Last updated: 2021-03-12
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Knit directory: RECCAP2_CESM_ETHZ_submission/
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library(tidyverse)
library(gt)This analysis is based on Table 3 of the RECCAP2-ocean protocol for model output, and statistics of the ETHZ CESM model output.
# read Table 3 from model protocol
table_3 <- read_csv(
here::here(
"data/overview",
"RECCAP2-ocean_data_products_overview - Model_protocol_table3.csv"
)
)
# replace placeholder variable name with actual CESM variable name
table_3_temp <- table_3 %>%
filter(variable_id == "epc100type / epc1000type") %>%
select(-variable_id)
table_3_temp <- expand_grid(
table_3_temp,
variable_id = c("epc100hard","epc1000hard","epc100soft","epc1000soft")
)
table_3 <- table_3 %>%
filter(variable_id != "epc100type / epc1000type")
table_3 <- bind_rows(table_3, table_3_temp)
rm(table_3_temp)The list of files and sizes of the ETHZ CESM model output refers to the content in this folder:
# set path to output
path_CESM <-
"/net/kryo/work/loher/CESM_output/RECCAP2/submit_March2021"
path_CESM[1] "/net/kryo/work/loher/CESM_output/RECCAP2/submit_March2021"# create list of CESM output files and sizes
CESM_files_names <- list.files(path = path_CESM)
CESM_files_sizes <-
file.size(paste(path_CESM, CESM_files_names, sep = "/"))
CESM_files <- bind_cols(file_name = CESM_files_names,
file_size_MB = round(CESM_files_sizes * 1e-6, 1))
rm(path_CESM, CESM_files_names, CESM_files_sizes)
# extract variable_id and experiment_id from file name
CESM_files <- CESM_files %>%
mutate(
variable_id = str_split(file_name,
pattern = "_CESM",
simplify = TRUE)[, 1],
experiment_id = str_sub(string = file_name, -19, -19)
) %>%
mutate(experiment_id = if_else(
experiment_id %in% c("A", "B", "C", "D"),
experiment_id,
"ancillary"
)) %>%
select(-c(file_name))
# correct errornous file name in CESM output
# CESM_files <- CESM_files %>%
# mutate(variable_id = if_else(variable_id == "atmpco2", "pco2atm", variable_id))# join file list and tab 3
overview <- full_join(table_3, CESM_files) %>%
arrange(variable_id)
# remove missing/additional variables
# overview <- overview %>%
# filter(!(variable_id %in% c("siconc", "fice")))
rm(CESM_files, table_3)
# write overview file
overview %>%
write_csv("data/overview/overview_files.csv")Overview table of output files created. Please note, that for each listed variable, four experiment_id (A-D) versions exist.
overview %>%
group_by(variable_id, dimension, priority) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE) %>%
arrange(dimension, priority) %>%
gt(
rowname_col = "variable_id",
groupname_col = c("dimension", "priority"),
row_group.sep = " | Priority: "
) %>%
summary_rows(groups = TRUE,
fns = list(total = "sum"))| file_size_MB | |
|---|---|
| 2D | Priority: 1 | |
| chlos | 485.2 |
| dissicos | 485.2 |
| epc100 | 485.2 |
| epcalc100 | 485.2 |
| fgco2 | 485.2 |
| fgco2_glob | 0.0 |
| fgco2_reg | 0.0 |
| fice | 485.2 |
| intphyc | 485.2 |
| intpp | 485.2 |
| intzooc | 485.2 |
| mld | 485.2 |
| sos | 485.2 |
| spco2 | 485.2 |
| talkos | 485.2 |
| tos | 485.2 |
| zeu | 485.2 |
| total | 7,278.00 |
| 2D | Priority: 2 | |
| dfeos | 485.2 |
| epc1000 | 485.2 |
| epc1000hard | 485.2 |
| epc1000soft | 485.2 |
| epc100hard | 485.2 |
| epc100soft | 485.2 |
| intdiac | 485.2 |
| intphynd | 485.2 |
| Kw | 485.2 |
| no3os | 485.2 |
| o2os | 485.2 |
| pco2atm | 485.2 |
| po4os | 485.2 |
| sios | 485.2 |
| total | 6,792.80 |
| 2D | Priority: 3 | |
| alpha | 485.2 |
| total | 485.20 |
| 3D | Priority: 1 | |
| dissic | 2426.0 |
| epc | 2426.0 |
| so | 2426.0 |
| talk | 2426.0 |
| thetao | 2426.0 |
| total | 12,130.00 |
| 3D | Priority: 2 | |
| no3 | 2426.0 |
| o2 | 2426.0 |
| po4 | 2426.0 |
| si | 2426.0 |
| total | 9,704.00 |
| NA | Priority: NA | |
| area | 0.3 |
| Area_tot_native | 0.0 |
| Atm_CO2 | 0.0 |
| mask_sfc | 0.3 |
| mask_vol | 15.6 |
| Vol_tot_native | 0.0 |
| volume | 15.6 |
| total | 31.80 |
In the following, the sum of file sizes is calculated for some variants to group the files. Grouping variables are named according to Table 3 in the model protocol.
