Workshop on Indices and Indicators for Climate Extremes, Asheville, NC, USA, 3–6 June 1997 Breakout Group C: Temperature Indices for Climate Extremes

Folland, Chris K.; Miller, Christopher D.; Bader, Daniel A.; Crowe, M. P.; Jones, Phil; Plummer, Neil; Richman, Michael B.; Parker, D. E.; Rogers, Jeffrey C.; Scholefield, Paul

doi:10.1007/978-94-015-9265-9_5

Cited by 26 publications

(13 citation statements)

References 14 publications

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“…The ETCCDMI recommended a total of 27 core indices (Table 2) with primary focuses on extremes to be derived from station daily data. They are a part of a larger list defined by the World Meteorological Organization Working Group on Climate Change Detection [ Folland et al , 1999; Jones et al , 1999; Peterson et al , 2001]. Most of the definitions for the indices were presented in the work of Peterson et al [2001], and have been computed for other regions [e.g., Aguilar et al , 2005; Haylock et al , 2005; Vincent et al , 2005; L. Alexander et al, Global observed changes in daily climate extremes of temperature and precipitation, submitted to Journal of Geophysical Research , 2005, hereinafter referred to as Alexander et al submitted manuscript, 2005; A. M. G. Klein Tank et al, Changes in daily temperature and precipitation extremes in central and South Asia, submitted to Geophysical Research Letters , 2005, hereinafter referred to as Klein Tank et al, submitted manuscript, 2005].…”

Section: The Analysesmentioning

confidence: 99%

Trends in Middle East climate extreme indices from 1950 to 2003

Zhang¹,

Aguilar

Şensoy³

et al. 2005

J. Geophys. Res.

471

360

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[1] A climate change workshop for the Middle East brought together scientists and data for the region to produce the first area-wide analysis of climate extremes for the region. This paper reports trends in extreme precipitation and temperature indices that were computed during the workshop and additional indices data that became available after the workshop. Trends in these indices were examined for 1950-2003 at 52 stations covering 15 countries, including Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, and Turkey. Results indicate that there have been statistically significant, spatially coherent trends in temperature indices that are related to temperature increases in the region. Significant, increasing trends have been found in the annual maximum of daily maximum and minimum temperature, the annual minimum of daily maximum and minimum temperature, the number of summer nights, and the number of days where daily temperature has exceeded its 90th percentile. Significant negative trends have been found in the number of days when daily temperature is below its 10th percentile and daily temperature range. Trends in precipitation indices, including the number of days with precipitation, the average precipitation intensity, and maximum daily precipitation events, are weak in general and do not show spatial coherence. The workshop attendees have generously made the indices data available for the international research community.

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Section: The Analysesmentioning

confidence: 99%

Trends in Middle East climate extreme indices from 1950 to 2003

Zhang¹,

Aguilar

Şensoy³

et al. 2005

J. Geophys. Res.

471

360

View full text Add to dashboard Cite

show abstract

“…Regional studies have shown an expansion of areas with extreme high temperatures since the mid-1970s (Katz & Brown 1992, Changnon 1993, Rohli & Keim 1994, Degaetano 1996, Folland et al 1999, Karl & Easterling 1999. It is likely that the frequency of hot spells will also increase along with the general increase in heat waves.…”

Section: Statistical Analysis and Resultsmentioning

confidence: 99%

“…In general, a hot spell is defined as a time period during which the maximum daily temperatures are higher than the mean maximum seasonal levels; other conditions must be satisfied as well (Macchiato et al 1993, Henderson & Muller 1997, Folland et al 1999, McCarthy et al 2001. Similarly, a heat wave is defined as an extended period of unusually high atmosphererelated heat stress, which causes temporary modifications in the way of living and which may have adverse health consequences for the vulnerable groups of human population (Matzarakis & Mayer 1991, Robinson 2001.…”

Section: Introductionmentioning

confidence: 99%

Analysis of hot spell characteristics in the Greek region

Katsoulis¹,

Hatzianastassiou

2005

Clim. Res.

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In the present study, hot spell conditions over the Greek region during the warm period (June to September) were examined by using daily maximum temperature data from 16 meteorological stations of the Hellenic National Meteorological Service (HNMS) and a combined deterministicstochastic model. The results show that daily maximum temperatures can be studied as a mixture of deterministic and stochastic components, whereas the properties of hot spells can be adequately described with statistical distributions. Trends and the geographical and temporal distribution of hot spells at the surface were investigated by linear regression, Fourier analysis and statistical distributions. Partial duration series of hot spells, fitted with Poisson, Gauss and exponential distributions, provide information on their frequency, timing, magnitude and geographical distribution. The most frequent hot spells are found over southern parts of inland Greece, as well as over the northern mainland, while less frequent events occur along the coastal belt of the Greek Peninsula and in the Aegean Islands. Hot spells are more frequent at inland than in marine areas, and occur from June to September. Statistical analysis of synoptic weather conditions that favour the development of hot spells shows that such events are most probable over Greece when (1) a persistent Azores anticyclone appears over North Africa and extends ridges of high pressure towards the NE Mediterranean, and (2) when western and central European anticyclones extend towards the region.

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“…Folland et al . [] suggest to fit a statistical distribution to the daily data sample in order to get a robust estimate of the threshold. Compared to this approach, the method described here is simpler to use as the fitting needs to be done only once and keeps the annual cycle in the focus.…”

Section: Discussionmentioning

confidence: 99%

Estimating daily climatologies for climate indices derived from climate model data and observations

et al. 2015

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Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use.Key PointsMore robust estimates of daily climate characteristicsStatistical fitting approachBased on a perfect model approach

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Workshop on Indices and Indicators for Climate Extremes, Asheville, NC, USA, 3–6 June 1997 Breakout Group C: Temperature Indices for Climate Extremes

Cited by 26 publications

References 14 publications

Trends in Middle East climate extreme indices from 1950 to 2003

Trends in Middle East climate extreme indices from 1950 to 2003

Analysis of hot spell characteristics in the Greek region

Estimating daily climatologies for climate indices derived from climate model data and observations

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