Mapping
Jens Daniel Müller
04 August, 2020
Last updated: 2020-08-04
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Knit directory: Cant_eMLR/
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| File | Version | Author | Date | Message |
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| Rmd | 810fc7b | jens-daniel-mueller | 2020-08-04 | first completed Cant map |
| html | eb244fb | jens-daniel-mueller | 2020-08-04 | Build site. |
| Rmd | c834496 | jens-daniel-mueller | 2020-08-04 | formatting |
| html | e29d39d | jens-daniel-mueller | 2020-08-04 | Build site. |
| Rmd | 1443a30 | jens-daniel-mueller | 2020-08-04 | Included gamma values from Clement based on WOA13 |
| html | 9dc5d7f | jens-daniel-mueller | 2020-07-29 | Build site. |
| html | 21524b4 | jens-daniel-mueller | 2020-07-29 | Build site. |
| html | 4f80a27 | jens-daniel-mueller | 2020-07-28 | Build site. |
| Rmd | c63f537 | jens-daniel-mueller | 2020-07-28 | included model coeffcients in mapping |
| html | 4eebe14 | jens-daniel-mueller | 2020-07-24 | Build site. |
| Rmd | 12f9ef2 | jens-daniel-mueller | 2020-07-24 | started neutral density calculation |
| html | 2e08795 | jens-daniel-mueller | 2020-07-24 | Build site. |
| html | 64978a1 | jens-daniel-mueller | 2020-07-24 | Build site. |
| Rmd | 7cbc7ec | jens-daniel-mueller | 2020-07-24 | first publish |
library(tidyverse)
library(lubridate)
library(oce)
library(marelac)1 Required data
Currently we use following data sets for mapping:
- GLODAPv2_2016b_MappedClimatologies
- Salinity
- Temperature
- Phosphate
- Nitrate
- Silicate
- oxygen
- World Ocean Atlas 2013
- Neutral densities calculated by D Clement
- World Ocean Atlas 2018
- basin mask
- eMLR model coefficients
We aim to use WOA18 instead of WOA13, but still need to implement neutral density calculation.
variables <- c("salinity", "temperature", "NO3", "oxygen", "PO4", "silicate")
for (i_variable in variables) {
# i_variable <- variables[2]
# print(i_variable)
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)
# GLODAP_depths <- unique(GLODAP_predictors$depth)
# GLODAP_lon <- unique(GLODAP_predictors$lon)
# min(GLODAP_lon)
# max(GLODAP_lon)WOA18_predictors <-
read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"WOA18_predictors.csv"))
WOA18_predictors <- WOA18_predictors %>%
rename(salinity = s_an, temperature = t_an)
# WOA18_depths <- unique(WOA18_predictors$depth)WOA13 <-
read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"WOA13_mask_gamma.csv"))
WOA13_gamma <- WOA13 %>%
select(-mask)
WOA13_gamma <- WOA13_gamma %>%
rename(lat = latitude, lon = longitude) %>%
mutate(lon = if_else(lon > 180, lon - 360, lon))
rm(WOA13)
# WOA13_depths <- unique(WOA13_gamma$depth)
# GLODAP_depths - WOA13_depthsbasinmask <- read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"))all_lm <- read_csv(here::here("data/eMLR",
"all_lm.csv"))2 Join predictor climatologies
CAVEAT: Coverage of GLODAP climatologies differs slightly for parameters (some are NA in some regions)
2.1 Control plots
Maps of number of observations per horizontal grid cell.
2.1.1 GLODAP climatology
GLODAP_n <- GLODAP_predictors %>%
drop_na() %>%
group_by(lat, lon) %>%
summarise(n = n()) %>%
ungroup()
GLODAP_n %>%
ggplot(aes(lon, lat, fill = n)) +
geom_raster() +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
rm(GLODAP_n)2.1.2 WOA13 climatology
WOA13_gamma_n <- WOA13_gamma %>%
drop_na() %>%
group_by(lat, lon) %>%
summarise(n = n()) %>%
ungroup()
WOA13_gamma_n %>%
ggplot(aes(lon, lat, fill = n)) +
geom_raster() +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
rm(WOA13_gamma_n)2.2 WOA13 + GLODAP
predictors <- full_join(GLODAP_predictors, WOA13_gamma)
rm(GLODAP_predictors, WOA13_gamma)
predictors <- predictors %>%
group_by(lat, lon) %>%
mutate(n_NO3 = sum(!is.na(NO3)),
n_oxygen = sum(!is.na(oxygen)),
n_PO4 = sum(!is.na(PO4)),
n_silicate = sum(!is.na(silicate)),
n_salinity = sum(!is.na(salinity)),
n_temperature = sum(!is.na(temperature)),
n_gamma = sum(!is.na(gamma))) %>%
ungroup()
predictors <- predictors %>%
filter(n_NO3 > 0,
n_oxygen > 0,
n_PO4 > 0,
n_silicate > 0,
n_salinity > 0,
n_temperature > 0,
n_gamma > 0) %>%
select(-c(n_NO3,
n_oxygen,
n_PO4,
n_silicate,
n_salinity,
n_temperature,
n_gamma))
predictors <- predictors %>%
drop_na()2.2.1 Spatial boundaries
min_depth <- 150
min_bottomdepth <- 500
max_lat <- 65Only mapped variables were taken into consideration with:
- minimum depth: 150m
- minimum bottom depth: 500m
- maximum latitude: 65°N
predictors <- predictors %>%
filter(depth >= min_depth)
predictors <- predictors %>%
filter(lat <= max_lat)
predictors <- predictors %>%
group_by(lat, lon) %>%
mutate(bottomdepth = max(depth)) %>%
ungroup()
predictors <- predictors %>%
filter(bottomdepth >= min_bottomdepth) %>%
select(-bottomdepth)
rm(min_depth, max_lat, min_bottomdepth)2.2.2 Basin mask
Please note that some predictor variables are available outside the WOA18 basin mask and will be removed for further analysis.
