OceanSODA

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-03-22

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: f69ff93

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 f69ff93. 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/

Unstaged changes:
    Modified:   analysis/child/pCO2_product_analysis.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/OceanSODA.Rmd) and HTML (docs/OceanSODA.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	98cf341	jens-daniel-mueller	2024-03-21	Build site.
html	e3e1491	jens-daniel-mueller	2024-03-21	Build site.
html	47238da	jens-daniel-mueller	2024-03-21	Build site.
html	2fdbfec	jens-daniel-mueller	2024-03-21	Build site.
Rmd	d476020	jens-daniel-mueller	2024-03-21	convert kw unit
html	83fcd67	jens-daniel-mueller	2024-03-21	Build site.
html	342018b	jens-daniel-mueller	2024-03-20	Build site.
Rmd	8120865	jens-daniel-mueller	2024-03-20	hovmoeller anomalies not without 2023 data
html	f0a1de7	jens-daniel-mueller	2024-03-20	Build site.
html	2d2fb75	jens-daniel-mueller	2024-03-20	Build site.
Rmd	23dde7a	jens-daniel-mueller	2024-03-20	new variables added
html	8698b51	jens-daniel-mueller	2024-03-20	Build site.
Rmd	39d9769	jens-daniel-mueller	2024-03-20	write summary output files
html	d520917	jens-daniel-mueller	2024-03-19	Build site.
Rmd	3b8f860	jens-daniel-mueller	2024-03-19	solubility and SSS included
html	03321bd	jens-daniel-mueller	2024-03-19	Build site.
Rmd	e80f0d8	jens-daniel-mueller	2024-03-19	units fixed
html	b41fa51	jens-daniel-mueller	2024-03-19	Build site.
html	bd3c1fe	jens-daniel-mueller	2024-03-19	Build site.
Rmd	fbfd936	jens-daniel-mueller	2024-03-19	run pco2 products with child document
html	5c97a86	jens-daniel-mueller	2024-03-19	Build site.
Rmd	60bf95f	jens-daniel-mueller	2024-03-19	run OceanSODA with child document
html	604281a	jens-daniel-mueller	2024-03-19	Build site.
Rmd	949479e	jens-daniel-mueller	2024-03-19	run OceanSODA with child document
html	14a6ce5	jens-daniel-mueller	2024-03-19	Build site.
Rmd	cdff298	jens-daniel-mueller	2024-03-19	run OceanSODA with child document
html	d3f3f52	jens-daniel-mueller	2024-03-18	Build site.
Rmd	c3f5bd6	jens-daniel-mueller	2024-03-18	run OceanSODA with child document

---

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

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

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

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

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

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


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

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

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

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

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

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

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

latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)

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

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

latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)

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

Read data

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

path_OceanSODA <-
  "/nfs/kryo/work/gregorl/projects/OceanSODA-ETHZ/releases/v2023-full_carbonate_system/OceanSODA_ETHZ_HRLR-v2023.01-co2fluxvars-netCDF/"

library(ncdf4)
nc <-
  nc_open(paste0(
    path_pCO2_products,
    "VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc"
  ))

nc <-
  nc_open(paste0(
    path_OceanSODA,
    "kw_OceanSODA_ETHZ_HR_LR-v2023.01-1982_2023.nc"
  ))

print(nc)

OceanSODA_files <- list.files(path = path_OceanSODA)

OceanSODA_files <-
  OceanSODA_files[OceanSODA_files %>% str_detect(c("mld|press|chl"))]

for (i_OceanSODA_files in OceanSODA_files) {
  
  # i_OceanSODA_files <- OceanSODA_files[2]
  
  i_pco2_product <-
    read_ncdf(paste0(path_OceanSODA,
                     i_OceanSODA_files),
              make_units = FALSE,
              ignore_bounds = TRUE)
  
  if (exists("pco2_product")) {
    pco2_product <-
      c(pco2_product,
                i_pco2_product)
  }
  
  if (!exists("pco2_product")) {
    pco2_product <- i_pco2_product
  }
  
}

rm(OceanSODA_files, i_OceanSODA_files, i_pco2_product)

pco2_product <- pco2_product %>%
  as_tibble()
pco2_product_temp <- pco2_product
rm(pco2_product)

# pco2_product_temp %>%
#   # mutate(chl_filled = 10^chl_filled) %>%
#   # filter(chl_filled < 5) %>%
#   ggplot(aes(chl_filled)) +
#   geom_histogram()

OceanSODA_files <- list.files(path = path_OceanSODA)

OceanSODA_files <-
  OceanSODA_files[OceanSODA_files %>% str_detect(c("dfco2|fgco2_O|kw|spco2|temp|sal|sol"))]

for (i_OceanSODA_files in OceanSODA_files) {
  
  # i_OceanSODA_files <- OceanSODA_files[2]
  
  i_pco2_product <-
    read_ncdf(paste0(path_OceanSODA,
                     i_OceanSODA_files),
              make_units = FALSE,
              ignore_bounds = TRUE)
  
  if (exists("pco2_product")) {
    pco2_product <-
      c(pco2_product,
                i_pco2_product)
  }
  
  if (!exists("pco2_product")) {
    pco2_product <- i_pco2_product
  }
  
}

rm(OceanSODA_files, i_OceanSODA_files, i_pco2_product)
# rm(pco2_product)

pco2_product <- pco2_product %>%
  as_tibble()

pco2_product <-
  full_join(pco2_product, pco2_product_temp)
rm(pco2_product_temp)

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

pco2_product <-
  pco2_product %>% 
  mutate(lon = if_else(lon < 20, lon + 360, lon),
         fgco2 = fgco2 * 1e-3 * 365,
         sol = sol * 1e-3,
         mld = 10^mld,
         chl_filled = 10^chl_filled,
         kw = kw * 1e-2 * 24 * 365) %>% 
  rename(chl = chl_filled)

pCO2productanalysis <-
  knitr::knit_expand(
    file = here::here("analysis/child/pCO2_product_analysis.Rmd"),
    product_name = "OceanSODA"
  )

Read data

path_reccap2 <-
  "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/"

print("RECCAP2_region_masks_all_v20221025.nc")

[1] "RECCAP2_region_masks_all_v20221025.nc"

biome_mask <-
  read_ncdf(
    paste(
      path_reccap2,
      "supplementary/RECCAP2_region_masks_all_v20221025.nc",
      sep = ""
    )
  ) %>%
  as_tibble()

Analysis settings

key_biomes <- c("global",
                "NA-SPSS",
                "NA-STPS",
                "NP-SPSS",
                "PEQU-E",
                "SO-SPSS")

key_biomes %>% 
  write_rds("../data/key_biomes.rds")

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

start_year <- 1990

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

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 data from the period `, and extrapolated to 2023.

anomaly_determination <- function(df,...) {
  
  group_by <- quos(...)
  
