Climate Models
CMIP6
For analysing future climate scenarios, understanding climate variability in a multi-model framework, and assessing climate change impacts, the CMIP6 multi-model ensemble is used. CMIP is a large international effort to advance climate model development and improve scientific understanding of the Earth system, coordinated by the World Climate Research Programme (WCRP) through its Working Group on Coupled Modelling (WGCM)More than 30 research groups worldwide contribute to CMIP6. In total, 21 model intercomparison projects were endorsed for their relevance to the WCRP’s Grand Challenges and the core scientific questions of CMIP6. The model simulations produced within CMIP6 have also been evaluated and used in major international climate assessments and negotiations, including the IPCC Assessment Reports.
The data produced by CMIP6 forms the foundation for the impact models used in the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project), which underpin the results presented in Climate Impacts Online.
Read more about CMIP6: www.wcrp-climate.org
ISIMIP3b
For the Climate Impacts Online web portal, bias-adjusted ISIMIP3b climate input data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) were used. This dataset is based on original global climate model (GCM) outputs from the CMIP6 archive. More information on this dataset can be found on the ISIMIP website
For bias correction and statistical downscaling, the ISIMIP3BASD method and W5E5 v2.0 observational dataset were applied (Lange, 2019a; Lange, 2020).
The data covers three time periods:
- - Pre-industrial (1601–1849)
- - Historical (1850–2014)
- - Future (2015–2100)
- - piControl (Pre-industrial control experiment)
- - historical
- - ssp126 (SSP1–RCP2.6)
- - ssp370 (SSP3–RCP7.0)
- - ssp585 (SSP5–RCP8.5)
The ISIMIP3b dataset includes a wide range of climate variables: near-surface relative and specific humidity, near-surface wind speed, daily maximum, mean, and minimum temperature, longwave and shortwave downwelling radiation, snowfall, surface air pressure, and total precipitation.
If working with data downloaded from Climate Impacts Online, please cite according to ISIMIP3b's "cite as" information
The ISIMIP3b data includes the following models. Under each link you will also find more information about each specific model:
| Model | Link |
|---|---|
| GFDL-ESM4 | GFDL, ISIMIP input data GFDL-ESM4 |
| IPSL-CM6A-LR | IPSL, ISIMIP input data IPSL-CM6A-LR |
| MPI-ESM1-2-HR | Max-Planck-Institut für Meterologie, ISIMIP input data MPI-ESM1-2-HR |
| MRI-ESM2-0 | Max-Planck-Institut, ISIMIP input data MRI-ESM2-0 |
| UKESM1-0-LL | UKESM, ISIMIP input data UKESM1-0-LL |
Models in detail
GDFL-ESM4The GDFL-ESM4 climate model, which is part of the CMIP6 project, was released in 2018. It consists of:
"aerosol: interactive (including aerosol indirect effect), atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa), atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry), land: GFDL-LM4.1 (land model with a new vegetation dynamics model with explicit treatment of plant age and height structure and soil microbes, with daily fire, crops, pasture, and grazing tiles),
landIce: GFDL-LM4.1, ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-COBALTv2, seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories), with radiative transfer and C-grid dynamics for compatibility with MOM6.
The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km."
Source: WDC Climate - GDFL-ESM4
IPSL-CM6A-LR
The IPSL-CM6A-LR climate model was released in 2017 and is also part of the CMIP project.
The model includes the components:
"atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3.
The model was run by the Institut Pierre Simon Laplace, Paris 75252, France (IPSL) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km."
Source: WDC Climate - GDFL-ESM4
MPI-ESM1-2-HR
The MPI-ESM consists of the coupled general circulation models for the atmosphere and the ocean,
ECHAM6 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa) and MPIOM (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m),
and the subsystem models for land and vegetation JSBACH3.20 and for the marine biogeochemistry HAMOCC6. It is run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions:
"aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km."
Source: WDC Climate - GDFL-ESM4
MRI-ESM2-0
The model MRI-ESM2.0 was released in 2017 and run by the Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan (MRI). It includes the following components:
"aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4.
The model was run in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km."
Source: WDC Climate - GDFL-ESM4
UKESM1
UKESM1 consists of the NEMO ocean model and the CICE sea-ice, as well as the JULES land surface model, with TRIFFID dynamic vegetation. Furthermore, the model simulates atmospheric chemistry and aerosols using UKCA, marine biogeochemistry with MEDUSA and dynamic ice-sheets with the BISICLES model. Source: UKESM - UKESM1
Sources:
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6(CMIP6) experimental design and organization. Geoscientific Model Development, 9, 1937−1958, https://doi.org/10.5194/gmd-9-1937-2016.Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019a.
Lange, S.: ISIMIP3BASD v2.4.1, https://doi.org/10.5281/zenodo.3898426, 2020.
