Mapping Cant
Jens Daniel Müller
01 October, 2020
Last updated: 2020-10-01
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Knit directory: Cant_eMLR/
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| html | 4578cfa | jens-daniel-mueller | 2020-09-19 | Build site. |
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library(tidyverse)
library(metR)
library(seacarb)
library(collapse)basinmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"
)
)
basinmask_AIP <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18_AIP.csv"
)
)
landmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"land_mask_WOA18.csv"
)
)
section_global_coordinates <-
read_csv(here::here("data",
"section_global_coordinates.csv"))1 Predictor fields
Currently, we use combined predictor fields:
- WOA18: S, T, and derived variables
- GLODAP16: Oxygen, PO4, NO3, Silicate, and derived variables
predictors <-
read_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn.csv"))
predictors_surface <-
read_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn_surface.csv"))2 Atm. pCO2
co2_atm_tref <-
read_csv(here::here(
"data/pCO2_atmosphere/_summarized_data_files",
"co2_atm_tref.csv"
))3 Load MLR models
lm_all_wide <-
read_csv(here::here("data/eMLR",
"lm_all_wide.csv"))4 Merge MLRs + climatologies
lm_all_wide <- lm_all_wide %>%
mutate(model = str_remove(model, "Cstar ~ "))
Cant <- full_join(predictors, lm_all_wide)
rm(predictors, lm_all_wide)5 Map Cant
5.1 Deep water
5.2 Apply MLRs to predictor
Cant <- Cant %>%
mutate(Cant = `delta_coeff_(Intercept)` +
delta_coeff_aou * aou +
delta_coeff_oxygen * oxygen +
delta_coeff_phosphate * phosphate +
delta_coeff_phosphate_star * phosphate_star +
delta_coeff_silicate * silicate +
delta_coeff_sal * sal +
delta_coeff_tem * tem)
Cant <- Cant %>%
mutate(Cant_pos = if_else(Cant < 0, 0, Cant))Cant <- Cant %>%
mutate(Cant_intercept = `delta_coeff_(Intercept)`,
Cant_aou = delta_coeff_aou * aou,
Cant_oxygen = delta_coeff_oxygen * oxygen,
Cant_phosphate = delta_coeff_phosphate * phosphate,
Cant_phosphate_star = delta_coeff_phosphate_star * phosphate_star,
Cant_silicate = delta_coeff_silicate * silicate,
Cant_sal = delta_coeff_sal * sal,
Cant_tem = delta_coeff_tem * tem,
Cant_sum = Cant_intercept +
Cant_aou +
Cant_oxygen +
Cant_phosphate +
Cant_phosphate_star +
Cant_silicate +
Cant_sal +
Cant_tem)5.2.1 Sections by model
Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.
library(scales)
for (i_eras in unique(Cant$eras)) {
# i_eras <- unique(Cant$eras)[2]
Cant_eras <- Cant %>%
filter(eras == i_eras)
for (i_lon in seq(20.5, 360, 20)) {
# i_lon <- seq(20.5, 360, 20)[7]
Cant_eras_lon <- Cant_eras %>%
filter(lon == i_lon)
Cant_eras_lon %>%
ggplot(aes(lat, depth, col = Cant)) +
geom_point() +
scale_color_gradient2(
name = "Cant",
high = muted("red"),
mid = "grey",
low = muted("blue")
) +
scale_y_reverse(limits = c(parameters$inventory_depth, NA)) +
scale_x_continuous(limits = c(-75, 65)) +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
labs(title = paste("eras:", i_eras, "| lon:", i_lon)) +
facet_wrap( ~ model, ncol = 5)
ggsave(
here::here(
"output/figure/mapping",
paste(i_eras,
"lon",
i_lon,
"model_Cant.png",
sep = "_")
),
width = 17,
height = 9
)
}
}5.3 Surface water
As outlined in Gruber et al. (2019), a transient equilibrium approach was applied to estimate Cant in surface waters, assuming that the CO2 system in these waters has followed the increase in atmospheric CO2 closely.
Using eq 10.2.16 from OBD, the change in anthropogenic CO2 in the upper ocean was computed as:
ΔtCanteq(t2ref − t1ref )= 1∕γ ⋅ DIC/pCO2 ⋅ (pCO2atm (t2ref)− pCO2atm (t1ref))
, where DIC and pCO2 are the in situ values, where γ is the buffer (Revelle) factor and where we evaluated the right-hand side using seacarb employing the Mehrbach constants as refitted by Dickson and Millero using the climatological values for temperature, salinity, DIC and Alk.
