Disentangling the influence of local and remote anthropogenic aerosols on South Asian monsoon daily rainfall characteristics
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Wet and dry periods within the South Asian summer monsoon season can have acute societal impacts. Recent studies have identified changes in daily rainfall characteristics of the monsoon, but the underlying causes are poorly understood. In particular, although the dominant role of anthropogenic aerosols in shaping historical changes in seasonal-mean monsoon rainfall has been documented, their influence on daily-scale rainfall remains unconstrained. Using an ensemble of single-forcing climate simulations, we find that anthropogenic aerosols have a stronger influence on late-twentieth century changes in the frequency of wet events, dry events and rainless days, compared with other climate forcings. We also investigate the role of aerosol-cloud interactions (“indirect effects”) in the total aerosol response, and the contribution of aerosols emitted from South Asia versus from remote sources. Based on additional simulations with the GFDL-CM3 climate model, we find that the simulated aerosol response over South Asia is largely associated with aerosol-indirect effects. In addition, local aerosols suppress wet-event frequency and enhance dry-event frequency over eastern-central India, where increases in aerosol loading are the largest. Remote aerosols cause a north–south dipole pattern of change in mean rainfall over India and fewer rainless days over western India. However, the overall spatial response of South Asian rainfall characteristics to total aerosol forcing is substantially influenced by the combined non-linear climate response to local and remote aerosols. Together, our results suggest that understanding the influence of different aerosol emissions trajectories on the regional climate dynamics is critical for effective climate-risk management in this populated, vulnerable region.
KeywordsSouth Asian Monsoon Anthropogenic aerosols Local and remote aerosols Daily-scale precipitation
This study is partially supported by the National Science Foundation Grant AGS16-07348 and the Lamont-Doherty Postdoctoral Fellowship. Massimo A. Bollasina was partially supported by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. We would like to thank Alexandra Karambelas for helpful discussions. We also thank NOAA Geophysical Fluid Dynamics Laboratory for providing access to the data used in this study. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output publicly available. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Output for CanESM2 and CSIRO was accessed from https://esgf-node.llnl.gov/projects/esgf-llnl/ and for CCSM4 was accessed from https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.cmip5.output.html. We also acknowledge the use of the NCAR Command Language Version 6.3.0 for the analysis and production of maps (Boulder, Colorado: UCAR/NCAR/CISL/TDD. https://doi.org/10.5065/D6WD3XH5). Any data or code used in this manuscript can be made available by contacting the corresponding author (email@example.com).
DS, MB, and NSD conceived the study. All authors designed the analysis. MB provided the data. DS performed the analysis and all authors wrote the manuscript.
Compliance with ethical standards
Conflict of interest
All authors declare no competing financial interests.
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