overview %>%
group_by(experiment_id) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE) %>%
arrange(file_size_MB, experiment_id)# A tibble: 5 x 2
experiment_id file_size_MB
<chr> <dbl>
1 ancillary 31.8
2 A 9098.
3 B 9098.
4 C 9098.
5 D 9098. overview <- overview %>%
filter(experiment_id != "ancillary",
!is.na(priority))Ancillary data will be excluded for the following analysis, but needs to be included into one of the tar levels, or provided seperately.
overview %>%
group_by(dimension, priority) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE)# A tibble: 5 x 3
# Groups: dimension [2]
dimension priority file_size_MB
<chr> <dbl> <dbl>
1 2D 1 7278
2 2D 2 6793.
3 2D 3 485.
4 3D 1 12130
5 3D 2 9704 overview %>%
group_by(dimension, experiment_id) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE)# A tibble: 8 x 3
# Groups: dimension [2]
dimension experiment_id file_size_MB
<chr> <chr> <dbl>
1 2D A 3639
2 2D B 3639
3 2D C 3639
4 2D D 3639
5 3D A 5458.
6 3D B 5458.
7 3D C 5458.
8 3D D 5458.overview %>%
group_by(dimension, priority, experiment_id) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE)# A tibble: 20 x 4
# Groups: dimension, priority [5]
dimension priority experiment_id file_size_MB
<chr> <dbl> <chr> <dbl>
1 2D 1 A 1820.
2 2D 1 B 1820.
3 2D 1 C 1820.
4 2D 1 D 1820.
5 2D 2 A 1698.
6 2D 2 B 1698.
7 2D 2 C 1698.
8 2D 2 D 1698.
9 2D 3 A 121.
10 2D 3 B 121.
11 2D 3 C 121.
12 2D 3 D 121.
13 3D 1 A 3032.
14 3D 1 B 3032.
15 3D 1 C 3032.
16 3D 1 D 3032.
17 3D 2 A 2426
18 3D 2 B 2426
19 3D 2 C 2426
20 3D 2 D 2426 phy_vars <- c(
"fgco2",
"fgco2_glob",
"fgco2_reg",
"spco2",
"fice",
"Kw",
"pco2atm",
"alpha",
"mld",
"tos",
"sos",
"dissicos",
"talkos",
"no3os",
"po4os",
"sios"
)To support the analysis of surface fluxes, following 2D-variables could be tarred separately:
fgco2, fgco2_glob, fgco2_reg, spco2, fice, Kw, pco2atm, alpha, mld, tos, sos, dissicos, talkos, no3os, po4os, sios
overview <- overview %>%
mutate(tar = case_when(
variable_id %in% phy_vars &
dimension == "2D" ~ "phy_surf",
TRUE ~ "rest"
))
overview %>%
group_by(tar, dimension) %>%
summarise_at("file_size_MB", sum, na.rm = TRUE)# A tibble: 3 x 3
# Groups: tar [2]
tar dimension file_size_MB
<chr> <chr> <dbl>
1 phy_surf 2D 6793.
2 rest 2D 7763.
3 rest 3D 21834
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] gt_0.2.2 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.18 haven_2.3.1 colorspace_1.4-1
[5] vctrs_0.3.5 generics_0.0.2 htmltools_0.5.0 yaml_2.2.1
[9] utf8_1.1.4 blob_1.2.1 rlang_0.4.9 later_1.1.0.1
[13] pillar_1.4.7 withr_2.3.0 glue_1.4.2 DBI_1.1.0
[17] dbplyr_1.4.4 modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0
[21] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6
[25] evaluate_0.14 knitr_1.30 httpuv_1.5.4 fansi_0.4.1
[29] broom_0.7.2 Rcpp_1.0.5 checkmate_2.0.0 promises_1.1.1
[33] backports_1.1.10 scales_1.1.1 jsonlite_1.7.1 fs_1.5.0
[37] hms_0.5.3 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.3 here_0.1 cli_2.1.0 tools_4.0.3
[45] sass_0.2.0 magrittr_1.5 crayon_1.3.4 whisker_0.4
[49] pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2 reprex_0.3.0
[53] lubridate_1.7.9 assertthat_0.2.1 rmarkdown_2.5 httr_1.4.2
[57] rstudioapi_0.13 R6_2.5.0 git2r_0.27.1 compiler_4.0.3