carbon cycle science in the big data era: opportunities and limitations paul stoy...
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Carbon cycle science in the Big Data era: opportunities and limitations
Paul [email protected]
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 [email protected]
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
genomegenome
RegionRegion
MacrosystemMacrosystem
GlobeGlobe
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
Ki
lom
eter
s
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
Ki
lom
eter
s
10
0s/1
000s
of K
ilom
eter
s
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
ORCHIDEE
Daily(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
ula
tive
Obs (t+1)
Forecast (t+1) Assimilation
77±3
127±2
140±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
genomegenome
RegionRegion
MacrosystemMacrosystem
GlobeGlobe
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 [email protected]
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