Arctic climate simulations by coupled models - an overview -

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1 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

2 Surface temperature anomalies in Observation Experiment 3 titude arge 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? Delworth and Knutson, 2000 [K] Anomalies relative to climatology

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 (1) Annual cycle of surface air temperature models observation temperature poleward 70 o N, excluding land 8 coupled models from IPCC/DDC; control Walsh et al., 2002

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

6 AOGCM summary ß Reasonable representation of mean state and variability by the ensemble, but considerable across-model scatter ß Biases in Arctic climate from an Arctic perspective: systematic differences in key variables (SP, clouds, sea ice) influence of global climate on Arctic & vice versa development of Arctic specific parameterizations (PB, clouds, permafrost, ) ß Resolution ( km horiz., few-tens of vertical levels) limits the ability to capture important aspects of climate (e.g., topographic effects, storms, sea ice-atmosphereinteraction) 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 and-sea mask & orography of the pan-arctic domain GCM (T30, 3.75 o ) RCM (0.5 o ) Courtesy W. Dorn

9 (1) Annual cycle of surface air temperature Temperature [ o C] model observation averaged over model domain (Period: , RCM:HIRHAM)

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

11 Arctic Regional Climate Model Intercomparison Project (ARCMIP) Participating Models 1. ARCSyM (USA) 2. COAMPS (S) 3. HIRHAM (D,DK) 4. NARCM (CAN) 5. RCA (S) 6. RegCM (N) 7. REMO (D) 8. PolarMM5 (USA) Experimental set-up ß Same horizontal resolution & boundary conditions ß Different dynamics & physics ß Simulation during SHEBA year (Sept 1997-Sept 1998) Same domain ß Beaufort Sea & pan-arctic

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 [ o C] [K]

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

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

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

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

18 Model development ß ß ß ß ß Outlook going to finer horizontal and vertical resolutions Arctic specific parameterizations (surf. albedo, clouds, PB) 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 ß ß ß 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