Our planet’s fields and forests, oceans and aquifers, and animals of the land, sea, and sky don’t exist in a vacuum. But too often, environmental and agricultural models treat them as if they do. To truly understand the role climate (and climate change) plays on them, an integrated approach is called for. That’s where the work of Joshua Elliott, research scientist and fellow at the University of Chicago Computation Institute, comes in. Whereas an individual crop model can only be run for one spatial point at a time, and for a limited number of scenarios, Elliott is leading an effort to make it possible to run each model “on hundreds of thousands of points for hundreds of simulated years and thousands of scenarios using coordinated inputs and processing methodologies.”
The project that Elliott leads is an outgrowth of the Center for Robust Decision Making on Climate and Energy Policy (RDCEP), established by the University of Chicago to bring together experts in all relevant fields and improve models, with the hope of giving policy makers the best computational models to understand climate change and the steps needed to mitigate it. Elliott’s project, the parallel System for Integrating Impact Models and Sectors (pSIMS), isn’t so much one big model as “a framework for leveraging multiple existing models, big and highly variable data resources, and high-performance computing to solve problems at scales previously unthinkable,” he says.
This was not a simple task. First, every model within pSIMS was developed separately, and none were developed in-house; some contained multiple models within themselves. “Each of our crop models supports anywhere from five to ten different crops as well as pasture grasses, biofuel crops, and other things we’ve added into the framework,” he explains. “Each is its own unique model which fits into a model crop framework. It’s really a model of model of models.” Furthermore, the models sometimes used input and output formats that were slightly different from each other; getting them to work together meant that pSIMS needed to provide tools to automatically transform data (such as geospatial coordinates) for the next model to use. Finally, pSIMS had to be flexible enough to accommodate for working on multiple kinds of platforms, including supercomputers, clusters, grid computing, and clouds.