FESOM-REcoM

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-09-09

Checks: 7 0

Knit directory: heatwave_co2_flux_2023/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20240307)

The command set.seed(20240307) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 72fc042

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 72fc042. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data
    Ignored:    output/

Unstaged changes:
    Modified:   analysis/child/pCO2_product_analysis.Rmd
    Modified:   analysis/child/pCO2_product_preprocessing.Rmd
    Modified:   code/Workflowr_project_managment.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/FESOM_REcoM.Rmd) and HTML (docs/FESOM_REcoM.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	923ec8f	jens-daniel-mueller	2024-08-23	Build site.
html	3a18c6c	jens-daniel-mueller	2024-07-22	Build site.
Rmd	37dc042	jens-daniel-mueller	2024-07-22	dic minus ta added
html	0db8b2b	jens-daniel-mueller	2024-07-10	Build site.
html	f6a4369	jens-daniel-mueller	2024-07-01	Build site.
html	f1954bc	jens-daniel-mueller	2024-06-27	Build site.
html	8df7b5b	jens-daniel-mueller	2024-06-26	Build site.
html	f0368da	jens-daniel-mueller	2024-06-13	Build site.
html	a60be97	jens-daniel-mueller	2024-06-12	Build site.
html	d46002d	jens-daniel-mueller	2024-06-12	manual commit
html	5b2b715	jens-daniel-mueller	2024-06-11	Build site.
Rmd	b30db73	jens-daniel-mueller	2024-06-11	low res analysis rebuild website
html	5261667	jens-daniel-mueller	2024-06-11	manual commit
html	2b34bf8	jens-daniel-mueller	2024-06-11	manual commit
html	6954c65	jens-daniel-mueller	2024-06-06	Build site.
html	e1e0ccb	jens-daniel-mueller	2024-05-27	Build site.
Rmd	d8f416a	jens-daniel-mueller	2024-05-27	consistency fixes
html	a3743ec	jens-daniel-mueller	2024-05-25	Build site.
html	ddef92a	jens-daniel-mueller	2024-05-25	Build site.
Rmd	a22aecf	jens-daniel-mueller	2024-05-25	C* included
html	d3d3c44	jens-daniel-mueller	2024-05-24	Build site.
Rmd	e2d6743	jens-daniel-mueller	2024-05-24	talk included, variable names adapted, monthly anomaly profiles addded
html	4a4e4df	jens-daniel-mueller	2024-05-24	Build site.
html	054921b	jens-daniel-mueller	2024-05-23	Build site.
Rmd	8331c52	jens-daniel-mueller	2024-05-23	read 3D ocean interior fields
html	38f6f6e	jens-daniel-mueller	2024-05-22	Build site.
Rmd	a7f3127	jens-daniel-mueller	2024-05-22	read bgc surface variables
html	be285dc	jens-daniel-mueller	2024-05-21	Build site.
html	51df30d	jens-daniel-mueller	2024-05-15	Build site.
Rmd	981d5e1	jens-daniel-mueller	2024-05-15	kw K0 product included, mean flux densities computed
html	009791f	jens-daniel-mueller	2024-05-14	Build site.
html	3b5d16b	jens-daniel-mueller	2024-05-13	Build site.
Rmd	1e1dee5	jens-daniel-mueller	2024-05-13	pco2 to fco2 conversions, changed output files
html	13225ec	jens-daniel-mueller	2024-05-08	Build site.
Rmd	fb237ae	jens-daniel-mueller	2024-05-08	2016 and 1998 included
html	fb21e78	jens-daniel-mueller	2024-05-08	Build site.
Rmd	51196a6	jens-daniel-mueller	2024-05-08	units fixed
html	c7897dc	jens-daniel-mueller	2024-05-08	Build site.
Rmd	d77b01c	jens-daniel-mueller	2024-05-08	read new data
html	7f9c687	jens-daniel-mueller	2024-04-23	Build site.
html	ce4e2a6	jens-daniel-mueller	2024-04-17	Build site.
Rmd	6b3a080	jens-daniel-mueller	2024-04-17	rebuild entire website with NRT_fco2residual

---

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_FESOM <-
  "/net/kryo/work/loher/GlobalMarineHeatwaves/AWI_FESOM2/"

library(ncdf4)

nc <-
  nc_open(paste0(
    path_FESOM,
    "dissic_FESOM2_REcoM_A_1_gr_1980-2023_v20220716.nc"
  ))

print(nc)

FESOM_files <- list.files(path = path_FESOM)

