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. Comparing model output and observed data is animportant step for assessing model performance and quality of simulationresults. However, such comparisons are often hampered by differences inspatial scales between local point observations and large-scale simulationsof grid cells or pixels. In this study, we propose a generic approach for apixel-to-point comparison and provide statistical measures accounting for theuncertainty resulting from landscape variability and measurement errors inecosystem variables. The basic concept of our approach is to determine thestatistical properties of small-scale (within-pixel) variability andobservational errors, and to use this information to correct for their effectwhen large-scale area averages (pixel) are compared to small-scale pointestimates. We demonstrate our approach by comparing simulated values ofaboveground biomass, woody productivity (woody net primary productivity, NPP)and residence time of woody biomass from four dynamic global vegetationmodels (DGVMs) with measured inventory data from permanent plots in theAmazon rainforest, a region with the typical problem of low dataavailability, potential scale mismatch and thus high model uncertainty. Wefind that the DGVMs under- and overestimate aboveground biomass by 25 %and up to 60 %, respectively. Our comparison metrics provide aquantitative measure for model–data agreement and show moderate to goodagreement with the region-wide spatial biomass pattern detected by plotobservations. However, all four DGVMs overestimate woody productivity andunderestimate residence time of woody biomass even when accounting for thelarge uncertainty range of the observational data. This is because DGVMs donot represent the relation between productivity and residence time of woodybiomass correctly. Thus, the DGVMs may simulate the correct large-scalepatterns of biomass but for the wrong reasons. We conclude that moreinformation about the underlying processes driving biomass distribution arenecessary to improve DGVMs. Our approach provides robust statistical measuresfor any pixel-to-point comparison, which is applicable for evaluation ofmodels and remote-sensing products.

A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region