Climatology variable A
Jens Daniel Müller
20 May, 2022
Last updated: 2022-05-20
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b6d47dd | jens-daniel-mueller | 2022-05-20 | write files without 3D_ALL insert |
| html | 0dd6813 | jens-daniel-mueller | 2022-05-15 | Build site. |
| Rmd | 1e98fee | jens-daniel-mueller | 2022-05-15 | included more models excp. planktom12 |
| html | 06054b2 | jens-daniel-mueller | 2022-05-10 | Build site. |
| Rmd | d43e8c0 | jens-daniel-mueller | 2022-05-10 | rerun all with multi model subsetting |
1 Select 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)2 Read nc file
Here we used annual output of cmorized (1x1) model with variable forcing (RECCAP2 RunA) in year 2007 as the predictor climatology. Predictors include:
- Salinity (sal)
- Potential temperature (theta, not predictor, used for temperature calculation)
- In-situ temperature (temp, calculated)
- DIC (tco2)
- ALK (talk)
- oxygen
- AOU (calculated)
- nitrate
- phosphate
- silicate
# set name of model to be subsetted
model_ID <- "A"
# for loop across variables
variables <-
c("so", "thetao", "dissic", "talk", "o2", "no3", "po4", "si")
models <- list.files(path_cmorized)
models <-
models[!str_detect(models, pattern = "\\.t|\\.z")]
models <-
models[str_detect(
models,
pattern = c(
"CESM|CNRM|EC-Earth3|FESOM_REcoM_LR|MOM6-Princeton|MRI-ESM2-0|NorESM-OC1.2|ORCA025-GEOMAR|ORCA1-LIM3-PISCES"
)
)]
# files chunked into decades
models <-
models[!str_detect(models, pattern = "ORCA025-GEOMAR")]
# "so" variable only NA's
models <-
models[!str_detect(models, pattern = "MOM6-Princeton")]# load python scripts
source_python(paste0(
path_functions,
"python_scripts/Gamma_GLODAP_python.py"
))models <- models[1]
for (i_model in models) {
# i_model <- models[8]
print(i_model)
variables_available <-
list.files(path = paste0(path_cmorized, i_model),
pattern = paste0("_", model_ID, "_"))
variables_available <-
str_split(variables_available,
pattern = "_",
simplify = TRUE)[, 1]
variables_available <-
variables_available[variables_available %in% variables]
variables_available <- unique(variables_available)
print(variables_available)
for (i_variable in variables_available) {
# i_variable <- variables_available[1]
# read list of all files
file <-
list.files(path = paste0(path_cmorized, i_model),
pattern = paste0(i_variable, "_"))
file <-
file[str_detect(file, pattern = paste0("_", model_ID, "_"))]
print(file)
# read in data
if (i_model == "NorESM-OC1.2_3D_ALL_v20211125") {
variable_data <-
tidync::tidync(paste(paste0(path_cmorized, i_model),
file,
sep = "/"))
} else {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
make_units = FALSE)
}
if (i_model == "CESM-ETHZ_3D_ALL_v20211122") {
variable_data <-
variable_data %>% slice(index = 28, along = "time_ann")
} else if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
variable_data <-
variable_data %>% slice(index = 28, along = "Time")
} else if (i_model == "planktom12_3d_all_v20220404") {
variable_data <-
variable_data %>% slice(index = 28, along = "TIME")
} else if (i_model == "MRI-ESM2-0_3D_ALL_v20210830" &
i_variable == "si") {
variable_data <-
variable_data %>% slice(index = 1, along = "time")
} else if (i_model == "NorESM-OC1.2_3D_ALL_v20211125") {
variable_data <- variable_data %>%
tidync::hyper_filter(time = index == 28) %>%
tidync::hyper_tibble() %>%
select(-time)
} else {
variable_data <- variable_data %>% slice(index = 28, along = "time")
}
print(variable_data)
# convert to tibble
variable_data_tibble <- variable_data %>%
as_tibble()
# remove open link to nc file
rm(variable_data)
# remove na values
variable_data_tibble <-
variable_data_tibble %>%
filter(!is.na(!!sym(i_variable)))
if (i_model == "CNRM-ESM2-1_3D_ALL_v20211208") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = lev)
}
if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
variable_data_tibble <- variable_data_tibble %>%
rename(
lat = Lat,
lon = Lon,
depth = Depth
)
}
if (i_model == "MOM6-Princeton_3D_ALL_v20220125") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = z_l)
}
if (i_model == "MRI-ESM2-0_3D_ALL_v20210830") {
variable_data_tibble <- variable_data_tibble %>%
rename(depth = lev)
}
if (i_model == "planktom12_3d_all_v20220404") {
variable_data_tibble <- variable_data_tibble %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
depth = DEPTH)
}
# harmonize longitudes
variable_data_tibble <- variable_data_tibble %>%
mutate(lon = if_else(lon < 20, lon + 360, lon))
# only consider model grids within basinmask
variable_data_tibble <-
inner_join(variable_data_tibble, basinmask) %>%
select(-basin_AIP)
if (exists("climatology")) {
climatology <- full_join(climatology, variable_data_tibble)
}
if (!exists("climatology")) {
climatology <- variable_data_tibble
}
}
if (i_model == "FESOM_REcoM_LR_3D_all_v20211119") {
climatology <- climatology %>%
