Extreme Cold Days Research Articles

Extreme Temperatures in the Antarctic

AbstractWe present the first Antarctic-wide analysis of extreme near-surface air temperatures based on data collected up to the end of 2019 as part of the synoptic meteorological observing programs. We consider temperatures at 17 stations on the Antarctic continent and nearby sub-Antarctic islands. We examine the frequency distributions of temperatures and the highest and lowest individual temperatures observed. The variability and trends in the number of extreme temperatures were examined via the mean daily temperatures computed from the 0000, 0600, 1200, and 1800 UTC observations, with the thresholds for extreme warm and cold days taken as the 5th and 95th percentiles. The five stations examined from the Antarctic Peninsula region all experienced a statistically significant increase (p < 0.01) in the number of extreme high temperatures in the late-twentieth-century part of their records, although the number of extremes decreased in subsequent years. For the period after 1979 we investigate the synoptic background to the extreme events using ECMWF interim reanalysis (ERA-Interim) fields. The majority of record high temperatures were recorded after the passage of air masses over high orography, with the air being warmed by the foehn effect. At some stations in coastal East Antarctica the highest temperatures were recorded after air with a high potential temperature descended from the Antarctic plateau, resulting in an air mass 5°–7°C warmer than the maritime air. Record low temperatures at the Antarctic Peninsula stations were observed during winters with positive sea ice anomalies over the Bellingshausen and Weddell Seas.

Do greenhouse gas emissions drive extreme weather conditions at the city level in China? Evidence from spatial effects analysis

This paper employs spatial econometric models to depict the impact of CO2 emissions on extreme temperature of Chinese cities. Ten indicators are used to measure the extreme temperature, including extreme heat and extreme cold. Meanwhile, both the geographical and climatic factors and the economic factors are considered to control the model. The results show that CO2 emissions have significant effect on extreme weather changes. An increase in emissions leads to an increase in the probability of extreme heatby 0.00843 and a decrease in the probability of extreme cold by 0.00737. Considering the spatial effects, CO2 emissions increased the probability of extreme heat in local city and decreased that in nearby cities, while CO2 emissions decreased the ratio of extreme cold in both local city and neighboring cities. Specifically, the extreme heat days increased 0.114 day in the local city and decreased 15.601 days in the neighboring cities, while the extreme cold days decreased about 0.347 and 12.695 days for the local city and all neighboring cities, respectively. As climate changes resulted from emission growth have significant policy implications for sustainable development, this cannot be neglected in policy-making.

Changes in the extreme air temperature in Czech Republic

This study is focused on air temperature in Czech Republic in relation to selected climatological indices for extreme hot and extreme cold days for the period 1961- 2017. The number of summer days (SU) and number of tropical days (SU30) have been chosen as the indicators of extreme hot days. For the indicators of extreme cold days, the number of frost days (FD) and number of icing days (ID) have been analyzed. The analyses are based on the daily data for air temperature measured at eight meteorological stations situated at the regions with different geographical and climate conditions. The data are freely available from the website of the Czech Hydrometeorological Institute. The results of the study show negative trend of extreme cold events and positive trend of extreme hot temperatures. With a few exceptions, the trend is statistically significant at p=0.05. The trend values for the indicators of cold days are higher than those for the indicators of warm days. The results show a faster change in air temperature for the cold part of the year

Trends of Temperature Extreme Indices over Arusha and Kilimanjaro Regions in Tanzania

The study aimed at analyzing the trends and variability of temperature extremes over the northeastern highlands in Tanzania, specifically over Arusha and Kilimanjaro regions. Quality controlled mean monthly, daily maximum and minimum temperature data for the period 1961 to 2020, obtained from Tanzania Meteorological Authority, were used in the study. Rclimdex and the National Climate Monitoring Products (NMCP) software, developed by the World Meteorological Organization (WMO), were used for computation of the indices at a monthly, seasonal and annual time scale. The computed indices were also subjected to trend analysis to determine their direction and magnitude of change. Extraction and assessment of the top five highest and lowest maximum and minimum temperatures were also done. Increasing trends of temperature anomalies for seasonal and annual timescales were observed for both Arusha and Kilimanjaro regions. Also, the increasing trends of warm and extreme warm days and nights and relatively increasing trends of cold and extreme cold days and nights were observed for both regions. The highest ever recorded temperatures since the establishment of the two stations were 36.3?C observed on 16th February 2011 and 38.6?C observed on 22nd February 2005 for Arusha and Kilimanjaro respectively. These results indicate that The last two decades have been characterized by enhanced warming, which is consistent with overall global temperature trend patterns as depicted in recent IPCC reports and the report of the State of Climate in Africa.