  # Linear regression models
  
  df_lm <-
    df %>%
    filter(year <= 2022,
           !(name %in% name_quadratic_fit)) %>%
    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_2023 <-
    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_2023
    )
  
  rm(df_lm_2023)
  
  # Quadratic regression models
  
  df_quadratic <-
    df %>%
    filter(year <= 2022,
           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_2023 <-
    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_2023
    )
  
  rm(df_quadratic_2023)
  
  # Join linear and quadratic regression results
  
  df_regression <-
    bind_rows(df_lm,
              df_quadratic)
  
  rm(df_lm,
     df_quadratic)
  
  
  return(df_regression)
  
}

Biome mask

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

land_mask <- biome_mask %>%
  filter(seamask == 0) %>% 
  select(lon, lat)

map <- ggplot(land_mask,
              aes(lon, lat)) +
  geom_tile(fill = "grey80") +
  scale_y_continuous(breaks = seq(-60,60,30)) +
  scale_x_continuous(breaks = seq(0,360,60)) +
  coord_quickmap(expand = 0, ylim = c(-80, 80)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank())

map %>%
  write_rds("../data/map.rds")
  
biome_mask <- biome_mask %>%
  filter(seamask == 1) %>% 
  select(lon, lat, atlantic:southern) %>% 
  pivot_longer(atlantic:southern,
               names_to = "region",
               values_to = "biome") %>%
  mutate(biome = as.character(biome))

biome_mask <- biome_mask %>%
  filter(biome != "0")

biome_mask <- biome_mask %>%
  mutate(biome = paste(region, biome, sep = "_"))

biome_mask <- biome_mask  %>% 
  mutate(biome = case_when(
    biome == "atlantic_1" ~ "NA-SPSS",
    biome == "atlantic_2" ~ "NA-STSS",
    biome == "atlantic_3" ~ "NA-STPS",
    biome == "atlantic_4" ~ "AEQU",
    biome == "atlantic_5" ~ "SA-STPS",
    # biome == "atlantic_6" ~ "MED",
    biome == "pacific_1" ~ "NP-SPSS",
    biome == "pacific_2" ~ "NP-STSS",
    biome == "pacific_3" ~ "NP-STPS",
    biome == "pacific_4" ~ "PEQU-W",
    biome == "pacific_5" ~ "PEQU-E",
    biome == "pacific_6" ~ "SP-STSS",
    biome == "indian_1" ~ "Arabian Sea",
    biome == "indian_2" ~ "Bay of Bengal",
    biome == "indian_3" ~ "Equatorial Indian",
    biome == "indian_4" ~ "Southern Indian",
    # biome == "arctic_1" ~ "ARCTIC-ICE",
    # biome == "arctic_2" ~ "NP-ICE",
    # biome == "arctic_3" ~ "NA-ICE",
    # biome == "arctic_4" ~ "Barents",
    # str_detect(biome, "arctic") ~ "Arctic",
    biome == "southern_1" ~ "SO-STSS",
    biome == "southern_2" ~ "SO-SPSS",
    # biome == "southern_3" ~ "SO-ICE",
    TRUE ~ "other"
  ))

biome_mask <-
  biome_mask %>%
  filter(biome != "other")

map +
  geom_tile(data = biome_mask,
            aes(lon, lat, fill = region)) +
  labs(title = "Considered ocean regions") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Past versions of prepare_region_mask-1.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

biome_mask %>%
  group_split(region) %>%
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = biome)) +
         labs(title = paste("Region:", .x$region)) +
         scale_fill_okabeito())

[[1]]

Past versions of prepare_region_mask-2.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of prepare_region_mask-3.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of prepare_region_mask-4.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of prepare_region_mask-5.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

map +
  geom_tile(data = biome_mask %>% filter(biome %in% key_biomes),
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected biomes to highlight") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Past versions of prepare_region_mask-6.png

Version	Author	Date
b41fa51	jens-daniel-mueller	2024-03-19

biome_mask <-
  biome_mask %>%
  select(-region)


super_biome_mask <- biome_mask  %>%
  mutate(
    biome = case_when(
      str_detect(biome, "NA-") ~ "North Atlantic",
      str_detect(biome, "NP-") ~ "North Pacific",
      str_detect(biome, "SO-") ~ "Southern Ocean",
      TRUE ~ "other"
    )
  )

super_biome_mask <-
  super_biome_mask %>%
  filter(biome != "other")

map +
  geom_tile(data = super_biome_mask,
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected super biomes") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Past versions of prepare_region_mask-7.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

super_biomes <-
  super_biome_mask %>%
  distinct(biome) %>%
  pull()

super_biomes %>%
  write_rds("../data/super_biomes.rds")

