ISIMIP3b simulation round simulation protocol - all sectors combined

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 all sectors combined. 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 (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.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Atmosphere: 288x180

Ocean: 720x576

r1i1p1f1 1
UKESM1-0-LL ukesm1-0-ll Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK

Atmosphere: 192x144

Ocean: 360x330

r1i1p1f2 2
MPI-ESM1-2-HR mpi-esm1-2-hr Max Planck Institute for Meteorology, Hamburg 20146, Germany

Atmosphere: 384x192

Ocean: 802x404

r1i1p1f1 3
IPSL-CM6A-LR ipsl-cm6a-lr Institut Pierre Simon Laplace, Paris 75252, France

Atmosphere: 144x143

Ocean: 362x332

r1i1p1f1 4
MRI-ESM2-0 mri-esm2-0 Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan

Atmosphere: 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
Ocean variables mandatory
Mass Concentration of Total Phytoplankton Expressed as Chlorophyll chl kg m-3
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Floor Depth deptho m
  • 1° grid
  • fixed
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Downward Flux of Particulate Organic Carbon expc-bot mol m-2 s-1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Particulate Organic Carbon Content intpoc kg m-2
  • 1° grid
  • monthly
  • GFDL-ESM4
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Primary Organic Carbon Production by All Types of Phytoplankton intpp mol m-2 s-1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Net Primary Organic Carbon Production by Diatoms intppdiat mol m-2 s-1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • UKESM1-0-LL
Net Primary Mole Productivity of Carbon by Diazotrophs intppdiaz mol m-2 s-1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • MPI-ESM1-2-HR
Maximum Ocean Mixed Layer Thickness Defined by Sigma T mlotstmax m
  • 1° grid
  • monthly
  • CESM2
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Dissolved Oxygen Concentration o2, o2-bot, o2-surf mol m-3
  • 1° grid
  • monthly
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
pH ph, ph-bot, ph-surf 1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Phytoplankton Carbon Concentration phyc, phyc-vint mol m-3
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Mole Concentration of Diatoms expressed as Carbon in sea water phydiat, phydiat-vint mol m-3
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • UKESM1-0-LL
Mole Concentration of Diazotrophs Expressed as Carbon in Sea Water phydiaz, phydiaz-vint mol m-3
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • MPI-ESM1-2-HR
Primary Carbon Production by Total Phytoplankton pp mol m-3 s-1
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
Sea Water Salinity so, so-bot, so-surf 0.001
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Net Downward Shortwave Radiation at Sea Water Surface rsntds W m-2
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
Sea Ice Area Fraction siconc %
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Water Potential Temperature thetao °C
  • 1° grid
  • 2° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Ocean Model Cell Thickness thkcello m
  • 1° grid
  • monthly
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Water Potential Temperature at Sea Floor tob °C
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Surface Temperature tos °C
  • 1° grid
  • 2° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Water X Velocity uo m s-1
  • 1° grid
  • monthly
  • CESM2
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Water Y Velocity vo m s-1
  • 1° grid
  • monthly
  • CESM2
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Sea Water Z Velocity wo m s-1
  • 1° grid
  • 2° grid
  • monthly
  • CESM2
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL
Mole Concentration of Mesozooplankton expressed as Carbon in sea water zmeso, zmeso-vint mol m-3
  • 1° grid
  • monthly
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • UKESM1-0-LL
Mole Concentration of Microzooplankton expressed as Carbon in sea water zmicro, zmicro-vint mol m-3
  • 1° grid
  • monthly
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • UKESM1-0-LL
Zooplankton Carbon Concentration zooc, zooc-vint mol m-3
  • 1° grid
  • monthly
  • CESM2
  • GFDL-ESM4
  • IPSL-CM6A-LR
  • MPI-ESM1-2-HR
  • UKESM1-0-LL

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
co2 ppm
  • global
  • annual

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

Lightning mandatory

Flash Rate Monthly Climatology

climate/lightning/lightning_fixed.nc
lightning km-2 d-1
  • 0.5° grid
  • monthly

Cecil, Daniel J. 2006. LIS/OTD 0.5 Degree High Resolution Monthly Climatology (HRMC) [indicate subset used]. Dataset available online from the NASA Global Hydrology Resource Center DAAC, Huntsville, Alabama, U.S.A., DOI: http://dx.doi.org/10.5067/LIS/LIS-OTD/DATA303

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. (2020), see also https://luh.umd.edu). For further information on the land use data refer to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/82/.

Downscaling to 5 crops

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-5crops_annual_<start_year>_<end_year>.nc
  • share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated)
  • share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated)
  • share of rainfed/irrigated C3 N-fixing crops (c3nfx_rainfed, c3nfx_irrigated)
  • share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated)
  • share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al. (2020), see also https://luh.umd.edu).

Downscaling to 15 crops

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-15crops_annual_<start_year>_<end_year>.nc
  • share of rainfed/irrigated maize (maize_rainfed, maize_irrigated)
  • share of rainfed/irrigated rice (rice_rainfed, rice_irrigated)
  • share of rainfed/irrigated oil crops (groundnut) (oil_crops_groundnut_rainfed, oil_crops_groundnut_irrigated)
  • share of rainfed/irrigated oil crops (rapeseed) (oil_crops_rapeseed_rainfed, oil_crops_rapeseed_irrigated)
  • share of rainfed/irrigated oil crops (soybean) (oil_crops_soybean_rainfed, oil_crops_soybean_irrigated)
  • share of rainfed/irrigated oil crops (sunflower) (oil_crops_sunflower_rainfed, oil_crops_sunflower_irrigated)
  • share of rainfed/irrigated pulses (pulses_rainfed, pulses_irrigated)
  • share of rainfed/irrigated temperate cereals (temperate_cereals_rainfed, temperate_cereals_irrigated)
  • share of rainfed/irrigated temperate roots (temperate_roots_rainfed, temperate_roots_irrigated)
  • share of rainfed/irrigated tropical cereals (tropical_cereals_rainfed, tropical_cereals_irrigated)
  • share of rainfed/irrigated tropical roots (tropical_roots_rainfed, tropical_roots_irrigated)
  • share of rainfed/irrigated C3 annual crops not covered by the above (others_c3ann_rainfed, others_c3ann_irrigated)
  • share of rainfed/irrigated C3 N-fixing crops not covered by the above (others_c3nfx_rainfed, others_c3nfx_irrigated)
  • share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated)
  • share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated)
  • share of pastures, both managed and rangeland (pastures)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

The C4 perennial crops are not further downscaled from the "5 crops" data set and currently only include sugarcane. Similarly, the C3 perennial crops are not downscaled either. The data is derived from the "5 crops" LUH2 data, and the crops have been downscaled to 15 crops according to the ratios given by the Monfreda data set (Monfreda, Ramankutty, & Foley, 2008).

Managed pastures and rangeland

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-pastures_annual_<start_year>_<end_year>.nc
  • share of managed pastures (managed_pastures)
  • share of rangeland (rangeland)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review., see also https://luh.umd.edu).

Urban areas

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-urbanareas_annual_<start_year>_<end_year>.nc
  • share of urban areas (urbanareas)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al. (2020), see also https://luh.umd.edu).

N-fertilizer mandatory

Nitrogen deposited by fertilizers on croplands

socioeconomic/n-fertilizer/<soc_scenario>/<soc_scenario>_n-fertilizer-5crops_annual_<start_year>_<end_year>.nc
  • deposition of N through fertilizer on cropland with C3 annual crops (fertl_c3ann)
  • C3 perennial crops (fertl_c3per)
  • C3 N-fixing crops (fertl_c3nfx)
  • C4 annual crops (fertl_c4ann)
  • C4 perennial crops (fertl_c4per)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual (growing season)

Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al. (2020), see also https://luh.umd.edu). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/28/.

N-deposition

Reduced nitrogen deposition

socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc
  • NHx deposition (nhx)
  • 1850-1900
  • 1901-2016
  • 0.5° grid
  • monthly

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/24/.

