About

Aneesh Subramanian is an Associate Professor at the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado Boulder and a visiting scientist at the Center for Western Weather and Water Extremes at Scripps Institution of Oceanography, UC San Diego. He completed his PhD at the Climate Research Division at Scripps Institution of Oceanography, UC San Diego in 2012. Prior to his current position, he was a visiting scholar and postdoctoral scholar in the Predictability of Weather and Climate group in the Physics Department at the University of Oxford. He also collaborates internationally with the Geophysical Flows Lab at the Indian Institute of Technology Madras, Chennai, India. His educational background includes an M.Sc. (Engg.) from the Indian Institute of Science in 2006 and a B.Tech from IIT Madras in 2004. His research focuses on the predictability and dynamics of weather and climate, with particular emphasis on tropical climate and weather dynamics including ocean-coupled effects, atmospheric river dynamics and predictions, and tropical-extratropical teleconnections. His group works on improving the understanding of processes influencing extreme weather across timescales from weather to long-term climate. They focus on modeling modes of climate variability such as the Madden-Julian Oscillation and its teleconnections using global climate models like the Community Earth System Model (CESM) and ECMWF IFS. The group actively develops stochastic parameterization and machine learning approaches to better represent physical processes in weather and climate models. Another major area of his research is data assimilation in coupled ocean-atmosphere models. His group has assimilated ship cruise and satellite data into a regional eddy-permitting ocean model of the South East Pacific using the Regional Ocean Modeling System (ROMS) to better understand mesoscale ocean processes. They also study nonlinear data assimilation techniques to improve upon the Ensemble Kalman Filter and Adjoint-based methods in simplified nonlinear atmospheric and climate models. His work integrates advances in computational sciences to enhance weather and climate modeling, including multi-scale earth system modeling and high-performance computing.

Research topics

• Geology
• Climatology
• Environmental science
• Geography
• Meteorology
• Computer Science
• Oceanography
• Mathematics
• Artificial Intelligence
• Political Science
• Atmospheric sciences
• Data science
• Engineering
• Applied mathematics
• Risk analysis (engineering)
• Ecology
• Business
• Management science
• Fishery
• Telecommunications
• Accounting
• Statistics

Selected publications

• Exploring air–sea interactions in a regional weakly coupled data assimilation system: An observing system experiment for the 2023 Indian Ocean monsoon and Cyclone Biparjoy

  2026-02-12

  articleOpen access

  We examined air–sea interactions in a weakly coupled ocean–atmosphere data assimilation (DA) system by performing an observing system experiment (OSE). The OSE is performed for the Arabian Sea monsoon onset event in 2023, which also coincides with the passage of the tropical cyclone Biparjoy. Results show that assimilating both oceanic and atmospheric data produces a better forecast than either experiment assimilating only oceanic or atmospheric data. This indicates that the coupled system provides improved initial conditions with more balanced oceanic and atmospheric states when assimilating all observations. However, we found that assimilating only atmospheric data improves both the ocean and atmosphere state forecasts, whereas assimilating only oceanic data improves the ocean state forecasts but degrades the atmospheric state forecasts, when compared with the free run. We found that this counterintuitive model performance is due to the potential misrepresentation of air–sea exchanges. First, errors in the cyclone path increase when atmospheric data are not assimilated. Then, assimilation of sea surface temperature (SST) leads to a warmer SST below the misplaced cyclone (with an offset of about 200 km), resulting in increased cyclone intensity. This misrepresentation of air–sea exchanges not only occurs during the cyclone passage but also persists throughout the early monsoon season. Since errors in one component can affect the other and result in positive feedback, leading to error growth, this study highlights an interesting effect on weakly coupled ocean–atmosphere DA systems when atmospheric observations are unavailable or limited, particularly during high-impact atmospheric events such as tropical cyclones.

