After Climategate and Cancun…. …What next for climate science? By Tim Palmer University of Oxford University of Cambridge From IPCC AR4 WG1 AR4 assessment of likely range Potential Amplifiers of Climate Change Water Vapour Water Vapour Aerosols Clouds Clouds Carbon cycle Ice albedo Methane Clathrates 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.” Wilhelm Bjerknes (1862-1951) Proposed weather forecasting as a deterministic initial value problem based on the laws of physics ECMWF Lewis Fry Richardson (1881-1953) The first numerical weather forecast ECMWF John von Neumann (1903-1957) Integrating the numerical weather forecast on a digital computer ECMWF Forecast Skill 1980-2010 ECMWF Data Assimilation (DA) Aim: to combine limited observations with our Knowledge of the laws of physics for optimal state estimation PHYSICAL LAWS OBS DATA DA MODEL OPTIMAL STATE ESTIMATE 11 M.J. Rodwell Data Assimilation Cycle: Unbiased Model 12 T Schematic diagram of data assimilation / forecast cycle (perfect model) Observations Analysis Analysis increment Mean Analysis Increment = 0 First guess forecast 0 1 2 Time (cycles) 3 4 Data Assimilation Central tool for determining the initial state for a weather forecast Find the state x given observations yi by minimising 1 J ( x) ( x0 xb )T B 1 ( x0 xb ) 2 T 1 n + H i ( xi ) yi Ri1 H i ( xi ) yi 2 i 0 • • • • Multiple time scales High dimensions Sparsity of data Nonlinearity of climate processes • Model Uncertainty Don’t worry. No hurricane! X X Y Y XZ rX Y Z XY bZ The Butterfly Effect X X Y Y XZ rX Y Ed Lorenz Z XY bZ Idealised model of weather AN 19871016, 06GMT EPS Cont FC +66 h 956 979 - mem no. 1 of 51 +66 h - mem no. 2 of 51 +66 h MLSP 66-hourWEATHER forecasts, VT: FORECAST 16-Oct-1987, 6FOR UTC AN ENSEMBLE TL399OCT EPS with TL95, moist SVs 1987 - mem no. 3 of 51 +66 h - mem no. 4 of 51 +66 h 978 984 - mem no. 11 of 51 +66 h 963 - mem no. 7 of 51 +66 h - mem no. 8 of 51 +66 h - mem no. 9 of 51 +66 h - mem no. 14 of 51 +66 h 962 - mem no. 15 of 51 +66 h - mem no. 16 of 51 +66 h 988 - mem no. 17 of 51 +66 h - mem no. 18 of 51 +66 h - mem no. 21 of 51 +66 h - mem no. 19 of 51 +66 h - mem no. 23 of 51 +66 h - mem no. 20 of 51 +66 h 984 990 965 - mem no. 22 of 51 +66 h 984 969 966 979 964 - mem no. 10 of 51 +66 h 981 981 - mem no. 13 of 51 +66 h - mem no. 6 of 51 +66 h 979 968 - mem no. 12 of 51 +66 h - mem no. 5 of 51 +66 h - mem no. 24 of 51 +66 h - mem no. 25 of 51 +66 h 976 965 - mem no. 26 of 51 +66 h - mem no. 27 of 51 +66 h - mem no. 28 of 51 +66 h 962 - mem no. 29 of 51 +66 h - mem no. 30 of 51 +66 h 979 982 979 970 967 966 961 - mem no. 31 of 51 +66 h - mem no. 32 of 51 +66 h 970 - mem no. 33 of 51 +66 h - mem no. 34 of 51 +66 h 964 975 - mem no. 35 of 51 +66 h - mem no. 36 of 51 +66 h 983 964 980 - mem no. 42 of 51 +66 h - mem no. 43 of 51 +66 h - mem no. 44 of 51 +66 h 986 977 979 981 972 974 980 988 985 - mem no. 38 of 51 +66 h 972 980 - mem no. 41 of 51 +66 h - mem no. 37 of 51 +66 h - mem no. 45 of 51 +66 h - mem no. 46 of 51 +66 h 964 - mem no. 47 of 51 +66 h - mem no. 39 of 51 +66 h - mem no. 40 of 51 +66 h 978 978 988 - mem no. 48 of 51 +66 h - mem no. 49 of 51 +66 h 960 - mem no. 50 of 51 +66 h 980 976 958 987 968 963 989 Probability of hurricane-force gusts on October 16th 1987 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! “How can we trust global climate forecasts 100 years into the future when the same models can’t demonstrate shorterrange forecasts?” X X Y Y