Regional Arctic Climate System Model (RACM) – Project Overview Participants: Wieslaw Maslowski (PI)- Naval Postgraduate School John Cassano (co-PI)- University of Colorado William Gutowski (co-PI)- Iowa State University Dennis Lettenmeier (co-PI)- University of Washington Greg Newby, Andrew Roberts,- Arctic Region Supercomputing Juanxiang He, Anton KulchitskyCenter Dave Bromwich (OSU), Gabriele Jost (HPCMO), Tony Craig (NCAR), Jaromir Jakacki (IOPAN), Mark Seefeldt (CU), Chenmei Zhu (UW), Justin Glisan (ISU), Jaclyn Kinney (NPS) A 4-year ( ) DOE / SciDAC-CCPP project IARC / Arctic System Model Workshop, Boulder, CO, May 19-21, 2008

Specific Goals develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, ocean, sea ice, and land hydrology components perform multi-decadal numerical experiments using high performance computers to understand feedbacks, minimize uncertainties, and fundamentally improve predictions of climate change in the pan-Arctic region provide guidance to field observations and to GCMs on required improvements of future climate change simulations in the Arctic To synthesize understanding of past and present states and thus improve decadal to centennial prediction of future Arctic climate and its influence on global climate. Main science objective

Regional Arctic climate model components and resolution Atmosphere - Polar WRF (gridcell ≤50km) Land Hydrology – VIC (gridcell ≤50km) Sea Ice – CICE/CSIM (gridcell ≤10km) Ocean - POP (gridcell ≤10km) Flux Coupler – CCSM/CPL7

RACM domain and elevations (red box represents the domain of ocean and sea ice models) Pan-Arctic region to include: - all sea ice covered ocean in the northern hemisphere - Arctic river drainage - critical inter-ocean exchange and transport - large-scale atmospheric weather patterns (AO, NAO, PDO)

Why develop a regional Arctic climate model? 1.Facilitate focused regional studies of the Arctic 2.Resolve critical details of land elevation, coastline and ocean bottom bathymetry 3.Improve representation of local physical processes and feedbacks (e.g. forcing and deformation of sea ice) 4.Minimize uncertainties and improve predictions of climate change in the pan-Arctic region

Arctic Sea Ice cover in September 2002 Comparison of sea ice conditions in September NCAR/CCSM3 case (b30.040b) prediction of summer ice-free Arctic by 2050 CCSM3(b) simulates too much ice on the Greenland shelf (1), too much/little melt in the eastern(2) / western(3) Arctic.

Comparison of areal sea ice fluxes through Fram Strait -CCSM3 sea ice export about twice as high as compared to Kwok et al. (2003) and NPS/NAME fluxes - Possibly too strong atmospheric forcing at Fram Strait - Consequences include - too much ice production in the Arctic Ocean - overestimate of buoyancy flux into the North Atlantic

CCSM3(b)NPS/NAME InOutNetInOutNet Fram Strait 2.0/17-6.9/ / -66.0/45-8.4/ / +9 Barents Sea Opening 4.8/ / -54.5/1105.0/ / /79 FJL-NZ4.7/ / /313.4/ / / year mean ocean volume transport (Sv) / heat transport (TW) Note: 1Sv = 10 m 6 /sec; 1TW = 3.6 Petajoules/hour or 86.4 Petajoules/day or 2592 Petajoules/month NPS/NAME TRANSPORTS (Maslowski et al., JGR, 2004) Fram Strait ‘in’ obs estimates: 7.0 Sv / 50 TW - Courtesy of A. Beszczynska-Möller, AWI FJL-NZ: near-zero heat transport (Gammelsrod et al., JMS submitted)

OCEAN BATHYMETRY/RESOLUTION IMPACTS Barents Sea outflows (north of Novaya Zemlya and through Kara Gate) look similar but: Mean paths significantly different due to representation of bathymetry (I.e. resolution) Mean paths significantly different due to representation of bathymetry (I.e. resolution) Velocity magnitudes differences Velocity magnitudes differences 9-km model circulation shown to match observed well (Maslowski et al., 2004) 9-km model circulation shown to match observed well (Maslowski et al., 2004) Implications for location of fronts, water mass transformations, heat and salt balances Implications for location of fronts, water mass transformations, heat and salt balances (from Maslowski et al., 2008) 18-km Model m (levels 1-7), every vector 9-km Model m (levels 1-15), every 2 nd vector

Increased horizontal resolution allows for improved representation of topography Topography impacts atmospheric circulation, precipitation, temperature, etc. ERA40 precipitation (above) is “smoothed” compared to higher resolution (50 km) Polar MM5 simulation (right) This will impact both atmosphere and land/ocean ERA40 Annual Precipitation Polar MM5 Annual Precipitation ERA40 Annual Precipitation LAND TOPOGRAPHY / RESOLUTION IMPACTS

Cyclone Central Pressure and Size Model resolution impacts the size and intensity of cyclones Comparison of AMPS ( 20 km; based on Polar MM5 and WRF) and three coarser reanalyses in the Southern Ocean AMPS simulates lower pressure in and smaller cyclones than all reanalyses Similar results are expected in Arctic

Wetlands Finer resolution captures dispersed features missed by coarse grids Gutowski et al. (2007) Unforced “noise” Wetlands cases RMS Difference vs. Baseline (500 hPa Heights)

Sea Ice Divergence near SHEBA Tower (Stern & Moritz, 2002) Ice strain (reds/yellows) (200 km x 200 km window)

Long-term streamflow changes (Peterson et al., 2002) are not captured by model in permafrost basins (particularly in discontinuous permafrost). Reasons include improper permanent ground ice initialization and lack of tracking. Attribution of observed trends in Eurasian Arctic river runoff: Why don’t model reconstructed trends match observations? (Visuals courtesy of Jennifer Adam, Washington State University) High Quality GHCN Precipitation Stations High Quality GHCN Temperature Stations Improvements to the VIC frozen soils algorithm to handle permafrost are underway.

1.Evaluate uncoupled model simulations for physical and numerical optimizations in RACM 2.Couple each climate model component to the coupler (CPL7) 3.Run and validate results as in #2 4.Couple all climate model components and run tests with RACM RACM Outlook