Daily Temperature Distribution Research Articles

Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

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.

Impact of ENSO and Trends on the Distribution of North American Wintertime Daily Temperature

Abstract The effect of the El Niño–Southern Oscillation (ENSO) teleconnection and climate change trends on observed North American wintertime daily 2-m temperature is investigated for 1960–2022 with a quantile regression model, which represents the variability of the full distribution of daily temperature, including extremes and changes in spread. Climate change trends are included as a predictor in the regression model to avoid the potentially confounding effect on ENSO teleconnections. Based on prior evidence of asymmetric impacts from El Niño and La Niña, the ENSO response is taken to be piecewise linear, and the regression model contains separate predictors for warm and cool ENSO. The relationship between these predictors and shifts in median, interquartile range, skewness, and kurtosis of daily 2-m temperature are summarized through Legendre polynomials. Warm ENSO conditions result in significant warming shifts in the median and contraction of the interquartile range in central-northern North America, while no opposite effect is found for cool ENSO conditions in this region. In the southern United States, cool ENSO conditions produce a warming shift in the median, while warm ENSO conditions have little impact on the median, but contracts the interquartile range. Climate change trends are present as a near-uniform warming in the median and across quantiles and have no discernable impact on interquartile range or higher-order moments. Trends and ENSO together explain a substantial fraction of the interannual variability of daily temperature distribution shifts across much of North America and, to a lesser extent, changes of the interquartile range.

Decadal Predictability of Seasonal Temperature Distributions

AbstractDecadal predictions focus regularly on the predictability of single values, like means or extremes. In this study we investigate the prediction skill of the full underlying surface temperature distributions on global and European scales. We investigate initialized hindcast simulations of the Max Planck Institute Earth system model decadal prediction system and compare the distribution of seasonal daily temperatures with estimates of the climatology and uninitialized historical simulations. In the analysis we show that the initialized prediction system has advantages in particular in the North Atlantic area and allow so to make reliable predictions for the whole temperature spectrum for two to 10 years ahead. We also demonstrate that the capability of initialized climate predictions to predict the temperature distribution depends on the season.

Spatiotemporal distribution and lag effect of extreme temperature exposure on mortality of residents in Jiangsu, China

BackgroundWith the ever-increasing occurrence of extreme weather events as a result of global climate change, the impact of extreme temperatures on human health has become a critical area of concern. Specifically, it is imperative to investigate the impact of extreme weather conditions on the health of residents. MethodsIn this study, we analyze the daily death data from 13 prefecture-level cities in Jiangsu Province from January 2014 to September 2022, using the distributed lag nonlinear model (DLNM) to comprehensively account for factors such as relative humidity, atmospheric pressure, air pollutants, and other factors to evaluate the lag and cumulative effects of extreme low temperature and high temperature on the death of residents across different age groups. Additionally, we utilize the Geographical Detector to analyze the effects of various meteorological and environmental factors on the distribution of resident death in Jiangsu Province. This provides valuable insights that can guide health authorities in decision-making and in the protection of residents. ResultsThe experimental results indicate that both extreme low and high temperatures increase the mortality of residents. We observe that the impact of extreme low temperatures has a delayed effect, peaking after 3–5 days and lasting up to 11–21 days. In contrast, the impact of extreme high temperature is greatest on the first day, and lasts only 2–4 days. ConclusionBoth extreme high and low temperatures increase the mortality of residents, with the former being more transient and stronger and the latter being more persistent and slower. Furthermore, residents over 75 years of age are more vulnerable to the effects of extreme temperatures. Finally, we note that the spatial distribution of resident deaths is most closely associated consistent with the spatial distribution of daily mean temperature, and there is significant spatial heterogeneity in deaths among residents in Jiangsu Province.

Pitfalls in diagnosing temperature extremes

Worsening temperature extremes are among the most severe impacts of human-induced climate change. These extremes are often defined as rare events that exceed a specific percentile threshold within the distribution of daily maximum temperature. The percentile-based approach is chosen to follow regional and seasonal temperature variations so that extremes can occur globally and in all seasons, and frequently uses a running seasonal window to increase the sample size for the threshold calculation. Here, we show that running seasonal windows as used in many studies in recent years introduce a time-, region-, and dataset-depended bias that can lead to a striking underestimation of the expected extreme frequency. We reveal that this bias arises from artificially mixing the mean seasonal cycle into the extreme threshold and propose a simple solution that essentially eliminates it. We then use the corrected extreme frequency as reference to show that the bias also leads to an overestimation of future heatwave changes by as much as 30% in some regions. Based on these results we stress that running seasonal windows should not be used without correction for estimating extremes and their impacts.

