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

Paul Stoypaul.stoy@montana.edu

www.watershed.montana.edu/flux

FLUXNET NACP Site Level Interim SynthesisABACUS (PI M. Williams)M. Dietze & labB. Ruddell & N. Brunsell

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

Paul Stoypaul.stoy@montana.edu

www.watershed.montana.edu/flux

What brings us together?

1. Carbon cycle science (obvious)

2. Enjoy scientific endeavors

3. Data intensive approach

Gray (2007) NRC-CSTB

We are all (mostly) computer scientists who work on the C cycle

How are we different?

1. Science vs. Policy

2. Measurers vs. Modelers *(MDF)

3. We work at different scales

Can information science bridge our differences?

A) Information scalingB) ‘Data mining’ (KDD)C) Model-data fusion

Are we arriving at a synthesis, or just playing w/data?

A) Jarvis (1995) Scaling Processes and ProblemsScaling is information transfer

Sources of error1) Aggregation (nonlinearity)2) Feedbacks3) Time/space heterogeneity

genome

Region

Macrosystem

Globe

Ecological scaling. A special case of Information Theory?

Ruddell, Brunsell & Stoy (2013)

Temporal Scale

Seconds Minutes Hours One Day One Week

Spati

al S

cale

Met

ers

Kilo

met

ers

Man

y Ki

lom

eter

s

Turbulent

Regional

Synoptic

LE

Rg

Cf

P

VPDTair

Tsoilθ

H

GEP NEE

Creating an information process network Ruddell and Kumar (2009a,b)

Temporal Scale

Seconds Minutes Hours One Day One Week

Spati

al S

cale

Met

ers

Kilo

met

ers

1

00s/

1000

s of

Kilo

met

ers

Turbulent

Regional

Synoptic

LE

Rg

Cf

P

GEP and NEE

VPDTair

Tsoilθ

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

How much information do we really need?

Stoy et al. (2013) AAAR. In press.

PLIRTLE model (Shaver et al. 2007)Inputs:PPFD, Ta, LAI (NDVI)

Outputs:Gross Primary ProductivityEcosystem Respiration

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

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

Stoy et al. (2009) BG

‘Multi-Annual’ spectral peaks in models

CANOAK

Long time seriesare required toquantify IAV

RE

GEP

NEE

ca. 7 – 11 y

Stoy et al. (2009) BG

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

ORCHIDEEDaily(24hrs)101.38

Annual(24hrs)103.94

Wavelet coherence: ED2 model, US-Ha1

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

“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)

State (t)Initial Forecast

State (t+1)

g C m-2

Cum

ulat

ive

Obs (t+1)

Forecast (t+1) Assimilation

77±3

127±2140±3

168±13

model

(EnKF)

Uncertainty is as importantas the observation / prediction

Ensemble Kalman Filter (DALEC model)

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

Jarvis (1995)

Understanding the C cycle across all time/space scales at which it varies

genome

Region

Macrosystem

Globe

Climate

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 SynthesisABACUS (PI M. Williams)M. Dietze & labB. Ruddell & N. Brunsell

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

Paul Stoypaul.stoy@montana.edu

www.watershed.montana.edu/flux

How much information minimizes scalewise bias?

Williams et al. (2008) GCBStoy et al. (2009) Land. Ecol.

NDVI

LAI

Also f(σNDVI2, information content)

Jensen’s Inequality