Last updated: 2020-09-14
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Knit directory: Cant_eMLR/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1a13af4 | jens-daniel-mueller | 2020-09-14 | replace all model sections plots chunk |
| html | da91d98 | jens-daniel-mueller | 2020-09-10 | Build site. |
| html | a34a239 | jens-daniel-mueller | 2020-09-10 | Build site. |
| html | e60e13c | jens-daniel-mueller | 2020-09-09 | Build site. |
| Rmd | 7040b80 | jens-daniel-mueller | 2020-09-09 | remove basin_AIP not covered by mapping |
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| Rmd | 0bea059 | jens-daniel-mueller | 2020-09-07 | plotted predictor contribution zonal mean |
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| html | da445a6 | jens-daniel-mueller | 2020-09-04 | Build site. |
| Rmd | 6a302ca | jens-daniel-mueller | 2020-09-04 | rebuild after new data cleaning and mapping Cant to surface |
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| html | 429aab3 | jens-daniel-mueller | 2020-09-01 | Build site. |
| html | f4216dd | jens-daniel-mueller | 2020-09-01 | Build site. |
| Rmd | 8f3ce45 | jens-daniel-mueller | 2020-09-01 | rebuild without PO4 star selection, oxygen only |
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| Rmd | 42e98c8 | jens-daniel-mueller | 2020-08-27 | section plots for all regular longitude added |
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| Rmd | b577ec6 | jens-daniel-mueller | 2020-08-26 | Analysis split in this and previous studies |
| html | 882b358 | jens-daniel-mueller | 2020-08-26 | Build site. |
| Rmd | db9fb4b | jens-daniel-mueller | 2020-08-26 | Cant mean zonal sections separate for Indian and Pacific |
| Rmd | ccacd26 | jens-daniel-mueller | 2020-08-25 | updated with PO4* oxygen based |
| html | ec20f40 | jens-daniel-mueller | 2020-08-24 | Build site. |
| Rmd | a804955 | jens-daniel-mueller | 2020-08-24 | split mapping into 2 rmds, po4star selection in parameters, use po4star nitrate |
library(tidyverse)
library(metR)basinmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18.csv"
)
)
basinmask_AIP <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"basin_mask_WOA18_AIP.csv"
)
)
landmask <-
read_csv(
here::here(
"data/World_Ocean_Atlas_2018/_summarized_files",
"land_mask_WOA18.csv"
)
)Currently, we use combined predictor fields:
predictors <-
read_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn.csv"))lm_all_wide <-
read_csv(here::here("data/eMLR",
"lm_all_wide.csv"))lm_all_wide <- lm_all_wide %>%
mutate(model = str_remove(model, "Cstar ~ "))
Cant <- full_join(predictors, lm_all_wide)
rm(predictors, lm_all_wide)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_sal * sal +
delta_coeff_tem * tem)Cant <- Cant %>%
mutate(Cant_intercept = `delta_coeff_(Intercept)`,
Cant_aou = delta_coeff_aou * aou,
Cant_oxygen = delta_coeff_oxygen * oxygen,
Cant_phosphate = delta_coeff_phosphate * phosphate,
Cant_phosphate_star = delta_coeff_phosphate_star * phosphate_star,
Cant_silicate = delta_coeff_silicate * silicate,
Cant_sal = delta_coeff_sal * sal,
Cant_tem = delta_coeff_tem * tem,
Cant_sum = Cant_intercept +
Cant_aou +
Cant_oxygen +
Cant_phosphate +
Cant_phosphate_star +
Cant_silicate +
Cant_sal +
Cant_tem)Zonal sections plots are produced for every 20° longitude, each era and for all models individually and can be downloaded here.
library(scales)
for (i_eras in unique(Cant$eras)) {
# i_eras <- unique(Cant$eras)[2]
Cant_eras <- Cant %>%
filter(eras == i_eras)
for (i_lon in seq(20.5, 360, 20)) {
# i_lon <- seq(20.5, 360, 20)[7]
Cant_eras_lon <- Cant_eras %>%
filter(lon == i_lon)
Cant_eras_lon %>%
ggplot(aes(lat, depth, col = Cant)) +
geom_point() +
scale_color_gradient2(
name = "Cant",
high = muted("red"),
mid = "grey",
low = muted("blue")
) +
scale_y_reverse(limits = c(parameters$inventory_depth, NA)) +
scale_x_continuous(limits = c(-75, 65)) +
coord_cartesian(expand = 0) +
guides(fill = guide_colorsteps(barheight = unit(10, "cm"))) +
labs(title = paste("eras:", i_eras, "| lon:", i_lon)) +
facet_wrap( ~ model, ncol = 5)
ggsave(
here::here(
"output/figure/mapping",
paste(i_eras,
"lon",
i_lon,
"model_Cant.png",
sep = "_")
),
width = 17,
height = 9
)
}
}Mean and sd are calculated for Cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all Cant values vs positive values only. This averaging step summarizes the information derived from ten best fitting MLRs.
