ISIMIP3a simulation round simulation protocol - Agriculture
Introduction
General concept
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.
ISIMIP is intended to be structured in successive rounds connected to the different phases of the climate model intercomparison CMIP (ISIMIP Mission & Implementation document).
The main components of the ISIMIP framework are:
- A common set of climate and other forcing data which will be distributed via a central database;
- A common modelling protocol to ensure consistency across sectors and scales in terms of data, format and scenario set-up;
- A central archive where the output data will be collected and made available to the scientific community.
ISIMIP3a
Historical model evaluation and attribution runs
The ISIMIP3a part of the third round framework is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are forced by observed climate and socio-economic information and a de-trended version of the observed climate allowing for the generation of a “no climate change” baseline (counterfactual).
You can find the ISIMIP3b protocol, which is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions, here.
Simulation protocol
In this protocol we describe the scenarios & experiments in ISIMIP3a simulation round, the different input datasets, the output variables, and how to report model results specifically for Agriculture. An overview of all sectors can be found at protocol.isimip.org.
Throughout the protocol we use specifiers that denote a particular scenario, experiment, variable or other parameter. We use these specifiers in the tables below, in the filenames of the input data sets, and ask you to use the same specifiers in your output files. More on reporting data can be found at the end of this document.
Model versioning
To ensure consistency between ISIMIP3a and ISIMIP3b as well as the different experiments within a simulation round, we require that modelling groups use the same version of an impact model for the experiments in ISIMIP3a and ISIMIP3b. If you cannot fulfill this, please indicate that by using a suffix for your model name (e.g. simple version numbering: MODEL-v1, MODEL-v2 or following semantic versioning: MODEL-2.0.0, see also reporting model results).
This versioning does not only apply to changes in the computational logic of the model, but also to input parameters, calibration or setup. If model versions are not reported, we will name them according to the simulation round (e.g. MODEL-isimip3a). We require the strict versioning to ensure that differences between model results are fully attributable to the changes in model forcings.
Scenarios & Experiments
Scenario definitions
Scenario specifier | Description |
---|---|
obsclim | Observed climate and CO₂ forcing used for model evaluation and the detection and attribution task. |
spinclim | Detrended version of the observed climate forcing used to spin-up the simulations based on a stable 1900 climate (see explanation below for details regarding the design of the spin-up) |
counterclim | Detrended version of the observed climate forcing used for the "no climate change" baseline simulations in the context of the detection and attributions task. |
Scenario specifier | Description |
---|---|
histsoc |
Varying direct human influences in the historical period (1850-2014) (e.g. observed changes in historical land use, nitrogen deposition and fertilizer input, fishing effort). Please label your model run |
2015soc |
Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort). Please label your simulations |
Scenario specifier | Description |
---|---|
default | For all experiments other than the sensitivity experiments. |
1901co2 | CO₂ concentration fixed at 1901 levels as a deviation from the “obsclim” climate + CO₂ forcing. |
General note regarding sensitivity experiments
The sensitivity experiments are meant to be "artificial" deviations from the default settings. So for example if your model does not at all account for changes in CO₂ concentrations (no option to switch it on or off) the run should be labeled as “default” in the sensitivity specifier of the file name even if the run would be identical to the 1901co2
sensitivity setting.
The particular sensitivity scenario for an experiment is given in the experiments table below. For most experiments no sensitivity scenario is given, so the default
label applies.
Experiments
Experiment | Short description |
Transition from Spin-up to experiment 1850-1900, only if spin-up is needed |
Historical 1901-2016 |
---|---|---|---|
model evaluationhistsoc 1st priority |
CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards |
spinclim |
obsclim |
DHF: Fixed 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
histsoc |
|
model evaluation2015soc 1st priority |
CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards |
spinclim |
obsclim |
DHF: Fixed 2015 levels of direct human forcing for the entire time period |
2015soc |
2015soc |
|
counterfactual climatehistsoc 1st priority |
CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level | "histsoc" version of the transition period of the model evaluation run |
counterclim |
DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
||
counterfactual climate2015soc 1st priority |
CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level | "2015soc" version of the transition period of the model evaluation run |
counterclim |
DHF: fixed 2015 levels of direct human influences for the entire time period |
2015soc |
||
CO₂ sensitivityhistsoc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "histsoc" version of the transition period of the model evaluation run |
obsclim Sensitivity scenario: 1901co2 |
DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards |
histsoc |
||
CO₂ sensitivity2015soc 2nd priority |
CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level | "2015soc" version of the transition period of the model evaluation run |
obsclim Sensitivity scenario: 1901co2 |
DHF: fixed 2015 levels of direct human influences for the entire time period |
2015soc |
Note regarding models requiring spin-up
For models requiring spin-up, we provide 100 years of spinclim data which is identical with the first 100 years of the counterclim data (files climate/atmosphere/spinclim/<dataset>/<dataset>_spinclim_<variable>_global_daily_<start-year>_<start-year>.nc
). If more than 100 years of spin-up are needed, these data can be repeated as often as needed.
