Atmospheric pCO2
Jens Daniel Müller
24 October, 2022
Last updated: 2022-10-24
Checks: 7 0
Knit directory: emlr_mod_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | e829a11 | jens-daniel-mueller | 2022-07-05 | Build site. |
| Rmd | 923e07e | jens-daniel-mueller | 2022-07-04 | rerun for all models |
| html | 89cbb74 | jens-daniel-mueller | 2022-05-26 | Build site. |
| html | 42bbb89 | jens-daniel-mueller | 2022-05-24 | Build site. |
| Rmd | e82a302 | jens-daniel-mueller | 2022-05-23 | bug fixes for 5 models |
| html | 40863a2 | jens-daniel-mueller | 2022-05-22 | Build site. |
| Rmd | d9d4d68 | jens-daniel-mueller | 2022-05-21 | rebuild with revised output structure and all models |
| html | df15df5 | jens-daniel-mueller | 2022-05-12 | Build site. |
| Rmd | b1c9920 | jens-daniel-mueller | 2022-05-12 | read data from all models and write individual files |
| html | 1e7641d | jens-daniel-mueller | 2022-05-12 | Build site. |
| Rmd | f91b22d | jens-daniel-mueller | 2022-05-12 | read data from all models |
| html | 06054b2 | jens-daniel-mueller | 2022-05-10 | Build site. |
| Rmd | d43e8c0 | jens-daniel-mueller | 2022-05-10 | rerun all with multi model subsetting |
1 Read data
models <- list.files(path_cmorized)
models <-
models[!str_detect(models, pattern = "\\.t|\\.z|.p")]
models <-
models[str_detect(
models,
pattern = c(
"CESM|CNRM|EC-Earth3|FESOM_REcoM_LR|MOM6-Princeton|MRI-ESM2-1|NorESM-OC1.2|ORCA025-GEOMAR|ORCA1-LIM3-PISCES|planktom12"
)
)]
# no Atm_CO2 data provided
models <-
models[!str_detect(models, pattern = "NorESM-OC1.2")]
# no Atm_CO2 data provided
models <-
models[!str_detect(models, pattern = "ORCA1-LIM3-PISCES")]# models <- models[1]
for (i_model in models) {
# i_model <- models[7]
print(i_model)
# read list of all files
file <-
list.files(path = paste0(path_cmorized, i_model),
pattern = "CO2")[1]
print(file)
# read in data
if (i_model %in% c(
"CESM-ETHZ_Ancillary_data_v20211122",
"MOM6-Princeton_Ancillary_data_v20220125",
"ORCA025-GEOMAR_Ancillary_data_v20210804"
)) {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
var = "Atm_CO2",
make_units = FALSE)
} else if (i_model == "CNRM-ESM2-1_Ancillary_data_v20211208") {
variable_data <-
read_delim(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
delim = " ") %>%
rename(year = Year,
Atm_CO2 = atmCO2)
} else if (i_model == "EC-Earth3_Ancillary_data_v20220323") {
variable_data <-
read_table(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
col_names = FALSE) %>%
rename(year = X1,
Atm_CO2 = X2)
} else if (i_model == "MRI-ESM2-1_Ancillary_data_v20220502") {
variable_data <-
tidync(paste(paste0(path_cmorized, i_model),
file,
sep = "/")) %>%
activate("D0") %>%
hyper_tibble() %>%
mutate(time = as.Date(time, origin = "1980-01-01"))
} else if (i_model == "planktom12_ancillary_data_v20220404") {
variable_data <-
read_table(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
col_names = FALSE) %>%
select(year = X1,
Atm_CO2 = X2)
} else {
variable_data <-
read_ncdf(paste(paste0(path_cmorized, i_model),
file,
sep = "/"),
make_units = FALSE)
}
# convert to tibble
variable_data_tibble <- variable_data %>%
as_tibble()
# remove open link to nc file
rm(variable_data)
if (i_model == "CESM-ETHZ_Ancillary_data_v20211122") {
variable_data_tibble <- variable_data_tibble %>%
rename(time = time_ann)
}
if (i_model == "FESOM_REcoM_LR_Ancillary_data_v20211119") {
variable_data_tibble <-
variable_data_tibble %>%
pivot_longer(
cols = -NumberOfMonths,
names_to = "year",
values_to = "Atm_CO2",
names_prefix = "AtmCO2_"
) %>%
mutate(year = as.numeric(year))
}
# mutate variables
if (!(
i_model %in% c(
