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Jesse Kenyon and Gabriele C. Hegerl

Abstract

The influence of large-scale modes of climate variability on worldwide summer and winter temperature extremes has been analyzed, namely, that of the El Niño–Southern Oscillation, the North Atlantic Oscillation, and Pacific interdecadal climate variability. Monthly indexes for temperature extremes from worldwide land areas are used describe moderate extremes, such as the number of exceedences of the 90th and 10th climatological percentiles, and more extreme events such as the annual, most extreme temperature. This study examines which extremes show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show that temperature extremes are substantially affected by large-scale circulation patterns, and they show distinct regional patterns of response to modes of climate variability. The effects of the El Niño–Southern Oscillation are seen throughout the world but most clearly around the Pacific Rim and throughout all of North America. Likewise, the influence of Pacific interdecadal variability is strongest in the Northern Hemisphere, especially around the Pacific region and North America, but it extends to the Southern Hemisphere. The North Atlantic Oscillation has a strong continent-wide effect for Eurasia, with a clear but weaker effect over North America. Modes of variability influence the shape of the daily temperature distribution beyond a simple shift, often affecting cold and warm extremes and sometimes daytime and nighttime temperatures differently. Therefore, for reliable attribution of changes in extremes as well as prediction of future changes, changes in modes of variability need to be accounted for.

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Jesse Kenyon and Gabriele C. Hegerl

Abstract

The probability of climate extremes is strongly affected by atmospheric circulation. This study quantifies the worldwide influence of three major modes of circulation on station-based indices of intense precipitation: the El Niño–Southern Oscillation, the Pacific interdecadal variability as characterized by the North Pacific index (NPI), and the North Atlantic Oscillation–Northern Annular Mode. The study examines which stations show a statistically significant (5%) difference between the positive and negative phases of a circulation regime. Results show distinct regional patterns of response to all these modes of climate variability; however, precipitation extremes are most substantially affected by the El Niño–Southern Oscillation. The effects of the El Niño–Southern Oscillation are seen throughout the world, including in India, Africa, South America, the Pacific Rim, North America, and, weakly, Europe. The North Atlantic Oscillation has a strong, continent-wide effect on Eurasia and affects a small, but not negligible, percentage of stations across the Northern Hemispheric midlatitudes. This percentage increases slightly if the Northern Annular Mode index is used rather than the NAO index. In that case, a region of increase in intense precipitation can also be found in Southeast Asia. The NPI influence on precipitation extremes is similar to the response to El Niño, and strongest in landmasses adjacent to the Pacific. Consistently, indices of more rare precipitation events show a weaker response to circulation than indices of moderate extremes; the results are quite similar, but of opposite sign, for negative anomalies of the circulation indices.

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Gabriele C. Hegerl and Myles R. Allen

Abstract

Two approaches to distinguishing anthropogenic greenhouse gas and sulfate aerosol signals in the observed surface temperature record are compared. Both rely on a variant of general regression called “optimal fingerprinting.” One approach is equivalent to a stepwise regression procedure estimating, first, a greenhouse gas signal and, in a second step, the sulfate aerosol signal. This is different from multiple regression, under which both signals are estimated simultaneously and treated symmetrically. The stepwise regression approach is a more powerful means of detecting greenhouse gas influence in the presence of a small and possibly poorly simulated sulfate aerosol signal. However, when both signals are of comparable size, multiple regression provides estimates of the amplitude of the greenhouse and sulfate responses that are, in principle, independent of each other, making it generally simpler to interpret. It is shown that there is a simple linear transform relating the stepwise and multiple regression approaches. Application of this transform to previous results of stepwise regression illustrates that estimated responses to anthropogenic greenhouse gas forcing are very similar between different climate models and are generally consistent with the signal estimated from the observations. The sulfate component of the anthropogenic signal appears to be responsible for the most prominent discrepancies between observations and some of the model simulations considered. The estimated contribution of anthropogenic greenhouse gases to the observed warming over the period of 1949–98 lies in the range of 0.39–1.29 K (50 yr)−1 or 0.28–1.16 K (50 yr)−1 (5%–95% range), depending on the model used to estimate the signal. These ranges depend only on the accuracy of the spatial pattern and the sign of the modeled sulfate forcing and response, not on its amplitude.

