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

Table 1: Climate scenario specifiers (`climate-scenario`).
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.

Table 2: Socio-economic scenario specifiers (`soc-scenario`).
Scenario specifier	Description
histsoc	Varying direct human influences in the historical period (e.g. observed changes in historical land use, nitrogen deposition and fertilizer input, fishing effort). Please label your model run `histsoc` even if it only partly accounts for varying direct human forcings while another part of the the direct human forcing is considered constant or is ignored.
2015soc	Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort). Please label your simulations `2015soc` if they do not at all account for historical changes in direct human forcing, but they do represent constant year-2015 levels of direct human forcing for at least some direct human forcings.

Table 3: Sensitivity scenario specifiers (`sens-scenario`).
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

Table 4: Experiment set-up: Each experiment is specified by the climate forcing (CF) and the Direct Human Forcing (DHF).
Experiment	Short description	Transition from Spin-up to experiment 1850-1900, only if spin-up is needed	Historical 1901-2016
model evaluation histsoc 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
model evaluation histsoc 1st priority	DHF: Fixed 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards	histsoc	histsoc
model evaluation 2015soc 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
model evaluation 2015soc 1st priority	DHF: Fixed 2015 levels of direct human forcing for the entire time period	2015soc	2015soc
counterfactual climate histsoc 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
counterfactual climate histsoc 1st priority	DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards		histsoc
counterfactual climate 2015soc 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
counterfactual climate 2015soc 1st priority	DHF: fixed 2015 levels of direct human influences for the entire time period		2015soc
CO₂ sensitivity histsoc 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
CO₂ sensitivity histsoc 2nd priority	DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards		histsoc
CO₂ sensitivity 2015soc 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
CO₂ sensitivity 2015soc 2nd priority	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

Table 5: Climate and climate-related forcing data (`climate-forcing`).
Title	Specifier	Time period	Reanalysis	Bias adjustment target	Comments	Priority
GSWP3-W5E5	gswp3-w5e5	1901-2019	ERA5	GPCC, CRU	Combination of W5E5 v2.0 (Cucchi et al., 2020, DOI: 10.5194/essd-2020-28 and Lange et al., 2021, DOI: 10.48364/ISIMIP.342217) for 1979-2019 with GSWP3 v1.09 (Kim, 2017, DOI: 10.20783/DIAS.501) homogenized to W5E5 for 1901-1978. The homogenization reduces discontinuities at the 1978/1979 transition and was done using the ISIMIP3BASD v2.5.0 bias adjustment method (Lange, 2019, DOI: 10.5194/gmd-12-3055-2019 and Lange, 2021, DOI: 10.5281/zenodo.4686991). For further information see also <https://www.isimip.org/gettingstarted/input-data-bias-correction/details/80/>	1

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.

Table 6: Climate forcing variables for ISIMIP3a simulations (`climate-variable`).
Variable	Variable specifier	Unit	Resolution	Datasets
Atmospheric variables mandatory
Near-Surface Relative Humidity	hurs	%	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Specific Humidity	huss	kg kg-1	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Precipitation	pr	kg m-2 s-1	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Snowfall Flux	prsn	kg m-2 s-1	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Air Pressure	ps	Pa	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Longwave Radiation	rlds	W m-2	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Shortwave Radiation	rsds	W m-2	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Wind Speed	sfcwind	m s-1	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Air Temperature	tas	K	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Maximum Near-Surface Air Temperature	tasmax	K	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Minimum Near-Surface Air Temperature	tasmin	K	0.5° grid daily	GSWP3 (obsclim and counterclim, 1901-2010) GSWP3-W5E5 (obsclim and counterclim, 1901-2016)

Other climate datasets

Table 7: Other climate datesets for ISIMIP3a simulation round.
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`
Atmospheric CO2 concentration	co2	ppm	global annual	Meinshausen et al. (2011) for 1850-2005 and Dlugokencky & Tans (2019) from 2006-2018.

