CSIO-MNR Argo Temperature Climatology
Pasqualina Vonlanthen & Jens Daniel Müller
31 March, 2022
Last updated: 2022-03-31
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Knit directory: bgc_argo_r_argodata/
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| Rmd | 11915d8 | pasqualina-vonlanthendinenna | 2022-03-31 | loaded in Mayot biomes and Roemmich temp climatology |
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| html | 6dd0945 | pasqualina-vonlanthendinenna | 2022-03-25 | Build site. |
| Rmd | 5b93849 | pasqualina-vonlanthendinenna | 2022-03-25 | added climatology pages |
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)
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())
map <-
read_rds(paste(path_emlr_utilities,
"map_landmask_WOA18.rds",
sep = ""))# RECCAP2-ocean region mask
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))
# 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$MLR_basins)[1]) %>%
select(-c(MLR_basins, basin))CSIO-MNR climatology
boa_clim_argo_temp_jan <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_01.nc"))
# 1 896 093 obs of 6 variables (2004-2019):
# temp (range between -3.9180 and 30.1866º)
# salt
# lon (range between 0.5 and 359.5)
# lat (range between -72.5 and 79.5)
# pres (range between 0 and 1975)
# time (15 days since 0000-01-01)
# range(boa_clim_argo_temp_jan$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$depth)map+
geom_point(data = boa_clim_temp_jan_SO %>% filter(depth < 20),
aes(x = lon,
y = lat),
size = 0.2,
pch = 2,
alpha = 0.2)
| Version | Author | Date |
|---|---|---|
| 6dd0945 | pasqualina-vonlanthendinenna | 2022-03-25 |
boa_clim_sst_jan_SO <- boa_clim_temp_jan_SO %>%
filter(depth <= 20) %>%
group_by(lon, lat) %>%
summarise(clim_sst = mean(clim_temp_jan, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'lon'. You can override using the `.groups` argument.map+
geom_tile(data = boa_clim_sst_jan_SO,
aes(x = lon,
y = lat,
fill = clim_sst))+
scale_fill_viridis_c()+
lims(y = c(-80, -25))+
labs(title = 'Li et al. CSIO January climatological argo SST')Warning: Removed 14609 rows containing missing values (geom_tile).
| Version | Author | Date |
|---|---|---|
| 6dd0945 | pasqualina-vonlanthendinenna | 2022-03-25 |
boa_clim_temp_jan_SO <- inner_join(boa_clim_temp_jan_SO, region_masks_all_1x1)Joining, by = c("lon", "lat")boa_clim_temp_jan_SO <- inner_join(boa_clim_temp_jan_SO,
basinmask)Joining, by = c("lon", "lat")boa_clim_temp_jan_SO %>%
ggplot(aes(x = clim_temp_jan,
y = depth))+
geom_point(size = 0.2, pch = 1, fill = NA)+
scale_y_reverse()+
facet_grid(basin_AIP~biome)
| Version | Author | Date |
|---|---|---|
| 6dd0945 | pasqualina-vonlanthendinenna | 2022-03-25 |
UCSD climatology
Roemmich and Gilson UCSD argo temperature climatology
# clim_argo_temp_jan_2022 <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/RG_ArgoClim_202201_2019.nc"))
#
# range(clim_argo_temp_jan_2022$LONGITUDE)
# # range between 20.5 and 379.5
# range(clim_argo_temp_jan_2022$LATITUDE)
# # range between -64.5 and 79.5
# range(clim_argo_temp_jan_2022$TIME)
# table(clim_argo_temp_jan_2022$TIME)
# # time = 216.5 in the whole dataset (216.5 months since January 1 2004, corresponds to 15-01-2022)
#
# clim_argo_temp_2004_2018 <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/RG_ArgoClim_Temperature_2019.nc"))
# range(clim_argo_temp$LONGITUDE)
# # 20.5 to 379.5
# table(clim_argo_temp$LONGITUDE)
# # 1 degree intervals
# range(clim_argo_temp$LATITUDE)
# # -64.5 to 79.5
# range(clim_argo_temp$TIME)
# # 0.5 to 179.5
# # -> 01-01-2014 to 31-12-2018, centered on the 15th of each month
# range(clim_argo_temp$ARGO_TEMPERATURE_ANOMALY)
# # -12.543 to 13.413
# range(clim_argo_temp$PRESSURE)
# # 2.5 to 1975.0
#
clim_argo_temp_year_mean <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/RG_ArgoClim_33pfit_2019_mean.nc"))
