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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Hydrological Models

Separate hydrological models were used for the water sector variables. The following are the the two hydrological models used:

ISIMIP - CWatM

CWatM is a spatially-distributed, rainfall-runoff and channel routing water resources model (Burek et al., 2020). It is process-based and used to quantify water availability, human water withdrawals of different sectors (industry, domestic, agriculture), and the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is designed at grid level, with two native versions for 0.5◦ and 0.0833° resolutions at global scales (with sub-grid resolution taking topography and land cover into account). It operates at daily time steps (with sub-daily time stepping for soil and river routing).

ISIMIP - H08

H08 is a grid-cell based global hydrological model. It consists of six sub-models, namely land surface hydrology, river routing, reservoir operation, crop growth, environmental flow, and water abstraction. The formulations of sub-models are described in detail in Hanasaki et al. (2008a,b, 2010). In the standard simulation settings, H08 spatially covers the whole globe at a resolution of 0.5°×0.5° in order to assess geographical heterogeneity of hydrology and water use. Simulation period is typically for several decades and calculation interval is a day. The six sub-models exchange water fluxes and updates water storage at each calculation interval with the complete closure of water balance (the error is less than 0.01% of the total input precipitation). These characteristics enable us to explicitly simulate the major interaction between natural water cycle and major human activities of the globe. Source code and the manuals of H08 is open to public, available at http://h08.nies.go.jp. In 2016, the water abstraction schemes of H08 has been substantially enhanced. In addition, a simple groundwater scheme was added to the land surface hydrology sub-model. It enabled us to estimate water abstraction from six major water sources, namely, streamflow regulated by global reservoirs (i.e. reservoirs regulating the flow of main channel of the world major rivers), aqueduct water transfer, local reservoirs, seawater desalination, renewable groundwater, and non-renewable groundwater. A model description paper is available at https://www.hydrol-earth-syst-sci.net/22/789/2018/. H08 is one of the 13 global hydrology models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a global water sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Water (global) Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.010 For ISIMIP2b, the new H08 (Hanasaki et al., 2018) was used. The model is substantially different from “classic H08” (Hanasaki et al. 2008a, 2008b, 2010) by mainly six points. - Groundwater scheme was added. - Groundwater abstraction scheme was added. - Aqueduct water transfer scheme was added - Scheme for return flow and delivery loss was added - Reservoir scheme was updated - Seawater desalination scheme was added For ISIMIP3a/3b, the hydrological parameters of H08 have been tuned based on the study of Yoshida et al. (2022; https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR030660 ). We believe now the overall water balance is better estimated compared to previous versions. Due to some technical reasons, the parameters for subarctic climate (Dc) vary by longitudinal sections. This may cause sudden jumps in hydrological variables (e.g. soil moisture) at the borders of sections. See Yoshida et al. (2022) for further details.

Human Forcings Experiment Specifiers (soc-scenarios)

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Agricultural Models

Agricultural data for the crop yield variables was taken from the ISIMIP3b agricultural output data. Shown data consists of an ensemble of 11 different agriculture models. The data presented are derived from global crop models, which simulate crop growth worldwide using generalized assumptions for climate, soil, and management conditions. Because regional environments vary, such models are typically calibrated with local data to improve accuracy. The results shown here have not been regionally calibrated, and therefore may not represent precise yield levels for this area. They should be interpreted as illustrating global or large-scale trends rather than exact local values.
Details about each of the agriculture models used as input are listed below

