Last updated: 2024-03-14

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

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

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

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

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

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


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

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

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

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

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

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

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

latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)

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

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

latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)

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

Read data

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

path_reccap2 <-
  "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/"
print("RECCAP2_region_masks_all_v20221025.nc")
[1] "RECCAP2_region_masks_all_v20221025.nc"
region_masks_all <-
  read_ncdf(
    paste(
      path_reccap2,
      "supplementary/RECCAP2_region_masks_all_v20221025.nc",
      sep = ""
    )
  ) %>%
  as_tibble()
library(ncdf4)
nc <-
  nc_open(paste0(
    path_pCO2_products,
    "VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc"
  ))

print(nc)
print("VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc")
[1] "VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc"
SOM_FFN <-
  read_ncdf(
    paste0(
      path_pCO2_products,
      "VLIZ-SOM_FFN/VLIZ-SOM_FFN_vBAMS2024.nc"
    ),
    var = c("dco2", "atm_co2", "sol", "kw", "spco2_smoothed", "fgco2_smoothed"),
    make_units = FALSE
  )

SOM_FFN <- SOM_FFN %>%
  as_tibble()

SOM_FFN <-
  SOM_FFN %>%
  rename(spco2 = spco2_smoothed, fgco2 = fgco2_smoothed)

SOM_FFN <-
  SOM_FFN %>%
  mutate(across(dco2:fgco2, ~ replace(., . >= 1e+19, NA)))

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

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

Analysis settings

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

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

start_year <- 1990

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

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) {
  
  # Linear regression models
  
  df_lm <-
    df %>%
    filter(year <= 2022,
           !(name %in% name_quadratic_fit)) %>%
    nest(data = -name) %>%
    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, term, estimate) %>%
        pivot_wider(names_from = term,
                    values_from = estimate) %>%
        mutate(fit = `(Intercept)` + year * 2023) %>%
        select(name, 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, 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 = -name) %>%
    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, term, estimate) %>%
        pivot_wider(names_from = term,
                    values_from = estimate) %>%
        mutate(fit = `(Intercept)` + year * 2023 + `I(year^2)` * 2023 ^ 2) %>%
        select(name, 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, 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)
  
}
# my_sum <- function(df, col_to_sum,...) {
# 
#     col_to_sum <- enquo(col_to_sum)
#     group_by <- quos(...)
# 
#     df %>%
#         group_by(!!!group_by) %>%
#         summarise(total_sum = sum(!!col_to_sum)) %>% 
#         ungroup()
# }
# 
# transactions %>% my_sum(amount, accountid, month)

anomaly_determination <- function(df) {
  
  # Linear regression models
  
  df_lm <-
    df %>%
    filter(year <= 2022,
           !(name %in% name_quadratic_fit)) %>%
    nest(data = -name) %>%
    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, term, estimate) %>%
        pivot_wider(names_from = term,
                    values_from = estimate) %>%
        mutate(fit = `(Intercept)` + year * 2023) %>%
        select(name, 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, 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 = -name) %>%
    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, term, estimate) %>%
        pivot_wider(names_from = term,
                    values_from = estimate) %>%
        mutate(fit = `(Intercept)` + year * 2023 + `I(year^2)` * 2023 ^ 2) %>%
        select(name, 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, 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

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

land_mask <- region_masks_all %>%
  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())
  
region_masks_all <- region_masks_all %>%
  filter(seamask == 1) %>% 
  select(lon, lat, atlantic:southern) %>% 
  pivot_longer(atlantic:southern,
               names_to = "region",
               values_to = "biome") %>%
  mutate(biome = as.character(biome))

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

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

region_masks_all <- region_masks_all  %>% 
  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"
  ))

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


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

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
a91341f jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
3f11106 jens-daniel-mueller 2024-03-12 
region_masks_all %>%
  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]]

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
a91341f jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
3f11106 jens-daniel-mueller 2024-03-12 

[[2]]

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
3f11106 jens-daniel-mueller 2024-03-12 

