Daily Minimum Temperature Research Articles

Trends and Patterns of Daily Maximum, Minimum and Mean Temperature in Brazil from 2000 to 2020

According to data obtained from meteorological towers, Brazil has significantly increased temperature in the past 20 years, particularly in the North and Midwest regions. Vapor pressure deficit and evapotranspiration were also analyzed, showing an increase across the entire country, confirming that the air is becoming drier. This warming trend is part of the global climate change phenomenon caused by the rise of greenhouse gases in the atmosphere, fires, poor soil management practices, deforestation, and logging. The increase in temperature and dryness has profoundly impacted Brazil’s climate and ecosystems, leading to intensified extreme weather events and changes in the distribution of both animal and plant species. This study highlights the importance of utilizing meteorological tower data to monitor and understand the effects of climate change in Brazil. It emphasizes the need for immediate action to address its causes and mitigate its negative impacts.

Exploratory analysis of local extreme-temperature attributable mortality in an urban city of Madrid

Aimto assess the Heat (HW) and Cold Waves (CW) risks on health in the urban municipality of Getafe. Methodologytime series analysis between 01/01/1999–31/10/2013. Dependent variable: daily mortality due to natural causes – (ICD-10): A99-R99–. Independent variables: the maximum (Tmax) and minimum (Tmin) daily temperature. The mortality-temperature relationship was analysed to determine the thresholds of HW (Thresholdheat) and CW (Thresholdcold). Using Poisson GLM (link = log), the Relative Risk (RR), Attributable Risk (AR) and Attributable Mortality (AM) were determined for each degree of the Tmax exceeding the Thresholdheat (Theat) and for each degree of Tmin under the Thresholdcold (Tcold). Finally, socioeconomic variables were analysed descriptively. ResultsThresholdheat was 36 °C while Thresholdcold was 0 °C. The RRs associated with Theat, i.e. 1.08 (1.03 1.14), are akin to those obtained for Tcold, i.e. 1.05 (1.03 1.08). There were 202 HW and 430 CW episodes. The AM to HW totalled 61 (25, 96) deaths, while that attributable to CW reached 146 (82,211) deaths. The vulnerability in Getafe seems to be lower than in surrounding similar urban and rural cities. ConclusionsThe singular urban development of the municipality may have granted it an advantage over surrounding municipalities regarding temperature extremes.

1991–2020 climate normal in the European Alps: focus on high-elevation environments

Alps are an important geographical area of the European continent and, in this area, temperature increase is most evident. However, the 1991–2020 climate normal in the Alps has still not been thoroughly investigated. Aiming to fill this gap with a focus on high-elevation environments, minimum and maximum daily air temperature acquired by 23 automatic weather station were used. The results show that the mean annual values of minimum and maximum temperature for the 1991–2020 climate normal in the Alps are −2.4°C and 4.4°C, respectively, with a warming rate of 0.5°C/10 years. The mean annual temperature comparison between 1961–1990 and 1971–2000, 1961–1990 and 1981–2010, 1961–1990 and 1991–2020 climate normal show an increase of 0.3°C, 0.5°C and 0.9°C, respectively. The results also confirm that seasonal and annual temperatures are rising through the whole Alpine arc, mainly in summer and autumn. This work highlights that annual minimum and maximum temperature do not seem to be affected by a positive elevation-dependent warming. Instead, a positive elevation-dependent warming in the maximum values of the annual minimum temperature was found. If anthropogenic emissions maintain the trend of the last decades, the expected mean annual temperature of the 2001–2030 climate normal is −0.2°C, with an increase of 0.5°C if compared to the 1991–2020 climate normal and with an increase of 1.5°C if compared to the 1961–1990 climate normal. This study highlights the warming rate that is now present in the European Alps, provides indications on the warming rate that will occur in the coming years and highlights the importance of carrying out investigations that consider not only the last 30-year climate normal, but also the most recent 30-year climate normal by comparing them with each other.

