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 (1850-2014) (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 1901-2018
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)	"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. 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.

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

Table 5: Climate and climate-related forcing data (`climate-forcing`).
Title	Specifier	Time period	Reanalysis	Bias adjustment target	Comments
GSWP3-W5E5	gswp3-w5e5	1901-2016	ERA5	GPCC, CRU	Combination of W5E5 for 1979-2016 with GSWP3 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.
GSWP3	gswp3	1901-2010	20CR	GPCC, GPCP, CPC-Unified, CRU, SRB	Dynamically downscaled and bias-adjusted 20th Century Reanalysis (20CR) from the Global Soil Wetness Project Phase 3 (GSWP3).

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)
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)

The climate forcing input files can be found using the following pattern:

climate/atmosphere/<climate-scenario>/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc

Greenhouse gas forcing

Table 7: Greenhouse gas forcing for ISIMIP3a simulation round.
Variable	Variable specifier	Unit	Resolution	Datasets
Atmospheric composition mandatory
Atmospheric CO2 concentration	`composition_atmosphere/co2/co2_historical_annual_1850_2018.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, in review, see also https://luh.umd.edu).
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., in review, 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., in review, 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., in in review, see also https://luh.umd.edu).
N-deposition
Reduced nitrogen deposition	`socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc`
	NHx deposition	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)
Oxidized nitrogen deposition	`socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc`
	NOy deposition	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)
Reservoirs & dams
Reservoirs & dams	`socioeconomic/reservoir_dams/reservoirs-dams_1850_2025.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) 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) length of reservoir (R_Lgth_km) main purpose(s) of dam (PURPOSE) source of information (SOURCE) other notes (COMMENTS)	1850-2025	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). 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). 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). 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 (based on WPP 2017 revision, following the methodology of Klein Goldewijk et al., 2017). 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 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

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. 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.
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`
	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)
ggcmi_soil_cropland	`geo_conditions/soil/ggcmi_soil_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	These data are based on the soil data from HWSD. The data originally sit on a 0°0'30" grid and were aggregated to the ISIMIP 0.5° grid. Only the top soil layer properties are given, however on request deeper soil layers could be provided. The aggregation has been performed with the agriculture sector in mind (the aggregation uses the MIRCA data set, see Portmann, F. T., Siebert, S., & Döll, P., 2010, http://doi.org/10.1029/2008GB003435). For further details please refer to the README in the `ISIMIP3a/InputData/geo_conditions/soil/` folder.
Other
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

Output variables

Table 10: Output variables for Agriculture (`variable`).
Variable	Variable specifier	Unit	Dimensions	Resolution	Comments
Key model output
Crop yields	yield-<crop>-<irrigation>	dry matter (t ha-1 per growing season)		0.5° grid seasonal	Crop-specific: Yield may be identical to above-ground biomass (biom) if the entire plant is harvested, e.g. for bioenergy production.
Potential irrigation water withdrawal (assuming unlimited water supply)	pirrww-<crop>-<irrigation>	mm per growing season		0.5° grid seasonal	Irrigation water withdrawn in case of optimal irrigation (in addition to rainfall), assuming no losses in conveyance and application.
Soil moisture for each layer	soilmoist	kg m-2	(time, depth, lat, lon)	0.5° grid daily	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
Actual evapotranspiration	aet-<crop>-<irrigation>	mm per growing season		0.5° grid seasonal	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)
Nitrogen application rate	initr-<crop>-<irrigation>	kg ha-1 per growing season		0.5° grid seasonal	Total nitrogen application rate. If organic and inorganic amendments are applied, rate should be reported as effective inorganic nitrogen input (ignoring residues).
Actual planting dates	plantday-<crop>-<irrigation>	day of year		0.5° grid seasonal	Julian dates.
Anthesis dates	anthday-<crop>-<irrigation>	day of year of anthesis		0.5° grid seasonal	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.
Maturity dates	matyday-<crop>-<irrigation>	day of year of maturity		0.5° grid seasonal	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.
Additional output variables (optional)
Above ground biomass (dry matter)	biom-<crop>-<irrigation>	t ha-1 per growing season		0.5° grid seasonal	The whole plant biomass above ground.
Soil carbon emissions	sco2-<crop>-<irrigation>	kg C ha-1		0.5° grid seasonal	Ideally should be modeled with realistic land-use history and initial carbon pools. Subject to extra study.
Nitrous oxide emissions	sn2o-<crop>-<irrigation>	kg N2O-N ha-1		0.5° grid seasonal	Ideally should be modeled with realistic land-use history and initial carbon pools. Subject to extra study.
Total N uptake (total growing season sum)	tnup-<crop>-<irrigation>	kg ha -1 yr -1		0.5° grid monthly	Nitrogen balance: uptake
Total N inputs (total growing season sum)	tnin-<crop>-<irrigation>	kg ha -1 yr -1		0.5° grid monthly	Nitrogen balance: inputs
Total N losses (total growing season sum)	tnloss-<crop>-<irrigation>	kg ha -1 yr -1		0.5° grid monthly	Nitrogen balance: losses
Growing season temperature sum	sumt-<crop>-<irrigation>	deg c-days yr-1		0.5° grid seasonal	Sum of daily mean temperature over growing season
Growing season radiation	gsrsds-<crop>-<irrigation>	w m-2 yr-1		0.5° grid seasonal	Average growing season shortwave solar radiation
Growing season precipitation	gsprcp-<crop>-<irrigation>	mm ha-1 yr-1		0.5° grid seasonal	Total growing season precipitation per crop
	leach			0.5° grid seasonal

Crop priority and naming

Table 11: Crop naming and priorities (`crop`).
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

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.
NetCDF file extension is .nc

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>_<timestep>_<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<timestep>[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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