"CNRM-ESM2-1_Ancillary_data_v20211208",
"EC-Earth3_Ancillary_data_v20220323",
"FESOM_REcoM_LR_Ancillary_data_v20211119",
"planktom12_ancillary_data_v20220404"
)
)) {
variable_data_tibble <- variable_data_tibble %>%
mutate(year = year(time)) %>%
select(-time)
}
variable_data_tibble <- variable_data_tibble %>%
mutate(model = i_model)
if (exists("atm_co2")) {
atm_co2 <- bind_rows(atm_co2, variable_data_tibble)
}
if (!exists("atm_co2")) {
atm_co2 <- variable_data_tibble
}
}[1] "CESM-ETHZ_Ancillary_data_v20211122"
[1] "Atm_CO2_CESM-ETHZ_1_gr_1980-2018_v20211122.nc"
[1] "CNRM-ESM2-1_Ancillary_data_v20211208"
[1] "Atm_CO2"
[1] "EC-Earth3_Ancillary_data_v20220323"
[1] "Atm_CO2_EC-Earth3_v20220323.txt"
[1] "FESOM_REcoM_LR_Ancillary_data_v20211119"
[1] "MonthlyAtmCO2_gcb2020.nc"
[1] "MOM6-Princeton_Ancillary_data_v20220125"
[1] "Atm_CO2_MOM6-Princeton_A_1_gr_1980-2018_v20220125.nc"
[1] "MRI-ESM2-1_Ancillary_data_v20220502"
[1] "Atm_CO2_MRI-ESM2-1_A_1_gm_1980-2018_v20220502.nc"
[1] "ORCA025-GEOMAR_Ancillary_data_v20210804"
[1] "Atm_CO2_ORCA025-GEOMAR_1_1980-2018_v20210804.nc"
[1] "planktom12_ancillary_data_v20220404"
[1] "Atm_CO2_PlankTOM12_v20220404.nc"atm_co2 <- atm_co2 %>%
mutate(year = round(year)) %>%
filter(year > 1979, year < 2019) %>%
group_by(model, year) %>%
summarise(Atm_CO2 = mean(Atm_CO2)) %>%
ungroup()
atm_co2 <- atm_co2 %>%
mutate(
model = str_remove(model, "Ancillary_data_"),
model = str_remove(model, "ancillary_data_")
)2 Time series
atm_co2 %>%
ggplot(aes(year, Atm_CO2, col = model)) +
geom_path() +
geom_point() +
scale_color_brewer(palette = "Dark2") +
theme(legend.position = "bottom",
legend.direction = "vertical")
3 Write files
atm_co2 %>%
group_by(model) %>%
group_walk( ~ write_csv(.x, paste0(
path_preprocessing,
.y$model,
"_co2_atm.csv"
)))
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/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] stars_0.5-5 sf_1.0-5 abind_1.4-5 tidync_0.2.4
[5] lubridate_1.8.0 geomtextpath_0.1.0 colorspace_2.0-2 marelac_2.1.10
[9] shape_1.4.6 ggforce_0.3.3 metR_0.11.0 scico_1.3.0
[13] patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1 stringr_1.4.0
[17] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[21] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ellipsis_0.3.2 class_7.3-20 rprojroot_2.0.2 fs_1.5.2
[5] rstudioapi_0.13 proxy_0.4-26 farver_2.1.0 bit64_4.0.5
[9] fansi_1.0.2 xml2_1.3.3 ncdf4_1.19 knitr_1.37
[13] polyclip_1.10-0 jsonlite_1.7.3 gsw_1.0-6 broom_0.7.11
[17] dbplyr_2.1.1 compiler_4.1.2 httr_1.4.2 backports_1.4.1
[21] assertthat_0.2.1 fastmap_1.1.0 cli_3.1.1 later_1.3.0
[25] tweenr_1.0.2 htmltools_0.5.2 tools_4.1.2 gtable_0.3.0
[29] glue_1.6.0 Rcpp_1.0.8 cellranger_1.1.0 jquerylib_0.1.4
[33] RNetCDF_2.5-2 vctrs_0.3.8 lwgeom_0.2-8 xfun_0.29
[37] ps_1.6.0 rvest_1.0.2 lifecycle_1.0.1 ncmeta_0.3.0
[41] oce_1.5-0 getPass_0.2-2 MASS_7.3-55 scales_1.1.1
[45] vroom_1.5.7 hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[49] RColorBrewer_1.1-2 yaml_2.2.1 sass_0.4.0 stringi_1.7.6
[53] highr_0.9 e1071_1.7-9 checkmate_2.0.0 rlang_1.0.2
[57] pkgconfig_2.0.3 systemfonts_1.0.3 evaluate_0.14 SolveSAPHE_2.1.0
[61] labeling_0.4.2 bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[65] seacarb_3.3.0 magrittr_2.0.1 R6_2.5.1 generics_0.1.1
[69] DBI_1.1.2 pillar_1.6.4 haven_2.4.3 whisker_0.4
[73] withr_2.4.3 units_0.7-2 modelr_0.1.8 crayon_1.4.2
[77] KernSmooth_2.23-20 utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[81] grid_4.1.2 readxl_1.3.1 data.table_1.14.2 callr_3.7.0
[85] git2r_0.29.0 reprex_2.0.1 digest_0.6.29 classInt_0.4-3
[89] httpuv_1.6.5 textshaping_0.3.6 munsell_0.5.0 bslib_0.3.1