Martin Jung, Miguel Mahecha, Markus Reichstein,
description
Transcript of 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
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)
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?!
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!
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
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
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
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
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
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
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)
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
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
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‘?