Publication Abstracts
Whaley et al. 2022
Whaley, C.H., R. Mahmood, K. von Salzen, B. Winter, S. Eckhardt, S. Arnold, S. Beagley, S. Becagli, R.-Y. Chien, J. Christensen, S.M. Damani, K. Eleftheriadis, N. Evangeliou, , 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, H. Skov, F. Taketani, M.A. Thomas, R. Traversi, , S. Tsyro, S. Turnock, V. Vitale, K.A. Walker, M. Wang, D. Watson-Parris, and T. Weiss-Gibbons, 2022: Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: A multi-species, multi-model study. Atmos. Chem. Phys., 22, no. 9, 5775-5828, doi: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 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.
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BibTeX Citation
@article{wh00100k,
author={Whaley, C. H. and Mahmood, R. and von Salzen, K. and Winter, B. and Eckhardt, S. and Arnold, S. and Beagley, S. and Becagli, S. and Chien, R.-Y. and Christensen, J. and Damani, S. M. and Eleftheriadis, K. and Evangeliou, N. and Faluvegi, G. S. and Flanner, M. and Fu, J. S. and Gauss, M. and Giardi, F. and Gong, W. and Hjorth, J. L. and Huang, L. and Im, U. and Kanaya, Y. and Krishnan, S. and Klimont, Z. and Kühn, T. and Langner, J. and Law, K. S. and Marelle, L. and Massling, A. and Olivié, D. and Onishi, T. and Oshima, N. and Peng, Y. and Plummer, D. A. and Popovicheva, O. and Pozzoli, L. and Raut, J.-C. and Sand, M. and Saunders, L. N. and Schmale, J. and Sharma, S. and Skov, H. and Taketani, F. and Thomas, M. A. and Traversi, R. and Tsigaridis, K. and Tsyro, S. and Turnock, S. and Vitale, V. and Walker, K. A. and Wang, M. and Watson-Parris, D. and Weiss-Gibbons, T.},
title={Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: A multi-species, multi-model study},
year={2022},
journal={Atmospheric Chemistry and Physics},
volume={22},
number={9},
pages={5775--5828},
doi={10.5194/acp-22-5775-2022},
}
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RIS Citation
TY - JOUR ID - wh00100k AU - Whaley, C. H. AU - Mahmood, R. AU - von Salzen, K. AU - Winter, B. AU - Eckhardt, S. AU - Arnold, S. AU - Beagley, S. AU - Becagli, S. AU - Chien, R.-Y. AU - Christensen, J. AU - Damani, S. M. AU - Eleftheriadis, K. AU - Evangeliou, N. AU - Faluvegi, G. S. AU - Flanner, M. AU - Fu, J. S. AU - Gauss, M. AU - Giardi, F. AU - Gong, W. AU - Hjorth, J. L. AU - Huang, L. AU - Im, U. AU - Kanaya, Y. AU - Krishnan, S. AU - Klimont, Z. AU - Kühn, T. AU - Langner, J. AU - Law, K. S. AU - Marelle, L. AU - Massling, A. AU - Olivié, D. AU - Onishi, T. AU - Oshima, N. AU - Peng, Y. AU - Plummer, D. A. AU - Popovicheva, O. AU - Pozzoli, L. AU - Raut, J.-C. AU - Sand, M. AU - Saunders, L. N. AU - Schmale, J. AU - Sharma, S. AU - Skov, H. AU - Taketani, F. AU - Thomas, M. A. AU - Traversi, R. AU - Tsigaridis, K. AU - Tsyro, S. AU - Turnock, S. AU - Vitale, V. AU - Walker, K. A. AU - Wang, M. AU - Watson-Parris, D. AU - Weiss-Gibbons, T. PY - 2022 TI - Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: A multi-species, multi-model study JA - Atmos. Chem. Phys. JO - Atmospheric Chemistry and Physics VL - 22 IS - 9 SP - 5775 EP - 5828 DO - 10.5194/acp-22-5775-2022 ER -
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