- 1 Select basin mask
- 2 Subset cmorized data according to GLODAP observation
Surface Ocean data
Jens Daniel Müller
08 June, 2021
Last updated: 2021-06-08
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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path_cmorized <-
"/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_June2021/"
path_preprocessing <-
paste(path_root, "/model/preprocessing/", sep = "")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 Subset cmorized data according to GLODAP observation
Here we subset cmorized (1x1) surface ocean data from the model with variable forcing.
Besides, Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1]. This refers to the variables:
- DIC
- ALK
- O2
- NO3
- PO4
- SiO3
- AOU (calculated)
For comparison, model results were converted from [mol m-3] to [µmol kg-1]
# set name of model to be subsetted
model_IDs <- c("A", "D")
# for loop across variables
variables <-
c("spco2",
"fgco2",
"talkos",
"dissicos",
"po4os",
"sios",
"sos",
"tos")
for (i_model_ID in model_IDs) {
# i_model_ID <- model_IDs[1]
for (i_variable in variables) {
# i_variable <- variables[1]
# read list of all files
file <- paste(i_variable,
"_CESM-ETHZ_",
i_model_ID,
"_1_gr_1980-2018_v20210607.nc",
sep = "")
print(file)
# read in data
variable_data <-
read_ncdf(paste(path_cmorized,
file,
sep = ""),
make_units = FALSE)
# 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 %>%
drop_na()
# 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)
# mutate variables
variable_data_tibble <- variable_data_tibble %>%
mutate(year = year(time_mon)) %>%
select(-time_mon)
# calculate annual average variable
variable_data_tibble_annual_average <- variable_data_tibble %>%
fgroup_by(lat, lon, year) %>% {
add_vars(fgroup_vars(., "unique"),
fmean(., keep.group_vars = FALSE))
}
# calculate climatology
variable_data_tibble_climatology <-
variable_data_tibble_annual_average %>%
fselect(-year) %>%
fgroup_by(lat, lon) %>% {
add_vars(fgroup_vars(., "unique"),
fmean(., keep.group_vars = FALSE))
}
# surface map of variable
print(
map +
geom_raster(data = variable_data_tibble_climatology,
aes(lon, lat, fill = !!sym(i_variable))) +
scale_fill_viridis_c(name = i_variable)
)
# write raw data file for GLODAP-based subsetting model variables
variable_data_tibble_annual_average %>%
write_csv(file = paste(path_preprocessing,
"surface_ocean_",
i_model_ID,
"/",
i_variable,
".csv",
sep = ""))
}
}[1] "spco2_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "fgco2_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "talkos_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "dissicos_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "po4os_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "sios_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "sos_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "tos_CESM-ETHZ_A_1_gr_1980-2018_v20210607.nc"
[1] "spco2_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "fgco2_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "talkos_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "dissicos_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "po4os_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "sios_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "sos_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
[1] "tos_CESM-ETHZ_D_1_gr_1980-2018_v20210607.nc"
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] reticulate_1.18 oce_1.2-0 marelac_2.1.10 shape_1.4.5
[5] gsw_1.0-5 testthat_2.3.2 rqdatatable_1.2.9 rquery_1.4.6
[9] wrapr_2.0.4 lubridate_1.7.9 stars_0.5-2 sf_0.9-8
[13] abind_1.4-5 metR_0.9.0 scico_1.2.0 patchwork_1.1.1
[17] collapse_1.5.0 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.5
[21] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[25] ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 httr_1.4.2 rprojroot_2.0.2
[4] tools_4.0.3 backports_1.1.10 R6_2.5.0
[7] KernSmooth_2.23-17 DBI_1.1.0 colorspace_1.4-1
[10] withr_2.3.0 tidyselect_1.1.0 compiler_4.0.3
[13] git2r_0.27.1 cli_2.1.0 rvest_0.3.6
[16] RNetCDF_2.4-2 xml2_1.3.2 labeling_0.4.2
[19] scales_1.1.1 checkmate_2.0.0 classInt_0.4-3
[22] digest_0.6.27 rmarkdown_2.5 pkgconfig_2.0.3
[25] htmltools_0.5.0 dbplyr_1.4.4 rlang_0.4.10
[28] readxl_1.3.1 rstudioapi_0.11 farver_2.0.3
[31] generics_0.0.2 jsonlite_1.7.1 magrittr_1.5
[34] ncmeta_0.3.0 Matrix_1.2-18 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_1.0.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] grid_4.0.3 blob_1.2.1 parallel_4.0.3
[46] promises_1.1.1 crayon_1.3.4 lattice_0.20-41
[49] haven_2.3.1 seacarb_3.2.14 hms_0.5.3
[52] knitr_1.30 pillar_1.4.7 reprex_0.3.0
[55] glue_1.4.2 evaluate_0.14 RcppArmadillo_0.10.1.2.0
[58] data.table_1.13.2 modelr_0.1.8 vctrs_0.3.5
[61] httpuv_1.5.4 cellranger_1.1.0 gtable_0.3.0
[64] assertthat_0.2.1 xfun_0.18 lwgeom_0.2-5
[67] broom_0.7.5 RcppEigen_0.3.3.7.0 e1071_1.7-4
[70] later_1.1.0.1 viridisLite_0.3.0 class_7.3-17
[73] units_0.6-7 ellipsis_0.3.1