[[3]]

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
f3b86fa jens-daniel-mueller 2024-03-12 

[[4]]

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
a91341f jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
f3b86fa jens-daniel-mueller 2024-03-12 
map +
  geom_tile(data = region_masks_all %>% filter(biome %in% key_biomes),
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected biomes to highlight") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Version Author Date
f1a1ba8 jens-daniel-mueller 2024-03-13
86b52e9 jens-daniel-mueller 2024-03-13
caeb7f1 jens-daniel-mueller 2024-03-12
f3b86fa jens-daniel-mueller 2024-03-12 
region_masks_all <-
  region_masks_all %>%
  select(-region)

Preprocessing

SOM_FFN <-
  SOM_FFN %>%
  filter(year >= start_year)
SOM_FFN <-
  full_join(SOM_FFN,
            region_masks_all)

# set all values outside biome mask to NA

SOM_FFN <-
  SOM_FFN %>%
  mutate(across(dco2:fgco2, ~ if_else(is.na(biome), NA, .)))

# map +
#   geom_tile(data = SOM_FFN %>% 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.

SOM_FFN_annual_mean_maps <-
  SOM_FFN %>%
  drop_na() %>% 
  group_by(year, lon, lat) %>%
  summarise(across(dco2:fgco2,
                   ~ mean(.))) %>%
  ungroup()

SOM_FFN_climatology <-
  SOM_FFN_annual_mean_maps %>%
  drop_na() %>% 
  filter(year <= 2022) %>%
  group_by(lon, lat) %>%
  summarise(across(dco2:fgco2,
                   ~ mean(.))) %>%
  ungroup()

SOM_FFN_2023_anomaly <-
  bind_rows(
    SOM_FFN_climatology %>% mutate(reference = "climatology"),
    SOM_FFN_annual_mean_maps %>%
      filter(year == 2023) %>%
      select(-year) %>% 
      mutate(reference = "2023")
  )

SOM_FFN_2023_anomaly <-
  SOM_FFN_2023_anomaly %>%
  pivot_longer(-c(lon, lat, reference)) %>%
  pivot_wider(names_from = reference,
              values_from = value) %>%
  mutate(anomaly = `2023` - climatology)

SOM_FFN_2023_anomaly %>%
  filter(!(name %in% name_divergent)) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ map +
      geom_tile(data = .x,
                aes(lon, lat, fill = climatology)) +
      labs(title = "Climatology") +
      scale_fill_viridis_c()
  )

SOM_FFN_2023_anomaly %>%
  filter(name %in% name_divergent) %>% 
  group_split(name) %>%
  # head(2) %>%
  map(~ map +
        geom_tile(data = .x,
                  aes(lon, lat, fill = climatology)) +
        labs(title = "Climatology") +
        scale_fill_divergent(.x$name))
SOM_FFN_coarse <-
  m_grid_horizontal_coarse(SOM_FFN)

SOM_FFN_coarse <-
  SOM_FFN_coarse %>%
  group_by(year, month, lon_grid, lat_grid) %>%
  summarise(across(dco2:fgco2,
                   ~ weighted.mean(., area))) %>%
  ungroup() %>%
  rename(lon = lon_grid, lat = lat_grid) %>%
  drop_na()

Annual means

2023 absolute

SOM_FFN_coarse_annual <-
  SOM_FFN_coarse %>%
  group_by(year, lon, lat) %>%
  summarise(across(dco2:fgco2,
                   ~ mean(.))) %>%
  ungroup()

SOM_FFN_coarse_annual <-
  SOM_FFN_coarse_annual %>% 
  pivot_longer(-c(year:lat))

SOM_FFN_coarse_annual_regression <-
  SOM_FFN_coarse_annual %>%
  group_by(lon, lat) %>%
  nest() %>%
  mutate(anomalies = map(.x = data, ~ anomaly_determination(.x))) %>%
  select(-data) %>%
  unnest(anomalies) %>%
  ungroup()
  