The relative value of sociocultural and infrastructural adaptations to heat in a very hot climate in northern Australia: a case time series of heat-associated mortality

Climate change is increasing heat-associated mortality particularly in hotter parts of the world. The Northern Territory is a large and sparsely populated peri-equatorial state in Australia. The Northern Territory has the highest proportion of Aboriginal and Torres Strait Islander people in Australia (31%), most of whom live in remote communities of over 65 Aboriginal Nations defined by ancient social, cultural, and linguistic heritage. The remainder non-Indigenous population lives mostly within the two urban centres (Darwin in the Top End region and Alice Springs in the Centre region of the Northern Territory). Here we aim to compare non-Indigenous (eg, high income) and Indigenous societies in a tropical environment and explore the relative importance of physiological, sociocultural, and technological and infrastructural adaptations to heat. In this case time series, we matched temperature at the time of death using a modified distributed lag non-linear model for all deaths in the Northern Territory, Australia, from Jan 1, 1980, to Dec 31, 2019. Data on deaths came from the national registry of Births, Deaths and Marriages. Cases were excluded if location or date of death were not recorded or if the person was a non-resident. Daily maximum and minimum temperature were measured and recorded by the Bureau of Meteorology. Hot weather was defined as mean temperature greater than 35°C over a 3-day lag. Socioeconomic status as indicated by Index of Relative Socioeconomic Disadvantage was mapped from location at death. During the study period, 34 782 deaths were recorded; after exclusions 31 800 deaths were included in statistical analysis (15 801 Aboriginal and 15 999 non-Indigenous). There was no apparent reduction in heat susceptibility despite infrastructural and technological improvements for the majority non-Indigenous population over the study period with no heat-associated mortality in the first two decades (1980-99; relative risk 1·00 [95% CI 0·87-1·15]) compared with the second two decades (2000-19; 1·14 [1·01-1·29]). Despite marked socioeconomic inequity, Aboriginal people are not more susceptible to heat mortality (1·05, [0·95-1·18]) than non-Indigenous people (1·18 [1·06-1·29]). It is widely believed that technological and infrastructural adaptations are crucial in preparing for hotter climates; however, this study suggests that social and cultural adaptations to increasing hot weather are potentially powerful mechanisms for protecting human health. Although cool shelters are essential during extreme heat, research is required to determine whether excessive exposure to air-conditioned spaces might impair physiological acclimatisation to the prevailing environment. Understanding sociocultural practices from past and ancient societies provides insight into non-technological adaptation opportunities that are protective of health. None.

Does choice of outdoor heat metric affect heat-related epidemiologic analyses in the US Medicare population?

We calculated daily maximum, minimum, and mean outdoor air temperature (T), heat index (HI), wet-bulb globe temperature (WBGT), and Universal Thermal Climate Index (UTCI) for populous US counties and linked estimates with daily all-cause mortality and heat-related hospitalizations among Medicare beneficiaries (2006-2016). We fit distributed-lag nonlinear models for each metric and compared relative risks (RRs) at the 99th percentile. Across all heat metrics, extreme heat was statistically significantly associated with elevated risks of morbidity and mortality. Associations were more pronounced for maximum daily values versus the corresponding minimum for the same metric. The starkest example was between HImax (RR = 1.14; 95% confidence interval [CI] = 1.12, 1.15) and HImin (RR = 1.10; 95% CI = 1.09, 1.11) for hospitalizations. When comparing RRs across heat metrics, we found no statistically significant differences within the minimum and maximum heat values (i.e., no significant differences between Tmax/HImax/WBGTmax/UTCImax or between Tmin/HImin/WBGTmin/UTCImin). We found similar relationships across the National Climate Assessment regions. Among Medicare beneficiaries in populous US counties, daily maximum and mean values of outdoor heat are associated with greater RRs of heat-related morbidity and all-cause mortality versus minimum values of the same metric. The choice of heat metric (e.g., temperature versus HI) does not appear to substantively affect risk calculations in this population.

A Facility’s Energy Demand Analysis for Different Building Functions

A more accurate determination of energy demands for buildings is of utmost importance for estimating future energy demands. This article presents two novel ideas that have the potential to contribute to a more precise determination of expected energy demands. The first idea involves accounting for a building’s function more thoroughly, which enables the determination of different energy demands for two or more identical buildings, depending on their respective usage functions. According to a case study, the heating energy demand can be up to twice as high in a commercial facility compared with a residential building. Similarly, the cooling energy requirement can also differ. The second idea concerns determining the heating degree day (HDD) and cooling degree day (CDD) values from the daily minimum and maximum temperatures. This idea may be relevant when few instantaneous values are available for the daily mean temperature. According to the case study, the calculated values from the daily minimum and maximum temperatures follow the HDD and CDD values specified from the daily mean temperature. However, the difference is less than 2% for the heating season and higher for the cooling season. Therefore, further research is required to refine the constants in the cooling equation.

