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Advancing Arctic Climate Prediction Capability from Sub-seasonal to Decadal Time Scales

Presentation Date
Tuesday, December 13, 2022 at 12:19pm - Tuesday, December 13, 2022 at 12:30pm
McCormick Place - S406b



Requirements for regionally relevant climate prediction capabilities have been steadily increasing. While the Arctic is not as populated region as the mid-latitudes, its amplified warming, increasing strategic importance, and teleconnections to lower latitude weather and climate place it near the top of the priority list for improved regional climate information. However, the Arctic is also one of the most challenging regions to model due to its complexity and the need for high resolution. While some of these challenges might be addressed in future Earth System Models (ESMs), dynamical downscaling offers an opportunity to bridge the gap between ESM limitations and the regional climate requirements.

The fully coupled Regional Arctic System Model (RASM) has been developed and used to better understand and predict the process-level operation of the Arctic System. Its pan-Arctic domain extends far into the North Pacific and the North Atlantic oceans, with the default atmosphere and land components configured on a 50-km grid and the ocean and sea ice components at 1/12-deg (~9.3km) in the horizontal space and with 45 vertical layers. High-resolution model configurations include the atmosphere/land at 25-km and ice-ocean at 2.4-km grids. The requirement of realistic boundary conditions in hindcast simulations allows comparisons of RASM results with observations in place and time, which is a unique capability not available in global ESMs, enabling diagnosis and potential reduction of biases. RASM has been used to produce probabilistic intra-annual (i.e., 6-month) forecasts each month for the past 3+ years as well as for decadal predictability studies. Here, we review some results from those efforts, including analysis of RASM sea ice predictive skill in comparison with observations and relative to the original global output. Examples of possible RASM improvements are discussed, related to optimized parameter space, improved initial conditions and higher spatial resolution.

Funding Program Area(s)