GLODAPv2_2020

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Last updated: 2021-06-03

Checks: 7 0

Knit directory: emlr_obs_preprocessing/

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File	Version	Author	Date	Message
html	9d8353f	jens-daniel-mueller	2021-05-31	Build site.
Rmd	b948168	jens-daniel-mueller	2021-05-31	ingest GLODAPv2_2021 beta data

---

path_glodapv2_2021  <- "/nfs/kryo/work/updata/glodapv2_2021/"
path_preprocessing  <- paste(path_root, "/observations/preprocessing/", sep = "")

1 Read files

Main data source for this project is GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

GLODAP <-
  read_csv(
    paste(
      path_glodapv2_2021,
      "GLODAPv2.2021_Merged_Master_File_beta_20210530.csv",
      sep = ""
    ),
    na = "-9999",
    col_types = cols(.default = col_double())
  )

# select relevant columns
GLODAP <- GLODAP %>%
  rename_with(~str_remove(., 'G2'))

# select relevant columns
GLODAP <- GLODAP %>%
  select(cruise:talkqc)

# create date column
GLODAP <- GLODAP %>%
  mutate(date = ymd(paste(year, month, day))) %>%
  relocate(date)

# harmonize column names
GLODAP <- GLODAP  %>%
  rename(sal = salinity,
         temp = temperature)

# harmonize coordinates
GLODAP <- GLODAP  %>%
  rename(lon = longitude,
         lat = latitude) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

# remove irrelevant columns
GLODAP <- GLODAP %>%
  select(-c(month:minute,
            maxsampdepth, bottle, sigma0:sigma4,
            nitrite:nitritef))

2 Data preparation

2.1 Subset tco2 data

The vast majority of rows is removed due to missing tco2 observations.

GLODAP <- GLODAP %>% 
  filter(!is.na(tco2))

2.2 Horizontal gridding

For merging with other data sets, all observations were grouped into latitude intervals of:

• 1° x 1°

GLODAP <- m_grid_horizontal(GLODAP)

2.3 Apply basin mask

# use only three basin to assign general basin mask
# ie this is not specific to the MLR fitting
basinmask <- basinmask %>% 
  filter(MLR_basins == "2") %>% 
  select(lat, lon, basin_AIP)

GLODAP <- inner_join(GLODAP, basinmask)

2.4 Create clean observations grid

GLODAP_obs_grid <- GLODAP %>% 
  count(lat, lon)

2.5 Add row number

GLODAP <- GLODAP  %>%  
  mutate(row_number = row_number()) %>% 
  relocate(row_number)

2.6 Write GLODAP file

GLODAP  %>%  
  write_csv(paste(path_preprocessing,
                             "GLODAPv2.2021_preprocessed.csv",
                             sep = ""))

3 Overview plots

3.1 Assign coarse spatial grid

For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:

• 5° x 5°

GLODAP <- m_grid_horizontal_coarse(GLODAP)

3.2 Histogram Zonal coverage

GLODAP_histogram_lat <- GLODAP %>%
  group_by(lat_grid) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_lat %>%
  ggplot(aes(lat_grid, n)) +
  geom_col() +
  coord_flip() +
  theme(legend.title = element_blank())

Past versions of coverage_histogram_zonal-1.png

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rm(GLODAP_histogram_lat)

3.3 Histogram temporal coverage

GLODAP_histogram_year <- GLODAP %>%
  group_by(year) %>%
  tally() %>%
  ungroup()

GLODAP_histogram_year %>%
  ggplot() +
  geom_col(aes(year, n)) +
  theme(
    axis.title.x = element_blank()
  )

Past versions of coverage_histogram_temporal-1.png

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rm(GLODAP_histogram_year)

3.4 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>%
  group_by(year, lat_grid) %>%
  tally() %>%
  ungroup()

GLODAP_hovmoeller_year %>%
  ggplot(aes(year, lat_grid, fill = log10(n))) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  theme(legend.position = "top",
        axis.title.x = element_blank())

Past versions of coverage_hovmoeller-1.png

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rm(GLODAP_hovmoeller_year)

3.5 Coverage map

map +
  geom_raster(data = GLODAP_obs_grid,
              aes(lon, lat, fill = log10(n))) +
  scale_fill_viridis_c(option = "magma",
                       direction = -1)

Past versions of coverage_map-1.png

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4 CANYON-B

4.1 Comparison to GLODAP

source("/net/kryo/work/uptools/co2_calculation/CANYON-B/CANYONB.R")

GLODAP_Can_B <- GLODAP %>%
  mutate(lon = if_else(lon > 180, lon - 360, lon)) %>%
  arrange(year) %>% 
  select(row_number, year, date, lat, lon, depth, basin_AIP,
         temp, sal, oxygen,
         talk, tco2, nitrate, phosphate, silicate)