Variations of Extreme Temperature Event Indices in Six Temperature Zones in China from 1961 to 2020

In this study, eight extreme temperature event indices were calculated based on daily maximum, minimum, and mean temperature data recorded at 699 National Reference Stations in China during 1961–2020. The yearly change of mean temperature and the magnitude, frequency, and duration of extreme temperature events in six temperature zones were evaluated. All temperature zones had a trend of an increase in mean temperature (rate: 2.1–3.3 °C per 10 years), and the warming was more significant in the warm temperate zone and the Qinghai–Tibet Plateau zone (QPZ). For extreme temperature events, the extreme maximum and minimum temperatures in most temperature zones showed significant trends of increase, and the rates of increase were greater in the northern zones and QPZ. The rate of increase in extreme minimum temperature was substantially (up to three times) higher than the rate of increase in extreme maximum temperature in the same temperature zone; however, the finding was the opposite for the cold temperate zone (CTZ), which is the northernmost region of China. The rate of increase in extreme maximum temperatures was the greatest (0.35 °C per 10 years), whereas the rate of increase in extreme minimum temperatures was the smallest (0.17 °C per 10 years). The number of warm days/nights and the warm spell duration index also showed significant trends of increase that were most obvious in the southern zones and QPZ. In the tropical zone (TZ), which is the southernmost part of mainland China, the number of warm nights was only 15.3 days in 1961–1970, whereas it increased to 61.9 days in 2011–2020 (an increase of 303.9%). The rate of increase in warm nights in TZ (8.8 days per 10 years) was four times that in CTZ (2.2 days per 10 years). The number of cold days/nights and the cold spell duration index showed significant trends of decrease, with the greatest rates of reduction in QPZ and TZ. In evaluating the frequency of extreme temperature events, the amplitude of warming of the night index was found to be greater than that of the day index. In evaluating the duration of extreme temperature events, the variation of the cold index was found to be greater than that of the warm index. The notable asymmetries found in the variations of the minimum/maximum temperatures, day/night indices, and cold/warm spell durations in China are direct manifestations of global warming.

Early Humidity Measurements by Louis Morin in Paris between 1701 and 1711—Data and Metadata

This paper discusses what is, to our knowledge, the oldest subdaily measurement series of humidity taken over several years. Louis Morin performed the measurements in Paris, three times a day, between May 1701 and June 1711. A correlation analysis of Morin’s humidity measurements with various meteorological variables yields results comparable to those of a parallel analysis of the relative humidity measurements of the E-OBS data: the Spearman correlation coefficient between the humidity and the daily minimum temperature is −0.43 (p < 0.01); with the mean temperature, it is −0.54 (p < 0.01); with the maximum temperature, it is −0.59 (p < 0.01); with the diurnal temperature range, it is −0.65 (p < 0.01); and with the total cloud cover, 0.33 (p < 0.01). However, with a Spearman correlation coefficient of 0.11 (p < 0.01), no correlation is found with the precipitation data. Further evidence for the plausibility of the measurements is shown by a day-by-day analysis of the first half-year of 1709. Here, abrupt changes in the humidity measurements of Morin can be explained by the other measurements/observations of Morin. According to the correlation analysis, indirect notes in his journal, and others, we argue that Morin used the hygrometer developed by Vincenzo Viviani. However, the conversion of the data to common units is not performed and is subject to further research.

Crop production in northwestern Ethiopian highlands under changing climate

AbstractClimate change is the most serious threat to agricultural productivity. Ethiopia's agricultural sector is affected by climate change. The northern part of Ethiopia is among the drought prone and topographically diversified areas. The purpose of this study was to examine the impact of climate change on crop yields, assess the pattern of rainfall maximum and lowest temperatures and assess the trajectory of selected crop yields. Historical weather station and National Aeronautics and Space Administration (NASA) power data were collected from 1981 to 2020. The Mann–Kendall (MK) trend test and Sen's slope (SS) method estimator were employed to estimate the trends and the direction of the trend. RClimDex package is used for climate extreme analysis. Nine rainfall and 11 temperature indices from a list of 27 extreme rainfall and temperature indices were used. The study found that the rainfall showed a significant decreasing and insignificant decreasing trend in Farta and Yilmana Densa districts, respectively. Maximum and minimum temperatures in both sites showed increasing trends. Rainfall happenings become more unanticipated, owing to the precipitation concentration index and anomaly trends. The majority of the indicators for extreme rainfall at both sites showed a downward trend. With the exception of the duration of cold spells, the annual lowest value of the daily minimum temperature, cold days and cold nights, the majority of the extreme temperature indices at both sites showed an increasing trend. The principal crop yields were connected with rainfall and temperature fluctuation, according to Pearson's product–moment correlation results, although the effects varied by crop and region. In summary, rising temperatures and decreasing precipitation may have a negative influence on agricultural activities, especially crop productivity.

