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

Abstract

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.

Arctic tropospheric ozone: assessment of current knowledge and model performance

“…Figure 7 shows that for the first 6 months of the year, the MMM is 20 %-30 % too low, but that in the summer, the MMM is much closer to observations. These CO results are very similar to those found in previous multi-model studies (Shindell et al, 2008;Monks et al, 2015;Whaley et al, 2022). Similar to O 3 , these results imply little change in the skill of models in simulating Arctic surface CO over the past decade.…”

Physical and Chemical Properties of Cloud Droplet Residuals and Aerosol Particles During the Arctic Ocean 2018 Expedition

“…The large differences between polar night and day in terms of, for example, radiation, sea ice, cloud type and phase (liquid, mixed‐phase, or ice), and atmospheric circulation result in large seasonal variations not only in aerosol particle abundance and composition but also in their impact on clouds (e.g., Willis et al., 2018 ). These conditions make the Arctic environment particularly challenging to represent in large‐scale climate models (e.g., Sand et al., 2017 ; Schmale et al., 2021 ; Whaley et al., 2021 ).…”

Arctic tropospheric ozone: assessment of current knowledge and model performance

“…To evaluate our process understanding of controls on the Arctic tropospheric O 3 budget and distribution, we evaluate a subset of the same model simulations that were used in AMAP (2022) and by Whaley et al (2022). A total of 12 atmospheric models participated in this study: seven chemical transport models (DEHM, EMEP MSC-W, GEOS-Chem, MATCH, MATCH-SALSA, OsloCTM, WRF-Chem) and five chemistry-climate models (CESM, CMAM, GISS-E2.1, MRI-ESM2, and UKESM1), with simulations of the years 2014-2015 for comparisons to observations.…”

“…Hence, to improve the quantification of O 3 radiative effects in the Arctic there is a need first to assess model performance in terms of seasonal cycles and vertical distributions. The annual mean vertical distributions of O 3 and CO were examined in AMAP (2022) and Whaley et al (2022) compared to the Tropospheric Emission Spectrometer (TES) and Measurement of Pollution in the Troposphere (MOPITT) satellite retrievals. Those studies showed good agreement between models and satellite measurements for O 3 in the free troposphere, where it is a strong GHG.…”