WGI AR5 Fig1-8.jpg
Figure 1.8 Schematic representations of the probability density function of daily temperature, which tends to be approximately Gaussian, and daily precipitation, which has a skewed distribution. Dashed lines represent a previous distribution and solid lines a changed distribution. The probability of occurrence, or frequency, of extremes is denoted by the shaded areas. In the case of temperature, changes in the frequencies of extremes are affected by changes (a) in the mean, (b) in the variance or shape, and (c) in both the mean and the variance. (d) In a skewed distribution such as that of precipitation, a change in the mean of the distribution generally affects its variability or spread, and thus an increase in mean precipitation would also imply an increase in heavy precipitation extremes, and vice-versa. In addition, the shape of the right-hand tail could also change, affecting extremes. Furthermore, climate change may alter the frequency of precipitation and the duration of dry spells between precipitation events. (Parts a–c modified from Folland et al., 2001,[1] and d modified from Peterson et al., 2008,[2] as in Zhang and Zwiers, 2012.)[3]

Climate change, whether driven by natural or human forcings, can lead to changes in the likelihood of the occurrence or strength of extreme weather and climate events such as extreme precipitation events or warm spells (see Chapter 3 of the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX); Seneviratne et al., 2012).[4] An extreme weather event is one that is rare at a particular place and/or time of year. Definitions of ‘rare’ vary, but an extreme weather event would normally be as rare as or rarer than the 10th or 90th percentile of a probability density function estimated from observations (see also Glossary in Annex III and FAQ 2.2). By definition, the characteristics of what is called extreme weather may vary from place to place in an absolute sense. At present, single extreme events cannot generally be directly attributed to anthropogenic influence, although the change in likelihood for the event to occur has been determined for some events by accounting for observed changes in climate (see Section 10.6). When a pattern of extreme weather persists for some time, such as a season, it may be classified as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., drought or heavy rainfall over a season). For some climate extremes such as drought, floods and heat waves, several factors such as duration and intensity need to be combined to produce an extreme event (Seneviratne et al., 2012).[4]

The probability of occurrence of values of a climate or weather variable can be described by a probability density function (PDF) that for some variables (e.g., temperature) is shaped similar to a Gaussian curve. A PDF is a function that indicates the relative chances of occurrence of different outcomes of a variable. Simple statistical reasoning indicates that substantial changes in the frequency of extreme events (e.g., the maximum possible 24-hour rainfall at a specific location) can result from a relatively small shift in the distribution of a weather or climate variable. Figure 1.8a shows a schematic of such a PDF and illustrates the effect of a small shift in the mean of a variable on the frequency of extremes at either end of the distribution. An increase in the frequency of one extreme (e.g., the number of hot days) can be accompanied by a decline in the opposite extreme (in this case the number of cold days such as frost days). Changes in the variability, skewness or the shape of the distribution can complicate this simple picture (Figure 1.8b, c and d).

While the SAR found that data and analyses of extremes related to climate change were sparse, improved monitoring and data for changes in extremes were available for the TAR, and climate models were being analysed to provide projections of extremes. In AR4, the observational basis of analyses of extremes had increased substantially, so that some extremes were now examined over most land areas (e.g., rainfall extremes). More models with higher resolution, and a larger number of regional models have been used in the simulation and projection of extremes, and ensemble integrations now provide information about PDFs and extremes.

Since the TAR, climate change studies have especially focused on changes in the global statistics of extremes, and observed and projected changes in extremes have been compiled in the so-called ‘Extremes’-Table (Figure 1.9). This table has been modified further to account for the SREX assessment. For some extremes (‘higher maximum temperature’, ‘higher minimum temperature’, ‘precipitation extremes’, ‘droughts or dryness’), all of these assessments found an increasing trend in the observations and in the projections. In the observations for the ‘higher maximum temperature’ the likelihood level was raised from likely in the TAR to very likely in SREX. While the diurnal temperature range was assessed in the Extremes-Table of the TAR, it was no longer included in the Extremes-Table of AR4, since it is not considered a climate extreme in a narrow sense. Diurnal temperature range was, however, reported to decrease for 21st century projections in AR4 (Meehl et al., 2007). In projections for precipitation extremes, the spatial relevance has been improved from very likely ‘over many Northern Hemisphere mid-latitudes to high latitudes land areas’ from the TAR to very likely for all regions in AR4 (these ‘uncertainty labels’ are discussed in Section 1.4). However, likelihood in trends in projected precipitation extremes was downscaled to likely in the SREX as a result of a perception of biases and a fairly large spread in the precipitation projections in some regions. SREX also had less confidence than TAR and AR4 in the trends for droughts and dryness, ‘due to lack of direct observations, some geographical inconsistencies in the trends, and some dependencies of inferred trends on the index choice’ (IPCC, 2012b).[5]

