Philosophy, Development, Application, and Communication of Future Climate Scenarios for the Pileus Project

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1 Philosophy, Development, Application, and Communication of Future Climate Scenarios for the Pileus Project Symposium on Climate Change in the Great Lakes Region Julie Winkler Michigan State University March 16, 2007

2 Climate Science for Decision Makers in the Great Lakes Region The Pileus Project is named after the Pileus cloud which appears as a cap or a hood above or attached to the top of a cumulonimbus cloud. The name was chosen to symbolize the The Pileus Project is named after the Pileus cloud which appears as a cap or a hood above or attached to the top of a cumulonimbus cloud. The name was chosen to symbolize the "overarching" or "capstone nature of the project. "overarching" or "capstone nature of the project.

3 Pileus Project Goals Identify, with stakeholder assistance, the influence of climate on the Michigan agriculture and tourism industries Create empirical and physicallybased models to quantify the impacts of past and projected climate variability and change on agriculture and tourism in Michigan. Develop decision support tools for weather and climate related risk management Golf TOURISM Skiing Climate Traffic AGRI CULTURE Grain Quality Tart Cherry Build strong stakeholder researcher partnerships

4 End to End Assessment Approach Climate Observations or Scenarios Industry Ecological or Activity Model Economic Model(s) Risk Management Decision Making Tools, Policy Framework

5 What is a Scenario? A scenario is not a forecast or a prediction. A scenario must be a plausible future state (Carter et al. 1996). A scenario is a tool, and the traditional starting point for a climate impact assessment.

6 Climate Scenario Requirements Local scale Daily temporal resolution Multiple locations in Michigan and the surrounding area Basic climatological variables (maximum temperature, minimum temperature, wet/dry days, and liquid equivalent precipitation) Temperature and precipitation need to be expressed in terms of industry relevant parameters (e.g., thresholds, spells, snowfall). Wider applicability beyond agriculture and tourism GCM based Scenarios for desirable with a particular focus on three 20 year time slices. Early Century ( ) Mid Century ( ) Late Century ( ) Ensemble of scenarios is required to estimate uncertainty

7 Several sources of uncertainty Many sources of uncertainty need to be considered Source: IPCC, 2001

8 Ensemble Approach Multiple scenarios are used to estimate the quantifiable range of uncertainty. Source: IPCC, 2001

9 Climate Scenarios For regional analyses, GCM coarse scale output must be downscaled. Dynamic downscaling, using a nested regional climate model Statistical/empirical downscaling We used an empirical downscaling methodology. Predictor variables are free atmosphere (circulation) variables.

10 Preprocessing Development Validation

11 Hadley A2, B2 NCAR ECHAM Canadian multiple downscaling methodologies >60 scenarios for each climate variable per location 15 Locations 3 climate parameters Tmax Tmin precipitation 4 GCMs CCC CGCM2 HadCM3 MPI ECHAM4 NCAR CSM1.2 2 Emission scenarios A2, B2 Multiple downscaling methodologies

12 Hadley A2, B2 NCAR ECHAM Canadian multiple downscaling methodologies >60 scenarios for each climate variable per location daily temperature and precipitation scenarios for

13 Future Scenarios Tool Organized around the agriculture and tourism sectors Designed to communicate uncertainty All scenarios shown as a projected change in the median value or frequency of a climate parameter for 20 year periods Five display options Includes audio visual learning modules and example user cases

14 Reference Climate Display Defined as Provides an indication of the year to year variability.

15 Early vs Mid Century and Mid Century vs Late Century Displays Display histograms of the projected changes in the median value (or frequency) of a climate parameter for two 20 year time slices. The spread of histogram is an indication of the quantifiable uncertainty range for the two time periods.

16 A2 vs B2 Displays Allows users to compare the scenarios developed using the A2 estimates of greenhouse gas emissions versus those developed using the B2 estimates. Option of displaying the A2 vs B2 scenarios for the early, mid, or late century periods.

17 Trend Displays the projected change in a climate parameter for 20 year overlapping periods beginning from and continuing through Uncertainty is shown by the maximum and minimum projected change across the entire scenario ensemble along with the 25th and 75th percentiles.

18 Designed audiovisual learning modules to convey key concepts to users including How to read the graphs, what is a histogram, etc. Definitions of statistical terms (e.g., what is a median) How to interpret uncertainty Learning Modules

19 Projected Temperature Changes Some examples for Eau Claire, Michigan

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25 Wet/Dry Day Scenarios

26 Projected change in the number of wet days in a 30 year period Projection of 30 year Overlapping Period, CGCM2, A2a Projection of 30 year Overlapping Period, HadCM3, A2a Projection of 30 year Overlapping Period, ECHAM4, A2a PN PS ZN ZS PN PS ZN ZS Projection of 30 year Overlapping Period, NCAR, A2a PN PS ZN ZS PN PS ZN ZS

27 SUMMER Projection of 30 year Overlapping Period, CGCM2, A2a, Sum m er Projection of 30 year Overlapping Period, HadCM3, A2a, Sum mer PN PS ZN ZS PN PS ZN ZS Projection of 30 year Overlapping Period, ECHAM4, A2a, Summ er Projection of 30 year Overlapping Period, NCAR, A2a, Summer PN PS ZN ZS PN PS ZN ZS

28 SPRING Projection of 30 year Overlapping Period, CGCM2, A2a, Spring Projection of 30 year Overlapping Period, HadCM3, A2a, Spring PN PS ZN ZS PN PS ZN ZS Projection of 30 year Overlapping Period, ECHAM4, A2a, Spring Projection of 30 year Overlapping Period, NCAR, A2a, Spring PN PS ZN ZS PN PS ZN ZS

29 Overview of Projected Changes Temperature scenarios for Michigan display consistent warming. Scenarios suggest that the number of wet days will decrease, particularly in the second half of the 21 st century. We anticipate that considerable uncertainty surrounding the scenarios of liquidequivalent precipitation.

30 Summary The Pileus Project is attempting to bring together stakeholders and researchers from different disciplines to Understand how current climate variability impacts Michigan s agriculture and tourism industries Evaluate how potential climate change may influence agriculture and tourism Provide user tools for stakeholders to better consider climate variability and change in their planning process Develop improved climate assessment methods Future work includes incorporating the international dimensions of an industry into the assessment process

31 Web Site The project web site is located at pileus.msu.edu Some of the tools are password protected: User name is weather Password is pileus Comments and suggestions are appreciated! Contact information: Julie Winkler Jeanne Bisanz

32 Acknowledgements We thank the many stakeholders who have contributed to the Pileus Project. The project is funded by the U.S. Environmental Protection Agency, project number R

33 Pileus Team Co Directors: Jeff Andresen and Julie Winkler Principal Investigators and Collaborators: Jeffrey Andresen, J. Roy Black, Donald Holecek, Sarah Nicholls, Peter Sousounis, Pang Ning Tan, Julie Winkler Administration and Web Development: Jeanne Bisanz and Lori Langone Post docs, Graduate Students and Undergraduate students: Tracy Beedy, Johnathan Clark, Galina Guentchev, Hai Kyung Min, Jeonghee Noh, Krerk Piromsopa, Haryono Prawiranata, Charles Shih, Ryan Torre, Jenni van Ravensway, and Costanza Zavalloni

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