XZ rX Y Z XY bZ 50% 50% “climate change” X X Y f Y XZ rX Y 80% Z XY bZ 20% Flat calm! Surface Pressure Potential Vorticity on 315K Rossby wave breaking Policy Relevance How much more frequent will persistent blocking anticyclones be under climate change? The weather forecast problem is fundamentally an initial value problem….. ….. whilst the climate change problem is fundamentally a forced problem 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 Why is there uncertainty in climate predictions? Chaos Future Emissions Model Uncertainty Standard ansatz for “ab initio” weather/climate models Eg u. u g p 2u t X1 X 2 X 3 ... ... X n Increasing scale Eg momentum“transport” by: •Turbulent eddies in boundary layer •Orographic gravity wave drag. •Convective momentum transport Simplified deterministic bulk-formula parametrisations P X n ; 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. “ ..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…. “There are no obvious problems with the high temperature models, Stainforth says…. The uncertainty at the upper end has exploded, says teammember Myles Allen.” Why is it so hard to produce a bias free climate model? “Truth” X X Y Y XZ rX Y Z XY bZ “Model” “Model= Truth + Bias” What is the origin of this bias? Let’s guess that the origin of the bias lies in the model’s equation for X, ie that the model equations are X X Y f Y XZ rX Y Z XY bZ Looks reasonable. “Model” f X X Y 2 In fact f Y XZ rX Y 2 Z XY bZ X X Y f cos Y XZ rX Y f sin Z XY bZ Direction of response Y 0 • 4 • Direction of f X 2 • 3 4 • Leading EOF (ie leading eigenvector of the covariance matrix C of L63) 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? Northern Annular Mode Z500 Difference eto4-er40 (12-3 1990-2005) 2 14 6 12 6 6 10 2 -2 8 2 2 -2 -2 -6 6 4 -2 -6 ECMWF Seasonal Forecast Systematic Error 2 -2 -2 -6 -2 -6 -4 -6 -10 -2 -2 -2 2 -8 -10 -12 2 -14 What is the root cause of this bias? CNTT511-CNTT95 CNTT95-ERA40 Z500 Difference f8o8-f18l (12-2 1962-2005) -2 SPBST95-CNTT95 Z500 Difference eut3-eto4 (12-3 1990-2005) 14 Z500 Difference ezeu-eto4 (12-3 1990-2005) 14 -2 -2 -6 12 10 -2 2 -2 10 8 2 2 2 2 -2 2 -2 8 2 -2 12 6 4 2 -2 6 -6 6 -2 -8 -4 2 -2 2 -4 2 2 -2 4 2 2 6 2 2 6 2 -2 6 -6 2 2 -8 -2 -10 -10 -12 -12 -14 -14 -2 2 Impact of changing P Impact of changing resolution Impact of adding stochastic noise 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?? 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. 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 M.J. Rodwell Data Assimilation Cycle: Unbiased Model 43 T Schematic diagram of data assimilation / forecast cycle (perfect model) Observations Analysis Analysis increment Mean Analysis Increment = 0 First guess forecast 0 1 2 Time (cycles) 3 4 M.J. Rodwell Data Assimilation Cycle: Biased Model 44 T Schematic diagram of data assimilation / forecast cycle (imperfect model) Observations Analysis Analysis increment Mean Analysis Increment ≠ 0 First guess forecast 0 1 2 Time (cycles) 3 4 −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) 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). Slide 45 ECMWF Mean Temperature Analysis Increment Tendencies Rodwell and Palmer, Quarterly Journal of the CLOUD T bias at D+5. Tendencies at step 2 Royal Meteorological Society, 2006 CONTROL T bias at D+5. Tendencies at step 2 CONTROL T bias at D+5. Tendencies at step 2 12 12 36 36 96 96 12 36 96 CONTROL 884 979 1012 -9 -6 -3 0 3 Kday-1 (K for bias) 6 9 