mutate(po4 = NA)
}
if (i_model == "planktom12_3d_all_v20220404") {
climatology <- climatology %>%
mutate(no3 = NA)
}
climatology <- climatology %>%
rename(
sal = so,
THETA = thetao,
tco2 = dissic,
talk = talk,
oxygen = o2,
nitrate = no3,
phosphate = po4,
silicate = si
)
climatology <- climatology %>%
mutate(depth = round(depth))
# In-situ temperature
climatology <- climatology %>%
mutate(temp = gsw_pt_from_t(
SA = sal,
t = THETA,
p = 10.1325,
p_ref = depth
))
# Example profile from North Atlantic Ocean
print(
climatology %>%
filter(lat == params_global$lat_Atl_profile,
lon == params_global$lon_Atl_section) %>%
ggplot() +
geom_line(aes(temp, depth, col = "insitu")) +
geom_point(aes(temp, depth, col = "insitu")) +
geom_line(aes(THETA, depth, col = "theta")) +
geom_point(aes(THETA, depth, col = "theta")) +
scale_y_reverse() +
labs(title = i_model) *
scale_color_brewer(palette = "Dark2", name = "Scale"))
# Unit conversion
# Model results are given in [mol m^-3^], whereas GLODAP data are in [µmol kg^-1^].
# For comparison, model results were converted from [mol m^-3^] to [µmol kg^-1^]
climatology <- climatology %>%
mutate(
rho = gsw_pot_rho_t_exact(
SA = sal,
t = temp,
p = depth,
p_ref = 10.1325
),
tco2 = tco2 * (1e+06 / rho),
talk = talk * (1e+06 / rho),
oxygen = oxygen * (1e+06 / rho),
nitrate = nitrate * (1e+06 / rho),
phosphate = phosphate * (1e+06 / rho),
silicate = silicate * (1e+06 / rho)
)
# AOU calculation
climatology <- climatology %>%
mutate(
oxygen_sat_m3 = gas_satconc(
S = sal,
t = temp,
P = 1.013253,
species = "O2"
),
oxygen_sat_kg = oxygen_sat_m3 * (1e+3 / rho),
AOU = oxygen_sat_kg - oxygen
) %>%
select(-oxygen_sat_kg, -oxygen_sat_m3)
# Neutral density calculation
# Neutral density gamma was calculated with a Python script provided by Serazin et al (2011)
# which performs a polynomial approximation of the original gamma calculation.
#calculate pressure from depth
climatology <- climatology %>%
mutate(CTDPRS = gsw_p_from_z(-depth,
lat))
# rename variables according to python script
climatology_gamma_prep <- climatology %>%
rename(LATITUDE = lat,
LONGITUDE = lon,
SALNTY = sal)
# calculate gamma
climatology_gamma_calc <-
calculate_gamma(climatology_gamma_prep)
# reverse variable naming
climatology <- climatology_gamma_calc %>%
select(-c(CTDPRS, THETA)) %>%
rename(
lat = LATITUDE,
lon = LONGITUDE,
sal = SALNTY,
gamma = GAMMA
)
climatology <- as_tibble(climatology)
rm(climatology_gamma_calc, climatology_gamma_prep)
# write_climatology_predictor_file
# select relevant columns
climatology <- climatology %>%
select(-c(rho))
# Remove 3D_all from model name
i_model <- str_remove(i_model, "3D_ALL_|3D_all_")
# write csv file
climatology %>%
write_csv(paste0(path_preprocessing,
i_model,
"_climatology_runA_2007.csv"))
rm(climatology)
}[1] "CESM-ETHZ_3D_ALL_v20211122"
[1] "dissic" "no3" "o2" "po4" "si" "so" "talk" "thetao"
[1] "dissic_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
dissic 1.53844 2.063345 2.121567 2.112429 2.172408 2.338243 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "no3_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
no3 0 0.0006632728 0.003444489 0.007649824 0.0142726 0.02791386 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "o2_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
o2 0.1949853 0.2097159 0.2504627 0.2672393 0.3263081 0.3876033 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "po4_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
po4 0 9.326071e-05 0.0002925115 0.0005661223 0.0009729294 0.001930219 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "si_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
si 0.0007265678 0.003127281 0.005360665 0.01249062 0.01393934 0.1167264 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "so_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
so 17.77432 34.11263 34.72358 34.70072 35.68783 40.53304 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "talk_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
talk 1.754645 2.341156 2.360543 2.36032 2.404855 2.692481 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375
[1] "thetao_CESM-ETHZ_A_1_gr_1980-2018_v20211122.nc"
stars object with 3 dimensions and 1 attribute
attribute(s), summary of first 1e+05 cells:
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
thetao -1.782697 2.924084 16.20286 14.43304 25.42252 30.07337 34720
dimension(s):
from to offset delta refsys point values x/y
lon 1 360 0 1 WGS 84 NA NULL [x]
lat 1 180 -90 1 WGS 84 NA NULL [y]
depth 1 60 NA NA NA NA [60] 5,...,5375 
| Version | Author | Date |
|---|---|---|
| 06054b2 | jens-daniel-mueller | 2022-05-10 |
3 Plots
Below, following subsets of the climatology are plotted for all relevant predictors:
- Horizontal planes at 4 depth levels
- Global section along track indicated by white lines in maps.
