After Climategate and Cancun….

1. After Climategate and Cancun…. …What next for climate science? By Tim Palmer University of Oxford University of Cambridge

2. From IPCC AR4 WG1 AR4 assessment of likely range

3. Potential Amplifiers of Climate Change Water Vapour Water Vapour Aerosols Clouds Clouds Carbon cycle Ice albedo Methane Clathrates

6. IPCC AR4 WG1 “…..there has been no apparent narrowing of the uncertainty range associated with cloud feedbacks in current climate change simulations. A straight-forward approach of model validation is not sufficient to constrain the models efficiently and a more dedicated approach is needed.”

7. Wilhelm Bjerknes (1862-1951) Proposed weather forecasting as a deterministic initial value problem based on the laws of physics

8. Lewis Fry Richardson (1881-1953) The first numerical weather forecast

9. John von Neumann (1903-1957) Integrating the numerical weather forecast on a digital computer

10. Forecast Skill 1980-2010

11. Data Assimilation (DA) Aim: to combine limited observations with our Knowledge of the laws of physics for optimal state estimation PHYSICAL LAWS OBS DATA MODEL DA OPTIMAL STATE ESTIMATE

12. Data Assimilation Cycle: Unbiased Model Mean Analysis Increment = 0

13. Data Assimilation • Central tool for determining the initial state for a weather forecast Find the state x given observations yi by minimising • Multiple time scales • High dimensions • Sparsity of data • Nonlinearity of climate processes • Model Uncertainty

14. Don’t worry. No hurricane!

16. The Butterfly Effect Ed Lorenz Idealised model of weather

18. AN ENSEMBLE WEATHER FORECAST FOR OCT 1987 MLSP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC TL399 EPS with TL95, moist SVs

19. Probability of hurricane-force gusts on October 16th 1987

20. In a nonlinear system, predictability is flow dependent – and predictable Very predictable part of the model Z X Very unpredictable part of the model. Oct 87!

21. “How can we trust global climate forecasts 100 years into the future when the same models can’t demonstrate shorter-range forecasts?”

22. 50% 50% “climate change” 80% 20%

23. Flat calm! Surface Pressure Policy Relevance How much more frequent will persistent blocking anticyclones be under climate change? Potential Vorticity on 315K Rossby wave breaking

24. The weather forecast problem is fundamentally an initial value problem…..

25. ….. whilst the climate change problem is fundamentally a forced problem

26. MLSP 66-hour forecasts, VT: 16-Oct-1987, 6 UTC TL399 EPS with TL95, moist SVs In recent years, ensemble prediction has also become a standard tool for climate forecasting….but for the latter there are more uncertainties than just initial-condition uncertainties

27. Why is there uncertainty in climate predictions? Chaos Future Emissions Model Uncertainty

28. Standard ansatz for “ab initio” weather/climate models Eg Increasing scale • Eg momentum“transport” by: • Turbulent eddies in boundary layer • Orographic gravity wave drag. • Convective momentum transport Simplified deterministic bulk-formula parametrisations

29. IPCC AR4 WG1 Chapter 8 “…models still show significant errors. Although these are generally greater at smaller scales, important large-scale problems also remain. ……The ultimate source of most such errors is that many important small-scale processes cannot be represented explicitly in models, and so must be included in approximate form as they interact with larger-scale features. …consequently models continue to display a substantial range of global temperature change in response to specified greenhouse gas forcing. “

30. ..we do not know how to close the equations with deterministic bulk formulae and produce a climate model which has no significant biases against observations .. Estimates of global warming also depend sensitively on the parameters α….

31. “There are no obvious problems with the high temperature models, Stainforth says…. The uncertainty at the upper end has exploded, says team-member Myles Allen.”

32. Why is it so hard to produce a bias free climate model?

33. “Truth”

34. “Model” “Model= Truth + Bias” What is the origin of this bias?

35. Let’s guess that the origin of the bias lies in the model’s equation for X, ie that the model equations are Looks reasonable.

36. “Model” In fact

37. Direction of response • • Y Leading EOF (ie leading eigenvector of the covariance matrix C of L63) Direction of f X • •

38. There is a tendency for systematic errors in “biased models” of Lorenz 63 to project onto the L63’s dominant mode of internal variability, regardless of the “root” cause of the error. This makes it difficult to identify the “root” cause of model error, by diagnosing the model’s systematic bias Is this true for comprehensive climate models as well?

39. Northern Annular Mode ECMWF Seasonal Forecast Systematic Error What is the root cause of this bias?

40. CNTT511-CNTT95 SPBST95-CNTT95 CNTT95-ERA40 Impact of changing resolution Impact of adding stochastic noise Impact of changing P Response to three very different model revisions each lies in the direction of the system’s principal mode of internal variability. So what is the primary cause of the bias??

41. 1905 - Annus Mirabilis • Special Theory of Relativity • Quantum explanation of the photoelectric effect • Brownian Motion • ..the same random forces which cause the erratic motion of a particle in Brownian motion would also cause drag if the particle were pulled through the fluid.

42. Fluctuation-Dissipation Theorem A very general result in statistical thermodynamics which links the response of a system to external forcing, to internal fluctuations of the system in thermal equilibrium. First applied to the climate system by Chuck Leith

43. Data Assimilation Cycle: Unbiased Model Mean Analysis Increment = 0

44. Data Assimilation Cycle: Biased Model Mean Analysis Increment ≠ 0 −Mean Analysis Increment = Mean Net Tendency = Convective + Radiative + … + Dynamical Tendency Can assess individual processes when acting on states close to the truth (Klinker and Sardeshmukh 1992)

45. Climate Sensitivity and Model Parameters (Stainforth et al, 2005) Greater than three times larger climate sensitivity with perturbed cloud parameter Circles: AGCM + Mixed-Layer model results from Stainforth et al. (2005) show combined RMSE of 8 year mean, annual mean T2m, SLP, precipitation and ocean-atmosphere sensible+latent heat fluxes (equally weighted and normalised by the control). Diamonds: AGCM results from Rodwell & Palmer (2006) show RMSE from 39 year mean, annual mean T850, SLP and precipitation (equally weighted and normalised by the control).

46. Mean Temperature Analysis Increment Tendencies Rodwell and Palmer, Quarterly Journal of the Royal Meteorological Society, 2006 CLOUD CONTROL ENTRAIN/5 and ENTRAINx3 are out of balance ENTRAINx3 ENTRAIN/5

47. Can a 6hr weather forecast determine the climate 100 years from now? It can strongly constrain what the climate will be like 100 years from now! So why not use data assimilation today to constrain climate predictions?

48. 1950 2000 Weather models No IPCC AR4 model had data assimilation capability…climate change is not primarily an initial value problem Climate models

49. Towards Comprehensive Earth System Models 1970 1997 2000 1975 1985 1992 Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Land surface Land surface Land surface Land surface Land surface Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Huge Demands on Human resources Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon cycle Carbon cycle Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models Sulphur cycle model Non-sulphate aerosols Ocean & sea-ice model Land carbon cycle model Carbon cycle model Ocean carbon cycle model The Met.OfficeHadley Centre Atmospheric chemistry Atmospheric chemistry

50. Many Demands on Computing Power and on Human Resources Decadal, Centennial, Paleo Integrations Data Assimilation Estimating uncertainty (ensembles) Horizontal resolution (eg Rossby Wave Breaking) Chemistry, Aerosols, Carbon Cycles… Vertical structure (inc. Stratosphere, Mesosphere)