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

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

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

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

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

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 ( 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

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

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

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

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

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  Same horizontal resolution & boundary conditions  Different dynamics & physics  Simulation during SHEBA year (Sept 1997-Sept 1998) Same domain  Beaufort Sea & pan-Arctic

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

Across-model std dev ARCMIP- Results: 850 hPa temperature May 1998 [ o C] [K]

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

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

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

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

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