Projected Trends of Wintertime North American Surface Mean and Extreme Temperatures over the Next Half-century in Two Generations of Canadian Earth System Models

ABSTRACT Based on two single-model initial-condition 50-member ensembles of climate simulations conducted with two generations of Canadian Earth System Models (CanESM2 and its successor CanESM5), we analyze the ensemble mean and spread of the projected trends of wintertime North American surface air temperature (SAT) and extreme indices of cold (TX10) and warm (TX90) days over the next half-century (2021–2070) and explore the contribution of internal climate variability to these trends. The ensemble mean of future climate simulations forced by the high-emissions scenario Representative Concentration Pathway 8.5 (RCP8.5) in CanESM2 and the Shared Socioeconomic Pathway 8.5 (SSP5-8.5) in CanESM5 reveals a poleward intensified warming, high risk of severe warm days over the west coast of North America and northern Canada, and a weakening belt of extreme cold days extending from Alaska to the northeastern United States. The warming trend is stronger in CanESM5 than in CanESM2, likely because of higher climate sensitivity and slightly higher CO2 emissions in CanESM5. Large ensemble spreads are apparent in the SAT trend and in the historical simulations and future projections of extreme temperatures, especially for the TX10 index. Individual realizations differ from the ensemble mean in both spatial pattern and magnitude of the projected trends. The signal-to-noise ratio reveals strong signals of the SAT and TX90 trends primarily over the west coast of North America and northern Canada, along with relatively strong signals of the TX10 trend over most of the central to eastern parts of North America in CanESM2 and western Canada and the southwestern and eastern United States in CanESM5. The components of the mean and extreme temperature trends generated by internal climate variability exhibit large-scale spatial coherences and are comparable to the externally anthropogenic-forced components of the trends, mostly in the central parts of North America. Overall, similar ensemble mean patterns of North American mean and extreme temperature trends are evident in the two models; CanESM5 tends to be less uncertain in projecting those trends than CanESM2.

Spatial Distribution and Temporal Trend Characteristics of Agro-Climatic Resources and Extreme Climate Events during the Soybean Growing Season in Northeast China from 1981 to 2017

Soybean is an important oil crop. Agro-climatic resources and extreme climate events during the growing season directly affect the crop growth and grain yield. In this study, we used historical climate data and phenology observation data to investigate the spatial distributions and temporal trends of agro-climatic resources and extreme climate events during the growing season for soybean in Northeast China (NEC). The results showed that during the soybean growing season, the thermal time increased while both the effective precipitation and photosynthetically active radiation (PAR) decreased. Within the growing season, the thermal time increased by 44.0°C day decade−1 during the vegetative stage but decreased by 16.5°C day decade−1 during the reproductive stage; the effective precipitation increased by 1.8 mm decade−1 during the vegetative stage but decreased by 7.3 mm decade−1 during the reproductive stage; PAR decreased by 6.5 and 11.9 MJ m−2 decade−1 during the vegetative and reproductive stages. The frequency of extreme cold days showed a decreasing trend during the four study phases of sowing to emergence, sowing-flowering, 15 days before flowering-flowering, and pod to physiological maturity. During the soybean growing season, the frequency of extreme heat days and the maximum number of consecutive dry days (CDD) increased, and the maximum number of consecutive wet days (CWD) and heavy precipitation days decreased. The results of this study could be used in selecting optimal management in soybean production in order to take advantage of beneficial climatic elements.