SOM_FFN_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 = .x$name))
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SOM_FFN_coarse_annual_regression %>%
  filter(year == 2023,
         name %in% name_divergent) %>%
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = value)) +
         labs(title = "Annual mean 2023") +
         scale_fill_divergent(name = .x$name))
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Change 1990 - 2023

SOM_FFN_coarse_annual_regression %>%
  filter(year %in% c(min(year), max(year))) %>%
  select(-c(value, resid)) %>% 
  pivot_wider(names_from = year,
              values_from = fit) %>% 
  mutate(change = `2023` - `1990`) %>% 
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = change)) +
         labs(title =  "Change 1990-2023") +
         scale_fill_divergent(name = .x$name))
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2023 anomaly

SOM_FFN_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 = .x$name))
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Monthly means

2023 absolute

SOM_FFN_coarse_monthly <-
  SOM_FFN_coarse %>%
  group_by(year, month, lon, lat) %>%
  summarise(across(dco2:fgco2,
                   ~ mean(.))) %>%
  ungroup()

SOM_FFN_coarse_monthly <-
  SOM_FFN_coarse_monthly %>% 
  pivot_longer(-c(year:lat))

SOM_FFN_coarse_monthly_regression <-
  SOM_FFN_coarse_monthly %>%
  group_by(lon, lat, month) %>%
  nest() %>%
  mutate(anomalies = map(.x = data, ~ anomaly_determination(.x))) %>%
  select(-data) %>%
  unnest(anomalies) %>%
  ungroup()
  

SOM_FFN_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 = .x$name) +
         facet_wrap(~ month, ncol = 2))
  
SOM_FFN_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 = .x$name) +
         facet_wrap(~ month, ncol = 2))

Change 1990 - 2023

SOM_FFN_coarse_monthly_regression %>%
  filter(year %in% c(min(year), max(year))) %>%
  select(-c(value, resid)) %>% 
  pivot_wider(names_from = year,
              values_from = fit) %>% 
  mutate(change = `2023` - `1990`) %>% 
  group_split(name) %>% 
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = change)) +
         labs(title = "Change 1990-2023") +
         scale_fill_divergent(name = .x$name) +
         facet_wrap(~ month, ncol = 2))

2023 anomaly

SOM_FFN_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 = .x$name) +
         facet_wrap(~ month, ncol = 2))

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

SOM_FFN_hovmoeller_monthly_annual <-
  SOM_FFN %>%
  group_by(year, lat) %>%
  summarise(across(dco2:spco2, 
                   ~weighted.mean(., area, na.rm = TRUE)),
            across(fgco2, 
                   ~sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>% 
  ungroup() %>% 
  drop_na()

SOM_FFN_hovmoeller_monthly_annual <-
  SOM_FFN_hovmoeller_monthly_annual %>%
  pivot_longer(-c(year, lat))

library(plotly)
SOM_FFN_hovmoeller_monthly_annual %>%
  group_split(name) %>%
  head(1) %>%
  map(~ ggplotly(ggplot(data = .x,
                  aes(year, lat, fill = value)) +
        geom_raster() +
        scale_fill_viridis_c(.x$name) +
        coord_cartesian(expand = 0) +
        labs(title = "Annual means",
             y = "Latitude") +
        theme(axis.title.x = element_blank())))
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Anomalies

SOM_FFN_hovmoeller_monthly_annual_regression <-
  SOM_FFN_hovmoeller_monthly_annual %>%
  group_by(lat) %>%
  nest() %>%
  mutate(anomalies = map(.x = data, ~ anomaly_determination(.x))) %>%
  select(-data) %>%
  unnest(anomalies) %>%
  ungroup()

  
SOM_FFN_hovmoeller_monthly_annual_regression %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(year, lat, fill = resid)) +
      geom_raster() +
      scale_fill_divergent(.x$name) +
      coord_cartesian(expand = 0) +
      labs(title = "Annual mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank())
  )
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Monthly means