FESOM_files <-
  FESOM_files[FESOM_files %>% str_detect(c(
    "dissic_|no3|thetao|talk_|so_"
  ))]


dates <- seq(ymd("1980-01-15"), ymd("2023-12-15"), "months")


for (i_FESOM_files in FESOM_files) {
  
  # i_FESOM_files <- FESOM_files[1]
  
  i_pco2_product_interior <-
    read_mdim(paste0(path_FESOM,
                     i_FESOM_files))
  
  i_pco2_product_interior <-
    i_pco2_product_interior %>%
    filter(Depth <= 500)
  
  i_pco2_product_interior <- 
    st_set_dimensions(i_pco2_product_interior, 4, values = dates, names = "time")

  if (exists("pco2_product_interior")) {
    pco2_product_interior <-
      c(pco2_product_interior,
                i_pco2_product_interior)
  }
  
  if (!exists("pco2_product_interior")) {
    pco2_product_interior <- i_pco2_product_interior
  }
  
}

rm(FESOM_files, i_FESOM_files, i_pco2_product_interior)
# rm(pco2_product_interior)

pco2_product_interior <- pco2_product_interior %>%
  as_tibble()

pco2_product_interior <- pco2_product_interior %>%
  drop_na()
gc()

             used    (Mb)  gc trigger    (Mb)    max used    (Mb)
Ncells    2875923   153.6     5463885   291.9     3988560   213.1
Vcells 3987874448 30425.1 11791951930 89965.5 10709865969 81709.8

pco2_product_interior <-
  pco2_product_interior %>%
  rename(lat = Lat,
         lon = Lon,
         depth = Depth)

pco2_product_interior <-
  pco2_product_interior %>%
  mutate(
    lon = if_else(lon < 20, lon + 360, lon),
    no3 = no3 * 1.025e3,
    dissic = dissic * 1.025e3,
    talk = talk * 1.025e3,
    sdissic = dissic / so * 35,
    stalk = talk / so * 35,
    sdissic_stalk = sdissic - stalk,
    cstar = dissic  - (117/16 * no3)  - 0.5 * (talk + no3)
  )
gc()

             used    (Mb)  gc trigger    (Mb)    max used    (Mb)
Ncells    2905542   155.2     5463885   291.9     3988560   213.1
Vcells 5757876697 43929.2 11791951930 89965.5 10709865969 81709.8

pco2_product_surf <-
  pco2_product_interior %>%
  filter(depth == min(depth))

gc()

             used    (Mb)  gc trigger    (Mb)    max used    (Mb)
Ncells    2907746   155.3     5463885   291.9     3988560   213.1
Vcells 6045496772 46123.5 11791951930 89965.5 10709865969 81709.8

FESOM_files <- list.files(path = path_FESOM)

FESOM_files <-
  FESOM_files[FESOM_files %>% str_detect(c(
    "alpha|chlos|fgco2|Kw|mld|fCO2atm|sfco2|sos|tos|intpp|dissicos|talkos"
  ))]

for (i_FESOM_files in FESOM_files) {
  
  # i_FESOM_files <- FESOM_files[1]
  
  i_pco2_product <-
    read_ncdf(paste0(path_FESOM,
                     i_FESOM_files))
  
  i_pco2_product <- 
    st_set_dimensions(i_pco2_product, 3, values = dates, names = "time")
  
  if (exists("pco2_product")) {
    pco2_product <-
      c(pco2_product,
                i_pco2_product)
  }
  
  if (!exists("pco2_product")) {
    pco2_product <- i_pco2_product
  }
  
}

rm(FESOM_files, i_FESOM_files, i_pco2_product)
# rm(pco2_product)

pco2_product <- pco2_product %>%
  as_tibble()


pco2_product <-
  pco2_product %>%
  rename(lat = Lat,
         lon = Lon) %>% 
  mutate(year = year(time),
         month = month(time))

pco2_product <-
  pco2_product %>%
  rename(sol = alpha,
         chl = chlos,
         kw = Kw,
         atm_fco2 = fCO2atm,
         salinity = sos,
         temperature = tos,
         dissic = dissicos,
         talk = talkos) %>%
  mutate(
    lon = if_else(lon < 20, lon + 360, lon),
    chl = log10(chl * 1e6),
    intpp = intpp * 60 * 60 * 24 * 365,
    kw = kw * 60 * 60 * 24 * 365,
    mld = -mld,
    sol = sol * 1.025e3 * 1e-6,
    fgco2 = -fgco2 * 60 * 60 * 24 * 365,
    dfco2 = sfco2 - atm_fco2,
    dissic = dissic * 1.025e3,
    talk = talk * 1.025e3,
    sdissic = dissic / salinity * 35,
    stalk = talk / salinity * 35,
    sdissic_stalk = sdissic - stalk
  )

