1. Issues of Climate Observing for Impacts William E. Easterling The Pennsylvania State University

2. General points on the draft report n Choice of essential surface climate variables is adequate for most biological and hydrological modeling n Terrestrial variables ok to zero order, but there are gaps, for example: –Frost-free season/growing season –Soil temperature and moisture –Monsoon onset or rainy season onset in Tropical Wet/Dry –Ice-out dates on lakes and rivers n Stop separating interannual climate variability from long-term climate change

3. General points on the draft report n Goals 4 (prediction), 5 (downscaling), and 6 (extremes) have most bearing on impact community –Interannual prediction has more skill and thus more potential utility to adpatation at present than long-term prediction

4. Source – Sonka, et al.: 2001 Midwestern U.S. farmers

5. Comparison of Three Drought Cases in NE Brazil: 1987 (no action taken according to climate forecast) 1992 (full action taken according to forecast & monitoring) 1993 (full action taken according to forecast & monitoring) Year Precipitation Grain Production Grain Production (% of mean) (in metric tons) (% of mean) 1987 70% 100,000 15% 1992 73% 530,000 82% 1993 60% 400,000 62%

6. General points on the draft report n Goals 4 (prediction), 5 (downscaling), and 6 (extremes) have most bearing on impact community –Interannual prediction has more skill and thus more potential utility to adpatation at present than long-term prediction < Little knowledge of forecast needs of potential users –Need to incorporate downscaling into seasonal to interannual predictions (not just long-term GCM experiments) n Importance of historical observations for impact analysis

10. General points on the draft report n Goals 4 (prediction), 5 (downscaling), and 6 (extremes) have most bearing on impact community –Interannual prediction has more skill and thus more potential utility to adpatation at present than long-term prediction < Little knowledge of forecast needs of potential users –Need to incorporate downscaling into seasonal to interannual predictions (not just long-term GCM experiments) n Importance of historical observations for impact analysis n Networks: focus on regions that matter! –Broaden concept of ecosystem networks to include: < major agricultural production zones < Major watersheds/river basins < Vulnerable zones (low-lying coastal areas, floodplains, ecotones) –Might focus observing on ecologically or socially vulnerable regions?

11. Heim et al, in press

12. General points on the draft report n Goals 4 (prediction), 5 (downscaling), and 6 (extremes) have most bearing on impact community –Interannual prediction has more skill and thus more potential utility to adpatation at present than long-term prediction < Little knowledge of forecast needs of potential users –Need to incorporate downscaling into seasonal to interannual predictions (not just long-term GCM experiments) n Importance of historical observations for impact analysis n Networks: focus on regions that matter! –Broaden concept of ecosystem networks to include: < major agricultural production zones < Major watersheds/river basins < Vulnerable zones (low-lying coastal areas, floodplains, ecotones) –Might focus observing on ecologically or socially vulnerable regions? n Leading Climate Indicators of Impacts: Another way of looking at integrated climate products

13. Indexes of Leading Climate Indicators of Impacts Leading indicators vary depending on the class of climate impact problems. Classes of Climate Impact Problems: - Occurrence of climate extremes - Anticipation/detection of greenhouse warming - Development of hazard warning capabilities - Assessment of climate effects on ecosystem health - involvement of climate in demand for energy and quality/quality of renewable natural resources. Leading indicators are historical-to-date integrations of climate data tailored to gage impacts (Easterling and Kates, 1995).

14. Index of Greenhouse Climate Response (GCRI) (Karl et al., 1995) Is derived as a percent of the U.S. with an arithmetic average of the following five indicators: - Above normal mean temperature - Above normal precipitation during cold season (Oct.-April) - Extreme or severe drought during warm season (May-Sept.) - Greater than normal proportion of precipitation derived from extreme 1-day events (exceeding 50.8 mm per day). - Below normal day-to-day temperature differences. Examples of Existing Climate Indexes

16. Moisture Stress Index (MSI) (Heim et al., 2003): - Is based on the effect of severe drought (Palmer Z index  –2) or catastrophic wetness (Z  +5) on crop yields. - Measures effect of drought and catastrophic wetness on corn and soybean yields and is calculated through the use of Palmer Z index reference and annual ten-year average crop productivity weights.