- For
historical
runs, use historical CO2 concentration and varying DHF, for the transition period from spin-up to the start of the experiment (1850-1900). When using a longer spin-up period that (nominally) extends back further than 1850, please keep CO2 concentration and DHF constant at 1850 level until reaching the year corresponding to 1850. - For experiments with fixed year-2015 direct human influences (
2015soc
), the spin-up should be based on the 2015 DHF. - For experiments with no direct human influences (
nat
), the spin-up should be not using DHF as well.
Input data
The base directory for input data at DKRZ is:
/work/bb0820/ISIMIP/ISIMIP3a/InputData/
Further information on accessing ISIMIP data can be found at ISIMIP - getting started.
Some of the datasets are tagged as mandatory. This does not mean that the data must be used in all cases, but if your models uses input data of this kind, we require to use the specified dataset. If an alterntive data set is used instead, we cannot consider it an ISIMIP simulation. If the mandatory label is not given, you may use alternative data (please document this clearly).
Climate forcing
The climate forcing input files can be found on DKRZ using the following pattern:
climate/atmosphere/<climate-scenario>/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc
Title | Specifier | Time period | Reanalysis | Bias adjustment target | Comments | Priority |
---|---|---|---|---|---|---|
GSWP3-W5E5 | gswp3-w5e5 | 1901-2016 | ERA5 | GPCC, CRU | Combination of W5E5 (Lange, 2019a; Cucchi et al., 2020) for 1979-2016 with GSWP3 (Dirmeyer et al., 2006) homogenized to W5E5 for 1901-1978. The homogenization reduces discontinuities at the 1978/1979 transition and was done using the ISIMIP3BASD v2.4.1 bias adjustment method (Lange, 2019b; Lange, 2020). | 1 |
GSWP3 | gswp3 | 1901-2010 | 20CR | GPCC, GPCP, CPC-Unified, CRU, SRB | Dynamically downscaled and bias-adjusted 20th Century Reanalysis (20CR; Compo et al., 2011) from the Global Soil Wetness Project Phase 3 (GSWP3; Dirmeyer et al., 2006). | 2 |
Note on climate forcing priority
The priority for the different climate forcing datasets is from top to bottom. If you cannot use all climate forcing datasets, please concentrate on those at the top of the table.
Variable | Variable specifier | Unit | Resolution | Datasets | ||
---|---|---|---|---|---|---|
Atmospheric variables mandatory | ||||||
Near-Surface Relative Humidity | hurs | % |
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Near-Surface Specific Humidity | huss | kg kg-1 |
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Precipitation | pr | kg m-2 s-1 |
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Surface Air Pressure | ps | Pa |
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Surface Downwelling Longwave Radiation | rlds | W m-2 |
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Surface Downwelling Shortwave Radiation | rsds | W m-2 |
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Near-Surface Wind Speed | sfcwind | m s-1 |
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Near-Surface Air Temperature | tas | K |
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Daily Maximum Near-Surface Air Temperature | tasmax | K |
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Daily Minimum Near-Surface Air Temperature | tasmin | K |
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Other climate datasets
Variable | Variable specifier | Unit | Resolution | Datasets | ||
---|---|---|---|---|---|---|
Atmospheric composition mandatory | ||||||
Atmospheric CO2 concentration |
climate/atmosphere_composition/co2/<climate-scenario>/co2_<climate-scenario>_annual_<start_year>_<end_year>.txt
|
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co2 | ppm |
|
Meinshausen et al. (2011) for 1850-2005 and Dlugokencky & Tans (2019) from 2006-2018. |
Socioeconomic forcing
Dataset | Included variables (specifier) | Covered time period | Resolution | Reference/Source and Comments | ||
---|---|---|---|---|---|---|
Land use mandatory | ||||||
Landuse totals |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-totals_annual_<start_year>_<end_year>.nc
|
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review, see also https://luh.umd.edu). |
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Downscaling to 5 crops |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-5crops_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu). |
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Downscaling to 15 crops |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-15crops_annual_<start_year>_<end_year>.nc
|
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The C4 perennial crops are not further downscaled from the "5 crops" data set and currently only include sugarcane. Similarly, the C3 perennial crops are not downscaled either. The data is derived from the "5 crops" LUH2 data, and the crops have been downscaled to 15 crops according to the ratios given by the Monfreda data set (Monfreda, Ramankutty, & Foley, 2008). |
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Managed pastures and rangeland |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-pastures_annual_<start_year>_<end_year>.nc
|
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review., see also https://luh.umd.edu). |
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Urban areas |
socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-urbanareas_annual_<start_year>_<end_year>.nc
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Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu). |
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N-fertilizer mandatory | ||||||
Nitrogen deposited by fertilizers on croplands |
socioeconomic/n-fertilizer/<soc_scenario>/<soc_scenario>_n-fertilizer-5crops_annual_<start_year>_<end_year>.nc
|
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Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al., in in review, see also https://luh.umd.edu). |
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N-deposition | ||||||
Reduced nitrogen deposition |
socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc
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Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018) |
