Abstract

Process-based models of atmosphere-biosphere interactions, along with empirical data mining techniques, are the primary tools used for scaling disparate observations through space and time. In the past few decades they have been developed in tandem with our understanding of ecological theory, resulting in models of various levels of complexity and detail. Model intercomparisons, however, show a large range in model performance, with no clear consensus as to whether model structural error (process mis-representation) or mis-parameterization is to blame. One potential reason for this lies in difficulties in using data sources at different scales to adequately test model performance and the common reliance on uni-variate model validations. Another is the lack of quantification of the uncertainty associated with model projections. In order to advance our ability to make policy-actionable projections of the future evolution of the earth system, we need to address these issues.

In this talk I will assess our ability to model land-atmosphere CO2 exchange at different spatial and temporal scales, both in the present climate and under future climate change. The analysis makes heavy use of model-data fusion techniques, which constitute a powerful framework by which to combine models with various data streams. Model benchmarking tools, such as empirical data mining techniques, also provide a strong alternative model evaluation. To illustrate the potential benefits of such an approach, we assess the performance of 17 process-based models of atmosphere-biosphere interactions, and two data mining tools, across 11 long-term eddy covariance forest sites. The results highlight details of model performance often overlooked by conventional model-data comparisons, and quantify the degree of coupling of terrestrial carbon sequestration to climate anomalies at multiple sites and time scales.