Frequency Of Temperature Extremes Research Articles

Medium‐range predictability of temperature extremes and biases in Rossby‐wave amplitude

AbstractThis study investigates the medium‐range predictability of warm and cold extremes in the Northern Hemisphere and the role that upper‐tropospheric circulation biases play in this regard. Deterministic ERA5 reforecasts for the period 1979–2019 are evaluated based on the ERA5 reanalysis of the respective period, thus providing a large sample for verification and bias identification. The predictability of temperature extremes at 850 hPa is assessed based on the Gilbert Skill Score and other metrics and is shown to exhibit regional and seasonal variations. Summer is generally characterized by lower forecast skill scores than winter for both warm and cold extremes. Moreover, cold extremes in summer have slightly lower skill scores than warm extremes, while the opposite is true in winter. Biases in the frequency of temperature extremes are, to some extent, consistent with biases in mean temperature and indicate an underestimation in the total amount of extremes for much of the hemisphere in summer. Associated with the latter, biases also emerge in the standard deviation of the daily temperature distribution, with the summer values being largely underestimated over most of the hemisphere. The role of upper‐tropospheric circulation in these biases is then assessed by verifying the representation of Rossby‐wave packet (RWP) properties. It is found that the amplitude of RWPs is systematically underestimated in most of the hemisphere in summer, while it is overestimated in many parts of the midlatitudes in winter. Overall, the results suggest that the underestimation of RWP amplitude in summer hinders the medium‐range predictability of temperature extremes in the explored retrospective and operational forecasts. Although operational European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts gradually improve between 2013 and 2022 in terms of the 850‐hPa temperature and 300‐hPa RWP amplitude absolute errors, the aforementioned summer biases remain qualitatively similar.

Assessment of historical and projected changes in extreme temperatures of Balochistan, Pakistan using extreme value theory.

The fundamental consequences of global warming include an upsurge in the intensity and frequency of temperature extremes. This study provides an insight into historical trends and projected changes in extreme temperatures on annual and seasonal scales across "Balochistan, Pakistan". Historical trends are analyzed through the Mann Kendal test, and extreme temperatures (Tmax and Tmin) are evaluated using generalized extreme value (GEV) distribution for historicalperiod (1991-2020) from the observational data and the two projected periods as near-future (2041-2070) and far-future (2071-2100) using a six-member bias-corrected ensemble of regional climate models (RCMs) projections from the coordinate regional downscaling experiment (CORDEX) based on the worst emission scenario (RCP8.5). The evaluation of historical temperature trends suggests that Tmax generally increase on yearly scale and give mixed signals on seasonal scale (winter, spring, summer, and autumn); however, Tmin trends gave mixed signals at both yearly and seasonal scale. Compared to the historical period, the return levels are generally expected to be higher for Tmax and Tmin during the both projection periods in the order as far-future > near-future > historical on yearly and seasonal basis; however, the changes in Tmin are more evident. Station-averaged anomalies of + 1.9°C and + 3.6°C were estimated in 100-year return levels for yearly Tmax for near-future and far-future, respectively, while the anomalies in Tmin were found to be + 3.5°C and + 4.8°C which suggest the intensified heatwaves but milder colder extreme in future. The findings provide guidance on improved quantification of changing frequencies and severity in temperature extremes and the associated impacts.

Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods

AbstractWe merge classical extreme value methods to extract high (high temperatures (HT)) and low (low temperatures (LT)) temperatures and form time series having at least one extreme value per year. Observed daily maximum and minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. We assess changes in the magnitude and frequency of extreme temperatures by introducing and applying novel methods that exploit the definition of stationarity. Analysis shows significant increasing (40.6% of the stations) and decreasing (41.1%) trends in the frequency of high and LT, respectively, and increasing trends in both high‐ and low‐temperature values (35.6% and 49.7%). Globally, HT and LT frequencies are increasing and decreasing, respectively, by 0.9% and 1.1% per year, relative to the expected frequencies under the assumption of stationarity. The global mean annual HT and LT magnitudes are increasing by 0.004 and 0.016°C/year compared to the expected ones under stationarity. The results indicate that the assumption of stationarity fails to explain the observed changes. The proposed methods are an alternative approach to classical extreme value methods and a useful tool to reveal changes in extremes in the era of earth‐system change.

