Mapping cant
Jens Daniel Müller
19 December, 2020
Last updated: 2020-12-19
Checks: 7 0
Knit directory: emlr_mod_v_XXX/
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1 Required data
1.1 Predictor fields
Currently, we use following combined predictor fields:
- WOA18: S, T, and derived variables
- GLODAP16: Oxygen, PO4, NO3, Silicate, and derived variables
predictors <-
read_csv(paste(path_version_data,
"W18_st_G16_opsn.csv",
sep = ""))
predictors_surface <-
read_csv(paste(path_version_data,
"W18_st_G16_opsn_surface.csv",
sep = ""))1.2 Atm. pCO2
Required only to estimate the change of Cant in surface water and assuming that the ocean pCO2 trend follows the atmospheric forcing.
co2_atm_tref <-
read_csv(paste(path_version_data,
"co2_atm_tref.csv",
sep = ""))1.3 MLR models
lm_best_cant <-
read_csv(paste(path_version_data,
"lm_best_cant.csv",
sep = ""))2 Join MLRs + climatologies
# remove predictor variable from model
lm_best_cant <- lm_best_cant %>%
mutate(model = str_remove(model, "Cstar ~ "))
# join predictors and MLR
cant <- full_join(predictors, lm_best_cant)
rm(predictors, lm_best_cant)3 Map Cant
3.1 Deep water
3.1.1 Apply MLRs to predictor
cant <- b_cant(cant)3.1.2 Sections by model
Zonal section plots are produced for every 20° longitude, each era and for all models individually. Plots can be accessed here:
- /nfs/kryo/work/jenmueller/emlr_cant/model/v_XXX/figures/Cant_model_sections/
if (params_local$plot_all_figures == "y") {
for (i_eras in unique(cant$eras)) {
# i_eras <- unique(cant$eras)[2]
cant_eras <- cant %>%
filter(eras == i_eras)
for (i_lon in params_global$longitude_sections_regular) {
# i_lon <- params_global$longitude_sections_regular[7]
cant_eras_lon <- cant_eras %>%
filter(lon == i_lon)
limits = max(abs(cant_eras_lon$cant)) * c(-1, 1)
cant_eras_lon %>%
ggplot(aes(lat, depth, z = cant)) +
stat_summary_2d(
fun = "mean",
na.rm = TRUE,
bins = 20,
col = "grey"
) +
scale_fill_scico(name = "Cant",
palette = "vik",
limit = limits) +
scale_y_reverse(limits = c(params_global$plotting_depth, NA)) +
scale_x_continuous(limits = c(-85, 85)) +
labs(title = paste(
"eras:",
i_eras,
"| lon:",
i_lon,
"|",
params_local$Version_ID
)) +
facet_wrap(~ model, ncol = 5)
ggsave(
paste(
path_version_figures,
"Cant_model_sections/",
paste("Cant_model",
i_eras,
"lon",
i_lon,
"section.png",
sep = "_"),
sep = ""
),
width = 17,
height = 9
)
}
}
}3.2 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:
\(\Delta\)tCant,eq(t2 − t1) = 1∕\(\gamma\) ⋅ DIC/pCO2 ⋅ (pCO2,atm (t2)− pCO2,atm(t1))
, where DIC and pCO2 are the in situ values, where \(\gamma\) is the buffer (Revelle) factor and where we evaluated the right-hand side using seacarb employing the Luecker constants using the climatological values for temperature, salinity, DIC and Alk.
3.2.1 pCO2 climatology
Plots below show the calculated climatolofical pCO2 values.
# calculate pCO2 from talk and tco2 climatology
predictors_surface <- predictors_surface %>%
mutate(
pCO2 = carb(
flag = 15,
var1 = TAlk * 1e-6,
var2 = TCO2 * 1e-6,
S = sal,
T = temp,
P = depth / 10,
Pt = phosphate * 1e-6,
Sit = silicate * 1e-6,
k1k2 = "l"
)$pCO2
)p_map_climatology(df = predictors_surface,
var = "pCO2")
p_section_climatology_regular(df = predictors_surface,
var = "pCO2",
surface = "y")
3.2.2 Revelle factor
Plots below show the calculated climatolofical Revelle factor values.
predictors_surface <- predictors_surface %>%
mutate(
rev_fac = buffer(
flag = 15,
var1 = TAlk * 1e-6,
var2 = TCO2 * 1e-6,
S = sal,
T = temp,
P = depth / 10,
Pt = phosphate * 1e-6,
Sit = silicate * 1e-6,
k1k2 = "l"
)$BetaD
)p_map_climatology(df = predictors_surface,
var = "rev_fac")
p_section_climatology_regular(df = predictors_surface,
var = "rev_fac",
surface = "y")
3.2.3 Cant calculation
# calculate increase in atm pCO2 between eras
co2_atm_tref <- co2_atm_tref %>%
arrange(pCO2_tref) %>%
mutate(d_pCO2_tref = pCO2_tref - lag(pCO2_tref),
eras = paste(lag(era), era, sep = " --> ")) %>%
drop_na() %>%
select(eras, d_pCO2_tref)
cant_surface <- full_join(predictors_surface, co2_atm_tref,
by = character())
# calculate cant
cant_surface <- cant_surface %>%
mutate(cant = (1 / rev_fac) * (TCO2 / pCO2) * d_pCO2_tref)
# calculate positive cant
cant_surface <- cant_surface %>%
mutate(cant_pos = if_else(cant < 0, 0, cant))3.2.4 Control plots
# i_eras <- unique(cant_average$eras)[2]
for (i_eras in unique(cant_surface$eras)) {
print(
p_map_climatology(df = cant_surface %>% filter(eras == i_eras),
var = "cant") +
labs(subtitle = paste("era:", i_eras))
)
}
# i_eras <- unique(cant_surface$eras)[2]
for (i_eras in unique(cant_surface$eras)) {
print(
p_section_climatology_regular(df = cant_surface %>% filter(eras == i_eras),
var = "cant",
surface = "y") +
labs(subtitle = paste("era:", i_eras))
)
}
3.3 Average model Cant
Mean and sd are calculated across 10 models for Cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all cant values vs positive values only.
