidync_0.4.0        terra_1.8-5        
[10] sf_1.0-19           rnaturalearth_1.0.1 geomtextpath_0.1.4 
[13] colorspace_2.1-1    marelac_2.1.11      shape_1.4.6.1      
[16] ggforce_0.4.2       metR_0.16.0         scico_1.5.0        
[19] patchwork_1.3.0     collapse_2.0.18     lubridate_1.9.3    
[22] forcats_1.0.0       stringr_1.5.1       dplyr_1.1.4        
[25] purrr_1.0.2         readr_2.1.5         tidyr_1.3.1        
[28] tibble_3.2.1        ggplot2_3.5.1       tidyverse_2.0.0    
[31] workflowr_1.7.1    

loaded via a namespace (and not attached):
 [1] DBI_1.2.3               rlang_1.1.4             magrittr_2.0.3         
 [4] git2r_0.35.0            e1071_1.7-16            compiler_4.4.2         
 [7] mgcv_1.9-1              getPass_0.2-4           systemfonts_1.1.0      
[10] callr_3.7.6             vctrs_0.6.5             pkgconfig_2.0.3        
[13] crayon_1.5.3            fastmap_1.2.0           backports_1.5.0        
[16] ncmeta_0.4.0            labeling_0.4.3          utf8_1.2.4             
[19] promises_1.3.2          rmarkdown_2.29          markdown_1.13          
[22] tzdb_0.4.0              ps_1.8.1                oce_1.8-3              
[25] gsw_1.2-0               bit_4.5.0               xfun_0.49              
[28] cachem_1.1.0            jsonlite_1.8.9          later_1.4.1            
[31] tweenr_2.0.3            parallel_4.4.2          R6_2.5.1               
[34] RColorBrewer_1.1-3      bslib_0.8.0             stringi_1.8.4          
[37] jquerylib_0.1.4         Rcpp_1.0.13-1           knitr_1.49             
[40] seacarb_3.3.3           Matrix_1.7-1            splines_4.4.2          
[43] httpuv_1.6.15           timechange_0.3.0        tidyselect_1.2.1       
[46] RNetCDF_2.9-2           rstudioapi_0.17.1       yaml_2.3.10            
[49] codetools_0.2-20        processx_3.8.4          lattice_0.22-6         
[52] withr_3.0.2             evaluate_1.0.1          rnaturalearthdata_1.0.0
[55] units_0.8-5             proxy_0.4-27            polyclip_1.10-7        
[58] xml2_1.3.6              pillar_1.9.0            whisker_0.4.1          
[61] CFtime_1.4.1            KernSmooth_2.23-24      checkmate_2.3.2        
[64] ncdf4_1.23              generics_0.1.3          vroom_1.6.5            
[67] rprojroot_2.0.4         hms_1.1.3               commonmark_1.9.2       
[70] munsell_0.5.1           class_7.3-22            glue_1.8.0             
[73] tools_4.4.2             data.table_1.16.2       fs_1.6.5               
[76] grid_4.4.2              nlme_3.1-166            cli_3.6.3              
[79] SolveSAPHE_2.1.0        textshaping_0.4.0       fansi_1.0.6            
[82] viridisLite_0.4.2       gtable_0.3.6            sass_0.4.9             
[85] digest_0.6.37           classInt_0.4-10         farver_2.1.2           
[88] memoise_2.0.1           htmltools_0.5.8.1       lifecycle_1.0.4        
[91] httr_1.4.7              here_1.0.1              gridtext_0.1.5         
[94] bit64_4.5.2             MASS_7.3-61