CSIO-MNR Argo Temperature Climatology

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Last updated: 2023-12-19

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Knit directory: bgc_argo_r_argodata/analysis/

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---

Task

This script loads the temperature climatology. The climatology netCDF has previously been downloaded. The lat and lon fields are harmonised to our requirements, by 0.5 degree and 20.5 ≥ lon ≤ 379.5.

CSIO-MNR 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

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

Dependencies

CSIO-MNR climatology - /nfs/kryo/work/updata/argo_climatology/temperature, monthly temperature climatology files BOA_Argo_monthly_MM.nc.

UCSD climatology - /nfs/kryo/work/updata/argo_climatology/temperature RG_ArgoClim_33pfit_2019_mean.nc and RG_ArgoClim_33pfit_2019_annual.nc

Outputs (in BGC preprocessed folder)

boa_temp_clim.rds – the CSIO-MNR temperature climatology by 1°x1° and month.

clim_argo_temp_ucsd.rds - the UCSD temperature climatology by 1°x1° and month.

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')

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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)))
  )

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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)))
  )

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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 grid

Harmonise 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')

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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)))
  )

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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)))
  )

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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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Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/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_1.7-10      gsw_1.1-1       forcats_0.5.2   stringr_1.5.0  
 [5] dplyr_1.1.3     purrr_1.0.2     readr_2.1.3     tidyr_1.3.0    
 [9] tibble_3.2.1    ggplot2_3.4.4   tidyverse_1.3.2

loaded via a namespace (and not attached):
 [1] httr_1.4.4          sass_0.4.4          viridisLite_0.4.1  
 [4] bit64_4.0.5         vroom_1.6.0         jsonlite_1.8.3     
 [7] modelr_0.1.10       bslib_0.4.1         assertthat_0.2.1   
[10] highr_0.9           googlesheets4_1.0.1 cellranger_1.1.0   
[13] yaml_2.3.6          pillar_1.9.0        backports_1.4.1    
[16] glue_1.6.2          digest_0.6.30       promises_1.2.0.1   
[19] rvest_1.0.3         colorspace_2.0-3    htmltools_0.5.3    
[22] httpuv_1.6.6        pkgconfig_2.0.3     broom_1.0.5        
[25] haven_2.5.1         scales_1.2.1        whisker_0.4        
[28] later_1.3.0         tzdb_0.3.0          timechange_0.1.1   
[31] git2r_0.30.1        googledrive_2.0.0   farver_2.1.1       
[34] generics_0.1.3      ellipsis_0.3.2      cachem_1.0.6       
[37] withr_2.5.0         cli_3.6.1           magrittr_2.0.3     
[40] crayon_1.5.2        readxl_1.4.1        evaluate_0.18      
[43] fs_1.5.2            ncdf4_1.19          fansi_1.0.3        
[46] xml2_1.3.3          tidync_0.3.0        tools_4.2.2        
[49] hms_1.1.2           gargle_1.2.1        lifecycle_1.0.3    
[52] munsell_0.5.0       reprex_2.0.2        compiler_4.2.2     
[55] jquerylib_0.1.4     RNetCDF_2.6-1       rlang_1.1.1        
[58] grid_4.2.2          rstudioapi_0.15.0   labeling_0.4.2     
[61] rmarkdown_2.18      gtable_0.3.1        DBI_1.1.3          
[64] R6_2.5.1            ncmeta_0.3.5        lubridate_1.9.0    
[67] knitr_1.41          fastmap_1.1.0       bit_4.0.5          
[70] utf8_1.2.2          workflowr_1.7.0     rprojroot_2.0.3    
[73] stringi_1.7.8       parallel_4.2.2      Rcpp_1.0.10        
[76] vctrs_0.6.4         dbplyr_2.2.1        tidyselect_1.2.0   
[79] xfun_0.35