Why it is good to be uncertain ? Martin Wattenbach, Pia Gottschalk, Markus Reichstein, Dario Papale, Jagadeesh Yeluripati, Astley Hastings, Marcel van Oijen, Pete Smith Members of JUTF

outline Uncertainty - the big unknown Some general thoughts about sources of uncertainty Uncertainty in CarboEurope –Measurement uncertainty –Model uncertainty JUTF activities Where do we go next ?

Uncertainty - the big unknown “We demand rigidly defined areas of doubt and uncertainty!” Douglas Adams

Uncertainty the big unknown Uncertainty is a measure of the lack of confidence we have in our experimental or modelling results after we have corrected for any known error We can not measure it; we can only estimate its range of probability Consequently, uncertainty is not error

Uncertainty - sources Incomplete or imperfect observations Incomplete conceptual frameworks In accurate prescriptions of known processes Chaos Lack of predictability Source: IPCC Martin Manning and Michel Petit

Uncertainty - sources Ecosystem Model model scenario uncertainty - type D baseline uncertainty - type C conceptual uncertainty - type E scientific judgement uncertainty – type B measured/statistical uncertainty - type A Scenario uncertainty measurement

Measurement Uncertainty

Model uncertainty Input parameters and variables Uncertainty site scale Fertilization (Nitrogen)+/- 10% each application Temperature+/- 1°C Precipitation+/- 5% Global radiation+/- 5% Clay content+/- 10% Initial soil carbon+/- 10% siteNEE measured kgC ha -1 best estimate run kgC ha -1 Mean value of the Monte Carlo simulation kgC ha -1 Oensingen The discrepancy between simulated mean value from the Monte Carlo runs and the annual value obtained from a single run using the best estimates. suggest that using the best estimate may not lead to the most probable model result. Input distribution Monte Carlo – multi model run DNDC* model * DeNitrification-DeComposition model Output distribution

Model uncertainty – global uncertainty Gottschalk et al. 2007

Model uncertainty - factor importance Oensingen In Oensingen Ex Laqueuille In Laqueuille Ex Gottschalk et al. 2007

JUTF - Joint Uncertainty Task Force Two projects – two shared aims: –CarboEurope : CarboEurope-IP aims to understand and quantify the present terrestrial carbon balance of Europe and the associated uncertainty at local, regional and continental scale. –NitroEurope: an observing system for N fluxes and pools [Component 1] a network of manipulation experiments [Component 2] plot-scale C-N modelling [Component 3] landscape analysis [Component 4] European up-scaling [Component 5] and uncertainty and verification of European estimates [Component 6] –Joint efforts = JUTF

JUTF key activities Workshop in spring 2007 that brought together people from both projects. We (CEU) learned about: –The NEU protocols for good-modelling practice and for uncertainty quantification and analysis Marcel von Oijen approach of Bayesian calibration and model comparison as one of the key features of the uncertainty analysis methods –Agreement to have a joint model comparison exercise across scales using the Bayesian approach –Implementation of the up-scaling approach used in NEU as one method for CEU croplands up-scaling

Where do we go next Prior pdf Posterior pdf model Bayesian calibration model data

Why do we go Bayesian ? 1.BC uses parameter pdf’s instead of best estimates 2.Takes into account data pdf’s 3.Use Bayes’ Theorem to calculate posterior parameter pdf 4.Use Bayes’ Theorem to quantify the plausibility of different models 5.It will reduce the uncertainty in our model results in the case the model represents the system correctly 6.Do all future model runs with samples from the parameter pdf (i.e. quantify uncertainty of model results) BC can use data to reduce parameter uncertainty for any process-based model

summary Uncertainty is already a key feature in CEU measurement and modelling However, implementation of NEU protocols are a useful addition to already existing methods in CarboEurope –Only the Bayesian approach can not only quantify but also reduce uncertainty in model parameters even with limited information available Why it is good to be uncertain ? –bad communication of uncertainties leads to misinterpretation, misunderstanding and finally to wrong decisions (e.g. Harwood and Stokes 2003) –Only “rigidly defined areas of doubt and uncertainty” will prevent this

“There is a theory which states that if ever for any reason anyone discovers what exactly the Universe is for and why it is here it will instantly disappear and be replaced by something even more bizarre and inexplicable. There is another that states that this has already happened.” Douglas Adams Thank you for your attention

Some thoughts about Uncertainty "probability theory is the logic of science" "all statements are conditional" "models can not be usefully evaluated without comparison to other models“ Marcel van Oijen