Associations between PM2.5, ambient heat exposure and congenital hydronephrosis in southeastern China.

This research aims to analyze how exposure to fine particulate matter (PM2.5) and ambient heat during pregnancy increases the risk of congenital hydronephrosis (CH) in newborns. A case-control study was conducted to investigate the relationship between exposure to PM2.5 and ambient heat during pregnancy and the occurrence of CH in newborns. The study, which was conducted from 2015 to 2020, included 409 infants with CH as the case group and 409 infants without any abnormalities as the control group. Using spatial remote sensing technology, the exposure of each pregnant mother to PM2.5 concentration was meticulously mapped. Additionally, data on the ambient temperature of exposure for each participant were also collected. A logistics regression model was used to calculate the influence of exposure to PM2.5 and ambient heat on the occurrence of CH. Stratified analysis and interaction analysis were used to study the interaction between ambient heat exposure and PM2.5 on the occurrence of CH. At the 6th week of gestation, exposure to PM2.5 may increase the risk of CH. For every 10 μg/m3 increase in PM2.5 exposure, the risk of CH increased by 2% (95%CI = 0.98, 1.05) at a p-value of >0.05, indicating that there was no significant relationship between the results. Exposure to intense heat at 6th and 7th weeks of gestation increased the risk of CH. Specifically, for every 1°C increase in heat exposure, the risk of CH in offspring increased by 21% (95%CI = 1.04, 1.41) during the 6th week and 13% during the 7th week (95%CI = 1.02, 1.24). At 5th and 6th weeks of gestation, the relative excess risk due to interaction (RERI) was greater than 0 at the 50th percentile (22.58°C), 75th percentile (27.25°C), and 90th percentile (29.13°C) of daily maximum temperature (Tmax) distribution, indicating that the risk of CH was higher when exposed to both ambient heat and PM2.5 at the same time compared to exposure to a single risk factor. Exposure to higher levels of PM2.5 and ambient heat during pregnancy increases the risk of CH in infants. There was a positive interaction between exposure to intense heat and high concentration of PM2.5 on the occurrence of CH.

Ambient temperature and seasonal variation in inpatient mortality from respiratory diseases: a retrospective observational study.

The seasonal fluctuation in mortality and hospital admissions from respiratory diseases, with a winter peak and a summer trough, is widely recognized in extratropical countries. However, little is known about the seasonality of inpatient mortality and the role of ambient temperature remains uncertain. We aimed to analyse the association between ambient temperature and in-hospital mortality from respiratory diseases in the provinces of Madrid and Barcelona, Spain. We used data on daily hospitalisations, weather (ie, temperature and relative humidity) and air pollutants (ie, PM2.5, PM10, NO2and O3) for the Spanish provinces of Madrid and Barcelona during 2006-2019. We applied a daily time-series quasi-Poisson regression in combination with distributed lag non-linear models (DLNM) to assess, on the one hand, the seasonal variation in fatal hospitalisations and the contribution of ambient temperature, and on the other hand, the day-to-day association between temperature and fatal hospital admissions. The analyses were stratified by sex, age and primary diagnostic of hospitalisation. The study analysed 1710012 emergency hospital admissions for respiratory diseases (mean [SD] age, 60.4 [31.0] years; 44.2% women), from which 103845 resulted in in-hospital death (81.4 [12.3] years; 45.1%). We found a strong seasonal fluctuation in in-hospital mortality from respiratory diseases. While hospital admissions were higher during the cold season, the maximum incidence of inpatient mortality was during the summer and was strongly related to high temperatures. When analysing the day-to-day association between temperature and in-hospital mortality, we only found an effect for high temperatures. The relative risk (RR) of fatal hospitalisation at the 99th percentile of the distribution of daily temperatures vs the minimum mortality temperature (MMT) was 1.395 (95% eCI: 1.211-1.606) in Madrid and 1.612 (1.379-1.885) in Barcelona. In terms of attributable burden, summer temperatures (June-September) were responsible for 16.2% (8.8-23.3) and 22.3% (15.4-29.2) of overall fatal hospitalisations from respiratory diseases in Madrid and Barcelona, respectively. Women were more vulnerable to heat than men, whereas the results by diagnostic of admission showed heat effects for acute bronchitis and bronchiolitis, pneumonia and respiratory failure. Unless effective adaptation measures are taken in hospital facilities, climate warming could exacerbate the burden of inpatient mortality from respiratory diseases during the warm season. European Research Council Consolidator Grant EARLY-ADAPT, European Research Council Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Neural Network and Regression Methods for Estimation of the Average Daily Temperature of Hyderabad for the Years 2018-2020

