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Rmd	b577ec6	jens-daniel-mueller	2020-08-26	Analysis split in this and previous studies

library(tidyverse)
library(patchwork)
library(scico)
library(scales)
library(metR)
library(marelac)
library(kableExtra)
library(threejs)

1 Data sources

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

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

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

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

C* estimates from this study:

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

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

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

Cleaned GLODAPv2_2020 file as used in this study

GLODAP <-
  read_csv(
    here::here(
      "data/GLODAPv2_2020/_summarized_data_files",
      "GLODAP_MLR_fitting_ready.csv"
    )
  )

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_inv <- left_join(Cant_inv, basinmask_AIP) 

Cant_inv_budget <- Cant_inv %>% 
  mutate(surface_area = earth_surf(lat, lon),
         cant_inv_grid = cant_inv*surface_area) %>% 
  group_by(eras, basin_AIP) %>% 
  summarise(cant_total = sum(cant_inv_grid)*12*1e-15,
            cant_total = round(cant_total,1)) %>% 
  ungroup() %>% 
  arrange(desc(eras)) %>% 
  pivot_wider(values_from = cant_total, names_from = basin_AIP) %>% 
  mutate(total = Atlantic + Indian + Pacific)

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

eras	Atlantic	Indian	Pacific	total
JGOFS_GO	9.9	10.1	17.5	37.5
GO_new	6.9	3.9	13.6	24.4

rm(Cant_inv_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_average_zonal <- Cant_average_zonal %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))

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

Cant_average_zonal <- Cant_average_zonal %>% 
  mutate(Cant_pos_mean_int = cut(Cant_pos_mean, 
                                breaks,
                                right = FALSE))


zonal_section <- function(df, i_basin_AIP, i_eras, var) {
  
  name_var <- var
  var <- sym(var)
  
  lat_max <- max(df$lat)
  lat_min <- min(df$lat)
  
  df_sub <- df %>% 
    filter(eras == i_eras,
           basin_AIP == i_basin_AIP)
  
  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, "| eras:", i_eras))
  
  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_average_zonal$basin_AIP)) {
  for (i_eras in rev(unique(Cant_average_zonal$eras))) {
    
    print(zonal_section(Cant_average_zonal,
                        i_basin_AIP = i_basin_AIP,
                        i_eras = i_eras,
                        "Cant_pos_mean"))
    
  }
  
}

rm(breaks, breaks_n)

4.2 Isoneutral slab distribution

Mean of positive Cant within each horizontal grid cell (lon x lat) per isoneutral slab.

Please note that:

density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping

Cant_gamma_maps <- Cant_average %>% 
  group_by(lat, lon, gamma_slab, eras) %>% 
  summarise(Cant_pos = mean(Cant_pos, na.rm = TRUE)) %>% 
  ungroup()


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

Cant_gamma_maps <- Cant_gamma_maps %>% 
  mutate(Cant_pos_int = cut(Cant_pos, 
                                breaks,
                                right = FALSE)) %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))


ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey30") +
  geom_raster(data = Cant_gamma_maps,
              aes(lon, lat, fill = Cant_pos_int)) +
  scale_fill_manual(values = Gruber_rainbow(breaks_n)) +
  facet_grid(gamma_slab ~ eras) +
  coord_quickmap(expand = 0) +
  theme(axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.position = "top")

rm(Cant_gamma_maps, breaks, breaks_n)

4.3 Inventory map

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

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

Cant_inv <- Cant_inv %>% 
  mutate(cant_inv_pos_int = cut(cant_inv_pos, 
                                breaks,
                                right = FALSE)) %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))

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

4.4 Global section

4.4.1 JGOFS_GO

section_global(Cant_average %>% filter(eras == "JGOFS_GO"),
                    "Cant_pos")

4.4.2 GO_new

section_global(Cant_average %>% filter(eras == "GO_new"),
                    "Cant_pos")

5 Cant - all

In a second series of plots we explore the distribution of Cant, taking positive and negative estimates into account.