Oxidized nitrogen deposition

socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc
  • NOy deposition (noy)
  • 1850-1900
  • 1901-2016
  • 0.5° grid
  • annual

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018). For further information refer also to https://www.isimip.org/gettingstarted/input-data-bias-correction/details/24/.

Reservoirs & dams

Reservoirs & dams

socioeconomic/reservoir_dams/reservoirs-dams_1850_2015.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)
  • 1850-2015
  • 0.5° grid and original coordinates (degree)
  • annual

Lehner et al. (2011a, https://doi.org/10.7927/H4N877QK), Lehner et al. (2011b, https://dx.doi.org/10.1890/100125), and Jida Wang et al. (KSU/Kansas State University, personal communication, data starting in 2016). Because the data from KSU is yet unpublished, modeling teams using it are asked to offer co-authorship to the team at KSU on any resulting publications. Please contact info@isimip.org in case of questions.

Water abstraction

Water abstraction for domestic and industrial purposes

socioeconomic/water_abstraction/[domw|indw][w|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc
  • domestic and industrial water withdrawal and consumption (domww, domwc, indww, indwc)
  • 1850-1900
  • 1901-2014
  • 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.

Fishing mandatory

Fishing effort

socioeconomic/fishing/effort_histsoc_1950_2014.csv
  • The generic name of the FAO area in which the Effort/Catch is occurring (FAO)
  • The EEZ / high seas name in which the Effort/Catch is occurring (EEZ)
  • The generic name of the Large Marine ecosystem in which the Effort/Catch is occurring (LME)
  • Functional Group of the target species (FGroup)
  • Nominal (i.e. not including the technological creep) fishing effort corresponding to the reported catch (kW.days) (NomEffReported)
  • Nominal (i.e. not including the technological creep) fishing effort corresponding to the illegal catch and discards (kW.days) (NomEffIUU)
  • Year (end of the year) when the Effort/Catch is occurring (Year)
  • Water surface area (km2) (AreaSqKm2)
  • Number of 0.5 degree cell making up the area (NbrCells)
  • Fishing country, in ISO3 notation. Ex supranational entities (USSR, Yugoslavia) are disaggregated to their constituent countries. Serbian Fishing Effort included with Montenegro (Fcountry)
  • Fishing sector (artisanal/industrial), defined by the law of the country (varies by country) (Sector)
  • A number code to the LME, as per NOAA notation (LMEnbr)
  • A number code to the FAO area (FAOnbr)
  • 1950-2014
  • Marine ecosystems or exclusive economic zones
  • annual

Data comprise the nominal effort of industrial and artisanal fleets aggregated into 6 functional groups. Source: Rousseau et al., 2019, PNAS 116 (25) 12238-12243.

Fish catch

socioeconomic/fishing/catch_histsoc_1950_2014.csv
  • The generic name of the FAO area in which the Effort/Catch is occurring (FAO)
  • The EEZ / high seas name in which the Effort/Catch is occurring (EEZ)
  • The generic name of the Large Marine ecosystem in which the Effort/Catch is occurring (LME)
  • Functional Group of the target species (FGroup)
  • Nominal (i.e. not including the technological creep) fishing effort corresponding to the reported catch (kW.days) (NomEffReported)
  • Nominal (i.e. not including the technological creep) fishing effort corresponding to the illegal catch and discards (kW.days) (NomEffIUU)
  • Year (end of the year) when the Effort/Catch is occurring (Year)
  • Water surface area (km2) (AreaSqKm2)
  • Number of 0.5 degree cell making up the area (NbrCells)
  • Fishing country, in ISO3 notation. Ex supranational entities (USSR, Yugoslavia) are disaggregated to their constituent countries. Serbian Fishing Effort included with Montenegro (Fcountry)
  • Fishing sector (artisanal/industrial), defined by the law of the country (varies by country) (Sector)
  • A number code to the LME, as per NOAA notation (LMEnbr)
  • A number code to the FAO area (FAOnbr)
  • 1950-2014
  • Marine ecosystems or exclusive economic zones
  • annual

Data comprise the nominal effort of industrial and artisanal fleets aggregated into 6 functional groups. Reference for data source: Watson and Tidd, 2018, Marine Policy, 93: 171-177.

Forest management

Forest management mandatory

http://doi.org/10.5880/PIK.2019.008
  • stem numbers (stemno)
  • tree species (species)
  • Bily Kriz: 1997-2015
  • Collelongo: 1992-2012
  • Hyytiälä: 1995-2011
  • KROOF: 1997-2010
  • Le Bray: 1986-2009
  • Peitz: 1948-2011
  • Solling-beech: 1967-2014
  • Solling-spruce: 1967-2014
  • Soro: 1944-2010
  • plot-specific
  • annual

Reyer et al. 2019, 2020 management prescribes stem numbers remaining after harvest, management data is annual but not every year has data.

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). For further information see https://www.isimip.org/gettingstarted/input-data-bias-correction/details/83/

Lakes

Lake area fraction

socioeconomic/lakes/pctlake_<soc_scenario>_<start_year>_<end_year>.nc
  • percentage of lakes in grid cell (pct_lake)
  • 1850-2014
  • 0.5° grid
  • annual

Accounts for growing lake area fraction due to reservoir construction. HydroLAKES polygons dataset v1.0 June 2019 and GRanD v1.3, rasterized using the polygon_to_cellareafraction tool (https://github.com/VUB-HYDR/polygon_to_cellareafraction). Reference: Messager et al. (2016, https://dx.doi.org/10.1038/ncomms13603, Lehner et al. (2011b, https://dx.doi.org/10.1890/100125).

Lake mask

socioeconomic/lakes/lakemask_<soc_scenario>_<start_year>_<end_year>.nc
  • total lake surface area in grid cell (tot_area)
  • average surface area of lakes in grid cell (avg_area)
  • 1850-2014
  • 0.5° grid
  • annual

Accounts for growing lake area fraction due to reservoir construction. HydroLAKES polygons dataset v1.0 June 2019 and GRanD v1.3, rasterized using the polygon_to_cellareafraction tool (https://github.com/VUB-HYDR/polygon_to_cellareafraction). Reference: Messager et al. (2016, https://dx.doi.org/10.1038/ncomms13603, Lehner et al. (2011b, https://dx.doi.org/10.1890/100125).

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 historical data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html). They are derived mainly from Penn World Tables (PWT) for recent decades, and from the Maddison Project database (2018 version, https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018) for earlier periods where no PWT data is available. Gaps were filled using World Bank data (WDI). No interpolation to SSPs is performed for this historical dataset.

Static geographic information

Table 9: Geographic data and information for 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 (Cucchi et al., 2020; Lange et al., 2021). Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land. For further information see also https://www.isimip.org/gettingstarted/input-data-bias-correction/details/41/.

landseamask_no-ant

geo_conditions/landseamask/landseamask_no-ant.nc
  • land-sea mask (mask)
0.5° grid

Same as landseamask but without Antarctica.

landseamask_water-global

geo_conditions/landseamask/landseamask_water-global.nc
  • land-sea mask (mask)
0.5° grid

This is the generic land-sea mask from ISIMIP2b that is to be used for global water simulations in ISIMIP3. It marks more grid cells as land than landseamask. Over those additional land cells, the climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are not guaranteed to represent climate conditions over land. Instead they may represent climate conditions over sea or a mix of conditions over land and sea.

