A parallel research program led by Michael Oppenheimer will focus on environmental impacts of climate change, links between air pollution and global warming, and the problem of time-frames in decision-making.


Using impacts of climate change as guides for policy

We will continue efforts to understand the future of earth’s major ice sheets. Oppenheimer and his students are using the GFDL ocean model to develop an improved model for the ocean-ice sheet interaction. This work may be the first step in development of a full ice sheet component for the Earth System Model. Oppenheimer will continue his work on synthesizing paleoclimatic data and model studies to forge a means to use ice sheet projections as a basis for implementation of Article 2 of the UNFCCC, and the definition of “dangerous anthropogenic interference” with the climate system. Collaborating with Brian O’Neill of IIASA, he is exploring scenarios for future greenhouse gas emissions in the context of constraints that preserve the ice sheets.


Oppenheimer’s group will analyze air pollution in India, with the aim of assessing policy approaches that reduce growth in fossil fuel dependence and biomass burning. They will develop an emissions data base and use it to develop a model for airborne concentrations in India and to investigate appropriate response policies. The work will be conducted in collaboration with scientists in India.


The problem of time scales

A key issue in climate change decision theory is: how to deal with the long timescale for scientific learning in the context of the short timescale for decison making on irreversible problems. Often our reckoning of the distribution of outcomes changes over decadal timescales, with the direction of learning shifting sharply over time. For example, the West Antarctic ice sheet was thought to be unstable for almost twenty years beginning in 1968. Then improved models caused a marked turn in the field toward expectation of stability. Now, with extensive observations of ice motion in response to warming, the “delivered wisdom” has shifted once again. Such erratic behavior for the trajectory of learning has been seen for other problems of global change, including ozone depletion, population projections, and energy projection.  The possibility of “negative learning” presents a profound difficulty for decision makers. This project will attempt to incorporate a wider concept of learning into decision models to explore avenues for improving the decision-making process in a world where scientific understanding is occasionally unstable. Collaborators include Mort Webster of University of North Carolina, Brian O’Neill of IIASA and Klaus Keller.