1. Lezlie C. Moriniere, ATMO529 (Fall07) Arid Land Resources Sciences / Global Change Focus: Climate Change  Xevents  Human Migration

2. Presentation Outline Scientific Motivation Introduction Terms IPCC Dataset Analysis Methods NOAA Standard Results Summary and Steps Ahead

3. Scientific Motivation: Climate Refugees? Global Change Natural Hazards  Extreme Events Human Migration 1. CREATE EXTREMES INDEX USING CLIMATE VARIABLES 2. EXTRACT DATA ON CLIMATE-RELATED DISASTER EVENTS 3.ALIGN WITH MIGRATION STATISTICS TO DETECT TRENDS

4. What is an Extreme Event? “an event that is rare within the statistical reference distribution at a particular place” (IPCC, 2001) Rare: x ≤ 10 th percentile or x ≥ 90 th percentile 4 attributes: rate of exceedence, mean excess, volatility, clustering in time (Stephenson, 2002) Measures: scale parameter (β), percentile thresholds empirical ranking Comfort in the Means vs. Intrigue in the Extremes

5. IPCC EXTREME EVENT Prediction: FAR PhenomenonAspect Direction and Likelihood TEMPERATURE Intensity and Frequency ↑ >90% probability PRECIPITATION Intensity and Frequency ↑ > 90 to 99% probability DROUGHTArea ↑ 66 to 90% probability CYCLONESIntensity ↑ 66 to 90% probability SEA LEVEL RISEFrequency ↑ 66 to 90% probability

6. CRU TS 2.1 Global Climate Database East Anglia University’s Climate Research Unit (CRU): Michael, T.D. and Jones, P.D., 2005. An improved method of constructing a database of monthly climate observations and associated high resolution grids. Int.J. Climatology 25: 693-712. Reformatted for ARCInfo: CGIAR (Consultative Group for Intl. Agricultural Research), Consortium for Spatial Information) Gridded 0.5°x0.5°, 11042 grids (Africa) 9 climate variables (Tmx, Tmn, Precip,Wet, Tmp, Dtr, Frs,Vap Cld): 102 years, monthly, 1901-2002 Software used: Analysis/Figures: MatLab Map: ESRI ArcGIS

7. Analysis Methods Precipitation: Beta Produce SPI for Continent of Africa Local Significance Composites Climate Extreme Index 2-tailed Exceedence per Variable Calculate Index Composites Local Significance All: Temporal Trends Spatial Trends

8. Climate Extremes Index (CEI) NOAA (Policy) coterminous USA, 1910-present Seasonal/Annual, 1° x 1 ° Grids Arithmetic average of 6 indicators: PERCENTAGE of AREA EXCEEDENCE : 1. ∑ (Max.Temperature HI, Max.Temperature LO ) 2. ∑ (Min.Temperature HI, Min.Temperature LO ) 3. ∑ ( PDSI HI, PDSI LO ) 4. 2 * ( 1-day Precipitation HI ) 5. ∑( WetDays HI DryDays HI ) 6. ∑ (Wind velocities^2) U of A (Research Application) Africa + islands, 1901-2002 Monthly/Seas./Ann., 0.5° x 0.5 ° Grids Arithmetic average of 5 indicators: FREQUENCY of TEMPORAL EXCEEDENCE: 1. ∑ (Max.Temperature HI, Max.Temperature LO ) 2. ∑ (Min.Temperature HI, Min.Temperature LO ) 3. ∑ ( SPI HI, SPI LO ) 4. ∑ ( Precipitation HI, Precipitation/Wetdays HI ) 5. ∑( WetDays HI, DryDays HI ) 6. ∑ (Wind velocities^2)

9. Step 1: Max. Monthly Temp

10. Step 2: Min. Monthly Temp

11. Step3: SPI (Drought and Moisture) Severe Sahelian droughts  1910-1914 Mid1 970s Mid 1980s

12. Africa SPI Africa Data Dissemination Service, Nov 2007 102 Year Monthly SPI

13. Precip: Winter Beta Step4: Precipitation & Intensity Precip: Summer Beta

14. Step5: Wet/Dry Days

15. CEI Composite: High >21% 1967,1968,1970,1974, 1975,1976,1995 Low <18% 1925,1927,1940,1943,1 944,1948

16. CEI: Century and Seasonal Means CEI: WinterCEI: Spring CEI: SummerCEI: Fall

17. Results: Local Significance What contributes most to the CEI? SPI HyØ Winter/Summer: Rejected, Yes 2 tailed Ttest, P Values: Summer: =0.11-0.14 Winter: =-0.18—0.15 Area >90 ci: Summer : 20 grids, Winter: 4 grids Max Temp: HyØ Winter/Summer: Rejected, No SPI: Winter Difference Hi-Lo/2 SPI: Summer Difference Hi-Lo/2 CEI: are the High and Low Composite Years significantly different? HyØ Winter/Summer: Rejected, Yes 2 tailed Ttest, P Values: Summer: =0.022-0.026 Winter: =0.046-0.052 Area >90 ci: Summer : 9 grids, Winter: 16 grids

18. Ttest results SPI Winter

19. Summary SPI and other variables complement each other Different perspectives on extremes Africa Xtremes beg confirmation and monitoring Details are lost: Africa: huge and heterogeneous Many confounding factors and widely varying climatic influences on the continent: Hadley Cell Circulation Mid-latitude Circulation Ever-mobile ITCZ El Nino Southern Oscillation, and NAO

20. Steps Ahead Master statistics specifically for extremes… Data acquisition: cyclone, 2002+ Spatial disaggregating (latitude or country ) Field Significance Global Triangulation: Disaster Events (lag time?) Human Migration