CSIO-MNR Argo Temperature Climatology
Pasqualina Vonlanthen & Jens Daniel Müller
25 March, 2022
Last updated: 2022-03-25
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Knit directory: bgc_argo_r_argodata/
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Task
CSIO-MNR Argo temperature climatology of Li et al. (2017)
Li, H., F. Xu, W. Zhou, D. Wang, J. S. Wright, Z. Liu, and Y. Lin (2017), Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res.Oceans, 122, doi: 10.1002/2016JC012285.6
User Manual: Shaolei Lu，Zenghong Liu，Hong Li，Zhaoqin Li，Xiaofen Wu，Chaohui Sun，Jianping Xu.(2020). Manual of Global Ocean Argo gridded data set (BOA_Argo) (Version 2019), 14 pp https://argo.ucsd.edu/wp-content/uploads/sites/361/2020/07/User_Manual_BOA_Argo-2020.pdf
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.1.4 ✓ stringr 1.4.0
✓ readr 2.1.1 ✓ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(ggOceanMaps)Loading required package: ggspatialSetting data download folder to a temporary folder /tmp/RtmpyCFvWc.
This means that any downloaded map data need to be downloaded again
when you restart R. To avoid this problem, change the default path to a
permanent folder on your computer. Add following lines to your
.Rprofile file: {.ggOceanMapsenv <- new.env(); .ggOceanMapsenv$datapath
<- 'YourCustomPath'}. You can use usethis::edit_r_profile() to edit the
file.'~/Documents/ggOceanMapsLargeData'would make it in a writable
folder on most operating systems.library(oce)Loading required package: gswLoad data
path_updata <- "/nfs/kryo/work/updata"
path_argo_clim_temp <- paste0(path_updata, "/argo_climatology/temperature")
path_emlr_utilities <- "/nfs/kryo/work/jenmueller/emlr_cant/utilities/files/"
path_argo <- '/nfs/kryo/work/updata/bgc_argo_r_argodata'
path_argo_preprocessed <- paste0(path_argo, "/preprocessed_bgc_data")
theme_set(theme_bw())January climatology
boa_clim_argo_temp_jan <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_01.nc"))
# 1 896 093 obs of 6 variables (2004-2019):
# temp (range between -3.9180 and 30.1866º)
# salt
# lon (range between 0.5 and 359.5)
# lat (range between -72.5 and 79.5)
# pres (range between 0 and 1975)
# time (15 days since 0000-01-01)
# range(boa_clim_argo_temp_jan$temp)
# -3.9180 to 30.1866
# range(boa_clim_argo_temp_jan$lon)
# 0.5 to 359.5 by 1
# range(boa_clim_argo_temp_jan$lat)
# -72.5 to 79.5 by 1
# range(boa_clim_argo_temp_jan$pres)
# 0 to 1975 (58 pressure levels)
# table(boa_clim_argo_temp_jan$pres)
# pressure levels: 0, 5, 10-170 by 10, 180-460 by 20, 500-1300 by 50, 1400-1900 by 100, 1975 dbar
# range(boa_clim_argo_temp_jan$time)
# days since 0000-01-01
# keep only data south of 30ºS
boa_clim_temp_jan_SO <- boa_clim_argo_temp_jan %>%
filter(lat <= -30) %>%
select(-salt)
# 766 434 obs of 5 variables
# range(boa_clim_temp_jan_SO$temp)
# range(boa_clim_temp_jan_SO$lat)
# range(boa_clim_temp_jan_SO$lon)
boa_clim_temp_jan_SO <- boa_clim_temp_jan_SO %>%
mutate(lon = if_else(lon < 20, lon + 360, lon),
depth = swDepth(pressure = pres, latitude = lat)) %>%
rename(clim_temp_jan = temp) %>%
mutate(month = rep(1, length(time)))
# range(boa_clim_temp_jan_SO$depth)region_masks_all_1x1 <- read_rds(file = paste0(path_argo_preprocessed,
'/region_masks_all_1x1.rds'))
