Edinburgh, June 2008Markus Reichstein Critical issues when using flux data for reducing Land Surfcace Model uncertainties – towards full uncertainty accounting?

Edinburgh, June 2008 Markus Reichstein

Critical issues when using flux data for reducing Land Surfcace Model

uncertainties – towards full uncertainty accounting?

Markus ReichsteinBiogeochemical Model-Data Integration Group

Max-Planck Institute for Biogeochemistry

CARBONFUSION workshop, University of Edinburgh, June 2008


Nominal uncertainties from CCDAS

Rayner et al. (2005)


Real uncertainties?

Rayner et al. (2005)


Nominal uncertainties by flux tower inversion

Knorr and Kattge (2005)

Parameter-based


Types of uncertainty in model-data fusion

• Model– Parameters– Structure– Model set-up

• Calibration data and drivers– Statistical error– (Selective) bias– Representation error


A toy experiment with artificial data…


Simple temperature response function with noise

6 8 10 12 14

X

0

1

2

3

4

Y

R2L=0.26, R2

P=0.26 (y=1.56-0.06x+0.01x2, p=0.000, N=200)

Temperature [°C]

Res

pir

atio

n


Estimating uncertainties via bootstrapping assuming a linear model

Temperature [°C]

Pre

dic

ted

res

pir

atio

n

2.68 2.80 2.91 3.02 3.13 3.24 3.35 3.46

Variable

0.00

0.05

0.10

0.15

0.20

0.25

59

15

33

47

68

99

74

55

3634

15

8

2

Distribution of prediction at 18°C


Introducing ‘model uncertainty’: use polynomials of higher order

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Variable

0.0

0.2

0.4

0.6

0.8

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Variable

0.0

0.2

0.4

0.6

0.8

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Variable

0.0

0.2

0.4

0.6

0.8

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Variable

0.0

0.2

0.4

0.6

0.8

Prediction uncertainty at 18°C

Respiration at 18°C

Linear model ‘correct’

Linear or quadratic

Linear, quadratic,

or cubic


Prediction uncertainty: confidence intervals

Density

5 10 15 20

X

0.32

1.65

2.98

4.32

Y

2

4

6

8

0.00

9.56

Temperature [°C]

Res

pir

atio

n

LinearLinear or quadr.Linear to cubic


Simulating systematic selective error

6 8 10 12 14

X

1

2

3

4

Y

R2L=0.34, R2

P=0.35 (y=1.60-0.09x+0.01x2, p=0.000, N=200)

R2L=0.46, R2

P=0.47 (y=0.52+0.07x+0.01x2, p=0.000, N=200)

Temperature [°C]

Res

pir

atio

n

Probability of 30% bias increasing from 10 to 5°C


Effect of this error depends on ‘model’

Density

5 10 15 20X

-0.58

1.28

3.15

5.02

Y

2

4

6

8

0.00

9.27

LinearLinear or quadr.Linear to cubic

Temperature [°C]

Res

pir

atio

n


What does that mean in our context (constraining LSMs with eddy-flux

data)?

• Random error rel. well characterized (Richardson et al. 2006, Lasslop et al. 2007)

• More important and less well understood: systematic errors (e.g. night-time fluxes, energy balance closure…)

• LSMs far from perfect or unique…..


Random error well characterized and ‘relatively’ unproblematic

Almost normal distribution in most cases

Fast decay of autocorrelation,almost no cross-correl

Lasslop et al. (2008)


Random errors: annual NEE

Based solely on random error statistics

Histogramm of confidence interval range for annual NEE


Assessing the syst. error: Uncertain u* - threshold

Bootstrapping technique is used to assess the uncertainty in the ustar threshold selection

BE-Vie 2001

cf. Reichstein et al. 2005, Papale et al. 2006

‘Barford’ plot, as sent before, blue triangle now show 95% confidence intervalls in u*threshold and NEE estimate, based on our bootstrapping

Box plots for NEE estimate and u*thresholds based on bootstrapping u* threshold, x-axis labels are years and annual NEE_fqcok. Boxes are 25-75 percentile, whiskers 5-95 perc. ~90% conf. intervall



Random versus systematic errors: annual NEE

Based solely on random error statistics

Based on bootstrappedustar uncertainty

Histogramm of confidence interval range for annual NEE


Selective systematic error leads to selective parameter errors…

… but can be attenuated by multiple constraints…

Lasslop et al. (2008)

CO2 flux constraint only CO2 and H2O constraint


Ideal model-data integration cycle (bottom-up)

Model(re)formulation(Definition of model

structure)Model

characterization(Forward runs, consistency check,

sensitivity, uncert. analysis)

Model parameter estimation

(Multiple constraint)

Parameterinterpretation

(Thinking)

Generalization(‘up-scaling’)

Model validation(against indep. data, by scale or quantity)

Model application

DATA


Addressing and reducing these uncertainties: ideas and questions

• Not only ‘formal uncertainties’; explore full range of uncertainty by data and model resampling strategies (‘data ensembles’)

• Disentangle parts of the system, i.e. look at sub-processes– Physiology, phenology, long-term dynamics ( different time scales)– e.g. first constrain and evaluate GPP, preferably while knowing

APAR, then constrain phenology parameters

• Combine process-oriented and data-mining approaches (e.g. finding patterns in residuals)

• Pattern-oriented modelling (only compare against ‘robust unbiased data patterns’, not the noise)

• Multiple-constraint approaches (but what if constraints contradict each other…?)

• Can Bayesian approaches help or does does it just ‘hide’ uncertainties?


Finally the 11 commandments…

1. Don’t kill your neighbor2. …3. …4. …5. …6. …7. …8. …9. …10. …11. Don’t understate uncertainties …

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Edinburgh, June 2008Markus Reichstein Critical issues when using flux data for reducing Land Surfcace Model uncertainties – towards full uncertainty accounting?