1. Changes in Floods and Droughts in an Elevated CO 2 Climate Anthony M. DeAngelis Dr. Anthony J. Broccoli

2. Outline of Presentation  Introduction and Motivation for Research  Model  Changes in Floods/ Droughts  Scaling Factor Hypothesis  Conclusions  Future Research  References

3. Importance of Research  Floods and droughts are major climatic events that can significantly impact human life and property.  Previous research has suggested that the frequency of these events has changed over the past century.  The frequency of floods and droughts may continue to change in a warmer climate over the United States.

4. Projected Changes in Precipitation Extremes Frequency of Dry Days Frequency of 95 th percentile events Anomalies in days/year. Diffenbaugh et al. 2005, RegCM3, Resolution: 25 km

5. Our Climate Model  CM2.1  Developed at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL)  Resolution: 2° latitude by 2.5° longitude.

6. Our Data  CM2.1U_Control-1860_D4 = Control data. Coupled (atmosphere + land) and (ocean + sea ice) model with forcing agents consistent with 1860.  CM2.1U-D4_1PctTo4X_J1 = Elevated CO 2 data. Increases CO 2 from 1860 levels by 1% per year to quadrupling, then holds CO 2 constant.

7. Using P-E  Instead of studying precipitation alone, we study precipitation minus evaporation (P- E).  The negative feedback between soil moisture and surface evaporation affects our results.  As evaporation increases, soil moisture decreases, and reduces the availability of water in the soil. Thus, evaporation increases slow or cease.

8. Assessing Changes in Extreme Precipitation Events in Elevated CO 2 Climate  Calculate 1 st and 99 th P-E percentiles for control and elevated CO 2 data for each location.  Calculate changes in frequencies of 99 th P-E percentile events between control and elevated CO 2 data.  Calculate changes in 99 th P-E percentile values between control and elevated CO 2 data.

9. Assessing Changes in Extreme Precipitation Events in Elevated CO 2 Climate  We look at changes in >99 th percentile P-E events of period lengths 1 and 7 days to assess changes in short and long term floods.  We look at changes in <1 st percentile P-E events for period lengths 90 and 360 days to assess changes in short and long term droughts.

10. Results: Changes in >99 th Percentile Frequencies (Floods) Annual, 1 Day:Summer, 1 Day:Winter, 1 Day: Annual, 7 Day:Summer, 7 Day:Winter, 7 Day: Percent Changes in >99 th percentile P-E frequencies ranging from -100 (blue) to 100 (red):

11. Results: Changes in <1 st Percentile Frequencies (Droughts) Annual, 90 Day:Summer, 90 Day:Winter, 90 Day: Annual, 360 Day: Percent Changes in <1 st percentile P-E frequencies ranging from -100 (blue) to 100 (red):

12. Results: Comparison of Mean Changes with Upper Percentile Changes Mean, Annual: Mean, Summer: Mean, Winter: 99 th, Annual, 1 Day: 99 th, Summer, 1 Day: 99 th, Winter, 1 Day: Mean P-E changes between control and elevated CO2 data: Ranging from -0.5 (blue) to 0.5 (red). 99 th Percentile daily P-E changes: Ranging from -10 (blue) to 10 (red). Units in mm/day.

13. Agreement with Previous Research  Diffenbaugh et al. 2005  RegCM3 model (CO 2 from A2 scenario)  Resolution: 25 km, Entire US  Increases in annual >95 th percentile precipitation events across east and northwest US.  Increases in annual mean precipitation across eastern US.  Similar patterns in direction of mean and precipitation extreme anomalies.

14. Agreement with Previous Research  Leung et al. 2004  PCM model (Doubling CO2 from 1995- 2100)  Resolution: 40 km, Western US  Increases in winter 95 th percentile precipitation values across parts of the northwestern US.  Decreases in winter mean precipitation across the western US.

15. Why does extreme precipitation change?  Our hypothesis: An intensification of the hydrologic cycle only.  Warmer temperatures  Increased evaporation  Increased water vapor  Heavier precipitation in areas and time periods of convergence  Increased droughts in areas and time periods of dry weather.  Scaling the hydrologic cycle by a constant factor may explain the changes.

