May 18, 2017
Feature

A Cheaper Way to Explore Distant Relations in Climate Models

Scientists simultaneously describe input-output relationships across timescales in one simulation

Teleconnections_BenKravitz_May2017-pnnl

During a global climate modeling run, low cloud cover response to a 1-degree Celsius warming in the northwest Indian Ocean reveals itself in intriguing ways. Different parts of the globe respond on various timescales.

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Teleconnections are related climate anomalies that happen far apart from each other.

We all have moments when we wish we could be in at least two places at once. Climate models have that power, but it often comes at a hefty price.

Researchers from Pacific Northwest National Laboratory, working with a colleague at Cornell University, borrowed a technique from engineering to efficiently discover distant input-output relationships in climate models. The scientists introduced a technique new to climate science in which these relationships—across different timescales—can be described at the same time in one simulation. By asking more focused input questions of climate models, researchers can get more information out of them with fewer simulations. The technique promises to save time and money in computing resources.

Why It Matters: Using a climate model, scientists run a possible scenario through to project future change. Most current climate modeling approaches can only adjust one variable at a time and only show long-term changes. However, teleconnections—related but physically distant climate anomalies—have varied timescales over which different responses occur. The new technique shows promise by zeroing in on teleconnections in ways that other modeling methods have not.

Methods: Researchers introduced a technique that involves multiple small changes of an input field while continually monitoring output fields to see how the model responds to the input adjustments. Modelers can identify different timescales of climate response to forcing—a change in the Earth's energy balance—without pushing the climate too far from its initial state.

Researchers used this technique to determine the steady-state, or equilibrium, responses of low cloud cover and latent heat flux (the heat given off when liquid water converts to vapor) to heating changes over 22 regions spanning the Earth's oceans. The new method described the responses for those regions at the same time. Meanwhile, the findings showed similarities between the response characteristics and those of step-change simulations, which often feature sudden changes to the input field. With this technique, researchers can estimate the timescale over which the steady-state response appears.

What's Next? The new method could be useful for building descriptions of teleconnections and uncertainty quantification to identify the effects of fine-tuning climate model inputs. 

Acknowledgments

Sponsors: The Department of Energy's Office of Science, Biological and Environmental Research supported this research as part of the Regional and Global Climate Modeling program, as a contribution to the High-Latitude Application and Testing of Global and Regional Climate Models (HiLAT) project.

Reference: Kravitz B, DG MacMartin, PJ Rasch, and H Wang. 2017. "Technical Note: Simultaneous Fully Dynamic Characterization of Multiple Input-Output Relationships in Climate Models." Atmospheric Chemistry and Physics 17: 2525-2541. DOI: 10.5194/acp-17-2525-2017

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About PNNL

Pacific Northwest National Laboratory draws on its distinguishing strengths in chemistry, Earth sciences, biology and data science to advance scientific knowledge and address challenges in energy resiliency and national security. Founded in 1965, PNNL is operated by Battelle and supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the DOE Office of Science website. For more information on PNNL, visit PNNL's News Center. Follow us on Twitter, Facebook, LinkedIn and Instagram.

Published: May 18, 2017

Research Team

Ben Kravitz, Phil Rasch, and Hailong Wang, PNNL
Douglas G. MacMartin, Cornell University