Surface flux products
Jens Daniel Müller
13 April, 2022
Last updated: 2022-04-13
Checks: 7 0
Knit directory: emlr_obs_preprocessing/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 016bac6 | jens-daniel-mueller | 2022-04-07 | Build site. |
| Rmd | 1a7cbdb | jens-daniel-mueller | 2022-04-07 | rerun all with lat max 65N and without arcic |
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| html | 569e06b | jens-daniel-mueller | 2022-04-01 | Build site. |
| html | 2565e71 | jens-daniel-mueller | 2022-02-17 | Build site. |
| Rmd | 78b36b3 | jens-daniel-mueller | 2022-02-17 | read Seaflux data |
| html | 163d599 | jens-daniel-mueller | 2022-02-17 | Build site. |
| Rmd | 7611648 | jens-daniel-mueller | 2022-02-17 | read Seaflux data |
| html | 6e65117 | jens-daniel-mueller | 2022-02-16 | Build site. |
| html | 008f1c0 | jens-daniel-mueller | 2022-02-15 | Build site. |
| Rmd | 5e4080a | jens-daniel-mueller | 2022-02-15 | testrun new script |
1 Read data
1.1 fgco2 global integrals
1.1.1 RECCAP2
products <- list.files(path_reccap2_surface_co2)
products <-
products[!str_detect(products, pattern = "\\.")]
# Remove data sets that do not meet formatting requirements
products <-
products[!str_detect(products, pattern = "JMAMLR_v20210312")]
products <-
products[!str_detect(products, pattern = "JMAMLR_v20211202")]
products <-
products[!str_detect(products, pattern = "LDEO_2021_clim_RECCAP2_v20210702")]
products <-
products[!str_detect(products, pattern = "spco2_LDEO_HPD_1985-2018_v20211210")]
products <-
products[!str_detect(products, pattern = "NIES-nn_v202011")]
products <-
products[!str_detect(products, pattern = "SOMFFN_v20211121")]
products <-
products[!str_detect(products, pattern = "AOML_EXTRAT_v20211130")]
products <-
products[!str_detect(products, pattern = "archive")]
### loop
for (i_products in products) {
# i_products <- products[5]
path_product <- paste(path_reccap2_surface_co2,
i_products,
sep = "/")
product_file_name <-
list.files(path_product, pattern = "fgco2_glob")
RECCAP2 <-
tidync(paste(path_product,
product_file_name,
sep = "/"))
RECCAP2 <- RECCAP2 %>%
hyper_tibble()
RECCAP2 <- RECCAP2 %>%
mutate(product = i_products)
if (exists("RECCAP2_all")) {
RECCAP2_all <- bind_rows(RECCAP2_all, RECCAP2)
}
if (!exists("RECCAP2_all")) {
RECCAP2_all <- RECCAP2
}
}1.1.2 Seaflux
SeaFlux_file_name <- paste0(path_seaflux_surface_co2,
"SeaFlux_v2021.04_fgco2_global.nc")
SeaFlux <-
tidync(SeaFlux_file_name) %>%
hyper_tibble()
ncmeta::nc_atts(SeaFlux_file_name)# A tibble: 9 × 4
id name variable value
<int> <chr> <chr> <named list>
1 0 units time <chr [1]>
2 1 calendar time <chr [1]>
3 0 description wind <chr [1]>
4 0 description product <chr [1]>
5 0 _FillValue fgco2_global <dbl [1]>
6 1 units fgco2_global <chr [1]>
7 2 long_name fgco2_global <chr [1]>
8 3 product fgco2_global <chr [1]>
9 4 description fgco2_global <chr [1]> ncmeta::nc_atts(SeaFlux_file_name, "time") %>% tidyr::unnest(cols = c(value))# A tibble: 2 × 4
id name variable value
<int> <chr> <chr> <chr>
1 0 units time days since 1982-01-15
