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

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 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.Assessments from the Russian ship-based campaign were performed with the support of RFBR project no. 20-55-12001 and according to the development program of the Interdisciplinary Scientific and Educational School of M.V. Lomonosov Moscow State University “Future Planet and Global Environmental Change”. Development of the methodology for aethalometric data treatment was supported by RSF project no. 19-77-30004. The BC observations on R/V Mirai were supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (Arctic Challenge for Sustainability (ArCS) project). Contributions by SMHI were funded by the Swedish Environmental Protection Agency under contract NV-03174-20 and the Swedish Climate and Clean Air Research program (SCAC) as well as partly by the Swedish National Space Board (NORD-SLCP, grant agreement ID: 94/16) and the EU Horizon 2020 project Integrated Arctic Observing System (INTAROS, grant agreement ID: 727890). Work on ACE-FTS analysis was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). Julia Schmale received funding from the Swiss National Science Foundation (project no. 200021_188478). Duncan Watson-Parris received funding from NERC projects NE/P013406/1 (A-CURE) and NE/S005390/1 (ACRUISE) as well as funding from the European Union's Horizon 2020 research and innovation program iMIRACLI under Marie Skłodowska-Curie grant agreement no. 860100. LATMOS has been supported by the EU iCUPE (Integrating and Comprehensive Understanding on Polar Environments) project (grant agreement no. 689443) under the European Network for Observing our Changing Planet (ERA-Planet), as well as access to IDRIS HPC resources (GENCI allocation A009017141) and the IPSL mesoscale computing center (CICLAD: Calcul Intensif pour le CLimat, l’Atmosphère et la Dynamique) for model simulations. Naga Oshima was supported by the Japan Society for the Promotion of Science KAKENHI (grant nos. JP18H03363, JP18H05292, and JP21H03582), the Environment Research and Technology Development Fund (grant nos. JPMEERF20202003 and JPMEERF20205001) of the Environmental Restoration and Conservation Agency of Japan, the Arctic Challenge for Sustainability II (ArCS II) under program grant no. JPMXD1420318865, and a grant for the Global Environmental Research Coordination System from the Ministry of the Environment, Japan (MLIT1753). The research with GISS-E2.1 has been supported by the Aarhus University Interdisciplinary Centre for Climate Change (iClimate) OH fund (no. 2020-0162731), the FREYA project funded by the Nordic Council of Ministers (grant agreement nos. MST-227-00036 and MFVM-2019-13476), and the EVAM-SLCF funded by the Danish Environmental Agency (grant agreement no. MST-112-00298). Jesper Christensen (for DEHM model) received funding from the Danish Environmental Protection Agency (DANCEA funds for Environmental Support to the Arctic Region project; grant no. 2019-7975). Maria Sand has been supported by the Research Council of Norway (grant 315195, ACCEPT).Peer Reviewed"Article signat per més de 50 autors/es: Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons "Postprint (published version

Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects

Low clouds persist in the summer Arctic with important consequences for the radiation budget. In this study, we simulate the linear relationship between liquid water content (LWC) and cloud droplet number concentration (CDNC) observed during an aircraft campaign based out of Resolute Bay, Canada, conducted as part of the Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments study in July 2014. Using a single-column model, we find that autoconversion can explain the observed linear relationship between LWC and CDNC. Of the three autoconversion schemes we examined, the scheme using continuous drizzle (Khairoutdinov and Kogan, 2000) appears to best reproduce the observed linearity in the tenuous cloud regime (Mauritsen et al., 2011), while a scheme with a threshold for rain (Liu and Daum, 2004) best reproduces the linearity at higher CDNC. An offline version of the radiative transfer model used in the Canadian Atmospheric Model version 4.3 is used to compare the radiative effects of the modelled and observed clouds. We find that there is no significant difference in the upward longwave cloud radiative effect at the top of the atmosphere from the three autoconversion schemes (p=0.05) but that all three schemes differ at p=0.05 from the calculations based on observations. In contrast, the downward longwave and shortwave cloud radiative effect at the surface for the Wood (2005b) and Khairoutdinov and Kogan (2000) schemes do not differ significantly (p=0.05) from the observation-based radiative calculations, while the Liu and Daum (2004) scheme differs significantly from the observation-based calculation for the downward shortwave but not the downward longwave fluxes.This research has been supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grants RGPIN-2014-05173 and RGPIN 155649) and the Marine Environmental Observation, Prediction and Response Network (MEOPAR), which is a federally funded Networks of Centres of Excellence (NCE) (EC1-RC-DAL).Peer ReviewedPostprint (published version

