R. Saravanan

About

R. Saravanan is a Professor and Department Head at the Texas A&M University College of Arts and Sciences in the Department of Atmospheric Sciences. His research focuses on the variability and predictability of climate on seasonal to millennial timescales, with particular emphasis on coupled ocean-atmosphere interactions and large-scale atmospheric and oceanic dynamics. He employs mathematical and physical approaches to study atmospheric dynamics, climate modeling, and ocean-atmosphere interactions, utilizing global and regional climate models to investigate phenomena such as midlatitude storms, tropical cyclones, and global low-frequency variability modes. A key goal of his work is to improve weather and climate prediction capabilities, especially through understanding how sea surface temperature influences atmospheric flow and how coupled climate models can predict sea surface temperature evolution over months to years. His recent research addresses questions related to the influence of large-scale phenomena like El Niño and the Atlantic Meridional Mode on tropical cyclone activity, the impact of mesoscale ocean eddies on atmospheric storms in mid-latitudes, and the application of statistical and machine learning methods to analyze the relationship between atmospheric states and satellite rainfall measurements. Dr. Saravanan has contributed to the scientific community through his involvement in various service activities, including membership on the Prediction and Research…

Selected publications

• Ocean fronts and eddies force atmospheric rivers and heavy precipitation in western North America

  Nature Communications · 2021 · 67 citations

  Atmospheric rivers (ARs) are responsible for over 90% of poleward water vapor transport in the mid-latitudes and can produce extreme precipitation when making landfall. However, weather and climate models still have difficulty simulating and predicting landfalling ARs and associated extreme precipitation, highlighting the need to better understand AR dynamics. Here, using high-resolution climate models and observations, we demonstrate that mesoscale sea-surface temperature (SST) anomalies along…

  DOI

• Central American mountains inhibit eastern North Pacific seasonal tropical cyclone activity

  Nature Communications · 2021-07-20 · 19 citations

  articleOpen access

  The eastern North Pacific (ENP) has the highest density of tropical cyclones (TCs) on earth, and yet the controls on TCs, from individual events to seasonal totals, remain poorly understood. One effect that has not been fully considered is the unique geography of the Central American mountains. Although observational studies suggest these mountains can readily fuel individual TCs through dynamical processes, here we show that these mountains indeed play the opposite role on the seasonal timescal…

  PublisherOA PDFDOI

• Improved Simulations of Atmospheric River Climatology and Variability in High‐Resolution CESM

  Journal of Advances in Modeling Earth Systems · 2022-09-01 · 16 citations

  articleOpen access

  Abstract Atmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, and Pacific‐North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal‐to‐decadal timescales. Up to now…

  PublisherDOI

• Statistical and machine learning methods applied to the prediction of different tropical rainfall types

  Environmental Research Communications · 2021-11-01 · 10 citations

  articleOpen access

  Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satelli…

  PublisherDOI

• Larger Cloud Liquid Water Enhances Both Aerosol Indirect Forcing and Cloud Radiative Feedback in Two Earth System Models

  Geophysical Research Letters · 2024-01-24 · 8 citations

  articleOpen accessSenior author

  Abstract Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two E…

  PublisherOA PDFDOI

Awards & honors

• College of Geosciences Distinguished Research Award, Texas A…

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