Mapping
Jens Daniel Müller
07 December, 2020
Last updated: 2020-12-07
Checks: 7 0
Knit directory: emlr_obs_v_XXX/
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path_preprocessing <-
"/nfs/kryo/work/updata/emlr_cant/observations/preprocessing/"
path_version_data <-
paste(
"/nfs/kryo/work/updata/emlr_cant/observations/",
params_local$Version_ID,
"/data/",
sep = ""
)
path_version_figures <-
paste(
"/nfs/kryo/work/updata/emlr_cant/observations/",
params_local$Version_ID,
"/figures/",
sep = ""
)1 Libraries
Loading libraries specific to the the analysis performed in this section.
library(metR)
library(marelac)
library(gsw)2 Required data
All required data sets were subsetted spatially in the read-in section Data base. Currently, following data sets are used for mapping:
2.1 GLODAPv2_2016b_MappedClimatologies
Following variables are currently used:
- Phosphate (+Phosphate*)
- Silicate
- Oxygen (+AOU)
- TAlk (surface only)
- TCO2 (surface only)
variables <-
c("oxygen", "PO4", "silicate")
# i_variable <- variables[1]
for (i_variable in variables) {
temp <- read_csv(paste(
path_preprocessing,
paste("GLODAPv2_2016_MappedClimatology_", i_variable, ".csv", sep = ""),
sep = ""
))
if (exists("GLODAP_predictors")) {
GLODAP_predictors <- full_join(GLODAP_predictors, temp)
}
if (!exists("GLODAP_predictors")) {
GLODAP_predictors <- temp
}
}
rm(temp, i_variable, variables)
GLODAP_predictors <- GLODAP_predictors %>%
rename(phosphate = PO4)
# removed na's attributable to slightly different coverage of predictor fields
GLODAP_predictors <- GLODAP_predictors %>%
drop_na()variables <-
c("PO4", "silicate", "TAlk", "TCO2")
for (i_variable in variables) {
temp <- read_csv(paste(
path_preprocessing,
paste("GLODAPv2_2016_MappedClimatology_", i_variable, ".csv", sep = ""),
sep = ""
))
if (exists("GLODAP_predictors_CO2")) {
GLODAP_predictors_CO2 <- full_join(GLODAP_predictors_CO2, temp)
}
if (!exists("GLODAP_predictors_CO2")) {
GLODAP_predictors_CO2 <- temp
}
}
rm(temp, i_variable, variables)
GLODAP_predictors_CO2 <- GLODAP_predictors_CO2 %>%
rename(phosphate = PO4)
# removed na's attributable to slightly different coverage of predictor fields
GLODAP_predictors_CO2 <- GLODAP_predictors_CO2 %>%
drop_na()2.2 World Ocean Atlas 2018
- Salinity
- Temperature
- Neutral density
WOA18_predictors <-
read_csv(paste(path_preprocessing,
"WOA18_sal_tem.csv",
sep = ""))3 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)
# unique(GLODAP_predictors$depth)
# unique(WOA18_predictors$depth)
predictors <- predictors %>%
drop_na()
predictors <- predictors %>%
filter(depth >= params_local$depth_min | gamma >= params_local$gamma_min)
rm(GLODAP_predictors)3.1 Apply density threshold
The predictor field was split into two parts:
- Deep water: neutral densities >= 26 and depth >= 150m
- Shallow water: rest
predictors_surface <- full_join(
GLODAP_predictors_CO2,
WOA18_predictors)
predictors_surface <- predictors_surface %>%
drop_na()
predictors_surface <- predictors_surface %>%
filter(depth < params_local$depth_min,
gamma < params_local$gamma_min)3.2 Control plots
3.2.1 Maps
Three maps are generated to control successful merging of data sets.
p_map_climatology(
df = predictors,
var = "phosphate")
p_map_climatology(
df = predictors,
var = "tem")
3.2.2 Maps surface
Three maps are generated to control successful merging of data sets.
