Annual cmorized Cant field
Jens Daniel Müller
19 December, 2020
Last updated: 2020-12-19
Checks: 7 0
Knit directory: model/
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1 Calculate annual Cant field
1.1 Read in cmorized RunA file
# read in cmorized variable forcing model file
A_annual <- tidync(paste(path_cmorized,
"RECCAP2_RunA.nc",
sep = ""))
A_annual <- A_annual %>% hyper_tibble()
# harmonize column names and coordinates
A_annual <- A_annual %>%
select(year = time_ann, lon, lat, depth, tco2_A = dissic, sal = so, theta = thetao) %>%
# select annual value in year of 2007
mutate(year = (year - 181) / 365 + 1980) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# calculate model temperature
A_annual <- A_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
A_annual <- A_annual %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
tco2_A = tco2_A * (1000000 / rho)
) %>%
select(year, lon, lat, depth, tco2_A)1.2 Read in cmorized RunB file
# read in cmorized variable 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) %>%
# select annual value in year of 2007
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 * (1000000 / rho)
) %>%
select(year, lon, lat, depth, tco2_B)
# join files and calculate Cant field
cant_annual <- inner_join(A_annual, B_annual) %>%
mutate(cant = tco2_A - tco2_B)
rm(A_annual, B_annual)1.3 Apply basin mask
# 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)
# restrict Cant field to basin mask grid
cant_annual <- inner_join(cant_annual, basinmask)1.4 Calculate dCant for year 1994 - 2007
cant_annual_1994 <- cant_annual %>%
filter(year == 1994) %>%
select(-c(tco2_A, tco2_B, year)) %>%
rename(cant_1994 = cant)
cant_annual_2007 <- cant_annual %>%
filter(year == 2007) %>%
select(-c(tco2_A, tco2_B, year)) %>%
rename(cant_2007 = cant)
dcant_gruber <- left_join(cant_annual_1994, cant_annual_2007) %>%
mutate(dcant = cant_2007 - cant_1994)
rm(cant_annual_1994, cant_annual_2007)1.5 Write Cant files
# write combined Cant file
cant_annual %>%
write_csv(paste(
path_preprocessing,
"cant_annual_field/cant_all_years.csv",
sep = ""
))
# write annual Cant files
years <- c(1982:2019)
for (i_year in years) {
# i_year = years[1]
cant_annual_year <- cant_annual %>%
filter(year == i_year) %>%
select(year, lon, lat, depth, cant)
cant_annual_year %>%
write_csv(paste(path_preprocessing,
"cant_annual_field/cant_", i_year, ".csv",
sep = ""))
}
# write dCant gruber file
dcant_gruber %>%
write_csv(paste(
path_preprocessing,
"cant_annual_field/dcant_gruber.csv",
sep = ""
))
rm(cant_annual_year)2 Zonal mean section
# Zonal mean section for Cant
cant_annual_zonal <- cant_annual %>%
select(-c(lon, tco2_A, tco2_B)) %>%
fgroup_by(lat, depth, year, basin_AIP) %>% {
add_vars(
fgroup_vars(., "unique"),
fmean(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_mean"),
fsd(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_sd")
)
}
# Zonal mean section for dCant
dcant_gruber_zonal <- dcant_gruber %>%
select(-c(lon, cant_1994, cant_2007)) %>%
fgroup_by(lat, depth, basin_AIP) %>% {
add_vars(
fgroup_vars(., "unique"),
fmean(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_mean"),
fsd(., keep.group_vars = FALSE) %>% add_stub(pre = FALSE, "_sd")
)
}3 Column inventory
3.1 Calculation
# Calculate column inventory for Cant in year 2007
for (i_inventory_depth in params_global$inventory_depths) {
# filter integration depth
cant_annual_temp <- cant_annual %>%
filter(year == 2007, depth <= i_inventory_depth)
depth_level_volume <- tibble(depth = unique(cant_annual_temp$depth)) %>%
arrange(depth)
# determine depth level volume of each depth layer
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_annual_temp <-
full_join(cant_annual_temp, depth_level_volume)
# calculate cant layer inventory
cant_annual_temp <- cant_annual_temp %>%
mutate(cant_layer_inv = cant * layer_thickness * 1.03) %>%
select(-layer_thickness)
# sum up layer inventories to column inventories
cant_annual_inv_temp <- cant_annual_temp %>%
group_by(lon, lat, basin_AIP) %>%
summarise(cant_inv = sum(cant_layer_inv, na.rm = TRUE) / 1000) %>%
ungroup()
cant_annual_inv_temp <- cant_annual_inv_temp %>%
mutate(inv_depth = i_inventory_depth)
if (exists("cant_annual_inv")) {
cant_annual_inv <- bind_rows(cant_annual_inv, cant_annual_inv_temp)
}
if (!exists("cant_annual_inv")) {
cant_annual_inv <- cant_annual_inv_temp
}
}
cant_annual_inv <- cant_annual_inv %>%
filter(inv_depth == params_global$inventory_depth_standard)
rm(cant_annual_inv_temp, cant_annual_temp)
# Calculate column inventory for dCant
for (i_inventory_depth in params_global$inventory_depths) {
# filter integration depth
dcant_gruber_temp <- dcant_gruber %>%
filter(depth <= i_inventory_depth)
