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WGI AR5 Fig2-32

Figure 2.32 Trends in annual frequency of extreme temperatures over the period 1951–2010, for (a) cold nights (TN10p), (b) cold days (TX10p), (c) warm nights (TN90p) and (d) warm days (TX90p) (Box 2.4, Table 1). Trends were calculated only for grid boxes that had at least 40 years of data during this period and where data ended no earlier than 2003. Grey areas indicate incomplete or missing data. Black plus signs (+) indicate grid boxes where trends are significant (i.e., a trend of zero lies outside the 90% confidence interval). The data source for trend maps is HadEX2 (Donat et al., 2013c) updated to include the latest version of the European Climate Assessment data set (Klok and Tank, 2009). Beside each map are the near-global time series of annual anomalies of these indices with respect to 1961–1990 for three global indices data sets: HadEX2 (red); HadGHCND (Caesar et al., 2006; blue) and updated to 2010 and GHCNDEX (Donat et al., 2013a; green). Global averages are only calculated using grid boxes where all three data sets have at least 90% of data over the time period. Trends are significant (i.e., a trend of zero lies outside the 90% confidence interval) for all the global indices shown.

AR4 concluded that it was very likely that a large majority of global land areas had experienced decreases in indices of cold extremes and increases in indices of warm extremes, since the middle of the 20th century, consistent with warming of the climate. In addition, globally averaged multi-day heat events had likely exhibited increases over a similar period. SREX updated AR4 but came to similar conclusions while using the revised AR5 uncertainty guidance (Seneviratne et al., 2012). Further evidence since then indicates that the level of confidence that the majority of warm and cool extremes show warming remains high.

A large amount of evidence continues to support the conclusion that most global land areas analysed have experienced significant warming of both maximum and minimum temperature extremes since about 1950 (Donat et al., 2013c). Changes in the occurrence of cold and warm days (based on daily maximum temperatures) are generally less marked (Figure 2.32). ENSO (Box 2.5) influences both maximum and minimum temperature variability especially around the Pacific Rim (e.g., Kenyon and Hegerl, 2008; Alexander et al., 2009) but often affecting cold and warm extremes differently. Different data sets using different gridding methods and/or input data (Supplementary Material 2.SM.7) indicate large coherent trends in temperature extremes globally, associated with warming (Figure 2.32). The level of quality control varies between these data sets. For example, HadEX2 (Donat et al., 2013c) uses more rigorous quality control which leads to a reduced station sample compared to GHCNDEX (Donat et al., 2013a) or HadGHCND (Caesar et al., 2006). However, despite these issues data sets compare remarkably consistently even though the station networks vary through time (Figure 2.32; Table 2.12). Other data sets that have assessed these indices, but cover a shorter period, also agree very well over the period of overlapping data, e.g., HadEX (Alexander et al., 2006) and Duke (Morak et al., 2011, 2013).

The shift in the distribution of nighttime temperatures appears greater than daytime temperatures although whether distribution changes are simply linked to increases in the mean or other moments is an active area of research (Ballester et al., 2010; Simolo et al., 2011; Donat and Alexander, 2012; Hansen et al., 2012). Indeed, all data sets examined (Duke, GHCNDEX, HadEX, HadEX2 and HadGHCND), indicate a faster increase in minimum temperature extremes than maximum temperature extremes. While DTR declines have only been assessed with 'medium confidence (Section 2.4.1.2), confidence of accelerated increases in minimum temperature extremes compared to maximum temperature extremes is high due to the more consistent patterns of warming in minimum temperature extremes globally.

Regional changes in a range of climate indices are assessed in Table 2.13. These indicate likely increases across most continents in unusually warm days and nights and/or reductions in unusually cold days and nights including frosts. Some regions have experienced close to a doubling of the occurrence of warm and a halving of the occurrence of cold nights, for example, parts of the Asia-Pacific region (Choi et al., 2009) and parts of Eurasia (Klein Tank et al., 2006; Donat et al.,2013a, 2013c) since the mid-20th century. Changes in both local and global SST patterns (Section 2.4.2) and large scale circulation patterns (Section 2.7) have been shown to be associated with regional changes bin temperature extremes (Barrucand et al., 2008; Scaife et al., 2008;

Alexander et al., 2009; Li et al., 2012), particularly in regions around the Pacific Rim (Kenyon and Hegerl, 2008). Globally, there is evidence of large-scale warming trends in the extremes of temperature, especially minimum temperature, since the beginning of the 20th century (Donat et al., 2013c).

