Climate Models

CMIP6

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)

Models in detail

The 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

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

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

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 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

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Forest & Biodiversity Models

Simulations for the Sector Forest & Biodiversity were carried out with the Dynamic Global Vegetation Model (DGVM) LPJmL4.0-VR (Sakschewski et al., 2021). DGVMs as LPJmL are an important tool to study the response of natural forest ecosystems, such as the Amazon rainforest, to climate change and human disturbance such as fire and deforestation.

The LPJmL (Lund-Potdam-Jena managed Land) model is a process-based DGVM that models the energy, water, and carbon cycles in the Earth system and simulates the growth and productivity of natural and agricultural vegetation (Schaphoff et al., 2018). Natural vegetation is characterized by different Plant Functional Types (PFTs), for example ‘Temperate Broadleaved Tree’ or ‘Boreal Needleleaved Tree’. The PFTs compete for light and water resources in grid cells of 0.5° (approx. 50 km x 50 km). As a result, forest communities establish that are adapted to local climatic conditions. In the tropics, broadleaved evergreen and raingreen trees coexist, depending on water availability and dry season length. A recent extension of LPJmL4.0 that now considers dynamic root growth, different rooting depths and root distributions in the soil, LPJmL4.0-VR (Variable Roots), was an important step towards a more realistic representation of the intra-annual productivity and evapotranspiration cycles, forest cover and spatial distribution of biomass in tropical and subtropical forests (Sakschewski et al., 2021).

References:

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

“Regional Rivalry” SSP3 - 7.0

“Fossil-fueled Development” SSP5 - 8.5

SSP Database

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.

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