depth_level_volume <- tibble(depth = unique(dcant_gruber_temp$depth)) %>%
arrange(depth)
# determine depth level volume of each depth layer
depth_level_volume <- depth_level_volume %>%
mutate(
layer_thickness_above = replace_na((depth - lag(depth)) / 2, 0),
layer_thickness_below = replace_na((lead(depth) - depth) / 2, 0),
layer_thickness = layer_thickness_above + layer_thickness_below
) %>%
select(-c(layer_thickness_above,
layer_thickness_below))
dcant_gruber_temp <-
full_join(dcant_gruber_temp, depth_level_volume)
# calculate cant layer inventory
dcant_gruber_temp <- dcant_gruber_temp %>%
mutate(dcant_layer_inv = dcant * layer_thickness * 1.03) %>%
select(-layer_thickness)
# sum up layer inventories to column inventories
dcant_gruber_inv_temp <- dcant_gruber_temp %>%
group_by(lon, lat, basin_AIP) %>%
summarise(cant_inv = sum(dcant_layer_inv, na.rm = TRUE) / 1000) %>%
ungroup()
dcant_gruber_inv_temp <- dcant_gruber_inv_temp %>%
mutate(inv_depth = i_inventory_depth)
if (exists("dcant_gruber_inv")) {
dcant_gruber_inv <- bind_rows(dcant_gruber_inv, dcant_gruber_inv_temp)
}
if (!exists("dcant_gruber_inv")) {
dcant_gruber_inv <- dcant_gruber_inv_temp
}
}
dcant_gruber_inv <- dcant_gruber_inv %>%
filter(inv_depth == params_global$inventory_depth_standard)
rm(dcant_gruber_inv_temp, dcant_gruber_temp)3.2 Inventory plots
3.2.1 Cant inventory
# Cant inventory plot in year 2007
p_map_cant_inv(
df = cant_annual_inv,
var = "cant_inv")
3.2.2 dCant inventory
# dCant inventory plot between 1994 to 2007
p_map_cant_inv(
df = dcant_gruber_inv,
var = "cant_inv")
4 Cant plots in 2007
4.1 Horizontal plane maps
# Cant horizontal plane plot in year 2007
cant_annual_year <- cant_annual %>%
filter(year == 2007) %>%
mutate(depth = round(depth))
p_map_climatology(df = cant_annual_year, var = "cant")
| Version | Author | Date |
|---|---|---|
| c3590c6 | Donghe-Zhu | 2020-12-18 |
4.2 Zonal mean section plot
# Cant zonal mean section plot in year 2007
for (i_basin_AIP in unique(cant_annual_zonal$basin_AIP)) {
print(
p_section_zonal(
df = cant_annual_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "cant_mean",
plot_slabs = "n",
subtitle_text = paste("Basin:", i_basin_AIP)
)
)
}
| Version | Author | Date |
|---|---|---|
| c3590c6 | Donghe-Zhu | 2020-12-18 |
| Version | Author | Date |
|---|---|---|
| c3590c6 | Donghe-Zhu | 2020-12-18 |
| Version | Author | Date |
|---|---|---|
| c3590c6 | Donghe-Zhu | 2020-12-18 |
4.3 Global sections plot
# Cant global mean section plot in year 2007
cant_annual_year <- cant_annual %>%
filter(year == 2007)
p_section_global(
df = cant_annual_year,
var = "cant",
col = "divergent")
| Version | Author | Date |
|---|---|---|
| c3590c6 | Donghe-Zhu | 2020-12-18 |
5 dCant plots in 2007
5.1 Horizontal plane maps
# Cant horizontal plane plot in year 2007
dcant_gruber_round <- dcant_gruber %>%
mutate(depth = round(depth))
p_map_climatology(df = dcant_gruber_round, var = "dcant")
rm(dcant_gruber_round)5.2 Zonal mean section plot
# Cant zonal mean section plot in year 2007
for (i_basin_AIP in unique(dcant_gruber_zonal$basin_AIP)) {
print(
p_section_zonal(
df = dcant_gruber_zonal %>% filter(basin_AIP == i_basin_AIP),
var = "dcant_mean",
plot_slabs = "n",
subtitle_text = paste("Basin:", i_basin_AIP)
)
)
}
5.3 Global sections plot
# Cant global mean section plot in year 2007
p_section_global(
df = dcant_gruber,
var = "dcant",
col = "divergent")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.1
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_2.3.2 stars_0.4-3 sf_0.9-6
[5] abind_1.4-5 tidync_0.2.4 metR_0.8.0 scico_1.2.0
[9] patchwork_1.1.0 collapse_1.4.2 forcats_0.5.0 stringr_1.4.0
[13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[17] tibble_3.0.4 ggplot2_3.3.2 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_1.3-2 tools_4.0.3 backports_1.1.10
[7] R6_2.5.0 KernSmooth_2.23-18 DBI_1.1.0
[10] colorspace_1.4-1 withr_2.3.0 tidyselect_1.1.0
[13] compiler_4.0.3 git2r_0.27.1 cli_2.1.0
[16] rvest_0.3.6 RNetCDF_2.4-2 xml2_1.3.2
[19] isoband_0.2.2 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_0.4.8 readxl_1.3.1
[31] rstudioapi_0.11 farver_2.0.3 generics_0.0.2
[34] jsonlite_1.7.1 magrittr_1.5 ncmeta_0.3.0
[37] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
[40] fansi_0.4.1 lifecycle_0.2.0 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_1.4.6
[55] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[58] RcppArmadillo_0.10.1.0.0 data.table_1.13.2 modelr_0.1.8
[61] vctrs_0.3.4 httpuv_1.5.4 cellranger_1.1.0
[64] gtable_0.3.0 assertthat_0.2.1 xfun_0.18
[67] lwgeom_0.2-5 broom_0.7.2 RcppEigen_0.3.3.7.0
[70] e1071_1.7-4 later_1.1.0.1 viridisLite_0.3.0
[73] class_7.3-17 ncdf4_1.17 units_0.6-7
[76] ellipsis_0.3.1