Diurnal Cycle Dependence of ENSO Influence on the Winter Surface Air Temperature in Southeastern China

Abstract It is well known that El Niño–Southern Oscillation (ENSO) can influence the East Asian winter climate by modifying the atmospheric circulation over the western North Pacific (WNP). While the impact on precipitation in southeastern China has been extensively studied, the ENSO signal in surface air temperature (SAT) remains overlooked. In this paper, we identify robust ENSO footprints in the winter daily minimum SAT in southeastern China, with El Niño winters generally accompanied by warmer-than-normal minimum SAT anomalies. In contrast, the responses of maximum SAT are weak and negligible, suggesting a diurnal cycle–dependent ENSO influence. Further analysis indicates that this diurnal cycle dependence stems primarily from the disparate surface radiative heating between day and night induced by ENSO-related local total cloud cover (TCC) change. The warmer minimum SAT occurring in the early morning of El Niño winters is mainly caused by the enhanced surface downward longwave radiative heating as a result of the TCC increase. However, in the afternoon of El Niño winters, although the anomalous horizontal advection of warm air plays a role, there is surface radiative cooling as the weakening of solar radiation due to TCC reflection overwhelms the increase in downward longwave radiation, which leads to a weakened sensible heat flux and thus has a cooling effect on the SAT. Ultimately, these two processes effectively cancel each other out and together produce insignificant maximum SAT responses. Our conclusions carry important implications for the seasonal to interannual winter climate prediction in southeastern China. Significance Statement The purpose of this study is to better understand how ENSO influences winter surface air temperature in southeastern China. Despite insignificant responses of winter daily maximum temperature to ENSO, we find here a robust ENSO influence on the daily minimum temperature, with warm anomalies tending to occur during El Niño events. Further evidence suggests that this diurnal cycle–dependent ENSO influence is mainly caused by ENSO-induced change in local cloudiness, which has a different radiative heating effect between day and night. Our findings are of great importance for improving regional climate prediction, which will benefit social and economic activities.

Detection and regional analysis of heatwave characteristics in İstanbul

Understanding heatwave characteristics is crucial for mitigating their adverse effects in İstanbul, a densely populated metropolis characterized by a high concentration of buildings, concrete structures, and limited green spaces. This study aims to develop a methodology for defining heatwaves by identifying appropriate thermal indicators. Utilizing the Distributed Lag Nonlinear Model (DLNM), this research investigates the complex and delayed effects of daily mean, maximum, and minimum temperatures, along with seven thermal indices, on daily mortality rates during the summer months of 2013–2017, leveraging the DLNM approach's flexibility in modeling the nonlinear exposure-response relationships. Heatwaves are defined based on the Minimum Mortality Temperature (MMT) and 90th percentile thresholds, and heatwave maps are constructed by their frequency, intensity, duration, and cumulative intensity characteristics. PETmean emerges as the optimal thermal index, exhibiting the lowest Akaike's Information Criteria (AIC: 3553) and estimates relative risk (RR) of 1.080 (95% CI: 1.051–1.110) for a 1°C increase above the 90th percentile. Additionally, Tmean, Tmax, PETmax, and Tmrtmean indices demonstrate favorable characteristics, with low AIC values and high RR estimates. Notably, heatwaves are intense and frequent in Çatalca despite low urban land use and abundant forests, but their duration was short due to fast adaptation capability of the region.

Is land use producing robust signals in future projections from Earth system models, all else being equal?

