Last updated: 2020-11-09

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library(tidyverse)
library(patchwork)
library(scico)
library(scales)
library(metR)
library(kableExtra)
library(collapse)
library(marelac)
library(gt)

1 Data sources

1.1 This study

cant estimates from this study:

Mean and SD per grid cell (lat, lon, depth)
Zonal mean and SD (basin, lat, depth)
Inventories (lat, lon)

cant_3d <-
  read_csv(here::here("data/mapping/_summarized_files",
                         "cant_3d.csv"))

cant_3d <- cant_3d %>%
  filter(eras == "JGOFS_GO") %>%
  select(lon,
         lat,
         depth,
         basin_AIP,
         cant,
         cant_pos) %>%
  mutate(estimate = "JDM")


cant_zonal <-
  read_csv(here::here("data/mapping/_summarized_files",
                         "cant_zonal.csv"))

cant_zonal <- cant_zonal %>%
  filter(eras == "JGOFS_GO") %>%
  select(lat,
         depth,
         basin_AIP,
         cant_mean,
         cant_pos_mean,
         cant_sd,
         cant_pos_sd) %>%
  mutate(estimate = "JDM")


cant_inventory <-
  read_csv(here::here("data/mapping/_summarized_files",
                         "cant_inv.csv"))

cant_inventory <- cant_inventory %>% 
  rename(cant_pos_inv = cant_inv_pos)

cant_inventory <- cant_inventory %>% 
  filter(eras == "JGOFS_GO") %>% 
  select(-c(eras, basin)) %>% 
  mutate(estimate = "JDM")

1.2 Gruber 2019

cant_3d_G19 <- 
  read_csv(here::here("data/Gruber_2019/_summarized_files",
                       "G19_cant_3d.csv"))

cant_3d_G19 <- cant_3d_G19 %>% 
  mutate(estimate = "G19") %>% 
  select(-eras)


cant_inventory_G19 <- 
  read_csv(here::here("data/Gruber_2019/_summarized_files",
                       "G19_cant_inv.csv"))

cant_inventory_G19 <- cant_inventory_G19 %>% 
  select(-eras) %>% 
  mutate(estimate = "G19")


cant_zonal_G19 <- 
  read_csv(here::here("data/Gruber_2019/_summarized_files",
                       "G19_cant_zonal.csv"))

cant_zonal_G19 <- cant_zonal_G19 %>%
  filter(eras == "JGOFS_GO") %>%
  select(lat,
         depth,
         basin_AIP,
         cant_mean,
         cant_pos_mean,
         cant_sd,
         cant_pos_sd) %>%
  mutate(estimate = "G19")

1.3 Join data sets

cant_inventory <- bind_rows(cant_inventory, cant_inventory_G19)

2 Color scale

For ease of comparison with Gruber et al (2019) we adapt their color scale, including the ranges and breaks applied in various types of visualizations.

rgb2hex <- function(r, g, b)
  rgb(r, g, b, maxColorValue = 100)

cols = c(rgb2hex(95, 95, 95),
         rgb2hex(0, 0, 95),
         rgb2hex(100, 0, 0),
         rgb2hex(100, 100, 0))

Gruber_rainbow <- colorRampPalette(cols)

rm(rgb2hex, cols)

3 cant budgets

Global cant inventories were estimated in Pg-C. Please note that here we only added positive cant values in the upper 3000m and do not apply additional corrections for areas not covered.

cant_inventory_budget <- cant_inventory %>% 
  mutate(surface_area = earth_surf(lat, lon),
         cant_inv_grid = cant_inv*surface_area,
         cant_pos_inv_grid = cant_pos_inv*surface_area) %>% 
  group_by(basin_AIP, estimate) %>% 
  summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
            cant_total = round(cant_total,1),
            cant_pos_total = sum(cant_pos_inv_grid)*12*1e-15,
            cant_pos_total = round(cant_pos_total,1)) %>% 
  ungroup()


cant_inventory_budget %>%
  gt(rowname_col = "basin_AIP",
     groupname_col = c("estimate")) %>% 
  summary_rows(
    groups = TRUE,
    fns = list(total = "sum")
  )

	cant_total	cant_pos_total
G19
Atlantic	10.8	11.0
Indian	5.6	6.9
Pacific	12.5	13.2
total	28.90	31.10
JDM
Atlantic	9.9	10.3
Indian	10.1	10.3
Pacific	17.5	18.1
total	37.50	38.70

# cant_inventory_budget %>% 
#   kableExtra::kable() %>% 
#   add_header_above() %>%
#   kable_styling(full_width = FALSE)

rm(cant_inventory_budget)

4 cant - positive

In a first series of plots we explore the distribution of cant, taking only positive estimates into account (positive here refers to the mean cant estimate across 10 eMLR model predictions available for each grid cell). Negative values were set to zero before calculating mean sections and inventories.

