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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
Ike




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




 Modelled present day African
 plant species richness
                                    low
 Assuming species occur at all
 climatically suitable places
 Sommer et al. Nees Institute for
 Biodiversity of Plants, Bonn

                                    high
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
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)
Shea distribution (present) predicted by a GAM using   Shea distribution (present) predicted by a GAM using
climate, topography, soil constraints and fire         climate variables only (mean temperature, moisture
intensity                                              index and driest month rainfall)
INNOVKAR WP2: Shea distribution under climate change                         Platts, Poudyal, McClean (February 2011)
Climate scenarios




   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).




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

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Lovett jon leeds univ shea climate 2013 26 feb2013+carbon

  • 1. Shea and Climate Jon Lovett University of Leeds
  • 4. 30-07-2008 Early season hurricane formation off Dakar from EUMETSAT http://www.eumetsat.int/Home/index.htm
  • 5. 08-08-2008 Hurricane Alley in full flow from EUMETSAT http://www.eumetsat.int/Home/index.htm
  • 6. Ike 04-09-2008 Hurricane Ike reaches category 4 coming out of Hurricane Alley from EUMETSAT http://www.eumetsat.int/Home/index.htm
  • 7. 09-09-2008 Hurricane Ike over Cuba from EUMETSAT http://www.eumetsat.int/Home/index.htm
  • 8. 17-09-2008 Hurricane Ike reaches Europe from EUMETSAT http://www.eumetsat.int/Home/index.htm
  • 9. 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.
  • 12. 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
  • 13. Effect of Climate Change Africa Modelled present day African plant species richness low Assuming species occur at all climatically suitable places Sommer et al. Nees Institute for Biodiversity of Plants, Bonn high
  • 14. 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
  • 15. 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)
  • 16. 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)
  • 17. Shea distribution (present) predicted by a GAM using Shea distribution (present) predicted by a GAM using climate, topography, soil constraints and fire climate variables only (mean temperature, moisture intensity index and driest month rainfall) INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)
  • 18. Climate scenarios 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)
  • 19. 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)
  • 20. 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). INNOVKAR WP2: Shea distribution under climate change Platts, Poudyal, McClean (February 2011)
  • 21. 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.
  • 22. Air passage over forested regions enhances seasonal rainfall by up to 10 mm per day
  • 23. 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
  • 24. 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