Optimal filtering for Bayesian detection and attribution of climate change
- 192 Downloads
In the conventional approach to the detection of an anthropogenic or other externally forced climate change signal, optimal filters (fingerprints) are used to maximize the ratio of the observed climate change signal to the natural variability noise. If detection is successful, attribution of the observed climate change to the hypothesized forcing mechanism is carried out in a second step by comparing the observed and predicted climate change signals. In contrast, the Bayesian approach to detection and attribution makes no distinction between detection and attribution. The purpose of filtering in this case is to maximize the impact of the evidence, the observed climate change, on the prior probability that the hypothesis of an anthropogenic origin of the observed signal is true. Whereas in the conventional approach model uncertainties have no direct impact on the definition of the optimal detection fingerprint, in optimal Bayesian filtering they play a central role. The number of patterns retained is governed by the magnitude of the predicted signal relative to the model uncertainties, defined in a pattern space normalized by the natural climate variability. Although this results in some reduction of the original phase space, this is not the primary objective of Bayesian filtering, in contrast to the conventional approach, in which dimensional reduction is a necessary prerequisite for enhancing the signal-to-noise ratio. The Bayesian filtering method is illustrated for two anthropogenic forcing hypotheses: greenhouse gases alone, and a combination of greenhouse gases plus sulfate aerosols. The hypotheses are tested against 31-year trends for near-surface temperature, summer and winter diurnal temperature range, and precipitation. Between six and thirteen response patterns can be retained, as compared with the one or two response patterns normally used in the conventional approach. Strong evidence is found for the detection of an anthropogenic climate change in temperature, with some preference given to the combined forcing hypothesis. Detection of recent anthropogenic trends in diurnal temperature range and precipitation is not successful, but there remains strong net evidence for anthropogenic climate change if all data are considered jointly.
This work was supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Change Data and Detection program and the US Department of Energy, Office of Energy Research, as part of the International ad hoc Detection Group effort, and by the European Commission QUARCC project, ENV4-96-0250.
- Cubasch U, Santer BD, Hellbach A, Hegerl G, Höck H, Maier-Reimer E, Mikolajewicz U, Stössel A, Voss R (1994) Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model. Clim Dyn 10:1–19Google Scholar
- Earman J (1992) Bayes or bust? A critical examination of Bayesian confirmation theory. MIT Press, Cambridge, Mass, USAGoogle Scholar
- Hasselmann K (1979) On the signal-to-noise problem in atmospheric response studies. In: Shaw DB (ed) Meteorology of tropical oceans. Royal Meteorological Society, London, pp 251–259Google Scholar
- Houghton JT, Meira Filho LG, Callendar BA, Harris N, Kattenberg A, Maskel K (eds) (1996) Climate change 1995: the science of climate change. Cambridge University Press, Cambridge, p 572Google Scholar
- Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskel K, Johnson CA (eds) (1996) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, p 881Google Scholar
- IDAG (members involved in paper listed in footnote) (2004) Detecting and attributing external influences on the climate system: a review of recent advances. J. Climate (in press)Google Scholar
- Kass RE, Raftery AE (1994) Bayes factors. Technical Report 254, Department of Statistics, University of Washington, U S A, p 56Google Scholar
- Preisendorfer RW, Overland JE (1982) A significance test for principal components applied to a cyclone climatology. Mon Weather Rev 110:1–4Google Scholar
- Risbey R Jr, Kandlikar M, Karoly DJ (2000) A protocol to articulate and quantify uncertainties in climate change detection and attribution. Clim Res 163:61–78Google Scholar
- Santer BD, Wigley TM, Barnett TP, Anyamba E (1996) Detection of climate change and attribution of causes. In: Houghton JT et al (eds) Climate change 1995, the science of climate change. Cambridge University Press, Cambridge, pp 411–443Google Scholar
- Stott PA, Tett SFB, Jones GS, Allen MR, Ingram WJ, Mitchell JFB (2001) Attribution of twentieth century climate change to natural and anthropogenic causes. Clim Dyn 15:1–22Google Scholar