Mapping
Jens Daniel Müller
28 August, 2020
Last updated: 2020-08-28
Checks: 7 0
Knit directory: Cant_eMLR/
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| Rmd | 94fbf0f | jens-daniel-mueller | 2020-08-27 | section plots for all regular longitude added |
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| Rmd | a804955 | jens-daniel-mueller | 2020-08-24 | split mapping into 2 rmds, po4star selection in parameters, use po4star nitrate |
library(tidyverse)
library(metR)
library(marelac)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:
- Salinity
- Temperature
- Phosphate (+Phosphate*)
- Silicate
- Oxygen (+AOU)
variables <-
c("salinity", "temperature", "oxygen", "PO4", "silicate", "NO3")
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()
GLODAP_predictors <- GLODAP_predictors %>%
rename(sal = salinity,
tem = temperature)1.2 World Ocean Atlas 2018
- Neutral density
- Basin mask
WOA18_predictors <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"WOA18_predictors.csv"
)
)1.3 WOA13
Neutral densities and the basin mask based on WOA13 and provided by Dominic Clement are 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)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 %>% select(-c(sal,tem)),
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*
3.1.1 Calculation
Currently, the predictor PO4* is calculated according to Clement and Gruber (2018), ie based on oxygen, as well as according to Gruber et al (2019), based on nitrate. Selection of one approach is done before mapping.
predictors <- predictors %>%
rename(phosphate = PO4) %>%
mutate(phosphate_star_oxy = phosphate + (oxygen / 170) - 1.95,
phosphate_star_nit = phosphate - NO3 / 16 + 2.9)3.1.2 Maps
map_climatology(predictors, "phosphate_star_nit")
map_climatology(predictors, "phosphate_star_oxy")
3.1.3 Sections
section_climatology(predictors, "phosphate_star_nit")
section_climatology(predictors, "phosphate_star_oxy")
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 = gas_satconc(S = sal,
t = tem,
P = 1.013253,
species = "O2"),
aou = oxygen_sat - oxygen) %>%
select(-oxygen_sat)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$longitude_sections_regular,
col = "white") +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
vars <-
c(
"gamma",
"sal",
"tem",
"phosphate",
"phosphate_star_oxy",
"phosphate_star_nit",
"oxygen",
"aou",
"silicate",
"NO3"
)
for (i_var in vars) {
print(section_climatology_regular(predictors, i_var))
}
5 Write csv
predictors %>%
write_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn.csv"))5.1 WOA13 + GLODAP
Currently not used.
predictors <- full_join(GLODAP_predictors, WOA13_gamma)
GLODAP_depths <- unique(GLODAP_predictors$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()6 Open tasks
- Check PO4* calculation
- Harmonize AOU calculation in fitting and mapping
- Use oxygen and/or nitrate as predictor?
7 Open questions
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] marelac_2.1.10 shape_1.4.4 metR_0.7.0 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[9] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2 tidyverse_1.3.0
[13] 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 gsw_1.0-5 grid_4.0.2 jsonlite_1.7.0
[41] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[45] magrittr_1.5 seacarb_3.2.13 scales_1.1.1 oce_1.2-0
[49] cli_2.0.2 stringi_1.4.6 farver_2.0.3 fs_1.4.2
[53] promises_1.1.1 testthat_2.3.2 xml2_1.3.2 ellipsis_0.3.1
[57] generics_0.0.2 vctrs_0.3.2 tools_4.0.2 glue_1.4.1
[61] hms_0.5.3 yaml_2.2.1 colorspace_1.4-1 isoband_0.2.2
[65] rvest_0.3.6 knitr_1.29 haven_2.3.1