ISIMIP3b simulation round simulation protocol - Regional forests

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 Regional forests. An overview of all sectors can be found at protocol.isimip.org.

Throughout the protocol we use specifiers that denote a particular scenario, experiment, variable or other parameter. We use these specifiers in the tables below, in the filenames of the input data sets, and ask you to use the same specifiers in your output files. More on reporting data can be found at the end of this document.

Model versioning

To ensure consistency between ISIMIP3a and ISIMIP3b as well as the different experiments within a simulation round, we require that modelling groups use the same version of an impact model for the experiments in ISIMIP3a and ISIMIP3b. If you cannot fulfill this, please indicate that by using a suffix for your model name (e.g. simple version numbering: MODEL-v1, MODEL-v2 or following semantic versioning: MODEL-2.0.0, see also reporting model results).

This versioning does not only apply to changes in the computational logic of the model, but also to input parameters, calibration or setup. If model versions are not reported, we will name them according to the simulation round (e.g. MODEL-isimip3a). We require the strict versioning to ensure that differences between model results are fully attributable to the changes in model forcings.

Scenarios & Experiments

Scenario definitions

Table 1: Climate scenario specifiers (climate-scenario).
Scenario specifier Description
picontrol Pre-industrial climate as simulated by the GCMs.
historical Historical climate as simulated by the GCMs.
ssp126 SSP1-RCP2.6 climate as simulated by the GCMs.
ssp370 SSP3-RCP7 climate as simulated by the GCMs.
ssp585 SSP5-RCP8.5 climate as simulated by the GCMs.
Table 2: Socio-economic scenario specifiers (soc-scenario).
Scenario specifier Description
1850soc

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

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

histsoc

Varying direct human influences in the historical period (1850-2014) (e.g. observed changes in historical land use, nitrogen deposition and fertilizer input, fishing effort).

Please label your model run histsoc even if it only partly accounts for varying direct human forcings while another part of the the direct human forcing is considered constant or is ignored.

2015soc

Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort).

Please label your simulations 2015soc if they do not at all account for historical changes in direct human forcing, but they do represent constant year-2015 levels of direct human forcing for at least some direct human forcings.

nat

No direct human influences (naturalized run).

Please only label your model run nat if it does not at all account for any direct human forcings, including e.g. human land use.

Table 3: Sensitivity scenario specifiers (sens-scenario).
Scenario specifier Description
default For all experiments other than the sensitivity experiments.
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 Original resolution Ensemble member Priority
GFDL-ESM4 gfdl-esm4 National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA 288x180 r1i1p1f1 1
UKESM1-0-LL ukesm1-0-ll Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK 192x144 r1i1p1f2 2
MPI-ESM1-2-HR mpi-esm1-2-hr Max Planck Institute for Meteorology, Hamburg 20146, Germany 384x192 r1i1p1f1 3
IPSL-CM6A-LR ipsl-cm6a-lr Institut Pierre Simon Laplace, Paris 75252, France 144x143 r1i1p1f1 4
MRI-ESM2-0 mri-esm2-0 Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan 320x160 r1i1p1f1 5

Note on climate forcing priority

The priority for the different climate forcing datasets is from top to bottom. If you cannot use all climate forcing datasets, please concentrate on those at the top of the table.

Table 6: Climate forcing variables for ISIMIP3b simulations (climate-variable).
Variable Variable specifier Unit Resolution Models
Atmospheric variables mandatory
Near-Surface Relative Humidity hurs %
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Specific Humidity huss kg kg-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Precipitation pr kg m-2 s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Snowfall Flux prsn kg m-2 s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Air Pressure ps Pa
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Downwelling Longwave Radiation rlds W m-2
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Surface Downwelling Shortwave Radiation rsds W m-2
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Wind Speed sfcwind m s-1
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Near-Surface Air Temperature tas K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Daily Maximum Near-Surface Air Temperature tasmax K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0
Daily Minimum Near-Surface Air Temperature tasmin K
  • 0.5° grid
  • daily
  • GFDL-ESM4
  • UKESM1-0-LL
  • MPI-ESM1-2-HR
  • IPSL-CM6A-LR
  • MRI-ESM2-0

