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

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

General considerations I Definitions Uncertainty: the state of being unsure of something In field science (ISO 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).

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

General considerations III Charaterization 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….)

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

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

Session plan Tuesday, 11:15-13:00 1.Overview about the taskforce objectives (Reichstein/Smith/Wattenbach/Gerbig) 2.Uncertainties in inventories (Luyssaert) 3.Uncertaintes in flux estimates (Aubinet)´ 4.Uncertainties in carbon balances inferred from atmospheric measurements (Rödenbeck/Peylin/Schumacher) 5.Integration of uncertainties in bottom-up modeling (Wattenbach) 6.Bottom-up modelling: Model input&structure uncertainties (Jung) 7.Bottom-up modelling: Parameter uncertainties (Zaehle) 8.Bottom-up modelling: Scaling-aggregation-representation uncertainties (Tenhunen) 9.Integrating and propagating uncertainties via CCDAS (Rayner) 10.Uncertainty quantification and analysis (UQ/UA) in NitroEurope (NEU) (van Oijen/Smith)

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

Also clear: we need distributions instead of point estimates

Show CE-IP integration (multiple- constraint) slide

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