Improvements in Numerical Modelling During the ACSYS Decade

1. Improvements in Numerical Modelling During the ACSYS Decade Gregory M. Flato Canadian Centre for Climate Modelling and Analysis Meteorological Service of Canada

2. Outline This overview is necessarily selective and focuses primarily on large-scale models, particularly global climate models. I will say something about the following topics: • Global climate models • Sea Ice • Snow • Hydrology • Atmospheric Circulation • Ocean Modelling As you will see, Model Intercomparison Projects were a ‘growth industry’ during this decade.

3. Introduction • At the beginning of the 1990s, most climate modelling was done with atmosphere-slab ocean models • These provided ‘equilibrium’ estimates of climate change, typically with doubled CO2 • They highlighted projected enhancement of warming at high latitudes, particularly in winter.

4. CCCma 2xCO2 winter (DJF) temperature change from three early climate models (IPCC, 1990). High-latitude amplification is attributed to positive feedbacks involving sea-ice albedo over ocean and snow albedo over land. GFDL UKMO

5. 37% Rind et al., 1995 Illustration of the effect of sea-ice feedbacks on CO2-induced warming response Interactive Sea ice Fixed sea ice

6. ‘Transient’ climate change simulations require coupling of 3-D ocean models. • This began in earnest in the early 1990s. • In parallel, there was growing appreciation that the meridional overturning circulation in the North Atlantic might be sensitive to freshwater perturbations in the Nordic Seas. • This raised the profile of modelling sea-ice motion so as to represent ice flux out of the Arctic through Fram Strait. overturning state ‘collapsed’ state Weaver and Sarachik, 1991

7. By the mid-1990s there were ~15 coupled models available and the Coupled Model Intercomparison Project (CMIP) was launched. • However, only 4 of these had a ‘physically-based’ representation of sea-ice dynamics. • Despite the fact that rather sophisticated sea-ice models had been available since the late 1970s (e.g. Hibler, 1979).

8. Global Climate Models of the mid 1990s  Motionless ice with a prognostic equation for ice growth and melt. 2 Prognostic equations for growth/melt and ice motion, including representation of internal ice stress. 3 Prognostic equation for ice growth/melt, ice motion diagnosed as a function of ocean surface current.

9. 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. G. Flato for IPCC 2001

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

11. As mentioned earlier, climate warming is enhanced over sea ice. • However, this is also the location of largest disagreement. • Representation of sea-ice processes and feedbacks is implicated. NH ensemble mean temperature change (C) NH intermodel standard deviation (C)

12. SH ensemble mean temperature change (C) SH intermodel standard deviation (C) There is a need to evaluate/improve representation of sea-ice processes …

13. ACSYS NEG Sea-Ice Model Intercomparison Project (SIMIP) • Initial intercomparison focused on ‘dynamics’ • Objective was to quantitatively evaluate performance of different dynamic schemes used in climate models. • “viscous-plastic” model performed best; it is appearing in many of the new coupled models. • A follow-on project, SIMIP2, focussed on ‘thermodynamics’, is underway now. Kreysher et al., JGR, 2000

14. Stand-alone sea-ice models, forced with historical re-analysis of atmospheric quantities, have also provided insight into aspects of sea-ice behaviour that we cannot observe.

15. r = 0.7 r = 0.06 Hilmer and Jung, 2000

16. Snow … • Has a profound effect on surface energy balance. • Rapidly-evolving and heterogeneous material. • Interacts with vegetation in complex ways. • Sophisticated snowpack models have been developed for applications such as avalanche forecasting. • Too computationally demanding for use in GCMs • GCM representations of snow typically use 1 or a few layers, with simplified physics. • Various approximations led to a considerable ‘diversity’ in early climate models. • As we’ll see, more recent models show considerable improvement.

17. Snow cover ‘error’ in AMIP1 models (early 1990s) Frei and Robinson, 1998

18. Snow cover ‘error’ in AMIP2 models (late 1990s) Frei et al., 2003

19. Snow Model Intercomparison Project (SnowMIP) • www.cnrm.meteo.fr/snowmip • considered two alpine sites in Europe and two sites in N. America. • point simulations at sites without tall vegetation. • simple snow schemes used in GCM and hydrological models as well as sophisticated schemes. Sleepers River, VT, USA Weissfluhjoch, Switzerland Large differences at Vermont site are attributed to differences in the representation of sublimation.

20. Connections to Hydrology • Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS) involves 21 models. • PILPS 2d: 18-year simulation of seasonally snow-covered grassland (Valdai, Russia). • PILPS 2e: basin in northern Scandinavia. • Sublimation of snow was a large source of intermodel discrepancy. PILPS 2e Observed (Added by GF) Provided by R. Essery, U. Aberystwyth

21. Spatial Pattern Thompson and Wallace, 1998 Atmospheric Circulation: the “Arctic Oscillation” http://www.cpc.ncep.noaa.gov/products/precip/CWlink/all_index.html

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

23. AMIP (the Atmospheric Model Intercomparison Project) allowed evaluation of various aspects of atmospheric circulation. Mean sea-level pressure exhibits certain biases, but there is a lot of variation from model to model. Bitz et al., J.Clim., 2002

24. Thickness field from a sea-ice model driven by different forcing Observation-based Forcing Climate Model Forcing Bitz et al., J.Clim., 2002

25. Circulation biases, along with other errors, impact other quantities, such as precipitation … Range of observational estimates Walsh et al., J. Clim., 1998 It is hard to isolate or identify shortcomings in process representation in a global model, particularly for some region …

26. Arctic Regional Climate Model Intercomparison Project (ARC-MIP) • Joint project of ACSYS/CliC NEG and GEWEX Working Group on Polar Clouds • RCM experiments using common domain and boundary conditions. • 5 RCM groups participating. • Comparing with observations from SHEBA year: Oct/97 – Oct/98 http://cires.colorado.edu/lynch/arcmip/background.html

27. Ocean Modelling … Models of Arctic Ocean Circulation have improved significantly during the ACSYS decade, in concert with availability of improved atmospheric forcing. Model studies have contributed substantially to understanding variability in the Arctic Ocean. Karcher, Gerdes, Kaucher and Koeberle, JGR, 2003

28. Arctic Ocean Model Intercomparison Project (AOMIP) • Initial evaluation of existing Arctic ocean model output. • Coordinated model experiments underway now. • Example shows transport streamfunction from various Arctic Ocean models, forced in various ways. Steiner et al., Ocean Modelling, 2003 http://fish.cims.nyu.edu/project_aomip/overview.html

29. A recent trend is to make use of alternate model grid configurations in global models to better resolve ocean (and ice) processes in the Arctic. These examples are from the POP ocean code, used in the NCAR community climate model. http://climate.lanl.gov/Models/POP/index.htm

30. Summary • Feedbacks involving the cryosphere lead to amplification of projected climate warming in the Arctic. • These feedbacks also amplify model errors • Although climate models are improving, errors in representing Arctic climate remain large … and must be improved. • 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.

31. The End

32. thermo-only dynamic (rheology) no flux-adj. flux adj. dynamic (drift) diagnostic Sea Ice and its Response to CO2 Forcing in Global Climate Models G.M. Flato – Canadian Centre for Climate Modelling and Analysis • Results from an ensemble of climate models are compared • mean state and response to increasing CO2 concentration • find a large range in model results that are not obviously connected to representation of sea-ice processes. Connection to initial ice stated is also not compelling.