data assimilation as a tool for biogeochemical studies mathew williams university of edinburgh
DESCRIPTION
Talk outline Assimilating C flux and stocks data to improve analyses of C dynamics Assimilating reflectance data Assimilating latent energy flux data to deconvolve net carbon fluxesTRANSCRIPT
Data assimilation as a tool for biogeochemical studies
Mathew WilliamsUniversity of Edinburgh
The carbon problem
Friedlingstein P., et al. 2006. Journal of Climate.
– 11 coupled climate-carbon models predicted very different future C dynamics
Conclusion – our models are flawed Solution – better model testing against data,
and better use of multiple data sets to test the representation of process interactions
Talk outline
Assimilating C flux and stocks data to improve analyses of C dynamics
Assimilating reflectance data Assimilating latent energy flux data to
deconvolve net carbon fluxes
Improving estimates of C dynamics
MODELS OBSERVATIONS
FUSION
ANALYSIS
MODELS+ Capable of interpolation
& forecasts- Subjective & inaccurate?
OBSERVATIONS+Clear confidence limits
- Incomplete, patchy- Net fluxes
ANALYSIS+ Complete
+ Clear confidence limits+ Capable of forecasts
Time update“predict”
Measurement update
“correct”
A prediction-correction system
Initial conditions
The Kalman Filter
MODEL At Ft+1 F´t+1OPERATOR
At+1
Dt+1
Assimilation
Initial state Forecast ObservationsPredictions
Analysis
P
Ensemble Kalman Filter
Drivers
Observations – Ponderosa Pine, OR (Bev Law)Flux tower (2000-2)Sap flowSoil/stem/leaf respirationLAI, stem, root biomassLitter fall measurements
GPP Croot
Cwood
Cfoliage
Clitter
CSOM/CWD
Ra
Af
Ar
Aw
Lf
Lr
Lw
Rh
D
C = carbon poolsA = allocationL = litter fallR = respiration (auto- & heterotrophic)
GPP Croot
Cwood
Cfoliage
Clitter
CSOM/CWD
Ra
Af
Ar
Aw
Lf
Lr
Lw
Rh
D
Temperature controlled
6 model pools10 model fluxes9 parameters10 data time series
Rtotal & Net Ecosystem Exchange of CO2
C = carbon poolsA = allocationL = litter fallR = respiration (auto- & heterotrophic)
Time (days since 1 Jan 2000) Williams et al (2005)
Time (days since 1 Jan 2000) Williams et al (2005)
= observation— = mean analysis| = SD of the analysis
Time (days since 1 Jan 2000) Williams et al (2005)
Time (days since 1 Jan 2000) Williams et al (2005)
= observation— = mean analysis| = SD of the analysis
0 365 730 1095-4
-3
-2
-1
0
1
2
0 365 730 1095-4
-2
0
2
Time (days, 1= 1 Jan 2000)
b) GPP data + model: -413±107 gC m-2
0 365 730 1095-4
-3
-2
-1
0
1
2c) GPP & respiration data + model: -472 ±56 gC m-2N
EE
(g C
m-2 d
-1)
0 365 730 1095-4
-2
0
2
a) Model only: -251 ±197 g c m-2
d) All data: -419 ±29 g C m-2
Data brings confidence
Williams et al (2005)
= observation— = mean analysis| = SD of the analysis
Assimilating EO reflectance data
DALECAt Ft+1
Reflectance
t+1
Radiativetransfer
At+1
MO
DIS
t+1
DA
Model only
AssimilatingMODIS NDVI
EO assimilation to improve photosynthesis predictions
= observation— = mean analysis| = SD of the analysis
Quaife et al. (RSE in press)
Cf
Cr
Cw
Cl
Csom
GPP
WS1
WS2
WS3
ETPpt
Rh
Carbon Hydrology
Ra
Constraining the C cycle via hydrology
Deconvolving net C fluxes
NEE = Reco – GPP Eddy flux towers also measure LE LE GPP (some complications…) Use a model of coupled C-water fluxes… Assimilate LE and NEE data, and use LE to
constrain GPP Improved flux deconvolution Improved model diagnosis and prognosis
Demonstration study
Generate a “true” system with a complex model Sample the “truth” and generate observations
(with errors) Attempt to reconstruct the truth through
assimilating the observations into a simple model
Experiment with NEE data alone, and NEE + LE data
Fluxes
Truth
Obs.
Analysis
Residuals
Obs.
Truth
Stocks
Truth
Obs.
Analysis
Summary
Data assimilation techniques are powerful tools for ecological research
Time series data are most useful For improved predictions, better constraints on
long time constant processes are required Error characterisation is vital EO data can be assimilated Hydrological assimilation can decompose net C
fluxes into components.
Thank you
Acknowledgements:Bev LawTris Quaife