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Rmd 0269854 Donghe-Zhu 2021-01-10 adding constant climate for regular and random sampling

1 Read nc file

Here we used annual output of cmorized (1x1) model with constant forcing (RECCAP2 RunC) in year 2007 as the predictor climatology. Predictors include:

  • Salinity (sal)
  • Potential temperature (theta, not predictor, used for temperature calculation)
  • In-situ temperature (temp, calculated)
  • DIC (tco2)
  • ALK (talk)
  • oxygen
  • AOU (calculated)
  • nitrate
  • phosphate
  • silicate
# read in RECCAP2 RunC file
climatology_temp <- tidync(paste(
  path_cmorized,
  "RECCAP2_RunC.nc",
  sep = ""
))

# read-in 2007 as tibble
climatology <- climatology_temp %>% 
  hyper_filter(time_ann = time_ann == 867110400) %>% 
  hyper_tibble()

# convert data variable to year
climatology <- climatology %>% 
  mutate(year = round(1980 + unique(climatology$time_ann)/(60*60*24*365))) %>% 
  select(-time_ann)

# select annual cmorized 2007 as climatology
climatology <- climatology %>%
  select(-epc) %>%
  rename(
    sal = so,
    THETA = thetao,
    tco2 = dissic,
    talk = talk,
    oxygen = o2,
    nitrate = no3,
    phosphate = po4,
    silicate = si
  ) %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

rm(climatology_temp)

1.1 Round depth levels

climatology <- climatology %>%
  mutate(depth = round(depth))

2 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)

# restrict predictor fields to basin mask grid
climatology <- inner_join(climatology, basinmask)

3 In-situ temperature

In-situ temperature was calculated as input paramter for AOU calculation.

climatology <- climatology %>%
  mutate(temp = gsw_pt_from_t(
    SA = sal,
    t = THETA,
    p = 10.1325,
    p_ref = depth
  ))

Example profile from North Atlantic Ocean.

climatology %>%
  filter(lat == params_global$lat_Atl_profile,
         lon == params_global$lon_Atl_section) %>%
  ggplot() +
  geom_line(aes(temp, depth, col = "insitu")) +
  geom_point(aes(temp, depth, col = "insitu")) +
  geom_line(aes(THETA, depth, col = "theta")) +
  geom_point(aes(THETA, depth, col = "theta")) +
  scale_y_reverse() +
  scale_color_brewer(palette = "Dark2", name = "Scale")

Version Author Date
843587f Donghe-Zhu 2021-01-11

4 Unit conversion

Model results are given in [mol m-3], whereas GLODAP data are in [µmol kg-1].

For comparison, model results were converted from [mol m-3] to [µmol kg-1]

# unit conversion from mol/m3 to µmol/kg
climatology <- climatology %>%
  mutate(
    rho = gsw_pot_rho_t_exact(
      SA = sal,
      t = temp,
      p = depth,
      p_ref = 10.1325
    ),
    tco2 = tco2 * (1e+06 / rho),
    talk = talk * (1e+06 / rho),
    oxygen = oxygen * (1e+06 / rho),
    nitrate = nitrate * (1e+06 / rho),
    phosphate = phosphate * (1e+06 / rho),
    silicate = silicate * (1e+06 / rho)
  )

5 AOU calculation

climatology <- climatology %>%
  mutate(
    oxygen_sat_m3 = gas_satconc(
      S = sal,
      t = temp,
      P = 1.013253,
      species = "O2"
    ),
    oxygen_sat_kg = oxygen_sat_m3 * (1e+3 / rho),
    AOU = oxygen_sat_kg - oxygen
  ) %>%
  select(-oxygen_sat_kg,-oxygen_sat_m3)

6 Neutral density calculation

Neutral density gamma was calculated with a Python script provided by Serazin et al (2011), which performs a polynomial approximation of the original gamma calculation.

