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: C. H. Whaley, R. Mahmood, K. von Salzen, B. Winter, S. Eckhardt, S. Arnold, S. Beagley, S. Becagli, R.-Y. Chien, J. Christensen, S. M. Damani, X. Dong, K. Eleftheriadis, N. Evangeliou, G. Faluvegi, M. Flanner, J. S. Fu, M. Gauss, F. Giardi, W. Gong, J. L. Hjorth, L. Huang, U. Im, Y. Kanaya, S. Krishnan, Z. Klimont, T. Kühn, J. Langner, K. S. Law, L. Marelle, A. Massling, D. Olivié, T. Onishi, N. Oshima, Y. Peng, D. A. Plummer, O. Popovicheva, L. Pozzoli, J.-C. Raut, M. Sand, L. N. Saunders, J. Schmale, S. Sharma, R. B. Skeie, H. Skov, F. Taketani, M. A. Thomas
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: Physics; QC1-999; Chemistry; QD1-999; Arctic; AMAP; black carbon; Global warming
• Online Access: https://doi.org/10.5194/acp-22-5775-2022; https://doaj.org/article/1808c4d43efe4e799ac4a0cc1eedbe9e

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 representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate 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 report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular 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 (CH 4 , O 3 , BC, and SO <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">4</mn><mrow><mn ...