ISIMIP3a simulation round simulation protocol - Permafrost

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:

ISIMIP3a

Historical model evaluation and attribution runs

The ISIMIP3a part of the third round framework is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are forced by observed climate and socio-economic information and a de-trended version of the observed climate allowing for the generation of a “no climate change” baseline (counterfactual).

You can find the ISIMIP3b protocol, which is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions, here.

Simulation protocol

In this protocol we describe the scenarios & experiments in ISIMIP3a simulation round, the different input datasets, the output variables, and how to report model results specifically for Permafrost. An overview of all sectors can be found at protocol.isimip.org.

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

Model versioning

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

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

Scenarios & Experiments

Scenario definitions

Table 1: Climate scenario specifiers (climate-scenario).
Scenario specifier Description
obsclim Observed climate and CO₂ forcing used for model evaluation and the detection and attribution task.
spinclim Detrended version of the observed climate forcing used to spin-up the simulations based on a stable 1900 climate (see explanation below for details regarding the design of the spin-up)
counterclim Detrended version of the observed climate forcing used for the "no climate change" baseline simulations in the context of the detection and attributions task.
Table 2: Socio-economic scenario specifiers (soc-scenario).
Scenario specifier Description
histsoc

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

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

2015soc

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

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

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.
1901co2 CO₂ concentration fixed at 1901 levels as a deviation from the “obsclim” climate + CO₂ forcing.

General note regarding sensitivity experiments

The sensitivity experiments are meant to be "artificial" deviations from the default settings. So for example if your model does not at all account for changes in CO₂ concentrations (no option to switch it on or off) the run should be labeled as “default” in the sensitivity specifier of the file name even if the run would be identical to the 1901co2 sensitivity setting.

The particular sensitivity scenario for an experiment is given in the experiments table below. For most experiments no sensitivity scenario is given, so the default label applies.

Experiments

Table 4: Experiment set-up: Each experiment is specified by the climate forcing (CF) and the Direct Human Forcing (DHF).
Experiment Short description

Transition from Spin-up to experiment

1850-1900, only if spin-up is needed

Historical

1901-2016

model evaluation

histsoc

1st priority

CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: Fixed 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

histsoc

model evaluation

2015soc

1st priority

CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: Fixed 2015 levels of direct human forcing for the entire time period

2015soc

2015soc

model evaluation

nat

2nd priority

CF: No climate change before 1901, observed forcing afterwards; 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: No direct human influences

nat

nat

counterfactual climate

histsoc

1st priority

CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level "histsoc" version of the transition period of the model evaluation run

counterclim

DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

counterfactual climate

2015soc

1st priority

CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level "2015soc" version of the transition period of the model evaluation run

counterclim

DHF: fixed 2015 levels of direct human influences for the entire time period

2015soc

counterfactual climate

nat

2nd priority

CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level "nat" version of the transition period of the model evaluation run

counterclim

DHF: No direct human influences

nat

CO₂ sensitivity

histsoc

2nd priority

CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level "histsoc" version of the transition period of the model evaluation run

obsclim

Sensitivity scenario: 1901co2

DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

CO₂ sensitivity

2015soc

2nd priority

CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level "2015soc" version of the transition period of the model evaluation run

obsclim

Sensitivity scenario: 1901co2

DHF: fixed 2015 levels of direct human influences for the entire time period

2015soc

Note regarding models requiring spin-up

For models requiring spin-up, we provide 100 years of spinclim data which is identical with the first 100 years of the counterclim data (files climate/atmosphere/spinclim/<dataset>/<dataset>_spinclim_<variable>_global_daily_<start-year>_<start-year>.nc). If more than 100 years of spin-up are needed, these data can be repeated as often as needed.

Input data

The base directory for input data at DKRZ is:

/work/bb0820/ISIMIP/ISIMIP3a/InputData/

Further information on accessing ISIMIP data can be found at ISIMIP - getting started.

Some of the datasets are tagged as mandatory. This does not mean that the data must be used in all cases, but if your models uses input data of this kind, we require to use the specified dataset. If an alterntive data set is used instead, we cannot consider it an ISIMIP simulation. If the mandatory label is not given, you may use alternative data (please document this clearly).

