Recent changes in heat waves and cold waves detected based on excess heat factor and excess cold factor in Romania

Abstract

In this paper, we investigated changes in heat and cold waves in Romania over the period 1961–2015 by employing a new and superior approach. It consists in using excess heat factor to identify heat waves and excess cold factor to identify cold waves. Five indices were calculated and then analysed for both heat waves and cold waves resulting in a set of ten indices. Indices for heat waves were analysed for the extended summer season (May–September), whereas those for cold waves were assessed for the extended winter (November–March). The intensity threshold was set to be equal or above the 90th percentile for heat waves, and equal or below the 10th percentile for cold waves, while the duration threshold for both heat and cold waves was of at least three consecutive days. For a better comparison with other studies conducted worldwide, and to get more information from the data sets, the percentile thresholds for heat and cold waves identification were calculated based on three reference periods: 1961–1990, 1971–2000, and 1981–2010. Trends were calculated using ordinary least square method, whereas statistical significance was assessed by the t‐test. The main results indicated that changes are more substantial in the case of indices calculated based on excess heat factor compared to those based on excess cold factor, suggesting that the warming process is more reflected in heat waves rather than in cold waves. Thus, heat waves became more frequent, longer, and more intense, while cold waves became less frequent, but more intense. When the reference period for percentile threshold calculation was changed from the earliest to the most recent ones, the frequency of increasing and significant increasing trends decreased for some of the heat wave indices, while for the cold wave indices the significant downward trends increased.

Heat waves in Berlin and Potsdam, Germany – Long‐term trends and comparison of heat wave definitions from 1893 to 2017

“…The modified EHF uses the 90th percentile of the 15‐day window T mean distribution in a reference period, centred on the respective calendar day, instead of T mean,95 p (Alexander and Herold, ). This version of the EHF was recently applied to detect HWs in Romania (Piticar et al ., ).…”

“…At the same time, the Mann–Kendall test could still detect a significant trend. The second method for trend estimation uses the ordinary‐least‐square (OLS) fitting. This method is also frequently applied to estimate trends in climate extremes (e.g., Moberg et al ., ; Smith et al ., ; Piticar et al ., ). The t test was used to assess the statistical significance of trends from OLS fitting.…”

“…In Europe, for example, HWs cause more human fatalities than any other natural hazard (period 1998–2009; European Environment Agency (EEA), ). As many studies have shown, the occurrence of HWs has increased until today (e.g., Alexander et al ., ; Moberg et al ., ; Della‐Marta et al ., ; Perkins et al ., ; Russo et al ., ; Kim et al ., ; Piticar et al ., ). Other studies show further increase for the coming decades in HW frequency, duration, and magnitude due to projected global climate change (e.g., Meehl and Tebaldi, ; Beniston et al ., ; Koffi and Koffi, ; Fischer and Schär, ; Russo et al ., ; ; Zacharias et al ., ; Kim et al ., ; Lhotka et al ., ).…”

Spatiotemporal variations in extreme precipitation and their potential driving factors in non-monsoon regions of China during 1961–2017

“…All extreme precipitation indices are calculated using R language-based RClimDex software after strict quality control (Zhang and Yang 2004 (Mann 1945, Kendall 1975 methods to obtain significant trends. OLS uses a linear model to estimate the slope magnitude, which was calculated using SPSS 24.0 software, while the M-K trend significance test was represented by statistical parameter Z (Liu et al 2016, Piticar et al 2018. When the slope is less than 0, a decreasing trend is seen in the extreme indices, an increasing trend is seen when the slope is greater than 0, and the magnitude of its absolute value reflects the rate at which the extreme indices rise or fall.…”

Assessing spatiotemporal variation of heat waves during 1961–2016 across mainland China

“…However, especially accurate results might not be obtained when studies about the relationship with morbidity and mortality are carried out . Therefore, in recent years, more and more scholars have introduced excess heat factors (EHFs) as indicators to carry out HW research (Rohini et al, 2016;Piticar, 2018;Piticar et al, 2018).…”