Impact Model - CYGMA1p74

CYGMA is a global gridded crop model. The model operates at 0.5° resolution in longitude and latitude and has a daily time step. In the model, crop development is modelled as a fraction of the accumulated growing degree days relative to the crop thermal requirements. For wheat, only spring wheat is considered, because the vernalization process is not currently incorporated into the model. Leaf growth and senescence are calculated according to the fraction of the growing season using the prescribed shape of the leaf area index curve. The yields are computed from the photosynthetically active radiation intercepted by the crop canopy, the radiation-use efficiency (RUE), the effects of CO2 fertilization on the RUE and the fraction of total biomass increments allocated to the harvestable component. The soil water balance sub-model, which is coupled with the snow cover sub-model, is used to calculate the actual evapotranspiration. In the model, crop development is modelled as a fraction of the accumulated growing degree days relative to the crop thermal requirements. For wheat, only spring wheat is considered, because the vernalization process is not currently incorporated into the model. Leaf growth and senescence are calculated according to the fraction of the growing season using the prescribed shape of the leaf area index curve. The yields are computed from the photosynthetically active radiation intercepted by the crop canopy, the radiation-use efficiency (RUE), the effects of CO2 fertilization on the RUE and the fraction of total biomass increments allocated to the harvestable component. The soil water balance sub-model, which is coupled with the snow cover sub-model, is used to calculate the actual evapotranspiration. Five different stress types, i.e., nitrogen (N) deficits, heat, cold, water deficits and water excesses are considered, and the most dominant stress type for a day decreases the daily potential increment in the leaf area for the vegetative growth period and in yield for the reproductive growth period. The growth and yield of soybeans in the model are less sensitive to N deficit stress than are theother crops considered here because the soybean is a legume that fix nitrogen. All of the stress types except N deficits are functions of daily weather, and the tolerance of each crop to these stresses increases as the knowledge stock increases. The knowledge stock is an economic indicator that is calculated as the sum of the public annual agricultural research and development (R&D) expenditures for each country since the year 1961 with a certain obsolescence rate, and it represents the average level of agronomic technology and management among farmers in a country. More details on the modelling are available in Iizumi et al. (2017).

Impact Model - CROVER

CROVER is one of the 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs; for a full technical description of the ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/

Impact Model - EPIC-IIASA

The EPIC-based global gridded crop modelling system “EPIC-IIASA” is used to assess the main global agricultural systems in response to management interventions such as cropping practices, fertilization and irrigation practices or conservation and organic agriculture options, and changing environment, including climate change and soil degradation. Furthermore, EPIC-IIASA is used to compare cropland management systems and their effects on environmental indicators like water availability, nitrogen and phosphorus levels in soil, and greenhouse gas emissions.
Building on the Environmental Policy Integrated Climate model and global or regional datasets on climate, soils, land cover, and land use, EPIC-IIASA can analyze numerous crop types and their management under different weather, topographical, and soil conditions. It investigates the trade-offs between plant growth and yield on the one hand, and environmental impacts and sustainability on the other.
For example, EPIC-IIASA can estimate—based on soil type and prevailing climatic conditions—the extent to which nutrients from fertilizer, such as nitrogen (N) are leaching into nearby river and stream networks. This problem is of growing concern as globally two-fifths of N used in agriculture is lost to ecosystems with harmful environmental effects.
EPIC-IIASA can analyze options of sustainable agriculture including soil erosion control, crop residue management, improving soil organic carbon stock and reducing GHG emissions. Global and regional EPIC-IIASA applications help informing on the potential of agricultural systems to contribute to meeting global climate and food security targets. 

Impact Model - ISAM

Integrated Science Assessment Model (ISAM) is a coupled biogeochemical and biogeophysical model with 0.5° × 0.5° spatial resolution and multiple temporal resolutions ranging from a half-hour to yearly time steps. It simulates C, N, energy, and water budgets for various terrestrial ecosystems through photosynthesis, surface hydrology, radiative transfer, carbon allocation, and ecosystem respiration (Barman et al., 2014a, 2014b; Yang et al., 2009). Moreover, ISAM incorporates crop growth processes for C3 and C4 food crops (maize, soybean, wheat, and rice) and bioenergy grasses (miscanthus, cave-in-rock, and alamo), which are evaluated at site-level, regional, and global scales (Gahlot et al., 2020; Lin et al., 2017; Niyogi et al., 2015; Song et al., 2013, 2015, 2016). Some of the important features, unique to ISAM and critical for crop yield calculations, include (i) dynamic crop-specific phenology and carbon allocation schemes (Song et al., 2013, 2015), accounting for the sensitivity of different crops to extreme environmental conditions; (ii) dynamic vegetation structures, which better capture seasonal variability in leaf area index (LAI), canopy height, and root depth; (iii) dynamic root distribution processes at the depth that improve simulated root-mediated soil water uptake and transpiration.

