Mapping
Jens Daniel Müller
10 September, 2020
Last updated: 2020-09-10
Checks: 7 0
Knit directory: Cant_eMLR/
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library(tidyverse)
library(metR)
library(marelac)
library(gsw)basinmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"
)
)
landmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"land_mask_WOA18.csv"
)
)1 Required data
All required data sets were subsetted spatially in the read-in section Data base. Currently, following data sets are used for mapping:
1.1 GLODAPv2_2016b_MappedClimatologies
Following variables are currently used:
- Phosphate (+Phosphate*)
- Silicate
- Oxygen (+AOU)
variables <-
c("oxygen", "PO4", "silicate")
for (i_variable in variables) {
temp <- read_csv(
here::here(
"data/GLODAPv2_2016b_MappedClimatologies/_summarized_files",
paste(i_variable, ".csv", sep = "")
)
)
if (exists("GLODAP_predictors")) {
GLODAP_predictors <- full_join(GLODAP_predictors, temp)
}
if (!exists("GLODAP_predictors")) {
GLODAP_predictors <- temp
}
}
rm(temp, i_variable, variables)
# removed na's attributable to slightly different coverage of predictor fields
GLODAP_predictors <- GLODAP_predictors %>%
drop_na()1.2 World Ocean Atlas 2018
- Salinity
- Temperature
- Neutral density
- Basin mask
WOA18_predictors <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"WOA18_predictors.csv"
)
)2 Join WOA18 + GLODAP
WOA18 and GLODAP predictor climatologies are merged. Only horizontal grid cells with observations from both predictor fields are kept.
CAVEAT: Coverage of GLODAP climatologies differs slightly for parameters (some are NA in some regions)
predictors <- full_join(
GLODAP_predictors,
WOA18_predictors)
predictors <- predictors %>%
drop_na()
rm(GLODAP_predictors, WOA18_predictors)2.1 Control plots
2.1.1 Maps
Three maps are generated to control successful merging of data sets.
map_climatology(predictors, "PO4")
map_climatology(predictors, "tem")
2.1.2 Predictor profiles
Likewise, predictor profiles for the North Atlantic (40.5 / 335.5) are plotted to control successful merging of the data sets.
N_Atl <- predictors %>%
filter(lat == parameters$lat_Atl_profile,
lon == parameters$lon_Atl_section)
N_Atl <- N_Atl %>%
select(-basin) %>%
pivot_longer(oxygen:gamma, names_to = "parameter", values_to = "value")
N_Atl %>%
ggplot(aes(value, depth)) +
geom_path() +
geom_point() +
scale_y_reverse() +
facet_wrap(~parameter,
scales = "free_x",
ncol = 2)
rm(N_Atl)3 Prepare predictor fields
3.1 PO4* calculation
The predictor PO4* was be calculated according to Clement and Gruber (2018), ie based on oxygen. Please note that an errornous equations for PO4* calculation is given in the supplement of Gruber et al (2019), based on nitrate.
predictors <- predictors %>%
rename(phosphate = PO4) %>%
mutate(phosphate_star = phosphate + (oxygen / 170) - 1.95)3.1.1 Maps
map_climatology(predictors, "phosphate_star")
3.1.2 Sections
section_climatology(predictors, "phosphate_star")
3.2 AOU
3.2.1 Calculation
AOU was calculated as the difference between saturation concentration and observed concentration.
CAVEAT: Algorithms used to calculate oxygen saturation concentration are not yet identical in GLODAP data set (fitting) and predictor climatologies (mapping).