pco2_product <-
  pco2_product %>% 
  mutate(kw_sol = kw * sol)

pco2_product <- 
  full_join(pco2_product,
            pco2_product_surf %>% select(-c(depth, thetao, dissic, talk, cstar, sdissic, stalk, sdissic_stalk)))

rm(pco2_product_surf)
gc()

             used    (Mb)  gc trigger    (Mb)    max used    (Mb)
Ncells    2947202   157.4     5463885   291.9     5463885   291.9
Vcells 6579110406 50194.7 11791951930 89965.5 10709865969 81709.8

# pco2_product %>% 
#   filter(time == max(time)) %>% 
#   select(lon, lat, temperature, sol, dissic) %>% 
#   ggplot(aes(lon, lat, fill = sol)) +
#   geom_tile() +
#   scale_fill_viridis_c()

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

Preprocessing

# model <- TRUE
model <- str_detect('FESOM-REcoM', "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_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 == "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_hov" = labels_breaks("fgco2_hov")$i_legend_title,
    "fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
    "thetao" = labels_breaks("thetao")$i_legend_title,
    "temperature" = labels_breaks("temperature")$i_legend_title,
    "salinity" = labels_breaks("salinity")$i_legend_title,
    "so" = labels_breaks("so")$i_legend_title,
    "chl" = labels_breaks("chl")$i_legend_title,
    "mld" = labels_breaks("mld")$i_legend_title,
    "press" = labels_breaks("press")$i_legend_title,
    "wind" = labels_breaks("wind")$i_legend_title,
    "SSH" = labels_breaks("SSH")$i_legend_title,
    "fice" = labels_breaks("fice")$i_legend_title,
    "resid_fgco2" = labels_breaks("resid_fgco2")$i_legend_title,
    "resid_fgco2_dfco2" = labels_breaks("resid_fgco2_dfco2")$i_legend_title,
    "resid_fgco2_kw_sol" = labels_breaks("resid_fgco2_kw_sol")$i_legend_title,
    "resid_fgco2_dfco2_kw_sol" = labels_breaks("resid_fgco2_dfco2_kw_sol")$i_legend_title,
    "resid_fgco2_sum" = labels_breaks("resid_fgco2_sum")$i_legend_title,
    "resid_fgco2_offset" = labels_breaks("resid_fgco2_offset")$i_legend_title
  )

Analysis settings

name_quadratic_fit <- c("atm_co2", "atm_fco2", "spco2", "sfco2")

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()

             used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells    3030711   161.9     5463885    291.9     5463885    291.9
Vcells 7864753381 60003.4 14150422316 107959.2 14146845143 107931.9

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()

pCO2productanalysis_2023 <-
  knitr::knit_expand(
    file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
    product_name = "FESOM-REcoM",
    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)
  # 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() %>% 
    nest(data = -c(name, !!!group_by)) %>%
    mutate(
      fit = map(data, ~ flm(
        formula = value ~ year + I(year ^ 2), data = .x))
    )
  
  df_quadratic <-
    left_join(
      df_quadratic %>%
        unnest_wider(fit) %>%
        select(name, !!!group_by,
               intercept = `(Intercept)`, slope = year, slope_squared = `I(year^2)`) %>%
        mutate(intercept = as.vector(intercept),
               slope = as.vector(slope),
               slope_squared = as.vector(slope_squared)),
      df
    ) %>%
    mutate(fit = intercept + year * slope + year^2 * slope_squared) %>%
    select(name, !!!group_by, year, fit, value) %>%
    mutate(resid = value - fit)
  
  
  # 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")
  
}

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

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-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_absolute-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_change-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_annual_anomaly-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

pco2_product_map_annual_slope %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "2023",
      "_map_annual_slope.csv"
    )
  )

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

rm(pco2_product_map_annual_anomaly,
   pco2_product_map_annual_slope)
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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

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-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_absolute-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

Past versions of 2023_map_monthly_anomaly-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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     3226480   172.4   323724888  17288.8   632275170  33767.2
Vcells 10188905688 77735.2 20376784134 155462.6 20376768662 155462.5

