ISIMIP3b 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:

ISIMIP3b

GCM-based simulations assuming fixed 2015 direct human influences for the future

The ISIMIP3b part of the third simulation round is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions. The group 1 simulations refer to the pre-industrial and historical period of the CMIP6-based climate simulations. Group 2 covers all future projections assuming fixed 2015 levels of socio-economic forcing and different future projections of climate (SSP126, SSP37 and SSP585). Group3 simulations account for future changes in socio-economic drivers and are intended to be started in summer 2021.

You can find the ISIMIP3a protocol, which is is dedicated to impact model evaluation and improvement and detection and attribution of observed impacts, here.

Simulation protocol

In this protocol we describe the scenarios & experiments in ISIMIP3b 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
picontrol Pre-industrial climate as simulated by the GCMs.
historical Historical climate as simulated by the GCMs.
ssp126 SSP1-RCP2.6 climate as simulated by the GCMs.
ssp370 SSP3-RCP7 climate as simulated by the GCMs.
ssp585 SSP5-RCP8.5 climate as simulated by the GCMs.
Table 2: Socio-economic scenario specifiers (soc-scenario).
Scenario specifier Description
1850soc

Fixed year-1850 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort).

Please label your simulations 1850soc if they do not at all account for historical changes in direct human forcing, but they do represent constant year-1850 levels of direct human forcing for at least some direct human forcings.

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.
2015co2 CO₂ concentration fixed at 2015 levels.

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 1850co2 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

Pre-industrial

1601-1849

Historical

1850-2014

Future

2015-2100

pre-industrial control

histsoc

1st priority

CF: no climate change, pre-industrial CO₂ fixed at 1850 levels

picontrol

picontrol

picontrol

DHF: varying management before 2015, then fixed at 2015 levels thereafter

1850soc

histsoc

2015soc

pre-industrial control

2015soc

1st priority

CF: no climate change, pre-industrial CO₂ fixed at 1850 levels does not have to be simulated as the following year already provide a large sample of years with stable climate and constant (2015soc) / no (nat) DHF for period

picontrol

picontrol

DHF: fixed at 2015 levels for all periods

2015soc

2015soc

RCP2.6

histsoc

1st priority

CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂ "histsoc" version of the pre-industrial period of the pre-industrial control experiment

historical

ssp126

DHF: varying management before 2015, then fixed at 2015 levels thereafter

histsoc

2015soc

RCP2.6

2015soc

1st priority

CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂ "2015soc" version of the pre-industrial period of the pre-industrial control experiment

historical

ssp126

DHF: fixed at 2015 levels for all periods

2015soc

2015soc

CO₂ sensitivity RCP2.6

histsoc

2nd priority

CF: RCP2.6 climate, CO2 after 2015 fixed at 2015 levels "histsoc" version of the pre-industrial period of the pre-industrial control experiment "histsoc" version of the historical period of the RCP2.6 experiment, as described above

ssp126

Sensitivity scenario: 2015co2

DHF: varying management before 2015, then fixed at 2015 levels thereafter

2015soc

RCP7

histsoc

1st priority

CF: SSP3-RCP7 climate & CO₂ "histsoc" version of pre-industrial of pre-industrial control experiment runs "histsoc" version of the historical period of the RCP2.6 experiment

ssp370

DHF: varying management before 2015, then fixed at 2015 levels thereafter

2015soc

RCP7

2015soc

1st priority

CF: SSP3-RCP7 climate & CO₂ "2015soc" version of pre-industrial of pre-industrial control experiment runs "2015soc" version of the historical period of the RCP2.6 experiment

ssp370

DHF: fixed at 2015 levels for all periods

2015soc

RCP8.5

histsoc

1st priority

CF: SSP5-RCP8.5 climate & CO₂ "histsoc" version of pre-industrial of pre-industrial control experiment runs "histsoc" version of the historical period of the RCP2.6 experiment

ssp585

DHF: varying management before 2015, then fixed at 2015 levels thereafter

2015soc

RCP8.5

2015soc

1st priority

CF: SSP5-RCP8.5 climate & CO₂ "2015soc" version of pre-industrial of pre-industrial control experiment runs "2015soc" version of the historical period of the RCP2.6 experiment