Other climate datasets

Table 7: Other climate datesets for ISIMIP3b simulation round.
Variable Variable specifier Unit Resolution Datasets
Atmospheric composition mandatory

Atmospheric CO2 concentration

climate/atmosphere_composition/co2/<climate-scenario>/co2_<climate-scenario>_annual_<start_year>_<end_year>.txt
co2 ppm
  • global
  • annual

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

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3b simulation round.
Dataset Included variables (specifier) Covered time period Resolution Reference/Source and Comments
Land use mandatory

Landuse totals

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-totals_annual_<start_year>_<end_year>.nc
  • share of the total cropland (cropland_total)
  • all of the rainfed cropland (cropland_rainfed)
  • all of the irrigated cropland (cropland_irrigated)
  • share of managed pastures or rangeland (pastures)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

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

Downscaling to 5 crops

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

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

Downscaling to 15 crops

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

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

Managed pastures and rangeland

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

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

Urban areas

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

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

N-fertilizer mandatory

Nitrogen deposited by fertilizers on croplands

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

Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al., in in review, see also https://luh.umd.edu).

N-deposition

Reduced nitrogen deposition

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

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018)

Oxidized nitrogen deposition

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

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018)

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).

Population mandatory

Population 5' grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-1900
  • 1901-2014
  • 5' grid
  • annual

HYDE v3.2.1 (Klein Goldewijk et al., 2017). Decadal data prior to year 2000 have been linearly interpolated in time.

Population 0.5° grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-2014
  • 0.5° grid
  • annual

HYDE v3.2.1 (Klein Goldewijk et al., 2017). Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution

Population national

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
  • total number of people per country
  • 1850-2014
  • national
  • annual

HYDE v3.2.1 (based on WPP 2017 revision, following the methodology of Klein Goldewijk et al., 2017). Decadal data prior to year 1950 have been linearly interpolated in time.

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). 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). 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 (based on WPP 2017 revision, following the methodology of Klein Goldewijk et al., 2017). Decadal data prior to year 1950 have been linearly interpolated in time.

GDP mandatory

GDP

socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
  • GDP PPP 2005 USD (gdp)
  • 1850-2014
  • country-level
  • annual

Historic country-level GDP data are an extension of the data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html), and are derived mainly from the Maddison Project database. Gridded GDP data will be provided by c. 07/2021.

Geographic data and information

Table 9: Geographic data and information for ISIMIP3b simulation round.
Dataset Included variables (specifier) Resolution Reference/Source and Comments
Land/Sea masks

landseamask

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

This is the land-sea mask of the W5E5 dataset (Lange, 2019a; Cucchi et al., 2020). Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land.

landseamask_no-ant

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

Same as landseamask but without Antarctica.

landseamask_water-global

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

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

Soil

gswp3_hwsd

geo_conditions/soil/gswp3_hwsd.nc
  • soiltexture
0.5° grid

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

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 Regional forests (variable).
Variable long name Variable specifier Unit Resolution Comments
Essential outputs
Mean DBH dbh-total, dbh-<species> cm
  • stand
  • annual
Mean DBH of 100 Highest Trees dbhdomhei cm
  • stand
  • annual

100 highest trees per hectare.

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

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

Dominant Height domhei m2 ha-1
  • stand
  • annual

Mean height of the 100 highest trees per hectare.

Stand Density density-total, density-<species> m2 ha-1
  • stand
  • annual
Basal Area ba-total, ba-<species> m2 ha-1
  • stand
  • annual
Volume of Dead Trees mort-total, mort-<species> m3 ha-1
  • stand
  • annual
Harvest by DBH-Class harv-total, harv-<species> m3 ha-1
  • stand
  • annual

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

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
Carbon Mass in Vegetation cveg-total, cveg-<species> kg m-2
  • stand
  • annual

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

Carbon Mass in Above Ground Vegetation Biomass cvegag-total, cvegag-<species> kg m-2
  • stand
  • annual

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

Carbon Mass in Below Ground Vegetation Biomass cvegbg-total, cvegbg-<species> kg m-2
  • stand
  • annual

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

Carbon Mass in Soil Pool csoil-total, csoil-<species> kg m-2
  • stand
  • annual

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

Soil carbon excluding belowground litter if your model reports clitter. If not including below-ground litter, i.e. only report csoil and document this in the model documentation. Grid cell total and 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.

