Combining Observations and Models: A Review of the CARDAMOM Framework for Data-Constrained Terrestrial Ecosystem Modeling.

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process-based models, and improve forecasting for decision support.

Bayesian weighting of climate models based on climate sensitivity.

Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO concentrations can result in biased projections. Various methods have been proposed to ameliorate this 'hot model' problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble.

Projecting groundwater storage changes in California's Central Valley.

Accurate and detailed knowledge of California's groundwater is of paramount importance for statewide water resources planning and management, and to sustain a multi-billion-dollar agriculture industry during prolonged droughts. In this study, we use water supply and demand information from California's Department of Water Resources to develop an aggregate groundwater storage model for California's Central Valley. The model is evaluated against 34 years of historic estimates of changes in groundwater storage derived from the United States Geological Survey's Central Valley Hydrologic Model (USGS CVHM) and NASA's Gravity Recovery and Climate Experiment (NASA GRACE) satellites.

Climate sensitive size-dependent survival in tropical trees.

Survival rates of large trees determine forest biomass dynamics. Survival rates of small trees have been linked to mechanisms that maintain biodiversity across tropical forests. How species survival rates change with size offers insight into the links between biodiversity and ecosystem function across tropical forests.

Probing the limits of predictability: data assimilation of chaotic dynamics in complex food webs.

The daunting complexity of ecosystems has led ecologists to use mathematical modelling to gain understanding of ecological relationships, processes and dynamics. In pursuit of mathematical tractability, these models use simplified descriptions of key patterns, processes and relationships observed in nature. In contrast, ecological data are often complex, scale-dependent, space-time correlated, and governed by nonlinear relations between organisms and their environment.