Last updated: 2021-03-12

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Knit directory: RECCAP2_CESM_ETHZ_submission/

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library(tidyverse)
library(gt)

1 Load data

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")

2 Overview CESM output

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

3 Compare tar variants

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.

3.1 experiment_id

overview %>% 
  group_by(experiment_id) %>% 
  summarise_at("file_size_MB", sum, na.rm = TRUE) %>% 
  arrange(file_size_MB, experiment_id)
# A tibble: 1 x 2
  experiment_id file_size_MB
  <chr>                <dbl>
1 ancillary           36422.
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.

3.2 dimension x priority

overview %>% 
  group_by(dimension, priority) %>% 
  summarise_at("file_size_MB", sum, na.rm = TRUE)
# A tibble: 0 x 3
# Groups:   dimension [0]
# … with 3 variables: dimension <chr>, priority <dbl>, file_size_MB <dbl>

3.3 dimension x experiment_ID

overview %>% 
  group_by(dimension, experiment_id) %>% 
  summarise_at("file_size_MB", sum, na.rm = TRUE)
# A tibble: 0 x 3
# Groups:   dimension [0]
# … with 3 variables: dimension <chr>, experiment_id <chr>, file_size_MB <dbl>

3.4 dimension x priority x experiment_ID

overview %>% 
  group_by(dimension, priority, experiment_id) %>% 
  summarise_at("file_size_MB", sum, na.rm = TRUE)
# A tibble: 0 x 4
# Groups:   dimension, priority [0]
# … with 4 variables: dimension <chr>, priority <dbl>, experiment_id <chr>,
#   file_size_MB <dbl>

3.5 Custom: 2D phy vs bio

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: 0 x 3
# Groups:   tar [0]
# … with 3 variables: tar <chr>, dimension <chr>, file_size_MB <dbl>

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