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 2026-2035 - µ 2001-2010 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 2001-2010 GLDAS NOAH 2.7.1 LSM daily data Variables - Potential evapotranspiration, PET (FA0-56 PM equation) - 2m air temperature

14. Model skill 0.50 - 0.75 0.25 - 0.50 < 0.25 R-square value June July August September Skill attributed to temperature

15. Model-estimated relationship: T & PET GLDAS Noah 2.7.1 LSM PET & T 2001:2010

16. Projected surface moisture loss 2026:2035 – 2001:2010 GLDAS Noah 2.7.1 LSM PET & T CMIP5 model ensemble mean monthly T 2001:2010 RFE2.0 rainfall 2001:2010

17. Moisture availability within growing season GLDAS Noah 2.7.1 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: harrison@geog.ucsb.edu

20. extra

22. PM equation

23. Temperature predictor coefficient by month From PET predictive model. 2001-10 data Results: Hot spots chapter1 Source: KNMI Climate Explorer Projected warming by month Ensemble mean. 2035 vs. 2001-10

24. Results: Hot spots chapter1 Source: KNMI Climate Explorer Projected warming by month Ensemble mean. 2035 vs. 2001-10 Temperature predictor coefficient by month From PET predictive model. 2001-10 data

25. JJAS, PET increase per 1 deg T anomaly