data assimilation as a tool for c cycle studies
DESCRIPTION
www.abacus-ipy.org. Data assimilation as a tool for C cycle studies. Mathew Williams, University of Edinburgh. Collaborators: P Stoy, J Evans, C Lloyd, A Prieto Blanco, M Disney, L Street, A Fox (Sheffield) M Van Wijk (Wageningen), E B Rastetter (MBL), G Shaver (MBL). - PowerPoint PPT PresentationTRANSCRIPT
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Data assimilation as a tool for C cycle studies
Collaborators: P Stoy, J Evans, C Lloyd, A Prieto Blanco, M Disney, L Street, A Fox (Sheffield)
M Van Wijk (Wageningen), E B Rastetter (MBL), G Shaver (MBL)
www.abacus-ipy.org
Mathew Williams, University of Edinburgh
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Transferring information across scales
The upscaling problem and data assimilation An Arctic C cycle application REFLEX – a comparison of DA approaches for
C flux estimation
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Upscaling C fluxes
How do we cope with spatial variation? What are the critical feedbacks over longer
time scales? How can model/parameters be improved? How can multiple data be combined? How trustworthy are such combinations?
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The Kalman Filter in theory
MODEL At Ft+1 F´t+1OPERATOR
At+1
Dt+1
Assimilation
Initial state Forecast ObservationsPredictions
Analysis
P
Drivers
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SWEDEN
What is the carbon balance of an Arctic landscape?
How will C balance change in the future?What measurements should we take to
improve understanding and forecast skills?
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A multiscale approach
Arctic Biosphere Atmosphere Coupling at multiple Scales
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Observation operator: NDVI-LAI
Van Wijk & Williams, 2005
LAI harvest calibrates indirect measurement (NDVI)
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Shaver et al. J. Ecol. (2007)
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GPP Croot
Cwood
Clitter
CSOM/CWD
Ra
Ar
Aw
Cfoliage
Af Lf
Lr
Lw
Rh
D
Temperature controlled
5 model pools9 model fluxes9 unknown parameters2 data time series
Net Ecosystem Exchange of CO2
C = carbon poolsA = allocationL = litter fallR = respiration (auto- & heterotrophic)
NDVI
DALEC
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The Kalman Filter in practice
DALECmodel
At Ft+1
NEE
NDVI
LAI-NDVIfit
At+1
NEE
NDVI
Assimilation
Initial state Forecast
Predictions
Analysis
Parameters
Met. drivers
Light responsecurves
Harvest calibrationFlux tower
Skye sensor
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Data time series
100 110 120 130 140 150 160 170 180 190 200-10
-5
0
5Net ecosystem exchange of C
m
ol m
-2 s
-1
100 110 120 130 140 150 160 170 180 190 2000.4
0.5
0.6
0.7
0.8Normalised difference vegetation index
ND
VI
Time (day of year 2007)
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Analysis
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Stocks
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Next steps
Isotopic tracer experiments C14 for SOM turnover Automated chambers Field determination of NPP (rhizotrons,
harvests) Spatial NDVI sampling (field and aircraft) PBL measurements (aircraft)
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REFLEX: GOALS
To identify and compare the strengths and weaknesses of various MDF techniques
To quantify errors and biases introduced when extrapolating fluxes made at flux tower sites using EO data
Closing date for contributions: 31 October
www.carbonfusion.org
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Regional Flux Estimation Experiment, stage 1
Flux dataMODIS LAI
MDF
Full analysisModel parameters
Forecasts
DALECmodel
Training Runs- FluxNet data- synthetic data
Deciduous forest sites
Coniferous forest sites
Assimilation
Output
www.carbonfusion.org
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REFLEX, stage 2
Flux dataMODIS LAI
MDF
Model parameters
DALECmodel
Testing predictionsWith only limited EO data
MDF
MODIS LAI
Analysis
Flux data
testing
Assimilation
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Thank you
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Time series data
Eddy covariance measurements at 3 m, open path LICOR 7500
EC: logical filter and U* filter (0.2 m s-1) applied EC: error assumed constant at 1 mol m-2 s-1
– Being actively explored
NDVI sensor at 2 m (Skye 2-channel) logged at 20 mins and averaged daily, with estimated 10% error (tbc)
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100 120 140 160 180 2000
2
4
6
8
10
12
14
m
ol m
-2 s
-1
100 120 140 160 180 2000
0.5
1
1.5
2
2.5
m
ol m
-2 s
-1
Time (day of year, 2007)
Analysis State Vector
100 120 140 160 180 2000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
m2 m
-2
100 120 140 160 180 2000.5
0.55
0.6
0.65
0.7
0.75
ND
VI
GPP Ecosystemrespiration
LAI NDVI
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Indirect, continuous LAI calibration
NDVI
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Observer
How good is the model?Are the parameters well known?How accurate are the observations?Are there complementary observations?