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3a simulation round.
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`
	share of the total cropland (cropland_total) all of the rainfed cropland (cropland_rainfed) all of the irrigated cropland (cropland_irrigated) share of managed pastures or rangeland (pastures)	1850-1900 1901-2018	0.5° grid annual	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. (2020), see also https://luh.umd.edu). For further information on the land use data refer to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/82/.
Downscaling to 5 crops	`socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-5crops_annual_<start_year>_<end_year>.nc`
	share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated) share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated) share of rainfed/irrigated C3 N-fixing crops (c3nfx_rainfed, c3nfx_irrigated) share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated) share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated)	1850-1900 1901-2018	0.5° grid annual	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. (2020), see also https://luh.umd.edu).
Downscaling to 15 crops	`socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-15crops_annual_<start_year>_<end_year>.nc`
	share of rainfed/irrigated maize (maize_rainfed, maize_irrigated) share of rainfed/irrigated rice (rice_rainfed, rice_irrigated) share of rainfed/irrigated oil crops (groundnut) (oil_crops_groundnut_rainfed, oil_crops_groundnut_irrigated) share of rainfed/irrigated oil crops (rapeseed) (oil_crops_rapeseed_rainfed, oil_crops_rapeseed_irrigated) share of rainfed/irrigated oil crops (soybean) (oil_crops_soybean_rainfed, oil_crops_soybean_irrigated) share of rainfed/irrigated oil crops (sunflower) (oil_crops_sunflower_rainfed, oil_crops_sunflower_irrigated) share of rainfed/irrigated pulses (pulses_rainfed, pulses_irrigated) share of rainfed/irrigated temperate cereals (temperate_cereals_rainfed, temperate_cereals_irrigated) share of rainfed/irrigated temperate roots (temperate_roots_rainfed, temperate_roots_irrigated) share of rainfed/irrigated tropical cereals (tropical_cereals_rainfed, tropical_cereals_irrigated) share of rainfed/irrigated tropical roots (tropical_roots_rainfed, tropical_roots_irrigated) share of rainfed/irrigated C3 annual crops not covered by the above (others_c3ann_rainfed, others_c3ann_irrigated) share of rainfed/irrigated C3 N-fixing crops not covered by the above (others_c3nfx_rainfed, others_c3nfx_irrigated) share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated) share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated) share of pastures, both managed and rangeland (pastures)	1850-1900 1901-2018	0.5° grid annual	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).
Managed pastures and rangeland	`socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-pastures_annual_<start_year>_<end_year>.nc`
	share of managed pastures (managed_pastures) share of rangeland (rangeland)	1850-1900 1901-2018	0.5° grid annual	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).
Urban areas	`socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-urbanareas_annual_<start_year>_<end_year>.nc`
	share of urban areas (urbanareas)	1850-1900 1901-2018	0.5° grid annual	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. (2020), see also https://luh.umd.edu).
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`
	deposition of N through fertilizer on cropland with C3 annual crops (fertl_c3ann) C3 perennial crops (fertl_c3per) C3 N-fixing crops (fertl_c3nfx) C4 annual crops (fertl_c4ann) C4 perennial crops (fertl_c4per)	1850-1900 1901-2018	0.5° grid annual (growing season)	Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al. (2020), see also https://luh.umd.edu). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/28/.
N-deposition
Reduced nitrogen deposition	`socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc`
	NHx deposition (nhx)	1850-1900 1901-2016	0.5° grid monthly	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). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/24/.
Oxidized nitrogen deposition	`socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc`
	NOy deposition (noy)	1850-1900 1901-2016	0.5° grid annual	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). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/24/.
Reservoirs & dams
Reservoirs & dams	`socioeconomic/reservoir_dams/reservoirs-dams_1850_2020.xls`
	Unique ID representing a dam and its associated reservoir corresponding to GRanD/KSU IDs (ID) name (DAM_NAME) original location (LON_ORIG, LAT_ORIG) location adjusted to the DDM30 routing network (LON_DDM30, LAT_DDM30) upstream area in DDM30 (CATCH_SKM_DDM30) upstream area in GRanD (CATCH_SKM_GRanD) maximum storage capacity of reservoir (CAP_MCM) year of construction/commissioning (YEAR) flag to indicate that the year of dam construction has been artificially set to 1850 if not existing (FLAG_ART=1, otherwise 0) alternative year may indicate a multi-year construction or secondary dam (ALT_YEAR) flag of correction if relocation was applied (FLAG_CORR) river name which the dam impounds (RIVER) height of dam (D_Hght_m) maximum inundation area of reservoir (R_Area_km2) main purpose(s) of dam (PURPOSE) source of information (SOURCE) other notes (COMMENTS)	1850-2020	0.5° grid and original coordinates (degree) annual	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, data starting in 2016). 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.
Water abstraction
Water abstraction for domestic and industrial purposes	`socioeconomic/water_abstraction/[domw\|indw][w\|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc`
	domestic and industrial water withdrawal and consumption (domww, domwc, indww, indwc)	1850-1900 1901-2018	0.5° grid annual	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.
Population mandatory
Population 5' grid	`socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc`
	total number of people (popc) rural number of people (rurc) urban number of people (urbc)	1850-1900 1901-2020	5' grid annual	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.
Population 0.5° grid	`socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc`
	total number of people (popc) rural number of people (rurc) urban number of people (urbc)	1850-1900 1901-2020	0.5° grid annual	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
Population national	`socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv`
	total number of people per country	1850-1900 1901-2020	national annual	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.
Population density 5' grid	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc`
	total number of people per square kilometer (popc) rural number of people per square kilometer (rurc) urban number of people per square kilometer (urbc)	1850-1900 1901-2020	5' grid annual	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.
Population density 0.5° grid	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc`
	total number of people per square kilometer (popc) rural number of people per square kilometer (rurc) urban number of people per square kilometer (urbc)	1850-1900 1901-2020	0.5° grid annual	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
Population density national	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv`
	total number of people per square kilometer	1850-1900 1901-2020	national annual	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.
GDP mandatory
GDP	`socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc`
	GDP PPP 2005 USD (gdp)	1850-1900 1901-2016	country-level annual	Historic country-level GDP data are an extension of the historical data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html). They are derived mainly from Penn World Tables (PWT) for recent decades, and from the Maddison Project database (2018 version, https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018) for earlier periods where no PWT data is available. Gaps were filled using World Bank data (WDI). No interpolation to SSPs is performed for this historical dataset.

