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

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

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?

The Kalman Filter in theory

MODEL At Ft+1 F´t+1OPERATOR

At+1

Dt+1

Assimilation

Initial state Forecast ObservationsPredictions

Analysis

P

Drivers

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?

A multiscale approach

Arctic Biosphere Atmosphere Coupling at multiple Scales

Observation operator: NDVI-LAI

Van Wijk & Williams, 2005

LAI harvest calibrates indirect measurement (NDVI)

Shaver et al. J. Ecol. (2007)

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

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

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)

Analysis

Stocks

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)

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

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

REFLEX, stage 2

Flux dataMODIS LAI

MDF

Model parameters

DALECmodel

Testing predictionsWith only limited EO data

MDF

MODIS LAI

Analysis

Flux data

testing

Assimilation

Thank you

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)

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

Indirect, continuous LAI calibration

NDVI

Observer

How good is the model?Are the parameters well known?How accurate are the observations?Are there complementary observations?