p_map_climatology(
df = predictors_surface,
var = "TAlk")
p_map_climatology(
df = predictors_surface,
var = "TCO2")
p_map_climatology(
df = predictors_surface,
var = "sal")
p_map_climatology(
df = predictors_surface,
var = "tem")
3.2.3 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 == params_global$lat_Atl_profile,
lon == params_global$lon_Atl_section)
N_Atl <- N_Atl %>%
select(-c(basin, basin_AIP)) %>%
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)4 Prepare predictor fields
4.1 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 %>%
mutate(phosphate_star = b_phosphate_star(phosphate, oxygen))4.1.1 Maps
p_map_climatology(
df = predictors,
var = "phosphate_star",
col = "divergent")
4.2 AOU
4.2.1 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(aou = b_aou(sal, tem, depth, oxygen))4.2.2 Maps
p_map_climatology(
df = predictors,
var = "aou",
col = "divergent")
4.3 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 <- m_cut_gamma(predictors, "gamma")5 Plot al predictor sections
5.1 Deep waters
Predictor sections along with lines are shown below for each (potential) predictor variable.
map +
geom_bin2d(data = predictors,
aes(lon, lat),
binwidth = c(1,1)) +
geom_vline(xintercept = params_global$longitude_sections_regular,
col = "white") +
scale_fill_viridis_c(direction = -1) +
theme(legend.position = "bottom")
vars <-
c(
"gamma",
"sal",
"tem",
"phosphate",
"phosphate_star",
"oxygen",
"aou",
"silicate"
)
# i_var <- vars[1]
for (i_var in vars) {
print(
p_section_climatology_regular(
df = predictors,
var = i_var)
)
}
5.2 Surface waters
Predictor sections along with lines are shown below for each (potential) predictor variable.
map +
geom_bin2d(data = predictors_surface,
aes(lon, lat),
binwidth = c(1,1)) +
geom_vline(xintercept = params_global$longitude_sections_regular,
col = "white") +
scale_fill_viridis_c(direction = -1) +
theme(legend.position = "bottom")
vars <-
c(
"gamma",
"sal",
"tem",
"TCO2",
"TAlk"
)
for (i_var in vars) {
print(
p_section_climatology_regular(
df = predictors_surface,
var = i_var)
)
}
6 Write csv
predictors %>%
write_csv(paste(path_version_data,
"W18_st_G16_opsn.csv",
sep = ""))
predictors_surface %>%
write_csv(paste(path_version_data,
"W18_st_G16_opsn_surface.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] gsw_1.0-5 testthat_3.0.0 marelac_2.1.10 shape_1.4.5
[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 viridisLite_0.3.0 jsonlite_1.7.1
[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] seacarb_3.2.14 haven_2.3.1 scales_1.1.1
[28] whisker_0.4 later_1.1.0.1 git2r_0.27.1
[31] farver_2.0.3 generics_0.0.2 ellipsis_0.3.1
[34] withr_2.3.0 cli_2.2.0 magrittr_2.0.1
[37] crayon_1.3.4 readxl_1.3.1 evaluate_0.14
[40] fs_1.5.0 fansi_0.4.1 xml2_1.3.2
[43] RcppArmadillo_0.10.1.2.0 oce_1.2-0 tools_4.0.3
[46] data.table_1.13.2 hms_0.5.3 lifecycle_0.2.0
[49] munsell_0.5.0 reprex_0.3.0 isoband_0.2.2
[52] compiler_4.0.3 rlang_0.4.9 grid_4.0.3
[55] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.5
[58] gtable_0.3.0 DBI_1.1.0 R6_2.5.0
[61] lubridate_1.7.9 knitr_1.30 rprojroot_2.0.2
[64] stringi_1.5.3 parallel_4.0.3 Rcpp_1.0.5
[67] vctrs_0.3.5 dbplyr_1.4.4 tidyselect_1.1.0
[70] xfun_0.18