Absolute

SOM_FFN_hovmoeller_monthly <-
  SOM_FFN %>%
  group_by(year, month, lat) %>%
  summarise(
    across(dco2:spco2,
           ~ weighted.mean(., area, na.rm = TRUE)),
    across(fgco2,
           ~ sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)
  ) %>%
  ungroup() %>%
  drop_na()

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

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

SOM_FFN_hovmoeller_monthly %>%
  group_split(name) %>%
  # head(1) %>%
  map(~ ggplot(data = .x,
                  aes(decimal, lat, fill = value)) +
        geom_raster() +
        scale_fill_viridis_c(.x$name) +
        coord_cartesian(expand = 0) +
        theme(axis.title.x = element_blank()))
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Anomalies

SOM_FFN_hovmoeller_monthly_regression <-
  SOM_FFN_hovmoeller_monthly %>%
  select(-c(decimal)) %>% 
  group_by(lat, month) %>%
  nest() %>%
  mutate(anomalies = map(.x = data, ~ anomaly_determination(.x))) %>%
  select(-data) %>%
  unnest(anomalies) %>%
  ungroup()

  
SOM_FFN_hovmoeller_monthly_regression <-
  SOM_FFN_hovmoeller_monthly_regression %>%
  mutate(decimal = year + (month - 1) / 12)
  
SOM_FFN_hovmoeller_monthly_regression %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplotly(
      ggplot(data = .x,
             aes(decimal, lat, fill = resid)) +
        geom_raster() +
        scale_fill_divergent(.x$name) +
        coord_cartesian(expand = 0) +
        labs(title = "Monthly mean anomalies",
             y = "Latitude") +
        theme(axis.title.x = element_blank())
    )
  )
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Anomalies since 2021

SOM_FFN_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(.x$name) +
      coord_cartesian(expand = 0) +
      labs(title = "Monthly mean anomalies",
           y = "Latitude") +
      theme(axis.title.x = element_blank())
  )
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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.

SOM_FFN_monthly_global <-
  SOM_FFN %>%
  group_by(time) %>% 
  drop_na() %>% 
  summarise(across(dco2:spco2, 
                   ~weighted.mean(., area, na.rm = TRUE)),
            across(fgco2, 
                   ~sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>% 
  ungroup()

SOM_FFN_monthly_biome <-
  SOM_FFN %>%
  drop_na() %>% 
  group_by(time, biome) %>% 
  summarise(across(dco2:spco2, 
                   ~weighted.mean(., area, na.rm = TRUE)),
            across(fgco2, 
                   ~sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>% 
  ungroup()
  
SOM_FFN_monthly <- 
  bind_rows(SOM_FFN_monthly_global %>% 
              mutate(biome = "Global"),
            SOM_FFN_monthly_biome)

rm(SOM_FFN_monthly_global,
   SOM_FFN_monthly_biome)


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

SOM_FFN_monthly <-
  SOM_FFN_monthly %>%
  pivot_longer(-c(time:month, biome))

Absolute values

Overview

SOM_FFN_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") +
  facet_grid(name ~ biome, scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.y = element_blank())

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Selected biomes

SOM_FFN_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 = "Absolute values") +
      facet_grid(name ~ biome, scales = "free_y") +
      theme(legend.title = element_blank(),
            axis.title.y = element_blank())
  )
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Anomalies

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.

Monthly anomalies

Absolute

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


SOM_FFN_monthly_detrended_anomaly %>%
  filter(biome == "Global") %>%
  pivot_longer(dco2:spco2)  %>%
  ggplot(aes(value, fgco2)) +
  geom_hline(yintercept = 0) +
  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(month)),
             shape = 21,
             size = 3)  +
  scale_fill_scico_d(palette = "buda",
                     guide = guide_legend(reverse = TRUE,
                                            order = 1),
                     name = "month\nof 2023") +
  labs(title = "Globally integrated fluxes") +
  facet_wrap( ~ name, ncol = 3, scales = "free_x") +
  theme(axis.title.x = element_blank())

