ISIMIP3b simulation round simulation protocol - Biomes

Introduction
Scenarios & Experiments
- Scenario definitions
- Experiments
Input data
Output data
- Output variables
- Information about PFT-specific outputs
Reporting model results
References

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.

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 Biomes. 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.
nat	No direct human influences (naturalized run). Please only label your model run `nat` if it does not at all account for any direct human forcings, including e.g. human land use.

Table 3: Sensitivity scenario specifiers (`sens-scenario`).
Scenario specifier	Description
default	For all experiments other than the sensitivity experiments.

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
pre-industrial control histsoc 1st priority	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
pre-industrial control 2015soc 1st priority	DHF: fixed at 2015 levels for all periods		2015soc	2015soc
pre-industrial control nat 2nd 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
pre-industrial control nat 2nd priority	DHF: No direct human influences		nat	nat
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
RCP2.6 histsoc 1st priority	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
RCP2.6 2015soc 1st priority	DHF: fixed at 2015 levels for all periods		2015soc	2015soc
RCP2.6 nat 2nd priority	CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂	"nat" version of the pre-industrial period of the pre-industrial control experiment	historical	ssp126
RCP2.6 nat 2nd priority	DHF: No direct human influences		nat	nat
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
CO₂ sensitivity RCP2.6 histsoc 2nd priority	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
RCP7 histsoc 1st priority	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
RCP7 2015soc 1st priority	DHF: fixed at 2015 levels for all periods			2015soc
RCP7 nat 2nd priority	CF: SSP3-RCP7 climate & CO₂	"nat" version of pre-industrial of pre-industrial control experiment runs	"nat" version of the historical period of the RCP2.6 experiment	ssp370
RCP7 nat 2nd priority	DHF: No direct human influences			nat
CO₂ sensitivity RCP7 histsoc 2nd priority	CF: RCP7 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 RCP7 experiment, as described above	ssp370 Sensitivity scenario: 2015co2
CO₂ sensitivity RCP7 histsoc 2nd priority	DHF: varying management before 2015, then fixed at 2015 levels thereafter			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
RCP8.5 histsoc 1st priority	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
RCP8.5 2015soc 1st priority	DHF: fixed at 2015 levels for all periods			2015soc
RCP8.5 nat 2nd priority	CF: SSP5-RCP8.5 climate & CO₂	"nat" version of pre-industrial of pre-industrial control experiment runs	"nat" version of the historical period of the RCP2.6 experiment	ssp585
RCP8.5 nat 2nd priority	DHF: No direct human influences			nat
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
CO₂ sensitivity RCP8.5 histsoc 1st priority	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
CO₂ sensitivity RCP8.5 2015soc 1st priority	DHF: fixed direct human forcing at 2015 levels for the entire simulation period			2015soc
CO₂ sensitivity RCP8.5 nat 1st priority	CF: Historical climate and CO₂ forcing up to 2015, fixed 2015 CO₂ afterwards	"nat" version of the pre-industrial period of the pre-industrial control experiment	"nat" version of the historical period of the RCP2.6 experiment	ssp585 Sensitivity scenario: 2015co2
CO₂ sensitivity RCP8.5 nat 1st priority	DHF: No direct human influences			nat

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

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

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

Table 5: Climate and climate-related forcing data (`climate-forcing`).
Title	Specifier	Institution	Native resolution	Ensemble member	Priority
GFDL-ESM4	gfdl-esm4	National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA	288x180	r1i1p1f1	1
UKESM1-0-LL	ukesm1-0-ll	Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK	192x144	r1i1p1f2	2
MPI-ESM1-2-HR	mpi-esm1-2-hr	Max Planck Institute for Meteorology, Hamburg 20146, Germany	384x192	r1i1p1f1	3
IPSL-CM6A-LR	ipsl-cm6a-lr	Institut Pierre Simon Laplace, Paris 75252, France	144x143	r1i1p1f1	4
MRI-ESM2-0	mri-esm2-0	Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan	320x160	r1i1p1f1	5

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 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

