SIGNAL-TO-NOISE ANALYSIS OF TIME-DEPENDENT GREENHOUSE WARMING EXPERIMENTS .1. PATTERN-ANALYSIS

Results from a control integration and time-dependent greenhouse warmi ng experiments performed with a coupled ocean-atmosphere model are ana lysed in terms of their signal-to-noise properties. The aim is to illu strate techniques for efficient description of the space-time evolutio n of signals and noise and to identify potentially useful components o f a multivariate greenhouse-gas ''fingerprint''. The three 100-year ex periments analysed here simulate the response of the climate system to a step-function doubling Of CO2 and to the time-dependent greenhouse- gas increases specified in Scenarios A (''Business as Usual'') and D ( ''Draconian Measures'') of the Intergovernmental Panel on Climate Chan ge (IPCC). If signal and noise patterns are highly similar, the separa tion of the signal from the natural variability noise is difficult. We use the pattern correlation between the dominant Empirical Orthogonal Functions (EOFs) of the control run and the Scenario A experiment as a measure of the similarity of signal and noise patterns. The EOF 1 pa tterns of signal and noise are least similar for near-surface temperat ure and the vertical structure of zonal winds, and are most similar fo r sea level pressure (SLP). The dominant signal and noise modes of pre cipitable water and stratospheric/tropospheric temperature contrasts s how considerable pattern similarity. Despite the differences in forcin g history, a highly similar EOF 1 surface temperature response pattern is found in all three greenhouse warming experiments. A large part of this similarity is due to a common land-sea contrast component of the signal. To determine the degree to which the signal is contaminated b y the natural variability (and/or drift) of the control run, we projec t the Scenario A data onto EOFs 1 and 2 of the control. Signal contami nation by the EOF 1 and 2 modes of the noise is lowest for near-surfac e temperature, a situation favorable for detection. The signals for pr ecipitable water, SLP, and the vertical structure of zonal temperature and zonal winds are significantly contaminated by the dominant noise modes. We use cumulative explained spatial variance, principal compone nt time series, and projections onto EOFs in order to investigate the time evolution of the dominant signal and noise modes. In the case of near-surface temperature, a single pattern emerges as the dominant sig nal component in the second half of the Scenario A experiment. The pro jections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the contro l run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2 x CO2 integration. The trajectories of the dominant Scenario A and control run modes evo lve differently, regardless of the basis vectors chosen for projection , thus making it feasible to separate signal and noise within the firs t two decades of the experiments. For Scenario D it may not be possibl e to discriminate between the dominant signal and noise modes until th e final 2-3 decades of the 100-year integration.