A qualitative study on temperature distribution has been executed in Hyderabad by several researchers. This study, however, is the first attempt to study temperature distribution quantitatively. Two different methods, i.e., Artificial Neural Network (ANN) and Regression Analysis (RA), have been used to determine the average daily temperature distribution for Hyderabad, a city in Pakistan. Both the methods are used to predict the average daily temperature of the years; 2018, 2019, and 2020. In Neural Network (NN) analysis, the network was trained and validated for three years with temperature recorded from 2015-2017. With the help of training and validation parameters of the hidden layer, the average d aily temperature was predicted for 2018-2020. Based on input parameters (dew point, relative humidity, and wind speed), a multiple regression model was developed, and average daily temperature for the years 2018-2020 was predicted again. For validation of the model statistical errors, Root Mean Square Error (RMSE), Mean Absolute Error (MABE), Mean Absolute Percent Error (MAPE), and coefficient of determination are calculated. The statistical errors show that multiple regression models and neural network models provide a good prediction of temperature distribution. However, the results of the neural network are better than the regression model.

Crop Insurance Model Based on Maximum Daily Rainfall and Maximum Daily Temperature Index

A loss insurance model of risk for agricultural commodities that considers maximum daily rainfall and maximum daily temperature is introduced in this paper. This model requires bivariate distribution of maximum daily rainfall and maximum daily temperature in a specific region. Characteristics of particular agricultural commodity is also needed in the region where the two variables are being insured. The bivariate distribution and commodity characteristics are combined to obtain exit. exit is a benchmark value that causes the total crop failure and gives full benefit claim to policyholder. The case study was demonstrated by using data on maximum daily rainfall and temperature in Dramaga Bogor from September to December during 38 years (1984-2021) . Data was collected from Jawa Barat Climatology Station. Frank Copula is better to represent the bivariate distribution of data. Furthermore, two scenarios had given the premiums as IDR 2 482 925 per hectare and IDR 1 495 660 per hectare. This crop insurance model based on maximum daily rainfall and maximum daily temperature index could be the basis for the next developing of crop insurance model.

Spatiotemporal investigation of wet–cold compound events in Greece

Abstract. Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. The complex geography and topography of Greece forms a variety of regions with different local climate conditions affecting the daily minimum temperature and precipitation distributions and subsequently the distribution of compound events of low temperature and high precipitation values. The aim of our study in this work is to identify these wet–cold events based on observational data from the Hellenic National Meteorological Service (HNMS) stations, which are divided into five different geographical categories, in the period 1980–2004 and coldest months of the year (November-April) on monthly basis. Two available reanalysis products, that of ERA-Interim downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution (WRF_5), and the coarser resolution (∼30 km) ERA5 Reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF), are adopted to derive a gridded monthly spatial distribution of wet–cold compound events, after performing a comparison with the observations. The results yield that the monthly maximum HNMS probabilities range from 0.07 % in April to 0.85 % in February, ERA5 range from 0.4 % in April to 2.97 % in February and WRF_5 from 10.4 % in November to 25.04 % in February. The results also displayed that February, January and December, are in this order, the months with the highest WCCEs.

Risk of ischemic and hemorrhagic stroke in relation to cold spells in four seasons

BackgroundCold winter weather increases the risk of stroke, but the evidence is scarce on whether the risk increases during season-specific cold weather in the other seasons. The objective of our study was to test the hypothesis of an association between personal cold spells and different types of stroke in the season-specific context, and to formally assess effect modification by age and sex.MethodsWe conducted a case-crossover study of all 5396 confirmed 25–64 years old cases with stroke in the city of Kaunas, Lithuania, 2000–2015. We assigned to each case a one-week hazard period and 15 reference periods of the same calendar days of other study years. A personal cold day was defined for each case with a mean temperature below the fifth percentile of the frequency distribution of daily mean temperatures of the hazard and reference periods. Conditional logistic regression was applied to estimate odds ratios (OR) and 95% confidence intervals (95% CI) representing associations between time- and place-specific cold weather and stroke.ResultsThere were positive associations between cold weather and stroke in Kaunas, with each additional cold day during the week before the stroke increases the risk by 3% (OR 1.03; 95% CI 1.00–1.07). The association was present for ischemic stroke (OR 1.05; 95% CI 1.01–1.09) but not hemorrhagic stroke (OR 0.98; 95% CI 0.91–1.06). In the summer, the risk of stroke increased by 8% (OR 1.08; 95% CI 1.00–1.16) per each additional cold day during the hazard period. Age and sex did not modify the effect.ConclusionsOur findings show that personal cold spells increase the risk of stroke, and this pertains to ischemic stroke specifically. Most importantly, cold weather in the summer season may be a previously unrecognized determinant of stroke.