We use six Earth system models (ESMs) run under SSP3-7.0, a scenario characterized by a relatively large land use change (LUC) over the 21st century, and under a variant of the same scenario where a significantly different pattern of LUC, taken from SSP1-2.6, was used, all else being equal. Our goal is to identify changes in climate extremes between the two scenarios that are statistically significant and robust across the ESMs. The motivation for this study is to test a long-held assumption of the shared socio-economic pathway-representative concentration pathway (SSP-RCP) scenario framework: that the signal from LUC can be safely disregarded when pairing different SSPs to the compatible RCPs, where compatibility only considers global radiative forcing, predominantly determined by well-mixed greenhouse gasses emissions. We analyze extremes of daily minimum and maximum temperatures and precipitation, after fitting non-stationary generalized extreme value distributions in a way that borrows strength along the length of the simulation (2015–2100) and across initial condition ensembles. We consider changes in the 20 year return levels (RL20s) of these metrics by 2100, and focus on eight locations where LUC is large within each scenario, and strongly differs between scenarios, averaging the RL20s over a neighborhood characterized by the same LUC to enhance the signal to noise. We find that precipitation extremes do not show significant differences attributable to LUC differences. For temperature extremes (cold and hot) results are mixed, with some location-index combination showing significant results for some of the ESMs but not all, and not many coherent changes appearing for indices across regions, or regions across indices. These ESMs are representative of what is typically adopted as the source of climate information for impact studies, when the SSP-RCP framework is put to use. Overall, our analysis suggests that the hypothesis to pair SSPs to RCPs in a flexible fashion is overall defensible. However, the appearance of some coherence in a few locations and for some indices invites further investigation.

Cause-specific mortality and burden attributable to temperature variability in China

BackgroundFew large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV). ObjectiveTo estimate associations between TV and cause-specific mortality and quantify the mortality burden in China. MethodsData on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens. ResultsOverall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0–7 days (TV0–7) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV0–7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders. ConclusionThis nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases.

Local weather affects body condition of three North American songbird species on the Texas Coast.

Body condition is a frequently used physiological indicator of avian health and can be affected by an array of environmental variables. Although a number of studies have investigated the specific effects of individual weather variables on body condition in birds, few have analyzed the effects of both temperature and precipitation within the context of an extreme weather event such as hurricanes. In this study, we examined the relationship between breeding-season body condition and daily maximum temperature, daily minimum temperature, and monthly total precipitation for three passerine bird species at the Welder Wildlife Refuge near Rockport, Texas. We also evaluated yearly changes in body condition over a 12-year period for northern cardinals (Cardinalis cardinalis), painted buntings (Passerina ciris), and white-eyed vireos (Vireo griseus), focusing on the extreme precipitation event of Hurricane Harvey, which caused heavy localized flooding. We found that body condition declined with average daily minimum and maximum temperatures, while precipitation had varied, species-specific effects in the three species analyzed. Our results also suggest that northern cardinals experienced a notable reduction in average body condition in the 2 years following Hurricane Harvey. Taken together, we conclude that short-term precipitation and temperature drivers can be important correlates of body condition in songbirds and that severe weather events may reduce body condition in some bird species.

The emergence of projected scaled patterns of extreme temperatures over Europe

This work investigates the scalability of extreme temperatures over the European domain with global warming levels. We have used the EURO-CORDEX ensemble of regional model simulations at 0.11° resolution for daily minimum and maximum temperatures to analyze future changes in extreme weather daily events. Scaling with the annual mean global warming modeled by the driving GCM was applied to future extreme temperature indices changes. Regional changes in each index were scaled by corresponding global warming levels obtained from GCMs. This approach asserts that regional patterns of climate change and average global temperature change are linearly related. It can provide information regarding climate change for periods or emission scenarios when no simulations exist. According to the results, the annual minimum of the lowest temperature of the day (TNn) increases more than the annual maximum of the highest temperature of the day (TXx) for Europe. The multi-model mean of the changes in scaled patterns of extreme temperatures emerges early, around 2020, even before it becomes robust. Individual scaled patterns of TNn and TXx emerge from around 2040.

Methodology for the Selection of the Representative Climate Scenario at Yongdam Watershed

The four representative concentration pathways (RCP) scenarios presented in the fifth Assessment Report (AR5) is still widely used by researchers to assess the future effects of climate change in water resources management. However, the lack of providing scientific basis in the selection and application of climate scenarios in numerical models can further increase the uncertainties in the results. Therefore, this study aims to present an analytical and practical methodology for the selection of the representative climate scenario at Yongdam watershed. Based on the findings presented in this study, RCP6.0 scenario best represented the daily minimum temperature at Yongdam watershed for the past decade; the daily precipitation was best represented by RCP4.5 scenario. Furthermore, all four climate scenarios had the tendency to overestimate the magnitude and frequency of precipitation, which is an important factor that should be considered in the selection of climate scenarios. The results and methodology presented in this study can serve as a guideline in the selection of the most applicable climate scenario in other regions.

