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A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere-Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.

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Previous observational studies in the stable boundary layer diverge appreciably on the values of dimensionless ratios between turbulence-related quantities and on their stability dependence. In the present study, the hypothesis that such variability is caused by the influence of locally dependent nonturbulent processes, referred to as submeso, is tested and confirmed. This is done using six datasets collected at sites with different surface coverage. The time-scale dependence of wind components and temperature fluctuations is presented using the multiresolution decomposition, which allows the identification of the turbulence and submeso contributions to spectra and cospectra. In the submeso range, the spectra of turbulence kinetic energy range increases exponentially with time scale. The exponent decreases with the magnitude of the turbulent fluctuations at a similar manner at all sites. This fact is used to determine the smaller time scale with relevant influence of submeso processes and a ratio that quantifies the relative importance of such nonturbulent processes with respect to turbulence. Based on that, values for the local stability parameter that are unaffected by nonturbulent processes are found. It is shown that the dimensionless ratios do not usually converge to a given value as the time scale increases and that it is as a consequence of the locally dependent submeso influence. The ratios and their stability dependence are determined at the time scales with least influence of nonturbulent processes, but significant site-to-site variability persists. Combining all datasets, expressions for the dependence of the dimensionless ratios on the local stability parameter that minimize the role of the submeso contribution are proposed.

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