Uncertainties on Climate Extreme Indices Estimated From U.S. Climate Reference Network (USCRN) Near‐Surface Temperatures

AbstractChanges in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

Attribution of observed changes in extreme temperatures to anthropogenic forcing using CMIP6 models

Global warming has clearly affected the occurrence of extreme events in recent years. Here, we assess changes in the frequency of temperature extremes and their causes, using percentile-based indices. Cold extremes are defined as temperatures below the 10th percentile of daily minimum (TN10) and maximum (TX10) temperatures while hot extremes exceed the 90th percentile of daily minimum (TN90) and maximum (TX90) temperatures. We analyze Berkeley Earth Surface Temperature (BEST) for observed changes in the last four decades 1981-2020, for two extended seasons, boreal summer April–September (AMJJAS) and boreal winter October–March (ONDJFM), and evaluate results using several reanalysis data sets. For the attribution of causes we use CMIP6 climate model simulations, analyzing natural-only and anthropogenic-only forcings. We use an attribution method that accounts for climate modeling uncertainty in both amplitude and pattern of responses.The observations show detectable changes in both cold and hot extreme temperatures. Hot extremes have increased in all regions and in both seasons while cold extremes have decreased over the past decades. Our attribution analysis revealed anthropogenic forcings are robustly detectable and the main drivers of observed changes in all indices for all regions, consistently in all data sets. Contributions from natural forcings are found small and detectable only in a few regions mainly for daytime cold extremes in ONDJFM. Anthropogenic forcing contributed to an increase of 3.4 days per decade in TN90 and of 2.7 days per decade in TX90, on average, at the global scale. Regionally, the anthropogenic contribution caused a range of decrease of 2–4.7 days per decade in TN10, 1.5–3.6 days per decade in TX10 while it caused an increase of 2.2–4.8 days per decade for TN90 and 2–3.3 days per decade in TX90. Anthropogenic-only warming in ONDJFM is slightly less than in AMJJAS.

CORDEX-EA Phase 2 다중 지역기후모델 앙상블을 이용한 전지구 온난화 제한 목표(1.5, 2.0℃)하에서의 한반도 미래 기온 전망

This study presents a future projection of temperature over the Korean Peninsula (KP) under the global warming targets of 1.5℃ and 2.0℃ set in the 2015 Paris Agreement. For projection, simulation data from the multi-Regional Climate Model (RCM) ensemble in Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia (EA) Phase 2 are used. Under the 2.0℃ (1.5℃) global warming condition, mean temperature over KP would increase 2.13℃ (1.51℃) compared to the present period (1985 ~ 2005 years). Extreme minimum and maximum temperature intensity indices (TNn and TXx) would also increase 2.61℃ (1.91℃) and 2.38℃ (1.58℃). In addition, changes in probabilities of extreme temperature events that occurs once in 20 years during the present period are investigated. Extreme cold events would occur once in 28.9 years (22.6 years) and warm events once in 5.9 years (7.2 years). Meanwhile, there are several noteworthy points about the future projection. The temperature over KP would increase more over the northern KP than the southern KP and this feature is more distinct in TNn. The uncertainties of the projection are higher for the 1.5℃ warming condition than the 2.0℃ warming condition and for extreme temperature versus mean temperature. The spatial pattern of change in extreme temperature frequency is different from that of intensity. In other words, a smaller increase in the frequency of extreme cold events is expected over the inland KP.

Projected increases in population exposure of daily climate extremes in eastern China by 2050

Climate extremes pose severe threats to human health, economic stability and environmental sustainability, especially in densely populated areas. It is generally believed that global warming drives increase in frequency, intensity and duration of climate extremes, and socioeconomic exposure plays a dominant role in climate impacts. In order to promote climate risk governance at regional level, the historical and projected trends in the population exposure to climate extremes are quantified in eastern China using downscaled climate simulations and population growth scenarios. The frequency of temperature extremes (tx35days) is projected to more than double by 2050 in nearly half of prefecture-level cities in eastern China, leading to an 81.8% increment of total exposure under SSP2-4.5 scenario. The increasing trend is also detectable in the frequency of precipitation extremes (r20mm) in eastern China, and the exposure increment is projected to be 22.9% by 2050, with a near equivalent contributions of both climate change and population growth. Spatially, temperature exposure mainly grows in southern Hebei, western Shandong and inland Guangdong provinces, while precipitation exposure raises principally in southeast coastal areas of China. Based on the historical baseline and projected amplification of population exposure, we identify some hotspot cities such as Guangzhou, Shanghai, Dongguan and Hangzhou that response to climate change dramatically and confront greater potential risk of climate extremes in the coming future.