3.3.1 Deep water averaging
cant_average <- m_cant_model_average(cant)
cant_average <- m_cut_gamma(cant_average, "gamma")
# split data set for individual predictor contributions and total cant
cant_predictor_average <- cant_average %>%
select(-c("cant", "cant_pos", ends_with("_sd")))
cant_average <- cant_average %>%
select(lon, lat, depth, eras, basin, basin_AIP,
cant, cant_pos, cant_sd, cant_pos_sd,
gamma, gamma_sd, gamma_slab)# i_eras <- unique(cant_average$eras)[2]
for (i_eras in unique(cant_surface$eras)) {
print(
p_map_climatology(
df = cant_average %>% filter(eras == i_eras),
var = "cant_pos",
subtitle_text = paste("era:", i_eras)
)
)
}
# i_eras <- unique(cant_surface$eras)[2]
for (i_eras in unique(cant_surface$eras)) {
print(
p_section_climatology_regular(
df = cant_average %>% filter(eras == i_eras),
var = "cant_pos",
subtitle_text = paste("era:", i_eras)
)
)
}
3.3.2 Surface water averaging
The averaging function is also applied to the surface data, although only one value per grid cell was mapped, to ensure consistency with the deep water values.
cant_surface_average <- m_cant_model_average(cant_surface)
cant_surface_average <- m_cut_gamma(cant_surface_average, "gamma")
rm(cant_surface)3.3.3 Join surface and deep water
cant_average <- full_join(cant_average, cant_surface_average)
rm(cant_surface_average)3.4 Zonal mean 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_zonal <- m_cant_zonal_mean(cant_average)
cant_average_zonal <- m_cut_gamma(cant_average_zonal, "gamma_mean")3.5 Mean cant sections by coefficient
For each basin and era combination, the zonal mean is calculated for the term of each predictor.
cant_predictor_average_zonal <-
m_cant_predictor_zonal_mean(cant_predictor_average)
cant_predictor_average_zonal <-
m_cut_gamma(cant_predictor_average_zonal, "gamma")3.6 Inventory calculation
To calculate Cant column inventories, we:
- Convert Cant concentrations to volumetric units
- Multiply layer thickness with volumentric Cant concentration to get a layer inventory
- For each horizontal grid cell and era, sum cant layer inventories for different inventory depths (100, 500, 1000, 3000, 10^{4} m)
Step 2 is performed separately for all Cant and positive Cant values only.
cant_inv <- m_cant_inv(cant_average)
p_map_cant_inv(df = cant_inv,
var = "cant_pos_inv",
subtitle_text = "for predefined integration depths") +
facet_grid(inv_depth ~ eras)
4 Write csv
cant_average %>%
write_csv(paste(path_version_data,
"cant_3d.csv", sep = ""))
cant_predictor_average %>%
write_csv(paste(path_version_data,
"cant_predictor_3d.csv", sep = ""))
cant_average_zonal %>%
write_csv(paste(path_version_data,
"cant_zonal.csv", sep = ""))
cant_predictor_average_zonal %>%
write_csv(paste(path_version_data,
"cant_predictor_zonal.csv", sep = ""))
cant_inv %>%
write_csv(paste(path_version_data,
"cant_inv.csv", sep = ""))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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_3.2.14 oce_1.2-0 gsw_1.0-5 testthat_3.0.0
[5] metR_0.9.0 scico_1.2.0 patchwork_1.1.0 collapse_1.4.2
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[17] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 jsonlite_1.7.1 viridisLite_0.3.0
[4] here_0.1 modelr_0.1.8 assertthat_0.2.1
[7] blob_1.2.1 cellranger_1.1.0 yaml_2.2.1
[10] pillar_1.4.7 backports_1.1.10 lattice_0.20-41
[13] glue_1.4.2 RcppEigen_0.3.3.7.0 digest_0.6.27
[16] promises_1.1.1 checkmate_2.0.0 rvest_0.3.6
[19] colorspace_2.0-0 htmltools_0.5.0 httpuv_1.5.4
[22] Matrix_1.2-18 pkgconfig_2.0.3 broom_0.7.2
[25] haven_2.3.1 scales_1.1.1 whisker_0.4
[28] later_1.1.0.1 git2r_0.27.1 farver_2.0.3
[31] generics_0.0.2 ellipsis_0.3.1 withr_2.3.0
[34] cli_2.2.0 magrittr_2.0.1 crayon_1.3.4
[37] readxl_1.3.1 evaluate_0.14 fs_1.5.0
[40] fansi_0.4.1 xml2_1.3.2 RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3 data.table_1.13.2 hms_0.5.3
[46] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0
[49] isoband_0.2.2 compiler_4.0.3 rlang_0.4.9
[52] grid_4.0.3 rstudioapi_0.13 labeling_0.4.2
[55] rmarkdown_2.5 gtable_0.3.0 DBI_1.1.0
[58] R6_2.5.0 lubridate_1.7.9 knitr_1.30
[61] rprojroot_2.0.2 stringi_1.5.3 parallel_4.0.3
[64] Rcpp_1.0.5 vctrs_0.3.5 dbplyr_1.4.4
[67] tidyselect_1.1.0 xfun_0.18