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

Abstract

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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Stephanie C. Herring, Andrew Hoell, Marty Hoerling, Nikolaos Christidis, and Peter A. Stott
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Stephanie C. Herring, Martin P. Hoerling, Thomas C. Peterson, and Peter A. Stott
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Nathan P. Gillett, Gabriele C. Hegerl, Myles R. Allen, Peter A. Stott, and Reiner Schnur

Abstract

Anthropogenic influences on surface temperature over the second half of the twentieth century are examined using output from two general circulation models (HadCM2 and ECHAM3). Optimal detection techniques involve the comparison of observed temperature changes with those simulated by a climate model, using a control integration to test the null hypothesis that all the observed changes are due to natural variability. Two recent studies have examined the influence of greenhouse gases and the direct effect of sulfate aerosol on surface temperature using output from the same two climate models but with many differences in the methods applied. Both detected overall anthropogenic influence on climate, but results on the separate detection of greenhouse gas and sulfate aerosol influences were different. This paper concludes that the main differences between the results can be explained by the season over which temperatures were averaged, the length of the climatology from which anomalies were taken, and the use of a time-evolving signal pattern as opposed to a spatial pattern of temperature trends. This demonstration of consistency increases confidence in the equivalence of the methodologies in other respects, and helps to synthesize results from the two approaches. Including information on the temporal evolution of the response to different forcings allows sulfate aerosol influence to be detected more easily in HadCM2, whereas focusing on spatial patterns gives better detectability in ECHAM3.

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Chunhui Lu, Fraser C. Lott, Ying Sun, Peter A. Stott, and Nikolaos Christidis

Abstract

In China, summer precipitation contributes a major part of the total precipitation amount in a year and has major impacts on society and human life. Whether any changes in summer precipitation are affected by external forcing on the climate system is an important issue. In this study, an optimal fingerprinting method was used to compare the observed changes of total, heavy, moderate, and light precipitation in summer derived from newly homogenized observation data with the simulations from multiple climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). The results demonstrate that the anthropogenic forcing signal can be detected and separated from the natural forcing signal in the observed increase of seasonal accumulated precipitation amount for heavy precipitation in summer in China and eastern China (EC). The simulated changes in heavy precipitation are generally consistent with observed change in China but are underestimated in EC. When the changes in precipitation of different intensities are considered simultaneously, the human influence on simultaneous changes in moderate and light precipitation can be detected in China and EC in summer. Changes attributable to anthropogenic forcing explain most of the observed regional changes for all categories of summer precipitation, and natural forcing contributes little. In the future, with increasing anthropogenic influence, the attribution-constrained projection suggests that heavy precipitation in summer will increase more than that from the model raw outputs. Society may therefore face a higher risk of heavy precipitation in the future.

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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
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Stephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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Lianchun Song, Siyan Dong, Ying Sun, Guoyu Ren, Botao Zhou, and Peter A. Stott
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Stephanie C. Herring, Nikolaos Christidis, Andrew Hoell, Martin P. Hoerling, and Peter A. Stott

Abstract

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2019 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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