Numerical models are a critical tool in many disciplines for generating and testing hypotheses, examining sensitivity to perturbation, hindcasting or filling in data gaps, and making predictions about future behavior. Numerical experiments offer advantages over field or laboratory experiments in that they provide complete control over critical variables and greatly expand the spatial and temporal scales over which experiments are run, though often at the expense of some of the complexities of reality. However, with ongoing advances in computing technology, highly complex, multi-scale direct simulations are possible. But does the ability to see everything (i.e., the perfect experiment) improve our overall understanding of phenomena?

Early in the advent of computing, models were by necessity of a reduced form, capturing only the essential physics needed to interpolate between limited experimental observations or mechanistically test an idea. Despite the present ability to construct detailed numerical models, reduced complexity models (RCMs) have persisted, and their applications within the biological and physical environmental sciences have grown tremendously over past years. Arguably, RCMs offer some advantages over reductionist models, including the ability to perform more extensive coupling of physical and biological dynamics and provide a highly intuitive yet quantitative understanding of basic system dynamics. However, the advantage of RCMs depends on the rigor of the physical and biological processes simulated, which has varied widely within the literature. A diverse array of different assumptions and strategies for simplifying the governing physics or biology has accompanied the burgeoning application of RCMs. As a result, there is a need for the community to come together for synthesis.

This material is based upon work supported by the National Science Foundation under Grant Number 1263851. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessary reflect the views of the National Science Foundation. 

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