# ESMValTool
# recipe_albedolandcover.yml
---
documentation:
  title: Landcover Albedo Relationship
  description: |
    This recipe analyzes the relationship between landcover and albedo
    in CMIP models

  authors:
    - crezee_bas
    - lejeune_quentin

  maintainer:
    - unmaintained


albedolandcover_parameters: &albedolandcover_parameters
  params:
    #
    # size of the big box in the latitudinal direction.
    # Recommended value: 5
    latsize_BB: 5
    #
    # size of the big box in the longitudinal direction.
    # Recommended value: 5
    lonsize_BB: 5
    #
    # minimum grid cell area fraction that must be
    # covered by the sum of the area fractions of all
    # land cover classes included in the local
    # regression for one grid cell, for this grid cell
    # to be included in the regression.
    # Recommended value: 90
    threshold_sumpred: 90
    #
    # minimum number of grid cells with non-zero area fraction
    # covered by one land cover class within a big box, for this
    # land cover class to be included as a predictor in the local
    # regression.
    # Recommended value: 2
    mingc: 2
    #
    # minimum number of grid cells with relevant
    # information required within a big box to perform
    # the local regression.
    # Recommended value: 15
    minnum_gc_bb: 15
    snowfree: true
    lc1_class: ['treeFrac']
    lc2_class: ['shrubFrac']
    lc3_class: ['grassFrac', 'cropFrac', 'pastureFrac']


CMIP6_landcover: &CMIP6_landcover
  additional_datasets:
    - {dataset: HadGEM3-GC31-LL, project: CMIP6, grid: gn, exp: historical,
       ensemble: r1i1p1f3}

CMIP5_landcover: &CMIP5_landcover
  additional_datasets:
    - {dataset: MPI-ESM-LR, project: CMIP5, ensemble: r1i1p1}

preprocessors:
  pp_cmip:
    custom_order: true
    regrid:
      target_grid: 2x2
      scheme: linear
    extract_month:
      month: 7
    climate_statistics:
      operator: mean
    mask_landsea:
      mask_out: sea
  pp_obs:
    mask_landsea:
      mask_out: sea
    extract_month:
      month: 7


diagnostics:
  albedolandcover_cmip5:
    description: "Multiple linear regression between albedo and xxFrac"
    variables:
      treeFrac: &variable_settings_cmip5
        preprocessor: pp_cmip
        mip: Lmon
        exp: historical
        start_year: 2000
        end_year: 2004
        <<: *CMIP5_landcover
      snc:
        <<: *variable_settings_cmip5
        mip: LImon
      alb:
        <<: *variable_settings_cmip5
        mip: Amon
        derive: true
        force_derivation: false
      cropFrac:
        <<: *variable_settings_cmip5
      grassFrac:
        <<: *variable_settings_cmip5
      shrubFrac:
        <<: *variable_settings_cmip5
      pastureFrac:
        <<: *variable_settings_cmip5
    scripts:
      albedolandcover:
        script: landcover/albedolandcover.py
        <<: *albedolandcover_parameters

  albedolandcover_cmip6:
    description: "Multiple linear regression between albedo and xxFrac"
    variables:
      treeFrac: &variable_settings_cmip6
        preprocessor: pp_cmip
        mip: Lmon
        exp: historical
        start_year: 2000
        end_year: 2004
        <<: *CMIP6_landcover
      snc:
        <<: *variable_settings_cmip6
        mip: LImon
      alb:
        <<: *variable_settings_cmip6
        mip: Amon
        derive: true
        force_derivation: false
      grassFrac:
        <<: *variable_settings_cmip6
      shrubFrac:
        <<: *variable_settings_cmip6
    scripts:
      albedolandcover:
        script: landcover/albedolandcover.py
        <<: *albedolandcover_parameters

  albedolandcover_obs:
    description: "Multiple linear regression between albedo and xxFrac"
    variables:
      albDiffiTr13:
        preprocessor: pp_obs
        mip: Amon
        additional_datasets:
          - {dataset: Duveiller2018, project: OBS, tier: 2, version: v2018,
             start_year: 2010, end_year: 2010, frequency: mon, type: clim}
    scripts:
      albedolandcover:
        script: landcover/albedolandcover.py
        <<: *albedolandcover_parameters