2 1 calendar time proleptic_gregorian SeaFlux <- SeaFlux %>%
mutate(date = as.Date(time, origin = '1982-01-15'),
year = year(date))2 Timeseries
2.1 RECCAP2
RECCAP2_all <- RECCAP2_all %>%
mutate(date = as.Date(time, origin = '1980-01-01'),
year = year(date)) %>%
select(product, year, date, fgco2_glob)
RECCAP2_all %>%
ggplot(aes(date, fgco2_glob, col=product)) +
geom_line() +
theme(legend.position = "bottom")
RECCAP2_all_annual <- RECCAP2_all %>%
group_by(year, product) %>%
summarise(fgco2_glob = mean(fgco2_glob)) %>%
ungroup()
RECCAP2_all_annual %>%
ggplot(aes(year, fgco2_glob, col=product)) +
geom_line() +
theme(legend.position = "bottom")
RECCAP2_all_annual_cum_1994 <- RECCAP2_all_annual %>%
filter(year >= 1994) %>%
arrange(year) %>%
group_by(product) %>%
mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
ungroup()
RECCAP2_all_annual_cum_1994 %>%
ggplot(aes(year, fgco2_glob_cum, col = product)) +
geom_line() +
geom_point(shape = 21, fill = "white") +
theme(legend.position = "bottom")
RECCAP2_all_annual_ensemble <- RECCAP2_all_annual %>%
filter(product != "UOEX_Wat20_1985_2019_v20211204") %>%
group_by(year) %>%
summarise(fgco2_glob_sd = sd(fgco2_glob),
fgco2_glob = mean(fgco2_glob)) %>%
ungroup()
ggplot() +
geom_ribbon(
data =
RECCAP2_all_annual_ensemble,
aes(
year,
ymax = fgco2_glob + fgco2_glob_sd,
ymin = fgco2_glob - fgco2_glob_sd,
fill = "ensemble SD"
), alpha = 0.3
) +
geom_line(data =
RECCAP2_all_annual,
aes(year, fgco2_glob, group = product, col = "individual products")) +
geom_line(data =
RECCAP2_all_annual_ensemble,
aes(year, fgco2_glob, col = "ensemble mean"), size = 1) +
scale_fill_manual(values = "red") +
scale_color_manual(values = c("red", "grey50")) +
theme(legend.position = "bottom",
legend.title = element_blank())
2.2 Seaflux
SeaFlux <- SeaFlux %>%
mutate(fgco2_glob = -fgco2_global) %>%
select(product, wind, year, date, fgco2_glob)
SeaFlux %>%
ggplot(aes(date, fgco2_glob, col=wind)) +
geom_line() +
theme(legend.position = "bottom") +
facet_wrap(~ product)
| Version | Author | Date |
|---|---|---|
| 163d599 | jens-daniel-mueller | 2022-02-17 |
SeaFlux_annual <- SeaFlux %>%
filter(wind %in% c("CCMP2", "ERA5", "JRA55")) %>%
group_by(year, product) %>%
summarise(fgco2_glob = mean(fgco2_glob)) %>%
ungroup()
SeaFlux_annual %>%
ggplot(aes(year, fgco2_glob, col=product)) +
geom_line() +
theme(legend.position = "bottom")
| Version | Author | Date |
|---|---|---|
| 163d599 | jens-daniel-mueller | 2022-02-17 |
SeaFlux_annual_cum_1994 <- SeaFlux_annual %>%
filter(year >= 1994) %>%
arrange(year) %>%
group_by(product) %>%
mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
ungroup()
SeaFlux_annual_cum_1994 %>%
ggplot(aes(year, fgco2_glob_cum, col = product)) +
geom_line() +
geom_point(shape = 21, fill = "white") +
theme(legend.position = "bottom")
| Version | Author | Date |
|---|---|---|
| 163d599 | jens-daniel-mueller | 2022-02-17 |
SeaFlux_annual_ensemble <- SeaFlux_annual %>%
group_by(year) %>%
summarise(fgco2_glob_sd = sd(fgco2_glob),
fgco2_glob = mean(fgco2_glob)) %>%
ungroup()
ggplot() +
geom_ribbon(
