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