Transcript Presentation

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
What’s the Purpose of Climate Change Research?
1. 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.
2. 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?
3. 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.
What sort of adaptation decisions?
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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]
Sources of Information
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Observations
Tell us what has happened.
Don’t tell us what the climate is.
Don’t tell us what it will be.
Models.
Complex climate models.
What is climate?
What is climate under climate change?
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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.
Interpretation of Models: The Stern Report
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“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.”
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.”
The Move to Probabilities
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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?]
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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.
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
Some Issues
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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.
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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?
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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.
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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.
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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.
And exploring uncertainty is important
Along with continually questioning the relevance of said uncertainty
exploration
Sources of Uncertainty In Climate Forecasts
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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:
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
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.
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, rapidly
mixing, scales?
• Response: Better Observations / Directed
Observations
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Ocean temperature and salinity structure.
Sutton and Hodson, Science, 2005
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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.
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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
• Can we constrain or
weight ensembles?
Stainforth et al.2006
Exploring Uncertainty: The Climateprediction.net Experiment
Initial Condition
Ensemble
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Forcing Ensemble
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Overall Grand
Ensemble
Standard model
set-up
Perturbed Physics
Ensemble
10000s
10s
Statistics
• > 300,000 participants.
• > 24M years simulated.
• > 110,000 completed simulations.
(Each 45years of model time)
10s
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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.
ClimatePrediction.net : What it looks like.
Teams.
P2P?
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
Distributions of Regional, Seasonal Precipitation
Mediterranean Basin
Northern Europe
Winter
Winter
Summer
Summer
Annual
Annual
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.
Consistency
Relationships With Global Temperature
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?