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Slide -1 (Announcement)

Slide -1 (Announcement). Breakout group “Against physical degradation of scientists” Where? Soccer field of Kalimera Hotel When? Friday, 5 pm (after plenary) Who? All interested colleagues Teams? Junior vs. senior?, Exp. vs. Mod?, Nitro vs. Carbo?, Italy vs. rest?.

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Slide -1 (Announcement)

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  1. Slide -1 (Announcement) Breakout group “Against physical degradation of scientists” Where?Soccer field of Kalimera Hotel When?Friday, 5 pm (after plenary) Who?All interested colleagues Teams?Junior vs. senior?, Exp. vs. Mod?, Nitro vs. Carbo?, Italy vs. rest?

  2. Open Science Conference on the GHG Cycle in the Northern HemisphereCrete, Nov. 2006 The European terrestrial biosphere affected by climate variability 1997-2005 : comparative analysis of processes and spatial patterns(CARBOEUROPE integrated activity) Markus Reichstein, Martin Jung, Dario Papale, Mona Vetter, Philippe Ciais, Alexander Knohl, Sebastiaan Luyssaert, Nicolas Viovy, Guerric le Maire, Miguel Mahecha, Martin Heimann

  3. Information to be extracted for biospheric modelling 2003 2005 Jul-Sep relative fAPAR-anomaly patterns from space (MODIS) against 2000-2005 Motivation for looking at inter-annual variability (IAV) over Europe … • IAV & extremes are important • Almost 10 yr of flux data • 6-8 yr of HQ remote sensing data • Strong IAV signal in many places

  4. Continental scale versus inter-annual variability Contintental Interannual GRL, in press cf. also Valentini et al. Law et al. (2002) Index of Water Availability: Ea/Ep (mm/mm) Mean annual temperature (°C)

  5. NEE signal decomposed by Time-series analysis Variability: high freq. > seasonal >> low freq. Analysis: Miguel Mahecha, cf. poster Data: San Rossore (G. Seufert)

  6. OBSERVATIONS ? MODELS ? Overall question: What are the controls over inter-annual variability (IAV) ? • Which processes drive IAV ? • GPP versus TER ? • Eco-physiological or bio-physical ? • Which environmental variables drive the IAV ? • What are the spatio-temporal structures in the flux IAV ?

  7. Which process drives IAV of NEP ? TER anomaly 2003 by bottom-up modelling Vetter et al. in prep.

  8. Remote sensing observation Site properties (Soil, veg.) Gridded soil properties Site meteo-rology Coarse-scale weather forecast data Gapfilling Regionalized weather forecast data (ECMWF, REMO) Pedo-Transferfunction Pedo-Transferfunction QC/GF, Aggregation, Fusion QC/Gapf. meteorology Regression on monthly data QC/spike det. Texture, water holding capacities Night-flux corr. Gap-filling Flux-partitioning fAPAR, vegetation type Harmonization Site met. data ‘Localized’ met. data Regional met. data Local ± 30% Gridded SITE LEVEL FLUX AND BIOMETRIC OBSERVATIONS Model-data fusion work-flow at site level ANN MOD17+ Biome-BGC LPJ ORCHIDEE OBSERVED NEE, GPP, TER, ET, SWC, LAI, NPP and derived properties MODELLED NEE, GPP, TER, ET, SWC, LAI, NPP and derived properties

  9. NEP_f GPP_f 2 2 [gC/m /month] [gC/m /month] 400 200 0 NEP_ORC2 400 2 [gC/m 300 200 100 0 Synoptic view on Flux-site: seasonal variability Observed Fluxes GPP_ORC2 2 /month] [gC/m /month] Modelled Tavg_REMO Tair_f 30 - [°C] [ °C] Meteo 20 10 0 fAPAR_B2 fAPAR_B_all - 1.0 Remote sens. 0.5 0.0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2000 2001 2002 2003 2004 2005 Data from: Tharandt (Grünwald/Köstner/Bernhofer)

  10. 100 NEP_f_anom 2 [gC/m /month] 50 0 -50 Synoptic view on Flux-site: monthly anomalies GPP_f_anom 2 [gC/m /month] Observed Fluxes NEP_ORC2_anom GPP_ORC2_anom 2 2 [gC/m /month] [gC/m /month] Modelled 0 Tavg_REMO_anom Tair_f_anom - [°C] [°C] 5 Meteo 0 -5 0.4 fAPAR_B2 fAPAR_Ball Remote sens. 0.2 0.0 -0.2 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2000 2001 2002 2003 2004 2005 Data from: Tharandt (Grünwald/Köstner/Bernhofer)

  11. Correlations GPP-NEP-TER:Full year and by seasons Eddy flux Orchidee model

  12. GPP-TER-NEP temporal correlations from ANN spatialization with MODIS-fPAR, and REMO-meteo n.s. Correlation GPP & NEP TER & NEP IAV of NEP ‘explained’: GPP minus TER Papale & Reichstein, Kalimera Hotel, Room P210, Crete, Nov. 2006

  13. Anomalies analysed by a self-organizing map … robust pattern! Luyssaert, Janssens et al. submitted

  14. Factors influencing variability

  15. Correlations with physical drivers differ fundamentally between time-scales Data: San Rossore (G. Seufert)Analysis: M. Mahecha

  16. Using a process-based model … 1:1 Eddy GPP anomaly [g m-2 mo-1] GPP anomaly with Orchidee model [g m-2 mo-1]

  17. Lag effects … Hainich Unmanaged mixed beech forest Leinefelde Managed pure beech forest Knohl et al. in prep. … site and species specific.

  18. Site specific Conclusions • Eddy flux IAV driven largely by GPP • but interesting exceptions • Factor correlations with IAV counter-intuitive and differ and between extreme events and ‘normal variability’ • Non-linearities & thresholds • Factor interactions (e.g. climate x soil x species) • Lag effects • Compared to short-term variability, IAV seems more influenced by biology and eco-physiology rather than by ‘physics’ (in forests!)

  19. Further steps… • More in-depth analysis • More sophisticated data mining (e.g. with Luyssaert, Mahecha, Papale, Janssens) • More process-model analysis and experiments (e.g. with Beer, Braakhekke, Ciais, Delpierre, leMaire, Vetter, Viovy, Zaehle) • Comparison with grassland dynamics (with Soussana) • More intensive exchange with the scientists at site level

  20. THANKS … For data from 12 ‘Golden’ sites: M. AubinetC. BernhoferN. BuchmannE. Moors A. GranierW. KutschA. Lindroth D. Lousteau K. Pilegaard J. PerreiraS. RambalE.-D. SchulzeG. SeufertR. Valentini T. Vesala EU Conference organizers!!! … for your attention !!

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