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SOM_FFN_monthly_detrended_anomaly %>%
  filter(biome != "Global") %>%
  pivot_longer(dco2:spco2) %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2)) +
      geom_hline(yintercept = 0) +
      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(month)),
        shape = 21,
        size = 3
      )  +
      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",
           x = .x$name)
  )
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Relative to spread

SOM_FFN_monthly_detrended_anomaly_spread <-
  SOM_FFN_monthly_detrended_anomaly %>%
  pivot_longer(-c(month, biome, year)) %>%
  filter(year < 2023) %>%
  group_by(month, biome, name) %>%
  summarise(spread = sd(value)) %>%
  ungroup()



SOM_FFN_monthly_detrended_anomaly_relative <-
  full_join(
    SOM_FFN_monthly_detrended_anomaly_spread,
    SOM_FFN_monthly_detrended_anomaly %>%
      pivot_longer(-c(month, biome, year))
  )

SOM_FFN_monthly_detrended_anomaly_relative <- 
SOM_FFN_monthly_detrended_anomaly_relative %>% 
  mutate(value = value / spread) %>% 
  select(-spread) %>% 
  pivot_wider() %>% 
  pivot_longer(-c(month, biome, year, fgco2))


SOM_FFN_monthly_detrended_anomaly_relative %>%
  group_split(name) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(value, fgco2)) +
      geom_vline(xintercept = 0) +
      geom_hline(yintercept = 0) +
      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(month)),
        shape = 21,
        size = 3
      )  +
      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(x = .x$name)
  )
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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] plotly_4.10.1       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         fs_1.5.2                rstudioapi_0.15.0      
  [7] proxy_0.4-27            farver_2.1.1            bit64_4.0.5            
 [10] fansi_1.0.3             xml2_1.3.3              splines_4.2.2          
 [13] codetools_0.2-18        cachem_1.0.6            knitr_1.41             
 [16] polyclip_1.10-4         jsonlite_1.8.3          gsw_1.1-1              
 [19] dbplyr_2.2.1            compiler_4.2.2          httr_1.4.4             
 [22] backports_1.4.1         Matrix_1.5-3            lazyeval_0.2.2         
 [25] assertthat_0.2.1        fastmap_1.1.0           gargle_1.2.1           
 [28] cli_3.6.1               later_1.3.0             tweenr_2.0.2           
 [31] htmltools_0.5.3         tools_4.2.2             rnaturalearthdata_0.1.0
 [34] gtable_0.3.1            glue_1.6.2              Rcpp_1.0.11            
 [37] RNetCDF_2.6-1           cellranger_1.1.0        jquerylib_0.1.4        
 [40] vctrs_0.6.4             nlme_3.1-160            crosstalk_1.2.0        
 [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         rlang_1.1.1            
 [67] pkgconfig_2.0.3         systemfonts_1.0.4       evaluate_0.18          
 [70] lattice_0.20-45         SolveSAPHE_2.1.0        htmlwidgets_1.5.4      
 [73] labeling_0.4.2          bit_4.0.5               processx_3.8.0         
 [76] tidyselect_1.2.0        seacarb_3.3.1           magrittr_2.0.3         
 [79] R6_2.5.1                generics_0.1.3          DBI_1.1.3              
 [82] mgcv_1.8-41             pillar_1.9.0            haven_2.5.1            
 [85] whisker_0.4             withr_2.5.0             units_0.8-0            
 [88] sp_1.5-1                modelr_0.1.10           crayon_1.5.2           
 [91] KernSmooth_2.23-20      utf8_1.2.2              tzdb_0.3.0             
 [94] rmarkdown_2.18          grid_4.2.2              readxl_1.4.1           
 [97] data.table_1.14.6       callr_3.7.3             git2r_0.30.1           
[100] reprex_2.0.2            digest_0.6.30           classInt_0.4-8         
[103] httpuv_1.6.6            textshaping_0.3.6       munsell_0.5.0          
[106] viridisLite_0.4.1       bslib_0.4.1