1. Dr. Gregory M. Flato Canadian Centre for Climate Modelling and Analysis. M.S., (University of Alberta, Edmonton) PhD (Dartmouth College) Research Interests: Global coupled climate modelling Sea-ice dynamics and thermodynamics Role of the cryosphere in climate

2. The Arctic in Global Climate Models and Projections of Future Change Gregory M. Flato Canadian Centre for Climate Modelling and Analysis Meteorological Service of Canada

3. Outline Arctic climate and its variability Global climate models Representation of Arctic climate and climate processes in global models Projections of future climate change Summary

4. Arctic Climate Observed Surface Temperature ( o C) Winter Fyfe (2004) Based on NCEP Reanalysis Annual Mean temperature anomaly Time series: 1850-present Jones and Moberg (2003)

5. John Walsh – U. Illinois

6. Atmospheric Circulation Mean Sea-Level Pressure (hPa) WinterSummer Fyfe (2004)

7. Arctic Oscillation Pressure anomaly Temperature Anomaly

8. International Arctic Buoy Program Sea-Ice Circulation

9. Variations in transport, deformation, growth and melt all contribute to observed variability and recent decline in ice coverage. Courtesy of J. Walsh, U. Illinois.

10. Transport of ice is balanced by net growth or melt. the associated salt or freshwater fluxes impact ocean mixing and circulation. Courtesy of M. Hilmer, IfM, Kiel

11. Jones (2001) Ocean Circulation Surface layer Atlantic layer

12. There are many important feedbacks and connections between these climate components in the Arctic. A model provides a framework for synthesizing our understanding of this complex system, and provides a tool for making quantitative projections of future change. However, the Arctic is part of, and interacts with, the global climate system, so it can’t be considered in isolation.

13. Global Climate Models Based on laws of physics Mathematical representation of –3-D atmosphere: its temperature, humidity, wind, radiative transfer, cloud formation/dissipation, precipitation, … –3-D ocean: its temperature, salinity, circulation, mixing, … –Sea-ice: its formation, melt, motion and deformation. –Land surface: its temperature, moisture content, reflectivity, evapotranspiration, …

14. The equations are solved numerically on a discrete grid. A problem peculiar to the Arctic is the convergence of meridians at the North Pole – this causes numerical difficulties, particularly in the ocean model. http://climate.lanl.gov/Models/POP/index.htm These examples are from the POP ocean code, used in the NCAR community climate model. A recent trend is to make use of alternate grid configurations to better resolve ocean (and ice) processes in the Arctic.

15. Model Intercomparison Projects There are perhaps 15 or so global climate models under development around the world. Intercomparison projects provide an opportunity to: evaluate models in a systematic fashion; compare/contrast results from different models; and hopefully, to identify reasons for the differences.

16. Model NameReferenceFlux-AdjustmentSea-Ice VariableSea-Ice Processes BMRC Power et al. (1993)nonethickness thermo-only   CCCma Flato et al. (2000)heat, watermass per unit areathermo-only CCSR Emori et al. (1999)heat, waterwater equivalent depththermo-only COLA Schneider et al. (1997) nonethicknessthermo-only CSIRO Gordon and O’Farrell (1997) heat, water, momentum thicknessdynamic- thermodynamic22 MPI_E4 Roeckner et al. (1996) heat, water (annual mean) thicknessdynamic- thermodynamic GFDL Manabe et al. (1991) heat, waterthicknessdrift-thermodynamic33 GISS_M Miller and Jiang (1996) nonemass per unit areathermo-only GISS_R Russel et al. (1995)none% time grid cell occupied by ice thermo-only MRI Tokioka et al. (1996) heat, waterthicknessdrift-thermodynamic NCAR_CSM Boville and Gent (1998) nonethicknessdynamic- thermodynamic NCAR_WM Washington and Meehl (1996) nonethicknessdynamic- thermodynamic UKMO Johns et al. (1996)heat, waterthicknessdrift-thermodynamic   Motionless ice with a prognostic equation for ice growth and melt. 22 Prognostic equations for growth/melt and ice motion, including representation of internal ice stress. 33 Prognostic equation for ice growth/melt, ice motion diagnosed as a function of ocean surface current. Global Climate Models of the mid 1990s Flato (2004)

17. One can look at ensemble mean quantities, or look at individual models … IPCC (2001)

18. Model disagreement is largest over area influenced by sea ice. Just as sea-ice feedbacks amplify climate change, they also amplify model errors and contribute to uncertainty in projections of future climate. Intermodel standard deviation of surface air temperature ( o C) based on CMIP archive data