There are some exceptions to this large-scale warming of temperature extremes including central North America, eastern USA (Alexander et al., 2006; Kunkel et al., 2008; Peterson et al., 2008) and some parts of South America (Alexander et al., 2006; Rusticucci and Renom, 2008; Skansi et al., 2013) which indicate changes consistent with cooling in these locations. However, these exceptions appear to be mostly associated with changes in maximum temperatures (Donat et al., 2013c). The so-called ‘warming hole’ in central North America and eastern USA, where temperatures have cooled relative to the significant warming elsewhere in the region, is associated with observed changes in the hydrological cycle and land–atmosphere interaction (Pan et al., 2004; Portmann et al., 2009a; Portmann et al., 2009b; Misra et al., 2012) and decadal and multi-decadal variability linked with the Atlantic and Pacific Oceans (Meehl et al., 2012; Weaver, 2012).

Since AR4 many studies have analysed local to regional changes in multi-day temperature extremes in more detail, specifically addressing different heat wave aspects such as frequency, intensity, duration and spatial extent (Box 2.4, FAQ 2.2). Several high-profile heat waves have occurred in recent years (e.g., in Europe in 2003 (Beniston, 2004), Australia in 2009 (Pezza et al., 2012), Russia in 2010 (Barriopedro et al., 2011; Dole et al., 2011; Trenberth and Fasullo, 2012a) and USA in 2010/2011 (Hoerling et al., 2012) (Section 10.6.2) which have had severe impacts (see WGII). Heat waves are often associated with quasi-stationary anticyclonic circulation anomalies that produce prolonged hot conditions at the surface (Black and Sutton, 2007; Garcia-Herrera et al., 2010), but long-term changes in the persistence of these anomalies are still relatively poorly understood (Section 2.7). Heat waves can also be amplified by pre-existing dry soil conditions in transitional climate zones (Ferranti and Viterbo, 2006; Fischer et al., 2007; Seneviratne et al., 2010; Mueller and Seneviratne, 2012) and the persistence of those soil-mositure anomalies (Lorenz et al., 2010). Dry soil-moisture conditions are either induced by precipitation deficits (Della-Marta et al., 2007b; Vautard et al., 2007), or evapotranspiration excesses (Black and Sutton, 2007; Fischer et al., 2007), or a combination of both (Seneviratne et al., 2010). This amplification of soil moisture–temperature feedbacks is suggested to have partly enhanced the duration of extreme summer heat waves in southeastern Europe during the latter part of the 20th century (Hirschi et al., 2011), with evidence emerging of a signature in other moisture-limited regions (Mueller and Seneviratne, 2012).

Table 2.13 shows that there has been a likely increasing trend in the frequency of heatwaves since the middle of the 20th century in Europe and Australia and across much of Asia where there are sufficient data. However, confidence on a global scale is medium owing to lack of studies over Africa and South America but also in part owing to differences in trends depending on how heatwaves are defined (Perkins et al., 2012). Using monthly means as a proxy for heatwaves Coumou et al. (2013) and Hansen et al. (2012) indicate that record-breaking temperatures in recent decades substantially exceed what would be expected by chance but caution is required when making inferences between these studies and those that deal with multi-day events and/ or use more complex definitions for heatwave events. There is also evidence in some regions that periods prior to the 1950s had more heatwaves (e.g., over the USA, the decade of the 1930s stands out and is also associated with extreme drought conditions (Peterson et al., 2013) whereas conversely in other regions heatwave trends may have been underestimated owing to poor quality and/or consistency of data (e.g., Della-Marta et al. (2007a) over Western Europe; Kuglitsch et al. (2009, 2010) over the Mediterranean). Recent available studies also suggest that the number of cold spells has reduced significantly since the 1950s (Donat et al., 2013a, 2013c).

In summary, new analyses continue to support the AR4 and SREX conclusions that since about 1950 it is very likely that the numbers of cold days and nights have decreased and the numbers of warm days and nights have increased overall on the global scale, that is, for land areas with sufficient data. It is likely that such changes have also occurred across most of North America, Europe, Asia and Australia. There is low to medium confidence in historical trends in daily temperature extremes in Africa and South America as there is either insufficient data or trends vary across these regions. This, combined with issues with defining events, leads to the assessment that there is medium confidence that globally the length and frequency of warm spells, including heat waves, has increased since the middle of the 20th century although it is likely that heatwave frequency has increased during this period in large parts of Europe, Asia and Australia.

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