Static geographic information

Table 9: Geographic data and information for ISIMIP3a simulation round.
Dataset	Included variables (specifier)	Resolution	Reference/Source and Comments
Land/Sea masks
landseamask	`geo_conditions/landseamask/landseamask.nc`
	land-sea mask (mask)	0.5° grid	This is the land-sea mask of the W5E5 dataset (Cucchi et al., 2020; Lange et al., 2021). 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. For further information see also https://www.isimip.org/gettingstarted/input-data-bias-correction/details/41/.
landseamask_no-ant	`geo_conditions/landseamask/landseamask_no-ant.nc`
	land-sea mask (mask)	0.5° grid	Same as landseamask but without Antarctica.
landseamask_water-global	`geo_conditions/landseamask/landseamask_water-global.nc`
	land-sea mask (mask)	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.
Soil
gswp3_hwsd	`geo_conditions/soil/gswp3_hwsd.nc`
	Soil texture (soiltexture)	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)
hwsd_soil_data_all_land	`geo_conditions/soil/hwsd_soil_data_all_land.nc`
	USDA soil texture class dominant HWSD on cropland (texture_class) domiant HWSD soil mapping unit within dominant USDA soil texture class on cropland (mu_global) Topsoil pH(H2O) (soil_ph) Topsoil Calcium Carbonate (soil_caco3) Topsoil Bulk Density (bulk_density) Topsoil Cation Exchange Capacity (soil) (cec_soil) Topsoil Organic Carbon (oc) depth of Obstacles to Roots (ESDB) (root_obstacles) depth of Impermeable Layer (ESDB) (impermeable_layer) Available Water Content (awc) Topsoil Sand Fraction (sand) Topsoil Silt Fraction (silt) Topsoil Clay Fraction (clay) Topsoil Gravel Content (gravel) Topsoil Salinity (ece) Topsoil Base Saturation (bs_soil) flag for valid soils (issoil)	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.
hwsd_soil_data_on_cropland	`geo_conditions/soil/hwsd_soil_data_on_cropland.nc`
	USDA soil texture class dominant HWSD on cropland (texture_class) domiant HWSD soil mapping unit within dominant USDA soil texture class on cropland (mu_global) Topsoil pH(H2O) (soil_ph) Topsoil Calcium Carbonate (soil_caco3) Topsoil Bulk Density (bulk_density) Topsoil Cation Exchange Capacity (soil) (cec_soil) Topsoil Organic Carbon (oc) depth of Obstacles to Roots (ESDB) (root_obstacles) depth of Impermeable Layer (ESDB) (impermeable_layer) Available Water Content (awc) Topsoil Sand Fraction (sand) Topsoil Silt Fraction (silt) Topsoil Clay Fraction (clay) Topsoil Gravel Content (gravel) Topsoil Salinity (ece) Topsoil Base Saturation (bs_soil) flag for valid soils (issoil)	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).
River routing
basins	`geo_conditions/river_routing/ddm30_basins_cru_neva.[nc\|asc]`
	basin number (basinnumber)	0.5° grid	DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.
flowdir	`geo_conditions/river_routing/ddm30_flowdir_cru_neva.[nc\|asc]`
	flow direction (flowdirection)	0.5° grid	DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.
slopes	`geo_conditions/river_routing/ddm30_slopes_cru_neva.[nc\|asc]`
	slope (slope)	0.5° grid	DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

