CSIO-MNR Argo Temperature Climatology
Pasqualina Vonlanthen & Jens Daniel Müller
11 May, 2022
Last updated: 2022-05-11
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Knit directory: bgc_argo_r_argodata/
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Task
library(tidyverse)
# library(ggOceanMaps)
library(oce)Load 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 = ""))# Mayot biome separations
nm_biomes <- read_rds(file = paste0(path_argo_preprocessed, "/nm_biomes.rds"))
# 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
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
Read data
jan <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_01.nc")) %>%
mutate(month = 1) %>%
select(-c(salt, time))
feb <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_02.nc")) %>%
mutate(month = 2) %>%
select(-c(salt, time))
mar <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_03.nc")) %>%
mutate(month = 3) %>%
select(-c(salt, time))
apr <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_04.nc")) %>%
mutate(month = 4) %>%
select(-c(salt, time))
may <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_05.nc")) %>%
mutate(month = 5) %>%
select(-c(salt, time))
jun <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_06.nc")) %>%
mutate(month = 6) %>%
select(-c(salt, time))
jul <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_07.nc")) %>%
mutate(month = 7) %>%
select(-c(salt, time))
aug <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_08.nc")) %>%
mutate(month = 8) %>%
select(-c(salt, time))
sept <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_09.nc")) %>%
mutate(month = 9) %>%
select(-c(salt, time))
oct <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_10.nc")) %>%
mutate(month = 10) %>%
select(-c(salt, time))
nov <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_11.nc")) %>%
mutate(month = 11) %>%
select(-c(salt, time))
dec <- tidync::hyper_tibble(paste0(path_argo_clim_temp, "/BOA_Argo_monthly_12.nc")) %>%
mutate(month = 12) %>%
select(-c(salt, time))Join data
boa_temp_clim <- bind_rows(jan, feb, mar, apr, may, jun, jul, aug, sept, oct, nov, dec)
rm(jan, feb, mar, apr, may, jun, jul, aug, sept, oct, nov, dec)Harmonise data
boa_temp_clim <- boa_temp_clim %>%
mutate(lon = if_else(lon < 20, lon + 360, lon),
depth = swDepth(pressure = pres, latitude = lat)) %>%
rename(clim_temp = temp)Write data to file
boa_temp_clim %>%
write_rds(file = paste0(path_argo_preprocessed, "/boa_temp_clim.rds"))Southern Ocean climatological SST
# keep only data south of 30ºS
boa_temp_clim_SO <- boa_temp_clim %>%
filter(lat <= -30)
# 9 196 740 obs of 6 variables map+
geom_point(data = boa_temp_clim_SO %>% filter(depth < 20),
aes(x = lon,
y = lat),
size = 0.1,
pch = 2,
alpha = 0.2) +
labs(title = 'CSIO observation coverage')
boa_sst_clim_SO <- boa_temp_clim_SO %>%
filter(depth <= 20) %>%
group_by(lon, lat, month) %>%
summarise(clim_sst = mean(clim_temp, na.rm = TRUE)) %>%
ungroup()
boa_sst_clim_SO %>%
group_split(month) %>%
map(
~map+
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = clim_sst))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
labs(title = paste0('Li et al. CSIO clim SST, month:', unique(.x$month)))
)[[1]]
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Southern Ocean climatological profiles
boa_temp_clim_SO <- inner_join(boa_temp_clim_SO, nm_biomes)
boa_temp_clim_SO <- inner_join(boa_temp_clim_SO,
basinmask)boa_temp_clim_SO %>%
group_split(month) %>%
map(
~ggplot(data = .x,
aes(x = clim_temp,
y = depth))+
geom_point(size = 0.1, pch = 1, fill = NA)+
scale_y_reverse()+
facet_grid(basin_AIP~biome_name)+
labs(title = paste0('CSIO climatological profiles, month:', unique(.x$month)))
)[[1]]
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UCSD climatology
Roemmich and Gilson UCSD argo temperature climatology
Roemmich, D. and J. Gilson, 2009: The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82, 81-100
Read data
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)
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 gridHarmonise data
# 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)
# calculate mean temperature anomaly in each 1x1 grid
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()
