ISIMIP3b simulation round simulation protocol - Diarrhoeal diseases
Introduction
General concept
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.
ISIMIP is intended to be structured in successive rounds connected to the different phases of the climate model intercomparison CMIP (ISIMIP Mission & Implementation document).
The main components of the ISIMIP framework are:
- A common set of climate and other forcing data which will be distributed via a central database;
- A common modelling protocol to ensure consistency across sectors and scales in terms of data, format and scenario set-up;
- A central archive where the output data will be collected and made available to the scientific community.
ISIMIP3b
GCM-based simulations assuming fixed 2015 direct human influences for the future
The ISIMIP3b part of the third simulation round is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions. The group 1 simulations refer to the pre-industrial and historical period of the CMIP6-based climate simulations. Group 2 covers all future projections assuming fixed 2015 levels of socio-economic forcing and different future projections of climate (SSP126, SSP37 and SSP585). Group3 simulations account for future changes in socio-economic drivers and are intended to be started in summer 2021.
You can find the ISIMIP3a protocol, which is is dedicated to impact model evaluation and improvement and detection and attribution of observed impacts, here.
Simulation protocol
In this protocol we describe the scenarios & experiments in ISIMIP3b simulation round, the different input datasets, the output variables, and how to report model results specifically for Diarrhoeal diseases. 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
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. |
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 |
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 |
2015soc |
Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort). Please label your simulations |
nat |
No direct human influences (naturalized run). Please only label your model run |
Scenario specifier | Description |
---|---|
default | For all experiments other than the sensitivity experiments. |
General note regarding sensitivity experiments
The sensitivity experiments are meant to be "artificial" deviations from the default settings. So for example if your model does not at all account for changes in CO₂ concentrations (no option to switch it on or off) the run should be labeled as default
in the sensitivity specifier of the file name even if the run would be identical to the 1850co2
sensitivity setting.
The particular sensitivity scenario for an experiment is given in the experiments table below. For most experiments no sensitivity scenario is given, so the default
label applies.
Experiments
Experiment | Short description |
Pre-industrial 1601-1849 |
Historical 1850-2014 |
Future 2015-2100 |
---|---|---|---|---|
pre-industrial controlhistsoc 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 control2015soc 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 |
||
RCP2.6histsoc 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 |
||
RCP2.62015soc 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 |
||
RCP7histsoc 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 |
|||
RCP72015soc 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 |
|||
RCP8.5histsoc 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 |
|||
RCP8.52015soc 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 |
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
Title | Specifier | Institution | Native resolution | Ensemble member | Priority |
---|---|---|---|---|---|
GFDL-ESM4 | gfdl-esm4 | National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA | 288x180 | r1i1p1f1 | 1 |
UKESM1-0-LL | ukesm1-0-ll | Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK | 192x144 | r1i1p1f2 | 2 |
MPI-ESM1-2-HR | mpi-esm1-2-hr | Max Planck Institute for Meteorology, Hamburg 20146, Germany | 384x192 | r1i1p1f1 | 3 |
IPSL-CM6A-LR | ipsl-cm6a-lr | Institut Pierre Simon Laplace, Paris 75252, France | 144x143 | r1i1p1f1 | 4 |
MRI-ESM2-0 | mri-esm2-0 | Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan | 320x160 | r1i1p1f1 | 5 |
Note on climate forcing priority
The priority for the different climate forcing datasets is from top to bottom. If you cannot use all climate forcing datasets, please concentrate on those at the top of the table.