5.3.1 pCO2 climatology
predictors_surface <- predictors_surface %>%
mutate(pCO2 = carb(flag = 15,
var1 = TAlk*1e-6,
var2 = TCO2*1e-6,
S = sal,
T = tem,
P = depth/10,
Pt = PO4*1e-6,
Sit = silicate*1e-6,
k1k2 = "l")$pCO2)
map_climatology(predictors_surface, "pCO2")
section_global_surface(predictors_surface, "pCO2")
5.3.2 Revelle factor
predictors_surface <- predictors_surface %>%
mutate(rev_fac = buffer(flag = 15,
var1 = TAlk*1e-6,
var2 = TCO2*1e-6,
S = sal,
T = tem,
P = depth/10,
Pt = PO4*1e-6,
Sit = silicate*1e-6,
k1k2 = "l")$BetaD)
map_climatology(predictors_surface, "rev_fac")
section_global_surface(predictors_surface, "rev_fac")
5.3.3 Cant
co2_atm_tref <- co2_atm_tref %>%
arrange(pCO2_tref) %>%
mutate(d_pCO2_tref = pCO2_tref - lag(pCO2_tref)) %>%
drop_na() %>%
mutate(eras = c("JGOFS_GO", "GO_new")) %>%
select(eras, d_pCO2_tref)
Cant_surface <- full_join(predictors_surface, co2_atm_tref,
by = character())
Cant_surface <- Cant_surface %>%
mutate(Cant = (1 / rev_fac) * (TCO2 / pCO2) * d_pCO2_tref)
Cant_surface <- Cant_surface %>%
mutate(Cant_pos = if_else(Cant < 0, 0, Cant))
map_climatology_eras(Cant_surface, "Cant")
section_global_surface_eras(Cant_surface, "Cant")
5.4 Mean Cant fields
Mean and sd are calculated for Cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all Cant values vs positive values only. This averaging step summarizes the information derived from ten best fitting MLRs.
5.4.1 Deep water averaging
Cant_predictor_average <- Cant %>%
fselect(lon, lat, depth, eras, basin,
Cant_intercept,
Cant_aou,
Cant_oxygen,
Cant_phosphate,
Cant_phosphate_star,
Cant_silicate,
Cant_tem,
Cant_sal,
Cant_sum,
gamma) %>%
fgroup_by(lon, lat, depth, eras, basin) %>% {
add_vars(fgroup_vars(.,"unique"),
fmean(., keep.group_vars = FALSE))
}
Cant_predictor_average_Atl <- Cant_predictor_average %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Atl))
Cant_predictor_average_Ind_Pac <- Cant_predictor_average %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Ind_Pac))
Cant_predictor_average <- bind_rows(Cant_predictor_average_Atl, Cant_predictor_average_Ind_Pac)
rm(Cant_predictor_average_Atl, Cant_predictor_average_Ind_Pac)Cant_average <- Cant %>%
fselect(lon, lat, depth, eras, basin, Cant, Cant_pos, gamma) %>%
fgroup_by(lon, lat, depth, eras, basin) %>% {
add_vars(fgroup_vars(.,"unique"),
fmean(., keep.group_vars = FALSE),
fsd(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_sd"))
}
rm(Cant)Cant_surface_average <- Cant_surface %>%
fselect(lon, lat, depth, eras, basin, Cant, Cant_pos, gamma) %>%
fgroup_by(lon, lat, depth, eras, basin) %>% {
add_vars(fgroup_vars(.,"unique"),
fmean(., keep.group_vars = FALSE),
fsd(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_sd"))
}
rm(Cant_surface)5.4.2 Join surface and deep water
Cant_average <- full_join(Cant_average, Cant_surface_average)
rm(Cant_surface_average)5.4.3 Gamma slab zonal mean
Cant_average_Atl <- Cant_average %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Atl))
Cant_average_Ind_Pac <- Cant_average %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Ind_Pac))
Cant_average <- bind_rows(Cant_average_Atl, Cant_average_Ind_Pac)
rm(Cant_average_Atl, Cant_average_Ind_Pac)5.5 Mean Cant sections
For each basin and era combination, the zonal mean Cant is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.
Cant_average <- left_join(Cant_average,
basinmask_AIP %>% select(-basin))
Cant_average_zonal <- Cant_average %>%
fselect(lat, depth, eras, basin, basin_AIP,
Cant, Cant_pos, gamma, Cant_sd, Cant_pos_sd, gamma_sd) %>%
fgroup_by(lat, depth, eras, basin, basin_AIP) %>% {
add_vars(fgroup_vars(.,"unique"),
fmean(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_mean"),
fsd(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_sd"))
}
# Cant_average_zonal <- Cant_average %>%
# group_by(lat, depth, eras, basin, basin_AIP) %>%
# summarise(across(
# c(
# "Cant_mean",
# "Cant_pos_mean",
# "Cant_sd",
# "Cant_pos_sd",
# "gamma_mean",
# "gamma_sd"
# ),
# list(
# mean = ~ mean(.x, na.rm = TRUE),
# sd = ~ sd(.x, na.rm = TRUE)
# )
# )) %>%
# ungroup()
Cant_average_zonal_Atl <- Cant_average_zonal %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))
Cant_average_zonal_Ind_Pac <- Cant_average_zonal %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))
Cant_average_zonal <- bind_rows(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)
rm(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)5.6 Mean Cant sections by coefficient
For each basin and era combination, the zonal mean Cant is calculated by model coefficient.