Opposing Trends of Winter Cold Extremes over Eastern Eurasia and North America under Recent Arctic Warming

Under recent Arctic warming, boreal winters have witnessed severe cold surges over both Eurasia and North America, bringing about serious social and economic impacts. Here, we investigated the changes in daily surface air temperature (SAT) variability during the rapid Arctic warming period of 1988/89–2015/16, and found the daily SAT variance, mainly contributed by the sub-seasonal component, shows an increasing and decreasing trend over eastern Eurasia and North America, respectively. Increasing cold extremes (defined as days with daily SAT anomalies below 1.5 standard deviations) dominated the increase of the daily SAT variability over eastern Eurasia, while decreasing cold extremes dominated the decrease of the daily SAT variability over North America. The circulation regime of cold extremes over eastern Eurasia (North America) is characterized by an enhanced high-pressure ridge over the Urals (Alaska) and surface Siberian (Canadian) high. The data analyses and model simulations show the recent strengthening of the high-pressure ridge over the Urals was associated with warming of the Barents–Kara seas in the Arctic region, while the high-pressure ridge over Alaska was influenced by the offset effect of Arctic warming over the East Siberian–Chukchi seas and the Pacific decadal oscillation (PDO)–like sea surface temperature (SST) anomalies over the North Pacific. The transition of the PDO-like SST anomalies from a positive to negative phase cancelled the impact of Arctic warming, reduced the occurrence of extreme cold days, and possibly resulted in the decreasing trend of daily SAT variability in North America. The multi-ensemble simulations of climate models confirmed the regional Arctic warming as the driver of the increasing SAT variance over eastern Eurasia and North America and the overwhelming effect of SST forcing on the decreasing SAT variance over North America. Therefore, the regional response of winter cold extremes at midlatitudes to the Arctic warming could be different due to the distinct impact of decadal SST anomalies.

A 21-year retrospective analysis of environmental impacts on paediatric acute gastroenteritis in an affluent setting

BackgroundExtreme weather events happen more frequently along with global warming and they constitute a challenge for public health preparedness. For example, many investigations showed heavy rainfall was associated with an increased risk of acute gastroenteritis. In this study, we examined the associations between different meteorological factors and paediatric acute gastroenteritis in an affluent setting in China controlling for pollutant effects. MethodsAggregated total weekly number of intestinal infection-related hospital admissions, and meteorological and air pollution data during 1998–2018 in Hong Kong were collected and analysed by a combination of quasi-Poisson generalized additive model and distributed lag nonlinear model. Study population was restricted to children under 5 years of age at the time of admission. ResultsWhile heavy rainfall did not exhibit a statistically significant association with the risk of paediatric admission due to intestinal infections, low temperature and humidity extremes (both relative humidity and vapour pressure) did. Compared with the temperature at which the lowest risk was detected (i.e. 22.5 °C), the risk was 6.4% higher (95% confidence interval: 0.0% to 13.0% at 15.1 °C (i.e. the 5th percentile)). We also found the risk of paediatric admission was statistically significantly associated with an increase in the number of extreme cold days in a week over the study period. ConclusionCold condition may have greater impact on disease transmission through increased stability and infectivity of enteric viruses in affluent settings like Hong Kong and thus resulted in an increased risk for paediatric acute gastroenteritis. On the contrary, an insignificant impact from heavy rainfall and high temperature may indicate a minor effect on disease transmission through bacterial growth in contaminated food and water. With the identified impacts of weather factors, extreme weather events are likely to distort the prevalence and seasonal pattern of diarrhoeal diseases in the future.

U.S. HOUSEHOLD PREFERENCES FOR CLIMATE AMENITIES: DEMOGRAPHIC ANALYSIS AND ROBUSTNESS TESTING

We estimate how climate amenities influence where households decide to reside in the United States with two main objectives in mind: (i) to produce estimates with sufficient demographic detail to inform demographic population projections for use in climate impact analysis; (ii) to study the robustness of estimates from the existing literature. With respect to the former goal, we find important differences in job-related migration motives by age group and in the overall propensity to migrate among households with children. With respect to the latter aim, our framework shares a common methodological approach with other, recent attempts to recover climate preferences, allowing us to explore the consequences of a number of key assumptions in a systematic manner. Consistent with the existing literature, we find relatively robust estimates of the impact of the frequency of extreme heat days on household location decisions. The impacts of other common measures of climate, including the frequency of extreme cold days, average summer and winter temperatures, annual precipitation, humidity and frequency of sunshine, do not show a strong enough signal in the data to be estimated with precision.

