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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.

The file list of the ETHZ CESM model output was created by running:

ls -l > /UP_home/jenmueller/Projects/RECCAP2_CESM_ETHZ_submission/data/overview/CESM_files.txt

while in folder:

/nfs/kryo/work/loher/CESM_output/RECCAP2/submit_Dec2020/split

# prepare tab 3
tab3 <- read_csv(
  here::here(
    "data/overview",
    "RECCAP2-ocean_data_products_overview - Model_protocol_table3.csv"
  )
)

tab3_temp <- tab3 %>% 
  filter(variable_id == "epc100type / epc1000type") %>% 
  select(-variable_id)

tab3_temp <- expand_grid(
  tab3_temp,
  variable_id = c("epc100hard","epc1000hard","epc100soft","epc1000soft")
)

tab3 <- tab3 %>% 
  filter(variable_id != "epc100type / epc1000type")

tab3 <- bind_rows(tab3, tab3_temp)
rm(tab3_temp)

# prepare cesm file list
CESM_files <- read_table2("data/overview/CESM_files.txt",
                          col_names = FALSE,
                          skip = 1)


CESM_files <- CESM_files %>%
  select(X5, X9) %>%
  rename(file_size = X5,
         file_name = X9) %>%
  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"
    ),
    variable_id = if_else(variable_id == "atmpco2", "pco2atm", variable_id),
    file_size_MB = file_size*1e-6
  ) %>%
  select(-c(file_name, file_size))

# join file list and tab 3
overview <- full_join(tab3, CESM_files) %>%
  arrange(variable_id)

overview <- overview %>%
  filter(!is.na(experiment_id),
         variable_id != "siconc") %>% 
  mutate(file_size_MB = round(file_size_MB,1))

rm(CESM_files, tab3)

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
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 6,792.80
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: 5 x 2
  experiment_id file_size_MB
  <chr>                <dbl>
1 ancillary             31.8
2 A                   8976. 
3 B                   8976. 
4 C                   8976. 
5 D                   8976. 
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        6793.
2 2D               2        6793.
3 2D               3         485.
4 3D               1       12130 
5 3D               2        9704 

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                    3518.
2 2D        B                    3518.
3 2D        C                    3518.
4 2D        D                    3518.
5 3D        A                    5458.
6 3D        B                    5458.
7 3D        C                    5458.
8 3D        D                    5458.

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                    1698.
 2 2D               1 B                    1698.
 3 2D               1 C                    1698.
 4 2D               1 D                    1698.
 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 

3.5 2D bio vs phy

phy_vars <- c("fgco2",
              "fgco2_glob",
              "fgco2_reg",
              "spco2",
              "fice",
              "Kw",
              "pco2atm",
              "alpha",
              "mld"
              )
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               2911.
2 rest     2D              11160.
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