ISIMIP3b
WGCM CMIP6
SWIM Model
A separate model was used for generating the water sector variables data in this region: the Soil and Water Integrated Model (SWIM).
What does the model do?
SWIM was specifically developed to investigate climate and land use change impacts at the regional scale, where the impacts are manifested and adaptation measures take place. The model simulates interlinked processes at the mesoscale such as runoff generation, plant and crop growth, nutrient and carbon cycling, and erosion. It provides numerous model outputs such as river discharge, crop yield, and nutrient concentrations and loads. The approach allows simulation of all interrelated processes within a single model framework at a daily time step using regionally available data (climate, land use and soil) and considering feedbacks. The model setup and postprocessing are supported by a GIS interface (based on GRASS). The model uses 3-level disaggregation scheme: basin – subbasins – hydrotopes. The results are presented as time series and maps for a number of variables. SWIM is a model of intermediate complexity for the river basin and regional scale, because it is a) more comprehensive than purely hydrological and precipitation-runoff models due to a more reliable representation of interlinked hydrological, vegetation and nutrient processes; b) driven by commonly available regional data and can be easier parametrized than more complex hydrological models.
Who maintains it?
SWIM is maintained and further developed by the regional hydrological modelling group of Research Department 2 on PIK's own GitLab source code repository. The development is coordinated by Michel Wortmann, Stefan Liersch and Fred Hattermann.
In what way is the model different from other models in the community?
Simulation of hydrological cycle (water discharge, groundwater level): the same or better than in other comparable ecohydrological and hydrological models;Simulation of hydrological cycle at the river basin scale: better and more detailed process description than in the continental-scale terrestrial models, and in large-scale water resources models;
Simulation of vegetation processes: better than in many hydrological models, which normally do not consider dynamical vegetation processes;
Simulation of water quality: better than in conceptual hydrological models which have no (or only minor) representation of soil processes;
Simulation of diffuse pollution: better than in other commonly used models because of a better temporal and spatial resolution and consideration of dynamical landscape processes;
Simulation of land use, agriculture management, crop yields and feedbacks of climate and land use changes: better than in other models with reduced representation of vegetation growth processes.
Sources:
Krysanova, V., F. Hattermann, Sh. Huang, C. Hesse, T. Vetter, S. Liersch, H. Koch and Z. W. Kundzewicz, 2014. Modelling climate and land use change impacts with SWIM: lessons learnt from multiple applications. Hydrological Sciences Journal, accepted.Krysanova, F. Wechsung, J. Arnold, R. Srinivasan, J. Williams, 2000. PIK Report Nr. 69 "SWIM (Soil and Water Integrated Model), User Manual", 239p. SWIM_Manual
Krysanova, V., Mueller-Wohlfeil, D.I., Becker, A., 1998. Development and test of a spatially distributed hydrological / water quality model for mesoscale water-sheds. Ecological Modelling, 106, 261-289.
Climate Scenarios
The so-called Representative Concentration Paths (RCPs) were developed for the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). They describe different levels of greenhouse gases-induced radiation drives that might occur in the period 2011 till 2100. However, they did not include any socioeconomic “narratives” to go alongside them. Over the past few years, an international team of climate scientists, economists and energy system modelers developed a range of new “pathways” that examine how global society, demographics and economics might change over the next century. They are known as the “Shared Socioeconomic Pathways” (SSPs). The SSPs were published in 2017 (Riahi et al., 2017) and they will play a major role in the IPCC Sixth Assessment Report in 2021/2022.
SSPs are baseline scenarios that describe future developments in the absence of new climate policies, beyond those already in place today. They offer five pathways that the world could take. The five SSPs differ mainly in population growth, urbanization, economic growth, investments in education and health, energy system and land use, rate of technological development as well as drivers of demand, such as lifestyle changes (Riahi et al., 2017).
As the RCPs the SSPs are also not complete in their design, as these are only social futures with do not include climate change impacts. Moreover, no mitigation or adaption measures are implemented in these (O'Neill et al., 2020). Therefore, ClimateImpactsOnline uses three SSP–RCP combination to visualize climate data corresponding to different socioeconomic and radiation scenarios.
