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Some challenges of model-data- integration a collection of issues and ideas based on model evaluation excercises Martin Jung, Miguel Mahecha, Markus Reichstein, Model Simulations by: Guerric Le Maire, Maarten Braakhekke, Sönke Zaehle, Mona Vetter

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Some challenges of model-data- integration a collection of issues and ideas based on model evaluation excercises. Martin Jung, Miguel Mahecha, Markus Reichstein, Model Simulations by: Guerric Le Maire, Maarten Braakhekke, Sönke Zaehle, Mona Vetter. Gross productivity. Net productivity. - PowerPoint PPT Presentation

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Page 1: Martin Jung, Miguel Mahecha, Markus Reichstein,

Some challenges of model-data- integration

a collection of issues and ideas based on model evaluation excercises

Martin Jung, Miguel Mahecha, Markus Reichstein,

Model Simulations by: Guerric Le Maire, Maarten Braakhekke, Sönke Zaehle, Mona Vetter

Page 2: Martin Jung, Miguel Mahecha, Markus Reichstein,

Forest age

Gross productivity

Ecosystem respiration

Net productivity

Ecosystem respiration

Net productivity

After Odum (1969), modified by from Alex Knohl

Model world

Real world

• Carbon balance simulated by process models is most likely biased

• Models may be useful to study variability of the carbon balance (anomalies, processes, …)

• Variability of the carbon balance results from variability of big constituent fluxes (GPP, TER, …)

• Models need to be quite precise at the constituent fluxes to get variability of the carbon balance right

Implications for data assimilation and model evaluations!

Models in steady-state

(see contribution by Nuno Carlvalhais)

Page 3: Martin Jung, Miguel Mahecha, Markus Reichstein,

Correlation of NEP residuals with GPP and TER residuals (based on site-level

runs, monthly data)If NEP is wrong it can be because of:-GPP-TER

If GPP is wrong it can be because of:-some parameter-LAI/fpar-soil water dynamics-temperature sensitivity function-sensitivity of gcan to VPD and soil moisture-coupling of Gcan and photosynthesis...

How to handle confounding effects?

Isolating model components as much as possible for evaluation/assimilation excercises?!

Sensitivity experiments?!

Page 4: Martin Jung, Miguel Mahecha, Markus Reichstein,

Agreement among models regarding inter-annual variability of GPP

Biome-BGC vs Orchidee & LPJ 1-R2

Based on annual GPP from 1981-2000

Models were run with the same input data!

Page 5: Martin Jung, Miguel Mahecha, Markus Reichstein,

APARAPAR

Biophysical vs. ecophysiological control of GPP interannual variability in the models

GPP = APAR x RUEGPP = APAR x RUEAPAR: Absorbed Photosynthetic Active Radiation [MJ/m2/yr]APAR: Absorbed Photosynthetic Active Radiation [MJ/m2/yr]RUE: Radiation Use Efficiency [gC/MJ]RUE: Radiation Use Efficiency [gC/MJ]

Simulated LAISimulated LAI

Input RadiationInput Radiation

Fraction of absorbed Fraction of absorbed radiation (FAPAR): radiation (FAPAR):

1 - exp(-0.5 x LAI)1 - exp(-0.5 x LAI)

Coefficient of variation (%

)

Correlation maps of GPP vs APAR and GPP vs RUECorrelation maps of GPP vs APAR and GPP vs RUE

Interannual Interannual variations of variations of radiation use radiation use efficiency are efficiency are the primary the primary

cause of GPP cause of GPP interannual interannual varibailityvaribaility

Jung et al., GBC, 2007

Page 6: Martin Jung, Miguel Mahecha, Markus Reichstein,

Correlation and sensitivity of summer (JJA) meteorology with GPP

Reducing meteorological variable space (radiation, temperature, vapour Reducing meteorological variable space (radiation, temperature, vapour pressure deficit, and precipitation) to principal componentspressure deficit, and precipitation) to principal components

PCA1 explains 84% of variance of the summer meteorological dataPCA1 explains 84% of variance of the summer meteorological data

PCA1 weights: RAD (-0.28), TEMP (-0.28), VPD (-0.28), RAIN (0.24)PCA1 weights: RAD (-0.28), TEMP (-0.28), VPD (-0.28), RAIN (0.24)Temperature/Radiation limitedTemperature/Radiation limitedMoisture limitedMoisture limited

Does nitrogen dynamics influence interannual variations of GPP?!Does nitrogen dynamics influence interannual variations of GPP?!

