Climate Science for Decision Support

Dave Stainforth

SoGAER,

Exeter University

; Centre for the Analysis of Timeseries,

London School of Economics

.

Tyndall Centre for Climate Research Oxford University Centre for the Environment

SIAM Minisymposium From Global Predictions to Local Action January 2008

Communication / Knowledge Transfer Is Tricky

Increasing GHGs / Increasing Temperatures and Sea Level

Source: IPCC Fourth Assessment Report

Climate Forecasts: A problem of extrapolation

We have no hope of confirming a climate forecasting system the way we might other systems; including weather forecasting.

- The lifetime of models is short.

- Predictions are for states of system for which we have no observations.

- Observations are in-sample.

- Confirmation of weather forecasting systems using related models can not inform us about the long timescale processes in question; or their interaction with short timescale processes.

[Q: It is not sufficient but is it a necessary condition for informative climate forecasts?] - We will only have one verification point (no good for probabilistic verification) and that will come too late to be of practical value.

Uncertainty analysis is therefore critical.

Climate: The Distribution of Weather

• Climate change is a change in this distribution. • Most if not all decision support is sensitive to more than the mean of that distribution.

• Climate prediction is the attempt to predict this changing distribution.

Uncertainty analysis is therefore critical.

What’s the Purpose of Climate Modelling?

1. Pursuit of knowledge / Process Understanding 2. Answering the questions: Is anthropogenic climate change a significant global problem?

3. Guiding (policy) decisions by governments, industry, societies and individuals.

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 or the New Orleans flood protection?

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]

Interpretation of Models: An Economist’s View

• • “Using climate models that follow basic physical laws, scientists can now assess the

likely

the atmosphere.” range of warming for a given level of greenhouse gases in “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.” Source: The STERN report.

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.” Source: IPCC Fourth Assessment Report

Interpretation of Models: UK Climate Impacts Programme

• 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

Uncertainty Analysis – Probabilistic but not Probabilities

• • • Multi-model analyses – Model Intercomparison Projects.

Perturbed physics ensembles.

Grand ensembles – perturbed physics / initial conditions / forcing. (climate

prediction

.net) • • Computing power is a significant issue.

But how should we design these experiments?

Sources of Uncertainty In Climate Forecasts

• Forcing Uncertainty.

• Microscopic Initial Condition Uncertainty.

• Macroscopic Initial Condition Uncertainty.

• Model Inadequacy. • Model Uncertainty.

Sources of Uncertainty

and How to Include Them In a Climate Forecast • Forcing uncertainty solar radiation etc.

Solution: : Changes due to factors external to the climate system e.g. greenhouse gas emissions (natural and anthropogenic), Scenarios for possible futures .

Source: IPCC Fourth Assessment Report Source: IPCC, Third Assessment

Sources of Uncertainty

and How to Include Them In a Climate Forecast • Microscopic Initial Condition Uncertainty How 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.

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.

Sources of Uncertainty

and How to Include Them In a Climate Forecast • Macroscopic Initial Condition Uncertainty How is the prediction is affected by our imprecise knowledge of the current state of the system on relatively large, slowly mixing, scales?

• Response: Better Observations / Directed Observations • Ocean temperature and salinity structure.

Sutton and Hodson, Science, 2005 • State of the quasi-biennial oscillation.

Sources of Uncertainty

and How to Include Them In a Climate Forecast • Model Inadequacy All 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.

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 Stainforth et al.2006

Exploring Uncertainty: The Climate

prediction

.net Experiment

• Perturbed Physics Ensemble Initial Condition Ensemble Forcing Ensemble • • • 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.

10000s 10s 10s • • • Statistics > 300,000 participants.

> 24M years simulated.

> 110,000 completed simulations.

(Each 45years of model time)

Climate

Prediction

.net : What it looks like.

Teams.

P2P?

climate

prediction

.net

Screensavers

First Results:

Grand Ensemble Frequency Distribution of Climate Sensitivity

From Stainforth et al. 2005 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))

Winter

Distributions of Regional, Seasonal Precipitation

Mediterranean Basin Northern Europe Winter Summer Annual Summer Annual

From Stainforth et al. 2006

• • • • Some Issues in the Interpretation of Perturbed-Physics and Multi-Model Ensembles 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.

Nevertheless we can be confident about some aspects of the future.

• • • • • Global mean annual mean temperature will continue to rise.

Annual mean temperatures will probably rise almost 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.

Society will therefore be less well adapted to the climate of the future than it is to the climate of today.

Therefore we do need to mitigate.

And we do need to adapt.

• We have information which may be useful in adaptation decisions. Identifying it is a significant challenge.

Combined Distributions of Regional, Seasonal Temperature and Precipitation

Sample The Range of GCM responses to Inform Decisions

6, 7. Downscale and evaluate impact.

8. Distribution of climate sensitive factor.

4. Extract range of GCM variables.

Temperature 5. Sample uniformly Climate sensitive decision factor e.g. agricultural production (tonnes/yr)

How Do We Go Further?

• • • • • • • How do we explore the boundaries of the types and extremes of behaviour consistent with current models?

How do we develop models and experiments to provide better info in the future?

How do we design experiments to: – Guide model development.

– Inform process understanding.

– Guide climate change influenced decisions.

How do we balance exploration of model uncertainty with exploration of initial value uncertainty?

How should we explore parameter space?

P2 High What is the role of emulators?

Stnd Does the distribution of model behaviour in parameter space have any relevance to probabilities of real world behaviour?

Low Low Stnd High P1

How Do We Go Further(2)?

• • • • How (when?) should we link impact / economic/ user system models with climate model ensembles?

How can we propagate uncertainty between different components of the climate system?

How often do we want new models? How much should be in them? When is better to concentrate on specific aspects to understand them better before constructing big models. Maybe waiting for cpu to catch up with what believe is necessary.

Can we constrain / weight models?

The End