Soil

gswp3_hwsd

geo_conditions/soil/gswp3_hwsd.nc
  • Soil texture (soiltexture)
0.5° grid

One fixed pattern to be used for all simulation periods. Upscaled Soil texture map (30 arc sec. to 0.5°x0.5° grid) based on Harmonized World Soil Database v1.1 (HWSD) using the GSWP3 upscaling method A (http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html)

hwsd_soil_data_all_land

geo_conditions/soil/hwsd_soil_data_all_land.nc
  • USDA soil texture class dominant HWSD on cropland (texture_class)
  • domiant HWSD soil mapping unit within dominant USDA soil texture class on cropland (mu_global)
  • Topsoil pH(H2O) (soil_ph)
  • Topsoil Calcium Carbonate (soil_caco3)
  • Topsoil Bulk Density (bulk_density)
  • Topsoil Cation Exchange Capacity (soil) (cec_soil)
  • Topsoil Organic Carbon (oc)
  • depth of Obstacles to Roots (ESDB) (root_obstacles)
  • depth of Impermeable Layer (ESDB) (impermeable_layer)
  • Available Water Content (awc)
  • Topsoil Sand Fraction (sand)
  • Topsoil Silt Fraction (silt)
  • Topsoil Clay Fraction (clay)
  • Topsoil Gravel Content (gravel)
  • Topsoil Salinity (ece)
  • Topsoil Base Saturation (bs_soil)
  • flag for valid soils (issoil)
0.5° grid

GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on all land.

hwsd_soil_data_on_cropland

geo_conditions/soil/hwsd_soil_data_on_cropland.nc
  • USDA soil texture class dominant HWSD on cropland (texture_class)
  • domiant HWSD soil mapping unit within dominant USDA soil texture class on cropland (mu_global)
  • Topsoil pH(H2O) (soil_ph)
  • Topsoil Calcium Carbonate (soil_caco3)
  • Topsoil Bulk Density (bulk_density)
  • Topsoil Cation Exchange Capacity (soil) (cec_soil)
  • Topsoil Organic Carbon (oc)
  • depth of Obstacles to Roots (ESDB) (root_obstacles)
  • depth of Impermeable Layer (ESDB) (impermeable_layer)
  • Available Water Content (awc)
  • Topsoil Sand Fraction (sand)
  • Topsoil Silt Fraction (silt)
  • Topsoil Clay Fraction (clay)
  • Topsoil Gravel Content (gravel)
  • Topsoil Salinity (ece)
  • Topsoil Base Saturation (bs_soil)
  • flag for valid soils (issoil)
0.5° grid

GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on current cropland (MIRCA2000 at 5 arc-minutes).

River routing

basins

geo_conditions/river_routing/ddm30_basins_cru_neva.[nc|asc]
  • basin number (basinnumber)
0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

flowdir

geo_conditions/river_routing/ddm30_flowdir_cru_neva.[nc|asc]
  • flow direction (flowdirection)
0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

slopes

geo_conditions/river_routing/ddm30_slopes_cru_neva.[nc|asc]
  • slope (slope)
0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

Lakes

lakedepth

geo_conditions/lakes/lakedepth.nc
  • lake depth (lakedepth)
0.5° grid

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 all sectors combined (variable).
Variable long name Variable specifier Unit Resolution Comments
Full ISIMIP variable list
Total Runoff qtot kg m-2 s-1
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • other: 0.5° grid
  • daily & monthly

Sectors: biomes, fire, permafrost, water_global, water_regional.

Total (surface + subsurface) runoff (qtot = qs + qsb). Please provide both daily and monthly resolution.

Surface Runoff qs kg m-2 s-1
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • other: 0.5° grid
  • monthly

Sectors: biomes, fire, permafrost, water_global, water_regional.

Water that leaves the surface layer (top soil layer) e.g. as overland flow / fast runoff.

Subsurface Runoff qsb kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Sum of water that flows out from subsurface layer(s) including the groundwater layer (if present). Equals qg in case of a groundwater layer below only one soil layer.

Groundwater Recharge qr kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Water that percolates through the soil layer(s) into the groundwater layer. In case seepage is simulated but no groundwater layer is present, report seepage as qr and qg.

Groundwater Runoff qg kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Water that leaves the groundwater layer. In case seepage is simulated but no groundwater layer is present, report seepage as qr and qg.

Discharge dis m3 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • daily & monthly

Sectors: water_global, water_regional.

River discharge or streamflow. Please provide both daily and monthly resolution.

Evapotranspiration
  • biomes: evap-total, evap-<pft>
  • fire: evap-total, evap-<pft>
  • forestry: evap-total, evap-<species>
  • water_global: evap-total
  • water_regional: evap-total
kg m-2 s-1
  • forestry: stand
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry, water_global, water_regional.

Sum of transpiration, evaporation, interception and sublimation.

Potential Evapotranspiration potevap kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

As evap, but with all resistances set to zero, except the aerodynamic resistance.

Total Soil Moisture Content soilmoist kg m-2
  • forestry: stand
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • other: 0.5° grid
  • daily if possible, else monthly

Sectors: agriculture, biomes, fire, forestry, permafrost, water_global, water_regional.

Level dimensions: (time, depth, lat, lon).

Please provide soil moisture for all depth layers (i.e. 3D-field), and indicate depth in m. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files.

Soil Moisture Content at Root Zone rootmoist kg m-2
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Level dimensions: (time, depth, lat, lon).

Total simulated soil moisture available for evapotranspiration. Please indicate the depth of the root zone for each vegetation type in your model. 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

Sectors: biomes, fire, permafrost, water_global.

Level dimensions: (time, depth, lat, lon).

Please provide soil moisture for all depth levels and indicate depth in m.

Temperature of Soil tsl K
  • forestry: stand
  • other: 0.5° grid
  • daily if possible, else monthly

Sectors: biomes, fire, forestry, permafrost, water_global.

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.

Snow depth snd m
  • 0.5° grid
  • daily if possible, else monthly

Sectors: biomes, fire, permafrost, water_global.

Grid cell mean depth of snowpack. This variable only for the purposes of the permafrost sector.

Snow Water Equivalent swe kg m-2
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • other: 0.5° grid
  • monthly

Sectors: biomes, fire, permafrost, water_global, water_regional.

Total water mass of the snowpack (liquid or frozen) averaged over grid cell. Please also deliver for the permafrost sector.

Total Water Storage tws kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in all compartments. Please indicate in the netcdf metadata which storage compartments are considered.

Canopy Water Storage canopystor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in the canopy.

Glacier Water Storage glacierstor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in glaciers.

Groundwater Storage groundwstor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in groundwater layer.

Lake Water Storage lakestor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in lakes (except reservoirs).

Wetland Water Storage wetlandstor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in wetlands.

River Water Storage riverstor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in rivers.

Reservoir Water Storage reservoirstor kg m-2
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water storage in reservoirs.

Annual Maximum Thaw Depth thawdepth m
  • 0.5° grid
  • annual

Sectors: biomes, fire, permafrost, water_global.

Calculated from daily thaw depths.

River Water Temperature triver K
  • 0.5° grid
  • monthly

Sectors: water_global.

Mean monthly water temperature in river (representative of the average temperature across the channel volume).

Potential Irrigation Water Withdrawal (assuming unlimited water supply) pirrww kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Irrigation water withdrawn in case of optimal irrigation (i.e. eliminating water stress for the plants). This includes the plant's water requirements over and above precipitation and soil moisture, as well as any losses due to conveyance or irrigation inefficiencies considered.

Actual Irrigation Water Withdrawal airrww kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Irrigation water withdrawal, taking water availability into account; please provide if computed.

Potential Irrigation Water Consumption pirruse kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Portion of withdrawal that is evapo-transpired, assuming unlimited water supply.

Cumulative Potential Net Irrigation Water Requirement pirnreqcum-<crop>-<irrigation> kg m-2
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Soil water demand required to avoid water stress accumulated across the growing season, excluding any water losses associated with application or transport and without constraints due to water availability; only needed for firr simulations.

Actual Irrigation Water Consumption airruse kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Portion of withdrawal that is evapo-transpired, taking water availability into account; if computed.

Actual Irrigation Green Water Consumption on Irrigated Cropland airrusegreen kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Actual evapotranspiration from rainwater over irrigated cropland; if computed.

Potential Irrigation Green Water Consumption on Irrigated Cropland pirrusegreen kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Potential evapotranspiration from rainwater over irrigated cropland; if computed and different from AIrrUseGreen.