4.1 Zonal mean sections

cant_zonal <- bind_rows(
  cant_zonal,
  cant_zonal_G19
)

rm(cant_zonal_G19)

breaks <- c(seq(0,18,1),Inf)
breaks_n <- length(breaks) - 1

cant_zonal <- cant_zonal %>% 
  mutate(cant_pos_mean_int = cut(cant_pos_mean, 
                                breaks,
                                right = FALSE))


zonal_section <- function(df, i_basin_AIP, i_estimate, var) {
  
  name_var <- var
  var <- sym(var)
  
  lat_max <- max(df$lat)
  lat_min <- min(df$lat)
  
  df_sub <- df %>% 
    filter(basin_AIP == i_basin_AIP,
           estimate == i_estimate)
  
  surface <- df_sub %>%
    ggplot(aes(lat, depth, z = !!var)) +
    geom_contour_filled(breaks = breaks) +
    scale_fill_manual(values = Gruber_rainbow(breaks_n),
                      name = "cant") +
    coord_cartesian(expand = 0,
                    ylim = c(500, 0),
                    xlim = c(lat_min, lat_max)) +
    scale_y_reverse() +
    scale_x_continuous(breaks = seq(-100,100,20)) +
    theme(
      axis.title.x = element_blank(),
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank()
    ) +
    labs(y = "Depth (m)",
         title = paste("Basin:", i_basin_AIP, "| estimate:", i_estimate))
  
  deep <- df_sub %>%
    ggplot(aes(lat, depth, z = !!var)) +
    geom_contour_filled(breaks = breaks) +
    scale_fill_manual(values = Gruber_rainbow(breaks_n),
                      name = "cant") +
    scale_y_reverse() +
    scale_x_continuous(breaks = seq(-100,100,20)) +
    coord_cartesian(expand = 0,
                    ylim = c(3000, 500),
                    xlim = c(lat_min, lat_max)) +
    labs(x = "latitude (°N)", y = "Depth (m)")
  
  surface / deep +
    plot_layout(guides = "collect")
  
}


for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
  for (i_estimate in unique(cant_zonal$estimate)) {

  print(zonal_section(cant_zonal,
                        i_basin_AIP = i_basin_AIP,
                        i_estimate = i_estimate,
                        "cant_pos_mean"))
    
  }
}

rm(breaks, breaks_n)

4.2 Inventory map

Column inventory of positive cant between the surface and 3000m water depth per horizontal grid cell (lat x lon).

cant_inventory <- bind_rows(
  cant_inventory,
  cant_inventory_G19
)

rm(cant_inventory_G19)

breaks <- c(seq(0,16,2),Inf)
breaks_n <- length(breaks) - 1

cant_inventory <- cant_inventory %>% 
  mutate(cant_pos_inv_int = cut(cant_pos_inv, 
                                breaks,
                                right = FALSE))

cant_inventory %>%
  ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat), fill = "grey30") +
  geom_raster(aes(lon, lat, fill = cant_pos_inv_int)) +
  coord_quickmap(expand = 0) +
  scale_fill_manual(values = Gruber_rainbow(breaks_n)) +
  # scale_fill_scico_d(palette = "batlow", direction = -1) +
  facet_wrap( ~ estimate, ncol = 1) +
  theme(axis.title = element_blank())

5 cant - all

5.1 Zonal mean sections

# breaks <- c(seq(0,18,1),Inf)
# breaks_n <- length(breaks) - 1
# 
# cant_zonal <- cant_zonal %>% 
#   mutate(cant_pos_mean_int = cut(cant_pos_mean, 
#                                 breaks,
#                                 right = FALSE))

  # cant_zonal %>%
  #   ggplot(aes(lat, depth, z = cant_mean)) +
  #   geom_contour_fill(breaks = MakeBreaks(5),
  #                     na.fill = TRUE) +
  #   scale_fill_divergent(guide = "colorstrip",
  #                        breaks = MakeBreaks(5),
  #                        #name = name_var
  #                        ) +
  #   scale_y_reverse() +
  #   coord_cartesian(expand = 0) +
  #   guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
  #   facet_grid(basin_AIP ~ estimate)



zonal_section <- function(df, i_basin_AIP, i_estimate, var) {
  
  # df <- cant_zonal
  # i_basin_AIP <- unique(cant_zonal$basin_AIP)[1]
  # i_estimate  <- unique(cant_zonal$estimate)[1]
  # var <- "cant_mean"
  
  name_var <- var
  var <- sym(var)
  
  lat_max <- max(df$lat)
  lat_min <- min(df$lat)
  
  df_sub <- df %>% 
    filter(basin_AIP == i_basin_AIP,
           estimate == i_estimate)
  
  surface <- df_sub %>%
    ggplot(aes(lat, depth, z = !!var)) +
    geom_contour_fill(breaks = MakeBreaks(5),
                      na.fill = TRUE) +
    scale_fill_divergent(guide = "colorstrip",
                         breaks = MakeBreaks(5),
                         name = name_var
                         ) +
    coord_cartesian(expand = 0,
                    ylim = c(500, 0),
                    xlim = c(lat_min, lat_max)) +
    scale_y_reverse() +
    scale_x_continuous(breaks = seq(-100,100,20)) +
    theme(
      axis.title.x = element_blank(),
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank()
    ) +
    labs(y = "Depth (m)",
         title = paste("Basin:", i_basin_AIP, "| estimate:", i_estimate))
  
  deep <- df_sub %>%
    ggplot(aes(lat, depth, z = !!var)) +
    geom_contour_fill(breaks = MakeBreaks(5),
                      na.fill = TRUE) +
    scale_fill_divergent(guide = "colorstrip",
                         breaks = MakeBreaks(5),
                         name = name_var
                         ) +
    scale_y_reverse() +
    scale_x_continuous(breaks = seq(-100,100,20)) +
    coord_cartesian(expand = 0,
                    ylim = c(3000, 500),
                    xlim = c(lat_min, lat_max)) +
    labs(x = "latitude (°N)", y = "Depth (m)")
  
  surface / deep +
    plot_layout(guides = "collect")
  
}


for (i_basin_AIP in unique(cant_zonal$basin_AIP)) {
  for (i_estimate in unique(cant_zonal$estimate)) {

  print(zonal_section(cant_zonal,
                        i_basin_AIP = i_basin_AIP,
                        i_estimate = i_estimate,
                        "cant_pos_mean"))
    
  }
}

rm(breaks, breaks_n)

5.2 Inventory map