Vertical profiles of light absorption and scattering associated with black carbon particle fractions in the springtime Arctic above 79◦ N

Despite the potential importance of black carbon (BC) for radiative forcing of the Arctic atmosphere, ver- tically resolved measurements of the particle light scatter- ing coefficient (σsp ) and light absorption coefficient (σap ) in the springtime Arctic atmosphere are infrequent, espe- cially measurements at latitudes at or above 80◦ N. Here, re- lationships among vertically distributed aerosol optical prop- erties (σap, σsp and single scattering albedo or SSA), par- ticle microphysics and particle chemistry are examined for a region of the Canadian archipelago between 79.9 and 83.4◦ N from near the surface to 500 hPa. Airborne data collected during April 2015 are combined with ground- based observations from the observatory at Alert, Nunavut and simulations from the Goddard Earth Observing Sys- tem (GEOS) model, GEOS-Chem, coupled with the TwO- Moment Aerosol Sectional (TOMAS) model (collectively GEOS-Chem–TOMAS; Kodros et al., 2018) to further our knowledge of the effects of BC on light absorption in the Arctic troposphere. The results are constrained for σsp less than 15 Mm−1, which represent 98 % of the observed σsp, be- cause the single scattering albedo (SSA) has a tendency to be lower at lower σsp, resulting in a larger relative contribution to Arctic warming. At 18.4 m2 g−1, the average BC mass ab- sorption coefficient (MAC) from the combined airborne and Alert observations is substantially higher than the two aver- aged modelled MAC values (13.6 and 9.1 m2 g−1) for two different internal mixing assumptions, the latter of which is based on previous observations. The higher observed MAC value may be explained by an underestimation of BC, the presence of small amounts of dust and/or possible differences in BC microphysics and morphologies between the obser- vations and model. In comparing the observations and simulations, we present σap and SSA, as measured, and σap/2 and the corresponding SSA to encompass the lower modelled MAC that is more consistent with accepted MAC values. Me- dian values of the measured σap, rBC and the organic com- ponent of particles all increase by a factor of 1.8 ± 0.1, going from near-surface to 750 hPa, and values higher than the sur- face persist to 600 hPa. Modelled BC, organics and σap agree with the near-surface measurements but do not reproduce the higher values observed between 900 and 600 hPa. The dif- ferences between modelled and observed optical properties follow the same trend as the differences between the mod- elled and observed concentrations of the carbonaceous com- ponents (black and organic). Model-observation discrepan- cies may be mostly due to the modelled ejection of biomass burning particles only into the boundary layer at the sources. For the assumption of the observed MAC value, the SSA range between 0.88 and 0.94, which is significantly lower than other recent estimates for the Arctic, in part reflecting the constraint of σsp < 15 Mm−1. The large uncertainties in measuring optical properties and BC, and the large differ- ences between measured and modelled values here and in the literature, argue for improved measurements of BC and light absorption by BC and more vertical profiles of aerosol chemistry, microphysics and other optical properties in the Arctic

The role of reduced aerosol precursor emissions in driving near-term warming

The representative concentration pathway (RCP) scenarios all assume stringent emissions controls on aerosols and their precursors, and hence include progressive decreases in aerosol and aerosol precursor emissions through the 21st century. Recent studies have suggested that the resultant decrease in aerosols could drive rapid near-term warming, which could dominate the effects of greenhouse gas (GHG) increases in the coming decades. In CanESM2 simulations, we find that under the RCP 2.6 scenario, which includes the fastest decrease in aerosol and aerosol precursor emissions, the contribution of aerosol reductions to warming between 2000 and 2040 is around 30%. Moreover, the rate of warming in the RCP 2.6 simulations declines gradually from its present-day value as GHG emissions decrease. Thus, while aerosol emission reductions contribute to gradual warming through the 21st century, we find no evidence that aerosol emission reductions drive particularly rapid near-term warming in this scenario. In the near-term, as in the long-term, GHG increases are the dominant driver of warming