Other climate datasets

Table 7: Other climate datesets for ISIMIP3b 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, 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) 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)	1850-2014	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.
Forest management
Wood harvesting	`socioeconomic/wood_harvesting/<variable>_<soc_scenario>_national_annual_<start_year>_<end_year>.nc`
	wood harvest area from primary forest land (primf-harv) wood harvest area from primary non forest land (primn-harv) wood harvest area from secondary mature forest land (secmf-harv) wood harvest area from secondary young forest land (secyf-harv) wood harvest area from secondary non forest land (secnf-harv) wood harvest biomass carbon from primary forest land (primf-bioh) wood harvest biomass carbon from primary non forest land (primn-bioh) wood harvest biomass carbon from secondary mature forest land (secmf-bioh) wood harvest biomass carbon from secondary young forest land (secyf-bioh) wood harvest biomass carbon from secondary non forest land (secnf-bioh)	1850-2017	national annual	Historic annual country-level wood harvesting data. Based on the LUH2 v2h Harmonization Data Set (see Hurtt, Chini et al. 2011; see also https://luh.umd.edu). Interpolated to a 0.5° grid using first-order conservative remapping and calculated over a fractional country mask (https://gitlab.pik-potsdam.de/isipedia/countrymasks/-/blob/master/) derived from ASAP-GAUL (https://data.europa.eu/euodp/data/dataset/jrc-10112-10004).
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). For 1950-2014, 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)	1850-2014	0.5° grid annual	HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, 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	1850-2014	national annual	HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, 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 1901-2014	5' grid annual	HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, 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)	1850-2014	0.5° grid annual	HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, 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	1850-2014	national annual	HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, 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)	1850-2014	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 (Lange, 2019a; Cucchi et al., 2020). Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land.
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)
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

ISIMIP output variables are usually reported with the dimensions (time,lat,lon). For variables with a number of levels (e.g. layers or depth), an alternative set of dimensions is given in the comment column in the table below. More information about level dimensions can be found here and here on the ISIMIP webpage.