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Oxidized nitrogen deposition |
socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc
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Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018) |
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Reservoirs & dams | ||||||
Reservoirs & dams |
socioeconomic/reservoir_dams/reservoirs-dams_1850_2025.xls
|
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Lehner et al. (2011a, https://doi.org/10.7927/H4N877QK), Lehner et al. (2011b, https://dx.doi.org/10.1890/100125), and Jida Wang et al. (KSU/Kansas State University, personal communication). Because the data from KSU is yet unpublished, modeling teams using it are asked to offer co-authorship to the team at KSU on any resulting publications. Please contact info@isimip.org in case of questions. |
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Water abstraction | ||||||
Water abstraction for domestic and industrial purposes |
socioeconomic/water_abstraction/[domw|indw][w|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc
|
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For modelling groups that do not have their own representation, we provide files containing the multi-model mean of domestic and industrial water withdrawal and consumption generated by WaterGAP, PCR-GLOBWB, and H08. This data is based ISIMIP2a varsoc simulations for 1901-2005, and on RCP6.0 simulations from the Water Futures and Solutions project (Wada et al., 2016, http://www.geosci-model-dev.net/9/175/2016/) for after 2005. Years before 1901 have been filled with the value for year 1901. |
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Population mandatory | ||||||
Population 5' grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
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Population 0.5° grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
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Population national |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
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Population density 5' grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
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Population density 0.5° grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
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Population density national |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
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HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2020, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
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GDP mandatory | ||||||
GDP |
socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
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Historic country-level GDP data are an extension of the data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html), and are derived mainly from the Maddison Project database. Gridded GDP data will be provided by c. 07/2021. |
Geographic data and information
Dataset | Included variables (specifier) | Resolution | Reference/Source and Comments | |||
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Land/Sea masks | ||||||
landseamask |
geo_conditions/landseamask/landseamask.nc
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0.5° grid | This is the land-sea mask of the W5E5 dataset (Lange, 2019a; Cucchi et al., 2020). Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land. |
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landseamask_no-ant |
geo_conditions/landseamask/landseamask_no-ant.nc
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0.5° grid | Same as landseamask but without Antarctica. |
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landseamask_water-global |
geo_conditions/landseamask/landseamask_water-global.nc
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0.5° grid | This is the generic land-sea mask from ISIMIP2b that is to be used for global water simulations in ISIMIP3. It marks more grid cells as land than landseamask. Over those additional land cells, the climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are not guaranteed to represent climate conditions over land. Instead they may represent climate conditions over sea or a mix of conditions over land and sea. |
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Soil | ||||||
gswp3_hwsd |
geo_conditions/soil/gswp3_hwsd.nc
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0.5° grid | One fixed pattern to be used for all simulation periods. Upscaled Soil texture map (30 arc sec. to 0.5°x0.5° grid) based on Harmonized World Soil Database v1.1 (HWSD) using the GSWP3 upscaling method A (http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html) |
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hwsd_soil_data_all_land |
geo_conditions/soil/hwsd_soil_data_all_land.nc
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0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on all land. |
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hwsd_soil_data_on_cropland |
geo_conditions/soil/hwsd_soil_data_on_cropland.nc
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0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on current cropland (MIRCA2000 at 5 arc-minutes). |
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River routing | ||||||
basins |
geo_conditions/river_routing/ddm30_basins_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
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flowdir |
geo_conditions/river_routing/ddm30_flowdir_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
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slopes |
geo_conditions/river_routing/ddm30_slopes_cru_neva.[nc|asc]
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0.5° grid | DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files. |
Output data
ISIMIP output variables are usually reported with the dimensions (time,lat,lon). For variables with a number of levels (e.g. layers or depth), an alternative set of dimensions is given in the comment column in the table below. More information about level dimensions can be found here and here on the ISIMIP webpage.
