Search

The article of record as published may be found at https://agu.confex.com/agu/osm20/meetingapp.cgi/Paper/641925 Ocean Sciences Meeting 2020 The Arctic is one of the most challenging regions to model climate change due to its complexity, including the cryosphere, small scale processes and feedbacks controlling its amplified response to global climate change. The combination of these factors defines the need for high spatial and temporal model resolution, which is commonly not practical for most state-of-the-art global Earth system models (ESMs), including those participating in the Coupled Model Intercomparison Project Phase 6. We offer an alternative approach to improve model physics and reduce uncertainties in modeling Arctic climate using a high resolution regional climate system model for dynamical downscaling of output from ESMs. The Regional Arctic System Model (RASM) has been developed to better understand the past and present operation of the Arctic climate system and to predict its change at time scales up to decades. RASM is a coupled model, consisting of the atmosphere, ocean, sea ice, land hydrology and river routing scheme components. Its domain is pan-Arctic, with 50-km or 25-km grids for the atmosphere and land components. The ocean and sea ice components are configured at ~9.3-km or ~2.4-km grids horizontally and with 45 or 60 vertical layers. For hindcast simulations, RASM derives boundary conditions from global atmospheric reanalyses, allowing comparison with observations in place and time, which is a unique capability not available with ESMs. We will discuss improvements to RASM model physics offered by high resolution and in generation of internally consistent realistic initial conditions for Arctic climate prediction. We will also discuss the need for fine-tuning of scale aware parameterizations of sub-grid physical processes in varying model configurations. Finally, selected results will be presented to demonstrate gains of dynamical downscaling in comparison with observations and with the global reanalysis and predictions.

C band backscatter parameters contain information about the upper snowpack/firn in the dry snow zone. The wide incidence angle diversity of the Advanced Scatterometer (ASCAT) gives unprecedented characterisation of backscatter anisotropy, revealing the backscatter response to climatic forcing. TheA(isotropic component) andM2(bi-sinusoidal azimuth anisotropy) parameters are investigated here, in conjunction with data from atmospheric and snowpack models, to identify the backscatter response to surface forcing parameters (wind speed and persistence, precipitation, surface temperature, density and grain size). The long-term meanAparameter is successfully recreated with a regression using these drivers, indicating strong links between theAparameter and precipitation on long timescales. While the ASCAT time series is too short to determine which factors drive observed trends, factors influencing the seasonal and short timescale variability are revealed. On these timescales,Astrongly responds to the propagation of surface temperature cycles/anomalies downward through the firn, via direct modulation of the dielectric constant. The influence of precipitation onAis small at shorter timescales. TheM2parameter is controlled by wind speed and persistence, through modification of monodirectionally-aligned surface roughness. This variability indicates that throughout much of coastal Antarctica, a microwave ‘snapshot’ is generally not representative of longer-term conditions.

The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-15-0775.1 The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work. U.S. Department of Energy (DOE) National Science Foundation (NSF) Grant DE-FG02-07ER64460 (DOE) Grant DE--FG02-07ER64463 (DOE) Grant DE-SC0006178 (DOE) Grant PLR-1107788 (NSF) Grant DE-SC0006693 (DOE) Grant DE-FG02-07ER64462 (DOE) Grant DE-SC0006856 (DOE) Grant PLR-1417818 (NSF)

Long-Term Goals: Our research combines state-of-the-art regional modeling of sea ice, ocean, atmosphere and ecosystem to provide a system approach to advance the knowledge and predictive capability of the diverse impacts of changing sea ice cover on the bio-physical marine environment of coastal Alaska and over the larger region of the western Arctic Ocean. The focus of this project on seasonally ice-free Alaskan coasts and shelves is in direct support of the ‘Coastal Effects of a Diminished-ice Arctic Ocean’ and littoral studies of interest to the U.S. Navy. Given the continued warming and summer sea ice cover decrease in the Arctic during the past decades, this research will have broader and long-term impacts by facilitating studies of the potential increased exploration of natural resources along the seasonally ice-free northern Alaskan coasts and shelves and of the use of northern sea routes from the Pacific Ocean to Europe. Such activities will change the strategic importance of the entire pan-Arctic region. The research will allow a better understanding and planning of current and future operational needs in support of the continued US commercial and tactical interests in the region. Award Number: N0001407WR20296