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

Output variables

Table 10: Output variables for Biomes (`variable`).
Variable long name	Variable specifier	Unit	Resolution	Comments
Carbon Pools
Carbon Mass in Vegetation	cveg-total, cveg-<pft>	kg m-2	0.5° grid annual	Grid cell total and PFT information is essential.
Carbon Mass in Above Ground Vegetation Biomass	cvegag-total, cvegag-<pft>	kg m-2	0.5° grid annual	Grid cell total and PFT information is essential.
Carbon Mass in Below Ground Vegetation Biomass	cvegbg-total, cvegbg-<pft>	kg m-2	0.5° grid annual	Grid cell total and PFT information is essential.
Carbon Mass in Soil Pool	csoil-total, csoil-<pft>	kg m-2	0.5° grid annual	Level dimensions: (time, depth, lat, lon). Soil carbon excluding belowground litter if your model reports clitter. If not including below-ground litter, i.e. only report csoil and document this in the model documentation. Grid cell total and PFT information is essential. If possible, provide soil carbon for all depth layers (i.e. 3D-field), and indicate depth in m. Otherwise, provide soil carbon integrated over entire soil depth.
Carbon Mass in Above Ground Litter Pool	clitterag-total, clitterag-<pft>	kg m-2	0.5° grid annual	Grid cell total and PFT information is essential.
Carbon Mass in Below Ground Litter Pool	clitterbg-total, clitterbg-<pft>	kg m-2	0.5° grid annual	Only if models separates below-ground litter and soil carbon. If not, only report csoil and document this in the model documentation. Grid cell total and PFT information is essential.
Carbon in Products of Land Use Change	cproduct-total, cproduct-<productclass>	kg m-2	0.5° grid annual	Products generated during Land-use change. Removed carbon should not go into the soil but into the product pool. Please use the product classes used within your model and document them in the model documentation on the ISIMIP homepage.
Carbon Fluxes
Carbon Mass Flux out of Atmosphere due to Gross Primary Production on Land	gpp-total, gpp-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Carbon Mass Flux out of Atmosphere due to Net Primary Production on Land	npp-total, npp-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Carbon Mass Flux out of Atmosphere due to Net Biospheric Production on Land	nbp-total, nbp-<pft>	kg m-2 s-1	0.5° grid monthly	This is the net mass flux of carbon between land and atmosphere calculated as photosynthesis MINUS the sum of plant and soil respiration, carbonfluxes from fire, harvest, grazing and land use change. Positive flux is into the land.
Carbon Mass Flux into Atmosphere due to Autotrophic (plant) Respiration on Land	ra-total, ra-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Carbon Mass Flux into Atmosphere due to Heterotrophic Respiration on Land	rh-total, rh-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Root autotrophic respiration	rr-total, rr-<pft>	kg m-2 s-1	0.5° grid monthly
Carbon Mass Flux into Atmosphere due to CO₂ Emission from Fire	ffire-total, ffire-<pft>	kg m-2 s-1	0.5° grid monthly
CO2 Flux to Atmosphere from Land Use Change	fluc-total, fluc-<pft>	kg m-2 s-1	0.5° grid monthly	For wood products only. Sum of CO₂ fluxes to wood production and wood storage turnover emsissions from previous years.
CO2 Flux to Atmosphere from Grazing	fgrazing-total, fgrazing-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
CO2 Flux to Atmosphere from Crop Harvesting	fcropharvest-total, fcropharvest-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Total Carbon Flux from Vegetation to Litter	flitter-total, flitter-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Total Carbon Flux from Litter to Soil	flittersoil-total, flittersoil-<pft>	kg m-2 s-1	0.5° grid monthly	Grid cell total and PFT information is essential.
Total Carbon Flux from Vegetation Directly to Soil	fvegsoil-total, fvegsoil-<pft>	kg m-2 s-1	0.5° grid monthly	Carbon going directly into the soil pool without entering litter (e.g., root exudate). Grid cell total and PFT information is essential.
Vegetation Cover & Structure
Fraction of Absorbed Photosynthetically Active Radiation	fapar-total, fapar-<pft>	%	0.5° grid daily else monthly	Value between 0 and 100. Grid cell total and PFT information is essential.
Leaf Area Index	lai-total, lai-<pft>	1	0.5° grid daily else monthly	Grid cell total and PFT information is essential.
Plant Functional Type Grid Fraction	pft-total, pft-<pft>	%	0.5° grid annual (or fixed if static)	The categories may differ from model to model, depending on their PFT definitions. This may include natural PFTs, anthropogenic PFTs, bare soil, lakes, urban areas, etc. Sum of all should equal the fraction of the grid cell that is land. For models that have grid cells partially covered by land and ocean, please document this in the model documentation and provide your land-sea mask along the data uploads.
Temperature of Soil	tsl	K	0.5° grid daily if possible, else monthly	Level dimensions: (time, depth, lat, lon). Temperature of each soil layer. Reported as "missing" for grid cells occupied entirely by "sea". This is the most important variable for the permafrost sector. If daily resolution not possible, please provide monthly. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files.
Burnt Area Fraction	burntarea-total, burntarea-<pft>	%	0.5° grid daily (total), monthly (pft/total)	Area percentage of grid cell that has burned at any time of the given day/month/year (for daily/monthly/annual resolution)
Hydrological variables
Evapotranspiration	evap-total, evap-<pft>	kg m-2 s-1	0.5° grid monthly	Sum of transpiration, evaporation, interception and sublimation.
Evaporation from Canopy (interception)	intercep-total, intercep-<pft>	kg m-2 s-1	0.5° grid monthly	The canopy evaporation+sublimation (if present in model).
Water Evaporation from Soil	esoil	kg m-2 s-1	0.5° grid monthly	Includes sublimation.
Transpiration	trans-total, trans-<pft>	kg m-2 s-1	0.5° grid monthly
Total Runoff	qtot	kg m-2 s-1	0.5° grid daily & monthly	Total (surface + subsurface) runoff (qtot = qs + qsb). Please provide both daily and monthly resolution.
Surface Runoff	qs	kg m-2 s-1	0.5° grid monthly	Water that leaves the surface layer (top soil layer) e.g. as overland flow / fast runoff.
Total Soil Moisture Content	soilmoist	kg m-2	0.5° grid daily	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.
Frozen Soil Moisture Content	soilmoistfroz	kg m-2	0.5° grid monthly	Level dimensions: (time, depth, lat, lon). Please provide soil moisture for all depth levels and indicate depth in m.
Snow depth	snd	m	0.5° grid monthly	Grid cell mean depth of snowpack. This variable only for the purposes of the permafrost sector.
Snow Water Equivalent	swe	kg m-2	0.5° grid monthly	Total water mass of the snowpack (liquid or frozen) averaged over grid cell. Please also deliver for the permafrost sector.
Annual Maximum Thaw Depth	thawdepth	m	0.5° grid monthly, else annual	Calculated from daily thaw depths.
Optional output
Carbon Mass in Leaves	cleaf-total, cleaf-<pft>	kg m-2	0.5° grid annual
Carbon Mass in Wood	cwood-total, cwood-<pft>	kg m-2	0.5° grid annual	Including sapwood and hardwood.
Carbon Mass in Roots	croot-total, croot-<pft>	kg m-2	0.5° grid annual	Including fine and coarse roots.
Carbon Mass in Coarse Woody Debris	ccwd-total, ccwd-<pft>	kg m-2	0.5° grid annual
Other
Soil Types	soil	-	0.5° grid constant	Soil types or texture classes as used by your model. Please include a description of each type or class, especially if these are different from the standard HSWD and GSWP3 soil types. Please also include a description of the parameters and values associated with these soil types (parameter values could be submitted as spatial fields where appropriate).
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.
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 Date	plantday-<crop>-<irrigation>	day of year	0.5° grid seasonal	Julian dates.
Anthesis Date	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 Date	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.
Total Above Ground Biomass Dry Matter Yields	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.
Surface Albedo of Land	landalbedo	1	0.5° grid monthly	Albedo of the land surface interacting with the atmosphere. Average of pfts, snow cover, bare ground.