Please note that unless otherwise defined, the variables in ISIMIP should be reported relative to the grid cell land area.
Output variables
Variable long name | Variable specifier | Unit | Resolution | Comments | ||
---|---|---|---|---|---|---|
Key model output | ||||||
Crop Yields | yield-<crop>-<irrigation> | dry matter (t ha-1 per growing season) |
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Crop-specific: Yield may be identical to above-ground biomass (biom) if the entire plant is harvested, e.g. for bioenergy production. |
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Potential Net Irrigation Water Requirement | pirnreq-<crop>-<irrigation> | mm per growing season |
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Soil water demand required to avoid water stress, excluding any water losses associate with application or transport. |
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Total Soil Moisture Content | soilmoist | kg m-2 |
|
Level dimensions: (time, depth, lat, lon). Please provide soil moisture for all depth layers (i.e. 3D-field), and indicate depth in m. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files. |
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Key diagnostic variables | ||||||
Actual Evapotranspiration | aet-<crop>-<irrigation> | mm per growing season |
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Portion of all water (including rain) that is evapo-transpired, the water amount should be accumulated over the entire growing period (not the calendar year) |
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Nitrogen Application Rate | initr-<crop>-<irrigation> | kg ha-1 per growing season |
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Total nitrogen application rate. If organic and inorganic amendments are applied, rate should be reported as effective inorganic nitrogen input (ignoring residues). |
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Actual Planting Date | plantday-<crop>-<irrigation> | day of year |
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Julian dates. |
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Anthesis Date | anthday-<crop>-<irrigation> | day of year of anthesis |
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Together with the year of anthesis added to the list of outputs (see below) it allows for clear identification of anthesis that is also easy to follow for potential users from outside the project. |
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Maturity Date | matyday-<crop>-<irrigation> | day of year of maturity |
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Together with the year of maturity added to the list of outputs (see below) it allows for clear identification of maturity that is also easy to follow for potential users from outside the project. |
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Additional output variables (optional) | ||||||
Total Above Ground Biomass Dry Matter Yields | biom-<crop>-<irrigation> | t ha-1 per growing season |
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The whole plant biomass above ground. |
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Soil Carbon Emissions | sco2-<crop>-<irrigation> | kg C ha-1 |
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Ideally should be modeled with realistic land-use history and initial carbon pools. Subject to extra study. |
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Nitrous Oxide Emissions | sn2o-<crop>-<irrigation> | kg N2O-N ha-1 |
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Ideally should be modeled with realistic land-use history and initial carbon pools. Subject to extra study. |
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Total N Uptake (total growing season sum) | tnup-<crop>-<irrigation> | kg ha-1 yr-1 |
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Nitrogen balance: uptake |
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Total N Inputs (total growing season sum) | tnin-<crop>-<irrigation> | kg ha-1 yr-1 |
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Nitrogen balance: inputs |
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Total N Losses (total growing season sum) | tnloss-<crop>-<irrigation> | kg ha-1 yr-1 |
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Nitrogen balance: losses |
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Growing Season Temperature Sum | sumt-<crop>-<irrigation> | deg c-days yr-1 |
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Sum of daily mean temperature over growing season |
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Growing Season Incoming Solar Shortwave Radiation | gsrsds-<crop>-<irrigation> | W m-2 yr-1 |
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Average growing season shortwave solar radiation |
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Accumulated Growing Season Precipitation | gsprcp-<crop>-<irrigation> | mm ha-1 yr-1 |
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Total growing season precipitation per crop |
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Nitrogen Leached | leach | kg ha-1 yr-1 |
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Crop priority and naming
