Statistical principles for climate change studies
- Univ. of California, Davis, CA (United States). Div. of Statistics
- Ohio State Univ., Columbus, OH (United States)
Predictions of climate change due to human-induced increases in greenhouse gas and aerosol concentrations have been an ongoing arena for debate and discussion. A major difficulty in early detection of changes resulting from anthropogenic forcing of the climate system is that the natural climate variability overwhelms the climate change signal in observed data. Statistical principles underlying fingerprint methods for detecting a climate change signal above natural climate variations and attributing the potential signal to specific anthropogenic forcings are discussed. The climate change problem is introduced through an exposition of statistical issues in modeling the climate signal and natural climate variability. The fingerprint approach is shown to be analogous to optimal hypothesis testing procedures from the classical statistics literature. The statistical formulation of the fingerprint scheme suggests new insights into the implementation of the techniques for climate change studies. In particular, the statistical testing ideas are exploited to introduce alternative procedures within the fingerprint model for attribution of climate change and to shed light on practical issues in applying the fingerprint detection strategies.
- OSTI ID:
- 335307
- Journal Information:
- Journal of Climate, Journal Name: Journal of Climate Journal Issue: 2 Vol. 12; ISSN JLCLEL; ISSN 0894-8755
- Country of Publication:
- United States
- Language:
- English
Similar Records
Detection and attribution of climate extremes in the observed record
Optimal fingerprints for the detection of time-dependent climate change