A104-10
Regional Modeling and Prediction of the Arctic Climate System

Thursday, 10 December 2020: 18:06
Virtual
Wieslaw Maslowski1, John J Cassano2, Jaclyn L Clement Kinney1, Anthony Craig1, Younjoo Lee1, Bart Nijssen3, Robert Osinski4, Mark W Seefeldt5 and Matthew Watts1, (1)Naval Postgraduate School, Monterey, CA, United States, (2)Univ Colorado, Boulder, CO, United States, (3)University of Washington Seattle Campus, Civil and Environmental Engineering, Seattle, WA, United States, (4)Institute of Oceanology Polish Academy of Sciences, Sopot, Poland, (5)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
Recorded Presentation
Abstract:
The climate research community has been increasingly compelled to improving regional climate modeling and prediction. Recent expansions of regional climate modeling for dynamical downscaling (e.g. the WCRP CORDEX Program) present an opportunity to bridge the gap between the limitations of Earth System Models (ESMs) and the regional requirements by stakeholders and decision makers to inform the development of future strategies and policies. Also, the emerging exascale capability for high performance computing further motivates the refinements of fully coupled regional climate model configurations to improve model fidelity.

The Arctic is one of the most challenging regions to model and predict climate change due to its complexity, including the cryosphere, small scale processes and interactions controlling its amplified response to global climate change. The Regional Arctic System Model (RASM) has been developed to better understand the process-level operation of the Arctic System and to predict its change at time scales from days to decades. RASM is a high-resolution limited-area model, consisting of the atmosphere, ocean, sea ice, marine biogeochemistry, land hydrology and river routing scheme components. Its pan-Arctic domain is configured on a 50-km or 25-km grid for the atmosphere / land and on a 1/12o(~9.3km) or 1/48o(~2.4km) grid for the ocean / sea ice components. As a regional climate model, RASM offers a unique capability not available with global ESMs to reproduce the observed natural environment in place and time in order to diagnose and reduce biases, given that its boundary conditions for hindcast simulations are derived from the global atmospheric reanalysis.

In this talk, we will focus on improvements to model physics offered by dynamical downscaling for producing internally consistent and realistic initial conditions to reduce errors and uncertainties in prediction of Arctic climate change. We will also discuss the need for high resolution and sensitivity studies of simulated sea ice states to scale dependent model parameter space. Finally, selected results will be presented to demonstrate predictive gains of dynamical downscaling at seasonal to decadal scales.

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