Broullon DIC/TA climatology
Pasqualina Vonlanthen & Jens Daniel Müller
13 December, 2023
Last updated: 2023-12-13
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Knit directory: bgc_argo_r_argodata/
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Task
Explore the Broullón et al. (2020) DIC / TA climatology
This climatology is not currently used. As this markdown file takes several hours to run it can be excluded from the website refresh process.
library(tidyverse)
library(lubridate)
library(stars)
library(seacarb)
library(gsw)path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_emlr_preprocessing <- "/nfs/kryo/work/jenmueller/emlr_cant/observations/preprocessing/"
path_updata <- '/nfs/kryo/work/updata'
path_broullon_clim <- paste0(path_updata, "/broullon_co2_monthly_climatology")
path_woa13_temp <- paste0(path_updata, "/woa2013/temperature/decav/1.00/")
path_woa13_sal <- paste0(path_updata, "/woa2013/salinity/decav/1.00/")theme_set(theme_bw())map <- map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))
# WOA 18 basin mask
basinmask <-
read_csv(
paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character())
)
basinmask <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(-c(MLR_basins, basin))Load Broullon data
DIC
DIC_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/TCO2_NNGv2LDEO_climatology.nc"))
nc_depth <- read_ncdf(paste0(path_broullon_clim, "/TCO2_NNGv2LDEO_climatology.nc"),
var = c("depth"))
nc_depth <- as_tibble(nc_depth)
nc_depth <- nc_depth %>%
mutate(depth_level = depth_level+0.5)
DIC_clim <- full_join(DIC_clim, nc_depth)
DIC_clim <- DIC_clim %>%
rename(DIC = TCO2_NNGv2LDEO,
month = time)
rm(nc_depth)
# table(unique(DIC_clim$latitude))
# table(unique(DIC_clim$longitude))
# table(unique(DIC_clim$time))
# table(unique(DIC_clim$depth))
# text <- read_file(paste0(path_broullon_clim, "/README_global_monthly_2020.txt"))
# Depth goes down to 5500 m, but below 1500 m DIC is an annual climatological value, rather than a monthly climatological value TA
TA_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/AT_NNGv2_climatology.nc"))
nc_depth <- read_ncdf(paste0(path_broullon_clim, "/AT_NNGv2_climatology.nc"),
var = c('depth'))
nc_depth <- as_tibble(nc_depth)
nc_depth <- nc_depth %>%
mutate(depth_level = depth_level+0.5)
TA_clim <- full_join(TA_clim, nc_depth)
rm(nc_depth)
# read_file(paste0(path_broullon_clim, "/README_Global_monthly_2019.txt"))
TA_clim <- TA_clim %>%
rename(TA = AT_NNGv2,
month = time)
# Depth goes down to 5500 m, but below 1500 m TA is an annual climatological value, rather than a monthly climatological value pco2_clim <- tidync::hyper_tibble(paste0(path_broullon_clim, "/pCO2_NNGv2LDEO_climatology.nc"))
pco2_clim <- pco2_clim %>%
mutate(depth_level = 1) %>%
rename(pco2 = pCO2_NNGv2LDEO,
month = time)Join data
broullon_clim <- full_join(DIC_clim, TA_clim)
# broullon_clim <- full_join(broullon_clim, pco2_clim)
rm(DIC_clim, TA_clim)Load WOA13 data
months <- sprintf("%02d", seq(1,12,1))
for (i_month in months) {
# i_month <- months[1]
# read temperature climatology
woa13_temp <-
read_ncdf(
paste0(path_woa13_temp, "woa13_decav_t", i_month, "_01.nc"),
var = "t_an",
make_units = FALSE,
make_time = FALSE
)
woa13_temp <- woa13_temp %>%
as_tibble()
woa13_temp <- woa13_temp %>%
mutate(month = i_month) %>%
select(-time) %>%
rename(temp = t_an) %>%
drop_na()
# read salinity climatology
woa13_sal <-
read_ncdf(
paste0(path_woa13_sal, "woa13_decav_s", i_month, "_01.nc"),
var = "s_an",
make_units = FALSE,
make_time = FALSE
)
woa13_sal <- woa13_sal %>%
as_tibble()
woa13_sal <- woa13_sal %>%
mutate(month = i_month) %>%
select(-time) %>%
rename(sal = s_an) %>%
drop_na()
# join temperature and salinity climatology
woa13_temp <- full_join(woa13_temp,
woa13_sal)
# bind months into joined data frame
if (exists("woa13")) {
woa13 <- bind_rows(woa13, woa13_temp)
}
if (!exists("woa13")) {
woa13 <- woa13_temp
}
}
woa13 <- woa13 %>%
mutate(month = as.numeric(month))
rm(woa13_temp, woa13_sal, months, i_month)Harmonise data