16. Testing Our Hypothesis  Multiply control data by constant scaling factor of 1.0581 (globally and time averaged percent increase in precipitation and evaporation between control and quadrupled CO 2 climate).  Perform Kolmogorov-Smirnov (KS) and Kuiper (KP) statistical tests on distributions of scaled control and elevated CO 2 data for all locations.

17. Testing Our Hypothesis  Kolmogorov-Smirnov Test (KS) Yields D value: The maximum distance between cumulative distribution functions of scaled control and elevated CO 2 data. Yields Probability: Ranging from 0 to 1 where small values show that the cumulative distribution functions of both data sets are significantly different.

18. Testing Our Hypothesis  Kuiper’s Statistic (KP) Variant on Kolmogorov-Smirnov statistic Yields V value: Sum of the absolute value of maximum negative and positive distances between the cumulative distribution functions of the scaled control and elevated CO 2 data. Yields Probability: Same as for KS statistic.

19. KS and KP Statistical Test Results for P- E 1 Day Annual Data Scaled control and elevated CO 2 distribution tested. Probability values ranging from 0 (blue) to 1 (red). KS TestKP Test ALL probabilities near 0

20. Annual Statistical Test Results for All Period Lengths  The KS test yields an overall lowest D value of about 0.0085, corresponding to a probability of 0.14.  The KP test yields an overall lowest V value of above 0.012, corresponding to a probability below 0.10.  These low probabilities indicate that the cumulative distribution functions between the scaled control and elevated CO 2 data are different for all locations and all period lengths (1, 2, 3, 7, 30, 60, 90, 180, 360 days).

21. Improvements in KS Test D Values and KP Test V Values After Scaling KS, 1 Day: KS, 30 Day: KS, 90 Day: KP, 1 Day: KP, 30 Day: KP, 90 Day: Change in D before and after scaling. ∆D values ranging from -0.05 (blue) to 0.05 (red). Positive values (yellow, orange, red) indicate improvement.

22. Comparison of Changes in 99 th Percentile Before and After Scaling 99 th, Annual, 1 Day: 99 th, Annual 1 Day: 99 th, Annual, 90 Day: Absolute changes in P-E annual data: Ranging from -10 (blue) to 10 (red) in 1 day and from -2 (blue) to 2 (red) in 90 day. Units in mm/day. Between Control and Elevated CO 2 Between Scaled Control and Elevated CO 2

23. Does Using a Higher Scaling Factor Yield Better Results?  Increasing the scaling factor improves agreement in cumulative distribution functions for many locations.  However, the improvement is not significant enough to conclude that the scaled control and elevated CO 2 distributions come from the same population.

24. Does Scaling Precipitation Alone Yield Better Results?  Scaling precipitation alone and comparing its cumulative distribution function with that of the elevated CO 2 data gives higher probabilities.  However, these probabilities are still close to zero, even when scaling factors are increased beyond 1.0581.

25. Conclusions  Frequency of floods increases across the north and east annually and in summer, and nearly everywhere in winter.  Frequency of droughts increases in east annually and in summer, and decreases in winter.  With the exception of a few regions, the direction of mean change is overall similar to the direction of upper percentile changes.

26. Conclusions  Magnitude of mean increases are significantly smaller than those of upper percentiles.  Cumulative Distribution functions between scaled control and elevated CO2 data are different for all locations.  Increasing scaling factors and performing the analysis on precipitation alone improves distribution agreement, but not significantly.

27. Conclusions  It appears that one reason for the large differences in cumulative distribution functions is the inability for the scaling factor to account for the large absolute increases in upper P-E percentiles (99 th ) between the control and elevated CO2 data.

28. Future Research  We seek to further understand how the scaled control distributions differ from the elevated CO 2 distributions.  If a simple linear scaling of the hydrological cycle alone cannot explain changes in extreme precipitation in a warmer climate, what can?

29. References  Diffenbaugh NS, Pal JS, Trapp RJ, et al., 2005: Fine-scale processes regulate the response of extreme events to global climate change. Proceedings of the National Academy of Sciences of the United States of America, 102, 15774-15778.  Leung LR, Qian Y, Bian XD, et al., 2004: Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change, 62, 75-113.