# re-grid annual mean temperature to 1x1 grid and compute depth
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)
# calculate mean yearly temperature in each lat/lon grid
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()
# check the compatability of the two depths in the two datasets
depth_year <- table(unique(round(clim_argo_temp_monthly_anomaly$depth, digits = 0)))
depth_month <- table(unique(round(clim_argo_temp_year_mean$depth, digits = 0)))
all.equal(depth_year, depth_month)[1] TRUE# same depth axis in both
rm(depth_year, depth_month)
clim_argo_temp_ucsd <- left_join(clim_argo_temp_monthly_anomaly,
clim_argo_temp_year_mean)
clim_argo_temp_ucsd <- clim_argo_temp_ucsd %>%
mutate(temp_monthly_clim = temp_annual_clim + temp_monthly_anomaly,
depth = round(depth, digits = 0),
month = time + 0.5)
rm(clim_argo_temp_monthly_anomaly, clim_argo_temp_year_mean)Write data to file
clim_argo_temp_ucsd %>%
write_rds(file = paste0(path_argo_preprocessed, "/clim_argo_temp_ucsd.rds"))Southern Ocean climatological SST
map+
geom_point(data = clim_argo_temp_ucsd %>%
filter(depth < 5),
aes(x = lon,
y = lat),
size = 0.2)+
facet_wrap(~time, ncol = 2)+
lims(y = c(-80, -30))+
labs(title = 'UCSD climatology obs coverage')
clim_sst_ucsd <- clim_argo_temp_ucsd %>%
filter(depth <= 20) %>%
group_by(lon, lat, month) %>%
summarise(clim_sst = mean(temp_monthly_clim, na.rm = TRUE)) %>%
ungroup()
clim_sst_ucsd %>%
group_split(month) %>%
map(
~map+
geom_tile(data = .x,
aes(x = lon,
y = lat,
fill = clim_sst))+
scale_fill_viridis_c()+
lims(y = c(-85, -28))+
labs(title = paste0('Roemmich & Gilson UCSD clim SST, month:', unique(.x$month)))
)[[1]]
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Southern Ocean January climatological temperature
clim_argo_temp_ucsd %>%
group_split(depth) %>%
map(
~map+
geom_tile(data = .x %>% filter(month == 1),
aes(x = lon,
y = lat,
fill = temp_monthly_clim))+
scale_fill_viridis_c()+
lims(y = c(-80, -29))+
labs(title = paste0('UCSD January climatological temperature ', unique(.x$depth), 'm'))
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Southern Ocean climatological profiles
# add in Mayot biome separations
clim_argo_temp_ucsd <- inner_join(clim_argo_temp_ucsd, nm_biomes)
# add in basin separations
clim_argo_temp_ucsd <- inner_join(clim_argo_temp_ucsd, basinmask)# plot climatological profiles per month and biome
clim_argo_temp_ucsd %>%
group_split(month) %>%
map(
~ggplot(data = .x,
aes(x = temp_monthly_clim,
y = depth))+
geom_point(size = 0.1, pch = 1, fill = NA)+
scale_y_reverse()+
facet_grid(basin_AIP~biome_name)+
labs(title = paste0('UCSD clim profiles, month:', unique(.x$month)))
)[[1]]
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CSIO-MNR vs UCSD climatology
boa_temp_clim_SO <- boa_temp_clim_SO %>%
rename(depth_boa = depth)
clim_argo_temp_ucsd <- clim_argo_temp_ucsd %>%
rename(depth_ucsd = depth)
boa_temp_clim_SO <- boa_temp_clim_SO %>%
mutate(depth = cut(depth_boa,
breaks = c(0, 10, 20, 30, 50, 70, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000),
include.lowest = TRUE,
labels = c(5, 15, 25, 40, 60, 85, 150, 250, 350, 450, 550, 650, 750, 850, 950, 1250, 1750)),
depth = as.numeric(as.character(depth)))
clim_argo_temp_ucsd <- clim_argo_temp_ucsd %>%
mutate(depth = cut(depth_ucsd,
breaks = c(0, 10, 20, 30, 50, 70, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000),
include.lowest = TRUE,
labels = c(5, 15, 25, 40, 60, 85, 150, 250, 350, 450, 550, 650, 750, 850, 950, 1250, 1750)),
depth = as.numeric(as.character(depth)))
# table(unique(boa_temp_clim_SO$depth))
# table(unique(clim_argo_temp_ucsd$depth))
temperature_climatology <- full_join(boa_temp_clim_SO, clim_argo_temp_ucsd)temperature_climatology %>%
group_split(month, depth) %>%
map(
~ggplot(data = .x,
aes(x = clim_temp,
y = temp_monthly_clim))+
geom_bin2d(data = .x,
aes(x = clim_temp,
y = temp_monthly_clim))+
geom_abline(slope = 1, intercept = 0)+
scale_fill_viridis_c()+
facet_grid(biome_name ~ basin_AIP)+
labs(title = paste0('month: ', unique(.x$month), '| depth: ', unique(.x$depth), 'm'),
x = 'UCSD climatological temp',
y = 'CSIO-MNR climatological temp')
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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_1.0.2
[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