Last updated: 2021-03-02
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Knit directory: RECCAP2_CESM_ETHZ_submission/
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path_GLODAP_preprocessing <-
paste(path_root, "/observations/preprocessing/", sep = "")
path_cmorized_annual <-
"/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_Dec2020/split/"
path_basin_mask <-
"/nfs/kryo/work/updata/reccapv2/"region_masks_all <-
read_csv("data/regions/RECCAP2_region_masks_all_clean.cvs")GLODAP <-
read_csv(paste(path_GLODAP_preprocessing,
"GLODAPv2.2020_preprocessed.csv",
sep = ""))
GLODAP <- GLODAP %>%
mutate(month = month(date))Here we subset cmorized (1x1) data from the model with variable forcing, according to the presence of GLODAP observations in a previously cleaned file.
Besides, Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1]. This refers to the variables:
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", "B", "C", "D")
# for loop across variables
variables <-
c(
"fgco2_glob"
# "fgco2_reg"
)
for (i_model_ID in model_IDs) {
for (i_variable in variables) {
# i_model_ID <- model_IDs[1]
# i_variable <- variables[2]
# read list of all files
file <- paste(i_variable,
"_CESM-ETHZ_",
i_model_ID,
"_1_gr_1980-2018.nc",
sep = "")
print(file)
# read in data
variable_data <-
tidync(paste(path_cmorized_annual,
file,
sep = ""))
# convert to tibble
variable_data_tibble <- variable_data %>%
hyper_tibble()
# remove open link to nc file
rm(variable_data)
variable_data_tibble %>%
ggplot(aes(time_mon,
!!sym(i_variable))) +
geom_path()
}
}[1] "fgco2_glob_CESM-ETHZ_A_1_gr_1980-2018.nc"
[1] "fgco2_glob_CESM-ETHZ_B_1_gr_1980-2018.nc"
[1] "fgco2_glob_CESM-ETHZ_C_1_gr_1980-2018.nc"
[1] "fgco2_glob_CESM-ETHZ_D_1_gr_1980-2018.nc"# set name of model to be subsetted
model_ID <- "A"
# for loop across variables
variables <-
c(
"fgco2_glob",
"fgco2_reg",
"fgco2",
"spco2",
"fice",
"intpp",
"epc100",
"epc1000",
"epc100type / epc1000type",
"epcalc100",
"Kw",
"pco2atm",
"alpha",
"tos",
"sos",
"dissicos",
"talkos",
"no3os",
"po4os",
"sios",
"dfeos",
"o2os",
"intphyc",
"intphynd",
"intdiac",
"intzooc",
"chlos",
"mld",
"zeu",
"dissic",
"talk",
"thetao",
"so",
"epc",
"no3",
"po4",
"si",
"o2")
for (i_variable in variables) {
# i_variable <- variables[1]
# read list of all files
file <- paste(i_variable,
"_CESM-ETHZ_",
model_ID,
"_1_gr_1980-2018.nc",
sep = "")
print(file)
# read in data
variable_data <-
read_ncdf(paste(path_cmorized_annual,
file,
sep = ""))
# convert to tibble
variable_data_tibble <- variable_data %>%
select(-time_mon) %>%
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)))
variable_data_tibble %>%
ggplot(aes(time_mon, as.numeric(fgco2_glob))) +
geom_path()
# # 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(month = month(time_mon), !!sym(i_variable) := as.numeric(!!sym(i_variable))) %>%
select(-time_mon)
# calculate model summary stats
stats <- variable_data_tibble %>%
pull(!!sym(i_variable)) %>%
summary()
stats <- c(year = i_year, variable = i_variable, stats)
if (exists("stats_summary")) {
stats_summary <- bind_rows(stats_summary, stats)
}
if (!exists("stats_summary")) {
stats_summary <- stats
}
rm(stats)
# subset model at month x lat x lon grid of observations
model_grid_horizontal <-
inner_join(Glodap_year_grid_horizontal, variable_data_tibble)
# join model and month x lat x lon x depth grid of observations
model_obs <-
full_join(model_grid_horizontal, Glodap_year_grid_depth)
# calculate nr of observations per month x lat x lon grid
model_obs <- model_obs %>%
group_by(month, lat, lon) %>%
mutate(n = sum(!is.na(!!sym(i_variable)))) %>%
ungroup()
# set variable value from model for observation depth, if only one model depth available
model_obs_set <- model_obs %>%
filter(n == 1) %>%
group_by(lon, lat, month) %>%
mutate(!!sym(i_variable) := mean(!!sym(i_variable), na.rm = TRUE)) %>%
ungroup()
