Elsevier

Global and Planetary Change

Volume 146, November 2016, Pages 109-121
Global and Planetary Change

Changes in heat waves indices in Romania over the period 1961–2015

https://doi.org/10.1016/j.gloplacha.2016.08.016Get rights and content

Highlights

  • In Romania, heat waves dramatically increased in the last 55 years.

  • Statistically significant increasing is dominant for most of the 10 indices.

  • Changes are more intense for heat waves detected based on maximum temperature.

  • Western and Central Romania are the most exposed to increasing in heat waves.

Abstract

In the last two decades many climate change studies have focused on extreme temperatures as they have a significant impact on environment and society. Among the weather events generated by extreme temperatures, heat waves are some of the most harmful. The main objective of this study was to detect and analyze changes in heat waves in Romania based on daily observation data (maximum and minimum temperature) over the extended summer period (May–Sept) using a set of 10 indices and to explore the spatial patterns of changes. Heat wave data series were derived from daily maximum and minimum temperature data sets recorded in 29 weather stations across Romania over a 55-year period (1961–2015). In this study, the threshold chosen was the 90th percentile calculated based on a 15-day window centered on each calendar day, and for three baseline periods (1961–1990, 1971–2000, and 1981–2010). Two heat wave definitions were considered: at least three consecutive days when maximum temperature exceeds 90th percentile, and at least three consecutive days when minimum temperature exceeds 90th percentile. For each of them, five variables were calculated: amplitude, magnitude, number of events, duration, and frequency. Finally, 10 indices resulted for further analysis. The main results are: most of the indices have statistically significant increasing trends; only one index for one weather station indicated statistically significant decreasing trend; the changes are more intense in case of heat waves detected based on maximum temperature compared to those obtained for heat waves identified based on minimum temperature; western and central regions of Romania are the most exposed to increasing heat waves.

Introduction

In the last two decades many climate change studies have focused on extreme temperatures as they have a significant impact on environment and society. Among the weather events generated by extreme temperatures, heat waves (HWs) are some of the most harmful. They are often associated with numerous disasters affecting society in terms of human health and mortality, water quality, and engineered systems (Nairn and Fawcett, 2013, Unal et al., 2013, Liu et al., 2015). Moreover, agricultural production, the retail industry, ecosystem services, and tourism may also be affected (Unal et al., 2013). Numerous studies have indicated the increased risk of heat-related deaths due to extreme heat events in populated areas of Europe (Le Tertre et al., 2006, D'Ippoliti et al., 2010), United States (Zanobetti and Schwartz, 2008, Peterson et al., 2013), and Australia (Loughnan et al., 2010, Tong et al., 2010). The most severe HWs occurred at the beginning of the XXIst century and caused many deaths in Europe and Western Russia (Dole et al., 2011, Runhaar et al., 2012, Bittner et al., 2013, Amengual et al., 2014). Thus, the analysis of changes and prediction of HWs occurrence is of a major importance.

In general, extreme temperature events are differently defined depending on the data series available (daily, monthly etc.) (Radinović and Ćurić, 2012). HWs are generally considered as a period of consecutive days with unusual high temperatures. Recent findings showed that the frequency, duration, and intensity of such events increased in many regions of the world (Coumou and Rahmstorf, 2012, Perkins and Alexander, 2013, Keggenhoff et al., 2015, Keellings and Waylen, 2014, Rusticucci et al., 2015). According to the outputs of global and regional climate models, climate change is expected to intensify the HWs, which will become more frequent and more severe in the following decades (Fischer and Schär, 2010, Seneviratne et al., 2012, Kirtman et al., 2013, Amengual et al., 2014). Amengual et al. (2014) analyzed projections of HWs with high impact on human health in Europe and concluded that the largest increase in the incidence of strong and extreme stress HW attributes are expected to take place in Southern Europe throughout this century. Jacob et al. (2014) also found statistically significant increase in the mean number of HWs over the interval May–September in Europe, especially in the southern areas.

Information on HWs for Romanian territory are available in some studies developed at global, or regional scale. In a recent study, Donat et al. (2013) analyzed indices of extreme temperatures at global scale, including a HW-related one (WSDI) and found significant increasing trends over the period 1951–2010 for Romania. Their results are in agreement with those got by Spinoni et al. (2015) who analyzed trends in heat and cold waves in the Carpathian region using a set of HWs indices calculated on gridded data. Their main findings are that the HWs events have become more frequent, longer, more severe and intense in the entire Carpathian Region, especially in summer in the Hungarian Plain and Southern Romania.

Recently, in Romania, few studies were also conducted based on observation data, but usually they focused on smaller areas such as Southern Romania or Barlad Plateau (Dragotă and Havriş, 2015, Huștiu, 2016) or even developed at local scale (Croitoru et al., 2014, Papathoma-Koehle et al., 2016).

The main objectives of this study were i. to detect and analyze changes in HWs in Romania based on daily temperature observation data over the extended summer period (May–Sept) using a set of 10 indices recommended by Expert Team on Sector-specific Climate Indices (ET-SCI) members, and ii. to explore the spatial patterns of changes over the entire Romanian territory. Similar multi-angle investigations of HWs have not been done so far in Romania.

Section snippets

Study area

The area under study is located in Eastern Europe (extending on latitude from 43°40′N to 48°11′N, and on longitude from 20°19′E to 29°66′E), and covers > 237,000 km2 (Fig. 1). It has a temperate climate with prevailing continental influences in the eastern and southern regions and dominated by moist oceanic conditions in central and western regions. Mean multiannual temperature ranges from > 11.0 °C in southern regions and on the Black Sea coastline to few degrees below 0, in the mountain areas.

Climatology of HWs in Romania

The percentile values changed from one baseline period to another, for both TX and TN datasets for each weather stations. The difference of percentile values calculated for different baseline period is higher in case of TX and during the summer months (Fig. 2) and lower for TN data series and during spring and autumn months (Fig. 3). The differences are higher, especially between the percentile calculated for 1981–2010 baseline period and the other two periods, with values that can reach 2.0 °C

Conclusions

In this study, changes in HWs for the extended summer season (May–September) over a 55-year period (1961–2015) were analyzed. The HWs were identified based on TX and TN, and after that the two HWs series were combined with five HWs variables (amplitude, magnitude, number, duration, and frequency), resulting in a set of 10 indices recommended by ET-SCI. The results suggest that assessing changes in HWs only by TX can lead to a lack of understanding of the complexity of HWs changes and

Acknowledgements

This research was developed under the framework of the research grant Extreme weather events related to air temperature and precipitation in Romania (project code: PN-II-RU-TE-2014-4-0736), funded by the Executive Unit for Financing Higher Education, Research, Development, and Innovation (UEFISCDI) in Romania.

The authors acknowledge the daily temperature data provided by European Climate Assessment & Dataset project (Klein Tank et al., 2002), Meteomanz data base, and National Meteorological

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