Variable | Variable specifier | Unit | Resolution | Models | ||
---|---|---|---|---|---|---|
Atmospheric variables mandatory | ||||||
Near-Surface Relative Humidity | hurs | % |
|
|
||
Near-Surface Specific Humidity | huss | kg kg-1 |
|
|
||
Precipitation | pr | kg m-2 s-1 |
|
|
||
Snowfall Flux | prsn | kg m-2 s-1 |
|
|
||
Surface Air Pressure | ps | Pa |
|
|
||
Surface Downwelling Longwave Radiation | rlds | W m-2 |
|
|
||
Surface Downwelling Shortwave Radiation | rsds | W m-2 |
|
|
||
Near-Surface Wind Speed | sfcwind | m s-1 |
|
|
||
Near-Surface Air Temperature | tas | K |
|
|
||
Daily Maximum Near-Surface Air Temperature | tasmax | K |
|
|
||
Daily Minimum Near-Surface Air Temperature | tasmin | K |
|
|
Other climate datasets
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 |
|
Meinshausen, Raper, & Wigley (2011) for 1850-2005 and 2016-2100 and Dlugokencky & Tans (2019) from 2006-2015 |
Socioeconomic forcing
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
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
|
|||||
|
|
|
Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018) |
|||
Reservoirs & dams | ||||||
Reservoirs & dams |
socioeconomic/reservoir_dams/reservoirs-dams_1850_2014.xls
|
|||||
|
|
|
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
|
|||||
|
|
|
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. |
|||
Population mandatory | ||||||
Population 5' grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
|||
Population 0.5° grid |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
|||
Population national |
socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
|||
Population density 5' grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. |
|||
Population density 0.5° grid |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution |
|||
Population density national |
socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
|
|||||
|
|
|
HYDE v3.2.1 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. Decadal data prior to year 1950 have been linearly interpolated in time. |
|||
GDP mandatory | ||||||
GDP |
socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
|
|||||
|
|
|
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
Dataset | Included variables (specifier) | Resolution | Reference/Source and Comments | |||
---|---|---|---|---|---|---|
Land/Sea masks | ||||||
landseamask |
geo_conditions/landseamask/landseamask.nc
|
|||||
|
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
|
|||||
|
0.5° grid | Same as landseamask but without Antarctica. |
||||
landseamask_water-global |
geo_conditions/landseamask/landseamask_water-global.nc
|
|||||
|
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
|
|||||
|
0.5° grid | One fixed pattern to be used for all simulation periods. Upscaled Soil texture map (30 arc sec. to 0.5°x0.5° grid) based on Harmonized World Soil Database v1.1 (HWSD) using the GSWP3 upscaling method A (http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html) |
||||
hwsd_soil_data_all_land |
geo_conditions/soil/hwsd_soil_data_all_land.nc
|
|||||
|
0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on all land. |
||||
hwsd_soil_data_on_cropland |
geo_conditions/soil/hwsd_soil_data_on_cropland.nc
|
|||||
|
0.5° grid | GGCMI Phase 3 soil input data set for usage in ISIMIP/GGCMI Phase 3 simulations, data aggregated by dominant soil profile (MU_GLOBAL) within dominant soil texture class from HWSD on current cropland (MIRCA2000 at 5 arc-minutes). |
||||
River routing | ||||||
basins |
geo_conditions/river_routing/ddm30_basins_cru_neva.[nc|asc]
|
|||||
|
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]
|
|||||
|
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]
|
|||||
|
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
No output variables have been defined for Diarrhoeal diseases, yet.
Reporting model results
The specification on how to submit the data, as well as further information and instructions are given on the ISIMIP website at:
https://www.isimip.org/protocol/preparing-simulation-files
It is important that you comply precisely with the formatting specified there, in order to facilitate the analysis of your simulation results in the ISIMIP framework. Incorrect formatting can seriously delay the analysis. The ISIMIP Team will be glad to assist with the preparation of these files if necessary.
File names consist of a series of identifier, separated by underscores. Things to note:
- Report one variable per file.
- In filenames, use lowercase letters only.
- Use underscore (
_
) to separate identifiers. - Variable names consist of a single word without hyphens or underscores.
- Use hyphens (
-
) to separate strings within an identifier, e.g. in a model name. - If no specific
sens-scenario
is given in the experiments table, usedefault
. - NetCDF file extension is
.nc
. - The reference year for the NetCDF files for ISIMIP3b is
1601
.