Heat-related first cardiovascular event incidence in the city of Madrid (Spain): Vulnerability assessment by demographic, socioeconomic, and health indicators

While climate change and population ageing are expected to increase the exposure and vulnerability to extreme heat events, there is emerging evidence suggesting that social inequalities would additionally magnify the projected health impacts. However, limited evidence exists on how social determinants modify heat-related cardiovascular morbidity. This study aims to explore the association between heat and the incidence of first acute cardiovascular event (CVE) in adults in Madrid between 2015 and 2018, and to assess how social context and other individual characteristics modify the estimated association.We performed a case-crossover study using the individual information collected from electronic medical records of 6514 adults aged 40–75 living in Madrid city that suffered a first CVE during summer (June–September) between 2015 and 2018. We applied conditional logistic regression with a distributed lag non-linear model to analyse the heat-CVE association. Estimates were expressed as Odds Ratio (OR) for extreme heat (at 97.5th percentile of daily maximum temperature distribution), compared to the minimum risk temperature. We performed stratified analyses by specific diagnosis, sex, age (40–64, 65–75), country of origin, area-level deprivation, and presence of comorbidities.Overall, the risk of suffering CVE increased by 15.3% (OR: 1.153 [95%CI 1.010–1.317]) during extreme heat. Males were particularly more affected (1.248, [1.059–1.471]), vs 1.039 [0.810–1.331] in females), and non-Spanish population (1.869 [1.28–2.728]), vs 1.084 [0.940–1.250] in Spanish). Similar estimates were found by age groups. We observed a dose-response pattern across deprivation levels, with larger risks in populations with higher deprivation (1.228 [1.031–1.462]) and almost null association in the lowest deprivation group (1.062 [0.836–1.349]). No clear patterns of larger vulnerability were found by presence of comorbidity.We found that heat unequally increased the risk of suffering CVE in adults in Madrid, affecting mainly males and deprived populations. Local measures should pay special attention to vulnerable populations.

Ambient temperature and nervous system diseases-related mortality in Japan from 2010 to 2019: A time-stratified case-crossover analysis

BackgroundDespite the substantial disease burden caused by nervous system diseases, few studies have examined the association between ambient temperature and nervous system diseases, especially during cold seasons. Evidence for specific disease subgroups such as Alzheimer's and Parkinson's is also lacking. ObjectivesThis study examined the association between short-term changes in ambient temperature and nervous system diseases-related mortality in ten Japanese prefectures from 1 January 2010 to 31 December 2019. MethodsA two-stage analysis based on a time-stratified case-crossover study design was conducted. A conditional quasi-Poisson regression model with a distributed lag non-linear model for temperature was applied followed by a multivariate random-effects meta-analysis to obtain average associations. Mortality due to all and cause-specific nervous system diseases (major neurodegenerative diseases, Alzheimer's, and Parkinson's) were analyzed, with consideration for sex and age (0–84 and 85+ years old) subgroups. ResultsWe analyzed 162,315 death cases due to nervous system diseases. Cold was associated with all categories of nervous system diseases-related mortality, but not heat. The estimated relative risks for cold, obtained by comparing the 2.5th percentile of daily mean temperature distribution to the minimum mortality temperature (MMT), were 1.43 (95 % CI: 1.28–1.60), 1.37 (95 % CI: 1.17–1.59), 1.53 (95 % CI: 1.07–2.01), and 1.54 (95 % CI: 1.13–2.10) for all nervous system diseases, major neurodegenerative diseases, Alzheimer's, and Parkinson's, respectively. These associations were also observed in the sex and age subgroups in general but without evidence of effect modification. Heat (the 97.5th percentile of daily mean temperature distribution vs. MMT) was associated with Parkinson’s disease-related mortality among those aged 85 years and over. DiscussionCold was associated with an increased risk of nervous system disease-related mortality. The effect of heat was not observed, except for mortality among the elderly aged 85 years and older with Parkinson's disease.

