TY - JOUR
T1 - Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme
T2 - A multi-species, multi-model study
AU - Whaley, Cynthia H.
AU - Mahmood, Rashed
AU - Von Salzen, Knut
AU - Winter, Barbara
AU - Eckhardt, Sabine
AU - Arnold, Stephen
AU - Beagley, Stephen
AU - Becagli, Silvia
AU - Chien, Rong You
AU - Christensen, Jesper
AU - Damani, Sujay Manish
AU - Dong, Xinyi
AU - Eleftheriadis, Konstantinos
AU - Evangeliou, Nikolaos
AU - Faluvegi, Gregory
AU - Flanner, Mark
AU - Fu, Joshua S.
AU - Gauss, Michael
AU - Giardi, Fabio
AU - Gong, Wanmin
AU - Hjorth, Jens Liengaard
AU - Huang, Lin
AU - Im, Ulas
AU - Kanaya, Yugo
AU - Krishnan, Srinath
AU - Klimont, Zbigniew
AU - Kühn, Thomas
AU - Langner, Joakim
AU - Law, Kathy S.
AU - Marelle, Louis
AU - Massling, Andreas
AU - Olivié, Dirk
AU - Onishi, Tatsuo
AU - Oshima, Naga
AU - Peng, Yiran
AU - Plummer, David A.
AU - Popovicheva, Olga
AU - Pozzoli, Luca
AU - Raut, Jean Christophe
AU - Sand, Maria
AU - Saunders, Laura N.
AU - Schmale, Julia
AU - Sharma, Sangeeta
AU - Skeie, Ragnhild Bieltvedt
AU - Skov, Henrik
AU - Taketani, Fumikazu
AU - Thomas, Manu A.
AU - Traversi, Rita
AU - Tsigaridis, Kostas
AU - Tsyro, Svetlana
AU - Turnock, Steven
AU - Vitale, Vito
AU - Walker, Kaley A.
AU - Wang, Minqi
AU - Watson-Parris, Duncan
AU - Weiss-Gibbons, Tahya
PY - 2022/5
Y1 - 2022/5
N2 - 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 SO2-4 ), 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.
AB - 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 SO2-4 ), 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.
UR - http://www.scopus.com/inward/record.url?scp=85130183795&partnerID=8YFLogxK
U2 - 10.5194/acp-22-5775-2022
DO - 10.5194/acp-22-5775-2022
M3 - Journal article
AN - SCOPUS:85130183795
SN - 1680-7316
VL - 22
SP - 5775
EP - 5828
JO - Atmospheric Chemistry and Physics
JF - Atmospheric Chemistry and Physics
IS - 9
ER -