Vulnerability to near-term warming in the Sahel Laura Harrison UCSB Geography Climate Hazards Group...

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: harrison@geog.ucsb.edu

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