Based on the daily minimum temperature data from 199 meteorological stations in northeast China (NEC), the cold wave (CW) processes in winter from 1980 to 2020 are identified. Then, by using the ...reanalysis 500‐hPa geopotential height data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF), the CWs are objectively classified into two types (abbreviated as Type 1 and Type 2) according to the corresponding atmospheric circulation through the hierarchical cluster algorithm (HCA). The relationships of the CWs with the Arctic sea sic (SIC) and sea surface temperature (SST) are revealed. For Type 1, the SIC in the north of the Barents Sea–Kara Sea is relatively less in the previous autumn, and a wave train propagating from the polar regions to Northeast Asia is stimulated under the ice–atmosphere interaction. This wave train further cooperates with the positive SST anomaly in the southern Indian Ocean to excite the low‐vortex circulation under the negative phase of Arctic Oscillation (AO) in winter, which is conducive to the occurrence of Type 1. However, the relatively low SST in the western Pacific warm pool in previous autumn stimulates a northward propagating wave train along the meridian, which increases the degree of meridionality of the atmospheric circulation over East Asia and thus indirectly causes the polar‐vortex‐splitting circulation under the positive AO phase, which favours the formation of Type 2. The numerical simulations can well reproduce the observational facts, further confirming the possible impact of the SIC and SST in the previous autumn on the two types of CW over the NEC.
Cold waves over northeast China (NEC) are categorized into two types by machine learning. The Arctic sea ice and Indian Ocean SST anomalies in early autumn jointly affect the cold‐vortex‐type cold waves, while the polar‐vortex‐splitting‐type cold waves are related with the SST anomalies in equatorial western Pacific.
The study aimed to explore the spatiotemporal patterns and characteristics of cold wave mortality in Bangladesh by developing a cold wave dataset using content analysis. Due to the absence of a ...formal database about cold wave-related mortality in Bangladesh, we first developed a cold wave mortality dataset using content analysis of four (04) leading national daily newspapers’ cold wave-related news from 2000 to 2019 and crosschecked with other related reports. Then, we analyzed the data to obtain the spatiotemporal trends and characteristics of cold wave mortality in Bangladesh. A total of 5610 people died in 81 cold wave events (spells) in Bangladesh during the 20 years, with an average of 281 deaths per year. The average duration of cold waves (spells) in Bangladesh was 25.4 days per year, with a statistically significant increasing trend over the period. The highest cold wave mortality rate was found in the Kurigram District; the rate was 163.63 deaths per million people per year. About 58% of cold wave spells and 58.5 % of cold wave mortality occurred in January, followed by December (22%) during the study period. However, cold wave mortality varied from district to district; northwestern districts of Bangladesh were more prone to cold waves and reported a higher mortality rate. During the study period, more children and males died due to cold waves than females in Bangladesh. The study findings serve as a foundation for future research and policy development to establish cold wave management guidelines to reduce the risk of cold wave exposures among vulnerable people in the country.
•Spatial and temporal changes in cold wave mortality in Bangladesh were evaluated using a 20-year longitudinal dataset.•81 cold wave events occurred from 2000 to 2019, with an average duration of 25.4 days per year.•5610 mortality cases were found during this period, with an average of 280.5 deaths per year.•The highest mortality number and mortality rate were found in the Kurigram District.•Northern and northwestern districts in Bangladesh were more vulnerable to cold waves in Bangladesh.
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 and cold waves in Romania were investigated by using 10 indices over the period 1961–2015. They were detected based on excess heat factor for extended summer season (May–September) and, respectively on excess cold factor for the extended winter period (November–March). The main results indicated that: (1) changes are more substantial for heat wave indices than for cold wave indices, and (2) heat waves became more frequent, longer and more intense, while cold waves became less frequent, but more intense.