data =
SeaFlux_annual_ensemble,
aes(
year,
ymax = fgco2_glob + fgco2_glob_sd,
ymin = fgco2_glob - fgco2_glob_sd,
fill = "ensemble SD"
), alpha = 0.3
) +
geom_line(data =
SeaFlux_annual,
aes(year, fgco2_glob, group = product, col = "individual products")) +
geom_line(data =
SeaFlux_annual_ensemble,
aes(year, fgco2_glob, col = "ensemble mean"), size = 1) +
scale_fill_manual(values = "red") +
scale_color_manual(values = c("red", "grey50")) +
theme(legend.position = "bottom",
legend.title = element_blank())
| Version | Author | Date |
|---|---|---|
| 163d599 | jens-daniel-mueller | 2022-02-17 |
2.3 Comparison
ggplot() +
geom_line(data = SeaFlux_annual,
aes(year, fgco2_glob, group = product,
col = "Seaflux")) +
geom_line(data = RECCAP2_all_annual,
aes(year, fgco2_glob, group = product,
col = "RECCAP2")) +
scale_color_brewer(palette = "Dark2") +
theme(legend.position = "bottom",
legend.title = element_blank())
ggplot() +
geom_line(data = SeaFlux_annual_cum_1994,
aes(year, fgco2_glob_cum, group = product,
col = "Seaflux")) +
geom_line(data = RECCAP2_all_annual_cum_1994,
aes(year, fgco2_glob_cum, group = product,
col = "RECCAP2")) +
scale_color_brewer(palette = "Dark2") +
theme(legend.position = "bottom",
legend.title = element_blank())
3 Write files
RECCAP2_all %>%
write_csv(paste0(path_preprocessing,
"fgco2_glob_RECCAP2_all.csv"))
RECCAP2_all_annual %>%
write_csv(paste0(path_preprocessing,
"fgco2_glob_RECCAP2_all_annual.csv"))
SeaFlux %>%
write_csv(paste0(path_preprocessing,
"fgco2_glob_Seaflux.csv"))
SeaFlux_annual %>%
write_csv(paste0(path_preprocessing,
"fgco2_glob_Seaflux_annual.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] lubridate_1.8.0 tidync_0.2.4 ggforce_0.3.3 metR_0.11.0
[5] scico_1.3.0 patchwork_1.1.1 collapse_1.7.0 forcats_0.5.1
[9] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 RColorBrewer_1.1-2 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.1.2 backports_1.4.1 bslib_0.3.1
[9] utf8_1.2.2 R6_2.5.1 DBI_1.1.2 colorspace_2.0-2
[13] withr_2.4.3 tidyselect_1.1.1 processx_3.5.2 bit_4.0.4
[17] compiler_4.1.2 git2r_0.29.0 cli_3.1.1 rvest_1.0.2
[21] RNetCDF_2.5-2 xml2_1.3.3 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 checkmate_2.0.0 callr_3.7.0 digest_0.6.29
[29] rmarkdown_2.11 pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9
[33] dbplyr_2.1.1 fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1
[37] rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.1 farver_2.1.0
[41] jsonlite_1.7.3 vroom_1.5.7 magrittr_2.0.1 ncmeta_0.3.0
[45] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
[49] stringi_1.7.6 whisker_0.4 yaml_2.2.1 MASS_7.3-55
[53] grid_4.1.2 parallel_4.1.2 promises_1.2.0.1 crayon_1.4.2
[57] haven_2.4.3 hms_1.1.1 knitr_1.37 ps_1.6.0
[61] pillar_1.6.4 reprex_2.0.1 glue_1.6.0 evaluate_0.14
[65] getPass_0.2-2 data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8
[69] tzdb_0.2.0 tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0
[73] gtable_0.3.0 polyclip_1.10-0 assertthat_0.2.1 xfun_0.29
[77] broom_0.7.11 later_1.3.0 ncdf4_1.19 ellipsis_0.3.2