Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study

Cynthia H. Whaley (1) , Rashed Mahmood (2, 3) , Knut von Salzen (1) , Barbara Winter (1) , Sabine Eckhardt (4) , Stephen Arnold (5) , Stephen Beagley (6) , Silvia Becagli (7) , Rong-You Chien (8) , Jesper Christensen (9) , Sujay M. Damani (1) , Xinyi Dong (8) , Kostas Eleftheriadis (10) , Nikolaos Evangeliou (4) , Gregory S. Faluvegi (11, 12) , Mark Flanner (13) , Joshua S. Fu (8) , Michael Gauss (7) , Fabio Giardi (14) , Wanmin Gong (6) , Jens Liengaard Hjorth (9) , Lin Huang (6) , Ulas Im (9) , Yugo Kanaya (15) , Srinath Krishnan (16) , Zbigniew Klimont (17) , Thomas Kühn (18, 19) , Joakim Langner (20) , Kathy S. Law (21) , Louis Marelle (22) , Andreas Massling (9) , Dirk Olivié (7) , Tatsuo Onishi (21) , Naga Oshima (23) , Yiran Peng (24) , David A. Plummer (1) , Olga Popovicheva (25) , Luca Pozzoli (26) , Jean-Christophe Raut (21) , Maria Sand (16) , Laura N. Saunders (27) , Julia Schmale (28) , Sangeeta Sharma (6) , Ragnhild Bieltvedt Skeie (16) , Henrik Skov (9) , Fumikazu Taketani (15) , Manu A. Thomas (20) , Rita Traversi (14) , Kostas Tsigaridis (11, 12) , Svetlana Tsyro (7) , Steven Turnock (29, 5) , Vito Vitale (26) , Kaley A. Walker (27) , Minqi Wang (24) , Duncan Watson-Parris (30) , Tahya Weiss-Gibbons (1)

1 CCCma - Canadian Centre for Climate Modelling and Analysis
2 BSC-CNS - Barcelona Supercomputing Center - Centro Nacional de Supercomputacion
3 Department of Geography [Montréal]
4 NILU - Norwegian Institute for Air Research
5 ICAS - Institute for Climate and Atmospheric Science [Leeds]
6 Climate Chemistry Measurements and Research
7 MET - Norwegian Meteorological Institute [Oslo]
8 The University of Tennessee [Knoxville]
9 ENVS - Department of Environmental Science [Roskilde]
10 INRASTES - Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety
11 GISS - NASA Goddard Institute for Space Studies
12 CCSR - Center for Climate Systems Research [New York]
13 University of Michigan [Ann Arbor]
14 Dipartimento di Chimica "Ugo schifo"
15 JAMSTEC - Japan Agency for Marine-Earth Science and Technology
16 CICERO - Center for International Climate and Environmental Research [Oslo]
17 IIASA - International Institute for Applied Systems Analysis [Laxenburg]
18 Department of Applied Physics [Kuopio]
19 Atmospheric Research Centre of Eastern Finland
20 SMHI - Swedish Meteorological and Hydrological Institute
21 TROPO - LATMOS
22 LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
23 MRI - Meteorological Research Institute [Tsukuba]
24 CESS - Center for Earth System Science [Beijing]
25 MSU - Lomonosov Moscow State University
26 JRC - European Commission - Joint Research Centre [Ispra]
27 University of Toronto
28 EERL - Extreme Environments Research Laboratory
29 MOHC - Met Office Hadley Centre
30 AOPP - Department of Atmospheric, Oceanic and Planetary Physics [Oxford]

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 represen- tation 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, sea- sonal 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−), the mmm was within ±25 % of the 4 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.

Domaines: Physique Atmosphérique et Océanique [physics.ao-ph]; Climatologie

Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, et al.. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study. Atmospheric Chemistry and Physics, 2022, 22 (9), pp.5775-5828. ⟨10.5194/acp-22-5775-2022⟩. ⟨insu-03454867v2⟩