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Cold wave (CW) and Severe Cold wave (SCW) prevail over India as a seasonal episode during winter season. The present study analyzes the changing spatio-temporal characteristics and trends of cold waves over India's meteorological subdivisions from 1951 to 2021 (Dec–Feb). It uses minimum temperature data obtained from the India Meteorological Department (IMD) at a spatial resolution of 0.5° × 0.5°. A declining trend in both CW and SCW days is found with a decrease of − 0.29 days/decade in CW days and 0.02 days/decade in SCW days. The study also explored the trend of spells of three, five and seven days of CW and SCW and found a significantly decreasing trend of − 0.02 days/decade and − 0.05 days/decade in 3 and 5-day SCW spell respectively. A consistent significant increase of 0.027 °C/decade in minimum temperature post 1980s is also reported in the study with a simultaneous decrease in cold wave. While the northwestern and northern meteorological subdivision record highest CW and SCW spells, a declining trend is reported in these regions, highest being in West Rajasthan (− 1.3 days/decade) and Punjab (− 1.3 days/decade). A significant increasing trend has been observed in CW, SCW days/spells in the eastern, eastern coastal and southern subdivisions such as Bihar (0.16 days/decade), Jharkhand (0.05 days/decade) and Odisha (0.2 days/decade). Overall, the study reports a decline in cold waves and identifies new cold wave-prone regions in the country. The study also highlights the emerging severe impact on agriculture sector in the scenario of declining cold extremes over major wheat producing belts.

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 km2. 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.

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.

Despite global warming, cold waves (CWs) remain one of the most severe, frequent, and damaging climatological hazards in most regions of the world. In this research, the most intense and widespread CW in Iran during the statistical period of 1836–2015 has been identified and analyzed. To identify and analyze the synoptic patterns of CWs, the following data were used: Minimum Daily Temperature (MDT) at the 2-meter land surface, Sea Level Pressure (SLP), Geopotential Height (GH) between the 1000 and 500 hPa levels, atmospheric thickness maps (500 to 1000 hPa), and temperature data from the lower and middle levels of the atmosphere. These data were obtained from the daily reanalysis dataset of the gridded NOAA-CIRES-DOE 20th Century Reanalysis V3. The criteria for selecting severe CWs include a temperature threshold of -20 °C or below, a wide spatial extent (regional or covering at least half of Iran), and a duration of at least two days. Based on the determined intensity and extent index, the most severe and widespread extreme CW in Iran, which occurred in January 1925, was selected for synoptic analysis. The synoptic analysis of the selected wave revealed that the extreme CW was caused by the combined influence of the Siberian high-pressure system and a migrating western high-pressure system with a pressure of 1030 hPa. The main cold core featured a high-pressure system of 1035 hPa associated with the migrating western system at the surface level. At upper atmospheric levels (1000 to 500 hPa), a blocking pattern over the North Atlantic Ocean played a significant role. The arrangement of these systems directed very cold northerly air currents toward Iran. At the 1000 to 850 hPa levels, wind flow divergence and temperature gradients caused the cold air to settle. Meanwhile, at the 700 and 500 hPa levels, blocking and the descent of cold air from the polar region and North Scandinavia, combined with a deep trough over Iran, were the primary factors contributing to the cooling and the occurrence of the extreme CW in January 1925.

