Radiative forcing due to aerosol-cloud interactions for shallow warm clouds over the Northern Indian Ocean
Abstract
Aerosol-Cloud Interactions (ACI) plays a crucial role in the Earth's energy balance and significantly contribute to global climate change. However, the radiative forcing due to ACI remain highly uncertain, in both observational and model-based studies. In this study, we utilize an observational approach to quantify the effective radiative forcing due to ACI (ERFaci) over the Northern Indian Ocean (NIO) during 2003 2021. The results show that warm marine boundary layer clouds exert a negative forcing (cooling effect) at the Top of the Atmosphere (TOA), driven by both cloud albedo effect (intrinsic forcing) and cloud fraction adjustment (extrinsic forcing). In details, on a basin-scale, the annual mean intrinsic radiative forcing is about 17 % stronger over the Bay of Bengal (BoB: ?0.48 0.52 Wm?2) than over the Arabian Sea (AS: ?0.41 0.37 Wm?2). In contrast, extrinsic forcing is almost similar over both the basins (AS: ?0.66 0.39 Wm?2; and BoB: ?0.64 0.68 Wm?2). Annually extrinsic forcing is 61 % higher than intrinsic forcing over the AS and 33 % higher over the BoB. However, on a seasonal basis, the relative difference increases up to 269 % (101 %) and 304 % (197 %) during winter (post-monsoon) over the AS and BoB, respectively. These seasonal variations are further supported by positive correlations between cloudy-sky albedo (? 0.02 0.06) and low-level liquid cloud fraction (? 0.02 0.04) with the aerosol index (AI) across the region. The shortwave components of ERFaci (SWRFaci) dominate the annual mean NetRFaci, contributing approximately 106 % and 80 % over the AS and BoB, respectively. The strong SWRFaci over the NIO could be attributed to two main factors: an increase in anthropogenic pollution during the study period or, possibly a retrieval bias inherent in satellite-based observation. Thus, if the former, our ERFaci estimates can serve as useful reference estimates for assessing ACI effects in regional model simulations.
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