19. MSLP ensemble mean error Atmosphere-only models Coupled models Walsh et al. (2002)

20. CCCma CGCM2 NCEP Reanalysis Annual Mean Sea-Level Pressure

21. Climate Model Forcing Observation- based Forcing Thickness field from a sea-ice model driven by different forcing Bitz et al. (2002)

22. Modelled ice extent in the 12 model CMIP ensemble 10% of models have less ice than this. Median ice edge. 10% of models have more ice than this. Interestingly, median model ice edge agrees well with observations. Flato, 2004

23. Snow cover ‘error’ in AMIP2 models (late 1990s) Frei et al., 2003 Frei and Robinson, 1998 Snow cover ‘error’ in AMIP1 models (early 1990s)

24. Projections of future change Coupled models are forced with GHG and aerosol forcing as observed from 1850 to the present, then increasing as per some prescribed future scenario. Concentration (ppmv)

25. CCCma CGCM2 -- Mean = 1.92 o C Projected Surface Air Temperature Change – 2050 vs 1980

26. Observations 1946-56 CCCma Model 1986-96 One can compare the evolution of temperature anomalies over time …

27. Projected climate warming is enhanced over sea ice; as in the case of ‘control’ climate, this is also the location of largest disagreement. (But all models predict warming) NH ensemble mean temperature change (C)NH intermodel standard deviation (C) Based on CMIP archive

28. Predictions of sea-ice changes likewise vary from model to model. Here we show NH annual mean ice extent from CCCma and Hadley Centre models. Walsh observations Both models underestimate ice extent somewhat. CCCma model indicates more rapid historical and future decline – not inconsistent with observed decline.

29. NH Ice Extent and its Change – CMIP2 model ensemble (CO2 increased at 1% per year for 80 years – the time of doubling dynamic (rheology) no flux-adj. flux adj. thermo-only dynamic (drift) diagnostic Initial Ice Extent Ice Extent Change No obvious connection between error and ice model characteristics But all models predict a decline Flato, 2004

30. –Feedbacks involving the cryosphere lead to amplification of projected climate warming in the Arctic. –These feedbacks also amplify model errors –Although global climate models are improving, the Arctic remains a challenge. –Model errors tend to be larger than elsewhere. –Nevertheless, models universally agree that climate change will be larger in the Arctic than at lower latitudes. –The last decade has seen an increased focus on modelling Arctic climate. –Various intercomparison projects yield quantitative evaluation of model shortcomings. –Representation of snow in climate models has improved demonstrably. –More sophisticated sea-ice models are being employed, and alternative grid configurations are being used to improve resolution of Arctic ice and ocean processes. Summary

31. The End

34. Stendel and Christensen, 2002 Model projection of change in permafrost

35. Figures courtesy J. Fyfe The ‘Arctic Oscillation’ Sea-Level Pressure Temperature

36. Fyfe et al., 1999 CCCma CGCM1 Observed Fyfe, 2004

37. Climate change scenario Observations to 2002 CCCma model Fyfe et al., 1999 GISS model Shindell et al., 1999 Stratosphere included No Stratosphere Arctic Oscillation

38. 900-year time series of NH ice extent from CCCma climate model

39. 1953-98 trend (p <0.1%) 1978-98 trend (p < 2%) Likelihood of observed trends based on 5000 yr control run of GFDL model Vinnikov et al. (1999) Recent trend is not likely a result of natural variability …

40. There is substantial interannual variability in Fram Strait outflow, but no obvious trend. Correlation with NAO is strong (r=0.7) for the period 1978-1997, but weak (r=0.1) for ‘58-’77 period. Hilmer and Jung (2000).

41. Observations are insufficient to say much about ice thickness variability, but model results give some indication. Variability is expected to be large near coastlines, due to wind-driven deformation.

42. Submarine observations do provide some evidence for long-term change in thickness. 40% decrease between 1958-1976 and 1993- 1997. Rothrock et al., 1999

43. However, model results indicate that wind-driven changes in thickness build-up pattern, and limited sampling, may be important. Holloway and Sou, 2002

44. Thickness change by middle of 21 st century CCCma Hadley

45. March September CCCma model projects seasonal Arctic ice cover by mid century. 1971-1990 2041-2060

46. Ensemble mean thickness Intermodel standard deviation

47. Composite results for Southern Hemisphere. 10 model ensemble

48. Ensemble mean thicknessIntermodel standard deviation JJA