Actual Green Water Consumption on Rainfed Cropland arainfusegreen kg m-2 s-1
  • water_global: 0.5° grid
  • water_regional: 0.5° grid if possible, otherwise at gauge location
  • monthly

Sectors: water_global, water_regional.

Actual evapotranspiration from rainwater over rainfed cropland; if computed.

Actual Domestic Water Withdrawal adomww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Domestic Water Consumption adomuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Manufacturing Water Withdrawal amanww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Manufacturing Water Consumption amanuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Electricity Water Withdrawal aelecww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Electricity Water Consumption aelecuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual Livestock Water Withdrawal aliveww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Actual livestock Water Consumption aliveuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential Domestic Water Withdrawal pdomww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential Domestic Water Consumption pdomuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential Manufacturing Water Withdrawal pmanww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential manufacturing Water Consumption pmanuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential electricity Water Withdrawal pelecww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential electricity Water Consumption pelecuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential livestock Water Withdrawal pliveww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Potential livestock Water Consumption pliveuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Total Actual Water Withdrawal (all sectors) atotww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed.

Total Actual Water Consumption (all sectors) atotuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Sum of actual consumptive water use from all sectors. Please indicate in metadata which sectors are included.

Total Potential Water Withdrawal (all sectors) ptotww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Sum of potential (i.e. assuming unlimited water supply) water withdrawal from all sectors. Please indicate in metadata which sectors are included.

Total Potential Water Consumption (all sectors) ptotuse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Sum of potential (i.e. assuming unlimited water supply) consumptive water use from all sectors. Please indicate in metadata which sectors are included.

Actual Industrial Water Consumption ainduse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed. There is no need to submit ainduse if its components are being submitted separately (ainduse = amanuse + aelecuse).

Actual Industrial Water Withdrawal aindww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed. There is no need to submit aindww if its components are being submitted separately (aindww = amanww + aelecww).

Potential Industrial Water Consumption pinduse kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed. There is no need to submit pinduse if its components are being submitted separately (pinduse = pmanuse + pelecuse).

Potential Industrial Water Withdrawal pindww kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

If computed. There is no need to submit pindww if its components are being submitted separately (pindww = pmanww + pelecww).

Potential Irrigation Water Withdrawal (assuming unlimited water supply) from groundwater resources pirrwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pirrww that is extracted from groundwater resources.

actual irrigation water withdrawal from groundwater resources airwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of airww that is extracted from groundwater resources.

Potential Irrigation Water Consumption from groundwater resources pirrusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pirruse that is extracted from groundwater resources.

Actual Irrigation Water Consumption from groundwater resources airrusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of airruse that is extracted from groundwater resources.

Potential Domestic Water Withdrawal from groundwater resources pdomwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pdomww that is extracted from groundwater resources.

Actual Domestic Water Withdrawal from groundwater resources adomwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of adomww that is extracted from groundwater resources.

Potential Domestic Water Consumption from groundwater resources pdomusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pdomuse that is extracted from groundwater resources.

Actual Domestic Water Consumption from groundwater resources adomusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of adomuse that is extracted from groundwater resources.

Potential Manufacturing Water Withdrawal from groundwater resources pmanwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pmanww that is extracted from groundwater resources.

Actual Manufacturing Water Withdrawal from groundwater resources amanwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of amanww that is extracted from groundwater resources.

Potential manufacturing Water Consumption from groundwater resources pmanusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pmanuse that is extracted from groundwater resources.

Actual Manufacturing Water Consumption from groundwater resources amanusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of amanuse that is extracted from groundwater resources.

Potential electricity Water Withdrawal from groundwater resources pelecwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pelecww that is extracted from groundwater resources.

Actual Electricity Water Withdrawal from groundwater resources aelecwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of aelecww that is extracted from groundwater resources.

Potential electricity Water Consumption from groundwater resources pelecusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pelecuse that is extracted from groundwater resources.

Actual Electricity Water Consumption from groundwater resources aelecusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of aelecuse that is extracted from groundwater resources.

Potential Industrial Water Withdrawal from groundwater resources pindwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pindww that is extracted from groundwater resources.

Actual Industrial Water Withdrawal from groundwater resources aindwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of aindww that is extracted from groundwater resources.

Potential Industrial Water Consumption from groundwater resources pindusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of pinduse that is extracted from groundwater resources.

Actual Industrial Water Consumption from groundwater resources aindusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of ainduse that is extracted from groundwater resources.

Potential livestock Water Withdrawal from groundwater resources plivwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of plivww that is extracted from groundwater resources.

Actual Livestock Water Withdrawal from groundwater resources alivwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of alivww that is extracted from groundwater resources.

Potential livestock Water Consumption from groundwater resources plivusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of plivuse that is extracted from groundwater resources.

Actual livestock Water Consumption from groundwater resources alivusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of alivuse that is extracted from groundwater resources.

Total Potential Water Withdrawal (all sectors) from groundwater resources ptotwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of ptotww that is extracted from groundwater resources.

Total Actual Water Withdrawal (all sectors) from groundwater resources atotwwgw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of atotww that is extracted from groundwater resources.

Total Potential Water Consumption (all sectors) from groundwater resources ptotusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of ptotuse that is extracted from groundwater resources.

Total Actual Water Consumption (all sectors) from groundwater resources atotusegw kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: water_global.

Part of atotuse that is extracted from groundwater resources.

Soil Types soil -
  • forestry: stand
  • other: 0.5° grid
  • constant

Sectors: biomes, forestry, water_global.

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)
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Yield may be identical to above-ground biomass (biom) if the entire plant is harvested, e.g. for bioenergy production. Yields are reported per growing seasons and not per year.

Cumulative Evapotranspiration evapcum-<crop>-<irrigation> kg m-2
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Evapotranspiration = sum of transpiration, evaporation, interception and sublimation aggregated across the growing season.

Cumulative Nitrogen Application initrcum-<crop>-<irrigation> kg ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Integral of the total nitrogen application rate over the growing season. If organic and inorganic amendments are applied, the nitrogen application should be reported as effective inorganic nitrogen input (ignoring residues).

Planting Date plantday-<crop>-<irrigation> day of year
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

As Julian dates. The planting date reported here corresponds to the actual planting date in the simulation and may diverge from the planting calendar used for harmonization.

Planting Year plantyear-<crop>-<irrigation> calendar year
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Anthesis Date anthday-<crop>-<irrigation> days from planting
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Together with the day and year of planting it allows for clear identification of anthesis.

Maturity Date matyday-<crop>-<irrigation> days from planting
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Together with the day and year of planting it allows for clear identification of maturity.

Harvest Year harvyear-<crop>-<irrigation> calendar year
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Usually the year when maturity is reached. Can also be computed from planting and maturuty day.

Total Above Ground Biomass Dry Matter Yields biom-<crop>-<irrigation> t ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

The whole plant biomass above ground.

Cumulative Nitrogen Uptake nupcum-<crop>-<irrigation> kg ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Nitrogen balance: Uptake (growing season sum)

Cumulative Nitrogen Inputs nincum-<crop>-<irrigation> kg ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Nitrogen balance: Inputs (growing season sum)

Cumulative Nitrogen Losses nlosscum-<crop>-<irrigation> kg ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Nitrogen balance: Losses (growing season sum)

Cumulative Nitrogen Leached nleachcum-<crop>-<irrigation> kg ha-1
  • 0.5° grid
  • annual

Sectors: agriculture, biomes, water_global.

Nitrogen balance: Leaching (growing season sum)

Amphibian Species Probability of Occurrence amphibianprob Probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from individual SDMs assuming no dispersal.

Terrestrial Bird Species Probability of Occurrence birdprob Probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from individual SDMs assuming no dispersal.

Terrestrial Mammal Species Probability of Occurrence mammalprob Probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from individual SDMs assuming no dispersal.