Impact Model - LDNDC

LandscapeDNDC is one of the 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs; for a full technical description of the ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/

Impact Model - LPJ-GUESS

The model is the crop-enabled version of LPJ-GUESS, described in Lindeskog et al. (2013). It is loosely based on LPJmL as described in Bondeau et al. (2007), but differs in several important aspects, including not being calibrated to observed country-level yields, a new phenology scheme, and a dynamic calculation of the potential heat units (PHU) required for a crop to achieve maturity. Sowing dates are calculated dynamically following Waha et al. (2012). The PHU sum needed for full development of a crop in a particular grid cell is calculated using a 10-year running mean of heat unit sums accumulated from the sowing date to the end of a sampling period (ranging from 190 to 245 days) derived from default sowing and harvest limit dates (Lindeskog et al., 2013). Crops are harvested upon full development. This dynamic variation of PHU to climate effectively assumes a perfect adaptation of crop cultivar to the prevailing climate. N limitation is not explicitly accounted for in this version of the model.

Impact Model - LPJmL

LPJmL is a dynamic global vegetation model that was extended to cover agricultural systems and the terrestrial hydrological cycle. It is capable of transient simulations of different crops, pasture systems and natural vegetation dynamics and can account for different management aspects in crop simulations.

Impact Model - pDSSAT

pDSSAT is one of the 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs; for a full technical description of the ISIMIP3 Simulation Data from the Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/
pDSSAT uses to the pSIMS environment to run the DSSAT crop model in a parallelized global way. In GGCMI/ISIMIP Phase 3a/3b (group 1 and 2) we use DSSAT version 4.6, which is now updated to version 4.8 for the Phase 3a ATTRICI and the Phase 3b group 3 simulations.

Impact Model - PEPIC

PEPIC is a Python-based Environmental Policy Integrated Climate (EPIC) model.

Impact Model - PROMET

PROMET is a hydrological land surface process model, which has been extended by a biophysical dynamic vegetation component to model crop growth and yield formation. It uses first order physical and physiological principles to determine net primary production and respiration based on approaches from Farquhar et al. (1980) and Ball et al. (1987), combined with a phenology and a two-layer canopy architecture component of Yin et al (2005). PROMET takes into account the dependency of net primary production and phenology on environmental conditions including meteorology, CO2 concentration for C3 and C4 pathways as well as water and temperature stress. The mass and energy balance of the canopy and underlying soil surface are iteratively closed for each simulation time step. The canopy and phenology component allocates assimilates into the different plant organs of the canopy depending on the phenological stage of development. Assimilates that are accumulated within the fruit fraction during the growing period determine the dry biomass available for yield formation. The simulation is performed on an hourly time step to account for non-linear reactions of crop growth to environmental conditions (mainly light, water, temperature and wind). Conversion of daily climate model data to hourly values is done by the TeddyTool v1.1 (Zabel and Poschlod 2023). Depending on the reaction of the considered crop to meteorological and soil-specific conditions, the crop may either die due to water, heat or cold stress before being harvested or it may not reach maturity. In both cases, this results in total yield loss.

Impact Model - SIMPLACE-LINTUL5

SIMPLACE—a versatile modelling and simulation framework for sustainable crops and agroecosystems hhttps://doi.org/10.1093/insilicoplants/diad006

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)
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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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