ENTRAIN/5 T bias at D+5. Tendencies at step 2 Pressure (approx) 539 728 Vertical Diffusion Cumulus Convection Large Scale Precipitation 539 353 Total D+5 Bias 728 Cloud Frac 884 539 979 1012 -9 728 12 36 96 ENTRAIN/5 202 -6 -3 0 3 Kday-1 (K for bias) 6 9 ENTRAINx3 T bias at D+5. Tendencies at step 2 ENTRAIN/5 and ENTRAINx3 are out of balance ENTRAINx3 202 884 Pressure (approx) Pressure (approx) Radiative 202 353 353 12 36 96 CLOUD Dynamic 202 Pressure (approx) Pressure (approx) 202 353 353 979 1012 539 539 -6 728 728 884 884 979 1012 979 1012 -9 -6 -3 0 3 Kday-1 (K for bias) 6 9 -9 -3 -6 -3 0 3 Kday-1 (K for bias) 0 Kday-1 (K for bias) 6 Slide 46 9 3 ECMWF 6 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? 1950 2000 Weather models Climate models No IPCC AR4 model had data assimilation capability…climate change is not primarily an initial value problem Towards Comprehensive Earth System Models 1975 1970 1985 1992 1997 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 Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Sulphate aerosol Non-sulphate aerosol Carbon cycle Carbon cycle Huge Demands on Human resources Ocean & sea-ice Off-line model model development Strengthening colours denote improvements in models Sulphur cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Atmospheric chemistry Non-sulphate aerosols Carbon cycle model Atmospheric chemistry The Met.Office Hadley Centre Ocean & sea-ice 2000 Many Demands on Computing Power and on Human Resources Decadal, Centennial, Paleo Integrations Data Assimilation Estimating uncertainty (ensembles) Chemistry, Aerosols, Carbon Cycles… Vertical structure (inc. Stratosphere, Mesosphere) Horizontal resolution (eg Rossby Wave Breaking) Could climate scientists do more to reduce uncertainty in climate predictions? • Yes. I have given one example of how this could be achieved in principle. However, the weather and climate forecast communities are still not well integrated, and hence not making enough one another’s expertise • Moreover, many institutes around the world view their climate models as matters of institutional (sometimes national) pride. The downside is that these same institutes individually have neither the human nor the computational resources needed to make significant inroads into the problem of reducing climate uncertainty – eg when such resources are needed merely to try to replicate developments made elsewhere. A community-wide approach to climate model development? Could governments/ the private sector do more to reduce uncertainty in climate predictions? • Current and planned high-performance computing devoted to climate (especially in Europe) is wholly inadequate to reduce substantially the uncertainties in climate change in the coming decade. • Governments should work together to provide dedicated leadership-scale computing facilities for use by the climate community. Conclusions • Discussions on global emissions cuts appear to be reaching stalemate. • I believe we need to focus on reducing uncertainty in climate predictions if we are to move policy forward. This is not straightforward scientifically. • Over the coming years we need a renewed effort by scientists and by governments: – By scientists to do all they can to focus on the problem of reducing uncertainty – mathematical multidisciplinary programmes like that at INI definitely help!! – By governments to provide collectively, the technology to enable new innovative methods to be implemented