GLODAP subset variable climate A
Jens Daniel Müller
02 March, 2021
Last updated: 2021-03-02
Checks: 7 0
Knit directory: RECCAP2_CESM_ETHZ_submission/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5611ec5 | jens-daniel-mueller | 2021-03-02 | maps and time series of 2D data |
| html | 6e5c0fd | jens-daniel-mueller | 2021-03-02 | Build site. |
| Rmd | edfb67c | jens-daniel-mueller | 2021-03-02 | plot spatially integrated time series |
| html | 026469a | jens-daniel-mueller | 2021-03-02 | Build site. |
| Rmd | 7ee6e95 | jens-daniel-mueller | 2021-03-02 | plot spatially integrated time series |
path_cmorized_annual <-
"/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_Dec2020/split/"1 Region masks
region_masks_all <-
read_csv("data/regions/RECCAP2_region_masks_all_clean.cvs")2 File names
overview <-
read_csv("data/overview/overview_files.csv")3 Time series plots
3.1 iT
Comments:
- fgco2_reg
- regions not accessible
- values seem identical to fgco2_glob
# set name of model to be subsetted
experiment_IDs <- c("A", "B", "C", "D")
# for loop across variables
variables <-
c(
"fgco2_glob"
# "fgco2_reg"
)
for (i_experiment_ID in experiment_IDs) {
for (i_variable in variables) {
# i_experiment_ID <- experiment_IDs[1]
# i_variable <- variables[1]
# read list of all files
file <- paste(i_variable,
"_CESM-ETHZ_",
i_experiment_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)
print(
variable_data_tibble %>%
ggplot(aes(time_mon,
!!sym(i_variable))) +
geom_path() +
labs(title = paste("experiment_ID:", i_experiment_ID))
)
}
}[1] "fgco2_glob_CESM-ETHZ_A_1_gr_1980-2018.nc"
| Version | Author | Date |
|---|---|---|
| 6e5c0fd | jens-daniel-mueller | 2021-03-02 |
[1] "fgco2_glob_CESM-ETHZ_B_1_gr_1980-2018.nc"
| Version | Author | Date |
|---|---|---|
| 6e5c0fd | jens-daniel-mueller | 2021-03-02 |
[1] "fgco2_glob_CESM-ETHZ_C_1_gr_1980-2018.nc"
| Version | Author | Date |
|---|---|---|
| 6e5c0fd | jens-daniel-mueller | 2021-03-02 |
[1] "fgco2_glob_CESM-ETHZ_D_1_gr_1980-2018.nc"
| Version | Author | Date |
|---|---|---|
| 6e5c0fd | jens-daniel-mueller | 2021-03-02 |
3.2 2D variables (XYT)
Comments:
# for loop across variables
variables <-
overview %>%
filter(shape == "XYT") %>%
distinct(variable_id) %>%
pull()
variables <- variables[1]
for (i_experiment_ID in experiment_IDs) {
for (i_variable in variables) {
# i_experiment_ID <- experiment_IDs[1]
# i_variable <- variables[1]
# read list of all files
file <- paste(i_variable,
"_CESM-ETHZ_",
i_experiment_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_climatology <-
variable_data_tibble %>%
group_by(lat, lon) %>%
summarise(!!paste(sym(i_variable),"mean",sep = "_") := mean(!!sym(i_variable), na.rm = TRUE),
!!paste(sym(i_variable),"min",sep = "_") := min(!!sym(i_variable), na.rm = TRUE),
!!paste(sym(i_variable),"max",sep = "_") := max(!!sym(i_variable), na.rm = TRUE)) %>%
ungroup()
variable_data_tibble_climatology <- variable_data_tibble_climatology %>%
pivot_longer(starts_with(i_variable),
values_to = "value",
names_to = "parameter")
print(
variable_data_tibble_climatology %>%
ggplot(aes(lon, lat, fill=value)) +
geom_raster() +
scale_fill_viridis_c() +
coord_quickmap(expand = 0) +
facet_grid(.~parameter) +
theme(axis.title = element_blank()) +
labs(title = paste("experiment_ID:", i_experiment_ID))
)
variable_data_tibble_time_series <-
variable_data_tibble %>%
group_by(time_mon) %>%
summarise("mean" := mean(!!sym(i_variable), na.rm = TRUE),
"min" := min(!!sym(i_variable), na.rm = TRUE),
"max" := max(!!sym(i_variable), na.rm = TRUE)) %>%
ungroup()
print(
variable_data_tibble_time_series %>%
ggplot() +
geom_ribbon(aes(
x = time_mon,
ymin = min,
ymax = max,
fill = "min/max"
), alpha = 0.3) +
geom_path(aes(
x = time_mon,
y = mean,
col = "mean"
)) +
scale_fill_viridis_d() +
scale_color_viridis_d() +
labs(title = paste("experiment_ID:", i_experiment_ID),
y = i_variable
) +
theme(legend.title = element_blank())
)
}
}[1] "alpha_CESM-ETHZ_A_1_gr_1980-2018.nc"
[1] "alpha_CESM-ETHZ_B_1_gr_1980-2018.nc"
[1] "alpha_CESM-ETHZ_C_1_gr_1980-2018.nc"
[1] "alpha_CESM-ETHZ_D_1_gr_1980-2018.nc"
# set name of model to be subsetted
experiment_ID <- "A"
# for loop across variables
variables <-
c("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_",
experiment_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/",
experiment_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 stars_0.4-3 sf_0.9-6 abind_1.4-5
[5] metR_0.9.0 scico_1.2.0 patchwork_1.1.1 collapse_1.5.0
[9] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
[17] 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_2.23-17 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] labeling_0.4.2 scales_1.1.1 checkmate_2.0.0
[22] classInt_0.4-3 digest_0.6.27 rmarkdown_2.5
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[28] rlang_0.4.9 readxl_1.3.1 rstudioapi_0.13
[31] generics_0.0.2 farver_2.0.3 jsonlite_1.7.1
[34] magrittr_1.5 ncmeta_0.3.0 Matrix_1.2-18
[37] Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
[40] lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
[43] yaml_2.2.1 grid_4.0.3 blob_1.2.1
[46] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[49] lattice_0.20-41 haven_2.3.1 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_0.7.2 RcppEigen_0.3.3.7.0 e1071_1.7-4
[70] later_1.1.0.1 class_7.3-17 ncdf4_1.17
[73] viridisLite_0.3.0 units_0.6-7 ellipsis_0.3.1