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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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Experimentally characterizing evapotranspiration (ET) in different biomes around the world is an issue of interest for different areas of science. ET in natural areas of the Brazilian Pampa biome has still not been assessed. In this study, the actual ET (ETact) obtained from eddy covariance measurements over two sites of the Pampa biome was analyzed. The objective was to evaluate the energy partition and seasonal variability of the actual ET of the Pampa biome. Results showed that the latent heat flux was the dominant component in available energy in both the autumn–winter (AW) and spring–summer (SS) periods. Evapotranspiration of the Pampa biome showed strong seasonality, with highest ET rates in the SS period. During the study period, approximately 65% of the net radiation was used for the evapotranspiration process in the Pampa biome. The annual mean ET rate was 2.45 mm d−1. ET did not show to vary significantly between sites, with daily values very similar in both sites. The water availability in the Pampa biome was not a limiting factor for ET, which resulted in a small difference between the reference ET and the actual ET. These results are helpful in achieving a better understanding of the temporal pattern of ET in relation to the landscape of the Pampa biome and its meteorological, soil, and vegetation characteristics.

Cassava [Manihot esculenta (L.) Crantz] plays an important role as staple food in the tropics. The GUMCAS model is a process-based dynamic simulation model for cassava that has been adapted to the Cropping System Model (CSM) framework of DSSAT (DSSAT-CSM). The objective of this study was to calibrate and evaluate the original GUMCAS model, a modified version of the GUMCAS model, and the current cassava model in DSSAT under potential conditions in the subtropical environment of Rio Grande do Sul State, Brazil. The modified original GUMCAS model consisted of three modifications in the code: we included a third independent "clock" in the cassava development for the onset of starch accumulation, we replaced the rate of leaf appearance submodel with the Wang and Engel model, and we modified the leaf senescence submodel. Model calibration was with a field experiment for cultivar Fepagro-RS 13 during the 2011/2012 growing season in Santa Maria, Brazil. Independent data from 16 experiments conducted at four sites in Rio Grande do Sul State were used for testing the performance of the three versions of the cassava model. The original GUMCAS model had the poorest performance, and the modified GUMCAS model slightly improved the predictions of stem and storage root yield compared with the current DSSAT cassava model. The modified GUMCAS model greatly improved the predictions of developmental stages, leaf development, and leaf area growth dynamics compared with the current DSSAT cassava model. Results from this study contribute to our understanding on how a cassava system functions in the subtropics.

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