“Sustainability” SSP1 - 2.6
The low climate change scenario (SSP1) presents a relatively sustainable path, often referred to as “Taking the Green Road”. The scenario assumes high investments in education and health. These lead to an acceleration of the demographic transition and to a slower population growth, especially in the developing countries. In total there is a broader emphasis on human well-being. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented toward low material growth and lower resource and energy intensity. The SSP1 baseline scenario has low challenges to climate mitigation and adaption, as climate targets can be reached globally (O’Neill et al., 2017). The SSP1 is combined with an forcing output of 2.6 Watt per square meter in 2100.“Regional Rivalry” SSP3 - 7.0
The moderate climate change scenario (SSP3) also refered to as “Regional Rivalry” is characterized by resurgent nationalism and regional conflicts. This pushes countries to increasingly focus on domestic or, at most, regional issues. For example energy and food security goals are achieved only in their own region. Investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities get worse over time. Furthermore, population growth is low in industrialized and high in developing countries. Overall SSP3 is characterized by a low international priority for addressing environmental concerns (Fujimori et al., 2017). The SSP3 scenario is combined with a forcing level of 7 Watt per square meter.“Fossil-fueled Development” SSP5 - 8.5
The extreme scenario (SSP5) is characterized by rapid technological progress and development of human capital. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, this is coupled with the exploitation of abundant fossil fuel resources and the adoption of resource and energy intensive lifestyles all around the world. There is faith in the ability to manage social and ecological systems, including by geo-engineering if necessary (Kriegler et al., 2017). ClimateImpactsOnline uses the combination of the SSP5 scenario with an level of greenhhouse gases-induced radiation of 8.5 Watt per square meter.See also:
The Shared Socio-Economic Pathways (SSPs): An Overview (Poster)
Sources:
O’Neill, B.C., Carter, T.R., Ebi, K. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0
Riahi, K. et al., The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, 153-168 (2017). https://doi.org/10.1016/j.gloenvcha.2016.05.009.
Observational Data
The W5E5 dataset consists of observed and reanalysis data and is used to represent the observed data. It is part of the Impact Model Intercomparison Project (ISIMIP3b), where the dataset is used for bias adjustment of impact assessments. The dataset consists of the land based WFDE5 data and the ocean based ERA5 dataset (data and reanalysis data) (Cucchi et al., 2020, Hersbach et al., 2020). An additional data source is the precipitation data from version 2.3 of the Global Precipitation Climatology Project (Adler et al., 2003).
W5E5 version 1.0 includes data for the period from 1979 to 2017. This means that for the decade 2011-2020, only 6 years are present and likewise the difference maps is only relative to the years 1991-2014. We will continously update and communicate further data of W5E5 in this decade.
The dataset is providing a horizontal spatial resolution of 0.5° and a daily temporal resolution.
The variables included in the dataset are as follows: relative humidity near the surface (abbreviation: hurs, unit: %), specific humidity near the surface (huss, kg kg-1), precipitation (pr, kg m-2 s-1), snowfall flux (prsn, kg m-2 s-1), surface air pressure (ps, Pa), sea level pressure (psl, Pa), descending longwave surface radiation (rlds, W m-2),
descending shortwave surface 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), surface elevation (orog, m) and WFDE5-ERA5 mask (mask, 1) (Lange, 2019).
The W5E5 data of the variables are, over land and ocean, the daily averages of the hourly WFDE5 data. The terrestrial variables hurs, pr, psl, tasmax and tasmin and the oceanic variables tasmax and tasmin are obtained in a different calculation. This is described on the following website: WFDE5 over land merged with ERA5 over the ocean (W5E5)
By selecting "Obsevations" in the Historical Data section of the Settings tab, you can visualise the observed data of the W5E5 dataset.
Sources:
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Nelkin, E. (2003). The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present). Journal of Hydrometeorology, 4(6), 1147–1167. doi:10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2
Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H., & Buontempo, C. (2020). WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth System Science Data, 12(3), 2097–2120. https://doi.org/10.5194/essd-12-2097-2020
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/QJ.3803
Lange, Stefan (2019): WFDE5 over land merged with ERA5 over the ocean (W5E5). V. 1.0. GFZ Data Services. https://doi.org/10.5880/pik.2019.023
Lange, S.: ISIMIP3BASD v2.4.1, https://doi.org/10.5281/zenodo.3898426, 2020.
Historical Simulation Data
To validate climate models, past climatic conditions are simulated and compared with observed data. An important indicator for validating the reliability of future climate projections from models is the simulated temporal and spatial change in global mean surface temperature from pre-industrial times to the present.
Part of the entry card to participate in the Coupled Model Intercomparison Project (Phase 6) is the historical simulation. It was added to Diagnosis, Evaluation, and Characterization of Klima (DECK) for a better seperation between CMIP and a specific phase, in this case CMIP6.
They also serve as a benchmark for CMIP6-Endorsed Model Intercomparison Projects (MIPs).
The simulations start at arbitrary equilibrium conditions from the pre-industrial control experiment (piControl). Then, various time-dependent forcings, consistent with CMIP6, such as greenhouse gas emissions, land use forcings, aerosols, solar forcing and others are fed into the models. These then generate a hindcast of historical conditions. CMIP6 historical simualtion cover the time period from 1850 to 2014.
By selecting "Historical Simulated Data" in the Historical Data section of the Settings tab, you can visualise the past simulations of the climate models.
Sources:
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937-1958, doi:10.5194/gmd-9-1937-2016, 2016.WGCM CMIP6