  PublisherDOI

• Persistent SST anomaly vs. dynamical ocean model in winter weather forecasts: Global Ensemble Prediction System versions 5 and 6 over the North Pacific and North Atlantic

  Weather and Climate Dynamics · 2026-04-23

  articleOpen access

  Abstract. The impact of coupling an atmosphere model to a dynamical ocean model, rather than using persistent SST anomalies, is assessed for wintertime medium-range forecasts over the North Pacific and North Atlantic. This assessment is based on 20 years (1998–2017) of hindcasts produced by the Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada (ECCC). We compare an uncoupled atmospheric model (versions 5, GEPS5) with an atmosphere–ocean coupled model (version 6, GEPS6) alongside European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) as the verification dataset. We find that by the third pentad, or days 11–15, coupling to a dynamic ocean model weakens the Aleutian Low, the Icelandic Low, and the Atlantic Subtropical High. This produces less integrated vapor transport (IVT) over the Pacific and Atlantic Oceans, whose spatial patterns are modulated by phases of Madden–Julian Oscillation (MJO). Coupling also results in colder sea surface temperature (SST) over the Kuroshio Current Extension region and produces a weaker Aleutian Low due to less upward latent heat fluxes. The weaker Aleutian Low further reinforces its weakening through a positive feedback loop. Lastly, the coupling to a dynamical ocean reduces the latent heat flux bias variance by 10 %–20 %, thus improving the IVT.

  PublisherOA PDFDOI

• Decision: Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network — R2/PR12

  2026-01-21

  peer-reviewOpen access

  Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.

  PublisherDOI

• Decision: Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network — R1/PR9

  2026-01-05

  peer-reviewOpen access

  Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.

  PublisherDOI

• Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network

  Environmental Data Science · 2026-01-01

  articleOpen access

  Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.

  PublisherDOI

• Recommendation: Skillful subseasonal Indian Ocean marine heatwave forecasts using a neural network — R2/PR11

  2026-01-21

  peer-reviewOpen access

  Marine heat waves (MHWs) are prolonged periods of elevated ocean temperatures that can devastate marine ecosystems, fisheries, and coastal communities. Skillfully predicting these events with sufficient lead time is crucial for mitigating their adverse effects. This study presents a probabilistic subseasonal MHW forecast tool using a U-Net-based neural network architecture, with a focus on the Northern Indian Ocean and the Arabian Sea. The model was trained using sea surface temperature and sea surface height reanalysis data. The U-Net-based forecast tool demonstrated significant predictive skill up to 10 weeks in advance across various deterministic and probabilistic skill metrics. The model outperformed persistence and climatology-based benchmarks, especially in the tropical warm pool. Future applications of explainable artificial intelligence (XAI) methods have the potential to identify the sources of predictive skill, inform understanding of underlying dynamics, and improve dynamic subseasonal to seasonal forecast models.

  PublisherDOI

• Physics‐Based Versus AI Weather Prediction Models: A Comparative Performance Assessment of Atmospheric River Prediction

  Geophysical Research Letters · 2026-02-14 · 1 citations

  articleOpen access

  Abstract Machine learning (ML) poses a potential paradigm shift in weather forecasting, but critical questions arise regarding its ability to predict high‐impact weather events. This study evaluates five state‐of‐the‐art ML models—Aurora, GraphCast, PanguWeather, FourCastNetV2, FourCastNet—in forecasting U.S. West Coast atmospheric rivers (ARs), compared to the high‐performing physics‐based European Center for Medium‐Range Weather Forecasts' high‐resolution system (HRES) model. Analysis of 152 daily forecast cycles (November 2023–March 2024) reveals significant performance differences between the systems. While ML models often show better variable‐specific root mean square error (RMSE), HRES has superior AR detection skill for the first four forecast days. PanguWeather matches HRES skill beyond day four; other ML models lag slightly. Aurora consistently exhibits the lowest AR detection performance, despite strong variable‐specific RMSE metrics, highlighting a disconnect between RMSE performance and its ability to predict AR events. These findings underscore the need for phenomenon‐specific metrics for ML‐based numerical weather prediction model assessment and operational implementation.

  PublisherOA PDFDOI

• Marine Heatwaves in the Arabian Sea: Drivers and Impacts on Atmospheric Circulation and Extreme Precipitation

