Modeling Climate Change and Its Impacts: Law, Policy, and Science

I. Introduction Use of models has become increasingly central to the regulatory process. Modeling is sometimes even explicitly mandated by Congress.1 The potential benefits are manifold. "Computer modeling," according to a leading environmental-law scholar, "narrows the range of uncertainties related to pollution impacts and causal pathways that have plagued environmental policymaking on all levels."2 In turn, "[b]etter forecasting allows potential problems to be spotted before they emerge and helps to target policy interventions."3 As a result of improved forecasting, he says, the "evolutionary process of policy evaluation and refinement through trial and error ... can be sped up dramatically."4 But not everyone would agree with this optimism about modeling. Environmental modeling is hardly foolproof,5 and we are still learning how it can best be used in environmental protection.6 There are some basic questions to be addressed about environmental modeling. How much of the potential of modeling has already been realized? What are the potential pitfalls of modeling? How can the regulatory and legal systems best make use of models? Climate modeling provides an ideal setting for exploring the larger issues related to modeling generally. These models are both important in their own right and as paradigms of sophisticated scientific models.7 This Article has two goals: providing legal and policy analysts with a basic understanding of the types of computer models used in studying climate change, and thinking through the uses and limitations of these models for courts and agencies. The Article proceeds in three stages. Part II analyzes the models used by climate scientists to understand climate change. Some of the models are mind-bogglingly complex, requiring weeks to run on the most powerful supercomputers. Even the simplest models may be challenging for scientific novices to understand. As we will see, we have good reason to rely on models, but we must also recognize that considerable areas of residual uncertainty remain. Another kind of computer modeling is helpful in bridging the gap between the models of climate change and the people who must apply their results. Part II also discusses the role of geographic information systems (GISs) in understanding climate impacts. These modeling systems convert data into interactive maps. Given the complexities of climate change, such models are especially important in making information accessible to policy makers and members of the public. For many users, GIS models will be the interface between them and the climate scientists' work. In Part III, this Article considers the probable response of courts to computer modeling-an issue that can arise in judicial review of administrative proceedings and in ordinary civil litigation. Courts are just beginning to confront the use of climate models, but it seems likely that they will find these models reliable enough for use in litigation and administrative decisions. The distinctive processes that climate scientists have designed for testing and improving models should provide additional assurances of reliability to courts. The main pitfall may be overreliance on any single model without acknowledging model limitations or recognizing the importance of confirmation from other models and observations. Part IV considers the implication of model uncertainty-the risk that a model has failed to capture the dynamics of the process it is simulating.8 Model uncertainty should diminish as our modeling efforts improve. Even now, models provide considerable confidence about the existence of humanoriginated, harmful climate change. However, they do leave uncertainty about regional effects and downside risks. Economic models are particularly subject to uncertainty. Climate policy must be designed with these uncertainties in mind. Because this Article covers a good deal of fairly diverse terrain, it may be helpful to identify four key take-away points: * Climate models establish a lower end estimate for globaltemperature impacts, but the distribution is less clearly bounded on the high side-or, in simpler terms, the high-end risk may be considerable. …