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Climate Dynamics

, Volume 12, Issue 2, pp 77–100 | Cite as

Towards the detection and attribution of an anthropogenic effect on climate

  • Benjamin D. Santer
  • Karl E. Taylor
  • Tom M. L. Wigley
  • Joyce E. Penner
  • Philip D. Jones
  • Ulrich Cubasch
Open Access
Article

Abstract

It has been hypothesized recently that regional-scale cooling caused by anthropogenic sulfate aerosols may be partially obscuring a warming signal associated with changes in greenhouse gas concentrations. Here we use results from model experiments in which sulfate and carbon dioxide have been varied individually and in combination in order to test this hypothesis. We use centered [R(t)] and uncentered [C(t)] pattern similarity statistics to compare observed time-evolving surface temperature change patterns with the model-predicted equilibrium signal patterns. We show that in most cases, the C(t) statistic reduces to a measure of observed global-mean temperature changes, and is of limited use in attributing observed climate changes to a specific causal mechanism. We therefore focus on R(t), which is a more useful statistic for discriminating between forcing mechanisms with different pattern signatures but similar rates of global mean change. Our results indicate that over the last 50 years, the summer (JJA) and fall (SON) observed patterns of near-surface temperature change show increasing similarity to the model-simulated response to combined sulfate aerosol/CO2 forcing. At least some of this increasing spatial congruence occurs in areas where the real world has cooled. To assess the significance of the most recent trends in R(t) and C(t), we use data from multi-century control integrations performed with two different coupled atmosphere-ocean models, which provide information on the statistical behavior of ‘unforced’ trends in the pattern correlation statistics. For the combined sulfate aerosol/CO2 experiment, the 50-year R(t) trends for the JJA and SON signals are highly significant. Results are robust in that they do not depend on the choice of control run used to estimate natural variability noise properties. The R(t) trends for the C02-only signal are not significant in any season. C(t) trends for signals from both the C02-only and combined forcing experiments are highly significant in all seasons and for all trend lengths (except for trends over the last 10 years), indicating large global-mean changes relative to the two natural variability estimates used here. The caveats regarding the signals and natural variability noise which form the basis of this study are numerous. Nevertheless, we have provided first evidence that both the largest-scale (global-mean) and smaller-scale (spatial anomalies about the global mean) components of a combined C02/anthropogenic sulfate aerosol signal are identifiable in the observed near-surface air temperature data. If the coupled-model noise estimates used here are realistic, we can be highly confident that the anthropogenic signal that we have identified is distinctly different from internally generated natural variability noise. The fact that we have been able to detect the detailed spatial signature in response to combined C02 and sulfate aerosol forcing, but not in response to C02 forcing alone, suggests that some of the regional-scale background noise (against which we were trying to detect a C02-only signal) is in fact part of the signal of a sulfate aerosol effect on climate. The large effect of sulfate aerosols found in this study demonstrates the importance of their inclusion in experiments designed to simulate past and future climate change.

Keywords

Sulfate Aerosol Spatial Congruence Anthropogenic Sulfate Observe Climate Change Spatial Anomaly 
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 1995

Authors and Affiliations

  • Benjamin D. Santer
    • 1
  • Karl E. Taylor
    • 1
    • 2
  • Tom M. L. Wigley
    • 3
  • Joyce E. Penner
    • 2
  • Philip D. Jones
    • 4
  • Ulrich Cubasch
    • 5
  1. 1.Program for Climate Model Diagnosis and IntercomparisonLawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Global Climate Research DivisionLawrence Livermore National LaboratoryLivermoreUSA
  3. 3.National Center for Atmospheric ResearchBoulderUSA
  4. 4.Climatic Research UnitUniversity of East AngliaNorwichUK
  5. 5.Deutsches KlimarechenzentrumBundesstrasseHamburgGermany

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