5.1 Zonal mean sections

section_zonal_average_divergent(Cant_average_zonal,
                                "Cant_mean",
                                "gamma_mean")

5.2 Isoneutral slab distribution

Mean of Cant within each horizontal grid cell (lon x lat) per isoneutral slab.

Please note that:

density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
gaps in the maps represent areas where (thin) density layers fit between discrete depth levels used for mapping

Cant_gamma_maps <- Cant_average %>%
  group_by(lat, lon, gamma_slab, eras) %>%
  summarise(Cant = mean(Cant, na.rm = TRUE)) %>%
  ungroup()


breaks <- c(-Inf, seq(-16, 16, 4), Inf)
breaks_n <- length(breaks) - 1

Cant_gamma_maps <- Cant_gamma_maps %>%
  mutate(Cant_int = cut(Cant,
                        breaks,
                        right = FALSE)) %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))

ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey30") +
  geom_raster(data = Cant_gamma_maps,
              aes(lon, lat, fill = Cant_int)) +
  scale_fill_brewer(palette = "RdBu",
                    direction = -1) +
  facet_grid(gamma_slab ~ eras) +
  coord_quickmap(expand = 0) +
  theme(
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    axis.text = element_blank(),
    legend.position = "top"
  )

rm(Cant_gamma_maps, breaks, breaks_n)

5.3 Inventory map

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

breaks <- c(-Inf, seq(-16,16,4),Inf)
breaks_n <- length(breaks) - 1

Cant_inv <- Cant_inv %>% 
  mutate(cant_inv_int = cut(cant_inv, 
                                breaks,
                                right = FALSE))
Cant_inv %>% 
  ggplot() +
    geom_raster(data = landmask,
                aes(lon, lat), fill = "grey80") +
    geom_raster(aes(lon, lat, fill = cant_inv_int)) +
    coord_quickmap(expand = 0) +
  scale_fill_brewer(palette = "RdBu",
                    direction = -1) +
  facet_wrap(~eras, ncol = 1) +
    theme(
      axis.title = element_blank()
    )

rm(breaks, breaks_n)

6 Cant - standard deviation

6.1 Across models

Standard deviation across Cant from all MLR models was calculate for each grid cell (XYZ). The zonal mean of this standard deviation should reflect the uncertainty associated to the predictor selection within each slab and era.

section_zonal_average_continous(Cant_average_zonal,
                                "Cant_sd_mean",
                                "gamma_mean")

6.2 Across basins

Standard deviation of mean Cant values was calculate across all longitudes. This standard deviation should reflect the zonal variability of Cant within the basin and era.

section_zonal_average_continous(Cant_average_zonal,
                                "Cant_sd",
                                "gamma_mean")

6.3 Correlation

6.3.1 Cant vs model SD

Cant_average <- Cant_average  %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))

Cant_average %>% 
  ggplot(aes(Cant, Cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(basin_AIP ~ eras)

Cant_average %>% 
  ggplot(aes(Cant, Cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(gamma_slab ~ basin_AIP)

6.3.2 Cant vs regional SD

Cant_average_zonal %>% 
  ggplot(aes(Cant_mean, Cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(basin_AIP ~ eras)

Cant_average_zonal %>% 
  ggplot(aes(Cant_mean, Cant_sd)) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 10) +
  geom_bin2d() +
  scale_fill_viridis_c(option = "magma",
                       direction = -1,
                       trans = "log10",
                       name = "log10(n)") +
  facet_grid(gamma_slab ~ basin_AIP)

7 Cant - predictor contribution

Cant_predictor_average_zonal <- Cant_predictor_average_zonal %>% 
  mutate(eras = factor(eras, c("JGOFS_GO", "GO_new")))


for (variable in c(
  "Cant_intercept",
  "Cant_aou",
  "Cant_oxygen",
  "Cant_phosphate",
  "Cant_phosphate_star",
  "Cant_silicate",
  "Cant_tem",
  "Cant_sal")) {

print(
section_zonal_average_continous(Cant_predictor_average_zonal,
                                variable,
                                "gamma")
)
    
