CSIO-MNR Argo Temperature Climatology

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Last updated: 2022-04-20

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Knit directory: bgc_argo_r_argodata/

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File	Version	Author	Date	Message
html	34f79af	pasqualina-vonlanthendinenna	2022-04-20	Build site.
Rmd	e81c502	pasqualina-vonlanthendinenna	2022-04-20	added temperature clim comparison
html	229c9d9	pasqualina-vonlanthendinenna	2022-04-14	Build site.
Rmd	5920e3e	pasqualina-vonlanthendinenna	2022-04-14	fixed full temperature climatology
html	f5f6b3f	pasqualina-vonlanthendinenna	2022-04-14	Build site.
Rmd	c2fa269	pasqualina-vonlanthendinenna	2022-04-14	added full temperature climatology
html	8805f99	pasqualina-vonlanthendinenna	2022-04-11	Build site.
Rmd	d21c526	pasqualina-vonlanthendinenna	2022-04-11	cleaned up code
Rmd	f3ca885	pasqualina-vonlanthendinenna	2022-04-07	added OceanSODA-Argo SST comparison
html	9875dd0	pasqualina-vonlanthendinenna	2022-04-05	Build site.
Rmd	72a65a7	pasqualina-vonlanthendinenna	2022-04-05	added new biomes to extreme pH
html	3edd3f0	pasqualina-vonlanthendinenna	2022-04-05	Build site.
Rmd	98f3235	pasqualina-vonlanthendinenna	2022-04-05	added UCSD temp climatology to loading data
html	fa5539a	pasqualina-vonlanthendinenna	2022-04-01	Build site.
Rmd	2f893b2	pasqualina-vonlanthendinenna	2022-04-01	added maps for Roemmich temp climatology
html	48573c4	pasqualina-vonlanthendinenna	2022-03-31	Build site.
Rmd	11915d8	pasqualina-vonlanthendinenna	2022-03-31	loaded in Mayot biomes and Roemmich temp climatology
html	b530d31	pasqualina-vonlanthendinenna	2022-03-25	Build site.
Rmd	34e771c	pasqualina-vonlanthendinenna	2022-03-25	added commented out sio temp climatology
html	6dd0945	pasqualina-vonlanthendinenna	2022-03-25	Build site.
Rmd	5b93849	pasqualina-vonlanthendinenna	2022-03-25	added climatology pages

---

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')

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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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fa5539a	pasqualina-vonlanthendinenna	2022-04-01

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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229c9d9	pasqualina-vonlanthendinenna	2022-04-14

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229c9d9	pasqualina-vonlanthendinenna	2022-04-14

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 4.1.2 (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