Toward Form-Function Relationships for Mesoscale Structure in Convection

Mesoscale patterns in atmospheric convection (between the inner scale of convecting-layer depth and the outer scales of domain constraints) are fascinating and ubiquitous. This review asks whether some aspects of that form (normalized for a given amount of convective activity) play a meaningful role or function in the total flow, especially in its more-predictable larger scales. Do some mesoscale features deserve to be called organization in its strong sense, acting like multi-cellular organs in an organism? After enumerating hypotheses from null (mesoscale arrangement doesn't matter) to various detailed ideas (rectification of nonlinear processes with spatial agglomeration, size-dependent top-heaviness of heating, vertical momentum flux effects, adjustment roles, and the character of stochastic noise), a tabular framework for categorizing form-function research is offered. Function measures are divided into micro (mere quantification of budget terms averaged over mesoscale patches) vs. macro (roles played through time in larger-scale phenomena). Tools and approaches are arrayed from literal and explicit (case observations) to conceptualized (models, ranging from theory to numerical to statistical depictions), on timelines both historical (contacting case observations in some way) and synthetic (theory, simulation, and composites). Efforts are further distinguished by whether their inferences are associative (derived from conditional sampling of either form or function) or causal (involving controlled experimentation). Literature examples are surveyed, albeit incompletely, and future research strategies are suggested across this tabular landscape or framework. One spotlighted result is an apparent internal optimum in the horizontal geometry continuum between isotropic horizontal two-dimensionality and horizontally one-dimensional squall lines. Form-function questions could help justify, orient, and capitalize scientifically on the field's costly multiscale activities (requiring both coverage and resolution) in both observational and modeling realms. Data assimilation is a motivating application, and also a potentially powerful research tool for achieving greater synthesis. An observant human sensibility remains crucial for discovering and interpreting form-function relationships, at the very least to design more salient algorithms in the age of big data and computing.