Shea and Climate

Jon Lovett University of Leeds

Sahara Dust 2005 http://earthobservatory.nasa.gov/NaturalHazards/shownh.php3?img_id=12836

February 2000 http://earthobservatory.nasa.gov/Newsroom/NewImages/Images/seawifs_canary_duststorm_large.jpg

30-07-2008 Early season hurricane formation off Dakar from EUMETSAT http://www.eumetsat.int/Home/index.htm

08-08-2008 Hurricane Alley in full flow from EUMETSAT http://www.eumetsat.int/Home/index.htm

04-09-2008 Hurricane Ike reaches category 4 coming out of Hurricane Alley from EUMETSAT http://www.eumetsat.int/Home/index.htm

Ike

09-09-2008 Hurricane Ike over Cuba from EUMETSAT http://www.eumetsat.int/Home/index.htm

17-09-2008 Hurricane Ike reaches Europe from EUMETSAT http://www.eumetsat.int/Home/index.htm

23-09-2008 Heavy rain in Spain: 250 l/m2 in two hours in Sueca in eastern Spain. Meteosat-9 HRV Europe RGB - 17:00 UTC.

http://earthobservatory.nasa.gov/Study/Desertification/

http://earthobservatory.nasa.gov/Study/Desertification/

IPCC ‘Climate Change 2007’

• Frequency of heavy precipitation events has increased

over most land areas - consistent with warming and increases of atmospheric water vapour

• Drying in the Sahel, the Mediterranean, southern Africa and parts of southern Asia

• More intense and longer droughts observed since the 1970s, particularly in the tropics and subtropics

high

low Modelled present day African plant species richness

Assuming species occur at all climatically suitable places

Sommer et al. Nees Institute for Biodiversity of Plants, Bonn

Effect of Climate Change Africa

Patterns of African plant species richness in 2100 Assuming species move into new habitats and occur at all climatically suitable places Sommer et al. Nees Institute for Biodiversity of Plants, Bonn

Effect of Climate Change Africa

Shea presence localities Distribution data from

John Hall (University of Wales, Bangor); plot locations from Burkina Faso, Mali and Ghana; one record from TROPICOS

– 241 points in total (216

paradoxa, 25 nilotica)

– 216 locations to nearest minute, remainder to nearest degree

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

Analysing present day distribution • Using generalised additive models (GAMs), the present

day shea distribution was correlated with climate, topography, agro-ecological zone data and fire radiative power

• Predictor variables were chosen using two contrasting stepwise selection procedures: beginning with a null/full model, variables were added/removed according to Akaike/Bayesian Information Criterion, respectively

• Platts, Poudyal, McClean INNOVKAR WP2

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

Shea distribution (present) predicted by a GAM using climate, topography, soil constraints and fire intensity

Shea distribution (present) predicted by a GAM using climate variables only (mean temperature, moisture index and driest month rainfall)

Climate scenarios

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

Summary of temperature changes forecast according to various IPCC SRES emissions scenarios (IPCC,

2007). A1: maximum energy requirements, with emissions differentiated according to fuel sources (A1Fl,

fossil intensive; A1T, technological development of non-fossil sources; A1B, balance across sources). B1:

minimum energy requirements and low emissions. A2: high energy requirements; emissions less than A1Fl.

B2: low energy requirements, but emission greater than B1. Considered here are SRES scenarios A2 and B2.

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

Maps showing modelled climatic suitability for shea under current and future climate scenarios (extrapolating the GAM predictions)

INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)

Plots showing forecast changes in the number of

10 arc-minute grid cells providing climatically

suitable conditions for shea. Viable conditions are

defined by model predictions of at least 0.5

The two SRES scenarios (A2 and B2) returned

similar results for the first half of the 21st century,

but thereafter the A2 scenario (higher emissions)

resulted in a more rapid increase in the amount of

climate-space suitable for shea, particularly within

its current latitudinal range (0°-15°N).

Satellite data and high resolution modelling studies show that storms need heterogeneous soil to initiate In particular, afternoon convection over dry Sahel regions needs this variation in ground surface to initiate. Convection forms over gradients in soil moisture.

Air passage over forested regions enhances seasonal rainfall by up to 10 mm per day

Carbon Estimates

• Shea parkland 20-50 MgC/ ha • 3.7 million km2 Sahel-Savannah wooded

parkland zone • = 74,000,000,000 tonnes C (@20 MgC/ ha) • = 271,000,000,000 tonnes CO2

• World CO2 emissions: 33,376,327,000 tonnes CO2

• Shea parkland C stock equivalent to more than 8 times global CO2 emissions

Conclusion

• Sahelian climate unpredictable • Climate change models predict a drier

Sahel • Bioclimatic envelope modelling shows

major shifts in vegetation; with Shea range expanding

• Vegetation affects Sahel rainfall; and carbon storage/ sequestration can mitigate climate change