Abstract
Extreme cold waves frequently occur in east of China that dramatically endanger ecological agriculture, power infrastructure and human life. In this study, we found that the ‘Warm ...Arctic-Cold Siberia’ pattern (WACS) significantly enhanced cold waves in east of China according to daily composites from 1979 to 2018. During the winter 2020/21, a record-breaking cold wave broke out following a noticeable WACS phenomenon and induced the record-low surface air temperature at 60 meteorological stations since they were established (nearly 60 years). On 3 January 2021, the difference in temperature anomaly between the Barents–Kara Sea and Siberia reached 20 °C, the peak of winter 2020/21. With a shrinking meridional temperature gradient, the atmospheric baroclinicity weakened correspondingly. The accompanying atmospheric anomalies, i.e. the persistent Ural Blocking High and Baikal deep trough effectively transported stronger cold air than the sole impact from Arctic warming. After 4 d, the east of China experienced a severe surface air temperature decrease of more than 8 °C, covering an area of 2500 000 km
2
. During the same winter, a record-breaking warm event occurred in February 2021, and the ‘Cold Arctic-Warm Eurasia’ pattern also appeared as a precursory signal. Furthermore, on the interannual scale, the connection between winter-mean temperature anomalies in east of China and the WACS pattern also existed and even performed more strongly in both observations and simulation data of CMIP6.
This paper presents the characteristics of extreme temperatures in the Czech Republic, as calculated from homogenized series of daily maximum (TMAX) and daily minimum (TMIN) temperatures recorded by ...133 climatological stations throughout the territory in the 1961–2020 period. In general, statistically significant increasing linear trends were recognized in series of absolute TMAX, absolute TMIN, numbers of summer days, tropical days, days with tropical nights, heat‐wave, and warm‐anomaly days. Significant decreasing linear trends appeared in series of numbers of frost days, ice days, cold‐wave, and cold‐anomaly days. Objective classification of circulation types demonstrated the importance of anticyclonic types (especially those with warm airflow from the southern quadrant) and an unclassified type in the development of summer hot extremes, while winter cold extremes were linked to cold (north‐)easterly advection. Significant changes in the frequency of certain circulation types emerged, as well as an increasing number of anticyclonic types conducive to hot extremes, a trend that contributed to their more frequent occurrence in recent decades. Existing trends in temperatures were complemented by spatiotemporal analysis of extreme temperatures, the characteristics of extremes and the circulation types in the two “normal” periods of 1961–1990 and 1991–2020. These exhibited significant differences in means and variances. The results obtained are also discussed in a broader context.
Fluctuations in mean annual numbers of summer (a) and tropical (b) days and in their mean earliest/latest occurrences (c) in the Czech Republic during the 1961–2020 period. Smoothed by 10‐year Gaussian filter and supplemented by linear trends.
As the climate warms, extreme low- and high-temperature events will become more intense, and the stable isotopes (δ2H and δ18O) in atmospheric precipitation have the potential to diagnose these ...extreme weather events. Based on daily-scale precipitation isotope data, this study explains the mechanism by which cold wave events affect precipitation isotopes in Eurasia. The results show that precipitation isotopes are noticeably depleted during cold wave events compared to other precipitation in the same month, which is associated with cooling and increased water vapor transport at high latitudes. The characteristics of LMWL can indicate changes in meteorological conditions during cold waves. The strengthening of the high-pressure and cold front systems makes a big difference in the increased latitudinal-variation gradient of precipitation isotopes in cold wave events. Additionally, the complicated variations in cold-wave precipitation d-excess reflect changes in the water vapor sources and the non-equilibrium fractionation process in the water vapor transport path. This study reveals the mechanism of precipitation isotope response to cold wave events, which provides a reference constraint for interpreting the paleoisotopic record accurately and reconstructing past cold wave activity.
•The isotope records preserve changes in meteorological factors and atmospheric circulation patterns during the cold wave.•The isotopic composition of cold wave precipitation is more depleted than other precipitation in the month.•The complicated variations in cold wave precipitation d-excess reflect the shifts in water vapor sources.•The increased spatial gradient of cold wave precipitation isotope reflects the enhanced high-pressure and cold front systems.
Extreme cold wave (ECW) events in Southwest China (SWC) often result in considerable socioeconomic impacts. Here, cold wave (CW) events over SWC are identified and classified into ECW and ordinary CW ...(OCW) events according to their intensity. Before a CW event, cold air accumulates over Siberia and the northern side of the Tibetan Plateau (NTP), with the latter determining the intensity of CW events over SWC, meaning it is a key factor in identifying the differences between the evolution processes of ECW and OCW events. The accumulation of cold air over the NTP is associated with a subtropical wave train from the North Atlantic, which is likely triggered by the tripole mode of the SST anomalies in the North Atlantic (NAT). Before an ECW event, the North Atlantic SST shows a negative-phase with a “warm–cold–warm” pattern. There are two wave trains—one from the subtropical North Atlantic and the other from high latitudes, which converge over the Tibetan Plateau and contribute to the development of a strong positive height anomaly that shapes the East Asian Trough (EAT). The northeasterly wind after the trough transports cold air to the NTP. There is no subtropical wave train during OCW events, the orientation of the EAT is only influenced by the high-latitude wave train. The cold air moves eastward and southward quickly and dissipates. On the interannual scale, the NAT is also conducive to the strengthening of the CW events over SWC. In the negative–phase NAT, the intensity of CW events over SWC is much stronger, and ECW events tend to occur in this period.