Cant_predictor_average <- Cant %>%
mutate(Cant_pos = if_else(Cant < 0, 0, Cant)) %>%
group_by(lon, lat, depth, eras, basin) %>%
summarise(Cant_intercept = mean(Cant_intercept, na.rm = TRUE),
Cant_aou = mean(Cant_aou, na.rm = TRUE),
Cant_oxygen = mean(Cant_oxygen, na.rm = TRUE),
Cant_phosphate = mean(Cant_phosphate, na.rm = TRUE),
Cant_phosphate_star = mean(Cant_phosphate_star, na.rm = TRUE),
Cant_silicate = mean(Cant_silicate, na.rm = TRUE),
Cant_tem = mean(Cant_tem, na.rm = TRUE),
Cant_sal = mean(Cant_sal, na.rm = TRUE),
Cant_sum = mean(Cant_sum, na.rm = TRUE),
gamma_mean = mean(gamma, na.rm = TRUE)
) %>%
ungroup()
Cant_predictor_average_Atl <- Cant_predictor_average %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))
Cant_predictor_average_Ind_Pac <- Cant_predictor_average %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))
Cant_predictor_average <- bind_rows(Cant_predictor_average_Atl, Cant_predictor_average_Ind_Pac)
rm(Cant_predictor_average_Atl, Cant_predictor_average_Ind_Pac)
# Cant <- Cant %>%
# select(lon, lat, depth, eras, basin, Cant, gamma, model)
Cant_average <- Cant %>%
mutate(Cant_pos = if_else(Cant < 0, 0, Cant)) %>%
group_by(lon, lat, depth, eras, basin) %>%
summarise(Cant_mean = mean(Cant, na.rm = TRUE),
Cant_sd = sd(Cant, na.rm = TRUE),
Cant_pos_mean = mean(Cant_pos, na.rm = TRUE),
Cant_pos_sd = sd(Cant_pos, na.rm = TRUE),
gamma_mean = mean(gamma, na.rm = TRUE),
gamma_sd = sd(gamma, na.rm = TRUE)) %>%
ungroup()
Cant_average_Atl <- Cant_average %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))
Cant_average_Ind_Pac <- Cant_average %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))
Cant_average <- bind_rows(Cant_average_Atl, Cant_average_Ind_Pac)
rm(Cant_average_Atl, Cant_average_Ind_Pac)For each basin and era combination, the zonal mean Cant is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.
Cant_average <- left_join(Cant_average,
basinmask_AIP %>% select(-basin))
Cant_average_zonal <- Cant_average %>%
group_by(lat, depth, eras, basin, basin_AIP) %>%
summarise(across(
c(
"Cant_mean",
"Cant_pos_mean",
"Cant_sd",
"Cant_pos_sd",
"gamma_mean",
"gamma_sd"
),
list(
mean = ~ mean(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE)
)
)) %>%
ungroup()
# Cant_average_zonal <- Cant_average %>%
# group_by(lat, depth, eras, basin) %>%
# summarise(Cant_mean_sd = sd(Cant_mean, na.rm = TRUE),
# Cant_mean = mean(Cant_mean, na.rm = TRUE),
# Cant_sd_mean = mean(Cant_sd, na.rm = TRUE),
# Cant_pos_mean_sd = sd(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_mean = mean(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_sd_mean = mean(Cant_pos_sd, na.rm = TRUE),
# gamma_mean = mean(gamma_mean)) %>%
# ungroup()
Cant_average_zonal_Atl <- Cant_average_zonal %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Atl))
Cant_average_zonal_Ind_Pac <- Cant_average_zonal %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Ind_Pac))
Cant_average_zonal <- bind_rows(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)
rm(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)For each basin and era combination, the zonal mean Cant is calculated by model coefficient.