predictors <- inner_join(predictors, basinmask)
rm(basinmask)2.2.3 Maps
predictors %>%
ggplot(aes(lon, lat)) +
geom_bin2d(binwidth = c(1,1)) +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
predictors %>%
filter(depth == 150) %>%
ggplot(aes(lon, lat, fill = PO4)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "150m values")
predictors %>%
filter(depth == 150) %>%
ggplot(aes(lon, lat, fill = gamma)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "150m values")
2.2.4 Predictor profiles
N_Atl <- predictors %>%
filter(lat == 40.5, lon == -20.5)
N_Atl <- N_Atl %>%
pivot_longer(salinity: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)2.3 WOA18 + GLODAP
WOA18 data are currently not used. Code chunks in this section are not executed.
predictors <- full_join(GLODAP_predictors, WOA18_predictors)
rm(GLODAP_predictors, WOA18_predictors)
predictors <- predictors %>%
group_by(lat, lon) %>%
mutate(n_NO3 = sum(!is.na(NO3)),
n_oxygen = sum(!is.na(oxygen)),
n_PO4 = sum(!is.na(PO4)),
n_silicate = sum(!is.na(silicate)),
n_salinity = sum(!is.na(salinity)),
n_temperature = sum(!is.na(temperature))) %>%
ungroup()
predictors <- predictors %>%
filter(n_NO3 > 1,
n_oxygen > 1,
n_PO4 > 1,
n_silicate > 1,
n_salinity > 1,
n_temperature > 1) %>%
select(-c(n_NO3 , n_oxygen , n_PO4 , n_silicate , n_salinity , n_temperature))
predictors %>%
ggplot(aes(lon, lat)) +
geom_bin2d(binwidth = c(1,1)) +
scale_fill_viridis_c(direction = -1) +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom")
predictors %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill = PO4)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "Surface values")
predictors %>%
filter(depth == 0) %>%
ggplot(aes(lon, lat, fill = temperature)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
theme(legend.position = "bottom") +
labs(title = "Surface values")predictors <- predictors %>%
group_by(lat, lon) %>%
arrange(depth) %>%
mutate(temperature = approxfun(depth, temperature, rule = 2)(depth),
salinity = approxfun(depth, salinity, rule = 2)(depth)) %>%
ungroup()
predictors <- predictors %>%
filter(depth %in% GLODAP_depths)3 Map Cant
3.1 Prepare model coefficients
all_lm <- all_lm %>%
select(term, estimate, basin, era, gamma_slab, model)
all_lm <- all_lm %>%
mutate(estimate = if_else(is.na(estimate), 0, estimate))
all_lm_wide <- all_lm %>%
pivot_wider(names_from = era, values_from = estimate,
names_prefix = "coeff_")
all_lm_wide <- all_lm_wide %>%
mutate(JGOFS_GO = coeff_JGOFS_WOCE - coeff_GO_SHIP,
GO_new = coeff_GO_SHIP - coeff_new_era) %>%
select(-c(coeff_JGOFS_WOCE,
coeff_GO_SHIP,
coeff_new_era))
all_lm_long <- all_lm_wide %>%
pivot_longer(JGOFS_GO:GO_new, names_to = "eras", values_to = "delta_coeff")
all_lm_wide <- all_lm_long %>%
pivot_wider(values_from = delta_coeff,
names_from = term,
names_prefix = "delta_coeff_",
values_fill = 0)
rm(all_lm_long, all_lm)4 PO4* calculation
Currently, the predictor PO4* is calculated according to Clement and Gruber (2018), ie based on oxygen rather than nitrate.
