1. Uncertainty and Climate Change Dealing with uncertainty in climate change impacts Daniel J. Vimont Atmospheric and Oceanic Sciences Department Center for Climatic Research University of Wisconsin - Madison “Preparing for Climate Change” Workshop Madison, WI, June 19, 2007

2. Uncertainty and Climate Change

4. Sources 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

5. Sources of Uncertainty Greenhouse Gas Concentrations What 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

6. Sources of Uncertainty Radiative Forcing How do different gasses affect the amount of radiation that Earth receives? IPCC AR4, WG I

7. Sources of Uncertainty Climate 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

8. Sources 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

9. Sources 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)

10. Regional Uncertainty IPCC AR4, WG I

11. Regional Uncertainty IPCC AR4, WG I

12. Using 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

13. Using 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

14. Risk 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 Consequence Probability x Consequence = Risk Adaptation Policies Mitigation Policies

15. Risk Assessment Jones, 2004 Determining Probability of Exceedence Probability distribution of future climate state determined by random sampling of future climate projections. Cumulative distribution determines probability of threshold exceedence.

16. Non-standard variables Reducing Model Bias Models 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.

17. Non-standard variables

18. Summary 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 options Scenarios 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 variables Models are appropriate for large-scale impacts, but not always for regional impacts. Combinations of models and observations can reduce regional bias, reducing “bad” uncertainty