Taking the megacity of Beijing as an example, a short‐time Fourier transform (STFT) method was employed to extract the multi‐timescale evolution pattern of the canopy urban heat island intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the evolution of the CUHII in Beijing and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual‐scale spectrum of CUHII exhibited an increasing trend with obvious seasonal variation of the canopy urban heat island (CUHI). The intra‐daily‐scale spectrum of CUHII showed an increasing trend with the nighttime CUHI developing faster. Increasing Western Pacific Subtropical High intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII is controlled by weather system evolution, showing that the frequency of cold/heat waves (CWs/HWs) in Beijing was significantly negatively correlated with the weather‐scale spectral intensity of the CUHII. CWs and HWs can increase the CUHII for a long duration. Plain Language Summary The canopy urban heat island (CUHI) phenomenon can affect human health and the ecological environment, and its multi‐timescale variability brings great uncertainty to the study of urban climates worldwide. In this study, taking the megacity of Beijing as an example, a novel short‐time Fourier transform (STFT) method was used to extract the multi‐timescale pattern of the CUHI intensity (CUHII) during 2000–2020. The STFT of CUHII showed a close relationship between the CUHI and the background meteorological forcing at intra‐annual, weather and intra‐daily scales. The intra‐annual spectrum of CUHII showed an increasing trend with a V‐shaped mode. The local climatic backgrounds of different cities can lead to differences in the seasonal development of the CUHI. The intra‐daily spectrum of CUHII showed an increasing trend due to the asymmetry in the day/night development of the CUHI. Increasing Western Pacific Subtropical High Intensity can enhance the seasonal and diurnal fluctuations of CUHII. The weather‐scale spectrum of CUHII was mainly controlled by weather system evolution, showing that cold waves and heat waves can increase the CUHII over a long duration. Our findings indicate that the evolution of CUHII is a nonlinear and complex process that is directly related to multi‐timescale background climate forcing. Key Points Using a short‐time Fourier transform to study the multi‐timescale evolution of the canopy urban heat island intensity (CUHII) Close relationships existed between CUHII and the background meteorological forcing at intra‐annual, intra‐daily, and weather scales The frequency of cold/heat waves in Beijing showed a significant negative correlation with the weather‐scale spectral intensity of the CUHII

An unprecedented cold wave intruded into East Asia in early January 2021 and led to record-breaking or historical extreme low temperatures over vast regions. This study shows that a major stratospheric sudden warming (SSW) event at the beginning of January 2021 exerted an important influence on this cold wave. The major SSW event occurred on 2 January 2021 and subsequently led to the displacement of the stratospheric polar vortex to the East Asian side. Moreover, the SSW event induced the stratospheric warming signal to propagate downward to the mid-to-lower troposphere, which not only enhanced the blocking in the Urals–Siberia region and the negative phase of the Arctic Oscillation, but also shifted the tropospheric polar vortex off the pole. The displaced tropospheric polar vortex, Ural blocking, and another downstream blocking ridge over western North America formed a distinct inverted omega-shaped circulation pattern (IOCP) in the East Asia–North Pacific sector. This IOCP was the most direct and impactful atmospheric pattern causing the cold wave in East Asia. The IOCP triggered a meridional cell with an upward branch in East Asia and a downward branch in Siberia. The meridional cell intensified the Siberian high and low-level northerly winds, which also favored the invasion of the cold wave into East Asia. Hence, the SSW event and tropospheric circulations such as the IOCP, negative phase of Arctic Oscillation, Ural blocking, enhanced Siberian high, and eastward propagation of Rossby wave eventually induced the outbreak of an unprecedented cold wave in East Asia in early January 2021.

The roles of planetary and synoptic-scale waves in extreme cold wave (ECW) events over the southeastern (SE) and northwestern (NW) United States (US) are studied using a spherical harmonic decomposition in conjunction with piecewise tendency diagnosis (PTD). Planetary waves and synoptic waves jointly work together to initiate ECW events. Notably, the planetary waves not only provide a direct contribution to circulation field enacting ECW events but also alter the background circulation field in such a manner that promotes synoptic waves growth via increases in regional barotropic deformation. The SE-ECW events, concurrent with the Northern Hemisphere annular mode (NAM) negative phase, feature high latitude intensification and subsequent southeastward movement of cold surface air temperature (SAT) anomalies. The planetary-scale pattern provides a sizable contribution to the total wave pattern on both sea level pressure (SLP) and upper level. Moreover, the negative NAM planetary anomaly acts to displace the jet equatorward and thereby increases the barotropic deformation of the synoptic-scale anomaly over southeastern US. PTD confirms that the planetary-scale barotropic deformation plays a key role in deepening the negative height anomaly with a secondary contribution from baroclinic growth. In contrast, NW-ECW events feature a regional SAT cold anomaly that intensified in situ in association with a quasi-stationary positive SLP anomaly with a substantial planetary-scale wave component. The upper level circulation is characterized by a pronounced anomalous ridge over the Gulf of Alaska and a northeast-southwest tilted negative height anomaly to its east. The negative height anomaly axis is orthogonal to the planetary-scale dilatation, result in a stronger planetary barotropic deformation of the incipient negative height anomaly.

Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases. Plain Language Summary The sudden drop in temperature and rapid spread of aerosols during cold waves resulted in a higher prevalence of infectious diseases. At the same time, low temperatures resulted in a higher risk of death due to infectious diseases and a variety of other factors during the epidemic. Key Points The rapid dispersion of aerosols during cold waves hastens the transmission of infectious diseases Low temperatures contribute to higher risk of death during infectious disease outbreaks

Cardiovascular disease is the most common cause of death globally. Examining the relationship between the extreme temperature events (e.g. heat and cold waves) and cardiovascular mortality has profound public significance. However, this evidence is scarce, particularly those from China. We collected daily data on cardiovascular mortality and meteorological conditions from 31 major Chinese cities during the maximum period of 2007-2013. A two-stage analysis was used to estimate the effects of heat and cold waves, and the potential effect modification of their characteristics (intensity, duration, and timing in season) on cardiovascular mortality. Firstly, a generalized quasi-Poisson regression combined with distributed lag nonlinear model was used to evaluate city-specific effects. Then, the meta-analysis was performed to pool effect estimates at the national scale. Overall, cardiovascular mortality risk increased by 19.03% (95%CI: 11.92%, 26.59%) during heat waves and 54.72% (95%CI: 21.20%, 97.51%) during cold waves. The effect estimates varied by the wave's characteristics. In heat wave days, the cardiovascular mortality risks increased by 3.28% (95%CI: −0.06%, 6.73%) for every 1 °C increase in intensity, 2.84% (95%CI: 0.92%, 4.80%) for every 1-d more in duration and −0.07% (95%CI: −0.38%, 0.24%) for every 1-d late in the staring of heat wave; the corresponding estimates for cold wave were 1.82% (95%CI: −0.04%, 3.72%), 1.52% (95%CI: 0.60%, 2.44%) and −0.26% (95%CI: −0.67%, 0.16%). Increased susceptibility to heat and cold waves was observed among patients with ischemic heart disease, females, the elderly, and those with lower education level. And consistent vulnerable populations were found for the effects of changes in cold and heat wave's characteristics. The findings have important implications for the development of early warning systems and plans in response to heat and cold waves, which may contribute to mitigating health threat to vulnerable populations.

Climate-related disasters cause substantial disruptions to human societies. With climate change, many extreme weather and climate events are expected to become more severe and more frequent. The International Disaster Database (EM-DAT) records climate-related disasters associated with observed impacts such as affected people and economic damage on a country basis. Although disasters are classified into different meteorological categories, they are usually not linked to observed climate anomalies. Here, we investigate countrywide climate features associated with disasters that have occurred between 1950 and 2015 and have been classified as droughts, floods, heat waves, and cold waves using superposed epoch analysis. We find that disasters classified as heat waves are associated with significant countrywide increases in annual mean temperature of on average 0.13 ∘C and a significant decrease in annual precipitation of 3.2%. Drought disasters show positive temperature anomalies of 0.08 ∘C and a 4.8 % precipitation decrease. Disasters classified as droughts and heat waves are thus associated with significant annual countrywide anomalies in both temperature and precipitation. During years of flood disasters, precipitation is increased by 2.8 %. Cold wave disasters show no significant signal for either temperature or precipitation. We further find that climate anomalies tend to be larger in smaller countries, an expected behavior when computing countrywide averages. In addition, our results suggest that extreme weather disasters in developed countries are typically associated with larger climate anomalies compared to developing countries. This effect could be due to different levels of vulnerability, as a climate anomaly needs to be larger in a developed country to cause a societal disruption. Our analysis provides a first link between recorded climate-related disasters and observed climate data, which is an important step towards linking climate and impact communities and ultimately better constraining future disaster risk.