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26

gc()

              used    (Mb)  gc trigger     (Mb)    max used     (Mb)
Ncells     3246811   173.4   258979911  13831.1   632275170  33767.2
Vcells 10415929105 79467.3 20376784134 155462.6 20376768662 155462.5

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

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

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

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

pco2_product_map_monthly_fCO2_decomposition %>%
  filter(year == 2023) %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_annual_anomaly-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15

Past versions of 2023_hovmoeller_annual_anomaly-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22

Past versions of 2023_hovmoeller_annual_anomaly-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_annual_anomaly-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_annual_anomaly-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_hovmoeller_annual_anomaly-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_annual_anomaly-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_annual_anomaly-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_annual_anomaly-20.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15

Past versions of 2023_hovmoeller_monthly_anomalies-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22

Past versions of 2023_hovmoeller_monthly_anomalies-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_monthly_anomalies-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_monthly_anomalies-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_hovmoeller_monthly_anomalies-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies-20.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-10.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-11.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-12.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
51df30d	jens-daniel-mueller	2024-05-15

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-13.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-14.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-15.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-16.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-17.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-18.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-19.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_hovmoeller_monthly_anomalies_recent_years-20.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

pco2_product_hovmoeller_monthly_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_absolute_means_and_integrals_key_biomes-2.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_absolute_means_and_integrals_key_biomes-3.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_absolute_means_and_integrals_key_biomes-4.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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 ~ x + I(x^2),
              aes(col = "quadratic"),
              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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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 ~ x + I(x^2),
              aes(col = "quadratic"),
              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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
a3743ec	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

pco2_product_biome_annual_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-2.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-3.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_annual_mean_trends_key_biomes-4.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

pco2_product_biome_annual_detrended %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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/",
      "FESOM-REcoM",
      "_",
      "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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-2.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-3.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

Past versions of 2023_anomalies_from_monthly_mean_trends_key_biomes-4.png

Version	Author	Date
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

pco2_product_biome_monthly_detrended %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
e1e0ccb	jens-daniel-mueller	2024-05-27
d3d3c44	jens-daniel-mueller	2024-05-24
054921b	jens-daniel-mueller	2024-05-23
38f6f6e	jens-daniel-mueller	2024-05-22
be285dc	jens-daniel-mueller	2024-05-21
51df30d	jens-daniel-mueller	2024-05-15
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
009791f	jens-daniel-mueller	2024-05-14
3b5d16b	jens-daniel-mueller	2024-05-13
fb21e78	jens-daniel-mueller	2024-05-08
c7897dc	jens-daniel-mueller	2024-05-08

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
f6a4369	jens-daniel-mueller	2024-07-01
8df7b5b	jens-daniel-mueller	2024-06-26

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
f6a4369	jens-daniel-mueller	2024-07-01
8df7b5b	jens-daniel-mueller	2024-06-26

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
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
f6a4369	jens-daniel-mueller	2024-07-01
8df7b5b	jens-daniel-mueller	2024-06-26

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/",
      "FESOM-REcoM",
      "_",
      "2023",
      "_biome_annual_flux_attribution.csv"
    )
  )

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

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

pco2_product_biome_monthly_fCO2_decomposition %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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/",
      "FESOM-REcoM",
      "_",
      "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())
  )

Past versions of 2023_anomaly_profiles_annual-1.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_annual-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_annual-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_annual-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_annual-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25

Past versions of 2023_anomaly_profiles_annual-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_annual-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_annual-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_annual-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

pco2_product_profiles_annual_anomaly %>%
  write_csv(
    paste0(
      "../data/",
      "FESOM-REcoM",
      "_",
      "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())
  )

Past versions of 2023_anomaly_profiles_monthly-1.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_monthly-2.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_monthly-3.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_monthly-4.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25
d3d3c44	jens-daniel-mueller	2024-05-24

Past versions of 2023_anomaly_profiles_monthly-5.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11
03d196b	jens-daniel-mueller	2024-06-11
a3743ec	jens-daniel-mueller	2024-05-25
ddef92a	jens-daniel-mueller	2024-05-25

Past versions of 2023_anomaly_profiles_monthly-6.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_monthly-7.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_monthly-8.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22
0db8b2b	jens-daniel-mueller	2024-07-10
430e926	jens-daniel-mueller	2024-07-10
8df7b5b	jens-daniel-mueller	2024-06-26
a60be97	jens-daniel-mueller	2024-06-12
775b99c	jens-daniel-mueller	2024-06-12
5b2b715	jens-daniel-mueller	2024-06-11