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Gabriele C. Hegerl and Gerald R. North

Abstract

Three statistically optimal approaches, which have been proposed for detecting anthropogenic climate change, are intercompared. It is shown that the core of all three methods is identical. However, the different approaches help to better understand the properties of the optimal detection. Also, the analysis allows us to examine the problems in implementing these optimal techniques in a common framework. An overview of practical considerations necessary for applying such an optimal method for detection is given. Recent applications show that optimal methods present some basis for optimism toward progressively more significant detection of forced climate change. However, it is essential that good hypothesized signals and good information on climate variability be obtained since erroneous variability, especially on the timescale of decades to centuries, can lead to erroneous conclusions.

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Gabriele C. Hegerl and John M. Wallace

Abstract

During the past 20 years, satellite measurements of tropospheric temperature have shown a slower rate of global temperature increase than surface air temperature, yielding an increase in the surface to lower-troposphere lapse rate of 0.12 K decade−1 from 1979 to August 2001. This increase in lapse rate was preceded by a decrease over the previous 15-yr interval.

The influence of patterns of climate variability on the global- and hemispheric-scale lapse rate was investigated, based on observations of surface and tropospheric temperature from satellite, radiosonde, surface air, and sea surface data. It was found that a substantial fraction of winter-to-winter lapse rate variability in the Northern Hemisphere mid- to high latitudes is dynamically induced. In the Tropics and subtropics, a distinctive signature of El Niño is apparent in the interannual variations in lapse rate. A small additional amount of month-to-month variability can be attributed to zonally symmetric circulation changes at lower latitudes that are linearly independent of ENSO. Trends in these patterns can account only for a small fraction of the observed trend in lapse rate. The combination of strong surface warming and very small tropospheric warming in the Tropics and subtropics over the recent 21 yr is extremely unusual in the context of data from a coupled climate model. The same can be said of the observed trends of the previous 15 yr. Thus, it is concluded that structured patterns of climate variability account for much of the variability in lapse rate on monthly and interannual timescales, but not on interdecadal timescales.

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Simone Morak, Gabriele C. Hegerl, and Nikolaos Christidis

Abstract

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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Ronald J. Stouffer, Gabriele Hegerl, and Simon Tett

Abstract

This study compares the variability of surface air temperature in three long coupled ocean–atmosphere general circulation model integrations. It is shown that the annual mean climatology of the surface air temperatures (SAT) in all three models is realistic and the linear trends over the 1000-yr integrations are small over most areas of the globe. Second, although there are notable differences among the models, the models’ SAT variability is fairly realistic on annual to decadal timescales, both in terms of the geographical distribution and of the global mean values. A notable exception is the poor simulation of observed tropical Pacific variability. In the HadCM2 model, the tropical variability is overestimated, while in the GFDL and HAM3L models, it is underestimated. Also, the ENSO-related spectral peak in the globally averaged observed SAT differs from that in any of the models. The relatively low resolution required to integrate models for long time periods inhibits the successful simulation of the variability in this region. On timescales longer than a few decades, the largest variance in the models is generally located near sea ice margins in high latitudes, which are also regions of deep oceanic convection and variability related to variations in the thermohaline circulation. However, the exact geographical location of these maxima varies from model to model. The preferred patterns of interdecadal variability that are common to all three coupled models can be isolated by computing empirical orthogonal functions (EOFs) of all model data simultaneously using the common EOF technique. A comparison of the variance each model associated with these common EOF patterns shows that the models generally agree on the most prominent patterns of variability. However, the amplitudes of the dominant modes of variability differ to some extent between the models and between the models and observations. For example, two of the models have a mode with relatively large values of the same sign over most of the Northern Hemisphere midlatitudes. This mode has been shown to be relevant for the separation of the temperature response pattern due to sulfate aerosol forcing from the response to greenhouse gas forcing. This indicates that the results of the detection of climate change and its attribution to different external forcings may differ when unperturbed climate variability in surface air temperature is estimated using different coupled models. Assuming that the simulation of variability of the global mean SAT is as realistic on longer timescales as it is for the shorter timescales, then the observed warming of more than 0.5 K of the SAT in the last 110 yr is not likely to be due to internally generated variability of the coupled atmosphere–ocean–sea ice system. Instead, the warming is likely to be due to changes in the radiative forcing of the climate system, such as the forcing associated with increases in greenhouse gases.