Please name the files in the Diarrhoeal diseases sector according to the following pattern:
<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>_<region>_<time-step>_<start-year>_<end-year>.nc
and replace the identifiers with the specifiers given in the tables of this document. Examples would be:
lpjml_gfdl-esm4_w5e5_picontrol_histsoc_default_qtot_global_annual_2001_2010.nc
lpjml_ukesm1-0-ll_w5e5_ssp585_2015soc_2015co2_yield-mai-noirr_global_annual_2006_2010.nc
The following regular expression can be used to validate and parse the file name for the diarrhoeal diseases sector:
(?P<model>[a-z0-9-+.]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<bias_adjustment>[a-z0-9-]+)_(?P<climate_scenario>[a-z0-9-]+)_(?P<soc_scenario>[a-z0-9-]+)_(?P<sens_scenario>[a-z0-9-]+)_(?P<variable>[a-z0-9]+)_(?P<region>(global))_(?P<time_step>[a-z0-9-]+)_(?P<start_year>\d{4})_(?P<end_year>\d{4}).nc
For questions or clarifications, please contact info@isimip.org or the data managers directly (isimip-data@pik‐potsdam.de) before submitting files.
References
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D. and Worley, S. J.: The twentieth century reanalysis project, Quarterly Journal of the Royal Meteorological Society, 137(654), 1–28, doi:10.1002/qj.776, 2011.
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H. and Buontempo, C.: WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies, Earth System Science Data, 12(3), 2097–2120, doi:10.5194/essd-12-2097-2020, 2020.
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, Bulletin of the American Meteorological Society, 87(10), 1381–1398, doi:10.1175/BAMS-87-10-1381, 2006.
Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, Natl. Ocean. Atmos. Adm. Earth Syst. Res. Lab. [online] Available from: https://www.esrl.noaa.gov/gmd/ccgg/trends/, 2019.
Geiger, T.: Continuous national gross domestic product (GDP) time series for 195 countries: Past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100), Earth System Science Data, 10(2), 847–856, doi:10.5194/essd-10-847-2018, 2018.
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Goldewijk, K. K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J., Kennedy, J., Kristzin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., Vuuren, D. P. van and Zhang, X.: Harmonization of Global Land-Use Change and Management for the Period 850–2100 (LUH2) for CMIP6, Geoscientific Model Development Discussions, 1–65, doi:https://doi.org/10.5194/gmd-2019-360, 2020.
Klein Goldewijk, K., Beusen, A., Doelman, J. and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth System Science Data, 9(2), 927–953, doi:10.5194/essd-9-927-2017, 2017.
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12(7), 3055–3070, doi:10.5194/gmd-12-3055-2019, 2019a.
Lange, S.: WFDE5 over land merged with ERA5 over the ocean (W5E5), v1.0, GFZ Data Services, doi:10.5880/pik.2019.023, 2019b.
Lange, S.: ISIMIP3BASD, v2.4.1, doi:10.5281/zenodo.3898426, 2020.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01. Palisades, NY, NASA Socioeconomic Data and Applications Center (SEDAC), doi:10.7927/H4N877QK, 2011a.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management, Frontiers in Ecology and the Environment, 9(9), 494–502, doi:10.1890/100125, 2011b.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M. and Vuuren, D. P. P. van: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109(1), 213, doi:10.1007/s10584-011-0156-z, 2011.
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., Berg, M. van den, Velders, G. J. M., Vollmer, M. K. and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13(8), 3571–3605, doi:10.5194/gmd-13-3571-2020, 2020.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I. and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature Communications, 7(1), 13603, doi:10.1038/ncomms13603, 2016.
Murakami, D. and Yamagata, Y.: Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling, Sustainability, 11(7), 2106, doi:10.3390/su11072106, 2019.
Portmann, F. T., Siebert, S. and Döll, P.: MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochemical Cycles, 24(1), doi:10.1029/2008GB003435, 2010.
Reyer, C., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Matteucci, G., Collalti, A., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Weedon, G., Sheffield, J., Vega del Valle, I., Suckow, F., Horemans, J., Martel, S., Bohn, F., Steinkamp, J., Chikalanov, A. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests. v. 0.1.12., GFZ Data Services, doi:10.5880/PIK.2019.008, 2019.
Reyer, C. P. O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T. G. M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J. A., Bohn, F., Steinkamp, J., Chikalanov, A., Weedon, G. P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on european forests, Earth System Science Data, 12(2), 1295–1320, doi:10.5194/essd-12-1295-2020, 2020.
Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., Arneth, A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos, F., Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E., Thompson, R. L., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang, K. and Zhu, Q.: The Global N2O Model Intercomparison Project, Bulletin of the American Meteorological Society, 99(6), 1231–1251, doi:10.1175/BAMS-D-17-0212.1, 2018.