vulnerability to near-term warming in the sahel laura harrison ucsb geography climate hazards group...
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Vulnerability to near-term warming in the Sahel
Laura HarrisonUCSB Geography
Climate Hazards GroupFamine Early Warning System Network
perspective
Efficient and optimal planning/response to climate hazards…
is dependent on our understandingregional vulnerability
to meteorological shock
goals
• Link climate hazards to impacts
• Identify areas most vulnerable to climate shocks/change
• Place risk in context to regional livelihoods
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
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, Ta
10N-20N, 20W-40E
CMIP5 ensemble meanprojected Ta
June
July
August
September
Ta = µ 2026-2035 - µ 2001-2010
Source: KNMI Climate Explorer
Projected near-term warmingVaries regionally and monthly
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-SeptemberA2 SRES scenario
Examine regional response to climate variability
Approach 1. Assume aspects of local climate will remain same2. Identify where:
Higher than normal heat is associated with……drier or windier or clearer sky than normal
conditions
Climate constraint to plant growth
Climate constraint and livelihoods
Moisture availability within growing season
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 Ta
Model anomalous PET
Where y(t) = Daily PET anomaly (mm)α = PET autocorrelation coefficient for lag 1β = Slope coefficient for temperature anomaly (mm °C-1 day-1)γ = Intercept termε = 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
Model skill
0.50 - 0.75
0.25 - 0.50
< 0.25
R-square valueJune
July
August
September
Skill attributed to temperature
Model-estimated relationship: T & PET
GLDAS Noah 2.7.1 LSMPET & T
2001:2010
Projected surface moisture loss
2026:2035 – 2001:2010
GLDAS Noah 2.7.1 LSMPET & T
CMIP5 model ensemblemean monthly T
2001:2010RFE2.0 rainfall
2001:2010
Moisture availability within growing season
GLDAS Noah 2.7.1 LSMPET
2001:2010RFE2.0 rainfall
2001:2010
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
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: [email protected]
extra
PM equation
Temperature predictor coefficient by monthFrom PET predictive model. 2001-10 data
Results: Hot spots
chapter1Source: KNMI Climate Explorer
Projected warming by monthEnsemble mean. 2035 vs. 2001-10
Results: Hot spots
chapter1Source: KNMI Climate Explorer
Projected warming by monthEnsemble mean. 2035 vs. 2001-10
Temperature predictor coefficient by monthFrom PET predictive model. 2001-10 data
JJAS, PET increase per 1 deg T anomaly