Analysis of Record-High Temperature over Southeast Coastal China in Winter 2018/19: The Combined Effect of Mid- to High-Latitude Circulation Systems and SST Forcing over the North Atlantic and Tropical Western Pacific

AbstractIn winter 2018/19, southeastern coastal China experienced extreme warm temperatures that were due to a weak East Asian winter monsoon. On the basis of observations from 10 meteorological stations and reanalysis data, the large-scale circulation patterns associated with this extreme warm winter and the possible driving mechanism of its related sea surface temperature (SST) anomalies are investigated in this study. During this winter, many places in this region reached their highest winter mean temperature record and had more extreme warm days and fewer extreme cold days relative to climatology. According to the circulation patterns during winter 2018/19, several large-scale circulation conditions associated mainly with the weak East Asian winter monsoon are identified: the eastward shift of the Siberian high and a shallower East Asian trough, which is related to the low blocking frequency over the Aleutian region, are both unfavorable for cold-air intrusion southward. Meanwhile, strong low-level southerly wind anomalies over southeastern China are related mainly to the 2018/19 El Niño event. Furthermore, the possible role of SST anomalies over the North Atlantic and tropical western Pacific Oceans is examined by using an atmospheric general circulation model, suggesting that both the “tripole pattern” of North Atlantic SST and tropical western Pacific SST anomalies in winter 2018/19 played a role in influencing the East Asian trough. The combined effect of all of these factors seems to be responsible for this extreme warm winter over southeastern coastal China.

Subseasonal Prediction of Land Cold Extremes in Boreal Wintertime

AbstractSubseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2–4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode) and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode, and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence. The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability.

Reversal of the seasonality of temperature-attributable mortality from respiratory diseases in Spain

A growing number of epidemiological studies have recently assessed temporal variations in vulnerability and/or mortality attributable to hot and cold temperatures. However, the eventual changes in the seasonal distribution of temperature-attributable mortality remain unexplored. Here, we analyse countrywide daily time-series of temperature and mortality counts from respiratory diseases by sex, age group and province of residence during the period 1980–2016 in Spain. We show the complete reversal of the seasonality of temperature-attributable mortality, with a significant shift of the maximum monthly incidence from winter to summer, and the minimum monthly incidence from early and late summer to winter. The reversal in the seasonal distribution of the attributable deaths is not driven by the observed warming in both winter and summer temperatures, but rather by the very large decrease in the risk of death due to cold temperatures and the relatively much smaller reduction due to hot temperatures. We conclude that the projected decrease in the number of moderate and extreme cold days due to climate warming will not contribute to a further reduction of cold-attributable respiratory deaths.

Non-Gaussian Cold-Side Temperature Distribution Tails and Associated Synoptic Meteorology

Abstract Non-Gaussian cold side temperature distribution tails occur in spatially coherent patterns in winter and summer across the globe. Under such conditions, future changes in extreme cold temperature exceedances may be manifested in more complex ways than if the underlying distribution were Gaussian. For example, under a uniform warm shift, locations with shorter- or longer-than-Gaussian cold side tails would experience a more or less rapid decrease in the number of extreme cold threshold exceedances, respectively, compared to if the tail were Gaussian. In many places in the mid- to high latitudes, shorter-than-Gaussian cold tails occur where there is a climatological limit on the magnitude of cold air to be transported by synoptic flow. For example, some high-latitude regions are already among the coldest in the hemisphere, thus limiting the availability of extremely cold air, in an anomalous sense, that can be transported to the region. In other short tail regions, anomalously cold air originates from or travels over large water bodies, which limits the magnitude of the cold anomaly. Long tails are often present when the cold source region is downstream of the climatological flow, requiring a highly anomalous circulation pattern to transport the cold air. The synoptic evolution of extreme cold days at several short- and long-tailed weather stations are presented to help diagnose the mechanisms behind extreme cold temperatures under conditions of non-Gaussianity. This provides a mechanistic view of how extreme cold occurs at each location, as well as an explanation for the notable deviations from Gaussianity.

