1. Christian Beer, CE-IP Crete 2006 Mean annual GPP of Europe derived from its water balance Christian Beer 1, Markus Reichstein 1, Philippe Ciais 2, Graham Farquhar 3, Dario Papale 4 (1)MDI-BGC, Max Planck Institute for Biogeochemistry, Germany (2)Laboratoire des Sciences du Climat et de L'Environnement, France (3)Research School of Biological Sciences, Australia (4)Forect ecology Lab., University of Tuscia, Italy

2. Christian Beer, CE-IP Crete 2006 Carbon balance – observations at ecosystem level Eddy Covariance Technique Inventory Carbon fluxes in Zotino, Siberia. Lloyd et al., 2002

3. Christian Beer, CE-IP Crete 2006 Global Scale: 1)Upscaling Inventory data 2)Models using -Remote Sensing Data (LUE) -Atm. [CO 2 ] (transport inversion) -Climate & Soil data (TEMs) Carbon balance at global scale: observations? GPP TER

4. Christian Beer, CE-IP Crete 2006 Global Scale: 1)Upscaling Inventory data 2)Models using -Remote Sensing Data (LUE) -Atm. [CO 2 ] (transport inversion) -Climate & Soil data (TEMs) Carbon balance at global scale: observations? GPP TER ?

5. Christian Beer, CE-IP Crete 2006 Objective Data-driven estimation of European mean GPP. Ball et al., 1987 From Sellers et al., 1997 Making use of linkage between C and H 2 O cycles: -Scaling WUE from stand level to watersheds -Multiplying WUE with water balance of watersheds -Summing up GPP of watersheds

6. Christian Beer, CE-IP Crete 2006 Outline Generalisation of WUE in forests WUE map of Europe Mean WUE and GPP of watersheds Uncertainties of European GPP number Plausibility

7. Christian Beer, CE-IP Crete 2006 Ecosystem-level WUE: Definitions GPP & ET: - NEE & LE from CE-IP database (Papale et al., 2006) -GPP derived by NEE partioning (Reichstein et al., 2005) -gap-filling of half-hourly data -aggregation to annual sums WHC at sites: Applying hydraulic parameters to reported soil texture classes (Cosby et al., 1984) FPC: Foliage Projective Cover

8. Christian Beer, CE-IP Crete 2006 Aim: WUE map

9. Christian Beer, CE-IP Crete 2006 Large variability of WUE between forest sites StationSpeciesWUE [g/kg] BE-VieFagus4.93 DE-HaiFagus5.15 DE-ThaPicea4.59 DK-SorFagus6.15 FI-HyyPinus3.50 FI-SodPinus2.90 FR-HesFagus4.03 FR-LBrPinus3.08 FR-PueQuercus3.78 IT-Ro1Quercus3.03 NL-LooPinus4.01 Environmental gradients!!

10. Christian Beer, CE-IP Crete 2006 Generalisation of forest WUE

11. Christian Beer, CE-IP Crete 2006 Generalisation of forest WUE

12. Christian Beer, CE-IP Crete 2006 Generalisation of forest WUE

13. Christian Beer, CE-IP Crete 2006 Generalisation of forest WUE  11 sets of (a 1,a 2,a 3 ) ‚Leave-one-out validation‘

14. Christian Beer, CE-IP Crete 2006 WUE map of Europe MODIS LAI, 1 km European soil texture map, 1 km WUE VPD, 1 km (33 maps) MODIS Land Cover ForestGrass/Cropland Mean WUE VPD : 18±5g*hPa/kg +

15. Christian Beer, CE-IP Crete 2006 LAISoil texture WUE VPD WUE map of Europe Mean WUE VPD of crop/grassland

16. Christian Beer, CE-IP Crete 2006 WUE, 10 kmVPD, 10 km WUE VPD, 10 km WUE VPD, 1 km WUE map of Europe

17. Christian Beer, CE-IP Crete 2006 Watershed-wide GPP MODIS LAI, 1 km European soil texture map, 1 km WUE VPD, 1 km (33 maps) WUE, 10 kmVPD, 10 km WUE VPD, 10 km (33 maps) WUE, watershed Precip for weighting average GPP, watershed ET=Precip-Runoff MODIS Land Cover ForestGrass/Cropland Mean WUE VPD : 18±5g*hPa/kg +

18. Christian Beer, CE-IP Crete 2006 Watershed-wide GPP – Basis for European GPP estimate Reichstein et al., 2006 RiverWUE [g/kg] GPP [gC/m²/a] Seine0.90545 Rhone2.931323 Tejo0.29141 Rhine3.521444 Elbe2.221084 Danube2.581278 Gota6.712388 Iijoki6.352269

19. Christian Beer, CE-IP Crete 2006 GPP result & uncertainties GPP of Europe = 3.21±0.36 PgC/a (11% uncertainty) 6 climate data sets: VPD: -DAO 2000-2003 -REMO 1961-2003 Precipitation: -GPCP 2000-2003 -CRU 1961-1990 -REMO 1961-2003 33 maps of WUE VPD + Not taken into account: Uncertainties due to soil texture, LAI, land cover

20. Christian Beer, CE-IP Crete 2006 Discussion Missing productive land: ~ Six-fold area of Ireland with GPP=1000 gC/m²/a  Underestimation of 0.4PgC/a (13%) Assuming GPP=1000 gC/m2/a for Gota, Iijoki, Oulujoki:  Overestimation of 0.1PgC/a (3%)

21. Christian Beer, CE-IP Crete 2006 Plausibility – Comparison of NPP assessments GPP = 3.2 PgC/a & NPP/GPP = 0.5  NPP ~ 1.6 PgC/a NPP(forest)~ 0.8 PgC/a (Schulze et al., 1999 + Nabuurs et al., 2003) NPP(crop)~ 0.5 PgC/a (Imhoff et al., 2004 + FAOSTAT, 2005) NPP(grass)~ 1 PgC/a (PASIM model, Vuichard, 2007) Total:~ 2.3 PgC/a Lower estimate compared to inventory!? Uncertainty of NPP/GPP ratio?

22. Christian Beer, CE-IP Crete 2006 Conclusions GPP can be estimated by the water balance on global scale Challenge: Extrapolating WUE in space  WUE VPD = f(WHC,LAI) Uncertainty of mean GPP at least 11%

23. Christian Beer, CE-IP Crete 2006 Perspectives Relationship WUE VPD =f(WHC,LAI) for grass? Interannual GPP estimates by annual water balance (P-R)  Comparison of GPP anomalies to NEE anomalies by atmospheric CO 2 inversions, or TEMs  Coupling such simple GPP model to inversions of atmospheric transport? (Comment by Christian Rödenbeck)  Parameterisation of large-scale TEMs

24. Christian Beer, CE-IP Crete 2006 Acknowledgments Spatial Data: Joint Research Center: Soil texture map MODIS Team: Land Cover and LAI Gridded climate data by REMO, CRU, DAO Mean river discharge: The Global Runoff Data Centre, D-56002 Koblenz, Germany Eddy Flux Obs., Thank you! M. Aubinet (2x) C. Bernhofer (2x) K. Pilegaard A. Granier S. Rambal R. Valentini D. Lousteau T. Vesala E. Moors T. Laurila D. Schulze N. Buchmann, A. Knohl W. Kutsch G. Kiely H. Soegaard Z. Nagy Z. Barkza Z. Tuba Comments during the ‚database workshop‘ in Amsterdam