# calculate pressure from depth
climatology <- climatology %>%
  mutate(CTDPRS = gsw_p_from_z(-depth,
                               lat))

# rename variables according to python script
climatology_gamma_prep <- climatology %>%
  rename(LATITUDE = lat,
         LONGITUDE = lon,
         SALNTY = sal)

# load python scripts
source_python(paste(
  path_functions,
  "python_scripts/Gamma_GLODAP_python.py",
  sep = ""
))

# calculate gamma
climatology_gamma_calc <-
  calculate_gamma(climatology_gamma_prep)

# reverse variable naming
climatology <- climatology_gamma_calc %>%
  select(-c(CTDPRS, THETA)) %>%
  rename(
    lat = LATITUDE,
    lon = LONGITUDE,
    sal = SALNTY,
    gamma  = GAMMA
  )

climatology <- as_tibble(climatology)

rm(climatology_gamma_calc, climatology_gamma_prep)

7 Write file

# select relevant columns
climatology <- climatology %>%
  select(-c(year, rho))

# write csv file
climatology %>%
  write_csv(paste(path_preprocessing,
                  "climatology_runC_2007.csv",
                  sep = ""))

8 Plots

Below, following subsets of the climatology are plotted for all relevant predictors:

  • Horizontal planes at 4 depth levels
  • Global section along track indicated by white lines in maps.
# define plotting variables
vars <- c(
  "tco2",
  "talk",
  "sal",
  "nitrate",
  "phosphate",
  "silicate",
  "oxygen",
  "temp",
  "AOU",
  "gamma"
)

# i_var <- vars[1]

for (i_var in vars) {
  # plot maps
  
  print(p_map_climatology(df = climatology,
                          var = i_var))
  
  # plot sections
  
  print(p_section_global(df = climatology,
                         var = i_var))
  
}

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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] marelac_2.1.10   shape_1.4.5      geosphere_1.5-10 oce_1.2-0       
 [5] gsw_1.0-5        testthat_3.0.1   reticulate_1.18  tidync_0.2.4    
 [9] metR_0.9.0       scico_1.2.0      patchwork_1.1.1  collapse_1.5.0  
[13] forcats_0.5.0    stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4     
[17] readr_1.4.0      tidyr_1.1.2      tibble_3.0.4     ggplot2_3.3.3   
[21] tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] fs_1.5.0                 lubridate_1.7.9          RColorBrewer_1.1-2      
 [4] httr_1.4.2               rprojroot_2.0.2          tools_4.0.3             
 [7] backports_1.1.10         R6_2.5.0                 DBI_1.1.0               
[10] colorspace_2.0-0         withr_2.3.0              sp_1.4-5                
[13] tidyselect_1.1.0         compiler_4.0.3           git2r_0.27.1            
[16] cli_2.2.0                rvest_0.3.6              RNetCDF_2.4-2           
[19] xml2_1.3.2               isoband_0.2.3            labeling_0.4.2          
[22] scales_1.1.1             checkmate_2.0.0          rappdirs_0.3.1          
[25] digest_0.6.27            rmarkdown_2.5            pkgconfig_2.0.3         
[28] htmltools_0.5.0          dbplyr_1.4.4             rlang_0.4.10            
[31] readxl_1.3.1             rstudioapi_0.13          farver_2.0.3            
[34] generics_0.1.0           jsonlite_1.7.2           magrittr_2.0.1          
[37] ncmeta_0.3.0             Matrix_1.2-18            Rcpp_1.0.5              
[40] munsell_0.5.0            fansi_0.4.1              lifecycle_0.2.0         
[43] stringi_1.5.3            whisker_0.4              yaml_2.2.1              
[46] grid_4.0.3               blob_1.2.1               parallel_4.0.3          
[49] promises_1.1.1           crayon_1.3.4             lattice_0.20-41         
[52] haven_2.3.1              hms_0.5.3                seacarb_3.2.15          
[55] knitr_1.30               pillar_1.4.7             reprex_0.3.0            
[58] glue_1.4.2               evaluate_0.14            RcppArmadillo_0.10.1.2.2
[61] data.table_1.13.6        modelr_0.1.8             vctrs_0.3.6             
[64] httpuv_1.5.4             cellranger_1.1.0         gtable_0.3.0            
[67] assertthat_0.2.1         xfun_0.20                broom_0.7.3             
[70] RcppEigen_0.3.3.9.1      later_1.1.0.1            viridisLite_0.3.0       
[73] ncdf4_1.17               ellipsis_0.3.1