Successes and Challenges in Linking Observations and Modeling of Marine and Terrestrial Cryospheric Processes

Friday, 19 December 2014: 4:00 PM
Ute C Herzfeld1, Elizabeth C Hunke2, Thomas Trantow3, Ralf Greve4, Brian McDonald1 and Bruce Wallin1, (1)Univ Colorado Boulder, Electrical, Computer and Energy Engineering, Boulder, CO, United States, (2)Los Alamos National Laboratory, T-3 Fluid Dynamics and Solid Mechanics Group, Los Alamos, NM, United States, (3)University of Colorado at Boulder, Applied Mathematics, Boulder, CO, United States, (4)Hokkaido University, Sapporo, Japan
Understanding of the state of the cryosphere and its relationship to other components of the Earth system requires both models of geophysical processes and observations of geophysical properties and processes, however linking observations and models is far from trivial. This paper looks at examples from sea ice and land ice model-observation linkages to examine some approaches, challenges and solutions.

In a sea-ice example, ice deformation is analyzed as a key process that indicates fundamental changes in the Arctic sea ice cover. Simulation results from the Los Alamos Sea-Ice Model CICE, which is also the sea-ice component of the Community Earth System Model (CESM), are compared to parameters indicative of deformation as derived from mathematical analysis of remote sensing data. Data include altimeter, micro-ASAR and image data from manned and unmanned aircraft campaigns (NASA OIB and Characterization of Arctic Sea Ice Experiment, CASIE). The key problem to linking data and model results is the derivation of matching parameters on both the model and observation side.

For terrestrial glaciology, we include an example of a surge process in a glacier system and and example of a dynamic ice sheet model for Greenland. To investigate the surge of the Bering Bagley Glacier System, we use numerical forward modeling experiments and, on the data analysis side, a connectionist approach to analyze crevasse provinces. In the Greenland ice sheet example, we look at the influence of ice surface and bed topography, as derived from remote sensing data, on on results from a dynamic ice sheet model.