Advanced Comput, Math & Data
New Modeling Approach Will Better Quantify Polar Ice Sheet Melts
Research to improve predictive ability of climate models
Results: PNNL scientists recently received funding from the DOE Advanced Scientific Computing Program to develop a new, novel approach for modeling ice sheets. The new research will improve the predictive ability of climate models and provide a clearer picture of the long-term impact of ice-sheet melts. The model development and validation will be conducted in collaboration with the BER project, "Improving the Characterization of Clouds, Aerosols and the Cryosphere in Climate Models."
Why It Matters: Large ice sheets are shrinking at both poles far faster than predicted a decade ago. Existing ice sheet models do not fully capture all the important mechanisms of ice-sheet evolution, and as a result, their predictions are highly uncertain and may substantially underestimate the rate of ice melt.
Mathematical modeling of ice sheets is complicated by the non-linearity of the underlying processes, and their governing equations and boundary conditions. Standard grid-based methods require complex front-tracking techniques; they are not good at handling large material deformations and they have limited scalability.
Significant model improvements across the range of relevant processes and scales are required to better quantify and understand the rate of ice cover change at high latitudes and the long-term impacts of reduced ice cover.
Methods: Researchers at PNNL will model the ice sheet with full 3D momentum conservation equation, coupled with mass and energy conservation equations and subject to the appropriate boundary conditions. They will also investigate the effects of neglected terms in the existing models. To solve the non-linear governing equations, researchers will develop new highly scalable algorithms based on SPH, a fully Lagrangian particle method.
"Because the novelty of our approach significantly differentiates it from existing ice sheet models and codes, we expect that this technology will create a new user base within the scientific community and become widely recognized as a unique and valuable capability," said Project Lead Alexandre Tartakovsky.
This work was funded by DOE’s Advanced Scientific Research Computing (ASCR).
Research team: Alexandre Tartakovsky, Bruce Palmer, Xin Sun, all of PNNL.