Amphibian Summed Probability of Occurrence amphibiansumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Terrestrial Bird Summed Probability of Occurrence birdsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Terrestrial Mammal Summed Probability of Occurrence mammalsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Endemic Amphibian Species endamphibiansumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Endemic Terrestrial Bird Species endbirdsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Endemic Terrestrial Mammal Species endmammalsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Threatened Amphibian Species thramphibiansumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Threatened Terrestrial Bird Species thrbirdsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Summed Probability of Threatened Terrestrial Mammal Species thrmammalsumprob Summed probability of occurrence per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Aggregated results from individual SDMs assuming no dispersal.

Amphibian Species Richness amphibiansr Estimated number of species (species richness) per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from macroecological richness models

Terrestrial Bird Species Richness birdsr Estimated number of species (species richness) per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from macroecological richness models

Terrestrial Mammal Species Richness mammalsr Estimated number of species (species richness) per cell
  • 0.5° grid
  • 30year-mean

Sectors: biodiversity.

Results from macroecological richness models

Carbon Mass in Vegetation
  • biomes: cveg-total, cveg-<pft>
  • fire: cveg-total, cveg-<pft>
  • forestry: cveg-total, cveg-<species>
  • permafrost: cveg-total, cveg-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻². Stand total and PFT information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass in Above Ground Vegetation Biomass
  • biomes: cvegag-total, cvegag-<pft>
  • forestry: cvegag-total, cvegag-<species>
  • permafrost: cvegag-total, cvegag-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻²Stand total and PFT information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass in Below Ground Vegetation Biomass
  • biomes: cvegbg-total, cvegbg-<pft>
  • forestry: cvegbg-total, cvegbg-<species>
  • permafrost: cvegbg-total, cvegbg-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻². Stand total and PFT information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass in Above Ground Litter Pool
  • biomes: clitterag-total, clitterag-<pft>
  • fire: clitterag-total, clitterag-<pft>
  • forestry: clitterag-total, clitterag-<species>
  • permafrost: clitterag-total, clitterag-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: Species information is essential. Stand total and PFT information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass in Below Ground Litter Pool
  • biomes: clitterbg-total, clitterbg-<pft>
  • fire: clitterbg-total, clitterbg-<pft>
  • forestry: clitterbg-total, clitterbg-<species>
  • permafrost: clitterbg-total, clitterbg-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, fire, forestry, permafrost.

biomes: 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.

fire: 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.

forestry: Only if models separates below-ground litter and soil carbon. Species information is essential.

permafrost: 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 Mass in Soil Pool
  • biomes: csoil-total, csoil-<pft>
  • fire: csoil-total, csoil-<pft>
  • forestry: csoil-total, csoil-<species>
  • permafrost: csoil-total, csoil-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, fire, forestry, permafrost.

Level dimensions: (time, depth, lat, lon).

biomes: 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.

fire: 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. 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.

forestry: 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 species 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.

permafrost: 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. 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 in Products of Land Use Change
  • biomes: cproduct-total, cproduct-<productclass>
  • fire: cproduct-total, cproduct-<productclass>
  • forestry: cproduct-total, cproduct-<productclass>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, fire, forestry.

biomes: 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.

fire: 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.

forestry: 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 Mass Flux out of Atmosphere due to Gross Primary Production on Land
  • biomes: gpp-total, gpp-<pft>
  • fire: gpp-total, gpp-<pft>
  • forestry: gpp-total, gpp-<species>
  • permafrost: gpp-total, gpp-<pft>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻² s⁻¹. Stand total and Species information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to Autotrophic (plant) Respiration on Land
  • biomes: ra-total, ra-<pft>
  • fire: ra-total, ra-<pft>
  • forestry: ra-total, ra-<species>
  • permafrost: ra-total, ra-<pft>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻² s⁻¹. Stand total and species information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass Flux out of Atmosphere due to Net Primary Production on Land
  • biomes: npp-total, npp-<pft>
  • fire: npp-total, npp-<pft>
  • forestry: npp-total, npp-<species>
  • permafrost: npp-total, npp-<pft>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻² s⁻¹. Stand total and species information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to Heterotrophic Respiration on Land
  • biomes: rh-total, rh-<pft>
  • fire: rh-total, rh-<pft>
  • forestry: rh-total, rh-<species>
  • permafrost: rh-total, rh-<pft>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry, permafrost.

biomes: Grid cell total and PFT information is essential.

fire: Grid cell total and PFT information is essential.

forestry: As kg carbon m⁻² s⁻¹. Stand total and Species information is essential.

permafrost: Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to CO₂ Emission from Fire ffire-total, ffire-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: biomes, fire, permafrost.

Burnt Area Fraction burntarea-total, burntarea-<pft> %
  • 0.5° grid
  • daily (total), monthly (pft/total)

Sectors: biomes, fire, permafrost.

biomes: Area percentage of grid cell that has burned at any time of the given day/month/year (for daily/monthly/annual resolution)

fire: Report <total> daily, <pft/total> monthly

permafrost: Area percentage of grid cell that has burned at any time of the given day/month/year (for daily/monthly/annual resolution)

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

Sectors: biomes, fire, permafrost.

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.

Root autotrophic respiration rr-total, rr-<pft> kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, forestry.

biomes:

forestry: As kg carbon*m-2*s-1

CO2 Flux to Atmosphere from Land Use Change fluc-total, fluc-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: biomes, fire, permafrost.

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

Sectors: biomes, fire, permafrost.

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

Sectors: biomes, fire, permafrost.

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

Sectors: biomes, fire, permafrost.

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

Sectors: biomes, permafrost.

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

Sectors: biomes, fire, permafrost.

Carbon going directly into the soil pool without entering litter (e.g., root exudate). Grid cell total and PFT information is essential.

Fraction of Absorbed Photosynthetically Active Radiation
  • biomes: fapar-total, fapar-<pft>
  • fire: fapar-total, fapar-<pft>
  • forestry: fapar-total, fapar-<species>
  • permafrost: fapar-total, fapar-<pft>
%
  • forestry: stand
  • other: 0.5° grid
  • daily else monthly

Sectors: biomes, fire, forestry, permafrost.

Value between 0 and 100. Grid cell total and PFT information is essential.

Leaf Area Index
  • biomes: lai-total, lai-<pft>
  • fire: lai-total, lai-<pft>
  • forestry: lai-total, lai-<species>
  • permafrost: lai-total, lai-<pft>
  • water_global: lai-total
1
  • forestry: stand
  • other: 0.5° grid
  • daily else monthly (fixed if static)

Sectors: biomes, fire, forestry, permafrost, water_global.

biomes: Grid cell total and PFT information is essential. If lai is static, the timestep specifier "fixed" can used.

fire: Grid cell total and PFT information is essential. If lai is static, the timestep specifier "fixed" can used.

forestry: Stand total and species information is essential. If lai is static, the timestep specifier "fixed" can used.

permafrost: Grid cell total and PFT information is essential. If lai is static, the timestep specifier "fixed" can used.

water_global: If used by, or computed by the model. If lai is static, the timestep specifier "fixed" can used.

Plant Functional Type Grid Fraction pft-total, pft-<pft> %
  • 0.5° grid
  • annual (or fixed if static)

Sectors: biomes, fire, permafrost.

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.

Evaporation from Canopy (interception)
  • biomes: intercep-total, intercep-<pft>
  • fire: intercep-total, intercep-<pft>
  • forestry: intercep-total, intercep-<species>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry.

The canopy evaporation+sublimation (if present in model).

Water Evaporation from Soil
  • biomes: esoil-
  • fire: esoil-total, esoil-<pft>
  • forestry: esoil-
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry.

Includes sublimation.

Transpiration
  • biomes: trans-total, trans-<pft>
  • fire: trans-total, trans-<pft>
  • forestry: trans-total, trans-<species>
kg m-2 s-1
  • forestry: stand
  • other: 0.5° grid
  • forestry: daily
  • other: monthly

Sectors: biomes, fire, forestry.

Carbon Mass in Leaves
  • biomes: cleaf-total, cleaf-<pft>
  • forestry: cleaf-total, cleaf-<species>
  • permafrost: cleaf-total, cleaf-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

Carbon Mass in Wood
  • biomes: cwood-total, cwood-<pft>
  • forestry: cwood-total, cwood-<species>
  • permafrost: cwood-total, cwood-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

Including sapwood and hardwood.

