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1 Data sources

Following Cant mean values per grid cell are used:

1.1 This study

Results from this study are referred to as JDM.

cant_JDM <-
  read_csv(paste(path_version_data,
                 "cant_3d.csv",
                 sep = ""))

cant_JDM <- cant_JDM %>%
  select(lon, 
         lat,
         depth,
         basin_AIP,
         eras,
         cant,
         cant_pos)

1.2 Model Cant

“True” Cant fields directly inferred from the model output are referred to as M.

cant_M <-
  read_csv(paste(path_version_data,
                  "cant_M.csv", sep = ""))

cant_M <- cant_M %>%
  select(lon, 
         lat,
         depth,
         basin_AIP,
         eras,
         cant,
         cant_pos)

1.3 Join data sets

Cant fields are merged, and differences calculate per grid cell and per eras.

# add estimate label
cant_long <- bind_rows(cant_JDM %>%  mutate(estimate = "JDM"),
                             cant_M %>%  mutate(estimate = "M"))

# pivot to wide format
cant_wide <- cant_long %>%
  pivot_wider(names_from = estimate, values_from = cant:cant_pos) %>%
  drop_na()

# calculate offset
cant_wide <- cant_wide %>%
  mutate(
    cant_pos_offset = cant_pos_JDM - cant_pos_M,
    cant_offset = cant_JDM - cant_M,
    estimate = "JDM - M"
  )

2 Global mean sections

2.1 Cant - positive only

for (i_eras in unique(cant_long$eras)) {
  for (i_estimate in unique(cant_long$estimate)) {
    print(
      p_section_global(
        df = cant_long %>% filter(estimate == i_estimate, eras == i_eras),
        var = "cant_pos",
        subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras)
      )
    )
    
  }
  print(
    p_section_global(
      df = cant_wide %>% filter(eras == i_eras),
      var = "cant_pos_offset",
      col = "divergent",
      subtitle_text = paste("Estimate: JDM - M | Eras:", i_eras)
    )
  )
}

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

2.2 Cant - all

for (i_eras in unique(cant_long$eras)) {
  for (i_estimate in unique(cant_long$estimate)) {
    print(
      p_section_global(
        df = cant_long %>% filter(estimate == i_estimate, eras == i_eras),
        var = "cant",
        col = "divergent",
        subtitle_text = paste("Estimate:", i_estimate, " | Eras:", i_eras)
      )
    )
    
  }
  print(
    p_section_global(
      df = cant_wide %>% filter(eras == i_eras),
      var = "cant_offset",
      col = "divergent",
      subtitle_text = paste("Estimate: JDM - M | Eras:", i_eras)
    )
  )
}

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14

Version Author Date
23151cd jens-daniel-mueller 2021-01-14


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] gt_0.2.2        marelac_2.1.10  shape_1.4.5     scales_1.1.1   
 [5] metR_0.9.0      scico_1.2.0     patchwork_1.1.1 collapse_1.5.0 
 [9] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
[13] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.2  
[17] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2               viridisLite_0.3.0        jsonlite_1.7.1          
 [4] here_0.1                 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_0.7.2             
[25] seacarb_3.2.14           haven_2.3.1              whisker_0.4             
[28] later_1.1.0.1            git2r_0.27.1             farver_2.0.3            
[31] generics_0.0.2           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] oce_1.2-0                tools_4.0.3              data.table_1.13.2       
[46] hms_0.5.3                lifecycle_0.2.0          munsell_0.5.0           
[49] reprex_0.3.0             gsw_1.0-5                isoband_0.2.2           
[52] compiler_4.0.3           rlang_0.4.9              grid_4.0.3              
[55] rstudioapi_0.13          labeling_0.4.2           rmarkdown_2.5           
[58] testthat_2.3.2           gtable_0.3.0             DBI_1.1.0               
[61] R6_2.5.0                 lubridate_1.7.9          knitr_1.30              
[64] rprojroot_2.0.2          stringi_1.5.3            parallel_4.0.3          
[67] Rcpp_1.0.5               vctrs_0.3.5              dbplyr_1.4.4            
[70] tidyselect_1.1.0         xfun_0.18