Comment on egusphere-2023-604

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Anthropogenic emissions of aerosols and precursor compounds are known to significantly affect the energy balance of the Earth-atmosphere system, alter the formation of clouds and precipitation, and have substantial impact on human health and the environment. Global models are an essential tool for examining the impacts of these emissions. In this study, we examine the sensitivity of model results to the assumed height of SO₂ injection, seasonality of SO₂ and BC emissions, and the assumed fraction of SO₂ emissions that is injected into the atmosphere as SO₄ in 11 climate and chemistry models, including both chemical transport models and the atmospheric component of Earth system models. We find a large variation in atmospheric lifetime across models for SO₂, SO₄, and BC, with a particularly large relative variation for SO₂, which indicates that fundamental aspects of atmospheric sulfur chemistry remain uncertain. Of the perturbations examined in this study, the assumed height of SO₂ injection had the largest overall impacts, particularly on global mean net radiative flux (maximum difference of -0.35 W m^-2), SO₂ lifetime over northern hemisphere land (maximum difference of 0.8 days), surface SO₂ concentration (up to 59 % decrease), and surface sulfate concentration (up to 23 % increase). Emitting SO₂ at height consistently increased SO₂ and SO₄ column burdens and shortwave cooling, with varying magnitudes, but had inconsistent effects across models on the sign of the change in implied cloud forcing. The assumed SO₄ emission fraction also had a significant impact on net radiative flux and surface sulfate concentration. Because these properties are not standardized across models this is a source of inter-model diversity typically neglected in model intercomparisons. These results imply a need to assure that anthropogenic emission injection height and SO₄ emission fraction are accurately and consistently represented in global models.