Revision 223ee354d84d0615853e1d40cb597024a397f33b authored by catherinehardacre on 04 April 2024, 23:14:39 UTC, committed by catherinehardacre on 04 April 2024, 23:14:39 UTC
1 parent a91915e
recipe_kcs.yml
# ESMValTool
# recipe_kcs.yml
---
documentation:
title: Reproduce KNMI '14 Climate Scenarios
description: >
This recipe reproduces the basic steps described in Lenderink 2014,
one scenario at a time.
references:
- lenderink14erl
authors:
- rol_evert
- kalverla_peter
- alidoost_sarah
maintainer:
- unmaintained
projects:
- eucp
cmip5: &cmip5
- {dataset: ACCESS1-0, project: CMIP5, mip: Amon, exp: [historical, rcp45], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: ACCESS1-0, project: CMIP5, mip: Amon, exp: [historical, rcp85], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: ACCESS1-3, project: CMIP5, mip: Amon, exp: [historical, rcp45], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: ACCESS1-3, project: CMIP5, mip: Amon, exp: [historical, rcp85], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: CanESM2, project: CMIP5, mip: Amon, exp: [historical, rcp45], ensemble: "r(1:5)i1p1", start_year: 1961, end_year: 2099}
- {dataset: CCSM4, project: CMIP5, mip: Amon, exp: [historical, rcp45], ensemble: "r(1:4)i1p1", start_year: 1961, end_year: 2099}
- {dataset: CCSM4, project: CMIP5, mip: Amon, exp: [historical, rcp60], ensemble: "r(1:4)i1p1", start_year: 1961, end_year: 2099}
- {dataset: CCSM4, project: CMIP5, mip: Amon, exp: [historical, rcp85], ensemble: "r(1:4)i1p1", start_year: 1961, end_year: 2099}
- {dataset: CSIRO-Mk3-6-0, project: CMIP5, mip: Amon, exp: [historical, rcp26], ensemble: "r(1:10)i1p1", start_year: 1961, end_year: 2099}
- {dataset: BNU-ESM, project: CMIP5, mip: Amon, exp: [historical, rcp26], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: BNU-ESM, project: CMIP5, mip: Amon, exp: [historical, rcp45], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
- {dataset: BNU-ESM, project: CMIP5, mip: Amon, exp: [historical, rcp85], ensemble: r1i1p1, start_year: 1961, end_year: 2099}
target: &target
- {dataset: CCSM4, project: CMIP5, mip: Amon, exp: [historical, rcp85], ensemble: "r(1:4)i1p1", start_year: 1961, end_year: 2099}
preprocessors:
preprocessor_global:
custom_order: true
area_statistics:
operator: mean
annual_statistics:
operator: mean
anomalies:
period: full
reference:
start_year: 1981
start_month: 1
start_day: 1
end_year: 2010
end_month: 12
end_day: 31
standardize: false
multi_model_statistics:
span: full
statistics:
- operator: percentile
percent: 10
- operator: percentile
percent: 90
preprocessor_local: &extract_NL
extract_point:
longitude: 6.25
latitude: 51.21
scheme: linear
diagnostics:
global_matching:
description: >
- Make a plot of the global mean temperature change according to all datasets (defined above)
- Get the global mean temperature change for specified years and specified percentiles (Delta T). These define our scenarios.
- Select the 30-year period from the target model (all ensemble members) where they match the Delta T for each scenario.
variables:
tas_cmip:
short_name: tas
preprocessor: preprocessor_global
additional_datasets: *cmip5
tas_target:
short_name: tas
preprocessor: preprocessor_global
additional_datasets: *target
scripts:
global_matching:
script: kcs/global_matching.py
scenario_years: [2050, 2085]
scenario_percentiles: [Percentile10, Percentile90]
local_resampling:
description: >
- Divide the 30-year dataset into 5-year blocks
- Create all possible combinations out of these 30/5 = 6 periods and x ensemble members (may contain the same block multiple times, but less is better and maximum three times (see third item below))
- Determine the 1000 best ...
- Determine the final best
variables:
pr_target:
short_name: pr
preprocessor: preprocessor_local
additional_datasets: *target
tas_target:
short_name: tas
preprocessor: preprocessor_local
additional_datasets: *target
pr_cmip:
short_name: pr
preprocessor: preprocessor_local
additional_datasets: *cmip5
tas_cmip:
short_name: tas
preprocessor: preprocessor_local
additional_datasets: *cmip5
scripts:
resample:
script: kcs/local_resampling.py
control_period: [1981, 2010]
n_samples: 8
scenarios:
ML_MOC:
description: "Moderate warming / low changes in seasonal temperature & precipitation, mid-century"
global_dT: 1.0
scenario_year: 2050
resampling_period: [2021, 2050]
dpr_winter: 4
pr_summer_control: [25, 55]
pr_summer_future: [45, 75]
tas_winter_control: [50, 80]
tas_winter_future: [20, 50]
tas_summer_control: [0, 100]
tas_summer_future: [0, 50]
ML_EOC:
description: "Moderate warming / low changes in seasonal temperature & precipitation, mid-century"
global_dT: 1.5
scenario_year: 2085
resampling_period: [2031, 2060]
dpr_winter: 6
pr_summer_control: [10, 40]
pr_summer_future: [60, 90]
tas_winter_control: [50, 80]
tas_winter_future: [20, 50]
tas_summer_control: [0, 100]
tas_summer_future: [0, 50]
WH_EOC:
description: "High warming / high changes in seasonal temperature & precipitation, end of century"
global_dT: 3.0
scenario_year: 2085
resampling_period: [2066, 2095]
dpr_winter: 24
pr_summer_control: [60, 100]
pr_summer_future: [0, 40]
tas_winter_control: [20, 50]
tas_winter_future: [50, 80]
tas_summer_control: [10, 50]
tas_summer_future: [60, 100]
![swh spinner](/static/img/swh-spinner.gif)
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