Past versions of 2023_anomaly_profiles_monthly-9.png

Version	Author	Date
923ec8f	jens-daniel-mueller	2024-08-23
3a18c6c	jens-daniel-mueller	2024-07-22

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

Session information

sessionInfo()

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

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

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

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

other attached packages:
 [1] scales_1.2.1        cmocean_0.3-1       ggtext_0.1.2       
 [4] broom_1.0.5         khroma_1.9.0        ggnewscale_0.4.8   
 [7] seacarb_3.3.1       SolveSAPHE_2.1.0    oce_1.7-10         
[10] gsw_1.1-1           lubridate_1.9.0     timechange_0.1.1   
[13] stars_0.6-0         abind_1.4-5         terra_1.7-65       
[16] sf_1.0-9            rnaturalearth_0.1.0 geomtextpath_0.1.1 
[19] colorspace_2.0-3    marelac_2.1.10      shape_1.4.6        
[22] ggforce_0.4.1       metR_0.13.0         scico_1.3.1        
[25] patchwork_1.1.2     collapse_1.8.9      forcats_0.5.2      
[28] stringr_1.5.0       dplyr_1.1.3         purrr_1.0.2        
[31] readr_2.1.3         tidyr_1.3.0         tibble_3.2.1       
[34] ggplot2_3.4.4       tidyverse_1.3.2     workflowr_1.7.0    

loaded via a namespace (and not attached):
  [1] readxl_1.4.1            backports_1.4.1         systemfonts_1.0.4      
  [4] lwgeom_0.2-10           sp_1.5-1                splines_4.2.2          
  [7] digest_0.6.30           htmltools_0.5.3         ncmeta_0.3.5           
 [10] fansi_1.0.3             magrittr_2.0.3          checkmate_2.1.0        
 [13] memoise_2.0.1           googlesheets4_1.0.1     tzdb_0.3.0             
 [16] modelr_0.1.10           vroom_1.6.0             rvest_1.0.3            
 [19] textshaping_0.3.6       haven_2.5.1             xfun_0.35              
 [22] callr_3.7.3             crayon_1.5.2            jsonlite_1.8.3         
 [25] glue_1.6.2              polyclip_1.10-4         gtable_0.3.1           
 [28] gargle_1.2.1            cubelyr_1.0.2           DBI_1.1.3              
 [31] Rcpp_1.0.11             viridisLite_0.4.1       gridtext_0.1.5         
 [34] units_0.8-0             bit_4.0.5               proxy_0.4-27           
 [37] httr_1.4.4              RColorBrewer_1.1-3      ellipsis_0.3.2         
 [40] pkgconfig_2.0.3         farver_2.1.1            sass_0.4.4             
 [43] dbplyr_2.2.1            utf8_1.2.2              here_1.0.1             
 [46] tidyselect_1.2.0        labeling_0.4.2          rlang_1.1.1            
 [49] later_1.3.0             munsell_0.5.0           cellranger_1.1.0       
 [52] tools_4.2.2             cachem_1.0.6            cli_3.6.1              
 [55] generics_0.1.3          evaluate_0.18           fastmap_1.1.0          
 [58] yaml_2.3.6              processx_3.8.0          knitr_1.41             
 [61] bit64_4.0.5             fs_1.5.2                RNetCDF_2.6-1          
 [64] nlme_3.1-160            whisker_0.4             xml2_1.3.3             
 [67] compiler_4.2.2          rstudioapi_0.15.0       e1071_1.7-12           
 [70] reprex_2.0.2            tweenr_2.0.2            bslib_0.4.1            
 [73] stringi_1.7.8           highr_0.9               ps_1.7.2               
 [76] lattice_0.20-45         Matrix_1.5-3            classInt_0.4-8         
 [79] commonmark_1.8.1        markdown_1.4            vctrs_0.6.4            
 [82] pillar_1.9.0            lifecycle_1.0.3         jquerylib_0.1.4        
 [85] data.table_1.14.6       httpuv_1.6.6            R6_2.5.1               
 [88] promises_1.2.0.1        KernSmooth_2.23-20      codetools_0.2-18       
 [91] MASS_7.3-58.1           assertthat_0.2.1        rprojroot_2.0.3        
 [94] withr_2.5.0             mgcv_1.8-41             parallel_4.2.2         
 [97] hms_1.1.2               grid_4.2.2              rnaturalearthdata_0.1.0
[100] class_7.3-20            rmarkdown_2.18          googledrive_2.0.0      
[103] git2r_0.30.1            getPass_0.2-2