Turbulence drives microscale patches of motile phytoplankton

Patchiness plays a fundamental role in phytoplankton ecology by dictating the rate at which individual cells encounter each other and their predators. The distribution of motile phytoplankton species is often considerably more patchy than that of non-motile species at submetre length scales, yet the mechanism generating this patchiness has remained unknown. Here we show that strong patchiness at small scales occurs when motile phytoplankton are exposed to turbulent flow. We demonstrate experimentally that Heterosigma akashiwo forms striking patches within individual vortices and prove with a mathematical model that this patchiness results from the coupling between motility and shear. When implemented within a direct numerical simulation of turbulence, the model reveals that cell motility can prevail over turbulent dispersion to create strong fractal patchiness, where local phytoplankton concentrations are increased more than 10-fold. This "unmixing" mechanism likely enhances ecological interactions in the plankton and offers mechanistic insights into how turbulence intensity impacts ecosystem productivity

A Framework for Validation and Benchmarking of Pyroclastic Current Models

Numerical models of pyroclastic currents are widely used for fundamental research and for hazard and risk modeling that supports decision-making and crisis management. Because of their potential high impact, the credibility and adequacy of models and simulations needs to be assessed by means of an established, consensual validation process. To define a general validation framework for pyroclastic current models, we propose to follow a similar terminology and the same methodology that was put forward by Oberkampf and Trucano (Prog Aerosp Sci, 38, 2002) for the validation of computational fluid dynamics (CFD) codes designed to simulate complex engineering systems. In this framework, the term validation is distinguished from verification (i.e., the assessment of numerical solution quality), and it is used to indicate a continuous process, in which the credibility of a model with respect to its intended use(s) is progressively improved by comparisons with a suite of ad hoc experiments. The methodology is based on a hierarchical process of comparing computational solutions with experimental datasets at different levels of complexity, from unit problems (well-known, simple CFD problems), through benchmark cases (complex setups having well constrained initial and boundary conditions) and subsystems (decoupled processes at the full scale), up to the fully coupled natural system. Among validation tests, we also further distinguish between confirmation (comparison of model results with a single, well-constrained dataset) and benchmarking (inter-comparison among different models of complex experimental cases). The latter is of particular interest in volcanology, where different modeling approaches and approximations can be adopted to deal with the large epistemic uncertainty of the natural system