Digitizing Ottoman daily weather observations of Halkali Agricultural School in Istanbul, Turkiye (1896–1917)

AbstractDaily weather observations measured by students and staff at Halkali Agricultural School (a school opened in 1892 on agriculture and animal husbandry during the Ottoman period) from 1896 to 1917 in Istanbul, Turkiye have been transcribed from the original publications into digital form and translated from Ottoman Turkish (the Perso‐Arabic script) to English (Latin alphabet). Over 55,000 observations of daily maximum, minimum and average temperature; rainfall, soil and under soil (0.25 m) temperature; humidity, pressure and wind speed were recovered. In addition, weather observations taken in Kandilli Observatory and Earthquake Research Institute from 1911 to 1936 and taken in Florya Meteorological Station from 1937 to 2022 could inform long‐term temperature changes in Istanbul. The publication of a new historical data set that includes, for the first time, digitized and quality‐controlled daily meteorological observations in Istanbul will enhance the understanding of weather changes in Turkiye back to the late 19th century. These observations will be used to fill gaps in existing temperature and pressure records and to the improvement of the accuracy of reanalysis products prior to the 1950s. It will be the first data set publication of other parameters such as soil temperature, wind speed and humidity for that period in that region. Data are available on the CEDA Archive in csv file format.

Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research.

Anthropogenic emissions and land use/cover change contributions to extreme temperature changes over China

Extreme weather and climate events tend to increase and strengthen as global warming intensifies, which severely affect human life and sustainable economic development. Therefore, based on the Detection and Attribution Model Intercomparison Project (DAMIP) and the Land Use Model Intercomparison Project (LUMIP) provided by the Coupled Model Intercomparison Project phase 6 (CMIP6), the optimal fingerprinting method is applied to quantify the greenhouse gases (GHG), aerosols (AA), natural forcing (NAT), and land use/cover change (LUCC) contributions to the intensity, frequency, and duration of extreme temperatures in China during 1960—2020. The results show that GHG is the main driver of extreme temperature changes in China, except for ice days (ID0) and cold spells (CSDI). GHG causes an increase in warm spells (WSDI) by about 10 days and an extension of the growing season length (GSL) by 6 to 8 days. The Tibetan Plateau is the region with the strongest extreme temperature changes influenced by GHG. AA forcing has a cooling effect that partially offsets the warming effect of GHG, especially in southeastern China. It should be pointed out that AA forcing is also the main driver for the changes in diurnal temperature range (DTR) in southeastern China, which exceeds the GHG contribution. Additionally, LUCC has greater impact in nighttime extreme temperature indices changes than the regional AA, and becomes the second dominant factor beside GHG. LUCC leads to an attributable cooling contribution of 0.34 °C for the maximum of daily minimum temperatures (TNx). Spatially, the LUCC effects on extreme temperature changes are stronger in western China than in eastern China. The more robust estimation of the GHG, AA, NAT, and LUCC contributions over distinct regions provides an advanced understanding of anthropogenic impacts on regional extreme temperatures, which is expected to be an important reference for regional climate change adaptation and mitigation.

Effect modification of the association between temperature variability and hospitalization for cardiovascular disease by comorbid diabetes mellitus: A nationwide time-stratified case-crossover analysis

AimsWe aimed to explore the association between short-term exposure to temperature variability (TV), and cardiovascular hospitalization stratified by the presence of comorbid diabetes. MethodsWe collected data on nationwide hospitalization for cardiovascular diseases and daily weather conditions during 2011–2018 in Japan. TV was calculated as the standard deviation of daily minimum and maximum temperatures within 0–7 lag days. We applied a two-stage time-stratified case-crossover design to estimate the association between TV and cardiovascular hospitalization with and without comorbid diabetes, adjusting for temperature and relative humidity. Furthermore, specific cardiovascular disease causes, demographic characteristics, and seasons were used for stratification. ResultsIn 3,844,910 hospitalizations for cardiovascular disease, each 1 °C increase in TV was associated with a 0.44% (95% CI: 0.22%, 0.65%) increase in the risk of cardiovascular admission. We observed a 2.07% (95% CI: 1.16%, 2.99%) and 0.61% (95% CI: −0.02%, 1.23%) increase per 1 °C in risk of heart failure admission in individuals with and those without diabetes, respectively. The higher risk among individuals with diabetes was mostly consistent in the analyses stratified by age, sex, body mass index, smoking status, and season. ConclusionComorbid diabetes may increase susceptibility to TV in relation to acute cardiovascular disease hospitalization.