Cant_predictor_average <- full_join(Cant_predictor_average,
basinmask_AIP %>% select(-basin))
Cant_predictor_average_zonal <- Cant_predictor_average %>%
group_by(lat, depth, eras, basin, basin_AIP) %>%
summarise(across(
Cant_intercept:gamma_mean,
list(mean = ~ mean(.x, na.rm = TRUE))
)) %>%
ungroup()
# Cant_average_zonal <- Cant_average %>%
# group_by(lat, depth, eras, basin) %>%
# summarise(Cant_mean_sd = sd(Cant_mean, na.rm = TRUE),
# Cant_mean = mean(Cant_mean, na.rm = TRUE),
# Cant_sd_mean = mean(Cant_sd, na.rm = TRUE),
# Cant_pos_mean_sd = sd(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_mean = mean(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_sd_mean = mean(Cant_pos_sd, na.rm = TRUE),
# gamma_mean = mean(gamma_mean)) %>%
# ungroup()
Cant_predictor_average_zonal_Atl <- Cant_predictor_average_zonal %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Atl))
Cant_predictor_average_zonal_Ind_Pac <- Cant_predictor_average_zonal %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Ind_Pac))
Cant_predictor_average_zonal <- bind_rows(Cant_predictor_average_zonal_Atl, Cant_predictor_average_zonal_Ind_Pac)
rm(Cant_predictor_average_zonal_Atl, Cant_predictor_average_zonal_Ind_Pac)To calculate Cant column inventories, we:
Step 2 is performed again for all Cant and positive Cant values only
depth_level_volume <- tibble(depth = unique(Cant_average$depth))
depth_level_volume <- depth_level_volume %>%
mutate(
layer_thickness_above = replace_na((depth - lag(depth)) / 2, 0),
layer_thickness_below = replace_na((lead(depth) - depth) / 2, 0),
layer_thickness = layer_thickness_above + layer_thickness_below
) %>%
select(-c(layer_thickness_above,
layer_thickness_below))
Cant_average <-
full_join(Cant_average, depth_level_volume)
Cant_average <- Cant_average %>%
mutate(layer_inv = Cant_mean * layer_thickness) %>%
mutate(layer_inv_pos = if_else(layer_inv < 0, 0, layer_inv)) %>%
select(-layer_thickness)
Cant_inv <- Cant_average %>%
filter(depth <= parameters$inventory_depth) %>%
group_by(lon, lat, basin, eras) %>%
summarise(
cant_inv_pos = sum(layer_inv_pos, na.rm = TRUE) / 1000,
cant_inv = sum(layer_inv, na.rm = TRUE) / 1000
) %>%
ungroup()# Cant %>%
# write_csv(here::here("data/mapping/_summarized_files",
# "Cant.csv"))
Cant_average %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_average.csv"))
Cant_predictor_average %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_predictor_average.csv"))
Cant_average_zonal %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_average_zonal.csv"))
Cant_predictor_average_zonal %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_predictor_average_zonal.csv"))
Cant_inv %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))
rm(Cant,
Cant_average,
Cant_predictor_average,
Cant_average_zonal,
Cant_predictor_average_zonal,
Cant_inv)
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] metR_0.7.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.16 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.2 generics_0.0.2 htmltools_0.5.0 yaml_2.2.1
[9] blob_1.2.1 rlang_0.4.7 later_1.1.0.1 pillar_1.4.6
[13] withr_2.2.0 glue_1.4.1 DBI_1.1.0 dbplyr_1.4.4
[17] modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6 evaluate_0.14
[25] knitr_1.29 httpuv_1.5.4 fansi_0.4.1 broom_0.7.0
[29] Rcpp_1.0.5 checkmate_2.0.0 promises_1.1.1 backports_1.1.8
[33] scales_1.1.1 jsonlite_1.7.0 fs_1.4.2 hms_0.5.3
[37] digest_0.6.25 stringi_1.4.6 rprojroot_1.3-2 grid_4.0.2
[41] here_0.1 cli_2.0.2 tools_4.0.2 magrittr_1.5
[45] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] data.table_1.13.0 xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9
[53] assertthat_0.2.1 rmarkdown_2.3 httr_1.4.2 rstudioapi_0.11
[57] R6_2.4.1 git2r_0.27.1 compiler_4.0.2