(In and) Out of Africa: estimating the carbon exchange of a continent Niall Hanan, Chris Williams, Bob Scholes, Scott Denning, Joe Berry, Jason Neff, Jeff Privette (and on behalf of collaborators in Africa, North America and Europe) or, more simply: Africa and the global carbon cycle MODIS Leaf Area Index

Acknowledgements This research funded by:  National Oceanic and Atmospheric Administration (NOAA) - Office of Global Programs  National Aeronautics and Space Administration (NASA) - Terrestrial Ecology Program

Spatial distribution of land cover and annual rainfall (400 mm yr -1 intervals) Sources: UMD Global Land Cover Facility ~8km IGBP land cover types, and FAO global half degree PPT data. Africa: Vegetation and Climate

What do we know about African carbon stocks and fluxes? 1.Plot/village scale inventory, growth and process studies, including CO2 fluxes and [CO2] 2.Regional-global scale biogeochemical (“forward”) models of stocks and/or fluxes (up-scaling of plot measurements, often using earth-observing satellites, and climatologies) 3.Regional-continental scale atmospheric inversions using the [CO2] observation network

What do we know...1 Brown, S., and G. Gaston Tropical Africa: Land Use, Biomass, and Carbon Estimates For ORNL/CDIAC Plot scale inventory, growth and process studies: Much work using traditional forest, ag, and pastoral sector methods at governmental level; FAO; NGOs; Millenium Assessment; Kyoto baseline assessments, etc. Often scattered / hard to find Some efforts at synthesis and regionalization of soil and vegetation biomass, land use change… Relatively few detailed process studies But increasing…

What do we know…2 Carbon inventory Statistics for Africa (summarized for 5 o latitude bands) Latitudinal distribution of (a) mean annual precipitation (mm y -1 ), (b) mean soil & plant carbon (kg C m -2 ), (c) annual NPP (g C m -2 ) Sources: (a) FAO PPT, (b) Olsen et al global live carbon and IGBP-DIS soil carbon, (c) CASA model and Potsdam model comparison results (a) (b) (c)

2. Regional-global scale biogeochemical (“forward”) models of stocks and/or fluxes (often part of global assessments or simulations) Cramer et al., Potsdam model comparison: Annual NPP (carbon g m -2 ; average of all models) What do we know …2 Other examples: Climatological & remote sensing based biogeochemical/process models Anthropogenic fluxes - land use, domestic and wildfires – at national- continental scales and so on…

What do we know …2 Historical analysis of spatial and temporal variability for 4 study sites across rainfall gradient, using: -SiB2 land surface model AVHRR archive -Climate reanalysis data -Regional soil and vegetation data Monthly NPP mean and variability

Carbon Emissions from Fires ICDC7 Poster LU204: G.R. van der Werf, L.Giglio, G.J. Collatz, J.T. Randerson, P.S. Kasibhatla, and A.F. Arellano What do we know …2

Carbon Statistics for Africa (Fraction of Global totals) Global totals “Error bars” show range of published estimates (small range may reflect error convergence among models, as much as true confidence in the estimates!)

3. Atmospheric Inversions of carbon source/sink distributions TRANSCOM-3 Model comparison study -11 terrestrial regions (2 in Africa) -11 oceanic regions (after Gurney et al. 2004) What do we know …3

(a) Net CO 2 flux, and (b) net CO 2 flux per unit area for Africa and comparison regions Positive values indicate a surface source Boxes show the range of +/- 1 standard deviation from the IPCC 2001 for in the 1980s (dark) and 1990s (light) Symbols report results from inverse analyses of atmospheric [CO 2 ]  = mean flux from individual inversion studies (error bars show the uncertainty) □ = mean flux estimate calculated from multiple source/sink estimates (Transcom inter-comparison) + = standard deviation among flux estimates (Transcom inter-comparison) o = average of individual uncertainty estimates (Transcom inter-comparison)

Why such uncertainty for Africa? Global [CO2] Observation Sites Global Eddy Flux (“Fluxnet”) Sites

What do we know about the C cycle of Africa ? We are aware that we don’t know much! Uncertainty in quantifying the carbon cycle of Africa related primarily to lack of observations This is more acute for Africa than most other regions of the world Therefore, forward and inverse models are poorly constrained for Africa But… New research and observations beginning in Africa…

Eddy flux and micrometeorology Soil CO 2 flux and stable isotope composition Near-surface precision CO 2 and flask samplers Physiological and biogeochemical studies New research…

Increasing Africa’s Representation in Global Carbon Cycle Observation Africa currently sparsely represented… but Afriflux and [CO2] observation network developing and Zambia

Global Atmospheric Inversions TRANSCOM Model-comparison -11 terrestrial regions (2 in Africa) -11 oceanic regions (results after Gurney et al. 2002)

Impact of Adding African Precision [CO 2 ] Stations on Inverse Solution Uncertainty -South Africa (operational) -Mali (Feb 2006) -Zambia (mid-2006) Data from the new sites will contribute to atmospheric inversions starting New terrestrial sampling sites Global Atmospheric Inversions

Regional Carbon Flux Estimation using a PBL-budget approach with near-surface [CO2] and atmospheric transport Betts, Helliker, and Berry, 2004, Coupling between CO2, water vapor, temperature and radon and their fluxes in an idealized equilibrium boundary layer over land. JGR, 109, D18103 Model results and animation from Joe Berry

Summary Understanding African C cycle limited by observations Inventory, forward & inverse methods: all data-poor for Africa Unique aspects of the continent: fire; prevalence of semi-arid & C4 vegetation, land use patterns and land use change Even given uncertainties it is clear that land use & fire fluxes far outweigh industrial emissions of the continent New and expanding observational capacity - particularly the developing Afriflux network and continental [CO2] measurements - is beginning to redress this imbalance Reducing uncertainty for Africa, using complementary approaches, will help reduce uncertainty for other regions and globally