Detecting greenhouse-gas-induced climate change with an optimal fingerprint method
- Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); and others
A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a {open_quotes}guess pattern{close_quotes} representing the expected time-space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near surface temperature trends defined for time intervals of 15-30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings. The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer. 71 refs., 12 figs., 14 tabs.
- OSTI ID:
- 411846
- Journal Information:
- Journal of Climate, Journal Name: Journal of Climate Journal Issue: 10 Vol. 9; ISSN 0894-8755; ISSN JLCLEL
- Country of Publication:
- United States
- Language:
- English
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