Deciphering Long-Term Variation of Aerosol Characteristics and Their Climatic Implication over the World’s Wettest Place: Mawsynram

Abstract

Decadal shifts in aerosol characteristics and their associated climatic impacts over the World’s wettest region, Mawsynram in Meghalaya, India, were investigated using satellite-derived data from 2014 to 2023. Moderate Resolution Imaging Spectroradiometer (MODIS) data highlights significant trends in the variation of aerosol characteristics such as Aerosol Optical Depth at 550 nm (AOD550) and Angstrom Exponent (AE). AOD550 displays a consistent rise of ~ 0.007 per year, while AE exhibits a rise of ~ 0.005 per year. Both AOD550 and AE show notable increases, with AOD550 escalating by ~ 14.63% over the decade and AE by ~ 9% during the study period. Elevated AOD550 values prevail notably during the pre-monsoon (0.63 ± 0.021) and winter season (0.61 ± 0.019), followed by the monsoon (0.53 ± 0.014) and post-monsoon (0.52 ± 0.015) periods. Atmospheric Aerosol Radiative Forcing (ARF) increases from 11.81 to 18.26 W m⁻2 in the winter and from 15.47 to 21.66 W m⁻2 in the pre-monsoon season. This results in an increase in the atmospheric heating rate from 0.4 to 0.6 K day⁻1 and from 0.5 to 0.7 K day⁻1 in the winter and pre-monsoon season, respectively. A significant positive correlation of AOD with AE (r = 0.368) and Aerosol Index (AICCN) (r = 0.404) indicate that higher aerosol loading enhances fine-mode aerosol concentration. In contrast, a negative correlation of AOD with Cloud Effective Radius (CER) (r = -0.480) and Cloud Optical Depth (COD) was found. This suggests that higher fine-mode aerosol concentrations may inhibit cloud formation by delaying droplet coalescence. A notable decline in regional annual rainfall, from 10,235 mm to 8838.2 mm over the past decade (2014–2023), with a significant negative correlation (r = -0.615) with AOD underscores the intricate interactions between aerosols and local climatic patterns. This study is crucial for understanding aerosol radiation and cloud interactions resolving uncertainties in model simulations of present-day climate change and improving future climate projections.