Chayan Roychoudhury

We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.

A novel chemical reanalysis over Asia spanning 2003-2019 at 12 km grid resolution.

Cities in dry regions struggle with ozone pollution, driven by two key chemicals: nitrogen dioxide (NO₂) and formaldehyde (CH₂O). Satellite data show that NO₂ has fallen in cities with strict controls (like Los Angeles and Madrid) but risen in fast-growing cities with weaker policies (like Tehran and Cairo). CH₂O has also increased, pointing to the need for more focus on volatile organic compounds (VOCs). The study further finds that ozone levels respond to both emissions and climate, especially temperature, in complex ways. As dry regions expand, effective pollution strategies must address both.

Our findings show that the weekend effect characteristics are most evident in winter and fall seasons. Monsoon period is shown to be distinct from other periods for O3 characteristics. Regional ozone reductions are influential for ozone levels during March–April of the COVID lockdown period.

A combination of data analysis techniques is introduced to separate local and regional influences on observed levels of carbon dioxide, carbon monoxide, and methane from an established ground-based remote sensing network. We take advantage of the covariations in these trace gases to identify the dominant type of sources driving these levels. Applying these methods in conjunction with existing approaches to other datasets can better address uncertainties in identifying sources and sinks.

This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model’s skill in capturing diurnal and MDA8 O3 levels.

Tropical cyclones are among the most high-impact weather events, and their strength over the North Indian Ocean has grown in the past two decades. This study shows that cyclone activity is closely tied to El Niño and La Niña climate patterns. In the Arabian Sea, El Niño years bring stronger storms due to warmer seas and favorable wind conditions, while in the Bay of Bengal, El Niño tends to weaken storms. La Niña generally boosts cyclone energy, especially in coastal regions during the post-monsoon season.

Understanding the changes in snow cover (SC) over glaciers in High Mountain Asia (HMA) is important yet challenging. Despite its impact on water resources, physical processes that drive these changes are complex. In particular, large-scale weather patterns, together with aerosol pollution hotspots in the vicinity, and its steep elevation strongly interact with each other. We use a statistical approach to assess the relevance of these interactions using geophysical data from present day reanalysis and observed SC extent from satellite products for two decades. We find that during the late snowmelt period from June to July, interactions between aerosols and meteorology are significant, specifically in low SC regions. Interactions of individual aerosol species, especially carbonaceous aerosols like black carbon are more important than total aerosol concentration. This approach in quantifying the interactions of these processes can help improve the monitoring and modeling of snow hydrology. Representing these relevant interactions in current models and reanalysis of hydrometeorology can lead to more accurate predictions of the state of snow for critical regions like HMA.