scholarly journals Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities

2021 ◽  
Author(s):  
Yong Yu ◽  
Siqi Luo ◽  
Yunquan Zhang ◽  
Linjiong Liu ◽  
Ke Wang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Temperature Variability ◽  
Hourly Temperature ◽  
Us Cities

scholarly journals Comparative Analysis of Daily and Hourly Temperature Variability in Association with All-Cause and Cardiorespiratory Mortality in 45 US Cities

2021 ◽  
Author(s):  
Yong Yu ◽  
Siqi Luo ◽  
Yunquan Zhang ◽  
Linjiong Liu ◽  
Ke Wang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Meteorological Data ◽  
Health Effect ◽  
Total Mortality ◽  
Temperature Variability ◽  
Daily Maximum ◽  
Mortality Risks ◽  
Mortality Burden ◽  
Hourly Temperature ◽  
Attributable Fractions

Abstract BackgroundTemperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days’ daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TVdaily and TVhourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited.MethodsWe collected daily mortality and meteorological data in 45 US metropolises, 1987–2000. A three-stage analyses were adopted to investigate TV-mortality associations using TVdaily and TVhourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationship, which were then pooled using random-effects meta-analysis with maximum likelihood estimation (MLE). We additionally calculated attributable fractions as a reflection of total mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risk.ResultsThere were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular and 1.2 million were from respiratory causes. Per 1 °C increase in TVdaily and TVhourly was associated with an increase of 0.53% (95% confidence interval: 0.31–0.76%) and 0.52% (0.26–0.79%) in cardiovascular mortality risks, 0.62% (0.26–0.98%) and 0.53% (0.13–0.94%) in respiratory mortality risks. Estimations of cardiovascular attributable fractions for TVdaily and TVhourly were 2.43% (1.42–3.43%) vs 1.63% (0.82–2.43%), whereas respiratory attributable fractions were 3.07% (1.11–4.99%) vs 1.89% (0.43–3.34%). Both daily and hourly indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TVdaily than those using TVhourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality.ConclusionsBoth TVdaily and TVhourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily TV metrics resembled hourly in risk effects, whilst greater mortality burden was found in TVdaily than TVhourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effect.

scholarly journals Abstract of Paper on the Diurnal Range of Temperature Variability at the Summit and Base of Ben Nevis, Lady Franklin Bay, and Hong Kong

1902 ◽  
Vol 42 ◽  
pp. 528-531
Author(s):  
R. C. Mossman
Keyword(s):  
Hong Kong ◽  
Mean Value ◽  
Temperature Variability ◽  
The Arctic ◽  
Mean Values ◽  
Hourly Temperature ◽  
Diurnal Range ◽  
The Mean ◽  
The Difference ◽  
Temperature Records

The average variability of temperature at any place is obtained by taking the difference of temperature at the same hour on successive days, and taking the mean value of this difference irrespective of sign. With a view to ascertaining whether this variability varied with the hour selected for comparison, the hourly temperature records at four places have been examined, and the day to day change of temperature at each hour noted. The places are—the Ben Nevis Observatory, the Fort-William Observatory, the Hong Kong Observatory, and the Arctic Station at Lady Franklin Bay, lat. 81° 44′ N., long. 64° 45′ W. At each place one year's record was taken, which, though too short a time to give a true mean, yet gives a fair approximation towards it. The mean values for each hour of the twelve months at the four stations are given in the accompanying tables, the highest value in each month being put in heavy type and the lowest in italic.

scholarly journals What’s the temperature in tropical caves?

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0237051
Author(s):  
Luis Mejía-Ortíz ◽  
Mary C. Christman ◽  
Tanja Pipan ◽  
David C. Culver
Keyword(s):  
Rainy Season ◽  
Temporal Pattern ◽  
Temperature Variability ◽  
Daily Temperature ◽  
The Other ◽  
Quintana Roo ◽  
Temperature Cycle ◽  
Hourly Temperature ◽  
Surface Variation ◽  
One Year

Hourly temperature was measured for approximately one year at 17 stations in three caves in Quintana Roo, Mexico. Thirteen of these stations were in the extensive twilight zones of all three caves. All seventeen stations showed seasonality in temperature with a 3°C drop during the Nortes season. Two of the caves, Muévelo Sabrosito and Muévelo Rico, showed greater variability during the winter months while in Río Secreto (Tuch) variability was greatest during the rainy season. Río Secreto is less open to the surface than the other two. All sites also showed a daily temperature cycle, although it was very faint in some Río Secreto (Tuch) sites. While temperature variability is diminished relative to surface variation, its temporal pattern is worthy of further study.

scholarly journals Contribution of clouds radiative forcing to the local surface temperature variability

2020 ◽  
Author(s):  
Oscar Rojas ◽  
Marjolaine Chiriaco ◽  
Sophie Bastin ◽  
Justine Ringard
Keyword(s):  
Heat Exchange ◽  
Decision Trees ◽  
Radiative Forcing ◽  
Temperature Variability ◽  
Local Climate ◽  
Hourly Temperature ◽  
Local Contribution ◽  
Bagged Decision Trees ◽  
The One ◽  
Local Surface Temperature

<p>The local contribution of clouds to the surface energy balance and temperature variability is an important topic in order to apprehend how this intake affects local climate variability and extreme events, how this contribution varies from one place to another, and how it evolves in a warming climate. The scope of this study is to understand how clouds impact temperature variability, to quantify their contribution, and to compare their effects to other surface processes. To do so, we develop a method to estimate the different terms that control temperature variability at the surface (∂T<sub>2m</sub> /∂t) by using this equation: <strong>∂T<sub>2m</sub> /∂t=R+HA+HG+Adv</strong> where R is the radiation that is separated into the cloud term (R<sub>cloud</sub>) and the clear sky one (R<sub>CS</sub>), HA the atmospheric heat exchange, HG the ground heat exchange, and Adv the advection. These terms are estimated hourly, almost only using direct measurements from SIRTA-ReOBS dataset (an hourly long-term multi-variables dataset retrieved from SIRTA, an observatory located in a semi-urban area 20-km South-West of Paris; Chiriaco et al., 2019) for a five-years period. The method gives good results for the hourly temperature variability, with a 0.8 correlation coefficient and a weak residual term between left part (directly measured) and right part of the equation.</p><p>A bagged decision trees analysis of this equation shows that R<sub>CS</sub> dominates temperature variability during daytime and is mainly modulated by cloud radiative effect (R<sub>cloud</sub>). During nighttime, the bagged decision trees analysis determines that R<sub>cloud</sub> is the term controlling temperature changes. When a diurnal cycle analysis (split into seasons) is performed for each term, HA becomes an important negative modulator in the late afternoon, chiefly in spring and summer, when evaporation and thermal conduction are increased. In contrast, HG and Adv terms do not play an essential role on temperature variability at this temporal scale and their contribution is barely considerable in the one-hour variability, but still they remain necessary in order to obtain the best coefficient estimator between the directly measured observations and the method estimated. All terms except advection have a marked monthly-hourly cycle.</p><p>Next steps consist in characterize the types of clouds and study their physical properties corresponding to the cases where R<sub>cloud</sub> is significant, using the Lidar profiles also available in the SIRTA-ReOBS dataset.</p>

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