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Climate Dynamics

, Volume 30, Issue 2–3, pp 321–332 | Cite as

The dynamics of learning about a climate threshold

  • Klaus Keller
  • David McInerney
Article

Abstract

Anthropogenic greenhouse gas emissions may trigger threshold responses of the climate system. One relevant example of such a potential threshold response is a shutdown of the North Atlantic meridional overturning circulation (MOC). Numerous studies have analyzed the problem of early MOC change detection (i.e., detection before the forcing has committed the system to a threshold response). Here we analyze the early MOC prediction problem. To this end, we virtually deploy an MOC observation system into a simple model that mimics potential future MOC responses and analyze the timing of confident detection and prediction. Our analysis suggests that a confident prediction of a potential threshold response can require century time scales, considerably longer that the time required for confident detection. The signal enabling early prediction of an approaching MOC threshold in our model study is associated with the rate at which the MOC intensity decreases for a given forcing. A faster MOC weakening implies a higher MOC sensitivity to forcing. An MOC sensitivity exceeding a critical level results in a threshold response. Determining whether an observed MOC trend in our model differs in a statistically significant way from an unforced scenario (the detection problem) imposes lower requirements on an observation system than the determination whether the MOC will shut down in the future (the prediction problem). As a result, the virtual observation systems designed in our model for early detection of MOC changes might well fail at the task of early and confident prediction. Transferring this conclusion to the real world requires a considerably refined MOC model, as well as a more complete consideration of relevant observational constraints.

Keywords

Loss Ratio Prediction Time Prediction Skill Force Threshold Hydrological Sensitivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank J. Annan, B. Haupt, J. Baehr, D. Ludwig, N. Urban, M. Oppenheimer, and D. Ricciuto for helpful discussions. Nathan Urban calculated the spectra shown in Fig. 1. Any potential remaining errors and omissions are, of course, ours. K. Zickfeld kindly provided the code for the adopted MOC model. Careful reviews by R. Stouffer and H. Held considerably improved the presentation of the manuscript. We gratefully acknowledge support from the National Science Foundation (SES #0345925). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding entity.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of GeosciencesPenn State UniversityUniversity ParkUSA

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