Define labels and breaks

labels_breaks <- function(i_name) {
  
  if (i_name == "dco2") {
    i_legend_title <- "ΔpCO<sub>2</sub><br>(µatm)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "dfco2") {
    i_legend_title <- "ΔfCO<sub>2</sub><br>(µatm)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "atm_co2") {
    i_legend_title <- "pCO<sub>2,atm</sub><br>(µatm)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "sol") {
    i_legend_title <- "CO<sub>2</sub> solubility<br>(mol m<sup>-3</sup> µatm<sup>-1</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "kw") {
    i_legend_title <- "K<sub>w</sub><br>(m yr<sup>-1</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "spco2") {
    i_legend_title <- "pCO<sub>2,ocean</sub><br>(µatm)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "sfco2") {
    i_legend_title <- "fCO<sub>2,ocean</sub><br>(µatm)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "fgco2") {
    i_legend_title <- "FCO<sub>2</sub><br>(mol m<sup>-2</sup> yr<sup>-1</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "fgco2_hov") {
    i_legend_title <- "FCO<sub>2</sub><br>(PgC deg<sup>-1</sup> yr<sup>-1</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "fgco2_int") {
    i_legend_title <- "FCO<sub>2</sub><br>(PgC yr<sup>-1</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "temperature") {
    i_legend_title <- "SST<br>(°C)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "salinity") {
    i_legend_title <- "SSS"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "chl") {
    i_legend_title <- "Chl-a<br>(mg m<sup>-3</sup>)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "mld") {
    i_legend_title <- "MLD<br>(m)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  if (i_name == "press") {
    i_legend_title <- "pressure<sub>atm</sub><br>(unit?)"
    # i_breaks <- c(-Inf, seq(0, 80, 10), Inf)
    # i_contour_level <- 50
    # i_contour_level_abs <- 2200
  }
  
  all_labels_breaks <- lst(i_legend_title,
                           # i_breaks,
                           # i_contour_level,
                           # i_contour_level_abs
                           )
  
  return(all_labels_breaks)
  
}


# labels_breaks("fgco2")

x_axis_labels <-
  c(
    "dco2" = labels_breaks("dco2")$i_legend_title,
    "dfco2" = labels_breaks("dfco2")$i_legend_title,
    "atm_co2" = labels_breaks("atm_co2")$i_legend_title,
    "sol" = labels_breaks("sol")$i_legend_title,
    "kw" = labels_breaks("kw")$i_legend_title,
    "spco2" = labels_breaks("spco2")$i_legend_title,
    "sfco2" = labels_breaks("sfco2")$i_legend_title,
    "fgco2_hov" = labels_breaks("fgco2_hov")$i_legend_title,
    "fgco2_int" = labels_breaks("fgco2_int")$i_legend_title,
    "temperature" = labels_breaks("temperature")$i_legend_title,
    "salinity" = labels_breaks("salinity")$i_legend_title,
    "chl" = labels_breaks("chl")$i_legend_title,
    "mld" = labels_breaks("mld")$i_legend_title,
    "press" = labels_breaks("press")$i_legend_title
  )

Preprocessing

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

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, .)))

# map +
#   geom_tile(data = pco2_product %>% filter(time == max(time),
#                                       !is.na(fgco2)),
#             aes(lon, lat))

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.

pco2_product_coarse <-
  m_grid_horizontal_coarse(pco2_product)
 
# pco2_product_coarse %>%
#   distinct(year)

pco2_product_coarse <-
  pco2_product_coarse %>%
  select(-c(lon, lat, time, biome)) %>% 
  group_by(year, month, lon_grid, lat_grid) %>%
  summarise(across(-area,
                   ~ weighted.mean(., area))) %>%
  ungroup() %>%
  rename(lon = lon_grid, lat = lat_grid)

pco2_product_coarse <-
  pco2_product_coarse %>%
  pivot_longer(-c(year, month, lon, lat)) %>% 
  drop_na() %>%
  pivot_wider()

Annual means

2023 absolute

pco2_product_coarse_annual <-
  pco2_product_coarse %>%
  select(-month) %>% 
  group_by(year, lon, lat) %>%
  summarise(across(where(is.numeric),
                   ~ mean(.))) %>%
  ungroup()

pco2_product_coarse_annual <-
  pco2_product_coarse_annual %>% 
  pivot_longer(-c(year, lon, lat))


pco2_product_coarse_annual_regression <-
  pco2_product_coarse_annual %>%
  anomaly_determination(lon, lat)

pco2_product_coarse_annual_regression <-
  pco2_product_coarse_annual_regression %>%
  drop_na()

pco2_product_coarse_annual_regression %>%
  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 = "Annual mean 2023") +
      scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown())
  )

[[1]]

Past versions of 2023_annual_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of 2023_annual_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of 2023_annual_mean_maps-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of 2023_annual_mean_maps-4.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of 2023_annual_mean_maps-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of 2023_annual_mean_maps-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of 2023_annual_mean_maps-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of 2023_annual_mean_maps-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_coarse_annual_regression %>%
  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 = "Annual mean 2023") +
         scale_fill_divergent(
           name = labels_breaks(.x %>% distinct(name))) +
         theme(legend.title = element_markdown())
  )

[[1]]

Past versions of 2023_annual_mean_maps-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Trends

pco2_product_coarse_annual_regression %>%
  group_by(name) %>%
  filter(year %in% c(min(year), max(year))) %>%
  ungroup() %>%
  select(-c(value, resid)) %>%
  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_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown())
  )

[[1]]

Past versions of change_1990_2023_annual_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of change_1990_2023_annual_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of change_1990_2023_annual_mean_maps-3.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of change_1990_2023_annual_mean_maps-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of change_1990_2023_annual_mean_maps-5.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of change_1990_2023_annual_mean_maps-6.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of change_1990_2023_annual_mean_maps-7.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of change_1990_2023_annual_mean_maps-8.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of change_1990_2023_annual_mean_maps-9.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

[[10]]

Past versions of change_1990_2023_annual_mean_maps-10.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

2023 anomaly

pco2_product_coarse_annual_regression %>%
  filter(year == 2023) %>%
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = resid)) +
         labs(title =  "2023 anomaly") +
         scale_fill_divergent(
           name = labels_breaks(.x %>% distinct(name))) +
         theme(legend.title = element_markdown())
  )

[[1]]

Past versions of anomaly_2023_annual_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of anomaly_2023_annual_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of anomaly_2023_annual_mean_maps-3.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of anomaly_2023_annual_mean_maps-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of anomaly_2023_annual_mean_maps-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of anomaly_2023_annual_mean_maps-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of anomaly_2023_annual_mean_maps-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of anomaly_2023_annual_mean_maps-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of anomaly_2023_annual_mean_maps-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_coarse_annual_regression %>%
  filter(year == 2023) %>%
  write_csv(paste0("../data/","OceanSODA","_anomaly_map_annual.csv"))

