Calculating Climate Extremes CEI Case Study: Africa

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

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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 - PowerPoint PPT Presentation

Transcript of Calculating Climate Extremes CEI Case Study: Africa

Lezlie C. Moriniere, ATMO529 (Fall07)

Arid Land Resources Sciences / Global Change

Focus: Climate ChangeXevents Human Migration

Presentation OutlineScientific MotivationIntroduction

TermsIPCCDataset

Analysis MethodsNOAA Standard

ResultsSummary and Steps Ahead

Scientific Motivation: Climate Refugees?

1. CREATE EXTREMES

INDEX USING CLIMATE

VARIABLES

3.ALIGN WITH MIGRATION

STATISTICS TO DETECT TRENDS

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

IPCC EXTREME EVENT Prediction: FAR

Phenomenon AspectDirection and

Likelihood

TEMPERATURE Intensity and Frequency ↑ >90% probability

PRECIPITATION Intensity and Frequency ↑ > 90 to 99%

probability

DROUGHT Area ↑ 66 to 90%

probability

CYCLONES Intensity ↑ 66 to 90%

probability

SEA LEVEL RISE Frequency ↑ 66 to 90%

probability

CRU TS 2.1 Global Climate DatabaseEast 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, 1901-2002Software used:

Analysis/Figures: MatLabMap: ESRI ArcGIS

Analysis MethodsPrecipitation: Beta Produce SPI for Continent of Africa

Local SignificanceComposites

Climate Extreme Index2-tailed Exceedence per VariableCalculate Index CompositesLocal Significance

All: Temporal Trends Spatial Trends

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)

Africa_CEI

Tmx_m

5.1 - 24

25 - 28

29 - 31

32 - 34

35 - 38

Step 1: Max. Monthly Temp

Step3: SPI (Drought and Moisture)

Severe Sahelian droughts • 1910-1914•Mid1 970s•Mid 1980s

Africa SPI

Africa_CEI

SPI_m

-1.2 - -0.51

-0.5 - -0.12

-0.11 - 0.13

0.14 - 0.4

0.41 - 0.92

Africa_CEI

SPI_m

-1.2 - -0.51

-0.5 - -0.12

-0.11 - 0.13

0.14 - 0.4

0.41 - 0.92

102 Year Monthly SPI

Africa_CEI

SPISUMm_HI

-1.9 - -0.69

-0.68 - -0.28

-0.27 - 0.067

0.068 - 0.48

0.49 - 1.4

Africa_CEI

SPISUMm_LO

-2.8 - -1.1

-1 - -0.54

-0.53 - -0.099

-0.098 - 0.35

0.36 - 1.3

Precip: Winter Beta

Precip:Summer Beta

Africa_CEI

Wet_aa

0.29 - 31

32 - 67

68 - 110

120 - 150

160 - 320

Africa_CEI

Wet_aa

0.29 - 31

32 - 67

68 - 110

120 - 150

160 - 320

Step5: Wet/Dry Days

Africa_CEI

Dry_aa

44 - 210

220 - 260

270 - 300

310 - 330

340 - 360

Africa_CEI

Dry_aa

44 - 210

220 - 260

270 - 300

310 - 330

340 - 360

CEIComposite:

High >21% 1967,1968,1970,1974,1975,1976,1995

Low <18%1925,1927,1940,1943,1944,1948

CEI

cru_af_grd1.img

CEI_m

0 - 0.117

0.117 - 0.184

0.184 - 0.2608

0.2608 - 0.3706

0.3706 - 1.6

Africa_CEI

CEI_m

0 - 0.12

0.13 - 0.18

0.19 - 0.26

0.27 - 0.37

0.38 - 1.6

CEI: Century and Seasonal Means

CEI: Winter CEI: Spring

CEI: Summer CEI: Fall

Results: Local SignificanceWhat 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, NoAfrica_CEI

SPIwindif

-1.4 - -0.55

-0.54 - -0.21

-0.2 - 0.049

0.05 - 0.33

0.34 - 1.5

SPI: Winter DifferenceHi-Lo/2

Africa_CEI

SPIsumdif

-1.1 - -0.35

-0.34 - -0.047

-0.046 - 0.23

0.24 - 0.56

0.57 - 1.9

SPI: Summer DifferenceHi-Lo/2

CEI: are the High and Low Composite Years significantly different?HyØ Winter/Summer:

Rejected, Yes2 tailed Ttest, P

Values: Summer: =0.022-0.026Winter: =0.046-0.052

Area >90 ci: Summer : 9 grids, Winter: 16 grids

Ttest resultsSPI Winter

Africa_tt.img

SPIT_win

<-.90

-0.9 to 0.9

>0.90

SummarySPI and other variables complement each other

Different perspectives on extremesAfrica Xtremes beg confirmation and monitoringDetails are lost:

Africa: huge and heterogeneousMany confounding factors and widely varying

climatic influences on the continent:Hadley Cell CirculationMid-latitude CirculationEver-mobile ITCZEl Nino Southern Oscillation, and NAO

Steps AheadMaster statistics specifically for extremes…Data acquisition: cyclone, 2002+Spatial disaggregating (latitude or country )Field SignificanceGlobal Triangulation:

Disaster Events (lag time?)Human Migration