Tropical South America plays a central role in global climate. Bowen ratio teleconnects to circulation and precipitation processes far afield, and the global CO2 growth rate is strongly influenced by carbon cycle processes in South America. However, quantification of basin‐wide seasonality of flux partitioning between latent and sensible heat, the response to anomalies around climatic norms, and understanding of the processes and mechanisms that control the carbon cycle remains elusive. Here, we investigate simulated surface‐atmosphere interaction at a single site in Brazil, using models with different representations of precipitation and cloud processes, as well as differences in scale of coupling between the surface and atmosphere. We find that the model with parameterized clouds/precipitation has a tendency toward unrealistic perpetual light precipitation, while models with explicit treatment of clouds produce more intense and less frequent rain. Models that couple the surface to the atmosphere on the scale of kilometers, as opposed to tens or hundreds of kilometers, produce even more realistic distributions of rainfall. Rainfall intensity has direct consequences for the “fate of water,” or the pathway that a hydrometeor follows once it interacts with the surface. We find that the model with explicit treatment of cloud processes, coupled to the surface at small scales, is the most realistic when compared to observations. These results have implications for simulations of global climate, as the use of models with explicit (as opposed to parameterized) cloud representations becomes more widespread.

Modeling the productivity of tropical forests is important for accurate climate predictions. The main reason is that the most productive biome in the world disproportionately drives global carbon and water cycles. Model estimates of current rainforest productivity vary by two-fold even after model differences in simulated precipitation are removed (

Previous research has documented that cetaceans can discriminate between humans, but the process used to categorize humans still remains unclear. The goal of the present study was to replicate and extend previous work on the discrimination between familiar and unfamiliar humans by three species of cetaceans. The current study manipulated the familiarity and activity level of humans presented to 12 belugas (Delphinapterus leucas) housed between two facilities, five bottlenose dolphins (Tursiops truncatus), and six Pacific white-sided dolphins (Lagenorhynchus obliquidens) during free-swim conditions. Two measures of discrimination were coded from video recordings of each trial: lateralized visual processing and gaze duration. No clear lateralization effects emerged at the species level, primarily due to extensive individual variability. The results also indicated that activity level influenced gaze durations across species, and for some individuals, the interaction between human familiarity and activity level influenced gaze durations and eye preferences. Unexpectedly, bottlenose dolphins had longer gaze durations for familiar humans whereas belugas and Pacific white-sided dolphins had longer gaze durations for unfamiliar humans. All three groups displayed longer gaze durations for active humans as compared to neutral humans, and belugas and bottlenose dolphins had significantly longer gaze durations than Pacific white-sided dolphins. These results indicate that the cetaceans can discriminate between unfamiliar and familiar humans and preferred active humans. However, discrimination of humans via lateralized visual processing did not appear at the group level, but rather at the individual level which countered previous research. This study is discussed within the contexts of attention and individual differences across animals of different species.

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