Carbon Mass in Roots
  • biomes: croot-total, croot-<pft>
  • forestry: croot-total, croot-<species>
  • permafrost: croot-total, croot-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

Including fine and coarse roots.

Carbon Mass in Coarse Woody Debris
  • biomes: ccwd-total, ccwd-<pft>
  • forestry: ccwd-total, ccwd-<species>
  • permafrost: ccwd-total, ccwd-<pft>
kg m-2
  • forestry: stand
  • other: 0.5° grid
  • annual

Sectors: biomes, forestry, permafrost.

Mean DBH dbh-total, dbh-<species> cm
  • stand
  • annual

Sectors: forestry.

Mean DBH of 100 Highest Trees dbhdomhei cm
  • stand
  • annual

Sectors: forestry.

100 highest trees per hectare.

Stand Height hei-total, hei-<species> m ha-1
  • stand
  • annual

Sectors: forestry.

For models including natural regeneration this variable may not make sense, please report domhei.

Dominant Height domhei m2 ha-1
  • stand
  • annual

Sectors: forestry.

Mean height of the 100 highest trees per hectare.

Stand Density density-total, density-<species> m2 ha-1
  • stand
  • annual

Sectors: forestry.

Basal Area ba-total, ba-<species> m2 ha-1
  • stand
  • annual

Sectors: forestry.

Volume of Dead Trees mort-total, mort-<species> m3 ha-1
  • stand
  • annual

Sectors: forestry.

Harvest by DBH-Class harv-total, harv-<species> m3 ha-1
  • stand
  • annual

Sectors: forestry.

Level dimensions: (time, dbhclass, lat, lon).

DBH class resolution: Either DBH classes or total per species

Remaining Stem Number after Disturbance and Management by DBH-Class stemno-total, stemno-<species> ha-1
  • stand
  • annual

Sectors: forestry.

Level dimensions: (time, dbhclass, lat, lon).

DBH-Class Resolution: Either DBH-Classes or Total per Species

Stand Volume vol-total, vol-<species> m3 ha-1
  • stand
  • annual

Sectors: forestry.

Tree Age by DBH-Class age-total, age-<species> yr
  • stand
  • annual

Sectors: forestry.

Level dimensions: (time, dbhclass, lat, lon).

DBH class resolution: Either DBH classes or total per species

Net Ecosystem Exchange nee-total, nee-<species> kg m-2 s-1
  • stand
  • daily if possible, else monthly

Sectors: forestry.

As kg carbon m⁻² s⁻¹

Mean Annual Increment mai-total, mai-<species> m3 ha-1
  • stand
  • annual

Sectors: forestry.

Species Composition species-total, species-<species> %
  • per ha
  • monthly

Sectors: forestry.

Removed Stem Numbers by DBH-Class by Natural Mortality mortstemno-total, mortstemno-<species> ha-1
  • stand
  • annual

Sectors: forestry.

Level dimensions: (time, dbhclass, lat, lon).

As trees per hectare. DBH class resolution: Either DBH classes or total per species

Removed Stem Numbers by DBH-Class by Management harvstemno-total, harvstemno-<species> ha-1
  • stand
  • annual

Sectors: forestry.

Level dimensions: (time, dbhclass, lat, lon).

As trees per hectare. DBH class resolution: Either DBH classes or total per species

Volume of Disturbance Damage dist-<dist-name>-<species/total> m3 ha-1
  • stand
  • annual

Sectors: forestry.

Nitrogen of Annual Litter nlit-total, nlit-<species> g m-2 a-1
  • stand
  • annual

Sectors: forestry.

As g Nitrogen m-2 a-1

Nitrogen in Soil nsoil g m-2 a-1
  • stand
  • annual

Sectors: forestry.

As g Nitrogen m-2 a-1

Thermal Stratification strat 1
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

1 if lake grid cell is thermally stratified, 0 if lake grid cell is not thermally stratified

Depth of Thermocline thermodepth m
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

Depth corresponding the maximum water density gradient

Temperature of Lake Water watertemp K
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

Depth resolution: Full Profile. Simulated water temperature. Layer averages and full profiles.

Temperature of Lake Surface Water surftemp K
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Average of the upper layer in case not simulated directly.

Temperature of Lake Bottom Water bottemp K
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Average of the lowest layer in case not simulated directly.

Lake Ice Cover ice 1
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

1 if ice cover is present in lake grid cell, 0 if no ice cover is present in lake grid cell

Lake Layer Ice Mass Fraction lakeicefrac 1
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Mean epi. Fraction of mass of a given layer taken up by ice.

Ice Thickness icethick m
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Snow Thickness snowthick m
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Ice Temperature at Upper Surface icetemp K
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

Snow Temperature at Upper Surface snowtemp K
  • Representative lake associated with grid cell
  • daily

Sectors: lakes_global, lakes_local.

Sensible Heat Flux at Lake-Atmosphere Interface
  • fire: sensheatf-total, sensheatf-<pft>
  • lakes_global: sensheatf-total
  • lakes_local: sensheatf-total
W m-2
  • fire: 0.5° grid
  • lakes_global: Representative lake associated with grid cell
  • lakes_local: Representative lake associated with grid cell
  • daily if possible, else monthly

Sectors: fire, lakes_global, lakes_local.

At the surface of the layer in contact with the atmosphere. Positive if upwards.

Latent Heat Flux at Lake-Atmosphere Interface latentheatf W m-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards.

Momentum Flux at Lake-Atmosphere Interface momf kg m-1 s-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards.

Upward Short-Wave Radiation Flux at Lake-Atmosphere Interface swup W m-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. Not to be confused with net shortwave radiation.

Upward Long-Wave Radiation Flux at Lake-Atmosphere Interface lwup W m-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. Not to be confused with net longwave radiation.

Downward Heat Flux at Lake-Atmosphere Interface lakeheatf W m-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. the residual term of the surface energy balance, i.e. the net amount of energy that enters the lake on daily time scale: lakeheatf = swdown - swup + lwdown - lwup - sensheatf - latenheatf (terms defined positive when directed upwards)

Turbulent Diffusivity of Heat turbdiffheat m2 s-1
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Only if computed by the model.

Surface Albedo of Lake lakealbedo 1
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Albedo of the lake surface interacting with the atmosphere (water, ice or snow).

Surface Albedo of Land landalbedo 1
  • 0.5° grid
  • monthly

Sectors: biomes, fire, permafrost.

Albedo of the land surface interacting with the atmosphere. Average of pfts, snow cover, bare ground.

Light Extinction Coefficient extcoeff m-1
  • Representative lake associated with grid cell
  • fixed

Sectors: lakes_global, lakes_local.

Only to be reported for global models, local models should use extcoeff as input.

Sediment Upward Heat Flux at Lake-Sediment Interface sedheatf W m-2
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Positive if upwards. Only if computed by the model.

Chlorophyll Concentration chl g-3 m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Total water chlorophyll concentration – indicator of phytoplankton.

Phytoplankton Functional Group Biomass phytobio mole m-3 as carbon
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Different models will have different numbers of functional groups so that the reporting of these will vary by model.

Phytoplankton Functional Group Biomass zoobio mole m-3 as carbon
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Total simulated Zooplankton biomass.

Total Phosphorus tp mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo.

Particulate Phosphorus pp mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo.

Total Dissolved Phosphorus tpd mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Some models may also output data for soluble reactive phosphorus (SRP).

Total Nitrogen tn mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo.

Particulate Nitrogen pn mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo.

Total Dissolved Nitrogen tdn mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Some models may also output data for Nitrate (N02) nitrite (NO3) and ammonium (NH4).

Dissolved Oxygen do mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo.

Dissolved Organic Carbon doc mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Not always available.

Dissolved Silica si mole m-3
  • Representative lake associated with grid cell
  • daily and monthly

Sectors: lakes_global, lakes_local.