Cant_predictor_average <- full_join(Cant_predictor_average,
basinmask_AIP %>% select(-basin))
Cant_predictor_average_zonal <- Cant_predictor_average %>%
fselect(lat, depth, eras, basin, basin_AIP,
Cant_intercept:gamma) %>%
fgroup_by(lat, depth, eras, basin, basin_AIP) %>% {
add_vars(fgroup_vars(.,"unique"),
fmean(., keep.group_vars = FALSE))
}
# Cant_predictor_average_zonal <- Cant_predictor_average %>%
# group_by(lat, depth, eras, basin, basin_AIP) %>%
# summarise(across(
# Cant_intercept_mean:gamma_mean,
# list(mean = ~ mean(.x, na.rm = TRUE))
# )) %>%
# ungroup()
Cant_predictor_average_zonal_Atl <- Cant_predictor_average_zonal %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Atl))
Cant_predictor_average_zonal_Ind_Pac <- Cant_predictor_average_zonal %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Ind_Pac))
Cant_predictor_average_zonal <- bind_rows(Cant_predictor_average_zonal_Atl, Cant_predictor_average_zonal_Ind_Pac)
rm(Cant_predictor_average_zonal_Atl, Cant_predictor_average_zonal_Ind_Pac)5.7 Inventory calculation
To calculate Cant column inventories, we:
- Multiple layer thickness with Cant concentration to get a layer inventory
- For each horizontal grid cell and era, sum Cant layer inventories from 150 - 3000 m
Step 2 is performed again for all Cant and positive Cant values only
depth_level_volume <- tibble(depth = unique(Cant_average$depth)) %>%
arrange(depth)
depth_level_volume <- depth_level_volume %>%
mutate(
layer_thickness_above = replace_na((depth - lag(depth)) / 2, 0),
layer_thickness_below = replace_na((lead(depth) - depth) / 2, 0),
layer_thickness = layer_thickness_above + layer_thickness_below
) %>%
select(-c(layer_thickness_above,
layer_thickness_below))
Cant_average <-
full_join(Cant_average, depth_level_volume)
Cant_average <- Cant_average %>%
mutate(layer_inv = Cant * layer_thickness) %>%
mutate(layer_inv_pos = if_else(layer_inv < 0, 0, layer_inv)) %>%
select(-layer_thickness)
Cant_inv <- Cant_average %>%
group_by(lon, lat, basin, eras) %>%
summarise(
cant_inv_pos = sum(layer_inv_pos, na.rm = TRUE) / 1000,
cant_inv = sum(layer_inv, na.rm = TRUE) / 1000
) %>%
ungroup()6 Write csv
# Cant %>%
# write_csv(here::here("data/mapping/_summarized_files",
# "Cant.csv"))
Cant_average %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_average.csv"))
Cant_predictor_average %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_predictor_average.csv"))
Cant_average_zonal %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_average_zonal.csv"))
Cant_predictor_average_zonal %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_predictor_average_zonal.csv"))
Cant_inv %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))
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] collapse_1.3.2 seacarb_3.2.13 oce_1.2-0 gsw_1.0-5
[5] testthat_2.3.2 metR_0.7.0 forcats_0.5.0 stringr_1.4.0
[9] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[13] tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 viridisLite_0.3.0 jsonlite_1.7.0 here_0.1
[5] modelr_0.1.8 Formula_1.2-3 assertthat_0.2.1 blob_1.2.1
[9] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.6 backports_1.1.8
[13] lattice_0.20-41 glue_1.4.1 digest_0.6.25 promises_1.1.1
[17] checkmate_2.0.0 rvest_0.3.6 colorspace_1.4-1 sandwich_2.5-1
[21] htmltools_0.5.0 httpuv_1.5.4 Matrix_1.2-18 pkgconfig_2.0.3
[25] broom_0.7.0 haven_2.3.1 xtable_1.8-4 scales_1.1.1
[29] whisker_0.4 later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[33] generics_0.0.2 ellipsis_0.3.1 withr_2.2.0 cli_2.0.2
[37] magrittr_1.5 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[41] fs_1.4.2 fansi_0.4.1 xml2_1.3.2 tools_4.0.2
[45] data.table_1.13.0 hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0
[49] reprex_0.3.0 isoband_0.2.2 compiler_4.0.2 lfe_2.8-5.1
[53] rlang_0.4.7 grid_4.0.2 rstudioapi_0.11 labeling_0.3
[57] rmarkdown_2.3 gtable_0.3.0 DBI_1.1.0 R6_2.4.1
[61] zoo_1.8-8 lubridate_1.7.9 knitr_1.29 rprojroot_1.3-2
[65] stringi_1.4.6 parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.2
[69] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.16