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This document consists of a summary of the components of the models contributing future regional climate change simulations to the Coordinated Regional climate Downscaling Experiment (CORDEX, http://cordex.org) initiative (Table 1), the institutions and contacts for the simulations (Table 2), and references for all components. Version 1.0 (2021-01-31) of this resource contributed to the IPCC AR6 WGI report (IPCC 2021: AnnexII), see version history. This information has been gathered from the modelling groups initially by the Copernicus Climate Change Service (C3S), aligned with the deadlines and activities of IPCC AR6. The full list of institutions and model names officially registered for CORDEX, including the Terms of Use for the corresponding data, is available at https://is-enes-data.github.io. This resource (as of version 2.1) is described and discussed in the journal publication: Diez-Sierra et al. (2022) The worldwide C3S CORDEX grand ensemble: A major contribution to assess regional climate change in the IPCC AR6 Atlas. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-22-0111.1

In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44g (g1/4g50gkm), 0.22g (g1/4g25gkm), and 0.11g (g1/4g12gkm) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12gkm grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM-RCM modeling chain., Geoscientific Model Development, 14 (8), ISSN:1991-9603, ISSN:1991-959X

Made available in DSpace on 2022-04-29T08:31:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Global warming is likely to cause a progressive drought increase in some regions, but how population and natural resources will be affected is still underexplored. This study focuses on global population and land-use (forests, croplands, pastures) exposure to meteorological drought hazard in the 21st century, expressed as frequency and severity of drought events. As input, we use a large ensemble of climate simulations from the Coordinated Regional Climate Downscaling Experiment, population projections from the NASA-SEDAC dataset, and land-use projections from the Land-Use Harmonization 2 project for 1981–2100. The exposure to drought hazard is presented for five SSPs (SSP1-SSP5) at four Global Warming Levels (GWLs, from 1.5 to 4°C). Results show that considering only Standardized Precipitation Index (SPI; based on precipitation), the combination SSP3-GWL4 projects the largest fraction of the global population (14%) to experience an increase in drought frequency and severity (vs. 1981–2010), with this value increasing to 60% if temperature is considered (indirectly included in the Standardized Precipitation-Evapotranspiration Index, SPEI). With SPEI, considering the highest GWL for each SSP, 8 (for SSP2, SSP4, and SSP5) and 11 (SSP3) billion people, that is, more than 90%, will be affected by at least one unprecedented drought. For SSP5 (fossil-fuelled development) at GWL 4°C, approximately 2·106 km2 of forests and croplands (respectively, 6 and 11%) and 1.5·106 km2 of pastures (19%) will be exposed to increased drought frequency and severity according to SPI, but for SPEI, this extent will rise to 17·106 km2 of forests (49%), 6·106 km2 of pastures (78%), and 12·106 km2 of croplands (67%), with mid-latitudes being the most affected areas. The projected likely increase of drought frequency and severity significantly increases population and land-use exposure to drought, even at low GWLs, thus extensive mitigation and adaptation efforts are needed to avoid the most severe impacts of climate change. European Commission Joint Research Centre (JRC) Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC Foundation—REMHI Division) Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of Atmospheric and Oceanic Sciences Snow and Ice Data Center University of Colorado Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE) University of Copenhagen Niels Bohr Institute (NBI) Danish Meteorological Institute Abdus Salam International Centre for Theoretical Physics (ICTP) Faculty of Science University of New South Wales Helmholtz-Zentrum Geesthacht (HZG) Institute of Coastal Research The Cyprus Institute (CyI) Climate and Atmosphere Research Center (CARE-C) Climate Service Center Germany (GERICS) Helmholtz-Zentrum Geesthacht Commonwealth Scientific and Industrial Research Organisation (CSIRO) Marine and Atmospheric Research Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Département des Sciences de la Terre et de l'Atmosphère Université du Quebec à Montréal (UQAM) University of Cape Town Climate System Analysis Group (CSAG) Department of Physics Faculty of Arts and Sciences Bogazici University Center for Climate Change and Policy Studies Bogazici University Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of Sciences (CAS) São Paulo State University and Bauru Meteorological Centre (IPMet/UNESP) Department of Physics Faculty of Arts and Sciences Isik University Institute of Meteorology and Climate Research Karlsruhe Institute of Technology (KIT) Departamento de Ciências Atmosféricas Universidade de São Paulo Facultad de Ciencias Exactas y Naturales Departamento de Ciencias de la Atmósfera y los Océanos (DCAO-FCEN-UBA) Universidad de Buenos Aires Universidad de Buenos Aires Centro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA) School of Biological Sciences The University of Queensland Department of Earth Sciences and Environment The National University of Malaysia (UKM) National Centre for Scientific Research (CNRS) Institut Pierre-Simon Laplace (IPSL) Laboratoire des Sciences du Climat et de l'Environnement (LSCE) NORCE Norwegian Research Centre AS Centro Italiano Ricerche Aerospaziali (CIRA) São Paulo State University and Bauru Meteorological Centre (IPMet/UNESP)

Previous studies based on observations and models are uncertain about the biophysical impact of af- and deforestation in the northern hemisphere mid-latitude summers, and show either a cooling or warming. The magnitude and direction is still uncertain. In this study, the effect of three different albedo parameterizations in the regional climate model COSMO-CLM (v5.09) is examined performing afforestation experiments at 0.44° horizontal resolution across the EURO-CORDEX domain during 1986-2015. Idealized de- and af-forestation simulations are compared to a simulation with no land cover change. Emphasis is put on the impact of changes in radiation and turbulent fluxes. A clear latitudinal pattern is found, which results partly due to the strong land cover conversion from forest- to grassland in the high latitudes and open land to forest conversion in mid-latitudes. Afforestation warms the climate in winter, and strongest in mid-latitudes. Results are indifferent in summer owing to opposing albedo and evapotranspiration effects of comparable size but different sign. Thus, the net effect is small for summer. Depending on the albedo parameterization in the model, the temperature effect can turn from cooling to warming in mid-latitude summers. The summer warming due to deforestation to grassland is up to 3°C higher than due to afforestation. The cooling by grass or warming by forest is in magnitude comparable and small in winter. The strength of the described near-surface temperature changes depends on the magnitude of the individual biophysical changes in the specific background climate conditions of the region. Thus, the albedo parameterization need to account for different vegetation types. Furthermore, we found that, depending on the region, the land use change effect is more important than the model uncertainty due to albedo parameterization. This is important information for model development.

West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its well-documented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system. In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and land-surface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.