Uncertainty and Climate ChangeUncertainty and Climate Change

Dealing with uncertainty in climate change impactsDealing with uncertainty in climate change impacts

Daniel J. VimontAtmospheric and Oceanic Sciences Department

Center for Climatic ResearchUniversity of Wisconsin - Madison

“Preparing for Climate Change” WorkshopMadison, WI, June 19, 2007

Uncertainty and Climate ChangeUncertainty and Climate Change

Uncertainty and Climate ChangeUncertainty and Climate Change

Sources of UncertaintySources of Uncertainty

Emissions Scenarios:How will our society evolve?What sort of technology will we develop?What mitigation strategies will we employ?

IPCC AR4, WG I

Sources of UncertaintySources of UncertaintyGreenhouse Gas ConcentrationsWhat are the life-spans of different gasses in Earth’s Atmosphere?How do anthropogenic, terrestrial and aquatic sources / sinks alter future GHG concentrations?

IPCC AR4, WG I

Sources of UncertaintySources of UncertaintyRadiative ForcingHow do different gasses affect the amount of radiation that Earth receives?

IPCC AR4, WG I

Sources of UncertaintySources of UncertaintyClimate Response:What climatic processes alter the way that Earth responds to changing GHG’s (good uncertainty)?What is the range of natural variability (good)?Can we account for model bias (bad uncertainty)?

IPCC AR4, WG I

Sources of UncertaintySources of Uncertainty

Emissions Scenarios:Different emissions scenarios imply different amounts of global warming. The range of scenarios provides an estimate of uncertainty (good uncertainty)

IPCC AR4, WG I

Sources of UncertaintySources of Uncertainty

Climate Response:Each model has its own assumptions, which leads to a slightly different amount of global warming. Multi-model ensembles provide an estimate of uncertainty due to limited physical understanding (good uncertainty)

Regional UncertaintyRegional Uncertainty

IPCC AR4, WG I

Regional UncertaintyRegional Uncertainty

IPCC AR4, WG I

Using Uncertainty: Top-downUsing Uncertainty: Top-down

Scenarios:

Useful when:• Impact is unknown or broad scale• Policy does not exist (can explore

different policy options)• Surprises are expected• Simple to implement

Disadvantages:• Computationally expensive• Poorer characterization of

uncertainty• Poor sampling of “climate space”

and “impact space”

Impact

Present Climate

Predicted Climate

Top-down impact assessment

Using Uncertainty: Bottom-upUsing Uncertainty: Bottom-up

Risk Assessment:

Advantages:• Useful when impacts are well

known• Uncertainty is well quantified• Adaption strategies are easily

explored

Disadvantages:• Projected climate variables may

not be relevant• Difficult to deal with “surprises”• Sometimes inflexible

Impact

Present Climate

Predicted Climate

Bottom-up impact assessment

Threshold Impact Region

Risk AssessmentRisk Assessment

Med Risk

Low Probability, High Consequence

High Risk

High Probability, High Consequence

Low Risk

Low Probability, Low Consequence

Med Risk

High Probability, Low Consequence

Probability

Con

sequ

ence

Probability x Consequence = Risk

AdaptationPolicies

MitigationPolicies

Risk AssessmentRisk Assessment

Jones, 2004

Determining Probability of ExceedenceProbability distribution of future climate state determined by random sampling of future climate projections. Cumulative distribution determines probability of threshold exceedence.

Non-standard variablesNon-standard variables

Reducing Model BiasModels have serious bias with certain non-standard variables (e.g. daily snow or rain). Combinations of models and observations can reduce bias and allow examination of non-standard variables.

Non-standard variablesNon-standard variables

SummarySummary

Uncertainty is unavoidable.Uncertainty arises through emissions scenarios, estimates of GHG concentration, radiative forcing, climate response, and impact sensitivity. This is unavoidable.

Uncertainty can be used to explore policy or adaption optionsScenarios are useful when impacts are not known, or policy does not exist. Risk Assessment is useful when policy does exist, or when thresholds are well defined (in terms of climates).

Debiasing techniques are useful for non-standard variablesModels are appropriate for large-scale impacts, but not always for regional impacts. Combinations of models and observations can reduce regional bias, reducing “bad” uncertainty