# yearly mean temperature values in each 1/6 lat/lon grid (1 value for the year)
# # 2004-2018
# # 66 030 591 obs of 6 variables, with columns:
# # ARGO_TEMPERATURE_MEAN
# # ARGO_SALINITY_MEAN
# # BATHYMETRY_MASK
# # LONGITUDE between 20.083 and 379.917º (1/6 degree intervals)
# # LATITUDE between -64.5 and 79.5º
# # PRESSURE between 2.5 and 1975.0 dbar
# range(clim_argo_temp$LONGITUDE)
# # 20.083 to 379.917
# range(clim_argo_temp$LATITUDE)
# # -64.5 to 79.5
# range(clim_argo_temp$ARGO_TEMPERATURE_MEAN)
# # -1.880 to 30.516
# range(clim_argo_temp$PRESSURE)
# # 2.5 to 1975.0
# table(clim_argo_temp$PRESSURE)
# # pressure levels: 2.5 dbar, 10-170 dbar by 10, 182.5, 200-440 dbar by 20, 462.5, 500-1350 dbar by 50, 12412.5, 1500-1900 dbar by 100, 1975 dbar
#
clim_argo_temp_monthly_anomaly <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/RG_ArgoClim_33pfit_2019_annual.nc"))
# monthly temperature anomaly from the annual mean, from January (time = 0.5) to December (time = 11.5), in each 1/2 lon/lat grid
# # 88 035 468 obs of 6 variables, with columns:
# # ARGO_TEMPERATURE_ANNUAL_ANOMALY range between -9.270 and 10.982, with some values of 9.9692e+36
# # ARGO_SALINITY_ANNUAL_ANOMALY
# # LONGITUDE range between 20.25 and 379.75º, in 0.5º intervals
# # LATITUDE range between -64.75 and 79.75º, in 0.5º intervals
# # PRESSURE range between 2.5 and 1975 dbar
# # TIME ranging from 0.5 to 11.5 (15 January to 15 December)
# range(clim_argo_temp$TIME)
# # 0.5 to 11.5 -> 15-January to 15-December
# range(clim_argo_temp$ARGO_TEMPERATURE_ANNUAL_ANOMALY)
# table(round(clim_argo_temp$ARGO_TEMPERATURE_ANNUAL_ANOMALY, digits = 0))
# clim_argo_temp_filtered <- clim_argo_temp %>%
# filter(ARGO_TEMPERATURE_ANNUAL_ANOMALY < 10^35)
# range(clim_argo_temp_filtered$ARGO_TEMPERATURE_ANNUAL_ANOMALY)
# # -9.270 to 10.982
# range(clim_argo_temp$LONGITUDE)
# # 20.25 to 379.75 (every 0.5 degrees)
# range(clim_argo_temp$LATITUDE)
# # -64.75 to 79.75 (every 0.5 degrees)
# range(clim_argo_temp$PRESSURE)
# # 2.5 to 1975.0 dbar
# put both dataframes onto 1/1 lon/lat grid and calculate mean temperature / mean anomaly in each grid
clim_argo_temp_monthly_anomaly <- clim_argo_temp_monthly_anomaly %>%
select(-ARGO_SALINITY_ANNUAL_ANOMALY) %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
pressure = PRESSURE,
temp_annual_anomaly = ARGO_TEMPERATURE_ANNUAL_ANOMALY,
time = TIME) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
lon = as.numeric(as.character(lon))) %>%
mutate(depth = swDepth(pressure = pressure, latitude = lat),
.after = pressure) %>%
filter(lat < -30)
clim_argo_temp_monthly_anomaly <- clim_argo_temp_monthly_anomaly %>%
group_by(lon, lat, depth, time) %>%
summarise(temp_monthly_anomaly = mean(temp_annual_anomaly, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'lon', 'lat', 'depth'. You can override using the `.groups` argument.clim_argo_temp_year_mean <- clim_argo_temp_year_mean %>%
