Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme:a multi-species, multi-model study

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Whaley, Cynthia, H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen, Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay, M., Dong, Xinyi, Eleftheriadis, Kostas, Evangeliou, Nikolaos, Faluvegi, Gregory, S., Flanner, Mark, Fu, Joshua, S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, Hjorth, Jens, Liengaard, Huang, Lin, Im, Ulas, Kanaya, Yugo, Krishnan, Srinath, Klimont, Zbigniew, Kühn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Olivié, Dirk, Onishi, Tatsuo, Oshima, Naga, Peng, Yiran, Plummer, David, A., Popovicheva, Olga, Pozzoli, Luca, Raut, Jean-Christophe, Sand, Maria, Saunders, Laura, N., Schmale, Julia, Sharma, Sangeeta, Skeie, Ragnhild Bieltvedt, Skov, Henrik, Taketani, Fumikazu, Thomas, Manu, A., Traversi, Rita, Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven, Vitale, Vito, Walker, Kaley, A., Wang, Minqi, Watson-Parris, Duncan, Weiss-Gibbons, Tahya
• Other Authors: Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada (ECCC), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC-CNS), Department of Geography Montréal, McGill University = Université McGill Montréal, Canada, Norwegian Institute for Air Research (NILU), Institute for Climate and Atmospheric Science Leeds (ICAS), School of Earth and Environment Leeds (SEE), University of Leeds-University of Leeds, Climate Chemistry Measurements and Research, Norwegian Meteorological Institute Oslo (MET), The University of Tennessee Knoxville, Department of Environmental Science Roskilde (ENVS), Aarhus University Aarhus, Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety (INRASTES), National Center for Scientific Research "Demokritos" (NCSR), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Climate Systems Research New York (CCSR), Columbia University New York, University of Michigan Ann Arbor, University of Michigan System, Dipartimento di Chimica "Ugo schifo", Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Center for International Climate and Environmental Research Oslo (CICERO), University of Oslo (UiO), International Institute for Applied Systems Analysis Laxenburg (IIASA), Department of Applied Physics Kuopio, University of Kuopio, Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute (FMI), Swedish Meteorological and Hydrological Institute (SMHI), TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Meteorological Research Institute Tsukuba (MRI), Japan Meteorological Agency (JMA), Center for Earth System Science Beijing (CESS), Tsinghua University Beijing (THU), Lomonosov Moscow State University (MSU), European Commission - Joint Research Centre Ispra (JRC), University of Toronto, Extreme Environments Research Laboratory (EERL), Ecole Polytechnique Fédérale de Lausanne (EPFL), Met Office Hadley Centre (MOHC), United Kingdom Met Office Exeter, Department of Atmospheric, Oceanic and Planetary Physics Oxford (AOPP), University of Oxford
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; AMAP; Arctic; black carbon; Global warming
• Online Access: https://insu.hal.science/insu-03454867; https://insu.hal.science/insu-03454867v2/document; https://insu.hal.science/insu-03454867v2/file/acp-22-5775-2022.pdf; https://doi.org/10.5194/acp-22-5775-2022

International audience While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios.In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their represen-tation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of differentchemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, andparticulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment reportare thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, sea-sonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percentmodel biases and correlation coefficients. The results show a large range in model performance, with no oneparticular model or model type performing well for all regions and all SLCF species. The multi-model mean(mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance.For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO2−), the mmm was within ±25 % of the 4measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs.Of the SLCFs in our study, model biases were smallest for CH4 and ...