# interpolate variable value from model to observation depth
model_obs_interpo <- model_obs %>%
filter(n > 1) %>%
group_by(lon, lat, month) %>%
arrange(depth) %>%
mutate(!!sym(i_variable) := approxfun(depth, !!sym(i_variable), rule = 2)(depth)) %>%
ungroup()
# join interpolated and set model values
model_obs_interpo <- full_join(model_obs_interpo, model_obs_set)
rm(model_obs_set)
# subsetted interpolated values at observation depth
model_obs_interpo <-
inner_join(Glodap_year_grid_depth, model_obs_interpo) %>%
select(-n) %>%
mutate(year = as.numeric(i_year))
# select observation grids without corresponding model subset
na_model <-
full_join(Glodap_year_grid_depth, model_obs_interpo) %>%
filter(is.na(!!sym(i_variable))) %>%
select(month, lat, lon) %>%
unique()
# rename interpolated model variable to indicate as model output
model_obs_interpo <- model_obs_interpo %>%
rename(!!sym(paste(i_variable, "model", sep = "_")) := !!sym(i_variable))
# add model subset to GLODAP
GLODAP <-
natural_join(
GLODAP,
model_obs_interpo,
by = c("year", "month", "lat", "lon", "depth"),
jointype = "FULL"
)
# calculate annual average variable
variable_data_tibble_annual_average <- variable_data_tibble %>%
fselect(-month) %>%
fgroup_by(lat, lon, depth) %>% {
add_vars(fgroup_vars(., "unique"),
fmean(., keep.group_vars = FALSE))
}
# select surface annual average variable
variable_data_tibble_annual_average_surface <-
variable_data_tibble_annual_average %>%
filter(depth == min(depth))
# surface map of variable
map +
geom_raster(data = variable_data_tibble_annual_average_surface, aes(lon, lat, fill = !!sym(i_variable))) +
scale_fill_viridis_c(name = i_variable) +
geom_point(data = model_obs_interpo,
aes(lon, lat), col = "black") +
geom_point(data = na_model,
aes(lon, lat), col = "red") +
labs(
title = "GLODAP-based cmorized subset distribution",
subtitle = paste(
"Model depth: 5m | Annual average of year",
i_year,
"| red = subsetting failed"
),
x = "Longitude",
y = "Latitude"
)
ggsave(
paste(
path_preprocessing,
"regular_subset_distribution_runA/",
i_variable,
"_",
i_year,
".png",
sep = ""
),
width = 5,
height = 3
)
}
}
# write raw data file for GLODAP-based subsetting model variables
GLODAP %>%
write_csv(
paste(
path_preprocessing,
"GLODAPv2.2020_preprocessed_model_runA_raw_subset.csv",
sep = ""
)
)
# write file for model summary statistics (original cmorized unit)
stats_summary %>%
write_csv(
paste(
path_preprocessing,
"regular_subset_distribution_runA/",
model_ID,
"_summary_stats.csv",
sep = ""
)
)
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] tidync_0.2.4 reticulate_1.18 oce_1.2-0 marelac_2.1.10
[5] shape_1.4.5 gsw_1.0-5 testthat_2.3.2 rqdatatable_1.2.9
[9] rquery_1.4.6 wrapr_2.0.4 lubridate_1.7.9 stars_0.4-3
[13] sf_0.9-6 abind_1.4-5 metR_0.9.0 scico_1.2.0
[17] patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0 stringr_1.4.0
[21] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[25] 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 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 scales_1.1.1
[19] checkmate_2.0.0 classInt_0.4-3 digest_0.6.27
[22] rmarkdown_2.5 pkgconfig_2.0.3 htmltools_0.5.0
[25] dbplyr_1.4.4 rlang_0.4.9 readxl_1.3.1
[28] rstudioapi_0.13 generics_0.0.2 jsonlite_1.7.1
[31] magrittr_1.5 ncmeta_0.3.0 Matrix_1.2-18
[34] Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[37] lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
[40] yaml_2.2.1 grid_4.0.3 blob_1.2.1
[43] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[46] lattice_0.20-41 haven_2.3.1 seacarb_3.2.14
[49] hms_0.5.3 knitr_1.30 pillar_1.4.7
[52] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[55] RcppArmadillo_0.10.1.2.0 data.table_1.13.2 modelr_0.1.8
[58] vctrs_0.3.5 httpuv_1.5.4 cellranger_1.1.0
[61] gtable_0.3.0 assertthat_0.2.1 xfun_0.18
[64] lwgeom_0.2-5 broom_0.7.2 RcppEigen_0.3.3.7.0
[67] e1071_1.7-4 later_1.1.0.1 ncdf4_1.17
[70] class_7.3-17 units_0.6-7 ellipsis_0.3.1