Output data

Variables are to be reported with the dimensions (time,lat,lon) where the time axis' unit is 'growing seasons since 1901-01-01 00:00:00' for all variables unless these are reported on a transient time axis, e.g. 'soilmoist1m'. In many cases growing seasons are equivalent to years, as there is always only one planting event per year. However, due to temperature-sensitive growing season lengths, the growing seasons are not fully equivalent to years and users should note the difference. Reported variables on start and end of the growing season are supplied to allow allocating events to transient time axes if desired.

Please note that unless otherwise defined, the variables in ISIMIP should be reported relative to the grid cell land area.

Output variables

Table 10: Output variables for Agriculture (`variable`).
Variable long name	Variable specifier	Unit	Resolution	Comments
Key model output
Crop Yields	yield-<crop>-<irrigation>	dry matter (t ha-1)	0.5° grid annual	Other sectors: biomes, water_global. Yield may be identical to above-ground biomass (biom) if the entire plant is harvested, e.g. for bioenergy production. Yields are reported per growing seasons and not per year.
Cumulative Potential Net Irrigation Water Requirement	pirnreqcum-<crop>-<irrigation>	kg m-2	0.5° grid annual	Other sectors: biomes, water_global. Soil water demand required to avoid water stress accumulated across the growing season, excluding any water losses associated with application or transport and without constraints due to water availability; only needed for `firr` simulations.
Cumulative Evapotranspiration	evapcum-<crop>-<irrigation>	kg m-2	0.5° grid annual	Other sectors: biomes, water_global. Evapotranspiration = sum of transpiration, evaporation, interception and sublimation aggregated across the growing season.
Cumulative Nitrogen Application	initrcum-<crop>-<irrigation>	kg ha-1	0.5° grid annual	Other sectors: biomes, water_global. Integral of the total nitrogen application rate over the growing season. If organic and inorganic amendments are applied, the nitrogen application should be reported as effective inorganic nitrogen input (ignoring residues).
Total Soil Moisture Content	soilmoist	kg m-2	0.5° grid daily if possible, else monthly	Other sectors: biomes, fire, forestry, permafrost, water_global, water_regional. 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.
Key diagnostic variables
Planting Date	plantday-<crop>-<irrigation>	day of year	0.5° grid annual	Other sectors: biomes, water_global. As Julian dates. The planting date reported here corresponds to the actual planting date in the simulation and may diverge from the planting calendar used for harmonization.
Planting Year	plantyear-<crop>-<irrigation>	calendar year	0.5° grid annual	Other sectors: biomes, water_global.
Anthesis Date	anthday-<crop>-<irrigation>	days from planting	0.5° grid annual	Other sectors: biomes, water_global. Together with the day and year of planting it allows for clear identification of anthesis.
Maturity Date	matyday-<crop>-<irrigation>	days from planting	0.5° grid annual	Other sectors: biomes, water_global. Together with the day and year of planting it allows for clear identification of maturity.
Harvest Year	harvyear-<crop>-<irrigation>	calendar year	0.5° grid annual	Other sectors: biomes, water_global. Usually the year when maturity is reached. Can also be computed from planting and maturuty day.
Additional output variables (optional)
Total Above Ground Biomass Dry Matter Yields	biom-<crop>-<irrigation>	t ha-1	0.5° grid annual	Other sectors: biomes, water_global. The whole plant biomass above ground.
Cumulative Nitrogen Uptake	nupcum-<crop>-<irrigation>	kg ha-1	0.5° grid annual	Other sectors: biomes, water_global. Nitrogen balance: Uptake (growing season sum)
Cumulative Nitrogen Inputs	nincum-<crop>-<irrigation>	kg ha-1	0.5° grid annual	Other sectors: biomes, water_global. Nitrogen balance: Inputs (growing season sum)
Cumulative Nitrogen Losses	nlosscum-<crop>-<irrigation>	kg ha-1	0.5° grid annual	Other sectors: biomes, water_global. Nitrogen balance: Losses (growing season sum)
Cumulative Nitrogen Leached	nleachcum-<crop>-<irrigation>	kg ha-1	0.5° grid annual	Other sectors: biomes, water_global. Nitrogen balance: Leaching (growing season sum)

