FLUXNET NACP Site Level Interim Synthesis ABACUS (PI M. Williams ) M. Dietze & lab

1. Carbon cycle science in the Big Data era:opportunities and limitationsPaul Stoypaul.stoy@montana.eduwww.watershed.montana.edu/flux

2. Carbon cycle science in the Big Data era: opportunities and limitationsPaul Stoypaul.stoy@montana.eduwww.watershed.montana.edu/flux FLUXNET NACP Site Level Interim Synthesis ABACUS (PI M. Williams) M. Dietze & lab B. Ruddell & N. Brunsell

3. Whatbrings us together?1. Carbon cycle science (obvious)2. Enjoyscientificendeavors3. Data intensive approach

4. Gray (2007) NRC-CSTB We are all (mostly) computer scientists who work on the C cycle

5. How are wedifferent?1. Science vs. Policy2. Measurersvs. Modelers *(MDF)3. Weworkatdifferentscales

6. Can information science bridge our differences? A) Information scaling B) ‘Data mining’ (KDD) C) Model-data fusion Are we arriving at a synthesis, or just playing w/data?

7. Jarvis (1995) ScalingProcesses and Problems • Scalingis information transfer Sources of error • Aggregation (nonlinearity) • Feedbacks • Time/spaceheterogeneity Globe Macrosystem Region genome

8. Ecologicalscaling. A special case of Information Theory? • Ruddell, Brunsell & Stoy (2013)

9. Creating an information process network Ruddell and Kumar (2009a,b) Synoptic VPD Tair Tsoil θ Regional Rg Cf Meters Kilometers Many Kilometers Spatial Scale Turbulent P LE H GEP NEE Seconds Minutes Hours One Day One Week Temporal Scale

10. Information Process Network: Mutual Information Flows Synoptic blue lines/arrows information severed during severe drought. Thin arrows: feedbacks Thick arrows:forcings VPD Tair Tsoil θ Regional Rg Cf Meters Kilometers 100s/1000s of Kilometers Spatial Scale Turbulent P LE H GEP and NEE • Ruddell, Brunsell & Stoy (2013) • AfterRuddell and Kumar (2009a,b) Seconds Minutes Hours One Day One Week Temporal Scale

11. How much information do we really need? PLIRTLE model (Shaver et al. 2007) Inputs: PPFD, Ta, LAI (NDVI) Outputs: Gross Primary Productivity Ecosystem Respiration Stoy et al. (2013) AAAR. In press.

12. The amount of information that preserves the information content (pdf) NDVI information content diverges from original Bias ensues Stoy et al. (2009) Land. Ecol., after Stoy et al. (2009) Ecosystems

13. B) Ecology: Pattern = Process (e.g. Turner 1989) Do our models match observed patterns? Stoyet al. (2009) BG

14. ‘Multi-Annual’ spectral peaks in models RE GEP CANOAK Long time series are required to quantify IAV NEE ca. 7 – 11 y Stoyet al. (2009) BG

15. Do models capture interannualvariabilty? Wavelet coherence: ED2 model, US-Ha1 Significant wavelet coherence with US-Ha1: ED2 LPJ Annual (24hrs) 103.94 LoTEC_DA ORCHIDEE Daily (24hrs) 101.38 Stoyet al.(2013) BGD. In press. See also Dietze et al. (2011)

16. Are we arriving at a synthesis, or just playing with data? So models don’t match measurements and scaling is important. What’s new? C) The ability to formally fuse models with data

17. “We have to do better at producing tools to support the whole research cycle – from data capture and data curation to data analysis and data visualization.” –Jim Gray (2007) Scientific workflow Recursive! PECaN (After Lebauer, Wang, Feng and Dietze, 2011)

18. Ensemble Kalman Filter (DALEC model) Obs (t+1) Initial Forecast model State (t) Forecast (t+1) Assimilation State (t+1) (EnKF) g C m-2 77±3 Uncertainty is as important as the observation / prediction 127±2 Cumulative 140±3 168±13

19. Scaling, Ecology, and C cycle synthesisaren’tgoingaway • Jarvis (1995) • Information sciencegives us a common set of tools • for scaling, pattern extraction, and synthesis

20. Understanding the C cycle across all time/spacescalesatwhichit varies Globe Climate Macrosystem Region genome

21. A. Arneth (Lund), D.D. Baldocchi (Berkeley), L.E. Band (UNC), A. Barr (Saskatoon), W. Bauerle (Colorado State), B. Cook (Oak Ridge), E. Daly (Melbourne), K. Davis (Penn State), E. DeLucia (Illinois), A. Desai (Wisconsin), M. Detto (Berkeley), M. Disney (UCL), D.E. Ellsworth (Sydney), E. Falge (MPI Mainz), L. Flanagan (Lethbridge), T.G. Gilmanov (SDSU), J.E. Hobbie (MBL), D. Hollinger (USFS), B. Huntley (Durham), R. Jackson (Duke), J-Y Juang (Tapei), M. Jung (MPI-Jena), G.G. Katul (Duke), B.E. Law (OSU), R. Leuning (CSIRO), P. Lewis (UCL), S. Liu (USGS), Y. Luo (Oklahoma), H.R. McCarthy (UC-Irvine), J.H. McCaughey (Queen’s), J.W. Munger (Harvard), K. Novick (Duke), S. Ollinger (UNH), R. Oren (Duke), D. Papale (Tuscia), K.T. Paw U. (Davis), G. Phoenix (Sheffield), E.B. Rastetter (MBL), M. Reichstein (MPI-Jena), A.D. Richardson (Harvard), S. Running (Montana), H-P. Schmid (Garmisch-Partenkirchen), G.R. Shaver (MBL), M.B.S. Siqueira (Duke), J. Tenhunen (Bayreuth), C. Trudinger (CSIRO), C. Song (UNC), S. Verma (Nebraska), S. Qian (Duke), T. Vesala (Helsinki), Y-P. Wang (Melbourne), M. van Wijk (Wageningen), M. Williams (Edinburgh), G. Wohlfahrt (Innsbruck), S.C. Wofsy (Harvard), W. Yuan (Beijing), S. Zimov (Cherskii) FLUXNET NACP Site Level Interim Synthesis ABACUS (PI M. Williams) M. Dietze & lab B. Ruddell & N. Brunsell Acknowledgements

22. Carbon cycle science in the Big Data era: opportunities and limitationsPaul Stoypaul.stoy@montana.eduwww.watershed.montana.edu/flux

23. How much information minimizes scalewise bias? LAI Jensen’s Inequality NDVI Williams et al. (2008) GCB Stoy et al. (2009) Land. Ecol. Also f(σNDVI2, information content)