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Carbon cycle science in the Big Data era: opportunities and limitations Paul Stoy

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Presentation on theme: "Carbon cycle science in the Big Data era: opportunities and limitations Paul Stoy"— Presentation transcript:

1 Carbon cycle science in the Big Data era: opportunities and limitations Paul Stoy

2 FLUXNET NACP Site Level Interim Synthesis ABACUS (PI M. Williams) M. Dietze & lab B. Ruddell & N. Brunsell Carbon cycle science in the Big Data era: opportunities and limitations Paul Stoy

3 What brings us together? 1. Carbon cycle science (obvious) 2. Enjoy scientific endeavors 3. Data intensive approach

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

5 How are we different? 1. Science vs. Policy 2. Measurers vs. Modelers *(MDF) 3. We work at different scales

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 A)Jarvis (1995) Scaling Processes and Problems Scaling is information transfer Sources of error 1)Aggregation (nonlinearity) 2)Feedbacks 3)Time/space heterogeneity genome Region Macrosystem Globe

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

9 Temporal Scale SecondsMinutes Hours One DayOne Week Spatial Scale Meters Kilometers Many Kilometers Turbulent Regional Synoptic LE RgRg CfCf P VPD T air T soil θ H GEP NEE Creating an information process network Ruddell and Kumar (2009a,b)

10 Temporal Scale SecondsMinutes Hours One DayOne Week Spatial Scale Meters Kilometers 100s/1000s of Kilometers Turbulent Regional Synoptic LE RgRg CfCf P GEP and NEE VPD T air T soil θ H Ruddell, Brunsell & Stoy (2013) After Ruddell and Kumar (2009a,b) blue lines/arrows information severed during severe drought. Thin arrows: feedbacks Thick arrows: forcings Information Process Network: Mutual Information Flows

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

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

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

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

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

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? 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 PECaN Recursive! (After Lebauer, Wang, Feng and Dietze, 2011)

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

19 Scaling, Ecology, and C cycle synthesis aren’t going away Information science gives us a common set of tools for scaling, pattern extraction, and synthesis Information science gives us a common set of tools for scaling, pattern extraction, and synthesis Jarvis (1995)

20 Understanding the C cycle across all time/space scales at which it varies genome Region Macrosystem Globe Climate

21 Acknowledgements 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

22 Carbon cycle science in the Big Data era: opportunities and limitations Paul Stoy

23 How much information minimizes scalewise bias? Williams et al. (2008) GCB Stoy et al. (2009) Land. Ecol. NDVI LAI Also f(σ NDVI 2, information content) Jensen’s Inequality


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