Decision Relevant Information for Climate Change Planning?

1. Decision Relevant Information for Climate Change Planning? Dave Stainforth, SoGAER, Exeter University; Centre for the Analysis of Timeseries, London School of Economics. ESF Workshop Econometric Time-Series Analysis Applied to Climate Research September 2007

2. What’s the Purpose of Climate Change Research? • Pursuit of knowledge.Understanding what triggers ENSO is like understanding how galaxies form, the search for the Higgs boson, the authorship of La Chanson de Roland. • Answering the questions:Is anthropogenic climate change a significant global problem?Is it a key task for human society to reduce anthropogenic greenhouse gas emissions to zero? • Guiding (policy) decisions by governments, industry, societies and individuals.Mostly adaptation decisions though there are some questions relevant to mitigation: When must anthropogenic GHG emissions be zero?Will it be enough?Must we invest in carbon capture and storage?What will the world be like here in 20/50 years time? – a key driver of public support for action.

3. What sort of adaptation decisions? • Here are some possibilities. We need more/better in order to direct climate science well. • How will climate change affect life expectancy? (By region, social class etc.?) [Insurance industry] • How should we design the next Thames barrier?How much will sea level rise in SE England? How will the characteristics of storm surges change? What are they now?[London/UK Government] • What are the relative risks to my pipeline from permafrost destabilisation for various designs and routes?[Oil/gas industries] • Given that climate change is likely to have a significant impact on agriculture, how do we optimise the investment in a developing country’s transport infrastructure for long term economic value and to minimise the impact of disasters?Which bridges/roads are/will be most susceptible to flood damage? How will climate change affect migration?[Aid Agencies, UN, DFID, USAID etc.] • What sort of electricity cables should be lain under the streets of London?[EdF, Energy Companies, Energy Regulators, Government]

4. Sources of Information • ObservationsTell us what has happened.Don’t tell us what the climate is.Don’t tell us what it will be. • Models.Complex climate models.

5. What is climate?What is climate under climate change? • Under constant boundary conditions weather is chaotic and climate may be taken as the distribution of states on some attractor of weather.Climate is the distribution of weather variables. • Under changing boundary condition the behaviour is not chaotic but pandemonium [Spiegel, 1987]. Climate has changed from the initial attractor but the distribution itself is in a transient state which may eventually stabilize towards some other attractor when boundary conditions are again constant.Climate is still the distribution of possible weather but it can not be evaluated in the real world.It can be defined for a model but its description requires very large initial condition ensembles; something we don’t currently have.

6. Interpretation of Models: The Stern Report • “Using climate models that follow basic physical laws, scientists can now assess the likely range of warming for a given level of greenhouse gases in the atmosphere.” • “It is currently impossible to pinpoint the exact change in temperature that will be associated with a level of greenhouse gases. Nevertheless, increasingly sophisticated climate models are able to capture some of the chaotic nature of the climate, allowing scientists to develop a greater understanding of the many complex interactions within the system and estimate how changing greenhouse gas levels will affect the climate. Climate models use the laws of nature to simulate the radiative balance and flows of energy and materials. These models are vastly different from those generally used in economic analyses, which rely predominantly on curve fitting. Climate models cover multiple dimensions, from temperature at different heights in the atmosphere, to wind speeds and snow cover. Also, climate models are tested for their ability to reproduce past climate variations across several dimensions, and to simulate aspects of present climate that they have not been specifically tuned to fit.”

7. Interpretation of Models: IPCC “There is considerable confidence that climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above. This confidence comes from the foundation of the models in accepted physical principles and from their ability to reproduce observed features of current climate and past climate changes. Confidence in model estimates is higher for some climate variables (e.g., temperature) than for others (e.g., precipitation). Over several decades of development, models have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases.”

8. The Move to Probabilities • Impact studies often use results from one or two GCMs as indicative of future possibilities.How should these be used in decision making? • In climate science there has been a shift towards the production of probability density functions:[How should these be used in decision making?] • Statistical representation of the belief that i) if more models make a given prediction then that result is more likely in the real world, and ii) models which better represent a given variable (at some scale) in the present are more reliable predictors of the climate change response in that variable. Tebaldi et al. (related: Frame et al., Forest et al.) • Scale the model predicted response according to consistency with observed patterns of change over the region of interest. (Stott et al. 2006) • Sparsely explore parameter space, use an emulator to fill in parameter space, weight by consistency with observations, assume the distribution of model behaviour tells us something about the probability distribution of real world behaviour. Murphy et al. 2004 and 2007. • Transfer functions. [Piani etal.,Knutti et al. Stainforth et al.] • Don’t give pdfs for future climate.

