CSCR Opening Plenary w/Art DeGaetano: Regional Climate Impacts on Ecosystems, Agriculture, and Human Communities.
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Climate Smart & Climate Ready Conference Opening Plenary on April 20, 2013 at Cinempolis in Ithaca, NY. Art Degaetano, Dept. Earth and Atmospheric Sciences, Cornell University. Global Climate Change in Our Backyard: Vulnerabilities, Risks, and Opportunities.
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CSCR Opening Plenary w/Art DeGaetano: Regional Climate Impacts on Ecosystems, Agriculture, and Human Communities.
- 1. Earth and Atmospheric Science Global Climate Change in our Backyard Vulnerabilities, Risks and Opportunities Art DeGaetano Professor and Associate Chair Dept of Earth and Atmospheric Science Cornell University Director, NOAA Northeast Regional Climate Center
- 2. Earth and Atmospheric Science 1.1 0.7 0.4 -0.7 -0.4 0.0 F
- 3. Earth and Atmospheric Science 1995-2004 vs. 1940-1980 -3.6 -1.8-2.7 -0.9 3.62.71.80.90.0 F
- 4. Earth and Atmospheric Science 43.5 44.5 45.5 46.5 47.5 48.5 49.5 1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 AnnualAverageTemperature(°F) Year Northeast U.S. Average Annual Temperature
- 5. Earth and Atmospheric Science +0.24 °F/decade +0.12 °F/decade+0.11 °F/decade +0.14 °F/decade WINTER SPRING FALLSUMMER
- 6. Earth and Atmospheric Science 0 10 20 30 40 50 60 70 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 DaysGreaterorEqualto90F Decade 0 5 10 15 20 25 30 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 DaysGreaterorEqualto90F Decade 0 5 10 15 20 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 DaysGreaterorEqualto90F Decade 0 1 2 3 4 5 DaysGreaterorEqualto90F Decade Days ≥ 90°F Washington DC New York Boston Montreal
- 7. Earth and Atmospheric Science Days ≥ 1 inch snow on ground 0 5 10 15 20 25 1890190019101920193019401950196019701980199020002010 Washington DC 0 5 10 15 20 25 30 35 40 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 New York 0 10 20 30 40 50 60 70 80 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Boston
- 8. Earth and Atmospheric Science 0.106 0.110 0.102 0.098 Precipitation(in/day)
- 9. Earth and Atmospheric Science 30.00 35.00 40.00 45.00 50.00 55.00 1895 1899 1903 1907 1911 1915 1919 1923 1927 1931 1935 1939 1943 1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 AnnualPrecipitation(inches) Year Northeast U.S. Average Annual Precipitation
- 10. Earth and Atmospheric Science Observed Trends in 1-day Very Heavy Precipitation (1958 to 2010) NOAA/NCDC
- 11. Earth and Atmospheric Science
- 12. Earth and Atmospheric Science Sea level rise
- 13. Earth and Atmospheric Science So What Does the FUTURE Hold ?
- 14. Earth and Atmospheric Science Climate Model Ensembles TEMPERATURE PRECIPITATION
- 15. Earth and Atmospheric Science GHG Scenarios
- 16. Earth and Atmospheric Science ClimAID Synthesis Report ClimAID Full Report
- 17. Earth and Atmospheric Science
- 18. Earth and Atmospheric Science
- 19. Earth and Atmospheric Science Climate Model Ensembles 67%
- 20. Earth and Atmospheric Science Climate Model Ensembles Adirondacks AnnualPrecipitation(inches)
- 21. Earth and Atmospheric Science
- 22. Earth and Atmospheric Science
- 23. Earth and Atmospheric Science Seasonality
- 24. Earth and Atmospheric Science
- 25. Earth and Atmospheric Science
- 26. Earth and Atmospheric Science
- 27. Earth and Atmospheric Science
- 28. Earth and Atmospheric Science
- 29. Earth and Atmospheric Science Average Snow Depth
- 30. Earth and Atmospheric Science Sea level rise • Directly from GCMs No downscaling • Thermal expansion • Glacier melt • Ice cap/ice sheet melt • Local land sinking/subsidence • Local water surface elevation • Rapid ice melt (included as option) •Local modeling for Hudson to Troy
- 31. Earth and Atmospheric Science NYC Troy New York City Baseline (1971-2000) 2020s 2050s 2080s Sea level rise (central range) NA + 2 to 5 in + 7 to 12 in + 12 to 23 in Rapid Ice-Melt Sea level rise NA 5 to 10 in 19 to 29 in 41 to 55 in Troy Baseline (1971-2000) 2020s 2050s 2080s Sea level rise (Central range) NA + 1 to 4 in + 5 to 9 in + 8 to 18 in Rapid Ice-Melt Sea level rise NA 4 to 9 in 17 to 26 in 37 to 50 in Sea level rise
- 32. Earth and Atmospheric Science
- 33. Earth and Atmospheric Science
- 34. Earth and Atmospheric Science
- 35. Earth and Atmospheric Science Questions?
Editor's Notes
- This method is one of the most straightforward and popular procedures for climate risk assessment, provided that the prerequisite climate model outputs are available. Change factors are typically calculated for calendar months by comparing the present and projected climatology in GCM for grid boxes overlying the target region.Change factors for temperature are calculated by subtracting the model averages representing baseline (1961–1990) from the future (e.g. 2020s, 2050s or 2080s) temperatures. Change factors for precipitation arenormally derived from the ratio of the projected-to-baseline averages, but absolute differences can also be applied. The temperature changes are then added to observations (or in the case of P multiplied by observations) to yield a climate series at the study location.Pros:Easy to apply; Can handle probabilistic climate model outputCons:1. Perturbs only baseline mean and variance
- This method is one of the most straightforward and popular procedures for climate risk assessment, provided that the prerequisite climate model outputs are available. Change factors are typically calculated for calendar months by comparing the present and projected climatology in GCM for grid boxes overlying the target region.Change factors for temperature are calculated by subtracting the model averages representing baseline (1961–1990) from the future (e.g. 2020s, 2050s or 2080s) temperatures. Change factors for precipitation arenormally derived from the ratio of the projected-to-baseline averages, but absolute differences can also be applied. The temperature changes are then added to observations (or in the case of P multiplied by observations) to yield a climate series at the study location.Pros:Easy to apply; Can handle probabilistic climate model outputCons:1. Perturbs only baseline mean and variance
- Snow depth in Wanakena, NY.SDSMCan generate sub-daily informationCan exactly reproduce many climate statisticsEasy to generate large ensembles
- Recommended further reading:R. L. Wilby and Coauthors. 2009: A review of climate risk information for adaptation and development planning, Int. J. Climatol. DOI: 10.1002/joc.1839