Climate forcing

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

climate/atmosphere/<climate-scenario>/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc
Table 5: Climate and climate-related forcing data (climate-forcing).
Title Specifier Time period Reanalysis Bias adjustment target Comments Priority
GSWP3-W5E5 gswp3-w5e5 1901-2016 ERA5 GPCC, CRU Combination of W5E5 (Lange, 2019a; Cucchi et al., 2020) for 1979-2016 with GSWP3 (Dirmeyer et al., 2006) homogenized to W5E5 for 1901-1978. The homogenization reduces discontinuities at the 1978/1979 transition and was done using the ISIMIP3BASD v2.4.1 bias adjustment method (Lange, 2019b; Lange, 2020). 1
GSWP3 gswp3 1901-2010 20CR GPCC, GPCP, CPC-Unified, CRU, SRB Dynamically downscaled and bias-adjusted 20th Century Reanalysis (20CR; Compo et al., 2011) from the Global Soil Wetness Project Phase 3 (GSWP3; Dirmeyer et al., 2006). 2

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 ISIMIP3a simulations (climate-variable).
Variable Variable specifier Unit Resolution Datasets
Atmospheric variables mandatory
Near-Surface Relative Humidity hurs %
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Specific Humidity huss kg kg-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Precipitation pr kg m-2 s-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Air Pressure ps Pa
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Longwave Radiation rlds W m-2
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Shortwave Radiation rsds W m-2
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Wind Speed sfcwind m s-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Air Temperature tas K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Maximum Near-Surface Air Temperature tasmax K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Minimum Near-Surface Air Temperature tasmin K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)

Other climate datasets

Table 7: Other climate datesets for ISIMIP3a 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 et al. (2011) for 1850-2005 and Dlugokencky & Tans (2019) from 2006-2018.

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3a simulation round.
Dataset Included variables (specifier) Covered time period Resolution Reference/Source and Comments
Land use mandatory

Landuse totals

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

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

Downscaling to 5 crops

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

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

Downscaling to 15 crops

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

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

Managed pastures and rangeland

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

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

Urban areas

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

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

N-fertilizer mandatory

Nitrogen deposited by fertilizers on croplands

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

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

N-deposition

Reduced nitrogen deposition

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

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

Oxidized nitrogen deposition

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

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

Reservoirs & dams

Reservoirs & dams

socioeconomic/reservoir_dams/reservoirs-dams_1850_2025.xls
  • Unique ID representing a dam and its associated reservoir corresponding to GRanD/KSU IDs (ID)
  • name (DAM_NAME)
  • original location (LON_ORIG, LAT_ORIG)
  • location adjusted to the DDM30 routing network (LON_DDM30, LAT_DDM30)
  • upstream area in DDM30 (CATCH_SKM_DDM30)
  • upstream area in GRanD (CATCH_SKM_GRanD)
  • maximum storage capacity of reservoir (CAP_MCM)
  • year of construction/commissioning (YEAR)
  • 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)
  • 1850-2025
  • 0.5° grid and original coordinates (degree)
  • annual

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

Water abstraction

Water abstraction for domestic and industrial purposes

socioeconomic/water_abstraction/[domw|indw][w|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc
  • domestic and industrial water withdrawal and consumption (domww, domwc, indww, indwc)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

For modelling groups that do not have their own representation, we provide files containing the multi-model mean of domestic and industrial water withdrawal and consumption generated by WaterGAP, PCR-GLOBWB, and H08. This data is based ISIMIP2a varsoc simulations for 1901-2005, and on RCP6.0 simulations from the Water Futures and Solutions project (Wada et al., 2016, http://www.geosci-model-dev.net/9/175/2016/) for after 2005. Years before 1901 have been filled with the value for year 1901.