Monthly means

2023 absolute

pco2_product_coarse_monthly <-
  pco2_product_coarse %>%
  group_by(year, month, lon, lat) %>%
  summarise(across(where(is.numeric),
                   ~ mean(.))) %>%
  ungroup()

pco2_product_coarse_monthly <-
  pco2_product_coarse_monthly %>% 
  pivot_longer(-c(year, month, lon, lat))

pco2_product_coarse_monthly_regression <-
  pco2_product_coarse_monthly %>%
  anomaly_determination(lon, lat, month)

pco2_product_coarse_monthly_regression <-
  pco2_product_coarse_monthly_regression %>% 
  drop_na()


pco2_product_coarse_monthly_regression %>%
  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 = "Monthly means 2023") +
      scale_fill_viridis_c(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      facet_wrap( ~ month, ncol = 2)
  )

[[1]]

Past versions of 2023_monthly_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of 2023_monthly_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of 2023_monthly_mean_maps-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of 2023_monthly_mean_maps-4.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of 2023_monthly_mean_maps-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of 2023_monthly_mean_maps-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of 2023_monthly_mean_maps-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of 2023_monthly_mean_maps-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_coarse_monthly_regression %>%
  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 = "Monthly means 2023") +
      scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      facet_wrap( ~ month, ncol = 2)
  )

[[1]]

Past versions of 2023_monthly_mean_maps-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Trends

pco2_product_coarse_monthly_regression %>%
  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_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      facet_wrap(~ month, ncol = 2)
  )

[[1]]

Past versions of change_1990_2023_monthly_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of change_1990_2023_monthly_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of change_1990_2023_monthly_mean_maps-3.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of change_1990_2023_monthly_mean_maps-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of change_1990_2023_monthly_mean_maps-5.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of change_1990_2023_monthly_mean_maps-6.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of change_1990_2023_monthly_mean_maps-7.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of change_1990_2023_monthly_mean_maps-8.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of change_1990_2023_monthly_mean_maps-9.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

[[10]]

Past versions of change_1990_2023_monthly_mean_maps-10.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20

2023 anomaly

pco2_product_coarse_monthly_regression %>%
  filter(year == 2023) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = resid)) +
      labs(title = "2023 anomaly") +
      scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      facet_wrap( ~ month, ncol = 2)
  )

[[1]]

Past versions of anomaly_2023_monthly_mean_maps-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of anomaly_2023_monthly_mean_maps-2.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of anomaly_2023_monthly_mean_maps-3.png

Version	Author	Date
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of anomaly_2023_monthly_mean_maps-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of anomaly_2023_monthly_mean_maps-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of anomaly_2023_monthly_mean_maps-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of anomaly_2023_monthly_mean_maps-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of anomaly_2023_monthly_mean_maps-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of anomaly_2023_monthly_mean_maps-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_coarse_monthly_regression %>%
  filter(year == 2023) %>%
  write_csv(paste0("../data/","OceanSODA","_anomaly_map_monthly.csv"))

Hovmoeller plots

The following Hovmoeller plots show the value of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.

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.

Annual means

Absolute

pco2_product_hovmoeller_monthly_annual <-
  pco2_product %>%
  select(-c(lon, time, month, biome)) %>%
  group_by(year, lat) %>%
  summarise(across(-c(fgco2, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup() %>%
  rename(fgco2_hov = fgco2) %>% 
  filter(fgco2_hov != 0)

pco2_product_hovmoeller_monthly_annual <-
  pco2_product_hovmoeller_monthly_annual %>%
  pivot_longer(-c(year, lat)) %>% 
  drop_na()

pco2_product_hovmoeller_monthly_annual %>%
  filter(!(name %in% name_divergent)) %>% 
  group_split(name) %>%
  # head(1) %>%
  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())
  )

[[1]]

Past versions of hovmoeller_annual_absolute-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of hovmoeller_annual_absolute-2.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of hovmoeller_annual_absolute-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of hovmoeller_annual_absolute-4.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of hovmoeller_annual_absolute-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of hovmoeller_annual_absolute-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of hovmoeller_annual_absolute-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of hovmoeller_annual_absolute-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of hovmoeller_annual_absolute-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_hovmoeller_monthly_annual %>%
  filter(name %in% name_divergent) %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, lat, fill = value)) +
      geom_raster() +
      scale_fill_divergent(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())
  )

[[1]]

Past versions of hovmoeller_annual_absolute-10.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Anomalies

pco2_product_hovmoeller_monthly_annual_regression <-
  pco2_product_hovmoeller_monthly_annual %>%
  anomaly_determination(lat) %>% 
  filter(!is.na(resid))

  
pco2_product_hovmoeller_monthly_annual_regression %>%
  # filter(name == "mld") %>% 
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, lat, fill = resid)) +
      geom_raster() +
      scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0) +
      labs(title = "Annual mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank())
  )

[[1]]

Past versions of hovmoeller_annual_anomaly-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of hovmoeller_annual_anomaly-2.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of hovmoeller_annual_anomaly-3.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of hovmoeller_annual_anomaly-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of hovmoeller_annual_anomaly-5.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of hovmoeller_annual_anomaly-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of hovmoeller_annual_anomaly-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of hovmoeller_annual_anomaly-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of hovmoeller_annual_anomaly-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[10]]

Past versions of hovmoeller_annual_anomaly-10.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Monthly means

Absolute

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

[[1]]

Past versions of hovmoeller_monthly_absolute-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of hovmoeller_monthly_absolute-2.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of hovmoeller_monthly_absolute-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of hovmoeller_monthly_absolute-4.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of hovmoeller_monthly_absolute-5.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of hovmoeller_monthly_absolute-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of hovmoeller_monthly_absolute-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of hovmoeller_monthly_absolute-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of hovmoeller_monthly_absolute-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

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_divergent(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())
  )

[[1]]

