Arctic climate simulationsby coupled models

- an overview -

Annette Rinke and Klaus DethloffAlfred Wegener Institute for Polar and Marine Research,Research Department Potsdam, Germany

DSAR konference 9. november 2000

Delworth and Knutson, 2000ObservationExperiment 3Experiment 5Experiment 4Surface temperature anomalies in 1890-2000Large internal variability of the coupled atmosphere-ocean systemTo what extent is polar warming amplificationattributed to real physical processes ratherthan to model imperfections?[K]Anomalies relative to1961-90 climatology

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

(1) Annual cycle of surface air temperatureWalsh et al., 2002poleward 70oN, excluding landtemperature8 coupled models from IPCC/DDC; control 1961-90

Dominant spatial pattern z500,NH,DJFData (NCEP, 1948-2001)AOGCM (ECHO-G, 1000 yrs)Handorf et al., 2002(3) Decadal variabilityAO Pattern and its temporal variability

AOGCM summaryReasonable representation of mean state and variability by the ensemble, but considerable across-model scatterBiases 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

Regional climate model (RCM) methodGCM (or observation-based analyses)RCMInitial & time-dependent boundary conditionsfor the RCM provided by GCM

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

(Period:1979-93, RCM:HIRHAM)Temperature [oC](1) Annual cycle ofsurface air temperatureaveraged over model domain

[K]1979-93NCEPHIRHAMHIRHAMNCEP[K]Interannual variability ofsurface air temperatureSeasonal mean ofsurface air temperatureSummer (JJA)

Arctic Regional Climate Model Intercomparison Project (ARCMIP)Participating ModelsARCSyM (USA)COAMPS (S)HIRHAM (D,DK)NARCM (CAN)RCA (S)RegCM (N)REMO (D)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

http://paos.colorado.edu/~currja/arcmip/index.html

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 devARCMIP- Results: 850 hPa temperature May 1998[oC][K]

ARCMIP- Results: Temporal development of the vertical atmospheric structureJanuary 1998

Anomalous sea ice retreat in Siberian Seas during summer 1990August 1990Sea ice concentrationMaslanik et al., 2000Rinke et al., 2003ObservationCoupled Regional ModelsHIRHAM-MOMARCSyM

- Mean sea level pressure -Maslanik et al., 2000Rinke et al., 2003ARCSyMModelsObservationAtmosphere-alonewith satellite sst/iceHIRHAMCoupled regional modelsAtmospheric circulation, August 1990

RCM summary Added value due to downscaling compared with GCM outputRCMs problems: large-scale errors of driving model nesting technique Importance of synoptic-scale processes in simulating strong regional variability of sea ice cover

OutlookModel developmentgoing to finer horizontal and vertical resolutionsArctic specific parameterizations (surf. albedo, clouds, PBL)extensive ensemble integrationsinclude more components of the climate systemcombined use of AOGCMs and RCMs EU project Global Implications of Arctic Climate Processes & Feedbacks

Understandingnatural climate variability on multiple scales in space & time atmosphere-ocean-ice-land interactions on regional scaleinterplay between Arctic regional climate feedbacks & global circulation patterns

Zonal mean anomalies of surface temperature (in K). Anomalies are relative to 1961-1990 climatologyGFDL,atmosphere 3.75lonx2.25 lat; ocean 1.87x2.2.5, flux corrected5 integrations with time-varying GHG concentration and sulfate aerosol (from 1865 to present) same design, but other inital conditions (selected from widely separate times in control)1000 yr Control (no year-to-year variations in external radiative forcing)8 coupled models from IPCC/DDCControl 1961-90 (from a longer run, e.g., 1900 onward)GHG prescribed according to observationsAtmosphere ECHAM4 - T30 horizontal resolution ~ 3.75Ocean HOPE-G - T42 horizontal resolution ~ 2.819 vertical hybrid layers in tropo- and stratosphere; fixed solar constant (1365 W/m2); constant CO2 concentration (353ppm)Wavelet coefficients describe temporal variations of the spectral energy of the 1. PC, Statistical significant with 95% if compared against a red-noise process thick black lines Dominant spatial pattern EOF1 ; 500 hPa geopotential height, NH, DJF 18% explained variancebasic strategy: use GCM to simulate the response of global circulation to large-scale forcings Use RCM to(a) account for sub-GCM grid scale forcings (complex topographic features, land cover inhomogenities) in a physically-based way (b) enhance the simulation of atmospheric circulation & climate variables at fine spatial scales Finer resolved orography and land-sea-sea ice contrastsBetter resolved nonlinear interactions between large and smaller scales (improved energy transfer)Improved simulation of hydrodynamic instability processes & synoptic cyclonesRCM reproduce well interannual variability, both in spatial pattern and magnitude Std dev=measure of interannual variabilitySmall domain: dynamics are determined primarily by lateral boundary forcingLarge domain: dynamics influenced by surface & parameter. processes Allows elucidation of the interaction of the parameter. processes with the atmosph. dynamicscoupling: vegetation; interaction snow and vegetation; boreal forest have a large effect on snow accumulation and ablation; permafrostCoarse resolution is a problem in modeling arctic PBL. Vertical resolution needs to resolve large temperature gradients, very sharp inversions

importance of synoptic scale processes in the simulation of the strong regional variability which dominates the analyses of trends in Arctic ice cover.