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Nikolaos Christidis
,
Richard A. Betts
, and
Peter A. Stott
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Philip W. Mote
,
Peter A. Stott
, and
Robert S. Harwood

Abstract

The authors have used a spectral, primitive equation mechanistic model of the stratosphere and mesosphere to simulate observed stratospheric flow through the winters of 1991–92 and 1994–95 by forcing the model at 100 hPa with observed geopotential height. The authors assess the model’s performance quantitatively by comparing the simulations with the United Kingdom Meteorological Office (UKMO) assimilated stratosphere–troposphere data. Time-mean, zonal-mean temperatures are generally within 5 K and winds within 5 m s−1; transient features, such as wave growth, are mostly simulated well. The phase accuracy of planetary-scale waves declines with altitude and wavenumber, and the model has difficulty correctly simulating traveling anticyclones in the upper stratosphere. The authors examine the minor warming of January 1995 which was unusual in its depth and development and which the model simulated fairly well. The authors also examine the minor warming of January 1992, which the model missed, and a major warming in February 1992 that occurred in the model but not in the observations.

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

Abstract

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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Donald P. Cummins
,
David B. Stephenson
, and
Peter A. Stott

Abstract

This study has developed a rigorous and efficient maximum likelihood method for estimating the parameters in stochastic energy balance models (with any k > 0 number of boxes) given time series of surface temperature and top-of-the-atmosphere net downward radiative flux. The method works by finding a state-space representation of the linear dynamic system and evaluating the likelihood recursively via the Kalman filter. Confidence intervals for estimated parameters are straightforward to construct in the maximum likelihood framework, and information criteria may be used to choose an optimal number of boxes for parsimonious k-box emulation of atmosphere–ocean general circulation models (AOGCMs). In addition to estimating model parameters the method enables hidden state estimation for the unobservable boxes corresponding to the deep ocean, and also enables noise filtering for observations of surface temperature. The feasibility, reliability, and performance of the proposed method are demonstrated in a simulation study. To obtain a set of optimal k-box emulators, models are fitted to the 4 × CO2 step responses of 16 AOGCMs in CMIP5. It is found that for all 16 AOGCMs three boxes are required for optimal k-box emulation. The number of boxes k is found to influence, sometimes strongly, the impulse responses of the fitted models.

Free access
Nikolaos Christidis
,
Peter A. Stott
, and
Simon J. Brown

Abstract

Formal detection and attribution analyses of changes in daily extremes give evidence of a significant human influence on the increasing severity of extremely warm nights and decreasing severity of extremely cold days and nights. This paper presents an optimal fingerprinting analysis that also detects the contributions of external forcings to recent changes in extremely warm days using nonstationary extreme value theory. The authors’ analysis is the first that attempts to partition the observed change in warm daytime extremes between its anthropogenic and natural components and hence attribute part of the change to possible causes. Changes in the extreme temperatures are represented by the temporal changes in a parameter of an extreme value distribution. Regional distributions of the trend in the parameter are computed with and without human influence using constraints from the global optimal fingerprinting analysis. Anthropogenic forcings alter the regional distributions, indicating that extremely warm days have become hotter.

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Thomas C. Peterson
,
Peter A. Stott
, and
Stephanie Herring

Attribution of extreme events shortly after their occurrence stretches the current state-of-theart of climate change assessment. To help foster the growth of this science, this article illustrates some approaches to answering questions about the role of human factors, and the relative role of different natural factors, for six specific extreme weather or climate events of 2011.

Not every event is linked to climate change. The rainfall associated with the devastating Thailand floods can be explained by climate variability. But long-term warming played a part in the others. While La Niña contributed to the failure of the rains in the Horn of Africa, an increased frequency of such droughts there was linked to warming in the Western Pacific– Indian Ocean warm pool. Europe's record warm temperatures would probably not have been as unusual if the high temperatures had been caused only by the atmospheric flow regime without any long-term warming.

Calculating how the odds of a particular extreme event have changed provides a means of quantifying the influence of climate change on the event. The heatwave that affected Texas has become distinctly more likely than 40 years ago. In the same vein, the likelihood of very warm November temperatures in the UK has increased substantially since the 1960s.

Comparing climate model simulations with and without human factors shows that the cold UK winter of 2010/2011 has become about half as likely as a result of human influence on climate, illustrating that some extreme events are becoming less likely due to climate change.

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Nikolaos Christidis
,
Kasemsan Manomaiphiboon
,
Andrew Ciavarella
, and
Peter A. Stott
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Nikolaos Christidis
,
Mark McCarthy
,
Andrew Ciavarella
, and
Peter A. Stott
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Peter A. Stott
,
Gareth S. Jones
, and
John F. B. Mitchell

Abstract

Current attribution analyses that seek to determine the relative contributions of different forcing agents to observed near-surface temperature changes underestimate the importance of weak signals, such as that due to changes in solar irradiance. Here a new attribution method is applied that does not have a systematic bias against weak signals.

It is found that current climate models underestimate the observed climate response to solar forcing over the twentieth century as a whole, indicating that the climate system has a greater sensitivity to solar forcing than do models. The results from this research show that increases in solar irradiance are likely to have had a greater influence on global-mean temperatures in the first half of the twentieth century than the combined effects of changes in anthropogenic forcings. Nevertheless the results confirm previous analyses showing that greenhouse gas increases explain most of the global warming observed in the second half of the twentieth century.

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Stephanie C. Herring
,
Nikolaos Christidis
,
Andrew Hoell
, and
Peter A. Stott

Abstract

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2020 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.

Open access