Past versions of hovmoeller_monthly_absolute-10.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Anomalies

pco2_product_hovmoeller_monthly_regression <-
  pco2_product_hovmoeller_monthly %>%
  select(-c(decimal)) %>% 
  anomaly_determination(lat, month) %>% 
  filter(!is.na(resid))

  
pco2_product_hovmoeller_monthly_regression <-
  pco2_product_hovmoeller_monthly_regression %>%
  mutate(decimal = year + (month - 1) / 12)
  
pco2_product_hovmoeller_monthly_regression %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
      geom_raster() +
      scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank())
  )

[[1]]

Past versions of hovmoeller_monthly_anomalies-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of hovmoeller_monthly_anomalies-2.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of hovmoeller_monthly_anomalies-3.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of hovmoeller_monthly_anomalies-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of hovmoeller_monthly_anomalies-5.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of hovmoeller_monthly_anomalies-6.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of hovmoeller_monthly_anomalies-7.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of hovmoeller_monthly_anomalies-8.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of hovmoeller_monthly_anomalies-9.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[10]]

Past versions of hovmoeller_monthly_anomalies-10.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

pco2_product_hovmoeller_monthly_regression %>%
  write_csv(paste0("../data/","OceanSODA","_anomaly_hovmoeller_monthly.csv"))

Anomalies since 2021

pco2_product_hovmoeller_monthly_regression %>%
  filter(year >= 2021) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
      geom_raster() +
      scale_fill_divergent(name = labels_breaks(.x %>% distinct(name))) +
      theme(legend.title = element_markdown()) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank())
  )

[[1]]

Past versions of hovmoeller_monthly_anomalies_recent_years-1.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of hovmoeller_monthly_anomalies_recent_years-2.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of hovmoeller_monthly_anomalies_recent_years-3.png

Version	Author	Date
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of hovmoeller_monthly_anomalies_recent_years-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of hovmoeller_monthly_anomalies_recent_years-5.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[6]]

Past versions of hovmoeller_monthly_anomalies_recent_years-6.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[7]]

Past versions of hovmoeller_monthly_anomalies_recent_years-7.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19

[[8]]

Past versions of hovmoeller_monthly_anomalies_recent_years-8.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

[[9]]

Past versions of hovmoeller_monthly_anomalies_recent_years-9.png

Version	Author	Date
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20

Regional means and integrals

The following plots show biome- or global- averaged/integrated values of each variable as provided through the pCO2 product, as well as the anomalies from the prediction of a linear/quadratic fit to the data from 1990 to 2022.

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.

pco2_product_monthly_global <-
  pco2_product %>%
  select(-c(lon, lat, year, month, biome)) %>% 
  group_by(time) %>%
  summarise(across(-c(fgco2, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_monthly_biome <-
  pco2_product %>%
  select(-c(lon, lat, year, month)) %>% 
  group_by(time, biome) %>%
  summarise(across(-c(fgco2, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_monthly_biome_super <-
  pco2_product %>%
  mutate(
    biome = case_when(
      str_detect(biome, "NA-") ~ "North Atlantic",
      str_detect(biome, "NP-") ~ "North Pacific",
      str_detect(biome, "SO-") ~ "Southern Ocean",
      TRUE ~ "other"
    )
  ) %>%
  filter(biome != "other") %>%
  select(-c(lon, lat, year, month)) %>%
  group_by(time, biome) %>%
  summarise(across(-c(fgco2, area),
                   ~ weighted.mean(., area, na.rm = TRUE)),
            across(fgco2,
                   ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>%
  ungroup()

pco2_product_monthly <-
  bind_rows(pco2_product_monthly_global %>%
              mutate(biome = "Global"),
            pco2_product_monthly_biome,
            pco2_product_monthly_biome_super)

rm(
  pco2_product_monthly_global,
  pco2_product_monthly_biome,
  pco2_product_monthly_biome_super
)


pco2_product_monthly <-
  pco2_product_monthly %>% 
  filter(!is.na(biome))

pco2_product_monthly <-
  pco2_product_monthly %>%
  rename(fgco2_int = fgco2)

pco2_product_monthly <-
  pco2_product_monthly %>%
  mutate(year = year(time),
         month = month(time),
         .after = time)

pco2_product_monthly <-
  pco2_product_monthly %>%
  pivot_longer(-c(time, year, month, biome))

Absolute values

Overview

fig.height <- pco2_product_monthly %>% 
  distinct(name) %>% 
  nrow() * 0.15

pco2_product_monthly %>%
  filter(biome %in% "Global") %>%
  ggplot(aes(month, value, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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") +
  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 absolute_global_means_and_integrals_overview-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly %>%
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, value, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 absolute_biome_means_and_integrals_overview_biomes-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_monthly %>%
  filter(biome %in% super_biomes) %>%
  ggplot(aes(month, value, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 super 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 absolute_biome_means_and_integrals_overview_biomes_super-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

Selected biomes

pco2_product_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(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
  )
  )

[[1]]

Past versions of absolute_global_means_and_integrals_key_biomes-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of absolute_global_means_and_integrals_key_biomes-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of absolute_global_means_and_integrals_key_biomes-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of absolute_global_means_and_integrals_key_biomes-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of absolute_global_means_and_integrals_key_biomes-5.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

Super biomes

pco2_product_monthly %>%
  filter(biome %in% super_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, value, group = as.factor(year))) +
      geom_path(data = . %>% filter(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
  )
  )

[[1]]

Past versions of absolute_global_means_and_integrals_key_biomes_super-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of absolute_global_means_and_integrals_key_biomes_super-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of absolute_global_means_and_integrals_key_biomes_super-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

