Last updated: 2021-03-03

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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 <-
  "/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_Dec2020/split"
path_CESM
[1] "/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_Dec2020/split"
# 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 484
dissicos 484
epc100 484
epcalc100 484
fgco2 484
fgco2_glob 0
fgco2_reg 0
intphyc 484
intpp 484
intzooc 484
mld 484
sos 484
spco2 484
talkos 484
tos 484
zeu 484
total 6,776.00
2D | Priority: 2
dfeos 484
epc1000 484
epc1000hard 484
epc1000soft 484
epc100hard 484
epc100soft 484
intdiac 484
intphynd 484
Kw 484
no3os 484
o2os 484
pco2atm 484
po4os 484
sios 484
total 6,776.00
2D | Priority: 3
alpha 484
total 484.00
3D | Priority: 1
dissic 2428
epc 2428
so 2428
talk 2428
thetao 2428
total 12,140.00
3D | Priority: 2
no3 2428
o2 2428
po4 2428
si 2428
total 9,712.00
NA | Priority: NA
area 0
Area_tot_native 0
Atm_CO2 0
mask_sfc 0
mask_vol 16
Vol_tot_native 0
volume 16
total 32.00

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: 5 x 2
  experiment_id file_size_MB
  <chr>                <dbl>
1 ancillary               32
2 A                     8972
3 B                     8972
4 C                     8972
5 D                     8972
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: 5 x 3
# Groups:   dimension [2]
  dimension priority file_size_MB
  <chr>        <dbl>        <dbl>
1 2D               1         6776
2 2D               2         6776
3 2D               3          484
4 3D               1        12140
5 3D               2         9712

3.3 dimension x experiment_ID

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                     3509
2 2D        B                     3509
3 2D        C                     3509
4 2D        D                     3509
5 3D        A                     5463
6 3D        B                     5463
7 3D        C                     5463
8 3D        D                     5463

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: 20 x 4
# Groups:   dimension, priority [5]
   dimension priority experiment_id file_size_MB
   <chr>        <dbl> <chr>                <dbl>
 1 2D               1 A                     1694
 2 2D               1 B                     1694
 3 2D               1 C                     1694
 4 2D               1 D                     1694
 5 2D               2 A                     1694
 6 2D               2 B                     1694
 7 2D               2 C                     1694
 8 2D               2 D                     1694
 9 2D               3 A                      121
10 2D               3 B                      121
11 2D               3 C                      121
12 2D               3 D                      121
13 3D               1 A                     3035
14 3D               1 B                     3035
15 3D               1 C                     3035
16 3D               1 D                     3035
17 3D               2 A                     2428
18 3D               2 B                     2428
19 3D               2 C                     2428
20 3D               2 D                     2428

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: 3 x 3
# Groups:   tar [2]
  tar      dimension file_size_MB
  <chr>    <chr>            <dbl>
1 phy_surf 2D                6292
2 rest     2D                7744
3 rest     3D               21852

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