Crop | Specifier | ||||
---|---|---|---|---|---|
Major crops | |||||
Wheat | whe | ||||
Maize | mai | ||||
Soy | soy | ||||
Rice | ric | ||||
Other crops | |||||
Barley | bar | ||||
Bean | ben | ||||
Cassava | cas | ||||
Cotton | cot | ||||
Eucalyptus | euc | ||||
Managed grass | mgr | ||||
Millet | mil | ||||
Miscanthus | mis | ||||
Groundnuts | nut | ||||
Field peas | pea | ||||
Poplar | pop | ||||
Potato | pot | ||||
Rapeseed | rap | ||||
Rye | rye | ||||
Sugar beet | sgb | ||||
Sorghum | sor | ||||
Sugarcane | sug | ||||
Sunflower | sun |
Irrigation
Irrigation type | Specifier |
---|---|
Full irrigation | firr |
No irrigation (rainfed land) | noirr |
Harmonization
Simulation | Specifier | Description |
---|---|---|
Default | default | Model should use their individual “best representation” of the historical period with regard to sowing dates, harvesting dates, fertilizer application rates and crop varieties. |
Fully harmonized | fullharm | Simulations based on prescribed “present day” fertilization rates (available for download) and fixed planting and harvesting dates (also available for download). Modelers should have planting as closely as possible to these dates, but it may be admissible to use these dates as indicators for planting windows (depending on model specifics). |
Harmonized seasons with no N constraints | harmnon | For models with an explicit description of the nitrogen cycle: harmnon simulations should be run with nitrogen stress turned off completely or (if that’s not possible) with very high N application rates to make model results comparable between those GGCMs that have explicit N dynamics and those that do not. For models without the nitrogen cycle: harmnon and fullharm simulations are the same and do not need to be duplicated. Please contact the sector coordination to push on with this side branch. |
Reporting per growing seasons
To resolve potential double harvests within one year, crop yields should be reported per growing season and not per calendar year. Thus, in the NetCDF output files, do not use a time dimension but instead a unitless coordinate variable with integer values; more information on how to construct these files is given below and on the ISIMIP website (https://www.isimip.org/protocol/preparing-simulation-files/).
Cumulative growing season variables such as, e.g., actual evapotranspiration or precipitation are to be accumulated over the growing season. The first season in the file (level=0) is then the first complete growing season of the time period provided by the input data without any assumed spin-up data, which equates to the growing season with the first planting after this date. To ensure that data can be matched to individual years in post-processing, it is essential to also provide the actual planting dates (as day of the year), actual planting years (year), anthesis dates (as day of the year), year of anthesis (year), maturity dates (day of the year), and year of maturity (year). This procedure is identical to the GGCMI convention (Elliott, et al., 2015).
Reporting model results
The specification on how to submit the data, as well as further information and instructions are given on the ISIMIP website at:
https://www.isimip.org/protocol/preparing-simulation-files
It is important that you comply precisely with the formatting specified there, in order to facilitate the analysis of your simulation results in the ISIMIP framework. Incorrect formatting can seriously delay the analysis. The ISIMIP Team will be glad to assist with the preparation of these files if necessary.
File names consist of a series of identifier, separated by underscores. Things to note:
- Report one variable per file.
- In filenames, use lowercase letters only.
- Use underscore (
_
) to separate identifiers. - Variable names consist of a single word without hyphens or underscores.
- Use hyphens (
-
) to separate strings within an identifier, e.g. in a model name. - If no specific
sens-scenario
is given in the experiments table, usedefault
. - NetCDF file extension is
.nc
. - The reference year for the NetCDF files for ISIMIP3a is
1901
.
Please name the files in the Agriculture sector according to the following pattern:
<model>_<climate-forcing>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>-<crop>-<irrigation>_<region>_<time-step>_<start-year>_<end-year>.nc
and replace the identifiers with the specifiers given in the tables of this document. Examples would be:
lpjml_gswp3_obsclim_histsoc_default_qtot_global_annual_1901_1910.nc
lpjml_gwsp3_counterclim_2015soc_1901co2_yield-mai-noirr_global_annual_2006_2010.nc
The following regular expression can be used to validate and parse the file name for the agriculture sector:
(?P<model>[a-z0-9-+.]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<climate_scenario>[a-z0-9-]+)_(?P<soc_scenario>[a-z0-9-]+)_(?P<sens_scenario>[a-z0-9-]+)_(?P<variable>[a-z0-9]+)-(?P<crop>[a-z]+)-(?P<irrigation>[a-z]+)_(?P<region>(global))_(?P<time_step>[a-z0-9-]+)_(?P<start_year>\d{4})_(?P<end_year>\d{4}).nc
For questions or clarifications, please contact info@isimip.org or the data managers directly (isimip-data@pik‐potsdam.de) before submitting files.
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