9. Guiding Societal Decisions • UKCIP (the United Kingdom Climate Impacts Programme) is contracted to provide to UK industry a new set of scenarios in 2008. “The UKCIPnext climate change scenarios will be presented … as probability distributions.” They will be available for 25km grid boxes. “Model outputs will include changes in temperature, precipitation, snowfall, wind speed, humidity, cloud cover, solar radiation, air pressure and soil moisture content.” Source : UKCIPnext Consultation

10. Some Issues • Climate models are not independent. • Climate models share methods, parameterizations, code. They are all constrained by the same limits on resolution and computational structures. • Exploration of parameter space is at the very least dependent on the definition of parameters. • We have no empirically adequate models. • Objective weighting by observations would rule out all models. • Given that all models are unrealistic and we are trying to extrapolate into the future, how do we know which are the most useful for predicting future changes? • We have no hope of verification. • Present day climate models will be long size abandoned by the time we have the time we have observations of the climate of 2030/2050/2100. • Even then we will have only one observational point. Not very useful in defining the distribution which is climate. • Making over-confident claims today risks undermining climate science just at the point when it is on the verge of providing valuable information.

11. Nevertheless we can be confident about some aspects of the future. • Global mean temperature will continue to rise. • Annual mean temperatures will probably rise everywhere. • Sea levels will continue to rise. • The climate in 40 years time will be very different from the climate now or 40 years ago.Therefore we need to mitigate.And we will need to adapt.

12. And exploring uncertainty is important Along with continually questioning the relevance of said uncertainty exploration

13. Sources of Uncertainty In Climate Forecasts • Forcing Uncertainty. • Microscopic Initial Condition Uncertainty. • Macroscopic Initial Condition Uncertainty. • Model Inadequacy. • Model Uncertainty.

14. Sources of Uncertainty and How to Include Them In a Climate Forecast • Forcing uncertainty:Changes due to factors external to the climate system e.g. greenhouse gas emissions (natural and anthropogenic), solar radiation etc.Solution: Scenarios for possible futures. Source: IPCC, Third Assessment

15. Sources of Uncertainty and How to Include Them In a Climate Forecast • Microscopic Initial Condition UncertaintyHow is the prediction is affected by our imprecise knowledge of the current state of the system at small, rapidly mixing, scales?Response: Initial Condition Ensembles Source: IPCC, Third Assessment Source: Large (50 member) IC ensemble from climateprediction.net.

16. Sources of Uncertainty and How to Include Them In a Climate Forecast • Macroscopic Initial Condition UncertaintyHow is the prediction is affected by our imprecise knowledge of the current state of the system on relatively large, rapidly mixing, scales? • Response: Better Observations / Directed Observations • Ocean temperature and salinity structure.Sutton and Hodson, Science, 2005 • State of the quasi-biennial oscillation.

17. Sources of Uncertainty and How to Include Them In a Climate Forecast • Model InadequacyAll models are unrealistic representations of many relevant aspects of the real world system. • Response: A context for all climate forecasts. • Processes known to be important are absent.e.g. ice sheet dynamics, atmospheric and oceanic chemistry, stratosphere circulation. • Parameterized processes are unlikely to capture small scale feedbacks. • Inadequate simulation of some processes which should result from the fundamental processes included.e.g. hurricanes, diurnal cycle of tropical precipitation.

18. Sources of Uncertainty and How to Include Them In a Climate Forecast • Model uncertainty:Climatic processes can be represented in models in different ways e.g. different parameter values, different parameterization schemes, different resolutions.What are the most useful parameter values and model versions to study within the available model class? What is the range of possibilities?Solution: Perturbed-Physics Ensembles • Can we constrain or weight ensembles? Stainforth et al.2006

19. Perturbed Physics Ensemble Initial Condition Ensemble Forcing Ensemble Overall Grand Ensemble Standard model set-up 10000s 10s 10s Exploring Uncertainty: The Climateprediction.net Experiment • To quantify uncertainty we need 100s of thousands of simulations. • Impossible with super computers but possible with distributed computing. • At www.climateprediction.net people can download the model to their PC. • Using a GCM means we can get regional detail as well as global averages. • Statistics • > 300,000 participants. • > 24M years simulated. • > 110,000 completed simulations.(Each 45years of model time)

20. ClimatePrediction.net : What it looks like. Teams. P2P?

21. First Results: Grand Ensemble Frequency Distribution of Climate Sensitivity In 2001 the IPCC concluded that the climate sensitivity was likely to be between 1.5 and 4.5°C Since then many studies have shown the possibility of high sensitivities (>6 °C)(e.g. Andronova et al. (2001), Forest et al. (2002), Knutti et al. (2002), Murphy et al. (2004)) From Stainforth et al. 2005

22. Distributions of Regional, Seasonal Precipitation Mediterranean Basin Northern Europe Winter Winter Summer Summer Annual Annual

23. Can We Go Further than a Range of Possibilities? • Possibly. • Relationships between forecast variables of interest and observable or large scale constrainable variables provide the “transfer function” method. • Observational constraints is another field under investigation.

24. Consistency

25. Relationships With Global Temperature

26. Some Questions / Issues • How do we optimise the exploration of uncertainty?For informing society?For improving understanding?For building better models? • How do we balance increased resolution, increased simulation length and increased exploration of uncertainty? • How do we extract decision relevant information?