# filter rows with essential variables for Canyon-B
GLODAP_Can_B <- GLODAP_Can_B %>%
  filter(across(c(lat, lon, depth,
                  temp, sal, oxygen), ~ !is.na(.x)))

GLODAP_Can_B <- GLODAP_Can_B %>%
  mutate(as_tibble(
    CANYONB(
      date = paste0(as.character(date), " 12:00"),
      lat = lat,
      lon = lon,
      pres = depth,
      temp = temp,
      psal = sal,
      doxy = oxygen,
      param = c("AT", "CT", "NO3", "PO4", "SiOH4")
    )
  ))

GLODAP_Can_B <- GLODAP_Can_B %>%
  select(-ends_with(c("_ci", "_cim", "_cin", "_cii")))


GLODAP_Can_B <- GLODAP_Can_B %>%
  rename(
    "talk_CANYONB" = "AT",
    "tco2_CANYONB" = "CT",
    "nitrate_CANYONB" = "NO3",
    "phosphate_CANYONB" = "PO4",
    "silicate_CANYONB" = "SiOH4"
  )


variables <- c("talk", "tco2", "nitrate", "phosphate", "silicate")

for (i_variable in variables) {
  # i_variable <- variables[1]
  
  # calculate equal axis limits and binwidth
  axis_lims <- GLODAP_Can_B %>%
    drop_na() %>% 
    summarise(max_value = max(c(max(
      !!sym(i_variable)
    ),
    max(!!sym(
      paste0(i_variable, "_CANYONB")
    )))),
    min_value = min(c(min(
      !!sym(i_variable)
    ),
    min(!!sym(
      paste0(i_variable, "_CANYONB")
    )))))
  
  binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 60
  axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
  
  print(
    ggplot(GLODAP_Can_B, aes(
      x = !!sym(i_variable),
      y = !!sym(paste0(i_variable, "_CANYONB"))
    )) +
      geom_bin2d(binwidth = binwidth_value) +
      scale_fill_viridis_c(trans = "log10") +
      geom_abline(slope = 1, col = 'red') +
      coord_equal(xlim = axis_lims,
                  ylim = axis_lims) +
      facet_wrap( ~ basin_AIP) +
      labs(title = "All years")
  ) 
  
  
  for (i_year in unique(GLODAP_Can_B$year)) {
    # i_year <- 2017
    
    print(
      ggplot(
        GLODAP_Can_B %>% filter(year == i_year),
        aes(x = !!sym(i_variable),
            y = !!sym(paste0(
              i_variable, "_CANYONB"
            )))
      ) +
        geom_bin2d(binwidth = binwidth_value) +
        scale_fill_viridis_c(trans = "log10") +
        geom_abline(slope = 1, col = 'red') +
        coord_equal(xlim = axis_lims,
                    ylim = axis_lims) +
        facet_wrap( ~ basin_AIP) +
        labs(title = paste("Year:", i_year))
    )
  }
  
}

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4.2 Write Canyon-B file

GLODAP_Can_B %>% 
  select(row_number,
         talk_CANYONB, tco2_CANYONB,
         nitrate_CANYONB, phosphate_CANYONB, silicate_CANYONB) %>% 
  write_csv(paste(path_preprocessing,
                             "GLODAPv2.2021_Canyon-B.csv",
                             sep = ""))

5 CONTENT

5.1 Comparison to GLODAP

source("/net/kryo/work/uptools/co2_calculation/CONTENT/CO2CONTENT.R")

GLODAP_Cont <- GLODAP %>%
  mutate(lon = if_else(lon > 180, lon - 360, lon)) %>%
  arrange(year) %>% 
  select(row_number, year, date, lat, lon, depth, basin_AIP,
         temp, sal, oxygen,
         talk, tco2) %>% 
  filter(year == 2015)

# filter rows with essential variables for Canyon-B
GLODAP_Cont <- GLODAP_Cont %>%
  filter(across(c(lat, lon, depth,
                  temp, sal, oxygen), ~ !is.na(.x)))

GLODAP_Cont <- GLODAP_Cont %>%
  mutate(as_tibble(
    CO2CONTENT(
      date = paste0(as.character(date), " 12:00"),
      lat = lat,
      lon = lon,
      pres = depth,
      temp = temp,
      psal = sal,
      doxy = oxygen,
      param = c("AT", "CT")
    )
  ))

GLODAP_Cont <- GLODAP_Cont %>%
  select(-ends_with(c("_ci", "_cim", "_cin", "_cii")))


