1 / 18

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

trent
Download Presentation

Delworth and Knutson, 2000

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related