ABSTRACT
The past two decades of the 20th century and the first of the 21st century have been characterized by global temperature rise and increased frequency of weather‐induced extreme events such ...as floods, droughts, heavy rainfall, and heat waves. We investigated the heat and the cold waves in the Carpathian Region, an area whose rich biosphere is endangered by extreme events. We used the daily minimum (TN) and maximum (TX) temperature data collected in the framework of the CARPATCLIM project. Such high‐resolution (0.1° × 0.1°) gridded data range from January 1961 to December 2010. In this study, a heat wave occurs when temperature is above the 90th percentile for at least five consecutive days and a cold wave occurs when temperature is below the 10th percentile for at least five consecutive days. The percentiles have been computed over the baseline period 1971–2000. We distinguish between night‐time and daytime events and we discuss heat (and cold) waves considering at least five consecutive night and days with temperature above (below) the selected percentile. For each heat or cold wave event, we assigned duration, severity, and intensity. For these parameters and for frequency, we performed linear trend analysis for the period 1961–2010. The trends have been computed on an annual and seasonal basis and tested for statistical significance. Different spatial patterns of heat and cold waves characterize the Carpathian Region: heat wave events show general increase in all the parameters considered, while cold wave events show a decrease in all the variables West to the Carpathians and an increase North–East to the Carpathians. We also compiled a list of the most relevant heat waves that hit the Carpathian Region from 1961 to 2010: out of seven events, four occurred from 2000 to 2010. Instead, the 1960s and the 1980s have been the decades most hit by severe cold waves.
According to the Indian Meteorological Department (IMD), heat wave and cold wave in India are the periods characterized by a significant increase/decrease in maximum/minimum temperatures. ...HW and ...CW events are significant socio-ecological concerns (Raymond et al., 2020, Kumar and Singh, 2021). The area experiences extreme weather conditions, including dust storms in summers, with temperatures occasionally exceeding 47.0°C in May and June, and frigid winters, notably in December and January, with minimum temperatures dropping to around 0.0°C. These seasonal variations in temperature and rainfall are significant drivers for the study. RESULTS AND DISCUSSION Frequency of heat and severe heat wave days The data presented in Fig. 1 illustrates the annual distribution of heat wave (HW) days and severe heat wave (SHW) days over the span of 23 years from 2000 to 2022. ...the years 2006 and 2017 also experienced considerable HW days with 12 and 14 days respectively, which might signify specific periods of heightened heat stress on the environment.
An unprecedented cold wave swept through most parts of East Asia in January 2016, leading to record-breaking low temperatures and widespread snowstorms in several regions. Our analysis indicated that ...this East Asian cold wave was triggered by a series of consecutive extreme events in the Northern Hemisphere from late 2015 to early 2016. (1) On 28 December 2015, a severe cyclonic storm emerged in the North Atlantic, and a downstream blocking high formed in Europe through the downstream development process. The strong southerly jet stream between the storm and its downstream blocking high steered the storm into the Arctic Circle, transported enormous warm and moist air masses, establishing warm conveyor belts, which led to an extraordinary Arctic warming event in late 2015. (2) This Arctic warming event in late 2015 resulted in a distinct Arctic dipole pattern resembling the negative phase of the Arctic Oscillation in early–mid-January 2016. (3) The dipole pattern induced eastward propagation of the Rossby wave and led to the occupation of two downstream blocking highs in the Urals and western North America. These two blocking highs, together with the low pressure between them, resulted in an inverted omega-shaped circulation pattern (IOCP) over the Siberia–North Pacific region. Additionally, the IOCP distinctly modulated the meridional circulation cell along the East Asia–Siberia regions, which generated negative vorticity and anticyclonic advection to the Siberian region, ultimately intensifying the Siberian high. The IOCP and the associated enhanced Siberian high eventually induced the outbreak of a mega-cold wave in East Asia on 21–25 January 2016.
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