COVARIABILITY OF WINTER AND SUMMER PRECIPITATION IN CENTRAL AMERICA AND MEXICO WITH TROPICAL SEA SURFACE TEMPERATURES UNDER A WARMING CLIMATE

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Zeng, Ning

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This dissertation investigates the influence of tropical sea surface temperatures (SSTs) on precipitation variability and the regional hydrological cycle in Central America and Mexico (CAM), focusing on both winter and summer. As global climate change progresses, the understanding of how tropical SSTs affect regional precipitation patterns has become crucial for improving seasonal climate predictions and assessing future climate risks. The study examines the relationships between CAM precipitation and tropical SSTs, with particular attention to the contributions of the Pacific and Atlantic Oceans. Through a combination of historical observational data, atmospheric model simulations, and climate projections, the research identifies significant covariability between CAM precipitation and tropical SSTs during both the winter and summer seasons. In the winter, ENSO-related SSTs in the Pacific and warming trends in the Atlantic Ocean are shown to modulate precipitation patterns, with both oceans contributing to precipitation variability. In summer, tropical SST warming, particularly in the Atlantic, is found to dominate precipitation variability, while ENSO still plays a secondary role. A key feature of CAM’s seasonal cycle, the mid-summer drought (MSD), is characterized in detail, revealing its sensitivity to both ENSO events and long-term trends. Future projections from climate models suggest a warmer, drier future for the region, with reductions in precipitation, soil moisture, and runoff, exacerbating challenges related to water resources and agriculture. The dissertation concludes that tropical SSTs, especially in the Pacific and Atlantic, are critical drivers of CAM precipitation and provides valuable insights for improving seasonal climate predictions. Further research is recommended to refine regional climate models, explore long-term trends in precipitation patterns, and better understand the socio-economic implications of projected hydrological changes.

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