Low order climate models as a tool for cross-disciplinary collaboration

Monday, 15 December 2014: 2:55 PM
Robert Newton, Columbia University of New York, Palisades, NY, United States, Stephanie L Pfirman, Barnard College, Closter, NJ, United States, Bruno Tremblay, McGill University, Montreal, QC, Canada and Peter Schlosser, Columbia University, Dept. of Earth and Environmental Engineering and Dept. of Earth and Environmental Sciences, New York, NY, United States
Human impacts on climate are pervasive and significant and project future states cannot be projected without taking human influence into account. We recently helped convene a meeting of climatologists, policy analysts, lawyers and social scientists to discuss the dramatic loss in Arctic summer sea ice. A dialogue emerged around distinct time scales in the integrated human/natural climate system. Climate scientists tended to discuss engineering solutions as though they could be implemented immediately, whereas lags of 2 or more decades were estimated by social scientists for societal shifts and similar lags were cited for deployment by the engineers. Social scientists tended to project new climate states virtually overnight, while climatologists described time scales of decades to centuries for the system to respond to changes in forcing functions. For the conversation to develop, the group had to come to grips with an increasingly complex set of transient effect time scales and lags between decisions, changes in forcing, and system outputs.

We use several low-order dynamical system models to explore mismatched timescales, ranges of lags, and uncertainty in cost estimates on climate outcomes, focusing on Arctic-specific issues. In addition to lessons regarding what is/isn’t feasible from a policy and engineering perspective, these models provide a useful tool to concretize cross-disciplinary thinking. They are fast and easy to iterate through a large region of the problem space, while including surprising complexity in their evolution. Thus they are appropriate for investigating the implications of policy in an efficient, but not unrealistic physical setting. (Earth System Models, by contrast, can be too resource- and time-intensive for iteratively testing “what if” scenarios in cross-disciplinary collaborations.)

Our runs indicate, for example, that the combined social, engineering and climate physics lags make it extremely unlikely that an ice-free summer ecology in the Arctic can be avoided. Further, if prospective remediation strategies are successful, a return to perennial ice conditions between one and two centuries from now is entirely likely, with interesting and large impacts on Northern economies.