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Kwano-Y. Kim, Gerald R. North, and Gabriele C. Hegerl

Abstract

In this study the magnitude and the temporal and spatial correlation scales of background fluctuations generated by three climate models, two different coupled ocean-atmosphere general circulation models and one energy balance model, were examined. These second-moment statistics of the models were compared with each other and with those of the observation data in several frequency bands. This exercise shows some discordance between the models and the observations and also significant discrepancy among different numerical models. The authors also calculated the empirical orthogonal functions and eigenvalues because these am important ingredients for formulating estimation and detection algorithms. There are significant model to model variations both in the shape of eigenfunctions and in the spectrum of eigenvalues. Also, consistency between the modeled eigenfunctions and eigenvalues and those of the observations are rather poor, especially in the low-frequency bands.

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Gabriele C. Hegerl, Philip D. Jones, and Tim P. Barnett

Abstract

The effect of sampling error in surface air temperature observations is assessed for detection and attribution of an anthropogenic signal. This error arises because grid-box values are based on varying densities of station and marine data. An estimate of sampling error is included in the application of an optimal detection and attribution method based on June–August trends over 50 yr. The detection and attribution method is applied using both the full spatial pattern of observed trends and spatial patterns from which the global mean warming has been subtracted.

Including the effect of sampling error is found to increase the uncertainty in estimates of the greenhouse gas–plus–sulfate aerosol signal from observations by less than 2%–6% for recent trend patterns (1949–98), and 3%–8% for signal estimates from observations in the first half of the twentieth century. Random instrumental error shows even smaller effects. However, the effects of systematic instrumental errors, such as changes in measurement practices or urbanization, cannot be estimated at present. The detection and attribution results for recent 50-yr summer trends are very similar between the case including and the case disregarding the global mean. However, results based on observations from the first half of the twentieth century yield high signal amplitudes with global mean and low ones without, suggesting little pattern agreement for that warming with the anthropogenic climate change signal.

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Tim P. Barnett, Gabriele C. Hegerl, Ben Santer, and Karl Taylor

Abstract

When long integrations of climate models forced by observed boundary conditions are compared against observations, differences appear that have spatial and temporal coherence. These differences are due to several causes, the largest of which are fundamental model errors and the internal variability inherent in a GCM integration. Uncertainties in the observations themselves are small in comparison. The present paper constitutes a first attempt to compare the time dependence of these spatial difference patterns with the time dependence of simulated spatial patterns of climate change associated with anthropogenic sources.

The analysis procedure was to project the model minus observed near-surface temperature difference fields onto estimates of the anthropogenic “signal” (in this case the response to greenhouse-gas and sulfate-aerosol forcing). The temporal behavior of this projection was then compared with the estimated temporal evolution of the anthropogenic signal. Such comparisons were performed on timescales of 10, 20, and 30 yr. For trends of only 10 yr in length, the model minus observed spatial difference patterns are of the same magnitude and have the same time rate of change as the expected anthropogenic signal. In the case of 20- and 30-yr trends, the prospects are favorable for discriminating between temperature changes due to anthropogenic signal changes and changes associated with model minus observed difference structures. This suggests that attempts to quantitatively detect anthropogenic climate change should be based on temporal samples of at least several decades in length. This study also shows the importance of distinguishing between purely statistical detection and what the authors term practical prediction. It is found that the results of the detection analysis are sensitive to the spatial resolution at which it is performed: for the specific case of near-surface temperature, higher spatial resolution improves ability to discriminate between an anthropogenic signal and the type of model error/internal variability “noise” considered here.

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