  arXiv (Cornell University) · 2026-03-18

  preprintOpen access

  Marine heatwaves (MHWs) threaten marine ecosystems and significantly impact weather patterns. In the Arabian Sea, summer MHWs are of particular concern due to their potential impacts on the Indian summer monsoon, a lifeline for nearly a billion people. However, the drivers of these MHWs and their influence on atmospheric circulation and monsoon rainfall remain poorly understood. Using satellite observations, reanalysis datasets, and numerical model experiments, we investigate the key drivers of MHW events and assess their impacts. When SST warming trends are retained, the eastern and northern Arabian Sea emerge as MHW hotspots, showing rapid increases during 1982-2023, largely due to anthropogenic warming. On detrending the SSTs to remove the influence of anthropogenic warming on individual MHWs, we find that most MHWs are short-lived (lasting <= 20 days) and are initiated by enhanced surface shortwave radiation and reduced latent heat loss associated with the suppressed convection phase of the Boreal Summer Intraseasonal Oscillations (BSISOs). Interannual SST anomalies, including ENSO and Indian Ocean Dipole (IOD), further modulate the year-to-year MHW variability. Conversely, the warm SSTs during MHWs exert strong atmospheric feedbacks. MHWs in the eastern Arabian Sea drive cyclonic winds, intensify moisture convergence and increase the risk of extreme precipitation along the southwest coast of India. In the northern Arabian Sea, MHW-induced cyclones trigger intense rainfall over northwestern India and Pakistan, contributing to extreme events like the 2022 Pakistan floods. These findings improve our capacity to predict Arabian Sea MHWs and assess their risks, offering significant socio-economic and ecological benefits.

  PublisherDOI

• ENSO-Driven Variability of Oxygen Content and Distribution in the Tropical Pacific

  Journal of Climate · 2026-02-16

  article

  Abstract The dissolved oxygen (O 2 ) content of the tropical Pacific exhibits substantial variability from interannual to decadal time scales, challenging the detection of ocean deoxygenation in this region. Using a global observational synthesis of O 2 along with eddying and noneddying global ocean–sea ice simulations, we examine the interannual variability of O 2 and its underlying drivers in the tropical Pacific. We find a tight relationship between El Niño–Southern Oscillation (ENSO) and the O 2 content and distribution across observations and models, with elevated O 2 in the eastern and central parts of the basin during El Niño and reduced O 2 in this region during La Niña. The variability of the O 2 content in this region is generally similar across models and observation-based products, though regional patterns differ. ENSO-driven variability of O 2 is shown to be the net balance of large and compensating effects between vertical advection and biological consumption that dominate over opposing changes in vertical mixing and lateral advection, such that the O 2 content increases during El Niño despite a major reduction in ventilation. This variability in O 2 ventilation is primarily driven by ENSO modulation of shear-driven turbulent mixing and the eastward transport of O 2 by the Equatorial Undercurrent (EUC). We also note that ENSO positively couples the tropical Pacific heat and O 2 contents, which contrasts sharply with their tight negative relationship at mid- and high latitudes, likely due to a larger role for ocean dynamics and biological processes in modulating the O 2 response to climate perturbations in the tropical Pacific.

  PublisherDOI

• Modulation of tropical cyclone intensity by current–wind interaction

  npj Climate and Atmospheric Science · 2026-01-16

  articleOpen access

  Current–wind interaction modulates air–sea momentum and turbulent heat fluxes, which are critical in the energy cycle of tropical cyclones (TCs). However, the effects of the surface currents on air–sea exchange under TCs have remained unclear. Here, using an atmosphere–ocean coupled model, we investigate the role of current–wind interaction in determining TC intensity. Surface currents generally align with surface winds. Accounting for the current–wind interaction, the alignment reduces both the air–sea turbulent heat flux and momentum flux (average 1.0% and 2.5%), which serve as the energy source and sink of TCs, respectively. The reduction in the energy source (sink) decreases (increases) the TC growth −1.9% (+1.3%) on average and up to −13.7% (+11.1%). For simulations extending beyond the seasonal scale, the accumulated impacts of current–wind interaction alter TC genesis, affecting surface wind speed and sea surface temperature during the TC season. These findings reveal an important feedback mechanism associated with TCs driven by the current–wind interaction.

  PublisherOA PDFDOI

Frequent coauthors

• F. Martin Ralph

  Scripps Institution of Oceanography

  154 shared

• Luca Delle Monache

  University of California, San Diego

  136 shared

• Jennifer S. Haase

  105 shared

• Anna M. Wilson

  Centre National d'Études Spatiales

  105 shared

• Zeke Hausfather

  Earth Island Institute

  102 shared

• Koen Venema

  Maastricht University

  102 shared

• Andrew Schurer

  University of Edinburgh

  102 shared

• Friederike E. L. Otto

  Imperial College London

  102 shared

Education

• Ph. D., Scripps Institution of Oceanography

  University of California San Diego

  2012