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, Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen R., Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay Manish, Dong, Xinyi, Eleftheriadis, Konstantinos, Evangeliou, Nikolaos, Faluvegi, Gregory, Flanner, Mark G., Fu, Joshua S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, Hjorth, Jens Liengaard, Huang, Lin, Im, Ulas, Kanaya, Yugo, Srinath, Krishnan, Klimont, Zbigniew, Kuhn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Oliviè, Dirk Jan Leo, Onishi, Tatsuo, Oshima, Naga, Peng, Yiran, Plummer, David A., Pozzoli, Luca, Popovicheva, Olga, Raut, Jean-Christophe, Sand, Maria, Saunders, Laura, Schmale, Julia, Sharma, Sangeeta, Skeie, Ragnhild Bieltvedt, Skov, Henrik, Taketani, Fumikazu, Thomas, Manu Anna, Traversi, Rita, Tsigaridis, Kostas, Tsyro, Svetlana
• Format: Article in Journal/Newspaper
• Language: English
• Published: EGU 2022
• Subjects: Arctic; AMAP; black carbon; Global warming
• Online Access: https://hdl.handle.net/11250/2997907; https://doi.org/10.5194/acp-22-5775-2022

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 (CH4, O3, BC, and SO), the mmm was within ±25 % of the measurements 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 greatest for OA. For ...