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.

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.

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.

Abstract

In this paper we develop a method to quantify the accuracy of different pattern extraction techniques for the additive space–time modes often assumed to be present in climate data. It has previously been shown that the standard technique of principal component analysis (PCA; also known as empirical orthogonal functions) may extract patterns that are not physically meaningful. Here we analyze two modern pattern extraction methods, namely dynamical mode decomposition (DMD) and slow feature analysis (SFA), in comparison with PCA. We develop a Monte Carlo method to generate synthetic additive modes that mimic the properties of climate modes described in the literature. The datasets composed of these generated modes do not satisfy the assumptions of any pattern extraction method presented. We find that both alternative methods significantly outperform PCA in extracting local and global modes in the synthetic data. These techniques had a higher mean accuracy across modes in 60 out of 60 mixed synthetic climates, with SFA slightly outperforming DMD. We show that in the majority of simple cases PCA extracts modes that are not significantly better than a random guess. Finally, when applied to real climate data these alternative techniques extract a more coherent and less noisy global warming signal, as well as an El Niño signal with a clearer spectral peak in the time series, and more a physically plausible spatial pattern.

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.

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.

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.

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.

Abstract

Atmospheric blocking is an important contributor to European temperature variability. It can trigger cold and warm spells, which is of specific relevance in spring because vegetation is particularly vulnerable to extreme temperatures in the growing season. The spring season is investigated as a transition period from predominant connections of blocking with cold spells in winter to predominant connections of blocking with warm spells in summer. Extreme temperatures are termed cold or warm spells if temperature stays outside the 10th to 90th percentile range for at least six consecutive days. Cold and warm spells in Europe over 1979–2014 are analyzed in observations from the European daily high-resolution gridded dataset (E-OBS) and the connection to blocking is examined in geopotential height fields from ERA-Interim. A highly significant link between blocking and cold and warm spells is found that changes during spring. Blocking over the northeastern Atlantic and Scandinavia is correlated with the occurrence of cold spells in Europe, particularly early in spring, whereas blocking over central Europe is associated with warmer conditions, particularly from March onward. The location of the block also impacts the spatial distribution of temperature extremes. More than 80% of cold spells in southeastern Europe occur during blocking whereas warm spells are correlated with blocking mainly in northern Europe. Over the analysis period, substantial interannual variability is found but also a decrease in cold spells and an increase in warm spells. The long-term change to a warmer climate holds the potential for even higher vulnerability to spring cold extremes.

Abstract

Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive are used to calculate the zonal-mean change in seasonal land precipitation for the second half of the twentieth century in response to a range of external forcings, including anthropogenic and natural forcings combined (ALL), greenhouse gas forcing, anthropogenic aerosol forcing, anthropogenic forcings combined, and natural forcing. These simulated patterns of change are used as fingerprints in a detection and attribution study applied to four different gridded observational datasets of global land precipitation from 1951 to 2005. There are large differences in the spatial and temporal coverage in the observational datasets. Yet despite these differences, the zonal-mean patterns of change are mostly consistent except at latitudes where spatial coverage is limited. The results show some differences between datasets, but the influence of external forcings is robustly detected in March–May, December–February, and for annual changes for the three datasets more suitable for studying changes. For June–August and September–November, external forcing is only detected for the dataset that includes only long-term stations. Fingerprints for combinations of forcings that include the effect of greenhouse gases are similarly detectable to those for ALL forcings, suggesting that greenhouse gas influence drives the detectable features of the ALL forcing fingerprint. Fingerprints of only natural or only anthropogenic aerosol forcing are not detected. This, together with two-fingerprint results, suggests that at least some of the detected change in zonal land precipitation can be attributed to human influences.