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State and progress of FLUXCOM – An ensemble of global data-driven carbon flux products

State and progress of FLUXCOM – An ensemble of global data-driven carbon flux products. Research in progress 19.12.2013 Martin Jung on behalf of the FLUXCOM TEAM. D. Papale. M. Reichstein. M. Jung. K. Ichii. G. Camps-Valls. S. Sickert. G. Tramontana. C. Schwalm. T. Hilton.

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State and progress of FLUXCOM – An ensemble of global data-driven carbon flux products

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  1. State and progress of FLUXCOM – An ensemble of global data-driven carbon flux products Research in progress 19.12.2013 Martin Jung on behalf of the FLUXCOM TEAM

  2. D. Papale M. Reichstein M. Jung K. Ichii G. Camps-Valls S. Sickert G. Tramontana C. Schwalm T. Hilton E. Tomelleri N. Carvalhais A. Bloom T. Keenan

  3. FLUXCOM goals • To deliver a best estimate ensemble product of carbon and energy fluxes from an ensemble of diverse data-oriented and FLUXNET based approaches. • To study and if possible quantify sources of uncertainty which hopefully leads to improved strategies with reduced uncertainty along the way of the FLUXCOM activity. • To come up with a practice guidance regarding regionalization of FLUXNET data.

  4. Data-driven approaches FLUXNET Remote Sensing Reanalysis Machine learning based methods (FLUXCOM-ML) Optimisation of (semi-empirical) models (FLUXCOM-MDF) Spatialization of parameters

  5. Empirical upscaling methodology The same gridded explanatory variables f • Site-level explanatory variables • Meteorology • Vegetation type • Remote sensing indices Target variable ecosystem-atmosphere flux Training Training Application Gridded target variable

  6. Machine Learning Methods Neural Networks D. Papale Support Vector Machine K. Ichii G. Camps-Valls Kernel Ridge Regression Gaussian Process Regression & GP Regression + Random Forests S. Sickert Random Forests & Model Tree Ensembles M. Jung

  7. Global Products Driven only by remote sensing data 8 daily temporal, 0.0833° spatial, 2000-2011 Driven by climate data & remote sensing mean seasonal cycles daily temporal, 0.5° spatial tiled by PFT, 1980-2011 Cross-validation & Training Tree ensembles Random Forests Model Tree Ensembles Kernel methods Support Vector Machines Kernel Ridge Regression Gaussian Process Regression GP Regression + Random Forests Neural Networks FLUXNET NEE, GPPMR, GPPGL, TERMR, TERGL, meteo Feature Selection Guided Hybrid Genetic Algorithm Quality control Explanatory variables (~200) Satellite Vegetation Indices (LAI, FPAR, EVI, NDVI, NDWI, LSWI) Land Surface Temperature (day, night) Reflectances (7 Bands) Climate Temperature (Tair, Tmin, Tmax) Radiation (Rg, Rpot, Rg/Rpot) Humidity (Rh, VPD) Moisture (precip, WAI1, WAI2, IWA) Mean seasonal cycles Mean, Max, Min, Amplitude

  8. Feature selection & Cross-validation • Fully consistent 10-fold cross-validation • all methods use same input variables • all methods use exactly the same data • all methods use same 10-fold stratification • entire sites are in each fold, i.e. predicting unseen sites (more challenging) • NDWI  water • LSTday, LSTnight,  temp, water • NDVI*Rg  ‘APAR’ • MSC(LAI)  phenology • max(MSC(LSTday))  temp. • amp(MSC(EVI))  seasonality • amp(MSC(MIR))  nitrogen ? • PFT  ecosystem type Efficient sampling of Pareto-frontier

  9. Cross-validation results

  10. Sensitivity of MEF to ‘outlier’ removal Removing 5% of largest residuals increase MEFGPP by 0.1 - 0.16 (!)

  11. Uncertainty of MEF to site selection 0.68 0.72 0.76 0.66 0.76 0.83 0.66 0.73 0.79 0.08 0.14 0.19 GPP 0.59 0.64 0.69 0.53 0.65 0.76 0.62 0.68 0.73 0.07 0.11 0.14 TER 0.43 0.49 0.55 0.26 0.44 0.57 0.47 0.57 0.66 0.05 0.11 0.16 NEE Modelling Efficiency 2.5th percentile 50th percentile 97.5th percentile Based on 1000 bootstrap samples (for RF, 8day, only remote sensing)

  12. Brand new global GPP from FLUXCOM gC/m2/year Mean annual Uncertainty (robust sigma) Jung et al 2011 FLUXCOM

  13. Mean GPP comparison gC/m2/year

  14. GPP – SIF comparison • Sun induced fluorescence is intrinsically linked to photosynthesis… … but the GPP – SIF relationship is complicated by: from L. Guanter • - SIF radiative transfer (and reabsorption) in the canopy (structure effect) • non-photochemical quenching (biochemistry effect) • SIF only for ‘clear sky’ at overpass time (sampling effect) GOME (Joiner et al 2013) High precision Low accuracy GOSAT (Frankenberg et al 2011) Low precision High accuracy Joiner et al 2013, AMT

  15. Improved consistency with SIF

  16. Strong consistency with SIF …

  17. … but SIF problems with snow and clouds

  18. Seasonal cycle of net ecosystem exchange Amplitude of seasonal cycle of NEE Day of year of maximum uptake Day of year of minimum uptake

  19. Outlook • Completing global runs / updates for the remote sensing only set-up • Repeating the entire procedure for the remote sensing – climate set-up • Doing everything for energy fluxes • Cross-consistency checks with process-models, atmospheric CO2 / inversions, catchment water balances • Harvest …

  20. Acknowledgements

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