Warm-season temperatures and emergency department visits among children with health insurance

High ambient temperatures have become more likely due to climate change and are linked to higher rates of heat-related illness, respiratory and cardiovascular diseases, mental health disorders, and other diseases. To date, far fewer studies have examined the effects of high temperatures on children versus adults, and studies including children have seldom been conducted on a national scale. Compared to adults, children have behavioral and physiological differences that may give them differential heat vulnerability. We acquired medical claims data from a large database of commercially insured US children aged 0–17 from May to September (warm-season) 2016–2019. Daily maximum ambient temperature and daily mean relative humidity estimates were aggregated to the county level using the Parameter-elevation Relationships on Independent Slopes dataset, and extreme heat was defined as the 95th percentile of the county-specific daily maximum temperature distribution. Using a case-crossover design and temperature lags 0–5 days, we estimated the associations between extreme heat and cause-specific emergency department visits (ED) in children aged <18 years, using the median county-specific daily maximum temperature distribution as the reference. Approximately 1.2 million ED visits in children from 2489 US counties were available during the study period. The 95th percentile of warm-season temperatures ranged from 71 °F to 112 °F (21.7 °C to 44.4 °C). Comparing 95th to the 50th percentile, extreme heat was associated with higher rates of ED visits for heat-related illness; endocrine, nutritional and metabolic diseases; and otitis media and externa, but not for all-cause admissions. Subgroup analyses suggested differences by age, with extreme heat positively associated with heat-related illness for both the 6–12 year (odds ratio [OR]: 1.34, 95% confidence interval [CI]: 1.16, 1.56) and 13–17 year age groups (OR: 1.55, 95% CI: 1.37, 1.76). Among children with health insurance across the US, days of extreme heat were associated with higher rates of healthcare utilization. These results highlight the importance of individual and population-level actions to protect children and adolescents from extreme heat, particularly in the context of continued climate change.

Impact of cold spells on COPD mortality in Jiangsu Province, China.

Ambient cold is associated with substantial population attributable fraction of mortality in China, and respiratory health is vulnerable to cold exposure. This study aimed to examine the effect of cold spells on risk of deaths from chronic obstructive pulmonary disease (COPD). We collected daily data on deaths from COPD and climatic factors from 1 January 2016 to 31 December 2019 in 13 cities of Jiangsu Province, China. We used a quasi-Poisson generalized linear model coupled with a distributed lag non-linear model to quantify the association between risk of COPD deaths and exposure to cold spells (defined as 2 or more consecutive days with mean temperature ≤ 5th percentile of daily mean temperature distribution in cold months). Stratification analyses by age, sex, education, and occupation were undertaken to identify vulnerable subgroups. The results suggested that exposure to cold spells was associated with a higher risk of COPD deaths in Lianyungang (relative risk (RR): 1.70; 95% confidence interval (CI): 1.31, 2.21), Nanjing (RR: 1.54; 95% CI: 1.16, 2.04), Nantong (RR: 1.97; 95% CI: 1.68, 2.31), Suzhou (RR: 1.97; 95% CI: 1.55, 2.50), Suqian (RR: 1.68; 95% CI: 1.23, 2.29), Taizhou (RR: 1.70; 95% CI: 1.32, 2.19), Wuxi (RR: 1.99; 95% CI: 1.53, 2.60), Xuzhou (RR: 1.71; 95% CI: 1.01, 2.90), Yancheng (RR: 1.78; 95% CI: 1.53, 2.06), Yangzhou (RR: 2.78; 95% CI: 2.06, 3.76), and Zhenjiang (RR: 1.79; 95% CI: 1.26, 2.55). All subgroups seemed to be vulnerable to the effect of cold spells. The recommendation of this study is that individuals with pre-existing COPD, regardless of age, sex, education, or occupation, should be made aware of the health risk posed by cold spells and should be encouraged to take cold adaptation actions before cold season arrives. The main limitation of this study is that it is subject to ecological fallacy.

