Last updated: 2020-09-04
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Knit directory: Cant_eMLR/
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| Rmd | 6a302ca | jens-daniel-mueller | 2020-09-04 | rebuild after new data cleaning and mapping Cant to surface |
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| Rmd | 42e98c8 | jens-daniel-mueller | 2020-08-27 | section plots for all regular longitude added |
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| 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 |
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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(lubridate)
# library(oce)
# library(marelac)
# library(reticulate)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"))Currently, we use combined predictor fields:
predictors <-
read_csv(here::here("data/mapping/predictor_fields",
"W18_st_G16_opsn.csv"))Only predictors were taken into consideration with:
The minimum sampling depth of 150m was only taken into account when gamma is >= 26.
predictors <- predictors %>%
filter(depth >= parameters$depth_min | gamma >= 26)
predictors <- predictors %>%
filter(lat <= parameters$lat_max)
predictors_grid <- predictors %>%
group_by(lat, lon) %>%
summarise(bottomdepth = max(depth)) %>%
ungroup()
predictors <- full_join(predictors, predictors_grid)
predictors <- predictors %>%
filter(bottomdepth >= parameters$bottomdepth_min) %>%
select(-bottomdepth)
predictors <- predictors %>% drop_na()all_lm_wide <-
read_csv(here::here("data/eMLR",
"all_lm_wide.csv"))all_lm_wide <- all_lm_wide %>%
mutate(model = str_remove(model, "Cstar ~ "))
Cant <- full_join(predictors, all_lm_wide)
#rm(predictors, all_lm_wide)Cant <- Cant %>%
mutate(Cant = `delta_coeff_(Intercept)` +
delta_coeff_aou * aou +
delta_coeff_oxygen * oxygen +
delta_coeff_phosphate * phosphate +
delta_coeff_phosphate_star * phosphate_star +
delta_coeff_silicate * silicate +
delta_coeff_sal * sal +
delta_coeff_tem * tem)Mean and sd are calculated for Cant in each grid cell (XYZ), basin and era combination. Calculations are performed for all Cant values vs positive values only. This averaging step summarizes the information derived from ten best fitting MLRs.
Cant <- Cant %>%
select(lon, lat, depth, eras, basin, Cant, gamma, model)
# Cant_average <- Cant %>%
# mutate(Cant_pos = if_else(Cant < 0, 0, Cant)) %>%
# group_by(lon, lat, depth, eras, basin) %>%
# summarise(across(c("Cant", "Cant_pos", "gamma"),
# list(
# mean = ~ mean(.x, na.rm = TRUE),
# sd = ~ sd(.x, na.rm = TRUE)
# ))) %>%
# ungroup()
Cant_average <- Cant %>%
mutate(Cant_pos = if_else(Cant < 0, 0, Cant)) %>%
group_by(lon, lat, depth, eras, basin) %>%
summarise(Cant_mean = mean(Cant, na.rm = TRUE),
Cant_sd = sd(Cant, na.rm = TRUE),
Cant_pos_mean = mean(Cant_pos, na.rm = TRUE),
Cant_pos_sd = sd(Cant_pos, na.rm = TRUE),
gamma_mean = mean(gamma, na.rm = TRUE),
gamma_sd = sd(gamma, na.rm = TRUE)) %>%
ungroup()
Cant_average_Atl <- Cant_average %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Atl))
Cant_average_Ind_Pac <- Cant_average %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean, parameters$slabs_Ind_Pac))
Cant_average <- bind_rows(Cant_average_Atl, Cant_average_Ind_Pac)
rm(Cant_average_Atl, Cant_average_Ind_Pac)For each basin and era combination, the zonal mean Cant is calculated, again for all vs positive only values. Likewise, sd is calculated for the averaging of the mean basin fields.
Cant_average <- full_join(Cant_average,
basinmask %>% select(-basin))
Cant_average_zonal <- Cant_average %>%
group_by(lat, depth, eras, basin, basin_AIP) %>%
summarise(across(
c(
"Cant_mean",
"Cant_pos_mean",
"Cant_sd",
"Cant_pos_sd",
"gamma_mean",
"gamma_sd"
),
list(mean = ~ mean(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE))
)) %>%
ungroup()
# Cant_average_zonal <- Cant_average %>%
# group_by(lat, depth, eras, basin) %>%
# summarise(Cant_mean_sd = sd(Cant_mean, na.rm = TRUE),
# Cant_mean = mean(Cant_mean, na.rm = TRUE),
# Cant_sd_mean = mean(Cant_sd, na.rm = TRUE),
# Cant_pos_mean_sd = sd(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_mean = mean(Cant_pos_mean, na.rm = TRUE),
# Cant_pos_sd_mean = mean(Cant_pos_sd, na.rm = TRUE),
# gamma_mean = mean(gamma_mean)) %>%
# ungroup()
Cant_average_zonal_Atl <- Cant_average_zonal %>%
filter(basin == "Atlantic") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Atl))
Cant_average_zonal_Ind_Pac <- Cant_average_zonal %>%
filter(basin == "Indo-Pacific") %>%
mutate(gamma_slab = cut(gamma_mean_mean, parameters$slabs_Ind_Pac))
Cant_average_zonal <- bind_rows(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)
rm(Cant_average_zonal_Atl, Cant_average_zonal_Ind_Pac)To calculate Cant column inventories, we:
Step 2 is performed again for all Cant and positive Cant values only
depth_level_volume <- tibble(depth = unique(Cant_average$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_average_zonal %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_average_zonal.csv"))
Cant_inv %>%
write_csv(here::here("data/mapping/_summarized_files",
"Cant_inv.csv"))
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