Depth resolution: Either full profile, or mean epi and mean hypo. Not always available.

Total Consumer Biomass Density tcb g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

All consumers (trophic level >1, vertebrates and invertebrates)

Total Consumer Biomass Density in log10 Weight Bins tcblog10 g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Level dimensions: (time, bins, lat, lon).

If the model is size-structured, please provide biomass in equal log 10 g weight bins (1-10g, 10-100g, 100g-1kg, 1-10kg, 10-100kg, >100kg)

Total Pelagic Biomass Density tpb g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

All pelagic consumers (trophic level >1, vertebrates and invertebrates)

Biomass Density of Small Pelagics <30cm bp30cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a pelagic species and L infinity is <30 cm, include in this variable

Biomass Density of Medium Pelagics >=30cm and <90cm bp30to90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a pelagic species and L infinity is >=30 cm and <90cm, include in this variable

Biomass Density of Large Pelagics >=90cm bp90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a pelagic species and L infinity is >=90cm, include in this variable

Total Demersal Biomass Density tdb g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

All demersal consumers (trophic level >1, vertebrates and invertebrates)

Biomass Density of Small Demersals <30cm bd30cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a demersal species and L infinity is <30 cm, include in this variable

Biomass Density of Medium Demersals >=30cm and <90cm bd30to90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a demersal species and L infinity is >=30 cm and <90cm, include in this variable

Biomass Density of Large Demersals >=90cm bd90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

If a demersal species and L infinity is >=90cm, include in this variable

Total Catch (all commercial functional groups / size classes) tc g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea (commercial landings plus discards, fish and invertebrates)

Total Catch in log10 Weight Bins tclog10 g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Level dimensions: (time, bins, lat, lon).

If the model is size-structured, please provide biomass in equal log 10 g weight bins (1-10g, 10-100g, 100g-1kg, 1-10kg, 10-100kg, >100kg)

Total Pelagic Catch tpc g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of all pelagic consumers (trophic level >1, vertebrates and invertebrates)

Catch Density of Small Pelagics <30cm cp30cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of pelagic species with L infinity <30 cm

Catch Density of Medium Pelagics >=30cm and <90cm cp30to90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of pelagic species with L infinity >=30 cm and <90 cm

Catch Density of Large Pelagics >=90cm cp90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of pelagic species with L infinity >=90 cm

Total Demersal Catch tdc g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of all demersal consumers (trophic level >1, vertebrates and invertebrates)

Catch Density of Small Demersals <30cm cd30cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of demersal species with L infinity <30 cm

Catch Density of Medium Demersals >=30cm and <90cm cd30to90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of demersal species with L infinity >=30 cm and <90 cm

Catch Density of Large Demersals >=90cm cd90cm g m-2
  • 0.5° grid
  • monthly

Sectors: marine-fishery_global, marine-fishery_regional.

Catch at sea of demersal species with L infinity >=90 cm

Number of Deaths Attributable to Cold ancold-<r> 1
  • 0.5° grid, location
  • daily

Sectors: health.

Level dimensions: (time, location).

For ERF models, this occurs when temperature is below threshold (e.g., minimum mortality temperature (MMT)). Report 0 if temperature above threshold. Can have gender, age, etc. dimensions; see below.

Number of Deaths Attributable to Heat anheat-<r> 1
  • 0.5° grid, location
  • daily

Sectors: health.

Level dimensions: (time, location).

Temperature above threshold (ERFs). Report 0 if temperature below threshold. Can have gender, age, etc. dimensions; see below.

Baseline Total Mortality btm 1
  • 0.5° grid, location
  • daily

Sectors: health.

Level dimensions: (time, location).

To be reported as annual series of mean daily total mortality, or as a single number of mean daily mortality; to be used for computations of attributable fractions. Can have gender, age, etc. dimensions; see below.

Population pop 1
  • 0.5° grid, location
  • annual, 5-year intervals

Sectors: health.

Level dimensions: (time, location).

Baseline population data should be provided for computations of mortality rates (i.e. deaths per total population). See Section ‎5.1.6. Can have gender, age, etc. dimensions; see below.

Burnt Area Fraction from Fire Mediated Land-Cover Change burntarealuc-total %
  • 0.5° grid
  • monthly

Sectors: fire.

Deforestation fires

Carbon emitted from peat fires ffirepeat-total kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: fire.

Should not be part of ffire

Carbon Mass Flux into Atmosphere from Fire Mediated Land-Cover Change ffireluc-total kg m-2 s-1
  • 0.5° grid
  • monthly

Sectors: fire.

C emitted from deforestation fires (if simulated).

Weighted Mean Fireline Intensity fireints-total kW m-1
  • 0.5° grid
  • monthly

Sectors: fire.

If calculated, weighted by burned area. Grid cell total and PFT information is essential.

Carbon in Different Fuel Classes cfuel-total kg m-2
  • 0.5° grid
  • monthly

Sectors: fire.

Level dimensions: (time, fuelclass, lat, lon).

as appropriate for your model

Combustion Completeness of Different Fuel Classes ccfuel-total 1
  • 0.5° grid
  • monthly

Sectors: fire.

Level dimensions: (time, fuelclass, lat, lon).

(between 0 and 1), fraction of fuel combusted in a fire

Fuel Moisture for Different Fuel Classes mfuel-total 1
  • 0.5° grid
  • monthly

Sectors: fire.

Level dimensions: (time, fuelclass, lat, lon).

as a fraction of fuel dry mass (not percentage)

Mean Number of Fires firenr-total km-2 day-1
  • 0.5° grid
  • monthly

Sectors: fire.

95th Percentile of Number of Fires firenrperc95-total km-2 day-1
  • 0.5° grid
  • monthly

Sectors: fire.

95th percentile of number of fires in one day during the month

Tree Mortality Caused by Fire firemortality-total 1
  • 0.5° grid
  • monthly

Sectors: fire.

fraction of area covered by trees

Weighted Mean Fire Size firesize-total km-2
  • 0.5° grid
  • monthly

Sectors: fire.

Monthly mean weighted with the number of fires of each day

95th Percentile of Fire Size firesizeperc95-total km-2
  • 0.5° grid
  • monthly

Sectors: fire.

95th percentile of fire size computed during the month

Weighted Mean Fire Duration fireduration-total s
  • 0.5° grid
  • monthly

Sectors: fire.

Mean needs to be weighted with the number of fires

Weighted Mean Fire Rate of Spread fireros-total m s-1
  • 0.5° grid
  • monthly

Sectors: fire.

Mean needs to be weighted with burned area

Ignitions Caused by Human ignhuman-total km-2 day-1
  • 0.5° grid
  • annual

Sectors: fire.

Ignitions Caused by Lightning ignlight-total km-2 day-1
  • 0.5° grid
  • monthly

Sectors: fire.

Mean Canopy Height canopyheight-total m
  • 0.5° grid
  • annual

Sectors: fire.

mean height of canopy formed by trees over 5m. Vegetation of less than 5m should be excluded from the canopy height calculation (i.e. not contributing with zero)

Low Vegetation Cover lowcover-total 1
  • 0.5° grid
  • annual

Sectors: fire.

fraction of grid cell covered with vegetation less than 5 m tall

High Vegetation Cover highcover-total 1
  • 0.5° grid
  • annual

Sectors: fire.

fraction of grid cell covered with vegetation more than 5 m tall

Grid Cell Area cellarea km**2
  • 0.5° grid
  • fixed

Sectors: water_global.

The total area associated with each grid cell in the model.

Continental Fraction of Grid Cell contfrac 1
  • 0.5° grid
  • fixed

Sectors: water_global.

The fraction of each grid cell that is assumed to be continent, i.e., not ocean. Should be 0 if the entire cell is assumed to be ocean, 1 if the entire cell is assumed to be covered by land or inland water bodies.