Information about PFT-specific outputs

Unless otherwise defined, variables in ISIMIP should be reported relative to the grid cell land area.
The output provided per Plant Functional Type (PFT) should be expressed relative to the respective PFT area so that e.g. sum(gpp-pft*pft-pft)=gpp-total.
When your model allows several PFTs to grow on the same area and hence the total cover fraction can be more than one, please scale the PFT area to one and report this step in the model documentation on the ISIMIP homepage.
When your model grows the same PFT on different land-use classes, e.g. the same c3-grass represents grasses growing on natural grasslands, pasture and possible even as crop, please differentiate this in your output by defining land-use specific PFT such as c3-grass-pasture, c3-grass-natural, c3-grass-crop and report this in model documentation on the ISIMIP homepage.

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.
The reference year for the NetCDF files for ISIMIP3b is 1601.

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

<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>(-<pft>)_<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_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 biomes 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<pft>[a-z0-9]+))?_(?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.: WFDE5 over land merged with ERA5 over the ocean (W5E5), v1.0, GFZ Data Services, doi:10.5880/pik.2019.023, 2019b.

Lange, S.: ISIMIP3BASD, v2.4.1, doi:10.5281/zenodo.3898426, 2020.

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.

ISIMIP3b simulation round simulation protocol - Biomes

Introduction

General concept

ISIMIP3b

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

Simulation protocol

Model versioning

Scenarios & Experiments

Scenario definitions

General note regarding sensitivity experiments

Experiments

pre-industrial control

pre-industrial control

pre-industrial control

RCP2.6

RCP2.6

RCP2.6

CO₂ sensitivity RCP2.6

RCP7

RCP7

RCP7

CO₂ sensitivity RCP7

RCP8.5

RCP8.5

RCP8.5

CO₂ sensitivity RCP8.5

CO₂ sensitivity RCP8.5

CO₂ sensitivity RCP8.5

Note regarding models requiring spin-up

Input data

Climate forcing

Note on climate forcing priority

Other climate datasets

Socioeconomic forcing

Geographic data and information

Output data

Output variables

Information about PFT-specific outputs

Reporting model results

References