Effects of temperature frequency trends on projected japonica rice (Oryza sativa L.) yield and dry matter distribution with elevated carbon dioxide.

In this study, we investigated the effects of temperature frequency trends on the projected yield and dry matter distribution of japonica rice (Oryza sativa L.) with elevated carbon dioxide (CO2) under future climate change scenarios in northwestern China. The Crop Environment Resource Synthesis (CERES)-Rice model was forced with the outputs from three general circulation models (GCMs) to project the rice growth and yield. Future temperature trends had the most significant impact on rice growth, and the frequency of higher than optimal temperatures (∼24–28 oC) for rice growth showed a marked increase in the future, which greatly restricted photosynthesis. The frequency of extreme temperatures (>35 oC) also increased, exerting a strong impact on rice fertilization and producing a significantly reduced yield. Although the increased temperature suppressed photosynthetic production, the elevated CO2 stimulated this production; therefore, the net result was determined by the dominant process. The aboveground biomass at harvest trended downward when temperature became the major factor in photosynthetic production and trended upward when CO2-fertilization dominated the process. The trends for the leaf and stem dry matter at harvest were affected not only by changes in photosynthesis but also by the dry matter distribution to the panicles. The trends for the rice panicle dry matter at harvest were closely related to the effects of temperature and CO2 on photosynthetic production, and extreme temperatures also remarkably affected these trends by reducing the number of fertilized spikelets. The trends of rice yield were very similar to those of panicle dry matter because the panicle dry matter is mostly composed of grain weight (yield). This study provides a better understanding of the japonica rice processes, particularly under extreme climate scenarios, which will likely become more frequent in the future.

Future changes in the frequency of temperature extremes may be underestimated in tropical and subtropical regions

In a warming world, temperature extremes are expected to show a distinguishable change over much of the globe even at 1.5 °C warming, and in many regions this change has already been detected in observations. Although many studies predict an increase in heat extreme events, the magnitude of the change varies greatly among different models even for the same mean warming. This uncertainty has been linked to differences in land–atmosphere feedback across models. Here we show that a significant constraint for future projections can be based on the ability of climate models to accurately simulate the present day variability of daily surface maximum temperature. An emergent constraint on Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6) models, applied to ERA5 reanalysis, indicates that the best estimate in hot extreme changes by the end of the century could be worse than previously estimated, mostly for tropical and subtropical regions as well as South and East Asia.

Regional changes in extreme temperature records over Pakistan and their relation to Pacific variability

The widespread changes in extreme temperatures record are of significant importance for climate risk management. Trends and variability of extreme temperature events in the agro-ecological zones of Pakistan are not fully understood, despite their importance. This study investigates the annual and seasonal trends of temperature extremes, and the role of Pacific variability during 1980–2019. The homogeneity of 40 synoptic stations, spatially distributed over Pakistan was assessed by removing non-climatic shifts from the data. There is distinct increases (decreases) in the upper (lower) tails are noticed in a spatially aggregated perspective. The strongest warming trends of 0.35 (0.49) ∘C decade−1 for the hottest nights (days) occur during spring, which is the hottest season of the year. The significant warming (hotter) trends are apparent for the agro-ecological zones located to central, western, and southeastern-Pakistan. The principal component analysis showed a robust increase in the intensity and frequency of extreme temperatures, with a total variance of 94.7%. In the spring season, we found strong connections between La Niña (El Niño) events over the western Pacific Ocean and changes in extreme temperatures. Analysis reveals that the strength of La Niña episodes has a much larger influence on the intensity of extremely high-temperature events over arid to semi-arid climatic zones. This information is of great use for the seasonal prediction of extremely hot days in the region and will help policymakers to formulate climate change adaptation plans.

