ISIMIP3 simulation protocol

Historical runs with climate-related forcing based on observations

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 direct human forcing and a de-trended version of the observed climate allowing for the generation of a “no climate change” baseline (counterfactual, if available).

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.

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 direct human forcing. 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 direct human forcing and different future projections of climate (SSP1-2.6, SSP3-7.0 and SSP5-8.5). Group 3 simulations account for future changes in the direct human forcing and are intended to be started once the corresponding direct human forcing input data is available.

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.

For models requiring spin-up, we provide 100 years of spinclim data which is identical with the first 100 years of the counterclim data. The files are located under ISIMIP3a/InputData/climate/atmosphere/spinclim at DKRZ and in the ISIMIP Repository. If more than 100 years of spin-up are needed, these data can be repeated as often as needed.

For historical runs, use the transclim climate time series, historical CO₂ concentration and varying DHF, for the transition period from spin-up to the start of the experiment (1850-1900). When using a longer spin-up period that (nominally) extends back further than 1850, please keep CO₂ concentration and DHF constant at 1850 level until reaching the year corresponding to 1850.

For experiments with fixed direct human influences (1901soc, 2015soc), the spin-up should be based on the 1901 DHF or 2015 DHF, respectively.

For sector-specific experiments without direct human influence (nat), the spin-up should not use any DHF as well. Likewise, the nofire experiment should include a spin-up without fire.

The model evaluation experiment starts in 1961. To capture historical fishing effort prior to 1961, we also provide input for a nominal spin-up (1841-1860, fishing held constant at 1861 levels) and pre-industrial transition period (1861-1960, reconstructed fishing effort).

To set-up climate-forcing variables for the entire 1841-1960 period, we ask modellers to use the "control run" (ctrlclim) monthly output for the years 1961-1980 (inclusive) on repeat for six cycles. These years have been selected because they correspond with an entire ENSO cycle and because no climate trend is detectable prior to 1980 from the GFDL model.

For models that require longer spin-up prior to 1841, please keep 1841 levels of fishing effort constant and, if needed, repeat the ENSO cycle (e.g. monthly values for 1961- 1980 inclusive from ctrlclim) for as many times necessary. For the ‘no fishing’ runs (nat), the spin-up and pre-industrial transition should not use any fishing effort.

We ask modellers to include all outputs from 1841 onwards for use in our analyses.

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.

For experiments with fixed year-2015 direct human influences (2015soc), spin-up should be based on the 2015 DHF.

For sector-specific experiments without direct human influence (nat), the spin-up should not use any DHF as well.

Please note that there is no "pre-industrial control run" from 1601-1849 for these experiments (2015soc, nat) and hence the spin-up links directly to the historical period.

The simulations with nitrogen cycling turned off should also be spun up with nitrogen cycling turned off.

General note regarding sensitivity experiments

The sensitivity experiments are meant to be 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 specifier for an experiment is given in the experiments table above. For most experiments no sensitivity specifier is given, so the default label applies.

General note regarding sensitivity experiments

The sensitivity experiments are meant to be 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 specifier for an experiment is given in the experiments table above. For most experiments no sensitivity specifier is given, so the default label applies.

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

ISIMIP3a/InputData/climate/atmosphere/<climate-scenario>/global/daily/historical/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc

# ocean data for marine-fishery
ISIMIP3a/InputData/climate/ocean/<climate-scenario>/global/monthly/historical/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_monthly_<start-year>_<end-year>.nc

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

ISIMIP3b/InputData/climate/atmosphere/bias-adjusted/global/daily/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<bias-adjustment>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc

# ocean data for marine-fishery
ISIMIP3b/InputData/climate/ocean/uncorrected/global/monthly/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<climate-scenario>_<climate-variable>_onedeg_global_monthly_<start-year>_<end-year>.nc  # 1° grid
ISIMIP3b/InputData/climate/ocean/uncorrected/global/monthly/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<climate-scenario>_<climate-variable>_halfdeg_global_monthly_<start-year>_<end-year>.nc  # 0.5° grid

Note on ocean data availability

Variable availability is mainly based on the data published in ESGF and may vary between the CMIP experiments.

Some variables are available as extracted versions from vertically resolved data. Their variable names have been suffixed with -bot (ocean bottom), -surf (surface values) or -vint (vertically integrated), respectively.

Note on ocean data availability

Some variables are available as extracted versions from vertically resolved data. Their variable names have been suffixed with -bot (ocean bottom), -surf (surface values) or -vint (vertically integrated), respectively.

Bias adjustment

Note on agriculture output

For agricultural output, variables are to be reported with the dimensions (time,lat,lon) where the time axis' unit is growing seasons since 1901-01-01 00:00:00 resp. growing seasons since 1601-01-01 00:00:00 for all variables unless these are reported on a monthly time axis, e.g. soilmoist. In many cases growing seasons are equivalent to years, as there is always only one planting event per year. However, due to temperature-sensitive growing season lengths, the growing seasons are not fully equivalent to years and users should note the difference. Reported variables on start and end of the growing season are supplied to allow allocating events to transient time axes if desired.

