Cant constant climate CB
Jens Daniel Müller
07 June, 2021
Last updated: 2021-06-07
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 72ba3bc | jens-daniel-mueller | 2021-06-02 | Build site. |
| Rmd | 125387e | jens-daniel-mueller | 2021-06-02 | rerun all with GLODAPv2.2021 beta subset, and march2021 cmorization |
| html | 45a1c9e | jens-daniel-mueller | 2021-05-20 | Build site. |
| html | 6aa4f34 | Donghe-Zhu | 2021-02-06 | Build site. |
| html | 2eb6652 | Donghe-Zhu | 2021-01-27 | Build site. |
| Rmd | e36b6ae | Donghe-Zhu | 2021-01-20 | zonal mean plot for offset |
| html | 843587f | Donghe-Zhu | 2021-01-11 | Build site. |
| Rmd | 0269854 | Donghe-Zhu | 2021-01-10 | adding constant climate for regular and random sampling |
# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>%
filter(MLR_basins == "2") %>%
select(lat, lon, basin_AIP)1 Calculate annual Cant field
1.1 Read in Run C
# read in cmorized constant forcing model file
C_annual <- tidync(paste(path_cmorized,
"RECCAP2_RunC.nc",
sep = ""))
C_annual <- C_annual %>% hyper_tibble()
# harmonize column names and coordinates
C_annual <- C_annual %>%
select(
year = time_ann,
lon,
lat,
depth,
tco2_C = dissic,
sal = so,
theta = thetao
) %>%
# compute year from days since Jan 1, 1980
mutate(year = (year - 181) / 365 + 1980) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# calculate model temperature
C_annual <- C_annual %>%
mutate(temp = gsw_pt_from_t(
SA = sal,
t = theta,
p = 10.1325,
p_ref = depth
))
# unit transfer from mol/m3 to µmol/kg
C_annual <- C_annual %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
tco2_C = tco2_C * (1e+6 / rho)
) %>%
select(year, lon, lat, depth, tco2_C)1.2 Read in Run
# read in cmorized constant forcing model file
B_annual <- tidync(paste(path_cmorized,
"RECCAP2_RunB.nc",
sep = ""))
B_annual <- B_annual %>% hyper_tibble()
# harmonize column names and coordinates
B_annual <- B_annual %>%
select(
year = time_ann,
lon,
lat,
depth,
tco2_B = dissic,
sal = so,
theta = thetao
) %>%
# compute year from days since Jan 1, 1980
mutate(year = (year - 181) / 365 + 1980) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# calculate model temperature
B_annual <- B_annual %>%
mutate(temp = gsw_pt_from_t(
SA = sal,
t = theta,
p = 10.1325,
p_ref = depth
))
# unit transfer from mol/m3 to µmol/kg
B_annual <- B_annual %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
tco2_B = tco2_B * (1e+6 / rho)
) %>%
select(year, lon, lat, depth, tco2_B)1.3 Join tco2 fields
cant <- inner_join(C_annual, B_annual)
rm(C_annual, B_annual)1.4 Round depth levels
cant <- cant %>%
mutate(depth = round(depth))1.5 Apply basin mask
# restrict Cant field to basin mask grid
cant <- inner_join(cant, basinmask)1.6 Calculate Cant total
cant <- cant %>%
mutate(cant_total = tco2_C - tco2_B) %>%
select(-c(tco2_C, tco2_B))1.7 Write Cant files
# write annual Cant files
years <- c(1982:2019)
for (i_year in years) {
# i_year = years[1]
cant_year <- cant %>%
filter(year == i_year)
cant_year %>%
write_csv(paste(path_preprocessing,
"cant_annual_field_CB/cant_", i_year, ".csv",
sep = ""))
}2 Calculate change in Cant 1994 - 2007
cant_1994 <- cant %>%
filter(year == 1994) %>%
select(-c(year)) %>%
rename(cant_1994 = cant_total)
cant_2007 <- cant %>%
filter(year == 2007) %>%
select(-c(year)) %>%
rename(cant_2007 = cant_total)
cant_gruber <- left_join(cant_1994, cant_2007) %>%
mutate(cant = cant_2007 - cant_1994)
cant_gruber <- cant_gruber %>%
mutate(cant_pos = if_else(cant <= 0, 0, cant))
rm(cant_1994, cant_2007)3 Zonal mean sections
cant_gruber <- cant_gruber %>%
mutate(eras = "JGOFS/WOCE")
cant_gruber_zonal <- m_zonal_mean_section(cant_gruber)3.1 1994 - 2007