}

rm(variable)

8 Neutral density

8.1 Slab depth

Please note that:

density slabs covering values >28.1 occur by definition only either in the Atlantic or Indo-Pacific basin
predictor density slabs are only shown for the upper 3000m as used for the mapping, whereas GLODAP observations are displayed for the entire water column as used for fitting eMLRs (in both cases shallow waters are excluded at low density)

gamma_maps <- Cant_average %>% 
  group_by(lat, lon, gamma_slab) %>% 
  summarise(depth_max = max(depth, na.rm = TRUE)) %>% 
  ungroup()

GLODAP_obs_coverage <- GLODAP %>% 
  count(lat, lon, gamma_slab, era) %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))
  
ggplot() +
  geom_raster(data = landmask,
              aes(lon, lat),
              fill = "grey80") +
  geom_raster(data = gamma_maps,
              aes(lon, lat, fill = depth_max)) +
  geom_raster(data = GLODAP_obs_coverage,
              aes(lon, lat), fill = "red") +
  facet_grid(gamma_slab ~ era) +
  coord_quickmap(expand = 0) +
  scale_fill_viridis_c(direction = -1) +
  theme(axis.title = element_blank(),
        axis.ticks = element_blank(),
        axis.text = element_blank(),
        legend.position = "top")

rm(gamma_maps, GLODAP_obs_coverage)

8.2 Surface maps

map_climatology_discrete(Cant_average, "gamma_slab")

8.3 Zonal sections

The mean zonal distribution of neutral densities was calculated. CAVEAT: Due to practical reasons, binning here does not include the two highest isoneutral density slabs in the Atlantic, yet.

8.3.1 Mean

Cant_average_zonal %>% 
  filter(eras == "JGOFS_GO") %>% 
  ggplot(aes(lat, depth, z = gamma_mean)) +
  geom_contour_filled(breaks = slab_breaks) +
  geom_contour(breaks = slab_breaks,
               col = "white") +
  geom_text_contour(breaks = slab_breaks,
               col = "white",
               skip = 1) +
  scale_fill_viridis_d(name = "Gamma",
                       direction = -1) +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(-100, 100, 20)) +
  coord_cartesian(expand = 0) +
  guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
  facet_grid(basin_AIP~.)

8.3.2 SD

Higher SD of gamma in shallow, subtropical waters results from a more pronounced longitudinal variability.

Cant_average_zonal %>% 
  filter(eras == "JGOFS_GO") %>% 
  ggplot(aes(lat, depth, z = gamma_sd)) +
  geom_contour_filled() +
  scale_fill_viridis_d(name = "Gamma SD",
                       direction = -1) +
  scale_y_reverse() +
  scale_x_continuous(breaks = seq(-100, 100, 20)) +
  coord_cartesian(expand = 0) +
  guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
  facet_grid(basin_AIP~.)

9 Cstar

9.1 Zonal mean sections

Cstar_average_zonal <- Cstar_average_zonal %>% 
    mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era"))) 

Cstar_average_zonal %>% 
    ggplot(aes(lat, depth, z = Cstar_mean_mean)) +
    geom_contour_filled(bins = 11) +
    scale_fill_viridis_d(name = "Cstar") +
    geom_contour(aes(lat, depth, z = gamma_mean_mean),
                 breaks = slab_breaks,
                 col = "white") +
    geom_text_contour(
      aes(lat, depth, z = gamma_mean_mean),
      breaks = slab_breaks,
      col = "white",
      skip = 1
    ) +
    scale_y_reverse() +
    scale_x_continuous(breaks = seq(-100,100,20)) +
    coord_cartesian(expand = 0) +
    guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
    facet_grid(basin_AIP ~ era)

10 Sections by model

Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.