Anomalies

Annual mean trends

pco2_product_annual <-
  pco2_product_monthly %>%
  group_by(year, biome, name) %>%
  summarise(value = mean(value)) %>%
  ungroup()
  
pco2_product_annual %>% 
  filter(biome %in% "Global") %>%
  ggplot(aes(year, value)) +
  geom_path() +
  geom_point() +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              aes(col = "linear"),
              se = FALSE) +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  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 = "Regression fit\n prior 2023") +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Annual mean trends | Global") +
  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 annual_mean_trends-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(year, value)) +
  geom_path() +
  geom_point(size = 0.2) +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              aes(col = "linear"),
              se = FALSE) +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  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 = "Regression fit\n prior 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 annual_mean_trends-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual %>% 
  filter(biome %in% super_biomes) %>%
  ggplot(aes(year, value)) +
  geom_path() +
  geom_point(size = 0.2) +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              aes(col = "linear"),
              se = FALSE) +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  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 = "Regression fit\n prior 2023") +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Annual mean trends | Selected super 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 annual_mean_trends-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_regression <-
  pco2_product_annual %>%
  anomaly_determination(biome)
  

pco2_product_annual_regression %>%
  filter(biome %in% "Global") %>%
  ggplot(aes(year, resid)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(data = . %>% filter(year < 2022),
             aes(fill = year),
             shape = 21) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(year >= 2022),
             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") +
  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 annual_mean_trends-4.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_regression %>%
  filter(biome %in% key_biomes) %>%
  ggplot(aes(year, resid)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(data = . %>% filter(year < 2022),
             aes(fill = year),
             shape = 21) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(year >= 2022),
             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 annual_mean_trends-5.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_regression %>%
  filter(biome %in% super_biomes) %>%
  ggplot(aes(year, resid)) +
  geom_hline(yintercept = 0) +
  geom_path() +
  geom_point(data = . %>% filter(year < 2022),
             aes(fill = year),
             shape = 21) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(year >= 2022),
             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 super 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 annual_mean_trends-6.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_annual_regression %>%
  filter(biome != "global") %>%
  ggplot(aes(biome, resid)) +
  geom_hline(yintercept = 0) +
  geom_jitter(data = . %>% filter(year < 2022),
             aes(fill = year),
             shape = 21, width = 0.2) +
  scale_fill_grayC() +
  new_scale_fill() +
  geom_point(data = . %>% filter(year >= 2022),
             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 = "Residual from fit to annual means (actual - predicted) | All biomes") +
  facet_grid(name ~ .,
             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(),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)
  )

Past versions of annual_mean_trends-7.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_annual_regression %>%
  write_csv(paste0("../data/","OceanSODA","_biome_annual_regression.csv"))

pco2_product_annual_detrended <-
  full_join(pco2_product_monthly,
            pco2_product_annual_regression %>% select(-c(value, resid))) %>%
  mutate(resid = value - fit)

pco2_product_annual_detrended %>% 
  filter(biome %in% "Global") %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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") +
  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 anomalies_from_annual_mean_trends_overview-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_detrended %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 anomalies_from_annual_mean_trends_overview-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_detrended %>% 
  filter(biome %in% super_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 super 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 anomalies_from_annual_mean_trends_overview-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_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(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
      )
  )

[[1]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of anomalies_from_annual_mean_trends_key_biomes-5.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_annual_detrended %>%
  filter(biome %in% super_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, resid, group = as.factor(year))) +
      geom_path(data = . %>% filter(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
      )
  )

[[1]]

Past versions of anomalies_from_annual_mean_trends_key_biomes_super-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of anomalies_from_annual_mean_trends_key_biomes_super-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of anomalies_from_annual_mean_trends_key_biomes_super-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_annual_detrended %>%
  write_csv(paste0("../data/","OceanSODA","_biome_annual_detrended.csv"))

Monthly mean trends

pco2_product_monthly %>% 
  filter(biome %in% "Global") %>%
  mutate(month = as.factor(month)) %>% 
  ggplot(aes(year, value, col = month)) +
  # geom_point() +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              se = FALSE,
              fullrange = TRUE) +
  geom_smooth(
    data = . %>% filter(year <= 2022,
                        name %in% name_quadratic_fit),
    method = "lm",
    fullrange = TRUE,
    formula = y ~ x + I(x ^ 2),
    se = FALSE
  ) +
  scale_color_scico_d(palette = "romaO", 
    name = "Regression fit\n prior 2023") +
  scale_x_continuous(breaks = seq(1980, 2020, 20)) +
  labs(title = "Monthly mean trends | Global") +
  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 monthly_mean_trends-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly %>% 
  filter(biome %in% key_biomes) %>%
  mutate(month = as.factor(month)) %>% 
  ggplot(aes(year, value, col = month)) +
  # geom_point() +
  geom_smooth(data = . %>% filter(year <= 2022,
                                  !(name %in% name_quadratic_fit)),
              method = "lm",
              fullrange = TRUE,
              se = FALSE) +
  geom_smooth(
    data = . %>% filter(year <= 2022,
                        name %in% name_quadratic_fit),
    method = "lm",
    fullrange = TRUE,
    formula = y ~ x + I(x ^ 2),
    se = FALSE
  ) +
  scale_color_scico_d(palette = "romaO",
    name = "Regression fit\n prior 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()
  )

Past versions of monthly_mean_trends-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_regression <-
  pco2_product_monthly %>%
  anomaly_determination(biome, month)


pco2_product_monthly_regression %>%
  filter(biome %in% "Global") %>%
  group_split(month) %>%
  head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, resid)) +
      geom_hline(yintercept = 0) +
      geom_path() +
      geom_point(
        data = . %>% filter(year < 2022),
        aes(fill = year),
        shape = 21
      ) +
      scale_fill_grayC() +
      new_scale_fill() +
      geom_point(
        data = . %>% filter(year >= 2022),
        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",
           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()
      )
  )

[[1]]

Past versions of monthly_mean_trends-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_regression %>%
  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(year < 2022),
        aes(fill = year),
        shape = 21
      ) +
      scale_fill_grayC() +
      new_scale_fill() +
      geom_point(
        data = . %>% filter(year >= 2022),
        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()
      )
  )

[[1]]

