Will the Earth’s polar ice caps melt within the next hundred years? Could this result, paradoxically, herald the onset of a new ice age? Will there be an increase in the frequency or intensity of hurricanes or mid-latitude cyclones in the near-future? These questions lie within the scope of climate dynamics—a relatively novel field, which was born in the 1960s and has developed rapidly since. Currently, climate dynamics research is conducted numerically using climate models, in which the physical processes (e.g., radiative transfer, convection, friction) that affect the distribution of climate fields, such as temperature or wind speed, are parameterized and formalized in terms of exchange of properties between adjacent members of a three-dimensional array of boxes covering the region of interest (e.g., the globe). This process is conceptualized in the figure below. State-of-the-art climate models are skillful in reproducing some aspects of the observed climate behavior, but suffer from biases due to ad hoc parameterizations of unresolved (sub-grid-scale) processes. Advanced statistical methods and data models are thus required to decipher the multi-scale complexity of both actual and simulated climate variability.
The research group led by Prof. Sergey Kravtsov addresses a wide spectrum of climate dynamics problems using a hierarchical modeling approach and advanced statistical analyses. Examples of recent work include the development of extremely numerically efficient, data-driven weather emulators; the robust identification of forced and internal components of climate variability in state-of-the-art climate models and observations; and skillful simulation of the Great Lakes climate regimes using an improved coupled lake–ice–atmosphere model. Please contact Prof. Kravtsov to learn more about these as well as other ongoing projects.