region_masks_all_1x1 <- region_masks_all_1x1 %>%
rename(biome = value) %>%
mutate(coast = as.character(coast))
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(region == 'southern',
biome != 0) %>%
select(-region)
region_masks_all_1x1 <- region_masks_all_1x1 %>%
filter(coast == "0")
# WOA 18 basin mask
basinmask <-
read_csv(
paste(path_emlr_utilities,
"basin_mask_WOA18.csv",
sep = ""),
col_types = cols("MLR_basins" = col_character())
)
basinmask <- basinmask %>%
filter(MLR_basins == unique(basinmask$MLR_basins)[1]) %>%
select(-c(MLR_basins, basin))
basinmask <- basinmask %>%
filter(lat <= -30)map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))map+
geom_point(data = boa_clim_temp_jan_SO %>% filter(depth < 20),
aes(x = lon,
y = lat),
size = 0.2,
pch = 2,
alpha = 0.2)
boa_clim_sst_jan_SO <- boa_clim_temp_jan_SO %>%
filter(depth <= 20) %>%
group_by(lon, lat) %>%
summarise(clim_sst = mean(clim_temp_jan, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'lon'. You can override using the `.groups` argument.map+
geom_tile(data = boa_clim_sst_jan_SO,
aes(x = lon,
y = lat,
fill = clim_sst))+
scale_fill_viridis_c()+
lims(y = c(-80, -25))+
labs(title = 'CSIO January climatological argo SST')Warning: Removed 14609 rows containing missing values (geom_tile).
boa_clim_temp_jan_SO <- inner_join(boa_clim_temp_jan_SO, region_masks_all_1x1)Joining, by = c("lon", "lat")boa_clim_temp_jan_SO <- inner_join(boa_clim_temp_jan_SO,
basinmask)Joining, by = c("lon", "lat")boa_clim_temp_jan_SO %>%
ggplot(aes(x = clim_temp_jan,
y = depth))+
geom_point(size = 0.2, pch = 1, fill = NA)+
scale_y_reverse()+
facet_grid(basin_AIP~biome)
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] oce_1.5-0 gsw_1.0-6 ggOceanMaps_1.2.6 ggspatial_1.1.5
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[13] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 sf_1.0-5 bit64_4.0.5 lubridate_1.8.0
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2 backports_1.4.1
[9] bslib_0.3.1 utf8_1.2.2 rgdal_1.5-28 R6_2.5.1
[13] KernSmooth_2.23-20 rgeos_0.5-9 DBI_1.1.2 colorspace_2.0-2
[17] raster_3.5-11 withr_2.4.3 sp_1.4-6 tidyselect_1.1.1
[21] processx_3.5.2 bit_4.0.4 compiler_4.1.2 git2r_0.29.0
[25] cli_3.1.1 rvest_1.0.2 RNetCDF_2.5-2 xml2_1.3.3
[29] labeling_0.4.2 sass_0.4.0 scales_1.1.1 classInt_0.4-3
[33] callr_3.7.0 proxy_0.4-26 digest_0.6.29 rmarkdown_2.11
[37] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[41] fastmap_1.1.0 tidync_0.2.4 rlang_0.4.12 readxl_1.3.1
[45] rstudioapi_0.13 farver_2.1.0 jquerylib_0.1.4 generics_0.1.1
[49] jsonlite_1.7.3 vroom_1.5.7 magrittr_2.0.1 ncmeta_0.3.0
[53] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
[57] terra_1.5-12 stringi_1.7.6 whisker_0.4 yaml_2.2.1
[61] grid_4.1.2 parallel_4.1.2 promises_1.2.0.1 crayon_1.4.2
[65] lattice_0.20-45 haven_2.4.3 hms_1.1.1 knitr_1.37
[69] ps_1.6.0 pillar_1.6.4 codetools_0.2-18 reprex_2.0.1
[73] glue_1.6.0 evaluate_0.14 getPass_0.2-2 modelr_0.1.8
[77] vctrs_0.3.8 tzdb_0.2.0 httpuv_1.6.5 cellranger_1.1.0
[81] gtable_0.3.0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[85] e1071_1.7-9 later_1.3.0 viridisLite_0.4.0 ncdf4_1.19
[89] class_7.3-20 units_0.7-2 ellipsis_0.3.2