Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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 Manish , Dong Xinyi , Eleftheriadis Konstantinos , Evangeliou Nikolaos , Faluvegi Gregory , 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 
Published: May 2022

Abstract. 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 SO42-), 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 most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.