Last updated: 2021-02-02

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Knit directory: emlr_mod_v_XXX/

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1 Definition

The following local parametrisations (i.e. relevant for this sensitivity run) were defined to run the analysis:

# neutral density thresholds to cut the Atlantic ocean into slabs
slabs_Atl <-
  c(
    -Inf,
    26.00,
    26.50,
    26.75,
    27.00,
    27.25,
    27.50,
    27.75,
    27.85,
    27.95,
    28.05,
    28.10,
    28.15,
    28.20,
    Inf
  )

# neutral density thresholds to cut the Indo-Pacific ocean into slabs
slabs_Ind_Pac <-
  c(-Inf,
    26.00,
    26.50,
    26.75,
    27.00,
    27.25,
    27.50,
    27.75,
    27.85,
    27.95,
    28.05,
    28.10,
    Inf)

# Predictors for MLR model
MLR_predictors <- c(
                "sal",
                "temp",
                "aou",
                "nitrate",
                "silicate",
                "phosphate",
                "phosphate_star")

params_local <-
  lst(
    # model climate forcing with options of variable ("AD") or constant ("CB")
    model_runs = "CB",
    # model subsetting with options of "GLODAP" or "random"
    subsetting = "random",
    # random subsetting with options of "grid" or "lat"
    random = "grid",
    # ID of current sensitivity run
    Version_ID = "v_XXX",
    # f flags accepted for GLODAP data
    flag_f = c(2),
    # qc flags accepted for GLODAP data
    flag_qc = c(1),
    # Should A16 cruise from 2013/14 be included in middle era (y/n)
    A16_GO_SHIP = "y",
    # Shallowest depth for data to be included in MLR fitting
    depth_min = 150,
    # Shallowest water depth for data to be included in MLR fitting
    bottomdepth_min = 0,
    # Lowest neutral density to map Cant with eMLR approach (in surface layer?)
    gamma_min = 26,
    # break years for eras, numbers indicate the upper end of the respective era
    era_breaks = c(1981, 1999, 2012, Inf),
    # ID for basins for MLR fits: 2, 5, SO, AIP
    MLR_basins = "2",
    # Select the target variable for MLR, either "tco2", "cstar" or "cstar_tref"
    MLR_target = "cstar_tref",
    # see above
    MLR_predictors = MLR_predictors,
    # Maxmimum number of MLR predictors
    MLR_predictors_max = 5,
    # Minimum number of MLR predictors
    MLR_predictors_min = 2,
    # Total number of MLR fits taken into account
    MLR_number = 10,
    # Criterion to select best MLR fits, either "rmse" or "aic"
    MLR_criterion = "rmse",
    # see above
    slabs_Atl = slabs_Atl,
    # see above
    slabs_Ind_Pac = slabs_Ind_Pac,
    # Stoichiometric ratio of C to P
    rCP = 117,
    # Stoichiometric ratio of N to P
    rNP = 16,
    # Stoichiometric ratio of P to O (PO4* calculation)
    rPO = 170,
    # Offset P to O (PO4* calculation)
    rPO_offset = 1.95,
    # surface DIC calculation with options of "revelle factor" or "atmospheric equilibrium"
    surface_DIC_calculation = "revelle factor",
    # Preindustrial atmospheric pCO2
    preind_atm_pCO2 = 280,
    # generate a high number of diagnostic plots while running the analysis (y/n)
    plot_all_figures = "n"
  )

2 Write file

Parameterization criteria are locally stored and used throughout this sensitivty case.

params_local %>%
  write_rds(here::here("data/auxillary",
                       "params_local.rds"))

3 Create folders

Folders for each new sensitivity run are automatically created.


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] forcats_0.5.0   stringr_1.4.0   dplyr_1.0.2     purrr_0.3.4    
 [5] readr_1.4.0     tidyr_1.1.2     tibble_3.0.4    ggplot2_3.3.3  
 [9] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.20        haven_2.3.1      colorspace_2.0-0
 [5] vctrs_0.3.6      generics_0.1.0   htmltools_0.5.0  yaml_2.2.1      
 [9] blob_1.2.1       rlang_0.4.10     later_1.1.0.1    pillar_1.4.7    
[13] withr_2.3.0      glue_1.4.2       DBI_1.1.0        dbplyr_1.4.4    
[17] modelr_0.1.8     readxl_1.3.1     lifecycle_0.2.0  munsell_0.5.0   
[21] gtable_0.3.0     cellranger_1.1.0 rvest_0.3.6      evaluate_0.14   
[25] knitr_1.30       httpuv_1.5.4     fansi_0.4.1      broom_0.7.3     
[29] Rcpp_1.0.5       promises_1.1.1   backports_1.1.10 scales_1.1.1    
[33] jsonlite_1.7.2   fs_1.5.0         hms_0.5.3        digest_0.6.27   
[37] stringi_1.5.3    rprojroot_2.0.2  grid_4.0.3       here_1.0.1      
[41] cli_2.2.0        tools_4.0.3      magrittr_2.0.1   crayon_1.3.4    
[45] whisker_0.4      pkgconfig_2.0.3  ellipsis_0.3.1   xml2_1.3.2      
[49] reprex_0.3.0     lubridate_1.7.9  assertthat_0.2.1 rmarkdown_2.5   
[53] httr_1.4.2       rstudioapi_0.13  R6_2.5.0         git2r_0.27.1    
[57] compiler_4.0.3