select(-ARGO_SALINITY_MEAN) %>%
rename(lon = LONGITUDE,
lat = LATITUDE,
temp_annual_mean = ARGO_TEMPERATURE_MEAN,
pressure = PRESSURE) %>%
mutate(lon = if_else(lon < 20, lon + 360, lon)) %>%
mutate(lat = cut(lat, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
lat = as.numeric(as.character(lat)),
lon = cut(lon, seq(20, 380, 1), seq(20.5, 379.5, 1)),
lon = as.numeric(as.character(lon))) %>%
mutate(depth = swDepth(pressure = pressure, latitude = lat),
.after = pressure) %>%
filter(lat < -30)
clim_argo_temp_year_mean <- clim_argo_temp_year_mean %>%
group_by(lon, lat, depth) %>%
summarise(temp_annual_clim = mean(temp_annual_mean, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'lon', 'lat'. You can override using the `.groups` argument.#
clim_argo_temp_ucsd <- left_join(clim_argo_temp_monthly_anomaly,
clim_argo_temp_year_mean)Joining, by = c("lon", "lat", "depth")clim_argo_temp_ucsd <- clim_argo_temp_ucsd %>%
mutate(temp_clim_month = temp_annual_clim + temp_monthly_anomaly,
depth = round(depth, digits = 0))map+
geom_point(data = clim_argo_temp_ucsd %>%
filter(depth < 5),
aes(x = lon,
y = lat),
size = 0.2)+
facet_wrap(~time, ncol = 2)+
lims(y = c(-80, -30))Warning: Removed 180180 rows containing missing values (geom_tile).
clim_jan_sst_ucsd <- clim_argo_temp_ucsd %>%
filter(time == 0.5,
depth <= 20) %>%
group_by(lon, lat) %>%
summarise(clim_sst_jan = mean(temp_clim_month, na.rm = TRUE)) %>%
ungroup()`summarise()` has grouped output by 'lon'. You can override using the `.groups` argument.map+
geom_tile(data = clim_jan_sst_ucsd,
aes(x = lon,
y = lat,
fill = clim_sst_jan))+
scale_fill_viridis_c()+
lims(y = c(-80, -30))+
labs(title = 'Roemmich & Gilson UCSD January Climatological Argo SST')Warning: Removed 15015 rows containing missing values (geom_tile).
map+
geom_tile(data = clim_argo_temp_ucsd %>%
filter(time == 0.5),
aes(x = lon,
y = lat,
fill = temp_clim_month))+
scale_fill_viridis_c()+
facet_wrap(~depth)+
lims(y = c(-80, -30))Warning: Removed 2402400 rows containing missing values (geom_tile).
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 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[9] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 viridisLite_0.4.0 bit64_4.0.5
[5] vroom_1.5.7 jsonlite_1.7.3 modelr_0.1.8 bslib_0.3.1
[9] assertthat_0.2.1 getPass_0.2-2 highr_0.9 cellranger_1.1.0
[13] yaml_2.2.1 pillar_1.6.4 backports_1.4.1 glue_1.6.0
[17] digest_0.6.29 promises_1.2.0.1 rvest_1.0.2 colorspace_2.0-2
[21] htmltools_0.5.2 httpuv_1.6.5 pkgconfig_2.0.3 broom_0.7.11
[25] haven_2.4.3 scales_1.1.1 processx_3.5.2 whisker_0.4
[29] later_1.3.0 tzdb_0.2.0 git2r_0.29.0 farver_2.1.0
[33] generics_0.1.1 ellipsis_0.3.2 withr_2.4.3 cli_3.1.1
[37] magrittr_2.0.1 crayon_1.4.2 readxl_1.3.1 evaluate_0.14
[41] ps_1.6.0 fs_1.5.2 ncdf4_1.19 fansi_1.0.2
[45] xml2_1.3.3 tidync_0.2.4 tools_4.1.2 hms_1.1.1
[49] lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1 callr_3.7.0
[53] compiler_4.1.2 jquerylib_0.1.4 RNetCDF_2.5-2 rlang_0.4.12
[57] grid_4.1.2 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.11
[61] gtable_0.3.0 DBI_1.1.2 R6_2.5.1 ncmeta_0.3.0
[65] lubridate_1.8.0 knitr_1.37 fastmap_1.1.0 bit_4.0.4
[69] utf8_1.2.2 rprojroot_2.0.2 stringi_1.7.6 parallel_4.1.2
[73] Rcpp_1.0.8 vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1
[77] xfun_0.29