Past versions of monthly_mean_trends-4.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_detrended <-
  full_join(pco2_product_monthly,
            pco2_product_monthly_regression %>% select(-c(value, resid, time))) %>%
  mutate(resid = value - fit)

pco2_product_monthly_detrended %>% 
  filter(biome %in% "Global") %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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") +
  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 anomalies_from_monthly_mean_trends_overview-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_detrended %>% 
  filter(biome %in% key_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 anomalies_from_monthly_mean_trends_overview-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_detrended %>% 
  filter(biome %in% super_biomes) %>%
  ggplot(aes(month, resid, group = as.factor(year))) +
  geom_path(data = . %>% filter(year < 2022),
            aes(col = year)) +
  scale_color_grayC() +
  new_scale_color() +
  geom_path(data = . %>% filter(year >= 2022),
            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 super 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 anomalies_from_monthly_mean_trends_overview-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_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(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
      )
  )

[[1]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes-1.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[2]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes-2.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[3]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes-3.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[4]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes-4.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

[[5]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes-5.png

Version	Author	Date
2fdbfec	jens-daniel-mueller	2024-03-21
83fcd67	jens-daniel-mueller	2024-03-21
342018b	jens-daniel-mueller	2024-03-20
f0a1de7	jens-daniel-mueller	2024-03-20
2d2fb75	jens-daniel-mueller	2024-03-20
d520917	jens-daniel-mueller	2024-03-19
03321bd	jens-daniel-mueller	2024-03-19
b41fa51	jens-daniel-mueller	2024-03-19

pco2_product_monthly_detrended %>%
  filter(biome %in% super_biomes) %>%
  group_split(biome) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(month, resid, group = as.factor(year))) +
      geom_path(data = . %>% filter(year < 2022),
                aes(col = year)) +
      scale_color_grayC() +
      new_scale_color() +
      geom_path(
        data = . %>% filter(year >= 2022),
        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()
      )
  )

[[1]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes_super-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes_super-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of anomalies_from_monthly_mean_trends_key_biomes_super-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

Flux anomaly correlation

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

Anomalies are first presented are first presented in absolute units. Due to the different flux magnitudes, we need to plot the globally and biome-integrated fluxes separately. Secondly, we normalize the anomalies to the monthly spread (expressed as standard deviation) of the anomalies from 1990 to 2022.

Annual anomalies

Absolute

pco2_product_annual_regression %>%
  filter(biome == "Global") %>%
  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(year <= 2022),
             aes(fill = year),
             shape = 21) +
  geom_smooth(
    data = . %>% filter(year <= 2022),
    method = "lm",
    se = FALSE,
    fullrange = TRUE,
    aes(col = "Regression fit\n prior 2023")
  ) +
  scale_color_grey() +
  scale_fill_grayC()+
  new_scale_fill() +
  geom_point(data = . %>% filter(year >= 2022),
             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 = "Globally 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 anomalies_correlation_absolute_global_annual-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

Monthly anomalies

Absolute

pco2_product_monthly_detrended_anomaly <-
  pco2_product_monthly_detrended %>%
  select(year, month, biome, name, resid) %>%
  pivot_wider(names_from = name,
              values_from = resid)


pco2_product_monthly_detrended_anomaly %>%
  filter(biome == "Global") %>%
  pivot_longer(-c(year, month, biome, fgco2_int))  %>%
  ggplot(aes(value, fgco2_int)) +
  geom_hline(yintercept = 0) +
  geom_point(data = . %>% filter(year <= 2022),
             aes(col = paste(min(year), max(year), sep = "-")),
             alpha = 0.2) +
  geom_smooth(
    data = . %>% filter(year <= 2022),
    aes(col = paste(min(year), max(year), sep = "-")),
    method = "lm",
    se = FALSE,
    fullrange = TRUE
  )  +
  scale_color_grey(name = "") +
  new_scale_color() +
  # geom_smooth(data = . %>% filter(year <= 2022),
  #             method = "lm", se = FALSE,
  #           aes(col = as.factor(month)))  +
  geom_path(data = . %>% filter(year > 2022),
            aes(col = as.factor(month), group = 1))  +
  geom_point(data = . %>% filter(year > 2022),
             aes(fill =  as.factor(month)),
             shape = 21,
             size = 3)  +
  scale_color_scico_d(palette = "buda",
                     guide = guide_legend(reverse = TRUE,
                                          order = 1),
                     name = "Month\nof 2023") +
  scale_fill_scico_d(palette = "buda",
                     guide = guide_legend(reverse = TRUE,
                                          order = 1),
                     name = "Month\nof 2023") +
  labs(title = "Globally 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 anomalies_correlation_absolute_global_monthly-1.png

Version	Author	Date
98cf341	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

pco2_product_monthly_detrended_anomaly %>%
  filter(!(biome %in% c(super_biomes, "Global"))) %>%
  pivot_longer(-c(year, month, biome, fgco2_int))  %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_hline(yintercept = 0) +
      geom_point(
        data = . %>% filter(year <= 2022),
        aes(col = paste(min(year), max(year), sep = "-")),
        alpha = 0.2
      ) +
      geom_smooth(
        data = . %>% filter(year <= 2022),
        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 > 2022),
                aes(col = as.factor(month), group = 1))  +
      geom_point(
        data = . %>% filter(year > 2022),
        aes(fill = as.factor(month)),
        shape = 21,
        size = 3
      )  +
      scale_color_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "Month\nof 2023"
      ) +
      scale_fill_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "Month\nof 2023"
      ) +
      facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
      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())
  )

[[1]]

Past versions of anomalies_correlation_absolute_biome_monthly-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of anomalies_correlation_absolute_biome_monthly-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of anomalies_correlation_absolute_biome_monthly-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[4]]

Past versions of anomalies_correlation_absolute_biome_monthly-4.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[5]]

Past versions of anomalies_correlation_absolute_biome_monthly-5.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[6]]

Past versions of anomalies_correlation_absolute_biome_monthly-6.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[7]]

Past versions of anomalies_correlation_absolute_biome_monthly-7.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[8]]

Past versions of anomalies_correlation_absolute_biome_monthly-8.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[9]]