climatology_files <- fs::dir_ls(path_preprocessing)
climatology <- climatology_files %>%
map_dfr(read_csv, .id = "model")
climatology <- climatology %>%
mutate(model = str_remove(model, path_preprocessing),
model = str_remove(model, "_climatology_runA_2007.csv"))# define plotting variables
vars <- c(
"tco2",
"talk",
"sal",
"nitrate",
"phosphate",
"silicate",
"oxygen",
"temp",
"AOU",
"gamma"
)
# i_var <- vars[1]
for (i_var in vars) {
# i_var <- vars[1]
# plot sections
print(p_section_global(df = climatology %>%
filter(model == models[1]),
var = i_var))
}
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] geosphere_1.5-14 oce_1.5-0 gsw_1.0-6 reticulate_1.23
[5] lubridate_1.8.0 stars_0.5-5 sf_1.0-5 abind_1.4-5
[9] colorspace_2.0-2 marelac_2.1.10 shape_1.4.6 ggforce_0.3.3
[13] metR_0.11.0 scico_1.3.0 patchwork_1.1.1 collapse_1.7.0
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[21] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[25] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ellipsis_0.3.2 class_7.3-20 rprojroot_2.0.2 fs_1.5.2
[5] rstudioapi_0.13 proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[9] fansi_1.0.2 xml2_1.3.3 knitr_1.37 polyclip_1.10-0
[13] jsonlite_1.7.3 broom_0.7.11 dbplyr_2.1.1 png_0.1-7
[17] compiler_4.1.2 httr_1.4.2 backports_1.4.1 assertthat_0.2.1
[21] Matrix_1.4-0 fastmap_1.1.0 cli_3.1.1 later_1.3.0
[25] tweenr_1.0.2 htmltools_0.5.2 tools_4.1.2 gtable_0.3.0
[29] glue_1.6.0 rappdirs_0.3.3 Rcpp_1.0.8 cellranger_1.1.0
[33] jquerylib_0.1.4 RNetCDF_2.5-2 vctrs_0.3.8 lwgeom_0.2-8
[37] xfun_0.29 ps_1.6.0 rvest_1.0.2 lifecycle_1.0.1
[41] ncmeta_0.3.0 getPass_0.2-2 MASS_7.3-55 scales_1.1.1
[45] vroom_1.5.7 hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[49] yaml_2.2.1 sass_0.4.0 stringi_1.7.6 highr_0.9
[53] e1071_1.7-9 checkmate_2.0.0 rlang_1.0.2 pkgconfig_2.0.3
[57] evaluate_0.14 lattice_0.20-45 SolveSAPHE_2.1.0 labeling_0.4.2
[61] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1 here_1.0.1
[65] seacarb_3.3.0 magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[69] DBI_1.1.2 pillar_1.6.4 haven_2.4.3 whisker_0.4
[73] withr_2.4.3 units_0.7-2 sp_1.4-6 modelr_0.1.8
[77] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.2.0
[81] rmarkdown_2.11 grid_4.1.2 readxl_1.3.1 data.table_1.14.2
[85] callr_3.7.0 git2r_0.29.0 reprex_2.0.1 digest_0.6.29
[89] classInt_0.4-3 httpuv_1.6.5 munsell_0.5.0 bslib_0.3.1