Forest management

Wood harvesting

socioeconomic/wood_harvesting/<variable>_<soc_scenario>_national_annual_<start_year>_<end_year>.nc
  • wood harvest area from primary forest land (primf-harv)
  • wood harvest area from primary non forest land (primn-harv)
  • wood harvest area from secondary mature forest land (secmf-harv)
  • wood harvest area from secondary young forest land (secyf-harv)
  • wood harvest area from secondary non forest land (secnf-harv)
  • wood harvest biomass carbon from primary forest land (primf-bioh)
  • wood harvest biomass carbon from primary non forest land (primn-bioh)
  • wood harvest biomass carbon from secondary mature forest land (secmf-bioh)
  • wood harvest biomass carbon from secondary young forest land (secyf-bioh)
  • wood harvest biomass carbon from secondary non forest land (secnf-bioh)
  • 1850-2017
  • national
  • annual

Historic annual country-level wood harvesting data. Based on the LUH2 v2h Harmonization Data Set (see Hurtt, Chini et al. 2011; see also https://luh.umd.edu). Interpolated to a 0.5° grid using first-order conservative remapping and calculated over a fractional country mask (https://gitlab.pik-potsdam.de/isipedia/countrymasks/-/blob/master/) derived from ASAP-GAUL (https://data.europa.eu/euodp/data/dataset/jrc-10112-10004).

Population mandatory

Population 5' grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-1900
  • 1901-2020
  • 5' grid
  • annual

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

Population 0.5° grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-1900
  • 1901-2020
  • 0.5° grid
  • annual

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

Population national

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
  • total number of people per country
  • 1850-1900
  • 1901-2020
  • national
  • annual

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

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
  • 1901-2020
  • 5' grid
  • annual

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

Population 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)
  • 1850-1900
  • 1901-2020
  • 0.5° grid
  • annual

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

Population density national

socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
  • total number of people per square kilometer
  • 1850-1900
  • 1901-2020
  • national
  • annual

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

GDP mandatory

GDP

socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
  • GDP PPP 2005 USD (gdp)
  • 1850-1900
  • 1901-2016
  • country-level
  • annual

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

Geographic data and information

Table 9: Geographic data and information for ISIMIP3a simulation round.
Dataset Included variables (specifier) Resolution Reference/Source and Comments
Land/Sea masks

landseamask

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

This is the land-sea mask of the W5E5 dataset (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)

River routing

basins

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

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

flowdir

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

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

slopes

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

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

Output data

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

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

Output variables

Table 10: Output variables for Permafrost (variable).
Variable long name Variable specifier Unit Resolution Comments
Key variable
Temperature of Soil tsl K
  • 0.5° grid
  • daily if possible, else monthly

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

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

Pools (as in Biomes sector)
Carbon Mass in Vegetation cveg-total, cveg-<pft> kg m-2
  • 0.5° grid
  • annual

Grid cell total and PFT information is essential.

Carbon Mass in Above Ground Vegetation Biomass cvegag-total, cvegag-<pft> kg m-2
  • 0.5° grid
  • annual

Grid cell total and PFT information is essential.

Carbon Mass in Below Ground Vegetation Biomass cvegbg-total, cvegbg-<pft> kg m-2
  • 0.5° grid
  • annual

Grid cell total and PFT information is essential.

Carbon Mass in Soil Pool csoil-total, csoil-<pft> kg m-2
  • 0.5° grid
  • annual

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

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

Fluxes (as in Biomes sector)
Carbon Mass Flux out of Atmosphere due to Gross Primary Production on Land gpp-total, gpp-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to Autotrophic (plant) Respiration on Land ra-total, ra-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Carbon Mass Flux out of Atmosphere due to Net Primary Production on Land npp-total, npp-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to Heterotrophic Respiration on Land rh-total, rh-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Structure (as in Biomes sector)
Fraction of Absorbed Photosynthetically Active Radiation fapar-total, fapar-<pft> %
  • 0.5° grid
  • daily else monthly

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

Leaf Area Index lai-total, lai-<pft> 1
  • 0.5° grid
  • daily else monthly

Grid cell total and PFT information is essential.

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

The categories may differ from model to model, depending on their PFT definitions. This may include natural PFTs, anthropogenic PFTs, bare soil, lakes, urban areas, etc. Sum of all should equal the fraction of the grid cell that is land. For models that have grid cells partially covered by land and ocean, please document this in the model documentation and provide your land-sea mask along the data uploads.

