Professor Myles Allen CBE FRS

Abstract:

Recent work suggests a discernible human influence on climate. This finding is supported, with less restrictive assumptions than those used in earlier studies, by a 1961 through 1995 data set of radiosonde observations and by ensembles of coupled atmosphere-ocean simulations forced with changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric ozone. On balance, agreement between the simulations and observations is best for a combination of greenhouse gas, aerosol, and ozone forcing. The uncertainties remaining are due to imperfect knowledge of radiative forcing, natural climate variability, and errors in observations and model response.

Abstract:

Extended empirical orthogonal functions (EEOFs), alternatively known as multi-channel singular systems (or singular spectrum) analysis (MSSA), provide a natural method of extracting oscillatory modes of variability from multivariate data. The eigen-functions of some simple non-oscillatory noise processes are, however, also solutions to the wave equation, so the occurrence of stable, wave-like patterns in EEOF/MSSA is not sufficient grounds for concluding that data exhibits oscillations. We present a generalisation of the "Monte Carlo SSA" algorithm which allows an objective test for the presence of oscillations at low signal-to-noise ratios in multivariate data. The test is similar to those used in standard regression, examining directions in state-space to determine whether they contain more variance than would be expected if the noise null-hypothesis were valid. We demonstrate the application of the test to the analysis of interannual variability in tropical Pacific sea-surface temperatures.