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails. To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

Relationships between short-term ambient temperature exposure and kidney disease hospitalizations in the warm season in Vietnam: A case-crossover study

BackgroundUnder a warming climate, adverse health effects of heat are an increasing concern. We evaluated associations between short-term ambient temperature exposure and hospital admission for kidney disease in Vietnam. MethodsWe linked province-level meteorologic data with admission data from 14 province-level hospitals (2003–2015). We used a case-crossover design to evaluate associations between daily ambient temperature metrics (mean, maximum, and minimum temperature and mean heat index) and risk of hospitalization for four kidney disease subtypes: glomerular diseases, renal tubulo-interstitial diseases, chronic kidney disease, and urolithiasis, including lagged (≤lag 14 days) and cumulative (≤lag 0–6 days) associations, during the warm season. We also evaluated independent associations with extreme heat days (defined as days with daily maximum temperature >95th percentile of the provincial daily maximum temperature distribution). Akaike's information criterion and patterns of risk estimates across cumulative exposure time windows and single-day lags informed our selection of final models. ResultsWe included 58,330 hospital admissions during the warm season. Daily mean temperature averaged over the same day and the previous six days (lag 0–6 days) was associated with risk of hospitalization for each kidney disease outcome with odds ratios (per 1 °C increase in daily mean temperature) of 1.07 (95% confidence interval [CI]: 0.99, 1.16) for glomerular diseases, 1.06 (95% CI: 0.96, 1.17) for renal tubulo-interstitial diseases, 1.12 (95% CI: 1.00, 1.24) for chronic kidney disease, and 1.09 (95% CI: 1.02, 1.16) for urolithiasis. We found no additional independent associations with extreme heat. Results for the four temperature metrics were similar. ConclusionsHigh ambient temperature was associated with increased risk of hospitalization for each kidney disease subtype, with the most convincing associations for chronic kidney disease and urolithiasis. Further laboratory and epidemiologic research is needed to confirm the findings and disentangle the underlying mechanisms.

Interpreting Observed Temperature Probability Distributions Using a Relationship between Temperature and Temperature Advection

Abstract The nonnormality of temperature probability distributions and the physics that drive it are important due to their relationships to the frequency of extreme warm and cold events. Here we use a conditional mean framework to explore how horizontal temperature advection and other physical processes work together to control the shape of daily temperature distributions during 1979–2019 in the ERA5 dataset for both JJA and DJF. We demonstrate that the temperature distribution in the middle and high latitudes can largely be linearly explained by the conditional mean horizontal temperature advection with the simple treatment of other processes as a Newtonian relaxation with a spatially variant relaxation time scale and equilibrium temperature. We analyze the role of different transient and stationary components of the horizontal temperature advection in affecting the shape of temperature distributions. The anomalous advection of the stationary temperature gradient has a dominant effect in influencing temperature variance, while both that term and the covariance between anomalous wind and anomalous temperature have significant effects on temperature skewness. While this simple method works well over most of the ocean, the advection–temperature relationship is more complicated over land. We classify land regions with different advection–temperature relationships under our framework, and find that for both seasons the aforementioned linear relationship can explain ∼30% of land area, and can explain either the lower or the upper half of temperature distributions in an additional ∼30% of land area. Identifying the regions where temperature advection explains shapes of temperature distributions well will help us gain more confidence in understanding the future change of temperature distributions and extreme events.

Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results

Increasingly extreme temperature events under global warming can have considerable impacts on sectors such as industrial activities, health, and transportation, suggesting that risk for these kinds of events under climate change and its regional sensitivity should be reassessed. In this study, the observation and multi-model simulations from CMIP6 are comprehensively used to explore the regional differences of the extreme temperature response to climate change from the perspective of return period (RP). The Gumbel model of generalized extremum distribution is applied to estimate the RP for the annual extremum of temperature based on Gaussian distribution of daily temperature. The analysis on the observation in selected three sites indicates that the regional inconsistency of RP variation is not only existed in extreme high temperature (HTx) but also in low temperature (LTn) during the past several decades. The annual amplitude of temperature extremum in the Northeast China is enlarged with summer becoming hotter and winter becoming colder while the opposite situation is detected in Huang-Huai River Basin with cooler summer and relatively stable winter, and South China is characterized by hotter summer and slight warmer winter. From the spatial distribution of the HTx and LTn variations of fix RP, it is found that the Northeast China and Jiang-Huai River Basin is the most sensitive areas, respectively, in the response of extreme low temperature and high temperature to global warming. However, the regional inconsistency of the extreme temperature change is only observed under SSP1-2.6 scenario in the CMIP6 simulation but gradually disappeared from SSP2-4.5 to SSP5-8.5.