Crop priority and naming

Irrigation

Harmonization

Species

Forest stands

Lake sites

A document with additional information is maintained by the sector coordinators and provided at https://docs.google.com/spreadsheets/d/1UY_KSR02o7LtmNoOs6jOgOxdcFEKrf7MmhR2BYDlm-Q/edit#gid=555498854.

Catchment gauging stations

No sector specific identifiers are available for this selection of simulation round and sectors.

Exposure

Reporting per growing seasons

To resolve potential double harvests within one year, crop yields should be reported per growing season and not per calendar year. Thus, in the NetCDF output files, do not use a time dimension but instead a unitless coordinate variable with integer values; more information on how to construct these files is given below and on the ISIMIP website (https://www.isimip.org/protocol/preparing-simulation-files/).

Cumulative growing season variables such as, e.g., actual evapotranspiration or precipitation are to be accumulated over the growing season. The first season in the file (level=0) is then the first complete growing season of the time period provided by the input data without any assumed spin-up data, which equates to the growing season with the first planting after this date. To ensure that data can be matched to individual years in post-processing, it is essential to also provide the actual planting dates (as day of the year), actual planting years (year), anthesis dates (as day of the year), year of anthesis (year), maturity dates (day of the year), and year of maturity (year). This procedure is identical to the GGCMI convention (Elliott et al., 2015, https://doi.org/10.5194/gmd-8-261-2015).

Information about PFT-specific outputs

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

Information about soil organic carbon pools of different turnover times

Some variables can be provided seperately by soil organic carbon pools of different turnover times, if your model simulates those. This is done using the -<pool> extension. Please indicate them as -fast, -slow, and -passive and describe your definition of the turnover times in your model description. The extension is used in addition to the extension expressing the Plant Functional Type (PFT) and needs to put before it.

Information about peat outputs

All variables should be reported separately for the peat fraction of the grid cell if they are calculated separately for peat and non-peat grid-cell fractions, with a peattype (<pt>) extension to the variable name. This extension can be -naturalpeat, -drainpeat, -restorepeat, or -minl for the non-peat (mineral) gridcell fraction. It is used in addition to the extension expressing the Plant Functional Type (PFT) and needs to put before it.

Additional instructions for the health sector

• If different realizations of the model are applied, then these should be indicated by the specifier <r>. E.g. to reflect a central, upper, and lower estimate of the ERF: <r> = lower, central, upper. Please explain the meaning of these realizations in the online model documentation; contact the ISIMIP coordination team in case of questions.
• If data are disaggregated e.g. by age group, gender, etc., they should be reported along an additional dimension, described by an auxiliary coordinate variable, in the NetCDF files. See the example provided at https://www.isimip.org/protocol/preparing-simulation-files/.
• For local (non-gridded) data, locations (cities/regions/countries) should be reported along an additional dimension called location, with the location name given as string in an auxiliary coordinate variable called location_name, in the NetCDF files. In addition, coordinates of the location should be reported in auxiliary variables called location_lat and location_lon. See the example provided at https://www.isimip.org/protocol/preparing-simulation-files/. The <region> specifier in the file name should be set to local. For gridded data, the <region> specifier in the file name should be global or indicate a region or country.

Additional instructions for the labour sector

• The analysis uses a large collection of micro-survey data to estimate new, and robust global and regional temperature and wet-bulb globe temperature exposure-response functions (ERFs) for labour supply at both individual and sub-national regions across the world. The specifications control for income and both location (sub-national region/country) and temporal (week/month/year) fixed-effects along with extreme events such as droughts. In the case of country-specific studies using micro-surveys (Antonelli et al. 2020; Shahegh et al. 2020; Shahegh and Dasgupta 2022), specifications also include socioeconomic and demographic drivers such as gender, age, education, and total income. The response-functions are estimated separately for working conditions; high-exposure/outdoor in the sun (e.g. agricultural, hunting, forestry and fishing; mining and quarrying; and construction) and low-exposure/outdoor in the shade or indoor (e.g. manufacturing and utilities).
• This sector uses an augmented mean function, created from an ensemble of five labour productivity response-functions reported in Dasgupta et al. 2021. M1) Pilcher et al., 2002: Psychological performance, e.g. reaction time, tracking or memory task, at global scale. M2) Dunne et al., 2013: Individual capacity to safely perform heavy labour under heat stress at global scale. M3) Kjellstrom et al., 2014: Reduction of hourly work capacity for heavy work following the ISO standard at global scale. M4) Sahu et al., 2013: Work output per hour of rice farmers calculated by number of rice bundles laid down, at country-level for India. M5) Li et al., 2016: Time efficiency measures; direct, indirect, and idle time of rebar construction workers, at country-level for China. Work is underway to estimate labour productivity response-functions using empirical analysis.

Additional instructions for the food-security and nutrition (FS-N) sector

• This sector will use additional data: cereal import dependency from FAOSTAT (to prescribe dependence on trade or subsistence farming), age distribution and income level data. Econometric specifications will control for income and inequality (HDI). Data on trade dependency, subsistence farming, and age distribution will allow segregated projections considering vulnerability in terms of population dependency ratio and import dependency.
• Please consider that for simulations based on empirical models, not all the socioeconomic variables are used for projections. This is standard practice to isolate the climate signal. In these cases, variables used should be specified in the model output documentation.

No sector specific notes are available for this selection of simulation round and sectors.