Effects of temperature and interspecific competition on population fitness of free-living marine nematodes

Global average temperature, frequency of temperature extremes and amplitude of daily fluctuations are all expected to change under prospected scenarios of climate change. The amplitudes of temperature variation and the ability of species to tolerate thermal stress are crucial for species fitness and survival in rapidly changing environments. Small differences in the life histories of species or in their responses to environmental variation can differentiate their niche and support coexistence; hence, even small changes in environmental conditions may not only affect the fitness of individual species, but also the outcome of species interactions. In the present experiments, the free-living nematode species Halomonhystera disjuncta is used as a model organism to investigate physiological and behavioural responses to environmental change in an ecological context of species interactions. We examine population fitness and food-taxis behaviour of H. disjuncta under different temperature regimes in the presence and absence of a competing species. Both stressors – temperature and competition – affected life-history traits and population development of the model species in different ways; the nematode populations exhibited higher fitness under a daily fluctuating temperature regime, but a lower fitness under an increased constant temperature. The presence of a competing species had a negative effect on its population fitness and food-finding behaviour at all temperature regimes, yet the most pronounced effects of competition were observed at the highest temperatures, suggesting that interspecific interactions such as competition may weaken a species’ intrinsic tolerance to environmental change. Thermal stress thus not only affects species fitness but also species interactions, which in turn will likely have repercussions for species coexistence.

A historical perspective on Australian temperature extremes

Global temperature increases are most clearly detected in the shifting distribution of extreme events. Australia’s warming climate has resulted in significant changes in the frequency of temperature extremes, with a general increase in heatwaves and a reduction in the number of cold days. Here, we present the longest historical analysis of daily Australian temperature extremes and their societal impacts compiled to date. We use a newly consolidated early instrumental dataset and a range of historical sources for the South Australia region of Adelaide—the nation’s driest state, containing the most heatwave-affected city in Australia—to investigate any changes in the characteristics of daily temperature extremes back to 1838. We identify multidecadal variability in heatwave and snow event frequency with a peak in the early twentieth century, with an overall decrease in cold extremes and an increase in heatwaves in the region over the 1838–2019 period. Documentary and instrumental records show a decrease in the number of snow events in Adelaide, and a clear increase in the number of heatwaves since the late twentieth century. To gain dynamical insight into historical extremes in South Australia, detailed case studies are presented to compare the synoptic characteristics of historical hot and cold extremes and their impacts. We place a particular emphasis on lesser-known events of the pre-1910 period and rare low-elevation snowfall. Significantly, this is the first study to provide long-term evidence for a reduction of low-elevation snow events and cold outbreaks in Australia. Finally, a discussion is provided on the value and limitations of using historical instrumental and documentary data to assess long-term changes in Australian temperature extremes and their potential to improve future climate change risk assessment.

Human influence on frequency of temperature extremes

We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951–2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called ‘warming hole’ in North America is much reduced in 1951–2018 compared with that in 1951–2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG’s warming effect is offset partially by AA’s cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.

Climate change contributes to widespread declines among bumble bees across continents.

Climate change could increase species' extinction risk as temperatures and precipitation begin to exceed species' historically observed tolerances. Using long-term data for 66 bumble bee species across North America and Europe, we tested whether this mechanism altered likelihoods of bumble bee species' extinction or colonization. Increasing frequency of hotter temperatures predicts species' local extinction risk, chances of colonizing a new area, and changing species richness. Effects are independent of changing land uses. The method developed in this study permits spatially explicit predictions of climate change-related population extinction-colonization dynamics within species that explains observed patterns of geographical range loss and expansion across continents. Increasing frequencies of temperatures that exceed historically observed tolerances help explain widespread bumble bee species decline. This mechanism may also contribute to biodiversity loss more generally.

