While there is evidence that increases in greenhouse gases have likely caused changes in some types of extremes, there is no simple answer to the question of whether the climate, in general, has become more or less extreme. Both the terms ‘more extreme’ and ‘less extreme’ can be defined in different ways, resulting in different characterizations of observed changes in extremes. Additionally, from a physical climate science perspective it is difficult to devise a comprehensive metric that encompasses all aspects of extreme behavior in the climate.
One approach for evaluating whether the climate is becoming more extreme would be to determine whether there have been changes in the typical range of variation of specific climate variables. For example, if there was evidence that temperature variations in a given region had become significantly larger than in the past, then it would be reasonable to conclude that temperatures in that region had become more extreme. More simply, temperature variations might be considered to be becoming more extreme if the difference between the highest and the lowest temperature observed in a year is increasing. According to this approach, daily temperature over the globe may have become less extreme because there have generally been greater increases in mean daily minimum temperatures globally than in mean daily maximum temperatures, over the second half of the 20th century. On the other hand, one might conclude that daily precipitation has become more extreme because observations suggest that the magnitude of the heaviest precipitation events has increased in many parts of the world. Another approach would be to ask whether there have been significant changes in the frequency with which climate variables cross fixed thresholds that have been associated with human or other impacts. For example, an increase in the mean temperature usually results in an increase in hot extremes and a decrease in cold extremes. Such a shift in the temperature distribution would not increase the ‘extremeness’ of day-to-day variations in temperature, but would be perceived as resulting in a more extreme warm temperature climate, and a less extreme cold temperature climate. So the answer to the question posed here would depend on the variable of interest, and on which specific measure of the extremeness of that variable is examined. As well, to provide a complete answer to the above question, one would also have to collate not just trends in single variables, but also indicators of change in complex extreme events resulting from a sequence of individual events, or the simultaneous occurrence of different types of extremes. So it would be difficult to comprehensively describe the full suite of phenomena of concern, or to find a way to synthesize all such indicators into a single extremeness metric that could be used to comprehensively assess whether the climate as a whole has become more extreme from a physical perspective. And to make such a metric useful to more than a specific location, one would have to combine the results at many locations, each with a different perspective on what is ‘extreme.’
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Brown et al., 2008). As a consequence, more complete and homogenous information about changes is now available for at least some variables and regions (Nicholls and Alexander, 2007; Peterson and Manton, 2008). For instance, the development of global databases of daily temperature and precipitation covering up to 70% of the global land area has allowed robust analyses of extremes (see Alexander et al., 2006). In addition, analyses of temperature and precipitation extremes using higher temporal resolution data, such as that available in the Global Historical Climatology Network – Daily data set (Durre et al., 2008) have also proven robust at both a global (Alexander et al., 2006) and regional scale (Sections 3.3.1 and 3.3.2). Nonetheless, as highlighted above, for many extremes, data remain sparse and problematic, resulting in lower ability to establish changes, particularly on a global basis and for specific regions.
3.2.2. The Causes behind the Changes
This section discusses the main requirements, approaches, and considerations for the attribution of causes for observed changes in extremes. In Sections 3.3 to 3.5, the causes of observed changes in
specific extremes are assessed. A global summary of these assessments is provided in Table 3-1. Climate variations and change are induced by variability internal to the climate system, and changes in external forcings, which include natural external forcings such as changes in solar irradiance and volcanism, and anthropogenic forcings such as aerosol and greenhouse gas emissions principally due to the burning of fossil fuels, and land use and land cover changes. The mean state, extremes, and variability are all related aspects of the climate, so external forcings that affect the mean climate would in general result in changes in extremes. For this reason, we provide in Section 3.2.2.1 a brief overview of human-induced changes in the mean climate to aid the understanding of changes in extremes as the literature directly addressing the causes of changes in extremes is quite limited.