New England Winters Are “Losing the Cold”

As extreme cold days wane, the northeastern United States has experienced an increase in mud days.

Variations in the Frequency of Winter Extreme Cold Days in Northern China and Possible Causalities

Abstract Weather and climate extremes often cause devastating disasters and motivate research to improve their understanding. Here, the spatiotemporal variations in the frequency of winter extreme cold days (WECDs) in northern China (NC) were investigated. The results show that the first EOF mode reflects the spatially consistent anomalies and explains 39% of the total variance. The second EOF mode represents the east–west contrasted anomalies and explains 17% of the total variance. In the winter, when the negative Arctic Oscillation (AO) co-occurs with the stronger Siberian high (SH), cold air from the polar region is more easily advected southward, causing the increased number of WECDs in all of NC. The co-occurrence of negative AO and stronger SH may be related to excessive snow in the preceding autumn over northeastern Eurasia through the local positive feedback between the snow and surface air temperatures and the tropospheric–stratospheric interaction. On the other hand, in the winter when the negative AO co-occurs with the weaker SH, eastern NC is still under the influence of cold-air intrusion from the polar region relevant to the negative AO, whereas western NC is impacted by the anomalous warm-air advection related to the weaker SH. The weakening of the SH may be caused by the El Niño teleconnection. The reproducibility of the observed variations and possible causalities of WECDs in NC by 10 coupled models participating in CMIP5 has been assessed. All the models can reproduce the observed dominant EOF modes. The relevant large-scale circulation anomalies and possible causalities can also be well simulated by the multimodel ensemble.

Skillful Seasonal Prediction of Eurasian Winter Blocking and Extreme Temperature Frequency

AbstractAtmospheric blocking is a major producer of extreme weather events in midlatitudes that have profound socioeconomic impacts. However, few strides toward seasonal prediction of atmospheric blocking have been made. Here, we developed a new statistical model for prediction of the winter seasonal blocking frequency over Eurasia 1 month in advance using sea surface temperature, geopotential height at 70‐hPa, and sea ice concentration as predictors, and the model captures more than 65% of the interannual variance. Furthermore, we applied the same predictors used for blocking prediction to predict the seasonal occurrence of winter extreme hot and cold days, and skillful prediction was achieved over Greenland and large portions of Eurasia. The predictive models provide insight into the seasonal predictability of atmospheric blocking and extreme temperature and also aide in valuable decisions across a variety of sectors.

The role of horizontal thermal advection in regulating wintertime mean and extreme temperatures over interior North America during the past and future

Horizontal thermal advection plays an especially prominent role in affecting winter climate over continental interiors, where both climatological conditions and extreme weather are strongly regulated by transport of remote air masses. Interior North America is one such region, and it experiences occasional cold-air outbreaks (CAOs) that may be related to amplified Arctic warming. Despite the known importance of dynamics in shaping the winter climate of this sector and the potential for climate change to modify heat transport, limited attention has been paid to the regional impact of thermal advection. Here, we use a reanalysis product and output from the Community Earth System Model’s Large Ensemble to quantify the roles of zonal and meridional temperature advection over the central United States during winter, both in the late twentieth and late twenty-first centuries. We frame our findings as a “tug-of-war” between opposing influences of the two advection components and between these dynamical forcings vs. thermodynamic changes under greenhouse warming. During both historical and future periods, zonal temperature advection is stronger than meridional advection east of the Rockies. The model simulates a future weakening of both zonal and meridional temperature advection, such that westerly flow provides less warming and northerly flow less cooling. On the most extreme cold days, meridional cold-air advection is more important than zonal warm-air advection. CAOs in the future feature stronger northerly flow but less extreme temperatures (even relative to the warmer climate), indicating the importance of other mechanisms such as snow cover and sea ice changes.

