Research interests:

Our research deals with ocean, atmosphere and climate dynamics, trying to understand physical processes that affect Earth's climate on time scales of a few years to millions of years. Climate variability results from a rich set of nonlinear, sometime chaotic, physical interactions of the oceans, atmosphere and at times the biosphere as well. Some of the very basic questions in this research area are still unanswered. E.g., why were there ice ages, why is El Nino difficult to predict, what future warming can we expect in a century, how can we understand specific observed features of past warm climates millions of years ago and what can they teach us about the future. These open challenges makes this a fascinating field to work in for those of us with an interest in applying physical and mathematical principles to the study of the natural world. In addition, climate is, of course, a research area with practical aspects directly affecting our life, creating an engaging combination of a scientific challenge and societal relevance. We normally use both simplified mathematical models of a given climate phenomenon to understand the mechanisms in question in detail, together with realistic state-of-the-art simulations for testing ideas developed using simpler models.

Please follow the links below for more details on our different activities.

El-Niño's dynamics, including its irregularity, predictability, possible chaotic behavior, and the role and dynamics of westerly wind bursts.

Large-scale oceanic circulation: the thermohaline circulation, climate stability and variability, ocean dynamics.

Past Climate dynamics: This has been a major focus of ours over the past few years, including,
Ice dynamics dabbling: including ice stream dynamics, ice flow on Snowball-Earth.

Future climate change: Often basic research in climate dynamics or the study of past climates lead us to insights on future climate change. Check here for a few examples.

Observations and models: Combining oceanographic data and models through sophisticated and powerful methods such as four dimensional variational data assimilation based on the adjoint method. The use of "transfer functions" for quantitatively testing climate models against observations. Using cluster analysis to analyze climate patterns, and make seasonal prediction of precipitation; and applying neural networks to learn and predict forest-atmosphere CO2 fluxes.

Our research has been supported by the NSF climate dynamics program, the NSF ESH program, the NSF P2C2 program, the NSF Physical Oceanography program, the McDonnell Foundation, the NOAA office of global programs, NASA and the DoE.

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