Increasing temperatures and declines

Pollinator Decline One aspect of climate change is an increasing number of days with extreme heat. Soroye et al. analyzed a large dataset of bumble bee occurrences across North America and Europe and found that an increasing frequency of unusually hot days is increasing local extinction rates, reducing colonization and site occupancy, and decreasing species richness within a region, independent of land-use change or condition (see the Perspective by Bridle and van Rensburg). As average temperatures continue to rise, bumble bees may be faced with an untenable increase in frequency of extreme temperatures. Science , this issue p. [685][1]; see also p. [626][2] [1]: /lookup/doi/10.1126/science.aax8591 [2]: /lookup/doi/10.1126/science.aba6432

Estimation of comfort of weather conditions and trends of their changes for the Kyiv region in the conditions of climate change

Climate change, the increasing variability of weather conditions and more often manifestation of extreme weather phenomena in Ukraine negatively affect the comfort of life, health, working capacity and mortality of the population. Evaluation of the population’s sensitivity towards the new climatic conditions and the establishment of tendencies in the distribution of comfortable and uncomfortable weather periods during the year were estimated based on the calculation of equivalent-effective temperatures (EET) according to the Missenard’s formula by using observation data from meteostations in the Kyiv region. A certain shift on a scale was established from a subcomfortable weather conditions in this region towards a more comfortable one for the period 1971―2018. However, the situation is becoming more complicated due to the increase in frequency of extreme temperatures in warm season, especially in summer, and significant variability in cold season (thermal and cold waves). Analysis of the periodical observations for meteorological parameters (surface air temperature, air humidity and wind speed) from 9 meteostations for the period 2006―2018 has showen that in cold season, against the general tendency to warming, significant variability of weather conditions appeared with an increase in frequency of frosty periods in winter and early spring. In warm period, the situation in the region is ambiguous, so for gradations (6÷12) and lower, there is a general tendency to decrease repeatability, and for gradations above (12÷16) to increase repeatability, which is associated with the appearing of heat waves. A possible scenario of the repeatability occurrence of comfortable and uncomfortable weather situations is also proposed for cold and warm periods of the year in the Kyiv region by the middle of the 21st century in the face of further climate change. Thus, biometeorological (bioclimatic) indices are effective indicators of the thermal structure of the environment, which surrounds a person, especially in the face of climate change, and their estimations are necessary to use in different spheres ― medicine, balneology, ecology, etc.

Projected changes in extreme warm and cold temperatures in China from 1.5 to 5°C global warming

AbstractLinking regional extreme temperature changes to global warming levels is important for understanding the impacts of global emission targets on regional climate. Here, we investigate how the temperature extremes in China change with different global warming levels using large ensemble runs from Canadian Earth System Model version 2. With the global mean near‐surface temperature increasing from 1.5 to 5°C above the preindustrial level, the absolute intensity of the warmest and coldest temperatures in China will change linearly, while the percentile‐based frequency of warm and cold temperatures will change nonlinearly. All the changes in the intensity and the frequency show clear regional differences, with the most obvious changes observed in northeastern China. The probability distribution functions (PDFs) for the intensity indices show clear shifts but with little change in shape, while the PDFs for the frequency indices show changes in both position and shape. Quantified analyses of risk ratio show that the risk changes in the frequency of temperature extremes will be larger than those for the intensity indices. The changes in the nighttime extremes are faster than those in the daytime extremes. The rarer the event is, the larger the change in the risk ratio. At the 2°C warming level, the cold days and nights with return periods of 5, 10, and 50 years in the current climate will become almost disappeared. At the 3°C level and beyond, the once‐in‐5‐year, 10‐year and 50‐year warm events in the current climate will occur every year.

Temperature Shocks and Industry Earnings News

Climate scientists project a rise in both average temperatures and the frequency of temperature extremes. We study how extreme temperatures affect companies' earnings across different industries and whether sell-side analysts understand these relationships. We combine granular daily data on temperatures across the continental U.S. with locations of public companies' establishments and build a panel of quarterly firm-level temperature exposures. Extreme temperatures significantly impact earnings in over 40% of industries, with bi-directional effects that harm some industries while others benefit. Analysts and investors do not immediately react to observable intra-quarter temperature shocks, but earnings forecasts account for temperature effects by quarter-end in many, though not all, industries.

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.

Climate Change, Operating Flexibility, and Corporate Investment Decisions

Extreme temperatures lead to large fluctuations in electricity demand and wholesale prices of electricity, which in turn affects the optimal production process for firms to use. Using a large international sample of planned power plant projects, we measure the way that electric utilities’ investment decisions depend on the frequency of extreme temperatures. We find that they invest more in regions with more extreme temperatures. These investments are mostly in flexible gas and oil-fired power plants that can easily adjust their output to improve their operating flexibility. Our results suggest that climate change is becoming a meaningful factor affecting firms’ behavior.