May 11, 2007 Conference

Global climate models, especially those that predict global warming induced by rising greenhouse gases, have been widely publicized recently. Those of us with a statistical background, but not experts in this field, cannot help but wonder whether these models are any good at all.

Just how good are global climate models? What success do they have in predicting present-day climate? What are the challenges in simulating future climate? (In this century? In the Midwest?) How can anyone quantify the sources of uncertainty in these predictions?

From a statistical point of view, there are even more fundamental issues here. What statistical methods are available for summarizing, modeling, estimating and visualizing spatial dependence on a global scale? How should observed data, such as data collected by satellites, be employed in modeling atmospheric phenomena on a global scale?

Finally, you have to wonder about the effects of climate change on the environment. How do climate-ecosystem models work? Is it possible to develop statistical approaches to identify and quantify vegetation feedbacks to climate? How can these climate-ecosystem models be used to study climate-ecosystem change due to past and future rising levels of greenhouse gases?

This conference will provide perspective on approaches used by experts to answer these questions.  It is sponsored by the Chicago Chapter of the American Statistical Association and the Department of Mathematics and Statistics, Loyola University Chicago.

 Conference Program

Abstracts and Biographical Sketches

Regional Climate Model Simulations of the Midwest U.S. Climate

Kenneth E. Kunkel
Center for Atmospheric Sciences
Illinois State Water Survey
Champaign, IL

Climate-Ecosystem Modeling and Applications

Michael Notaro, Zhengyu Liu, Robert Gallimore, Adrien Mauss
Center for Climatic Research
University of Wisconsin-Madison
mnotaro@wisc.edu

Climate-ecosystem models have emerged as critical tools in examining the response of terrestrial ecosystems to changes or variability in climate, in addition to the feedbacks of vegetation on climate. Thus, terrestrial vegetation is no longer seen as a static boundary condition but a dynamic component of the climate system. I will briefly explore the evolution of climate-ecosystem models, from offline models which lack feedbacks, to their asynchronous coupling to global climate models, and finally to their synchronous and fully dynamic coupling to climate models. I will examine the role and applications of both offline vegetation models and fully coupled models. In particular, I will discuss the structure of our fully dynamic atmosphere-ocean-ice-land model, FOAM-LPJ, including the details of the coupling between the atmosphere and land. Differences in time scales among various components of the climate system will be explored.

Novel approaches to identifying and quantifying vegetation feedbacks to climate will be examined. It is quite challenging to dissect the vegetation feedback signal from raw observations, although a statistical methodology offers some promise. The first approach to identifying vegetation feedbacks will be statistical, beginning with simple lead-lag correlations and advancing to a more complex method using lagged covariances. The statistical approach is applied to quantify both observed and simulated feedbacks. The statistical assessment of vegetation feedbacks will be evaluated through a dynamical approach, using initial value ensemble experiments. These explicit model experiments will offer deep insight into feedback mechanisms. I will discuss the applications of ensembles to climate-ecosystem modeling, including methods to separate signal from noise.

Several applications of the offline dynamical global vegetation model and fully coupled model will be introduced. The first set of applications focuses on climate-ecosystem change due to past and future rising levels of greenhouse gases. Other applications include examining remote vegetation feedbacks, simulating vegetation changes and feedbacks during the mid-Holocene period, and regional feedbacks on temperature in Siberia and precipitation in North Africa.

Biographical Sketch: Michael Notaro

Michael Notaro is an Assistant Scientist, at the Center for Climatic Research, University of Wisconsin-Madison. He began as a research associate at CCR in 2002 and has been an assistant scientist at CCR since 2005, primarily studying the interaction between climate and vegetation in a fully coupled atmosphere-ocean-vegetation model, FOAM-LPJ, and also in observations. He holds a B.S., M.S, and Ph.D. in Atmospheric Science, from the State University of New York at Albany and has co-authored several papers on global vegetation-climate feedbacks and mechanisms for vegetation change.

Statistical Processes on a Global Scale

Michael L. Stein
Center for Integrating Statistical and Environmental Science
University of Chicago

This talk explores some of the issues that arise in statistical modeling of atmospheric phenomena on a global scale, using total column ozone as measured by the satellite-based Total Ozone Mapping Spectrometer (TOMS) as a case study.

A basic issue in all statistical models for natural phenomena is finding statistical regularities that enable one to take meaningful averages. Since the statistical characteristics of total column ozone strongly depend on latitude, we consider the use of axial symmetry (invariance of statistical properties to rotations about the Earth's axis) as a possible exploitable regularity. Methods for summarizing, modeling, estimating and visualizing spatial dependence for axially symmetric processes are addressed. A computationally convenient approach to modeling axially symmetric processes using truncated series expansions of spherical harmonics is shown to capture much of the larger-scale latitudinal variation in spatial dependence. However, the approach performs poorly in terms of describing the local behavior of the process. By combining the spherical harmonic expansion with a covariance function with finite range it is possible to obtain a better model without sacrificing all of the computational benefits of the series expansion approach.

Biographical sketch: Michael Stein
Michael Stein, University of Chicago, is the Ralph and Mary Otis Isham Professor of Statistics. He received his PhD in Statistics from Stanford. He is presently the director of the Center for Integrating Statistical and Environmental Science, a large environmental statistics center with over 20 principal investigators, funded by the U.S. Environmental Protection Agency. His research focuses on statistical models and methods for spatial and spatial-temporal processes with applications to environmental processes. Dr. Stein is a fellow of the American Statistical Association, and the Institute of Mathematical Statistics. He is an elected member of the International Statistical Institute. He has published a book on the interpolation of spatial data and over sixty papers and has directed or co-directed the doctoral dissertations of 17 students. He is a member of the National Academy of Sciences’, Board of Mathematical Sciences and their Applications’, Committee on Applied and Theoretical Statistics. His invited talks include a Special Invited Paper and a Medallion Lecture for the Institute of Mathematical Statistics, the 2004 Chotey Lal and Mohra Devi Rustagi Memorial Lecture at Ohio State University and the 2005 Hunter Lecturer for the International Environmetrics Society.