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Delworth and Knutson, 2000

Arctic climate simulations by coupled models - an overview - Annette Rinke and Klaus Dethloff Alfred Wegener Institute for Polar and Marine Research, Research Department Potsdam, Germany. Surface temperature anomalies in 1890-2000. Observation. Experiment 3. Large internal variability

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Delworth and Knutson, 2000

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  1. Arctic climate simulationsby coupled models- an overview -Annette Rinke and Klaus DethloffAlfred Wegener Institute for Polar and Marine Research,Research Department Potsdam, Germany

  2. Surface temperature anomalies in 1890-2000 Observation Experiment 3 Large internal variability of the coupled atmosphere-ocean system Experiment 5 Experiment 4 To what extent is polar warming amplification attributed to real physical processes rather than to model imperfections? Anomalies relative to 1961-90 climatology Delworth and Knutson, 2000 [K]

  3. Global Coupled Models (AOGCMs) • AOGCMs performance in the Arctic • (seasonal cycle, interannual & decadal variability) • Regional Models (RCMs) • atmospheric RCMs performance in the Arctic • (seasonal cycle, interannual variability) • coupled RCMs for the Arctic • (case studies) • Outlook

  4. models observation (1) Annual cycle of surface air temperature temperature poleward 70oN, excluding land 8 coupled models from IPCC/DDC; „control“ 1961-90 Walsh et al., 2002

  5. Variability of dominant pattern (3) Decadal variability AO Pattern and its temporal variability Data (NCEP, 1948-2001) AOGCM (ECHO-G, 1000 yrs) Dominant spatial pattern z500,NH,DJF Handorf et al., 2002

  6. AOGCM summary • Reasonable representation of mean state and variability by the ensemble, but considerable across-model scatter • Biases in Arctic climate  from an Arctic perspective: systematic differences in key variables (SLP, clouds, sea ice)  influence of global climate on Arctic & vice versa  development of Arctic specific parameterizations (PBL, clouds, permafrost,…) • Resolution (200-300 km horiz., few-tens of vertical levels)  limits the ability to capture important aspects of climate (e.g., topographic effects, storms, sea ice-atmosphere- interaction)  higher resolution

  7. Regional climate model (RCM) method GCM (or observation-based analyses) RCM Initial & time-dependent boundary conditions for the RCM provided by GCM

  8. Regional climate model (RCM) method Land-sea mask & orography of the pan-Arctic domain GCM (T30, 3.75o) RCM (0.5o) Courtesy W. Dorn

  9. model observation (1) Annual cycle ofsurface air temperature Temperature [oC] averaged over model domain (Period:1979-93, RCM:HIRHAM)

  10. Seasonal mean of surface air temperature Interannual variability of surface air temperature HIRHAM HIRHAM [K] [K] NCEP NCEP Summer (JJA) 1979-93

  11. Arctic Regional Climate Model Intercomparison Project (ARCMIP) • Experimental set-up • Same horizontal resolution & boundary conditions • Different dynamics & physics • Simulation during SHEBA year (Sept 1997-Sept 1998) Participating Models • ARCSyM (USA) • COAMPS (S) • HIRHAM (D,DK) • NARCM (CAN) • RCA (S) • RegCM (N) • REMO (D) • PolarMM5 (USA) • Same domain • Beaufort Sea & pan-Arctic http://paos.colorado.edu/~currja/arcmip/index.html

  12. Different domains • allows elucidation of the interaction of the parameterized processes with the atmospheric dynamics  influence of resolution • Different boundary conditions •  separate errors associated with • - lateral boundary advection • - interaction with ice/ocean surface

  13. ARCMIP- Results: 850 hPa temperature May 1998 Across-model std dev [oC] [K]

  14. ARCMIP- Results: Temporal development of the vertical atmospheric structure January 1998

  15. Anomalous sea ice retreat in Siberian Seas during summer 1990 Observation Coupled Regional Models HIRHAM-MOM August 1990 Sea ice concentration ARCSyM Maslanik et al., 2000 Rinke et al., 2003

  16. HIRHAM-MOM H L L L H H L L L L L L H H L H L Atmospheric circulation, August 1990 - Mean sea level pressure - Atmosphere-alone with satellite sst/ice Coupled regional models ARCSyM HIRHAM Models Observation Maslanik et al., 2000 Rinke et al., 2003

  17. RCMs improve (should we expect to): • reduction of mean bias • better spatial variability • more realistic variance • better tail behaviour (i.e., extremes) RCM summary • Added value due to downscaling compared with GCM output • Importance of synoptic-scale processes in simulating strong regional variability of sea ice cover • RCM‘s problems: • large-scale errors of driving model • nesting technique

  18. Outlook Model development • going to finer horizontal and vertical resolutions • Arctic specific parameterizations (surf. albedo, clouds, PBL) • extensive ensemble integrations • include more components of the climate system • combined use of AOGCMs and RCMs  EU project “Global Implications of Arctic Climate Processes & Feedbacks” Understanding • natural climate variability on multiple scales in space & time • atmosphere-ocean-ice-land interactions on regional scale • interplay between Arctic regional climate feedbacks & global circulation patterns

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