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Climate Dynamics

, Volume 9, Issue 6, pp 267–285 | Cite as

Signal-to-noise analysis of time-dependent greenhouse warming experiments

Part 1: pattern analysis
  • Benjamin D Santer
  • Wolfgang Brüggemann
  • Ulrich Cubasch
  • Klaus Hasselmann
  • Heinke Höck
  • Ernst Maier-Reimer
  • Uwe Mikolajewica
Article

Abstract

Results from a control integration and time-dependent greenhouse warming experiments performed with a coupled ocean-atmosphere model are analysed in terms of their signal-to-noise properties. The aim is to illustrate techniques for efficient description of the space-time evolution of signals and noise and to identify potentially useful components of a multivariate greenhouse-gas “fingerprint”. The three 100-year experiments 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 Change (IPCC). If signal and noise patterns are highly similar, the separation 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 patterns of signal and noise are least similar for near-surface temperature and the vertical structure of zonal winds, and are most similar for sea level pressure (SLP). The dominant signal and noise modes of precipitable water and stratospheric/tropospheric temperature contrasts show considerable pattern similarity. Despite the differences in forcing 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 by the natural variability (and/or drift) of the control run, we project the Scenario A data onto EOFs 1 and 2 of the control. Signal contamination by the EOF 1 and 2 modes of the noise is lowest for near-surface temperature, a situation favorable for detection. The signals for precipitable 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 component 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 signal component in the second half of the Scenario A experiment. The projections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the control run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2 × CO2 integration. The trajectories of the dominant Scenario A and control run modes evolve differently, regardless of the basis vectors chosen for projection, thus making it feasible to separate signal and noise within the first two decades of the experiments. For Scenario D it may not be possible to discriminate between the dominant signal and noise modes until the final 2–3 decades of the 100-year integration.

Keywords

Zonal Wind Empirical Orthogonal Function Precipitable Water Noise Pattern Noise Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • Benjamin D Santer
    • 1
  • Wolfgang Brüggemann
    • 2
  • Ulrich Cubasch
    • 3
  • Klaus Hasselmann
    • 2
  • Heinke Höck
    • 2
  • Ernst Maier-Reimer
    • 2
  • Uwe Mikolajewica
    • 2
  1. 1.Program for Climate Model Diagnosis and IntercomparisonLawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Max-Planck-Institut für MeteorologicHamburgGermany
  3. 3.Deutsches KlimarechenzentrumHamburgGermany

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