for (i_basin_AIP in unique(cant_gruber_zonal$basin_AIP)) {
print(
p_section_zonal(
cant_gruber_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "cant_pos_mean",
plot_slabs = "n",
subtitle_text = paste("Basin: ", i_basin_AIP)
)
)
}
| Version | Author | Date |
|---|---|---|
| 843587f | Donghe-Zhu | 2021-01-11 |
| Version | Author | Date |
|---|---|---|
| 843587f | Donghe-Zhu | 2021-01-11 |
3.2 Cant total 1994
for (i_basin_AIP in unique(cant_gruber_zonal$basin_AIP)) {
print(
p_section_zonal(
cant_gruber_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "cant_1994_mean",
plot_slabs = "n",
breaks = seq(0,100,10),
subtitle_text = paste("Basin: ", i_basin_AIP)
)
)
}
| Version | Author | Date |
|---|---|---|
| 2eb6652 | Donghe-Zhu | 2021-01-27 |
| Version | Author | Date |
|---|---|---|
| 2eb6652 | Donghe-Zhu | 2021-01-27 |
3.3 Cant total 2007
for (i_basin_AIP in unique(cant_gruber_zonal$basin_AIP)) {
print(
p_section_zonal(
cant_gruber_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "cant_2007_mean",
plot_slabs = "n",
breaks = seq(0,100,10),
subtitle_text = paste("Basin: ", i_basin_AIP)
)
)
}
| Version | Author | Date |
|---|---|---|
| 843587f | Donghe-Zhu | 2021-01-11 |
| Version | Author | Date |
|---|---|---|
| 843587f | Donghe-Zhu | 2021-01-11 |
4 Column inventory
4.1 1994 - 2007
cant_gruber_inv <- m_cant_inv(cant_gruber)p_map_cant_inv(cant_gruber_inv %>% filter(inv_depth == 3000))
4.2 Cant total 1994
# this is just a work around, because the function is designed to calculate cant inventories, but not cant_total inventories
cant_gruber_total_inv <- m_cant_inv(
cant_gruber %>%
select(-cant_pos) %>%
rename(cant_pos = cant_1994))p_map_cant_inv(cant_gruber_total_inv %>% filter(inv_depth == 3000),
breaks = seq(0,100,10),
subtitle_text = "Cant total in 1994")
4.3 Cant total 2007
# this is just a work around, because the function is designed to calculate cant inventories, but not cant_total inventories
cant_gruber_total_inv <- m_cant_inv(
cant_gruber %>%
select(-cant_pos) %>%
rename(cant_pos = cant_2007))p_map_cant_inv(cant_gruber_total_inv %>% filter(inv_depth == 3000),
breaks = seq(0,100,10),
subtitle_text = "Cant total in 2007")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
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_3.0.1 stars_0.4-3 sf_0.9-8
[5] abind_1.4-5 tidync_0.2.4 metR_0.9.0 scico_1.2.0
[9] patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[17] tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 httr_1.4.2
[4] rprojroot_2.0.2 tools_4.0.3 backports_1.1.10
[7] R6_2.5.0 KernSmooth_2.23-18 DBI_1.1.0
[10] colorspace_2.0-0 withr_2.3.0 tidyselect_1.1.0
[13] compiler_4.0.3 git2r_0.27.1 cli_2.2.0
[16] rvest_0.3.6 RNetCDF_2.4-2 xml2_1.3.2
[19] isoband_0.2.3 labeling_0.4.2 scales_1.1.1
[22] checkmate_2.0.0 classInt_0.4-3 digest_0.6.27
[25] rmarkdown_2.5 pkgconfig_2.0.3 htmltools_0.5.0
[28] dbplyr_1.4.4 rlang_0.4.10 readxl_1.3.1
[31] rstudioapi_0.13 farver_2.0.3 generics_0.1.0
[34] jsonlite_1.7.2 magrittr_2.0.1 ncmeta_0.3.0
[37] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[40] fansi_0.4.1 lifecycle_1.0.0 stringi_1.5.3
[43] whisker_0.4 yaml_2.2.1 grid_4.0.3
[46] blob_1.2.1 parallel_4.0.3 promises_1.1.1
[49] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
[52] hms_0.5.3 knitr_1.30 pillar_1.4.7
[55] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[58] RcppArmadillo_0.10.1.2.2 data.table_1.13.6 modelr_0.1.8
[61] vctrs_0.3.6 httpuv_1.5.4 cellranger_1.1.0
[64] gtable_0.3.0 assertthat_0.2.1 xfun_0.20
[67] lwgeom_0.2-5 broom_0.7.5 RcppEigen_0.3.3.9.1
[70] e1071_1.7-4 later_1.1.0.1 class_7.3-17
[73] ncdf4_1.17 units_0.6-7 ellipsis_0.3.1