Past versions of anomalies_correlation_absolute_biome_monthly-9.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

pco2_product_monthly_detrended_anomaly %>%
  filter(biome %in% super_biomes) %>%
  pivot_longer(-c(year, month, biome, fgco2_int))  %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2_int)) +
      geom_hline(yintercept = 0) +
      geom_point(
        data = . %>% filter(year <= 2022),
        aes(col = paste(min(year), max(year), sep = "-")),
        alpha = 0.2
      ) +
      geom_smooth(
        data = . %>% filter(year <= 2022),
        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 > 2022),
                aes(col = as.factor(month), group = 1))  +
      geom_point(
        data = . %>% filter(year > 2022),
        aes(fill = as.factor(month)),
        shape = 21,
        size = 3
      )  +
      scale_color_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "Month\nof 2023"
      ) +
      scale_fill_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "Month\nof 2023"
      ) +
      facet_wrap( ~ biome, ncol = 3, scales = "free_x") +
      labs(
        title = "Super 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())
  )

[[1]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[4]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-4.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[5]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-5.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[6]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-6.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[7]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-7.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[8]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-8.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

[[9]]

Past versions of anomalies_correlation_absolute_super_biome_monthly-9.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21

pco2_product_monthly_detrended_anomaly %>%
  write_csv(paste0("../data/","OceanSODA","_biome_monthly_detrended_anomaly.csv"))

Relative to spread

pco2_product_monthly_detrended_anomaly_spread <-
  pco2_product_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_monthly_detrended_anomaly_relative <-
  full_join(
    pco2_product_monthly_detrended_anomaly_spread,
    pco2_product_monthly_detrended_anomaly %>%
      pivot_longer(-c(month, biome, year))
  )

pco2_product_monthly_detrended_anomaly_relative <-
  pco2_product_monthly_detrended_anomaly_relative %>%
  mutate(value = value / spread) %>%
  select(-spread) %>%
  pivot_wider() %>%
  pivot_longer(-c(month, biome, year, fgco2_int))



pco2_product_monthly_detrended_anomaly_relative %>%
  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 <= 2022),
        aes(col = paste(min(year), max(year), sep = "-")),
        alpha = 0.2
      ) +
      geom_smooth(
        data = . %>% filter(year <= 2022),
        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 > 2022),
                aes(col = as.factor(month), group = 1))  +
      geom_point(
        data = . %>% filter(year > 2022),
        aes(fill = as.factor(month)),
        shape = 21,
        size = 3
      )  +
      scale_color_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "Month\nof 2023"
      ) +
      scale_fill_scico_d(
        palette = "buda",
        guide = guide_legend(reverse = TRUE,
                             order = 1),
        name = "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())
  )

[[1]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-1.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[2]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-2.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[3]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-3.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[4]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-4.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[5]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-5.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[6]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-6.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[7]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-7.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[8]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-8.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

[[9]]

Past versions of anomalies_correlation_realtive_to_spread_monthly-9.png

Version	Author	Date
e3e1491	jens-daniel-mueller	2024-03-21
47238da	jens-daniel-mueller	2024-03-21

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

loaded via a namespace (and not attached):
  [1] googledrive_2.0.0       ellipsis_0.3.2          class_7.3-20           
  [4] rprojroot_2.0.3         markdown_1.4            fs_1.5.2               
  [7] gridtext_0.1.5          rstudioapi_0.15.0       proxy_0.4-27           
 [10] farver_2.1.1            bit64_4.0.5             fansi_1.0.3            
 [13] xml2_1.3.3              splines_4.2.2           codetools_0.2-18       
 [16] cachem_1.0.6            knitr_1.41              polyclip_1.10-4        
 [19] jsonlite_1.8.3          gsw_1.1-1               dbplyr_2.2.1           
 [22] compiler_4.2.2          httr_1.4.4              backports_1.4.1        
 [25] Matrix_1.5-3            assertthat_0.2.1        fastmap_1.1.0          
 [28] gargle_1.2.1            cli_3.6.1               later_1.3.0            
 [31] tweenr_2.0.2            htmltools_0.5.3         tools_4.2.2            
 [34] rnaturalearthdata_0.1.0 gtable_0.3.1            glue_1.6.2             
 [37] Rcpp_1.0.11             RNetCDF_2.6-1           cellranger_1.1.0       
 [40] jquerylib_0.1.4         vctrs_0.6.4             nlme_3.1-160           
 [43] lwgeom_0.2-10           xfun_0.35               ps_1.7.2               
 [46] rvest_1.0.3             ncmeta_0.3.5            lifecycle_1.0.3        
 [49] googlesheets4_1.0.1     oce_1.7-10              getPass_0.2-2          
 [52] MASS_7.3-58.1           scales_1.2.1            vroom_1.6.0            
 [55] hms_1.1.2               promises_1.2.0.1        parallel_4.2.2         
 [58] RColorBrewer_1.1-3      yaml_2.3.6              memoise_2.0.1          
 [61] sass_0.4.4              stringi_1.7.8           highr_0.9              
 [64] e1071_1.7-12            checkmate_2.1.0         commonmark_1.8.1       
 [67] rlang_1.1.1             pkgconfig_2.0.3         systemfonts_1.0.4      
 [70] evaluate_0.18           lattice_0.20-45         SolveSAPHE_2.1.0       
 [73] labeling_0.4.2          bit_4.0.5               processx_3.8.0         
 [76] tidyselect_1.2.0        here_1.0.1              seacarb_3.3.1          
 [79] magrittr_2.0.3          R6_2.5.1                generics_0.1.3         
 [82] DBI_1.1.3               mgcv_1.8-41             pillar_1.9.0           
 [85] haven_2.5.1             whisker_0.4             withr_2.5.0            
 [88] units_0.8-0             sp_1.5-1                modelr_0.1.10          
 [91] crayon_1.5.2            KernSmooth_2.23-20      utf8_1.2.2             
 [94] tzdb_0.3.0              rmarkdown_2.18          grid_4.2.2             
 [97] readxl_1.4.1            data.table_1.14.6       callr_3.7.3            
[100] git2r_0.30.1            reprex_2.0.2            digest_0.6.30          
[103] classInt_0.4-8          httpuv_1.6.6            textshaping_0.3.6      
[106] munsell_0.5.0           viridisLite_0.4.1       bslib_0.4.1