Taskforce IV: Treatment, quantification and integration of uncertainties in CarboEurope-IP

1. Propagation, CCDAS Bottom-up modelling Top-down modelling European carbon balance uncertainties Taskforce IV: Treatment, quantification and integration of uncertainties in CarboEurope-IP Component uncertainties (Inventory, Eddy fluxes, Atmosphere measurements,…)

2. Objectives • The overall goal of this task force is to develop a coherent strategy of how uncertainties in CarboEurope have to be treated in order to achieve a scientifically defensible estimate of the European carbon balance and the associated uncertainties at different temporal and spatial scales”. • 1. Sectoral component: • Common definitions • Guidelines for quantification • Importance ranking of uncertainties •  Recommendations for strategies to reduce uncertainties • 2. Integrative component: • Multiple constraint approach  make use of the complementary information in the different data streams • Analysis of data flow between components • Define UA/UQ in bottom-up modelling

3. General considerations IDefinitions • Uncertainty: the state of being unsure of something • In field science (ISO 1995 - the GUM ): “Uncertainty: parameter, associated with the result of a measurement, that characterizes the dispersion of the values that could reasonably be attributed to the measure” • The uncertainty in the result of a measurement arises from the remaining variance in the random component and the uncertainties connected to the correction for systematic effects (ISO 1995).

4. General considerations II(Importance ranking) • When we are considering a ranking of uncertainties within the different sectors reduced the following general equations should be considered: • Importance of Uncertainty = Magnitude * Sensitivity of goal value • Efficiency of reduction = Magnitude * Sensitivity * ‘Cost’ per Reduction of Magnitude

5. General considerations IIICharaterization of uncertainties • Spatial characteristic (scale, domain, absolute values versus gradients) • Temporal characteristic to considered (mean fluxes, trend, interannual variability, seasonal, synoptic, temporal domain) • Type of uncertainty (random, systematic, autocorrelation, scaling/aggregation, total) • Quantity of interest (GPP, NPP; NEP, NBP….)

6. General considerations IVCombination of uncertainties Method A ‘Truth’ Method B Method A: good a variability (also different scales!) Method B: good at mean

7. Large-scale constraint Spatial/temporally consistent data, stochastic events High precision, Spatial coverage Extrapolation cap., incl. of history High precision, high temporal resolution Provide understanding General considerations IVCombination of uncertainties

8. Session planTuesday, 11:15-13:00 • Overview about the taskforce objectives (Reichstein/Smith/Wattenbach/Gerbig) • Uncertainties in inventories (Luyssaert) • Uncertaintes in flux estimates (Aubinet)´ • Uncertainties in carbon balances inferred from atmospheric measurements (Rödenbeck/Peylin/Schumacher) • Integration of uncertainties in bottom-up modeling (Wattenbach) • Bottom-up modelling: Model input&structure uncertainties (Jung) • Bottom-up modelling: Parameter uncertainties (Zaehle) • Bottom-up modelling: Scaling-aggregation-representation uncertainties (Tenhunen) • Integrating and propagating uncertainties via CCDAS(Rayner) • Uncertainty quantification and analysis (UQ/UA) in NitroEurope (NEU) (van Oijen/Smith)

9. Superficially: need of CE-IP to provice uncertaintes (contract)

10. Also clear: we need distributions instead of point estimates

11. Show CE-IP integration (multiple-constraint) slide

12. There is a new view emerging: there no ‘validation’ of models or methods, but only via a new combination of methods with their uncertainties we can reduce those