# put longitude and latitude labels to the center of the grid (.5º)
broullon_clim <- broullon_clim %>%
rename(lon = longitude,
lat = latitude) %>%
select(-depth_level) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
broullon_clim <- broullon_clim %>%
drop_na()# put longitude and latitude labels to the center of the grid (.5º)
woa13 <- woa13 %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))Join Broullon/WOA13
broullon_clim <- full_join(broullon_clim,
woa13)
# remove grid cells with only one sal value to allow for interpolation
broullon_clim <- broullon_clim %>%
group_by(month, lat, lon) %>%
mutate(n = sum(!is.na(sal))) %>%
ungroup()
broullon_clim <- broullon_clim %>%
filter(n > 1) %>%
select(-n)
# interpolate sal/temp to broullon depth levels
broullon_clim <- broullon_clim %>%
group_by(lon, lat, month) %>%
arrange(depth) %>%
mutate(sal := approxfun(depth, sal, rule = 2)(depth),
temp := approxfun(depth, temp, rule = 2)(depth)) %>%
ungroup()
# remove sal/temp data on original woa13 depth levels
broullon_clim <- broullon_clim %>%
filter(!is.na(DIC))Apply basin mask
# subset Southerh Ocean data
broullon_clim_SO <- broullon_clim %>%
filter(lat <= -30,
depth <= 2000)
# join regional separations
broullon_clim_SO <- inner_join(broullon_clim_SO, nm_biomes)
broullon_clim_SO <- inner_join(broullon_clim_SO, basinmask)
broullon_clim <- inner_join(broullon_clim, basinmask)Write all data
broullon_clim %>%
write_rds(file = paste0(path_argo_preprocessed, "/broullon_TA_DIC_clim_all.rds"))Load Gruber 2019
G19_dcant_3d <-
read_csv(paste0(path_emlr_preprocessing,
"G19_dcant_3d.csv"))
G19_dcant_3d <- G19_dcant_3d %>%
select(lon, lat, depth, dcant = dcant_pos)
G19_dcant_3d <- inner_join(G19_dcant_3d,
nm_biomes %>% select(lon, lat))Scale DIC to 2016
# unique(G19_dcant_3d$depth)
# unique(broullon_clim_SO$depth)
broullon_clim_SO <-
full_join(broullon_clim_SO,
G19_dcant_3d %>% filter(depth <= 2000))
# remove grid cells with only one sal value to allow for interpolation
broullon_clim_SO <- broullon_clim_SO %>%
group_by(month, lat, lon) %>%
mutate(n = sum(!is.na(dcant))) %>%
ungroup()
broullon_clim_SO <- broullon_clim_SO %>%
filter(n > 1) %>%
select(-n)
# interpolate dcant to Broullon clim depth levels
broullon_clim_SO <- broullon_clim_SO %>%
group_by(lon, lat, month) %>%
arrange(depth) %>%
mutate(dcant := approxfun(depth, dcant, rule = 2)(depth)) %>%
ungroup()
# remove sal/temp data on original woa13 depth levels
broullon_clim_SO <- broullon_clim_SO %>%
filter(!is.na(DIC))
broullon_clim_SO <- broullon_clim_SO %>%
mutate(DIC = DIC + dcant * ((2019-1995)/(2007-1994))) %>%
select(-dcant)Calculate pH
rm(broullon_clim, woa13)
# calculate pressure from depth
broullon_clim_SO <- broullon_clim_SO %>%
mutate(pressure = gsw_p_from_z(z = -depth,
latitude = lat))
# broullon_clim_SO <- broullon_clim_SO %>%
# arrange(lon, lat)
#
# # calculate pHT from DIC, TA and ancillary parameters
# for (i_lon in unique(broullon_clim_SO$lon)) {
# print("***")
# print(i_lon)
#
# broullon_clim_SO_lon <- broullon_clim_SO %>%
# filter(lon == i_lon)
#
# for (i_lat in unique(broullon_clim_SO_lon$lat)) {
# print(i_lat)
#
# broullon_clim_SO_lon_lat <-
# broullon_clim_SO_lon %>%
# filter(lat == i_lat) %>%
# mutate(
# pH = carb(
# flag = 15,
# var1 = TA * 1e-6,
# var2 = DIC * 1e-6,
# S = sal,
# T = temp,
# P = pressure / 10,
# Pt = phosphate * 1e-6,
# Sit = silicate * 1e-6,
# k1k2 = "l"
# )[,6]
# )
#
# # bind months into joined data frame
# if (exists("broullon_clim_SO_pH")) {
# broullon_clim_SO_pH <- bind_rows(broullon_clim_SO_pH, broullon_clim_SO_lon_lat)
# }
#
# if (!exists("broullon_clim_SO_pH")) {
# broullon_clim_SO_pH <- broullon_clim_SO_lon_lat
# }
#
# }
# }
#
# rm(broullon_clim_SO_lon_lat, broullon_clim_SO_lon)
broullon_clim_SO_pH <-
broullon_clim_SO %>%
mutate(
pH = carb(
flag = 15,
var1 = TA * 1e-6,
var2 = DIC * 1e-6,
S = sal,
T = temp,
P = pressure / 10,
Pt = phosphate * 1e-6,
Sit = silicate * 1e-6,
k1k2 = "l"
)[, 6]
)Write SO+pH data
broullon_clim_SO_pH %>%
write_rds(file = paste0(path_argo_preprocessed, "/broullon_TA_DIC_clim_SO_pH.rds"))Plot data
pH