3.2.2.1. Human-Induced Changes in the Mean Climate that Affect Extremes
The occurrence of extremes is usually the result of multiple factors, which can act either on the large scale or on the regional (and local) scale (see also Section 3.1.6). Some relevant large-scale impacts of Three types of metrics have been considered to avoid these problems, and thereby allow an answer to this question. One approach is to count the number of record-breaking events in a variable and to examine such a count for any trend. However, one would still face the problem of what to do if, for instance, hot extremes are setting new records, while cold extremes are not occurring as frequently as in the past. In such a case, counting the number of records might not indicate whether the climate was becoming more or less extreme, rather just whether there was a shift in the mean climate. Also, the question of how to combine the numbers of record-breaking events in various extremes (e.g., daily precipitation and hot temperatures) would need to be considered. Another approach is to combine indicators of a selection of important extremes into a single index, such as the Climate Extremes Index (CEI), which measures the fraction of the area of a region or country experiencing extremes in monthly mean surface temperature, daily precipitation, and drought. The CEI, however, omits many important extremes such as tropical cyclones and tornadoes, and could, therefore, not be considered a complete index of ‘extremeness.’ Nor does it take into account complex or multiple extremes, nor the varying thresholds that relate extremes to impacts in various sectors.
A third approach to solving this dilemma arises from the fact that extremes often have deleterious economic consequences. It may therefore be possible to measure the integrated economic effects of the occurrence of different types of extremes into a common instrument such as insurance payout to determine if there has been an increase or decrease in that instrument. This approach would have the value that it clearly takes into account those extremes with economic consequences. But trends in such an instrument will be dominated by changes in vulnerability and exposure and it will be difficult, if not impossible, to disentangle changes in the instrument caused by non-climatic changes in vulnerability or exposure in order to leave a residual that reflects only changes in climate extremes.
For example, coastal development can increase the exposure of populations to hurricanes; therefore, an increase in damage in coastal regions caused by hurricane landfalls will largely reflect changes in exposure and may not be indicative of increased hurricane activity.
Moreover, it may not always be possible to associate impacts such as the loss of human life or damage to an ecosystem due to climate extremes to a measurable instrument.
None of the above instruments has yet been developed sufficiently as to allow us to confidently answer the question posed here. Thus we are restricted to questions about whether specific extremes are becoming more or less common, and our confidence in the answers to such questions, including the direction and magnitude of changes in specific extremes, depends on the type of extreme, as well as on the region and season, linked with the level of understanding of the underlying processes and the reliability of their simulation in models.
external forcings affecting extremes include net increases in temperature induced by changes in radiation, enhanced moisture content of the atmosphere, and increased land-sea contrast in temperatures, which can, for example, affect circulation patterns and to some extent monsoons.
At regional and local scales, additional processes can modulate the overall changes in extremes, including regional feedbacks, in particular linked to land-atmosphere interactions with, for example, soil moisture or snow (e.g., Section 3.1.4). This section briefly reviews the current understanding of the causes (i.e., in the sense of attribution to either external forcing or internal climate variability) of large-scale (and some regional) changes in the mean climate that are of relevance to extreme events, to the extent that they have been considered in detection and attribution studies.
Regarding observed increases in global average annual mean surface temperatures in the second half of the 20th century, we base our analysis on the following AR4 assessment (Hegerl et al., 2007): Most of the observed increase in global average temperatures is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.
Greenhouse gas forcing alone would likely have resulted in a greater warming than observed if there had not been an offsetting cooling effect from aerosol and other forcings. It is extremely unlikely (<5%) that the global pattern of warming can be explained without external forcing, and very unlikely that it is due to known natural external causes alone. Anthropogenically forced warming over the second half of the 20th century has also been detected in ocean heat content and air temperatures in all continents (Hegerl et al., 2007; Gillett et al., 2008b).