Changes in Means and Extreme Events of Sea Surface Temperature in the East China Seas Based on Satellite Data from 1982 to 2017

Marginal seas are fundamental to humans for their importance in mariculture resources and commerce. Based on the NOAA 0.25 degree daily Optimum Interpolation (OI) sea surface temperature (SST) data set, spatiotemporal changes in mean and extreme SST in the East China Seas (ECSs) were examined for from 1982 to 2017. As a regional average, the annual mean SST has notably increased at a rate of 0.21 ± 0.08 °C per decade. The warming SST during 1982–2017 is probably related to the influence from a recent strengthening and westward extension of the WPSH. There are also notable warming trends in annual minimum and maximum SST. Spatially, the rapid warming of annual mean SSTs are located in the vicinity of the Yangtze Estuary, exceeding 0.2 °C per decade and part of the ECS-Kuroshio. This pattern may be largely affected by the spatial changes of minimum SST. Rapid warming of maximum SST can be found across the region, from the northern East China Sea (ECS) to the Bohai Sea. Since 1982, extreme hot days (EHDs) have undergone an obvious increasing trend, at a rate of 15.2 days per decade. Conversely, extreme cold days (ECDs) have been decreasing. Notably, the largest increase of EHDs appears in the western ECS and the Bohai Sea, which both have rich marine ecosystems. The trend of EHDs has a significant relationship to mean SST, suggesting that there will be a further increase in EHDs under continued warming in the ECSs. These findings emphasize the importance and urgency of strategies which should be planned for the adaptation and mitigation of specific types of extreme hot events in this region.

Decadal Variations of Winter Extreme Cold Days in Northern China

Weather and climate extremes often lead to dramatic losses in our society and warrant improvement of their understanding. In this study, the decadal variations in the first two dominant empirical orthogonal function (EOF) modes of winter extreme cold days (WECDs) in northern China (NC) have been investigated. Results show that both EOF modes show distinct decadal variations that together explain around 24% of total variances. At the decadal time scale, the EOF1 is closely related to the decadal Arctic Oscillation (AO); the negative AO can lead to spatially consistent increase of WECDs in NC. On the other hand, the decadal EOF2 can be influenced by the decadal El Niño-Southern Oscillation (ENSO). The decadal El Niño can result in the large-scale negative sea level pressure (SLP) anomalies in the Eurasian continent west of the western NC and the positive ones over western China. The anomalous southwesterlies between the contrasted SLP anomalies can advect the warmer air from the lower latitudes to the western NC, decrease the WECDs there, and contribute to the east-west asymmetric WECD anomalies in NC. The impacts of El Niño are confirmed by the numerical simulations in the Atmospheric Model 2.1 (AM2.1) when forced by the El Niño-related sea surface temperature (SST) anomalies in the tropical Pacific.

Türkiye’nin Sıcak ve Soğuk Ekstremlerindeki Uzun Süreli Değişimler

Extreme weathers are the leading cause of weather-related disasters all over the world. Many people lose their lives each year due to the triggering effect of cold or hot weather. The extreme days are generally defined as a period of several days of abnormally cold or hot weather. Increased frequency of these days could lead to death and disasters. This study analyzes the variability of minimum and maximum temperature defined anomalous temperature events. Daily minimum and maximum air temperature data from 156 weather stations in Turkey are analyzed to determine percentile threshold values (99th, 95th and 1st, 5th) at each station. Two statistical tests are applied to analyze trend in extreme values, namely Mann-Kendall trend analysis and cluster analysis. The Mann-.Kendall analysis demonstrates an increase in frequency of hot and extremely hot days, whereas cold and extremely cold days show a decreasing trend in Turkey. The extreme cold days have been the highest of the year 1972, 1983, 1992 and 1993, respectively. After 2005, extreme cold days begin to decrease. The frequency of extreme hot day has increased since 2000. Especially in 2010, it has been the warmest year in Turkey from the records of the stations during the study period.