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = pH))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) pH clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = pH,
y = depth))+
geom_point(data = .x,
aes(x = pH,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim pH, month: ', unique(.x$month)),
x = 'pH')
)[[1]]
[[2]]
DIC
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = DIC))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) DIC clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = DIC,
y = depth))+
geom_point(data = .x,
aes(x = DIC,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim DIC, month: ', unique(.x$month)),
x = 'DIC')
)[[1]]
[[2]]
TA
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = TA))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) TA clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = TA,
y = depth))+
geom_point(data = .x,
aes(x = TA,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim TA, month: ', unique(.x$month)))
)[[1]]
[[2]]
Oxygen
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = oxygen))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) oxygen clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = oxygen,
y = depth))+
geom_point(data = .x,
aes(x = oxygen,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim oxygen, month: ', unique(.x$month)))
)[[1]]
[[2]]
Nitrate
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = nitrate))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) nitrate clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = nitrate,
y = depth))+
geom_point(data = .x,
aes(x = nitrate,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim nitrate, month: ', unique(.x$month)))
)[[1]]
[[2]]
Phosphate
broullon_clim_SO_pH %>%
group_split(depth) %>%
head(2) %>%
map(
~map +
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = phosphate))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
facet_wrap(~month, ncol = 2)+
labs(title = paste0('Broullon et al. (2020) phosphate clim, depth: ', unique(.x$depth)))
)[[1]]
[[2]]
broullon_clim_SO_pH %>%
# filter(depth <= 1500) %>%
group_split(month) %>%
head(2) %>%
map(
~ ggplot(data = .x,
aes(x = phosphate,
y = depth))+
geom_point(data = .x,
aes(x = phosphate,
y = depth),
size = 0.2,
pch = 1)+
scale_y_reverse()+
facet_grid(biome_name~basin_AIP)+
labs(title = paste0('Broullon et al. clim phosphate, month: ', unique(.x$month)))
)[[1]]
[[2]]
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] seacarb_3.3.1 SolveSAPHE_2.1.0 oce_1.7-10 gsw_1.1-1
[5] stars_0.6-0 sf_1.0-9 abind_1.4-5 lubridate_1.9.0
[9] timechange_0.1.1 forcats_0.5.2 stringr_1.5.0 dplyr_1.1.3
[13] purrr_1.0.2 readr_2.1.3 tidyr_1.3.0 tibble_3.2.1
[17] ggplot2_3.4.4 tidyverse_1.3.2
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 httr_1.4.4
[4] rprojroot_2.0.3 tools_4.2.2 backports_1.4.1
[7] bslib_0.4.1 utf8_1.2.2 R6_2.5.1
[10] KernSmooth_2.23-20 DBI_1.1.3 colorspace_2.0-3
[13] withr_2.5.0 tidyselect_1.2.0 bit_4.0.5
[16] compiler_4.2.2 git2r_0.30.1 cli_3.6.1
[19] rvest_1.0.3 RNetCDF_2.6-1 xml2_1.3.3
[22] labeling_0.4.2 sass_0.4.4 scales_1.2.1
[25] classInt_0.4-8 proxy_0.4-27 digest_0.6.30
[28] rmarkdown_2.18 pkgconfig_2.0.3 htmltools_0.5.3
[31] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
[34] rlang_1.1.1 tidync_0.3.0 readxl_1.4.1
[37] rstudioapi_0.15.0 farver_2.1.1 jquerylib_0.1.4
[40] generics_0.1.3 jsonlite_1.8.3 vroom_1.6.0
[43] googlesheets4_1.0.1 magrittr_2.0.3 ncmeta_0.3.5
[46] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.3
[49] lifecycle_1.0.3 stringi_1.7.8 whisker_0.4
[52] yaml_2.3.6 grid_4.2.2 parallel_4.2.2
[55] promises_1.2.0.1 crayon_1.5.2 haven_2.5.1
[58] hms_1.1.2 knitr_1.41 pillar_1.9.0
[61] reprex_2.0.2 glue_1.6.2 evaluate_0.18
[64] modelr_0.1.10 vctrs_0.6.4 tzdb_0.3.0
[67] httpuv_1.6.6 cellranger_1.1.0 gtable_0.3.1
[70] assertthat_0.2.1 cachem_1.0.6 xfun_0.35
[73] lwgeom_0.2-10 broom_1.0.5 e1071_1.7-12
[76] later_1.3.0 viridisLite_0.4.1 ncdf4_1.19
[79] class_7.3-20 googledrive_2.0.0 gargle_1.2.1
[82] workflowr_1.7.0 units_0.8-0 ellipsis_0.3.2