predictors <- predictors %>%
mutate(oxygen_sat_m3 = gas_satconc(S = sal,
t = tem,
P = 1.013253,
species = "O2"),
rho = gsw_pot_rho_t_exact(SA = sal, t = tem, p = depth, p_ref = 10.1325),
oxygen_sat_kg = oxygen_sat_m3 * (1000/rho),
aou = oxygen_sat_kg - oxygen) %>%
select(-c(oxygen_sat_m3, rho, oxygen_sat_kg))3.2.2 Maps
map_climatology(predictors, "aou")
3.2.3 Sections
section_climatology(predictors, "aou")
3.3 Isoneutral slabs
The following boundaries for isoneutral slabs were defined:
- Atlantic: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1, 28.15, 28.2,
- Indo-Pacific: -, 26, 26.5, 26.75, 27, 27.25, 27.5, 27.75, 27.85, 27.95, 28.05, 28.1,
Continuous neutral density (gamma) values based on WOA18 are grouped into isoneutral slabs.
predictors_Atl <- predictors %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Atl))
predictors_Ind_Pac <- predictors %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma, parameters$slabs_Ind_Pac))
predictors <- bind_rows(predictors_Atl, predictors_Ind_Pac)
rm(predictors_Atl, predictors_Ind_Pac)4 Plot al predictor sections
Predictor sections along with lines are shown below for each (potential) predictor variable.
predictors %>%
ggplot(aes(lon, lat)) +
geom_bin2d(binwidth = c(1,1)) +
geom_vline(xintercept = parameters$longitude_sections_regular,
col = "white") +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
vars <-
c(
"gamma",
"sal",
"tem",
"phosphate",
"phosphate_star",
"oxygen",
"aou",
"silicate"
)
for (i_var in vars) {
print(section_climatology_regular(predictors, i_var))
}
5 Write csv
predictors %>%
write_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn.csv"))6 Alternative predictors
6.1 WOA13 + GLODAP
Currently not used.
WOA13 <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2013_Clement/_summarized_files",
"WOA13_mask_gamma.csv"
)
)
WOA13_gamma <- WOA13 %>%
select(-mask)
rm(WOA13)predictors <- full_join(GLODAP_predictors, WOA13_gamma)
GLODAP_depths <- unique(GLODAP_predictors$depth)
rm(GLODAP_predictors, WOA13_gamma)
predictors <- predictors %>%
group_by(lat, lon) %>%
mutate(n_oxygen = sum(!is.na(oxygen)),
n_PO4 = sum(!is.na(PO4)),
n_silicate = sum(!is.na(silicate)),
n_sal = sum(!is.na(sal)),
n_tem = sum(!is.na(tem)),
n_gamma = sum(!is.na(gamma))) %>%
ungroup()
predictors <- predictors %>%
filter(n_oxygen > 0,
n_PO4 > 0,
n_silicate > 0,
n_sal > 0,
n_tem > 0,
n_gamma > 0) %>%
select(-c(n_oxygen,
n_PO4,
n_silicate,
n_sal,
n_tem,
n_gamma))
predictors <- predictors %>%
drop_na()
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] gsw_1.0-5 testthat_2.3.2 marelac_2.1.10 shape_1.4.4
[5] metR_0.7.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[9] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[13] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 here_0.1 lubridate_1.7.9 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 R6_2.4.1 cellranger_1.1.0
[9] backports_1.1.8 reprex_0.3.0 evaluate_0.14 httr_1.4.2
[13] pillar_1.4.6 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
[17] data.table_1.13.0 whisker_0.4 blob_1.2.1 checkmate_2.0.0
[21] rmarkdown_2.3 labeling_0.3 munsell_0.5.0 broom_0.7.0
[25] compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8 xfun_0.16
[29] pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0 viridisLite_0.3.0
[33] fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4 withr_2.2.0
[37] later_1.1.0.1 grid_4.0.2 jsonlite_1.7.0 gtable_0.3.0
[41] lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1 magrittr_1.5
[45] seacarb_3.2.13 scales_1.1.1 oce_1.2-0 cli_2.0.2
[49] stringi_1.4.6 farver_2.0.3 fs_1.4.2 promises_1.1.1
[53] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2
[57] tools_4.0.2 glue_1.4.1 hms_0.5.3 yaml_2.2.1
[61] colorspace_1.4-1 isoband_0.2.2 rvest_0.3.6 knitr_1.29
[65] haven_2.3.1