Seamless Prediction Across Time and Space Scales

Earth System Models are increasingly being applied to prediction, in support of scientifically-based decision-making. Such predictions occur on a variety of spatial and temporal scales, dependent on the problem of interest. In order to make the best use of resources, increasingly the same code base is being to applied to different prediction problems at different resolutions. Many of the model advances described in the Earth System Modeling research theme allow the same model to be applied to prediction at different scales, by incorporating scale-aware physically-based parameterizations, a choice of model formulation with the appropriate physics for the scale of interest (e.g., nonhydrostatic v. hydrostatic), and varying degrees of comprehensiveness and complexity. CIMES focuses on two different aspects of prediction across time and space scales, the very high resolution modeling necessary to resolve extreme weather phenomena, and the predictability of different weather and climate phenomena.

High resolution modeling: the CIMES contribution to high-resolution modeling at NOAA-GFDL, focuses on the development of model components that accurately represent physical processes as model resolution is increased beyond the current norm of 25-100 km, and the computational and data issues associated with the development of high-resolution coupled models. The unique strengths of the NOAA-GFDL modeling system include the FV3 atmospheric dynamical core; the MOM6 ocean model; and the Flexible Modeling System (FMS) infrastructure underlying those, to which Princeton scientists have made substantial technical and scientific contributions.

Predictability: The next generation earth system modeling tools that have been and will be developed at CIMES and NOAA/GFDL present a rich opportunity for collaborative research aimed at understanding of the underlying predictability of the earth system, on timescales from hours to decades, and building systems that realize this predictive potential. Predictability on these timescales can arise from both the dynamical evolution of the earth system from its initial state (“initial value problem”) and from changes in factors external to the system in question (“boundary value problem”). CIMES undertakes research into predictability, data assimilation and realizing prediction skill in close collaboration with scientists at NOAA/GFDL.