When James Hack came to Oak Ridge National Laboratory in late 2007, he was given two hats: one as the director of ORNL's National Center for Computational Sciences and the other as leader of ORNL's laboratory-wide climate science effort.
In one role, he guides the world's foremost open science supercomputing center. As leader of ORNL's Climate Change Initiative, he is responsible for pulling together scientists and engineers from across the laboratory to address one of the nation's greatest scientific challenges. Hack is uniquely qualified to take on this role. Before coming to ORNL, he headed the Climate Modeling Section at the National Center for Atmospheric Research in Boulder, Colorado, and served as deputy director of the center's Climate and Global Dynamics Division.
We asked Hack about the future of climate science and the climate initiative at ORNL.
Q. How do you see climate research evolving in the coming years?
Climate science has largely been curiosity-driven research, but the growing acceptance that humans affect the evolution of atmospheric composition and land use, which in turn affects the climate state, provides more focus and greater urgency to taking a harder look at what new modeling tools are capable of providing in the form of specific consequences for society.
That to me is the transformation. There's a growing need for improvements in simulation fidelity and predictive skill. The potential consumers of that kind of simulation information will be leaning hard on the climate change community to provide answers to their questions. That's the change that's going to differentiate the next 10 years of climate change science from the previous 30.
Q. Give us an example of this information.
We know from observations over the last 50 years that the snowpack in the Pacific Northwest has been decreasing. At the same time, temperature in the same region has been increasing. If that trend continues, it raises lots of concerns for water resource managers who have counted on storing their water in the form of snow until a certain time of year when it starts melting.
If precipitation never comes down as snow or if it starts melting sooner than we need it, we may not able to meet water demands. This is a good example of an infrastructure that's vulnerable to specific changes in a region's climate state. Many of the solutions to this problem may also bring with them other environmental consequences.
Q. How accurate is climate prediction?
We think we might currently have sufficient skill to project climate change on regional scales about the size of the Southeast, Pacific Northwest, Rocky Mountain West or Farm Belt. As a scientific community we need to demonstrate the potential and quantify the uncertainties. Although thus far we haven't done a very good job with this challenge, climate researchers are starting to realize that we have an opportunity to take a step back and ask, "What can we do on regional scales and timescales that we think are predictable?"
For example, there's a belief that climate statistics have some predictive skill on decadal timescales. The driver for that is going to reside in the ocean, where motion scales have a very, very long time frame. There is a belief in the scientific community that the ocean's behavior can be predicted several decades into the future.
If we can solve the ocean part of the problem, given the fact that 70 percent of the planet is covered with water, we have a very strong constraint on the other parts of the system. The question then becomes, "Will the other component models follow?"
Q. How do you convince critics that you're getting it right?
We develop numerical experiments to assess whether the global model can produce useful information on the timescales and space scales of most importance to resource managers and planners. They may want to know where the temperature's headed locally, how the hydrological cycle is likely to behave, or how extreme events will change. Do the models provide us with the kind of predictive skill we need, and if not, how can they be improved?
Q. What is the role of computing in this effort?
fully evaluate the skill in our modeling tools, we need very large computer systems—petascale machines. Assimilating data streams that will be used in the evaluation of modeling frameworks requires very large computer and data systems.
Clearly, a significant computational piece is modeling—building models that have all the components they need to accurately predict the evolution of the earth's climate system. That's computationally very intensive. Incorporating the complexities of the carbon cycle in these models, using the expertise of ORNL's Environmental Sciences Division, contributes to the computational demands. And then mining the data to deal with questions of human impacts and climate extremes is also very computationally intensive.
So, computation in fact ties the whole effort together. It cuts across all the various climate science applications. There are certain areas of science where you need a virtual laboratory to explore the "what-if" experiments, which is what computation provides for the climate problem.
Q. You are leading a new multidisciplinary effort at ORNL focused on climate science. What is the goal?
ORNL has identified climate change as an opportunity that could very effectively exploit existing competencies, particularly high-performance computing and ORNL's long history in contributing to fundamental knowledge about carbon science and in global modeling. The lab also has expertise in evaluating impacts on societal infrastructure. Take rising sea levels. Most of the folks living around the world live close to coastlines, so if the sea level rises even a meter, it has a huge societal impact. The people who are displaced must go somewhere else, maybe moving into areas that were previously used for agriculture. That displaces agricultural activities. ORNL has a very strong geographic information systems group that can contribute to quantification of these scenarios.
We are looking at how we can bring these various competencies together to provide a capability that's unique among the laboratories. The goal is to provide stakeholders, resource managers and others with information they need to deal with the consequences of climate change.
Q. What will ORNL's climate initiative look like?
We're trying to engage people from across the laboratory to stretch the kind of work they're doing in such a way that it requires partnerships with other ORNL staff. So far, many of the more promising proposals include collaborations that cut across the directorates of Biological and Environmental Sciences and Computing and Computational Sciences.
As the initiative matures, I hope we'll begin to incorporate more people in the energy arena, another strong part of the ORNL scientific program. These things could include ways to link climate change and the hard questions we're facing in energy production, like bioenergy and renewable energy technologies, as well as energy consumption. Dealing directly with climate mitigation questions, such as strategies for the sequestration of carbon, is an opportunity for this initiative.
From an energy production point of view, planning has a multidecadal timeframe. Anyone planning investments in the energy infrastructure needs to understand what role the environment might play. That's the goal—to be able to say 20 years from now, "Here's what we anticipate will happen with environmental change on a regional scale."
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