ssp585

DHF: fixed at 2015 levels for all periods

2015soc

CO₂ sensitivity RCP8.5

histsoc

1st priority

CF: Historical climate and CO₂ forcing up to 2015, fixed 2015 CO₂ afterwards "histsoc" version of the pre-industrial period of the pre-industrial control experiment "histsoc" version of the historical period of the RCP2.6 experiment

ssp585

Sensitivity scenario: 2015co2

DHF: varying management before 2015, then fixed at 2015 levels thereafter

2015soc

CO₂ sensitivity RCP8.5

2015soc

1st priority

CF: Historical climate and CO₂ forcing up to 2015, fixed 2015 CO₂ afterwards "2015soc" version of the pre-industrial period of the pre-industrial control experiment "2015soc" version of the historical period of the RCP2.6 experiment

ssp585

Sensitivity scenario: 2015co2

DHF: fixed direct human forcing at 2015 levels for the entire simulation period

2015soc

Note regarding models requiring spin-up

For models requiring spin-up, please use the pre-industrial control data and CO₂ concentration and DHF fixed at 1850 levels for the spin up as long as needed. Please note that the "pre-industrial control run" from 1601-1849 is part of the regular experiments that should be reported and hence the spin-up has to be finished before that.

Input data

The base directory for input data at DKRZ is:

/work/bb0820/ISIMIP/ISIMIP3b/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 Institution Original resolution Ensemble member
GFDL-ESM4 gfdl-esm4 National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA 288x180 r1i1p1f1
UKESM1-0-LL ukesm1-0-ll Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK 192x144 r1i1p1f2
MPI-ESM1-2-HR mpi-esm1-2-hr Max Planck Institute for Meteorology, Hamburg 20146, Germany 384x192 r1i1p1f1
IPSL-CM6A-LR ipsl-cm6a-lr Institut Pierre Simon Laplace, Paris 75252, France 144x143 r1i1p1f1
MRI-ESM2-0 mri-esm2-0 Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan 320x160 r1i1p1f1
Table 6: Climate forcing variables for ISIMIP3b simulations (climate-variable).
Variable Variable specifier Unit Resolution Models
Atmospheric variables mandatory
Near-Surface Relative Humidity hurs %
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Specific Humidity huss kg kg-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Precipitation pr kg m-2 s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Snowfall Flux prsn kg m-2 s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Air Pressure ps Pa
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Downwelling Longwave Radiation rlds W m-2
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Downwelling Shortwave Radiation rsds W m-2
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Wind Speed sfcwind m s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Air Temperature tas K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Daily Maximum Near-Surface Air Temperature tasmax K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Daily Minimum Near-Surface Air Temperature tasmin K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0

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

# atmospheric variables
climate/atmosphere/bias-adjusted/global/daily/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<bias-adjustment>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc
# ocean variables
climate/ocean/uncorrected/global/monthly/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<climate-scenario>_<climate-variable>_<resolution>_global_daily_<start-year>_<end-year>.nc

The ocean variables are not yet available.

Greenhouse gas forcing

Table 7: Greenhouse gas forcing for ISIMIP3b simulation round.
Variable Variable specifier Unit Resolution Datasets
Atmospheric composition mandatory

Atmospheric CO2 concentration

composition_atmosphere/co2/co2_<cmip6-experiment>_annual_<start_year>_<end_year>.txt
co2 ppm
  • global
  • annual

Meinshausen, Raper, & Wigley (2011) for 1850-2005 and 2016-2100 and Dlugokencky & Tans (2019) from 2006-2015

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3b 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_2014.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)
  • 1850-2014
  • 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
  • 1901-2014
  • 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
  • 1901-2014
  • 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)
  • 1850-2014
  • 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
  • 1850-2014
  • 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)
  • 1850-2014
  • 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 ISIMIP3b 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:

Please name the files in the Agriculture sector according to the following pattern:

<model>_<climate-forcing>_<bias-adjustment>_<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_gfdl-esm4_w5e5_picontrol_histsoc_default_qtot_global_annual_2001_2010.nc
lpjml_ukesm1-0-ll_w5e5_ssp585_2015soc_2015co2_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<bias_adjustment>[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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