Crop priority and naming

Table 11: Crop naming and priorities (`crop`).
Crop	Specifier
Major crops
Wheat	whe
Maize	mai
Soy	soy
Rice (first growing period)	ri1
Rice (second growing period)	ri2
Other crops
Winter wheat	wwh
Summer wheat	swh
Rice	ric
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

Table 12: Irrigation specifiers (`irrigation`).
Irrigation type	Specifier
Full irrigation	firr
No irrigation (rainfed land)	noirr

Harmonization

Table 13: Harmonization specifiers (`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, use default.
Data model is NETCDF4_CLASSIC with minimum compression level of 5.
NetCDF file extension is .nc.
The relative time axis' reference year for ISIMIP3a is 1901.
for daily data use standard, proleptic_gregorian, 366_day, 365_day or 360_day calendar depending on the temporal resolution of your model and the 360_day calendar for non-daily data.

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-z0-9]+)-(?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.

References

Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D. and Worley, S. J.: The twentieth century reanalysis project, Quarterly Journal of the Royal Meteorological Society, 137(654), 1–28, doi:10.1002/qj.776, 2011.

Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H. and Buontempo, C.: WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies, Earth System Science Data, 12(3), 2097–2120, doi:10.5194/essd-12-2097-2020, 2020.

Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, Bulletin of the American Meteorological Society, 87(10), 1381–1398, doi:10.1175/BAMS-87-10-1381, 2006.

Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, Natl. Ocean. Atmos. Adm. Earth Syst. Res. Lab. [online] Available from: https://www.esrl.noaa.gov/gmd/ccgg/trends/, 2019.

Geiger, T.: Continuous national gross domestic product (GDP) time series for 195 countries: Past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100), Earth System Science Data, 10(2), 847–856, doi:10.5194/essd-10-847-2018, 2018.

Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Goldewijk, K. K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J., Kennedy, J., Kristzin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., Vuuren, D. P. van and Zhang, X.: Harmonization of Global Land-Use Change and Management for the Period 850–2100 (LUH2) for CMIP6, Geoscientific Model Development Discussions, 1–65, doi:https://doi.org/10.5194/gmd-2019-360, 2020.

Klein Goldewijk, K., Beusen, A., Doelman, J. and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth System Science Data, 9(2), 927–953, doi:10.5194/essd-9-927-2017, 2017.

Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12(7), 3055–3070, doi:10.5194/gmd-12-3055-2019, 2019a.

Lange, S., Menz, C., Gleixner, S., Cucchi, M., Weedon, G.P., Amici, A., Bellouin, N., Müller Schmied, H., Hersbach, H., Buontempo, C. and Cagnazzo, C.: WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0), ISIMIP Repository, doi:10.48364/ISIMIP.342217, 2021.

Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01. Palisades, NY, NASA Socioeconomic Data and Applications Center (SEDAC), doi:10.7927/H4N877QK, 2011a.

Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management, Frontiers in Ecology and the Environment, 9(9), 494–502, doi:10.1890/100125, 2011b.

Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M. and Vuuren, D. P. P. van: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109(1), 213, doi:10.1007/s10584-011-0156-z, 2011.

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., Berg, M. van den, Velders, G. J. M., Vollmer, M. K. and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13(8), 3571–3605, doi:10.5194/gmd-13-3571-2020, 2020.

Messager, M. L., Lehner, B., Grill, G., Nedeva, I. and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature Communications, 7(1), 13603, doi:10.1038/ncomms13603, 2016.

Murakami, D. and Yamagata, Y.: Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling, Sustainability, 11(7), 2106, doi:10.3390/su11072106, 2019.

Portmann, F. T., Siebert, S. and Döll, P.: MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochemical Cycles, 24(1), doi:10.1029/2008GB003435, 2010.

Reyer, C., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Matteucci, G., Collalti, A., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Weedon, G., Sheffield, J., Vega del Valle, I., Suckow, F., Horemans, J., Martel, S., Bohn, F., Steinkamp, J., Chikalanov, A. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests. V. 0.1.12., GFZ Data Services, doi:10.5880/PIK.2019.008, 2019.

Reyer, C. P. O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T. G. M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J. A., Bohn, F., Steinkamp, J., Chikalanov, A., Weedon, G. P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on european forests, Earth System Science Data, 12(2), 1295–1320, doi:10.5194/essd-12-1295-2020, 2020.

Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., Arneth, A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos, F., Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E., Thompson, R. L., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang, K. and Zhu, Q.: The Global N2O Model Intercomparison Project, Bulletin of the American Meteorological Society, 99(6), 1231–1251, doi:10.1175/BAMS-D-17-0212.1, 2018.