predictors <- predictors %>%
rename(phosphate = PO4) %>%
mutate(phosphate_star = phosphate + (oxygen / 170) - 1.95)4.1 AOU climatology
4.1.1 Calculation
predictors <- predictors %>%
mutate(oxygen_sat = gas_satconc(S = salinity,
t = temperature,
P = 1.013253,
species = "O2"),
aou = oxygen_sat - oxygen) %>%
select(-oxygen_sat)4.1.2 Atlantic section
Atl_lon <- 335.5 - 360
predictors %>%
filter(lon == Atl_lon) %>%
ggplot(aes(lat, depth, z = aou)) +
geom_contour_filled() +
scale_y_reverse() +
coord_cartesian(expand = 0) +
theme(legend.position = "top")
rm(Atl_lon)4.2 Isoneutral slabs
slabs_Atl <- c(
-Inf,
26.00,
26.50,
26.75,
27.00,
27.25,
27.50,
27.75,
27.85,
27.95,
28.05,
28.10,
28.15,
28.20,
Inf)
slabs_Ind_Pac <- c(
-Inf,
26.00,
26.50,
26.75,
27.00,
27.25,
27.50,
27.75,
27.85,
27.95,
28.05,
28.10,
Inf)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,
Continous neutral densities (gamma) values from GLODAP are grouped into isoneutral slabs.
predictors_Atl <- predictors %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma, slabs_Atl))
predictors_Ind_Pac <- predictors %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma, slabs_Ind_Pac))
predictors <- bind_rows(predictors_Atl, predictors_Ind_Pac)
rm(predictors_Atl, predictors_Ind_Pac, slabs_Atl, slabs_Ind_Pac)4.3 Merge model + climatology
Cant <- full_join(predictors, all_lm_wide)4.4 Calculate Cant
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_salinity * salinity +
delta_coeff_temperature * temperature)
Cant_model_average <- Cant %>%
mutate(Cant = -Cant) %>%
mutate(Cant = if_else(Cant < 0, 0, Cant)) %>%
group_by(lon, lat, depth, eras, basin) %>%
summarise(Cant_mean = mean(Cant)) %>%
ungroup()
Cant_model_average_section <- Cant_model_average %>%
group_by(lat, depth, eras, basin) %>%
summarise(Cant_zonal_mean = mean(Cant_mean, na.rm = TRUE)) %>%
ungroup()4.5 Atlantic section
Cant_model_average_section %>%
filter(depth <= 500,
lat > -60.6) %>%
ggplot(aes(lat, depth, z = Cant_zonal_mean)) +
geom_contour_filled(binwidth = 1) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
theme(legend.position = "top") +
facet_wrap(~eras, ncol = 1)
Cant_model_average_section %>%
filter(depth <= 4500,
lat > -60.6) %>%
ggplot(aes(lat, depth, z = Cant_zonal_mean)) +
geom_contour_filled(binwidth = 1) +
scale_y_reverse() +
coord_cartesian(expand = 0) +
theme(legend.position = "top") +
facet_wrap(~eras, ncol = 1)
5 Neutral density calculation
GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
"GLODAP_MLR_fitting_ready.csv"))
cruises_meridional <- c("1041")
GLODAP_cruise <- GLODAP %>%
filter(cruise %in% cruises_meridional)
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_calc = swRho(salinity = salinity,
temperature = temperature,
pressure = depth,
longitude = lon,
latitude = lat,
eos = "gsw"))
GLODAP_cruise <- GLODAP_cruise %>%
mutate(gamma_calc = gamma_calc - 1000,
delta_gamma = gamma - gamma_calc)
lat_section <-
GLODAP_cruise %>%
ggplot(aes(lat, depth)) +
scale_y_reverse() +
scale_color_viridis_c() +
theme(legend.position = "bottom")
lat_section +
geom_point(aes(col = gamma))
lat_section +
geom_point(aes(col = gamma_calc))
lat_section +
geom_point(aes(col = delta_gamma))
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 oce_1.2-0 gsw_1.0-5
[5] testthat_2.3.2 lubridate_1.7.9 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] Rcpp_1.0.5 here_0.1 assertthat_0.2.1 rprojroot_1.3-2
[5] digest_0.6.25 R6_2.4.1 cellranger_1.1.0 backports_1.1.8
[9] reprex_0.3.0 evaluate_0.14 httr_1.4.2 pillar_1.4.6
[13] rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11 whisker_0.4
[17] blob_1.2.1 rmarkdown_2.3 labeling_0.3 munsell_0.5.0
[21] broom_0.7.0 compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8
[25] xfun_0.16 pkgconfig_2.0.3 htmltools_0.5.0 tidyselect_1.1.0
[29] viridisLite_0.3.0 fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[33] withr_2.2.0 later_1.1.0.1 grid_4.0.2 jsonlite_1.7.0
[37] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 git2r_0.27.1
[41] magrittr_1.5 seacarb_3.2.13 scales_1.1.1 cli_2.0.2
[45] stringi_1.4.6 farver_2.0.3 fs_1.4.2 promises_1.1.1
[49] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.2
[53] tools_4.0.2 glue_1.4.1 hms_0.5.3 yaml_2.2.1
[57] colorspace_1.4-1 isoband_0.2.2 rvest_0.3.6 knitr_1.29
[61] haven_2.3.1