Total Soil Moisture Content soilmoist kg m-2
  • 0.5° grid
  • daily

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

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

Frozen Soil Moisture Content soilmoistfroz kg m-2
  • 0.5° grid
  • monthly

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

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

Snow depth snd m
  • 0.5° grid
  • monthly

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

Annual Maximum Thaw Depth thawdepth m
  • 0.5° grid
  • monthly, else annual

Calculated from daily thaw depths.

Snow Water Equivalent swe kg m-2
  • 0.5° grid
  • monthly

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

Total Runoff qtot kg m-2 s-1
  • 0.5° grid
  • daily & monthly

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

Optional outputs
Carbon Mass in Leaves cleaf-total, cleaf-<pft> kg m-2
  • 0.5° grid
  • annual
Carbon Mass in Wood cwood-total, cwood-<pft> kg m-2
  • 0.5° grid
  • annual

Including sapwood and hardwood.

Carbon Mass in Roots croot-total, croot-<pft> kg m-2
  • 0.5° grid
  • annual

Including fine and coarse roots.

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

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

Other
Surface Runoff qs kg m-2 s-1
  • 0.5° grid
  • monthly

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

Carbon Mass in Above Ground Litter Pool clitterag-total, clitterag-<pft> kg m-2
  • 0.5° grid
  • annual

Grid cell total and PFT information is essential.

Carbon Mass in Below Ground Litter Pool clitterbg-total, clitterbg-<pft> kg m-2
  • 0.5° grid
  • annual

Only if models separates below-ground litter and soil carbon. If not, only report csoil and document this in the model documentation. Grid cell total and PFT information is essential.

Carbon Mass Flux into Atmosphere due to CO₂ Emission from Fire ffire-total, ffire-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly
Carbon Mass Flux out of Atmosphere due to Net Biospheric Production on Land nbp-total, nbp-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

This is the net mass flux of carbon between land and atmosphere calculated as photosynthesis MINUS the sum of plant and soil respiration, carbonfluxes from fire, harvest, grazing and land use change. Positive flux is into the land.

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

For wood products only. Sum of CO₂ fluxes to wood production and wood storage turnover emsissions from previous years.

CO2 Flux to Atmosphere from Grazing fgrazing-total, fgrazing-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

CO2 Flux to Atmosphere from Crop Harvesting fcropharvest-total, fcropharvest-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Total Carbon Flux from Vegetation to Litter flitter-total, flitter-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Total Carbon Flux from Litter to Soil flittersoil-total, flittersoil-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

Grid cell total and PFT information is essential.

Total Carbon Flux from Vegetation Directly to Soil fvegsoil-total, fvegsoil-<pft> kg m-2 s-1
  • 0.5° grid
  • monthly

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

Carbon Mass in Coarse Woody Debris ccwd-total, ccwd-<pft> kg m-2
  • 0.5° grid
  • annual
Surface Albedo of Land landalbedo 1
  • 0.5° grid
  • monthly

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

Information about PFT-specific outputs

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 Permafrost sector according to the following pattern:

<model>_<climate-forcing>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>(-<crop>-<irrigation>|-<pft>)_<region>_<time-step>_<start-year>_<end-year>.nc

and replace the identifiers with the specifiers given in the tables of this document. Examples would be:

lpjml_gswp3_obsclim_histsoc_default_qtot_global_annual_1901_1910.nc
lpjml_gwsp3_counterclim_2015soc_1901co2_yield-mai-noirr_global_annual_2006_2010.nc

The following regular expression can be used to validate and parse the file name for the permafrost sector:

(?P<model>[a-z0-9-]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<climate_scenario>[a-z0-9]+)_(?P<soc_scenario>[a-z0-9]+)_(?P<sens_scenario>[a-z0-9-]+)_(?P<variable>[a-z0-9]+)(-(?P<crop>[a-z0-9]+)-(?P<irrigation>(firr|cirr|noirr))|-(?P<pft>[a-z0-9-]+))?_(?P<region>(global))_(?P<time_step>[a-z]+)_(?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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