GLODAP_Cont <- GLODAP_Cont %>%
  rename(
    "talk_CANYONB" = "AT",
    "tco2_CANYONB" = "CT"
  )


variables <- c("talk", "tco2")

for (i_variable in variables) {
  # i_variable <- variables[1]
  
  # calculate equal axis limits and binwidth
  axis_lims <- GLODAP_Cont %>%
    drop_na() %>% 
    summarise(max_value = max(c(max(
      !!sym(i_variable)
    ),
    max(!!sym(
      paste0(i_variable, "_CANYONB")
    )))),
    min_value = min(c(min(
      !!sym(i_variable)
    ),
    min(!!sym(
      paste0(i_variable, "_CANYONB")
    )))))
  
  binwidth_value <- (axis_lims$max_value - axis_lims$min_value) / 60
  axis_lims <- c(axis_lims$min_value, axis_lims$max_value)
  
  print(
    ggplot(GLODAP_Cont, aes(
      x = !!sym(i_variable),
      y = !!sym(paste0(i_variable, "_CANYONB"))
    )) +
      geom_bin2d(binwidth = binwidth_value) +
      scale_fill_viridis_c(trans = "log10") +
      geom_abline(slope = 1, col = 'red') +
      coord_equal(xlim = axis_lims,
                  ylim = axis_lims) +
      facet_wrap( ~ basin_AIP) +
      labs(title = "All years")
  ) 
  
  
  for (i_year in unique(GLODAP_Cont$year)) {
    # i_year <- 2017
    
    print(
      ggplot(
        GLODAP_Cont %>% filter(year == i_year),
        aes(x = !!sym(i_variable),
            y = !!sym(paste0(
              i_variable, "_CANYONB"
            )))
      ) +
        geom_bin2d(binwidth = binwidth_value) +
        scale_fill_viridis_c(trans = "log10") +
        geom_abline(slope = 1, col = 'red') +
        coord_equal(xlim = axis_lims,
                    ylim = axis_lims) +
        facet_wrap( ~ basin_AIP) +
        labs(title = paste("Year:", i_year))
    )
  }
  
}

5.2 Write Canyon-B file

GLODAP_Cont %>% 
  select(row_number,
         talk_CANYONB, tco2_CANYONB) %>% 
  write_csv(paste(path_preprocessing,
                             "GLODAPv2.2021_Canyon-B.csv",
                             sep = ""))

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2

Matrix products: default
BLAS:   /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] mgcv_1.8-33     nlme_3.1-149    lubridate_1.7.9 metR_0.9.0     
 [5] scico_1.2.0     patchwork_1.1.1 collapse_1.5.0  forcats_0.5.0  
 [9] stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4     readr_1.4.0    
[13] tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3   tidyverse_1.3.0
[17] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               splines_4.0.3            jsonlite_1.7.1          
 [4] viridisLite_0.3.0        modelr_0.1.8             assertthat_0.2.1        
 [7] blob_1.2.1               cellranger_1.1.0         yaml_2.2.1              
[10] pillar_1.4.7             backports_1.1.10         lattice_0.20-41         
[13] glue_1.4.2               RcppEigen_0.3.3.7.0      digest_0.6.27           
[16] promises_1.1.1           checkmate_2.0.0          rvest_0.3.6             
[19] colorspace_1.4-1         htmltools_0.5.0          httpuv_1.5.4            
[22] Matrix_1.2-18            pkgconfig_2.0.3          broom_0.7.5             
[25] haven_2.3.1              scales_1.1.1             whisker_0.4             
[28] later_1.1.0.1            git2r_0.27.1             generics_0.0.2          
[31] farver_2.0.3             ellipsis_0.3.1           withr_2.3.0             
[34] cli_2.1.0                magrittr_1.5             crayon_1.3.4            
[37] readxl_1.3.1             evaluate_0.14            fs_1.5.0                
[40] fansi_0.4.1              xml2_1.3.2               RcppArmadillo_0.10.1.2.0
[43] tools_4.0.3              data.table_1.13.2        hms_0.5.3               
[46] lifecycle_1.0.0          munsell_0.5.0            reprex_0.3.0            
[49] compiler_4.0.3           rlang_0.4.10             grid_4.0.3              
[52] rstudioapi_0.11          labeling_0.4.2           rmarkdown_2.5           
[55] gtable_0.3.0             DBI_1.1.0                R6_2.5.0                
[58] knitr_1.30               rprojroot_2.0.2          stringi_1.5.3           
[61] parallel_4.0.3           Rcpp_1.0.5               vctrs_0.3.5             
[64] dbplyr_1.4.4             tidyselect_1.1.0         xfun_0.18