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Vulnerability to near-term warming in the Sahel Laura Harrison UCSB Geography Climate Hazards Group Famine Early Warning System Network.

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Presentation on theme: "Vulnerability to near-term warming in the Sahel Laura Harrison UCSB Geography Climate Hazards Group Famine Early Warning System Network."— Presentation transcript:

1 Vulnerability to near-term warming in the Sahel Laura Harrison UCSB Geography Climate Hazards Group Famine Early Warning System Network

2 perspective Efficient and optimal planning/response to climate hazards… is dependent on our understanding regional vulnerability to meteorological shock

3 goals Link climate hazards to impacts Identify areas most vulnerable to climate shocks/change Place risk in context to regional livelihoods

4 general method Examine recent land-atmosphere interaction in response to climate variability Water & surface energy balance Where there is systematic response explore climate change scenarios

5 CMIP5 ensemble mean RCP4.5 PET projections for the Sahel Q: How will warming over next 25 years impact plant stress in the Sahel? + ~0.75 °C Air temperature, T a 10N-20N, 20W-40E

6 CMIP5 ensemble mean projected T a June July August September T a = µ µ Source: KNMI Climate Explorer Projected near-term warming Varies regionally and monthly

7 Sahel rainfall change uncertain JAS White: < 66% of models agree on direction of change Gray: > 80% of models show no significant change Source: James and Washington, 2012 Inter-model rain change agreement (CMIP3) Rainfall change with 1 °C global warming July-September A2 SRES scenario

8 Examine regional response to climate variability Approach 1.Assume aspects of local climate will remain same 2.Identify where: Higher than normal heat is associated with… …drier or windier or clearer sky than normal conditions

9 Climate constraint to plant growth

10 Climate constraint and livelihoods

11 Moisture availability within growing season

12 Model anomalous PET Build statistical model to explain recent PET variability as a function of temperature Quantify the role of temperature Estimate effect of projected T a

13 Model anomalous PET Where y(t) = Daily PET anomaly (mm) α = PET autocorrelation coefficient for lag 1 β = Slope coefficient for temperature anomaly (mm °C -1 day -1 ) γ = Intercept term ε = Model error Build statistical model to explain recent PET variability as a function of temperature GLDAS NOAH LSM daily data Variables - Potential evapotranspiration, PET (FA0-56 PM equation) - 2m air temperature

14 Model skill < 0.25 R-square value June July August September Skill attributed to temperature

15 Model-estimated relationship: T & PET GLDAS Noah LSM PET & T 2001:2010

16 Projected surface moisture loss 2026:2035 – 2001:2010 GLDAS Noah LSM PET & T CMIP5 model ensemble mean monthly T 2001:2010 RFE2.0 rainfall 2001:2010

17 Moisture availability within growing season GLDAS Noah LSM PET 2001:2010 RFE2.0 rainfall 2001:2010

18 Further research Physical mechanisms of Temperature-PET relationship -Stronger vapor pressure gradient -Higher incoming radiation (LW, SW) Use station-estimated T trends (CHG) Results in context to rangeland conditions Wet vs. dry years

19 Thank you Collaborators: Chris Funk, Joel Michaelsen, Leila Carvalho, Phaedon Kyriakidis, Chris Still, Michael Marshall, Elena Tarnavsky, Molly Brown Climate Hazards Group USGS FEWS NET USAID Questions, comments:

20 extra

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22 PM equation

23 Temperature predictor coefficient by month From PET predictive model data Results: Hot spots chapter1 Source: KNMI Climate Explorer Projected warming by month Ensemble mean vs

24 Results: Hot spots chapter1 Source: KNMI Climate Explorer Projected warming by month Ensemble mean vs Temperature predictor coefficient by month From PET predictive model data

25 JJAS, PET increase per 1 deg T anomaly


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