For some extremes (e.g., ‘changes in tropical cyclone activity’) the definition changed between the TAR and the AR4. Whereas the TAR only made a statement about the peak wind speed of tropical cyclones, the AR4 also stressed the overall increase in intense tropical cyclone activity. The ‘low confidence’ for any long term trend (>40 years) in the observed changes of the tropical cyclone activities is due to uncertainties in past observational capabilities (IPCC, 2012b). The ‘increase in extreme sea level’ has been added in the AR4. Such an increase is likely according to the AR4 and the SREX for observed trends, and very likely for the climate projections reported in the SREX.

The assessed likelihood of anthropogenic contributions to trends is lower for variables where the assessment is based on indirect evidence. Especially for extremes that are the result of a combination of factors such as droughts, linking a particular extreme event to specific causal relationships is difficult to determine (e.g., difficult to establish the clear role of climate change in the event) (see Section 10.6 and Peterson et al., 2012).[6] In some cases (e.g., precipitation extremes), however, it may be possible to estimate the human-related contribution to such changes in the probability of occurrence of extremes (Pall et al., 2011;[7] Seneviratne et al., 2012).[4]

WGI AR5 Fig1-9.jpg
1 More intense precipitation events
2 Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases
3 Statistically significant trends in the number of heavy precipitation events in some regions. It is likely that more of these regions have experienced increases than decreases.
4 See SREX Table 3-3 for details on precipitation extremes for the different regions.
5 Increased summer continental drying and associated risk of drought
6 Area affected by droughts increases
7 Some areas include southern Europe and the Mediterranean region, central Europe, central North America and Mexico, northeast Brazil and southern Africa
8 Increase in tropical cyclone peak wind intensities
9 Increase in intense tropical cyclone activity
10 In any observed long-term (i.e., 40 years or more) after accounting for past changes in observing capabilities (see SREX, section 3.4.4)
11 Increase in average tropical cyclone maximum wind speed is, although not in all ocean basins; either decrease or no change in the global frequency of tropical cyclones
12 Increase in extreme coastal high water worldwide related to increases in mean sea level in the late 20th century
13 Mean sea level rise will contribute to upward trends in extreme coastal high water levels

Figure 1.9 Change in the confidence levels for extreme events based on prior IPCC assessments: TAR, AR4 and SREX. Types of extreme events discussed in all three reports are highlighted in green. Confidence levels are defined in Section 1.4. Similar analyses for AR5 are discussed in later chapters. Please note that the nomenclature for confidence level changed from AR4 to SREX and AR5.


  1. Folland, C. K., et al., 2001: Observed climate variability and change. In: Climate Change 2001: [ The Scientific Basis]. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [J. T. Houghton, Y. Ding, D. J. Griggs, M. Noquer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 101–181.
  2. Peterson, T. C., et al., 2008: Why weather and climate extremes matter. In: Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands, [Karl, T. R., G. A. Meehl, C. D. Miller, S. J. Hassol, A. M. Waple, and W. L. Murray (eds.)]. A Report by the U.S.Climate Change Science Program and the Subcommittee on Global Change Research, Washington, DC., USA, 11–33.
  3. Zhang, X., and F. Zwiers, 2012: Statistical indices for the diagnosing and detecting changes in extremes. In: Extremes in a Changing Climate: Detection, Analysis and Uncertainty [A. AghaKouchak, D. Easterling, K. Hsu, S. Schubert, and S. Sorooshian (eds.)]. Springer Science+Business Media, Heidelberg, Germany and New York, NY, USA, 1–14.
  4. 4.0 4.1 4.2 Seneviratne, S. I., et al., 2012: Chapter 3: Changes in climate extremes and their Impacts on the Natural Physical Environment. In: SREX: Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [C. B. Field, et al. (eds.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp.109–230.
  5. IPCC , 2012b: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change. [ Field, C. B., V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, and P. M. Midgley (Eds.)]. Cambridge University Press, Cambridge, United Kingdom, 582 pp.
  6. Peterson, T. C., P. A. Stott, and S. Herring, 2012: Explaining extreme events of 2011 from a climate perspective. Bull. Am. Meteorol. Soc., 93, 1041–1067.
  7. Pall, P., et al., 2011: Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature, 470, 382–385.
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