Crops

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

Species

Table 14: Specifiers for species, disturbance names and DBH classes (species).
Specifier Species
fasy Fagus sylvatica
quro Quercus robur
qupe Quercus petraea
pisy Pinus sylvestris
piab Picea abies
pipi Pinus pinaster
lade Larix decidua
acpl Acer platanoides
eugl Eucalyptus globulus
bepe Betula pendula
bepu Betula pubescens
rops Robinia pseudoacacia
frex Fraxinus excelsior
poni Populus nigra
soau Sorbus aucuparia
c3gr C3 grass
hawo hard woods
fi fire
wi wind
ins Insects

Forest stands

Table 15: Overview of the forest stands (forrest-stand).
Stand Specifier Country Coordinates (Lat, Lon) Forest type Species Thinning during historical time period Comment
Hyytiälä hyytiala FI 61.8475, 24.295 Even-aged conifer pisy, piab below Note that an experimental plot of pine contains a lot of data while footprint of flux tower is larger. Please note that the deciduous admixtures only appear in the data at a later stage and hence do not need to be simulated. Only simulate pine and spruce (no hard-woods) and regenerate as pure pine stand
Peitz peitz DE 51.9166, 14.35 Even-aged conifer pisy below Managed using a weak thinning from below.
Solling (beech) solling-beech DE 51.77, 9.57 Even-aged deciduous fasy above
Solling (spruce) solling-spruce DE 51.77, 9.57 Even-aged conifer piab below
Sorø soro DK 55.485844, 11.644616 Even-aged deciduous fasy above
Kranzberg Roof Project kroof DE 48.25, 11.4 Mixed deciduous and conifers fasy, piab, acpl, lade, pisy, quro below Unmanaged/ thinning from below in past 20 years for all species.
Le Bray le-bray FR 44.71711, -0.7693 Even-aged conifer pipi below
Collelongo collelongo IT 41.8494, 13.5881 Even-aged deciduous fasy above
Bílý Kříž bily-kriz CZ 49.3, 18.32 Even-aged conifer piab below

Lake sites

Table 16: Lake site specifications for local lake models (lake-site).
Lake name Specifier Reservoir or lake Country Coordinates (Lat, Lon)
Alqueva Reservoir alqueva reservoir Portugal 38.2, -7.49
Lake Annecy annecy lake France 45.87, 6.17
Lake Annie annie lake USA 27.21, -81.35
Lake Argyle argyle reservoir Australia -16.31, 128.68
Lake Biel biel lake Switzerland 47.08, 7.16
Big Muskellunge Lake big-muskellunge lake USA 46.02, -89.61
Black Oak Lake black-oak lake USA 46.16, -89.32
Lake Bourget bourget lake France 45.76, 5.86
Lake Burley Griffin burley-griffin reservoir Australia -35.3, 149.07
Crystal Lake crystal-lake lake USA 46.0, -89.61
Crystal Bog crystal-bog lake USA 46.01, -89.61
Delavan Lake delavan lake USA 42.61, -88.6
Dickie Lake dickie lake Canada 45.15, -79.09
Eagle Lake eagle lake Canada 44.68, -76.7
Ekoln basin of Mälaren ekoln lake Sweden 59.75, 17.62
Lake Erken erken lake Sweden 59.84, 18.63
Esthwaite Water esthwaite-water lake United Kingdom 54.37, -2.99
Falling Creek Reservoir falling-creek reservoir USA 37.31, -79.84
Lake Feeagh feeagh lake Ireland 53.9, -9.5
Fish Lake fish lake USA 43.29, -89.65
Lake Geneva geneva lake France/Switzerland 46.45, 6.59
Great Pond great lake USA 44.53, -69.89
Green Lake green lake USA 43.81, -89.0
Harp Lake harp lake Canada 45.38, -79.13
Kilpisjärvi kilpisjarvi lake Finland 69.03, 20.77
Lake Kinneret kinneret lake Israel 32.49, 35.35
Lake Kivu kivu lake Rwanda/DR Congo -1.73, 29.24
Klicava Reservoir klicava reservoir Czechia 50.07, 13.93
Lake Kuivajarvi kuivajarvi lake Finland 60.47, 23.51
Lake Langtjern langtjern lake Norway 60.37, 9.73
Laramie Lake laramie lake USA 40.62, -105.84
Lower Lake Zurich lower-zurich lake Switzerland 47.28, 8.58
Lake Mendota mendota lake USA 43.1, -89.41
Lake Monona monona lake USA 43.06, -89.36
Mozhaysk reservoir mozhaysk reservoir Russia 55.59, 35.82
Mt Bold mt-bold reservoir Australia -35.12, 138.71
Lake Müggelsee mueggelsee lake Germany 52.43, 13.65
Lake Neuchâtel neuchatel lake Switzerland 46.54, 6.52
Ngoring ngoring lake China 34.9, 97.7
Lake Nohipalo Mustjärv nohipalo-mustjaerv lake Estonia 57.93, 27.34
Lake Nohipalo Valgejärv nohipalo-valgejaerv lake Estonia 57.94, 27.35
Okauchee Lake okauchee lake USA 43.13, -88.43
Lake Pääjärvi paajarvi lake Finland 61.07, 25.13
Rappbode Reservoir rappbode reservoir Germany 51.74, 10.89
Rimov Reservoir rimov reservoir Czechia 48.85, 14.49
Lake Rotorua rotorua lake New Zealand -38.08, 176.28
Lake Sammamish sammamish lake USA 47.59, -122.1
Sau Reservoir sau reservoir Spain 41.97, 2.4
Sparkling Lake sparkling lake USA 46.01, -89.7
Lake Stechlin stechlin lake Germany 53.17, 13.03
Lake Sunapee sunapee lake USA 43.23, -72.5
Lake Tahoe tahoe reservoir USA 39.09, -120.03
Lake Tarawera tarawera lake New Zealand -38.21, 176.43
Lake Taupo taupo lake New Zealand -38.8, 175.89
Toolik Lake toolik lake USA 68.63, -149.6
Trout Lake trout-lake lake USA 46.03, -89.67
Trout Bog trout-bog lake USA 46.04, -89.69
Two Sisters Lake two-sisters lake USA 45.77, -89.53
Lake Vendyurskoe vendyurskoe lake Russia 62.1, 33.1
lake Võrtsjärv vortsjaerv lake Estonia 58.31, 26.01
Lake Waahi waahi lake New Zealand 37.33, 175.07
Lake Washington washington lake USA 47.64, -122.27
Windermere windermere lake United Kingdom 54.31, -2.95
Lake Wingra wingra lake USA 43.05, -89.43
Zlutice Reservoir zlutice reservoir Czechia 50.09, 13.11

Ocean regions

Table 17: Ocean regions (ocean-region).
Ocean region Specifier Coordinates (west, south, east, north)
North Sea north-sea -4.5, 50.5, 9.5, 62.5
Baltic Sea baltic-sea 15.5, 55.5, 23.5, 64.5
North-west Meditteranean nw-med-sea -1.5, 36.5, 6.5, 43.5
Adriatic Sea adriatic-sea 11.5, 39.5, 20.5, 45.5
Mediterranean Sea med-glob -6.5, 29.5, 35.5, 45.5
South-East Australia se-australia 120.5, -47.5, 170.5, -23.5
Eastern Bass Strait east-bass-strait 145.5, -41.5, 151.5, -37.5
Cook Strait cook-strait 174.5, -46.5, 179.5, -40.5
North Humboldt Sea humboldt-n -93.5, -20.5, -69.5, 6.5
Hawaii hawaii 0.0, 0.0, 0.0, 0.0
Benguela Current benguela 0.0, 0.0, 0.0, 0.0
Eastern Bering Sea east-bering-sea 0.0, 0.0, 0.0, 0.0

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 all sectors combined sector according to the following pattern:

<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>_<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 all sectors combined 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<region>(global))_(?P<time_step>[a-z0-9-]+)_(?P<start_year>\d{4})_(?P<end_year>\d{4}).nc

For questions or clarifications, please contact info@isimip.org or the data managers directly (isimip-data@pik‐potsdam.de) before submitting files.

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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