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DáithíA. Stone, Andrew J. Weaver, and Ronald J. Stouffer


Two possible interpretations of forced climate change view it as projecting, either linearly or nonlinearly, onto the dominant modes of variability of the climate system. An evaluation of these two interpretations is performed using annual mean sea level pressure (SLP) and surface air temperature (SAT) fields obtained from integrations of the Geophysical Fluid Dynamics Laboratory coupled general circulation model forced with varying concentrations of greenhouse gases.

The dominant modes of SLP both represent much of the total variability and remain important in warmer climates. With SAT, however, the dominant modes are often related to variations in the sea-ice edge and so do not remain important once the ice has retreated; those unrelated to sea ice remain dominant in the warmer climates but represent smaller fractions of the total variability.

In general, climate change tends to project most strongly onto the more dominant modes. The change in SLP projects partially onto the top two modes in the Northern Hemisphere, reflecting both an overall decrease in hemispheric SLP as well as the pattern of change. In the Southern Hemisphere the change projects negligibly onto the dominant patterns between equilibrium climates but very strongly onto the Antarctic oscillation–like mode in the transient integrations. Changes in SAT project partially onto the dominant modes but relate more to the mean warming rather than the pattern of change. In general, the change projects most strongly onto the more dominant modes.

In all SLP domains, the projection of climate change overwhelmingly manifests itself as a linear translation in the mode, consistent with the linear interpretation. In SAT domains related to sea-ice variability, the projection reflects an increased tendency toward ice-free regimes, consistent with the nonlinear perspective; however this nonlinear projection represents only a small portion of the overall climate change.

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DáithíA. Stone, Myles R. Allen, and Peter A. Stott


This paper presents an update on the detection and attribution of global annual mean surface air temperature changes, using recently developed climate models. In particular, it applies a new methodology that permits the inclusion of many more general circulation models (GCMs) into the analysis, and it also includes more recent observations. This methodology involves fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings.

Despite considerable spread in estimated EBM parameters, characteristics of model performance, such as the transient climate response, appear to be more constrained for each of the forcings. The resulting estimated response patterns are provided as input to the standard fingerprinting method used in previous studies. The estimated GCM responses to changes in greenhouse gases are detected in the observed record for all of the GCMs, and are generally found to be consistent with the observed changes; the same is generally true for the responses to changes in stratospheric aerosols from volcanic eruptions. GCM responses to changes in tropospheric sulfate aerosols and solar irradiance also appear consistent with the observed record, although the uncertainty is larger. Greenhouse gas and solar irradiance changes are found to have contributed to a best guess of ∼0.8 and ∼0.3 K warming over the 1901–2005 period, respectively, while sulfate aerosols have contributed a ∼0.4 K cooling. This analysis provides an observationally constrained estimate of future warming, which is found to be fairly robust across GCMs. By 2100, a warming of between about 1.5 and 4.5 K can be expected according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B emissions scenario.

These results indicate an emerging constraint for global mean surface temperature responses to external forcings across GCMs, which is corroborated in the observed record. This implies that observationally constrained estimates of past warming and predictions of future warming are indeed becoming robust.

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DáithíA. Stone, Myles R. Allen, Frank Selten, Michael Kliphuis, and Peter A. Stott


The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings.

This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis.

Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources.

The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques.

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Kamoru A. Lawal, Abayomi A. Abatan, Oliver Angélil, Eniola Olaniyan, Victoria H. Olusoji, Philip G. Oguntunde, Benjamin Lamptey, Babatunde J. Abiodun, Hideo Shiogama, Michael F. Wehner, and DáithíA. Stone
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