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WIREs Clim Change
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Indices for monitoring changes in extremes based on daily temperature and precipitation data

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Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically‐based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147

Figure 1.

The probability distributions of daily temperature and precipitation. The higher the black line, the more often weather with those characteristics occurs. Extremes are denoted by the shaded areas. (Reprinted with permission from Ref 4. Copyright 2005 Intergovernmental Panel on Climate Change)

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Figure 2.

Top: Percent of days exceeding the 90th (blue), 95th (black) and 97.5th (red dashed line) percentiles North American minimum temperature. Bottom: Standardized anomalies of the time series in the top panel. The thick smoothed lines are from a LOWESS filter applied to the annual time series. The climate change signal averaged over North America is essentially identical for each of these three thresholds for extremes. (Reprinted with permission from Ref 6. Copyright 2008 American Geophysical Union)

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Figure 3.

Number of days on which daily temperature exceeds its (top) 90th and (bottom) 99th percentiles averaged over 210 Canadian stations. Rates for the in‐base period computed with the bootstrap resampling procedure of Zhang et al.51 are shown with the dashed lines. Trend estimation for the 99th percentile index would be affected by the step changes. (Reprinted with permission from Ref 51. Copyright 2005 American Meteorological Society)

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Figure 4.

Digital daily maximum temperature from Berberati, Central African Republic. Black indicates before the regional climate change workshop and red indicates a few months after the workshop. The improvements are a result of a digitization effort. (Reprinted with permission from Ref 57. Copyright 2009 American Geophysical Union)

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Figure 5.

Correlation between de‐trended Indo‐Pacific region annual series of (a) TN90p and (b) R20mm, and sea surface temperature dataset HadISST. (Reprinted with permission from Ref 65. Crown copyright 2011 The Met Office)

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Figure 6.

Trends (in days per decade, shown as maps) and annual time series anomalies relative to 1961–1990 mean values (shown as plots) for annual series of percentile temperature indices for 1951–2003 for (a) cold nights (TN10p), (b) warm nights (TN90p), (c) cold days (TX10p), and (d) warm days (TX90p). Trends were calculated only for the grid boxes with sufficient data (at least 40 years of data during the period and the last year of the series is no earlier than 1999). Black lines enclose regions where trends are significant at the 5% level. The red curves on the plots are nonlinear trend estimates obtained by smoothing using a 21‐term binomial filter. (Reprinted with permission from Ref 26. Copyright 2006 American Geophysical Union)

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Figure 7.

As Figure 6 but for precipitation indices (a) R10 in days, (b) R95pT (i.e., (R95p/PRCPTOT) * 100) in %, (c) CDD in days, and (d) SDII in mm/day. (Reprinted with permission from Ref 26. Copyright 2006 American Geophysical Union)

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