Last updated: 2024-03-12

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Knit directory: heatwave_co2_flux_2023/

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Rmd 8c60127 jens-daniel-mueller 2024-03-12 quadratic regression model added
html 0473a50 jens-daniel-mueller 2024-03-12 Build site.
Rmd cab2d84 jens-daniel-mueller 2024-03-12 regression trends in annual and monthly means
html f3b86fa jens-daniel-mueller 2024-03-12 Build site.
Rmd d0a504d jens-daniel-mueller 2024-03-12 biome seasonality plots
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Rmd 65f52e0 jens-daniel-mueller 2024-03-12 all climatology and anomaly maps
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Rmd a67e799 jens-daniel-mueller 2024-03-11 regional analysis for SOM-FFN started
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Rmd f4af74f jens-daniel-mueller 2024-03-11 started pCO2 products analysis 
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/"
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)
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 %>%
  mutate(across(dco2:fgco2_smoothed, ~ 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 < 0, lon + 360, lon))

Biome mask

land_mask <- region_masks_all %>%
  filter(seamask == 0)

map <- ggplot(land_mask,
              aes(lon, lat)) +
  geom_tile(fill = "grey80") +
  coord_quickmap(expand = 0) +
  theme(axis.title = 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 = "_"))


# map +
#   geom_raster(data = region_masks_all,
#               aes(lon, lat, fill = biome))


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)) +
  scale_fill_muted()

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region_masks_all %>%
  group_split(region) %>%
  # head(2) %>%
  map(~ map +
        geom_tile(data = .x,
                    aes(lon, lat, fill = biome)) +
        labs(title = .x$region) +
        scale_fill_okabeito())
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key_biomes <- c("global",
                "NA-SPSS",
                "NA-STPS",
                "NP-SPSS",
                "PEQU-E",
                "SO-SPSS")


map +
  geom_tile(data = region_masks_all %>% filter(biome %in% key_biomes),
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected key biomes") +
  scale_fill_muted()

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pCO2 products

Climatology maps

SOM_FFN_annual_mean_maps <-
  SOM_FFN %>%
  group_by(year, lon, lat) %>%
  summarise(across(dco2:fgco2_smoothed,
                   ~ mean(.))) %>%
  ungroup()

SOM_FFN_climatology <-
  SOM_FFN_annual_mean_maps %>%
  filter(year >= 1990,
         year <= 2022) %>%
  group_by(lon, lat) %>%
  summarise(across(dco2:fgco2_smoothed,
                   ~ 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 %>%
  group_split(name) %>%
  # head(1) %>%
  map(~ map +
        geom_tile(data = .x,
                  aes(lon, lat, fill = climatology)) +
        labs(title = .x$name) +
        scale_fill_viridis_c())
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Anomaly maps

SOM_FFN_2023_anomaly %>%
  group_split(name) %>%
  # head(1) %>%
  map(~ map +
        geom_tile(data = .x,
                  aes(lon, lat, fill = anomaly)) +
        labs(title = .x$name) +
        scale_fill_divergent())
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Regional means and integrals

SOM_FFN <-
  inner_join(SOM_FFN,
             region_masks_all)

SOM_FFN_monthly_global <-
  SOM_FFN %>%
  group_by(time) %>% 
  summarise(across(dco2:spco2_smoothed, 
                   ~weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_smoothed, 
                   ~sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>% 
  ungroup()

SOM_FFN_monthly_biome <-
  SOM_FFN %>%
  group_by(time, region, biome) %>% 
  summarise(across(dco2:spco2_smoothed, 
                   ~weighted.mean(., area, na.rm = TRUE)),
            across(fgco2_smoothed, 
                   ~sum(. * area, na.rm = TRUE) * 12.01 * 1e-15)) %>% 
  ungroup()
  
SOM_FFN_monthly <- 
  bind_rows(SOM_FFN_monthly_global %>% 
              mutate(biome = "global",
                     region = "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, region))

Seasonality

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

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