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Nikolaos Christidis and Peter A. Stott

Abstract

The new Hadley Centre system for attribution of weather and climate extremes provides assessments of how human influence on the climate may lead to a change in the frequency of such events. Two different types of ensembles of simulations are generated with an atmospheric model to represent the actual climate and what the climate would have been in the absence of human influence. Estimates of the event frequency with and without the anthropogenic effect are then obtained. Three experiments conducted so far with the new system are analyzed in this study to examine how anthropogenic forcings change the odds of warm years, summers, or winters in a number of regions where the model reliably reproduces the frequency of warm events. In all cases warm events become more likely because of human influence, but estimates of the likelihood may vary considerably from year to year depending on the ocean temperature. While simulations of the actual climate use prescribed observational data of sea surface temperature and sea ice, simulations of the nonanthropogenic world also rely on coupled atmosphere–ocean models to provide boundary conditions, and this is found to introduce a major uncertainty in attribution assessments. Improved boundary conditions constructed with observational data are introduced in order to minimize this uncertainty. In more than half of the 10 cases considered here anthropogenic influence results in warm events being 3 times more likely and extreme events 5 times more likely during September 2011–August 2012, as an experiment with the new boundary conditions indicates.

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Nikolaos Christidis and Peter A. Stott
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Nikolaos Christidis and Peter A. Stott
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Nikolaos Christidis and Peter A. Stott
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Nikolaos Christidis, Andrew Ciavarella, and Peter A. Stott

Abstract

Attribution analyses of extreme events estimate changes in the likelihood of their occurrence due to human climatic influences by comparing simulations with and without anthropogenic forcings. Classes of events are commonly considered that only share one or more key characteristics with the observed event. Here we test the sensitivity of attribution assessments to such event definition differences, using the warm and wet winter of 2015/16 in the United Kingdom as a case study. A large number of simulations from coupled models and an atmospheric model are employed. In the most basic case, warm and wet events are defined relative to climatological temperature and rainfall thresholds. Several other classes of events are investigated that, in addition to threshold exceedance, also account for the effect of observed sea surface temperature (SST) anomalies, the circulation flow, or modes of variability present during the reference event. Human influence is estimated to increase the likelihood of warm winters in the United Kingdom by a factor of 3 or more for events occurring under any atmospheric and oceanic conditions, but also for events with a similar circulation or oceanic state to 2015/16. The likelihood of wet winters is found to increase by at least a factor of 1.5 in the general case, but results from the atmospheric model, conditioned on observed SST anomalies, are more uncertain, indicating that decreases in the likelihood are also possible. The robustness of attribution assessments based on atmospheric models is highly dependent on the representation of SSTs without the effect of human influence.

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Simone Morak, Gabriele C. Hegerl, and Nikolaos Christidis

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This study determines whether observed recent changes in the frequency of hot and cold extremes over land can be explained by climate variability or whether they show a detectable response to external influences. The authors analyze changes in the frequency of moderate-to-extreme daily temperatures—namely, the number of days exceeding the 90th percentile and the number of days not reaching the 10th percentile of daily minimum (tn90 and tn10, respectively) and maximum (tx90 and tx10, respectively) temperature—for both cold and warm seasons. The analysis is performed on a range of spatial scales and separately for boreal cold- and warm-season data. The fingerprint for external forcing is derived from an ensemble of simulations produced with the Hadley Centre Global Environmental Model, version 1 (HadGEM1), with both anthropogenic and natural forcings. The observations show an increase in warm extremes and a decrease in cold extremes in both seasons and in almost all regions that are generally well captured by the model. Some regional differences between model and observations may be due to local forcings or changes in climate dynamics. A detection analysis, using both optimized and nonoptimized fingerprints, shows that the influence of external forcing is detectable in observations for both cold and warm extremes, and cold and warm seasons, over the period 1951–2003 at the 5% level. It is also detectable separately for the Northern and Southern Hemispheres, and over most regions analyzed. The model shows a tendency to significantly overestimate changes in warm daytime extremes, particularly in summer.

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Nikolaos Christidis, Kasemsan Manomaiphiboon, Andrew Ciavarella, and Peter A. Stott
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Nikolaos Christidis, Richard A. Betts, and Peter A. Stott
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Eleanor J. Burke, Simon J. Brown, and Nikolaos Christidis

Abstract

Meteorological drought in the Hadley Centre global climate model is assessed using the Palmer Drought Severity Index (PDSI), a commonly used drought index. At interannual time scales, for the majority of the land surface, the model captures the observed relationship between the El Niño–Southern Oscillation and regions of relative wetness and dryness represented by high and low values of the PDSI respectively. At decadal time scales, on a global basis, the model reproduces the observed drying trend (decreasing PDSI) since 1952. An optimal detection analysis shows that there is a significant influence of anthropogenic emissions of greenhouse gasses and sulphate aerosols in the production of this drying trend. On a regional basis, the specific regions of wetting and drying are not always accurately simulated. In this paper, present-day drought events are defined as continuous time periods where the PDSI is less than the 20th percentile of the PDSI distribution between 1952 and 1998 (i.e., on average 20% of the land surface is in drought at any one time). Overall, the model predicts slightly less frequent but longer events than are observed. Future projections of drought in the twenty-first century made using the Special Report on Emissions Scenarios (SRES) A2 emission scenario show regions of strong wetting and drying with a net overall global drying trend. For example, the proportion of the land surface in extreme drought is predicted to increase from 1% for the present day to 30% by the end of the twenty-first century.

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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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