Effects of Effects of water stresswater stress on photosynthesis largely control GPP interannual variability on photosynthesis largely control GPP interannual variability

canopy conductance and coupling with carbon assimilationcanopy conductance and coupling with carbon assimilation

representation of soil, roots, below ground processesrepresentation of soil, roots, below ground processes Jung et al., GBC, 2007

Page 7: Martin Jung, Miguel Mahecha, Markus Reichstein,

Do the models have a systematic bias during drought?

(Model_site_month_DryYear – Model_site_month_WetYear) –

(Eddy_site_month_DryYear – Eddy_site_month_WetYear)

Drought effect too strong

Dro

ught

effe

ct

too

wea

kn.s. n.s.significant

Site-l

evel

runs

Page 8: Martin Jung, Miguel Mahecha, Markus Reichstein,

The models response to meteorology - How to tackle equifinality?

• 21 day sliding correlation window between C-fluxes and Temp, Rad, VPD, SWC

Cons

iste

ncy

Response of simulated NEP to meteo is more consistent with site data than the gross fluxes ‘equifinality’ or artifact of flux separation?

Largest differences with respect to TER

Consistency: how often does the simulated flux correlate with the same meteo driver as the eddy-based flux

sum(Var_maxR_site == Var_maxR_model)/sum(significant correlations)

Site-l

evel

runs

Confounding effects because meteo variabels are co-linear

Page 9: Martin Jung, Miguel Mahecha, Markus Reichstein,

Model RMSE as a function of time scale

Mahecha et al. In prep.

RMSE

(nor

m b

y da

ta ra

nge)

High frequency components & seasonal cycle work better than inter- and intra-annual components

Significance of changing pools & ecosystem properties?

Inter-annual components of GPP vs Gcan

Page 10: Martin Jung, Miguel Mahecha, Markus Reichstein,

What is an adequate model?• ‚scatter‘ is ok, bias not (data are noisy, simulations not)• RMSE, R2, ... are not really good measures of model performance• Looking for robust patterns in the FLUXNET data!• Can ‚patterns‘ be assimilated into models?

Jung et al 2007, Biogeosciences

Page 11: Martin Jung, Miguel Mahecha, Markus Reichstein,

What about using patterns from upscaled carbon fluxes?

• Advantages: noise goes away; no issues of ‚site specific pecularities‘; no representation bias; matches the scale of the models

• Disadvantages: uncertainties from drivers (meteo data, remote sensing products); model specific sensitivity to meteo; no effects from changing pools ( IAV)

Page 12: Martin Jung, Miguel Mahecha, Markus Reichstein,

Comparison of European mean GPP pattern: Process- vs. data-oriented models

Process oriented modelsProcess oriented models

Data driven modelsData driven models

R2R2

Mean annual GPP patterns from data-oriented models are becoming sufficiently robust for benchmarking process-oriented models

Page 13: Martin Jung, Miguel Mahecha, Markus Reichstein,

2003 GPP anomaly from different data-oriented models

Inter-annual variability from data-oriented models is not sufficiently robust for benchmarking process-oriented models

Jung et al., GCB, in press

Page 14: Martin Jung, Miguel Mahecha, Markus Reichstein,

Final Remarks/Questions• How to deal with important input data that are

usually not available (effective rooting depth, water holding capacity)?

• To what extent are parameters allowed to compensate for inadequate structure?

• What is an adequate model structure?• How to identify not adequate structure

components?• Should we concentrate on ‚patterns‘ rather than

on ‚values‘?