Hegerl et al. (2007) assessed literature that considered detection in temperature trends at scales as small as approximately 500 km. Recent work has provided more evidence of detection of an anthropogenic influence at increasingly smaller spatial scales and for seasonal averages (Stott et al., 2010). For instance, Min and Hense (2007) found that estimates of response to anthropogenic forcing from the multi-model Coupled Model Intercomparison Project 3 (CMIP3) ensemble (see Section 3.2.3.3) provided a better explanation for observed continental-scale seasonal temperature changes than alternative explanations such as natural external forcing or internal variability. In another study, an anthropogenic signal was detected in 20th-century summer temperatures in Northern Hemisphere subcontinental regions except central North America, although the results were more uncertain when anthropogenic and natural signals were considered together (Jones et al., 2008). An anthropogenic signal has also been detected in multi-decadal trends in a US climate extreme index (Burkholder and Karoly, 2007), in the hydrological cycle of the western United States (Barnett et al., 2008), in New Zealand temperatures (Dean and Stott, 2009), and in European temperatures (Christidis et al., 2011a).
Attribution has more stringent demands than those for the detection of an external influence in observations. Overall, attribution at scales smaller than continental has still not yet been established primarily due to the low signal-to-noise ratio and the difficulties of separately attributing effects of the wider range of possible driving processes
(either attributable to external forcing or internal climate variability) at these scales (Hegerl et al., 2007). One reason is that averaging over smaller regions reduces the internal variability less than does averaging over large regions. In addition, the small-scale details of external forcing, and the responses simulated by models, are less credible than large-scale features. For instance, temperature changes are poorly simulated by models in some regions and seasons (Dean and Stott, 2009; van Oldenborgh et al., 2009). Also the inclusion of additional forcing factors, such as land use change and aerosols that can be more important at regional scales, remains a challenge (Lohmann and Feichter, 2007;
Pitman et al., 2009; Rotstayn et al., 2009).
One of the significant advances since AR4 is emerging evidence of human influence on global atmospheric moisture content and precipitation.
According to the Clausius-Clapeyron relationship, the saturation vapor pressure increases approximately exponentially with temperature. It is physically plausible that relative humidity would remain roughly constant under climate change (e.g., Hegerl et al., 2007). This means that specific humidity increases about 7% for a one degree increase in temperature in the current climate. Indeed, observations indicate significant increases between 1973 and 2003 in global surface specific humidity but not in relative humidity (Willett et al., 2008), and at the largest spatial-temporal scales moistening is close to the Clausius-Clapeyron scaling of the saturated specific humidity (~7% K-1; Willett et al., 2010), though relative humidity over low- and mid-latitude land areas decreased over a 10-year period prior to 2008 possibly due to a slower temperature increase in the oceans than over the land (Simmons et al., 2010). By comparing observations with model simulations, changes in the global surface specific humidity for 1973-2003 (Willett et al., 2007), and in lower tropospheric moisture content over the 1988-2006 period (Santer et al., 2007) can be attributed to anthropogenic influence.
The increase in the atmospheric moisture content would be expected to lead to an increase in extreme precipitation when other factors do not change. Min et al. (2011) detected an anthropogenic influence in annual maxima of daily precipitation over Northern Hemisphere land areas. The influence of anthropogenic forcing has been detected in the latitudinal pattern of land precipitation trends though the model-simulated magnitude of changes is smaller than that observed (X. Zhang et al., 2007).
The smaller changes in model simulations may be due in part to averaging precipitation trends from different model simulations, as spatial patterns of trends simulated by different models are not exactly the same. The influence of anthropogenic greenhouse gases and aerosols on changes in precipitation over high-latitude land areas north of 55°N has also been detected (Min et al., 2008). Detection is possible there, despite limited data coverage, in part because the response to forcing is relatively strong, and because internal variability in precipitation is low in this region.
3.2.2.2. How to Attribute a Change in Extremes to Causes The good practice guidance paper on detection and attribution (Hegerl et al., 2010) reconciles terminologies of detection and attribution used
by Working Groups I and II in the AR4. It provides detailed guidance on the procedures that include two main approaches to attribute a change in climate to causes. One is single-step attribution, which involves assessments that attribute an observed change within a system to an external forcing based on explicitly modelling the response of the
variable to the external forcings. The alternate procedure is multi-step attribution, which combines an assessment that attributes an observed change in a variable of interest to a change in climate, with a separate assessment that attributes the change to external forcings. Attribution of changes in climate extremes has some unique issues. Observed data