11 Known issues

Deviations between this study and the results by Gruber et al (2019), short G19, for the same period, might be attributable to following known differences in the implementation of the eMLR(C*) method:

GLODAPv2_2020 here vs an extended version of GLODAPv2 in G19
flagging: Here, we accept f flags 0 and 2 (except for tco2, where only 0 is accepted). G19 claim to use 0 throughout, yet have a high coverage of talk observations in the SE Pacific
Neutral density calculation: Here and in GLODAPv2_2020 a polynomial approximation is used, whereas G19 uses the original Matlab code
Predictor climatology: Here we used WOA18, whereas G19 used WOA13
Missing data in the GLODAP mapped climatology, eg NO3 at surface, where not filled in this study
Cant on neutral density levels calculate as slab mean, rather than on one surface
Here, surface delta Cant were calculated based on Luecker constants, rather than Mehrbach as in G19
Here, pCO2 was calculated from DIC/TA Climatology

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

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

other attached packages:
 [1] threejs_0.3.3    igraph_1.2.5     kableExtra_1.1.0 marelac_2.1.10  
 [5] shape_1.4.4      metR_0.7.0       scales_1.1.1     scico_1.2.0     
 [9] patchwork_1.0.1  forcats_0.5.0    stringr_1.4.0    dplyr_1.0.0     
[13] purrr_0.3.4      readr_1.3.1      tidyr_1.1.0      tibble_3.0.3    
[17] ggplot2_3.3.2    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.4.2           lubridate_1.7.9    gsw_1.0-5          RColorBrewer_1.1-2
 [5] webshot_0.5.2      httr_1.4.2         rprojroot_1.3-2    tools_4.0.2       
 [9] backports_1.1.8    R6_2.4.1           DBI_1.1.0          colorspace_1.4-1  
[13] sp_1.4-2           withr_2.2.0        tidyselect_1.1.0   compiler_4.0.2    
[17] git2r_0.27.1       cli_2.0.2          rvest_0.3.6        xml2_1.3.2        
[21] isoband_0.2.2      labeling_0.3       checkmate_2.0.0    digest_0.6.25     
[25] rmarkdown_2.3      oce_1.2-0          base64enc_0.1-3    pkgconfig_2.0.3   
[29] htmltools_0.5.0    dbplyr_1.4.4       highr_0.8          htmlwidgets_1.5.1 
[33] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    farver_2.0.3      
[37] generics_0.0.2     jsonlite_1.7.0     crosstalk_1.1.0.1  magrittr_1.5      
[41] Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1        lifecycle_0.2.0   
[45] stringi_1.4.6      whisker_0.4        yaml_2.2.1         plyr_1.8.6        
[49] grid_4.0.2         blob_1.2.1         promises_1.1.1     crayon_1.3.4      
[53] lattice_0.20-41    haven_2.3.1        hms_0.5.3          seacarb_3.2.13    
[57] knitr_1.30         pillar_1.4.6       reprex_0.3.0       glue_1.4.1        
[61] evaluate_0.14      data.table_1.13.0  modelr_0.1.8       vctrs_0.3.2       
[65] httpuv_1.5.4       testthat_2.3.2     cellranger_1.1.0   gtable_0.3.0      
[69] assertthat_0.2.1   xfun_0.16          broom_0.7.0        later_1.1.0.1     
[73] viridisLite_0.3.0  memoise_1.1.0      ellipsis_0.3.1     here_0.1

Analysis of Cant estimates

Jens Daniel Müller

03 November, 2020

1 Data sources

2 Color scale

3 Cant budgets

4 Cant - positive

4.1 Zonal mean sections

4.2 Isoneutral slab distribution

4.3 Inventory map

4.4 Global section

4.4.1 JGOFS_GO

4.4.2 GO_new

5 Cant - all

5.1 Zonal mean sections

5.2 Isoneutral slab distribution

5.3 Inventory map

6 Cant - standard deviation

6.1 Across models

6.2 Across basins

6.3 Correlation

6.3.1 Cant vs model SD

6.3.2 Cant vs regional SD

7 Cant - predictor contribution

8 Neutral density

8.1 Slab depth

8.2 Surface maps

8.3 Zonal sections

8.3.1 Mean

8.3.2 SD

9 Cstar

9.1 Zonal mean sections

10 Sections by model

11 Known issues