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Research on SRM has largely been from developed nations and their pioneering work over the last decade has advanced the research, policy and social spaces to where they are today. However, developing nations have historically had relatively little input into broader research and discussions on the theme. Given the global south is highly vulnerable to the impacts of climate change, and, as some recent papers suggest, climate intervention strategies, it is imperative to entrain and develop the capacity of researchers from developing nations to contribute their unique perspectives in research, discursive and practical spaces concerning SRM.Given this imperative, why are developing nations so under-represented in the SRM / climate intervention community, especially considering how highly vulnerable they are to climate change and potentially to climate interventions? Through a series of meetings we posed the following questions to understand the reasons behind this, and what might be done to remedy this:Why have developing nations generally not been involved in SRM research?Why is the perspective of developing nations important in this discussion?What factors hinder developing nation actors from participation in this research?How can we overcome these barriers and entrain more developing nation researchers into SRM research?In this paper we report on responses to these questions and make practical suggestions for potential entrainment of southern researchers into SRM research. We also report on recent initiatives that begin addressing this problem and propose what global research into SRM may look like in ten years.&nbsp, The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

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)

This document reports on the methods used to create and the results of the WRF-based numerical wind atlases developed for the Wind Atlas for South Africa Phase 3 (WASA3) project.The report is divided into four main parts. In the first part, we document the method used to run the mesoscale simulations and to select the best suited WRF model configuration using the measurements from the WASA masts. In the second part, we describe the method used to generalise and downscale the WRF model wind climate. We compare the results from the downscaled numerical wind atlas against the observed wind statistics from the 19 WASA masts in the third part. In the last part, we present the new wind resource maps and their long-term climatology. In WASA3, there have been many updates to the configuration of the 2018 WASA2 simulations documented inHahmann et al (2018). Among the most important:• We ran thirteen two-year simulations covering the period most observed in all the WASA sites to find the WRF model configuration most suited to the simulation of the wind climatology over South Africa.• We used a new method of generalisation and downscaling of the WRF-derived wind climate that uses the PyWAsP engine and was demonstrated more accurate than the previous approach.• We produced the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km x 3.33 km spatial resolution and 30 minutes time output.The final error statistics of the WASA3 wind atlas show that the WRF+PyWAsP method has a MAPE of 14.2 % and 4.3 % for the long-term power density and wind speed, respectively. This is improved from the same validation in WASA2. When ignoring the two more complex masts, WM09 and WM11, the WRF and WRF+PyWAsP downscaling significantly narrows the error distributions for both long-term wind speed and power density.