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

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

DBH class resolution: Either DBH classes or total per species

Carbon Mass Flux out of Atmosphere due to Gross Primary Production on Land gpp-total, gpp-<species> kg m-2 s-1
  • stand
  • daily

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

Carbon Mass Flux out of Atmosphere due to Net Primary Production on Land npp-total, npp-<species> kg m-2 s-1
  • stand
  • daily

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

Carbon Mass Flux into Atmosphere due to Autotrophic (plant) Respiration on Land ra-total, ra-<species> kg m-2 s-1
  • stand
  • daily

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

Carbon Mass Flux into Atmosphere due to Heterotrophic Respiration on Land rh-total, rh-<species> kg m-2 s-1
  • stand
  • daily

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

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

As kg carbon m⁻² s⁻¹

Mean Annual Increment mai-total, mai-<species> m3 ha-1
  • stand
  • annual
Fraction of Absorbed Photosynthetically Active Radiation fapar-total, fapar-<species> %
  • stand
  • daily else monthly

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

Leaf Area Index lai-total, lai-<species> 1
  • stand
  • daily else monthly

Stand total and species information is essential.

Species Composition species-total, species-<species> %
  • per ha
  • monthly
Evapotranspiration evap-total, evap-<species> kg m-2 s-1
  • stand
  • daily

Sum of transpiration, evaporation, interception and sublimation.

Evaporation from Canopy (interception) intercep-total, intercep-<species> kg m-2 s-1
  • stand
  • daily else monthly

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

Water Evaporation from Soil esoil kg m-2 s-1
  • stand
  • daily else monthly

Includes sublimation.

Transpiration trans-total, trans-<species> kg m-2 s-1
  • stand
  • daily else monthly
Total Soil Moisture Content soilmoist kg m-2
  • stand
  • daily

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

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

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

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

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

As g Nitrogen m-2 a-1

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

As g Nitrogen m-2 a-1

Root autotrophic respiration rr-total, rr-<pft> kg m-2 s-1
  • stand
  • daily

As kg carbonm-2s-1

Carbon Mass in Leaves cleaf-total, cleaf-<species> kg m-2
  • stand
  • annual
Carbon Mass in Wood cwood-total, cwood-<species> kg m-2
  • stand
  • annual

Including sapwood and hardwood.

Carbon Mass in Roots croot-total, croot-<species> kg m-2
  • stand
  • annual

Including fine and coarse roots.

Temperature of Soil tsl K
  • stand
  • daily if possible, else monthly

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

Temperature of each soil layer. Reported as "missing" for grid cells occupied entirely by "sea". This is the most important variable for the permafrost sector. If daily resolution not possible, please provide monthly. If depth varies over time or space, see instructions for depth layers on https://www.isimip.org/protocol/preparing-simulation-files.

Other
Soil Types soil -
  • stand
  • constant

Soil types or texture classes as used by your model. Please include a description of each type or class, especially if these are different from the standard HSWD and GSWP3 soil types. Please also include a description of the parameters and values associated with these soil types (parameter values could be submitted as spatial fields where appropriate).

Carbon Mass in Above Ground Litter Pool clitterag-total, clitterag-<species> kg m-2
  • stand
  • annual

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

Carbon Mass in Below Ground Litter Pool clitterbg-total, clitterbg-<species> kg m-2
  • stand
  • annual

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

Carbon in Products of Land Use Change cproduct-total, cproduct-<productclass> kg m-2
  • stand
  • annual

Products generated during Land-use change. Removed carbon should not go into the soil but into the product pool. Please use the product classes used within your model and document them in the model documentation on the ISIMIP homepage.

Carbon Mass in Coarse Woody Debris ccwd-total, ccwd-<species> kg m-2
  • stand
  • annual

Species

Table 11: 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 12: 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

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 Regional forests sector according to the following pattern:

<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>(-<species>)_<forest-stand>_<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 regional forests 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<species>[a-z0-9]+))?_(?P<forest_stand>[a-z0-9-]+)_(?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.

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