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Calculating Climate Extremes CEI Case Study: Africa

Calculating Climate Extremes CEI Case Study: Africa. Lezlie C. Moriniere , ATMO529 (Fall07) Arid Land Resources Sciences / Global Change Focus: Climate Change Xevents Human Migration. Presentation Outline. Scientific Motivation Introduction Terms IPCC Dataset Analysis Methods

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Calculating Climate Extremes CEI Case Study: Africa

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  1. Calculating Climate Extremes CEI Case Study: Africa 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? 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 ≤ 10th percentile or x ≥ 90th 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

  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 : • ∑ (Max.TemperatureHI, Max.Temperature LO) • ∑ (Min.Temperature HI , Min.TemperatureLO ) • ∑ ( PDSIHI,PDSI LO ) • 2 * ( 1-day PrecipitationHI ) • ∑( WetDaysHIDryDays HI ) • ∑ (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: • ∑ (Max.TemperatureHI , Max.TemperatureLO) • ∑ (Min.TemperatureHI, Min.TemperatureLO ) • ∑ ( SPIHI,SPI LO ) • ∑( PrecipitationHI, Precipitation/WetdaysHI ) • ∑( WetDaysHI , DryDaysHI ) • ∑ (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 Precip: Summer Beta Step4: Precipitation & Intensity

  14. Step5: Wet/Dry Days

  15. CEI • Composite: • High >21% 1967,1968,1970,1974,1975,1976,1995 • Low <18%1925,1927,1940,1943,1944,1948

  16. CEI: Winter CEI: Spring CEI: Summer CEI: Fall CEI: Century and Seasonal Means

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

  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

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