Regional and global climate response to anthropogenic SO2 emissions from China in three climate models

Published

Journal Article

© Author(s) 2016. We use the HadGEM3-GA4, CESM1, and GISS ModelE2 climate models to investigate the global and regional aerosol burden, radiative flux, and surface temperature responses to removing anthropogenic sulfur dioxide (SO2) emissions from China. We find that the models differ by up to a factor of six in the simulated change in aerosol optical depth (AOD) and shortwave radiative flux over China that results from reduced sulfate aerosol, leading to a large range of magnitudes in the regional and global temperature responses. Two of the three models simulate a near-ubiquitous hemispheric warming due to the regional SO2 removal, with similarities in the local and remote pattern of response, but overall with a substantially different magnitude. The third model simulates almost no significant temperature response. We attribute the discrepancies in the response to a combination of substantial differences in the chemical conversion of SO2 to sulfate, translation of sulfate mass into AOD, and differences in the radiative forcing efficiency of sulfate aerosol in the models. The model with the strongest response (HadGEM3-GA4) compares best with observations of AOD regionally, however the other two models compare similarly (albeit poorly) and still disagree substantially in their simulated climate response, indicating that total AOD observations are far from sufficient to determine which model response is more plausible. Our results highlight that there remains a large uncertainty in the representation of both aerosol chemistry as well as direct and indirect aerosol radiative effects in current climate models, and reinforces that caution must be applied when interpreting the results of single-model studies of aerosol influences on climate. Model studies that implicate aerosols in climate responses should ideally explore a range of radiative forcing strengths representative of this uncertainty, in addition to thoroughly evaluating the models used against observations.

Full Text

Duke Authors

Cited Authors

  • Kasoar, M; Voulgarakis, A; Lamarque, JF; Shindell, DT; Bellouin, N; Collins, WJ; Faluvegi, G; Tsigaridis, K

Published Date

  • January 18, 2016

Published In

Volume / Issue

  • 2016 /

Electronic International Standard Serial Number (EISSN)

  • 1680-7375

International Standard Serial Number (ISSN)

  • 1680-7367

Digital Object Identifier (DOI)

  • 10.5194/acp-2015-1017

Citation Source

  • Scopus