Linkage of extreme temperature change with atmospheric and locally anthropogenic factors in China mainland

Zhang, Siqi; Ren, Guoyu; Ren, Yue; KealdrupTysa, Suonam

doi:10.1016/j.atmosres.2022.106307

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…Using a regional climate model coupled with urban canopy and building energy models, Nakajima et al (2021) found that the COVID-19 restrictions could reduce 0.13 C in the urban areas of Osaka, Japan. Additionally, our findings also concur with the response of UHII and urban temperatures on weekends and holidays (Forster and Solomon, 2003;Fujibe, 2010;Wu et al, 2015;Zhang et al, 2015a;2015b;Ohashi et al, 2016;Dou and Miao, 2017;Earl et al, 2016;Bäumer and Voge, 2007). For instance, the reduction was between 0.20-0.25 C on weekends and holidays in Tokyo and between 0.10-0.20 C in Osaka (Fujibe, 2010).…”

Section: Discussionsupporting

confidence: 91%

“…Besides the aerosol response to the strict antivirus measures (e.g., quarantine, closed social services, and limited vehicle use), the anthropogenic heat (AH) released due to human activities also decreased. AH is associated with the urban energy balance; hence, it likely impacts the urban heat island (UHI) effect (Tong et al, 2004;Fan and Sailor, 2005;Narumi et al, 2009;Oleson et al, 2011;Feng et al, 2012;Varentsov et al, 2018;Raj et al, 2020), according to which land surface and/or air temperatures are higher in urban areas than in rural surroundings (e.g., Oke, 1982;Arnfield, 2003;Ren et al, 2007;Narumi et al, 2009;Raj et al, 2020;Oleson et al, 2011;Gedzelman et al, 2013;Chen et al, 2014;Zhou et al, 2014;Zhang et al, 2015a;2015b;Varentsov et al, 2018). Based on a boundary-layer model with an AH emission inventory for Beijing, Tong et al (2004) showed that daytime and night-time UHI intensity (UHII) in winter increased by 0.5 and 1-2 C due to AH, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID‐19) lockdown in Wuhan, China: Observational evidence

Sun

Zhou

Chen

et al. 2022

Intl Journal of Climatology

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The Coronavirus Disease 2019 (COVID-19) lockdown in early 2020 reduced human activities in Wuhan, China, thereby altering the urban heat island (UHI) effect. The epidemic period (EP) during January 1, 2020-May 31, 2020 is divided into four stages, including pre-lockdown, first (EP_LS1) and second (EP_LS2) lockdown, and after-lockdown (EP_ALS) stages, which are identified according to different antivirus measures in early 2020 and confirmed by comparing the daily electricity consumption over the Wuhan metropolitan area during 2020 and 2019. Then, the Wuhan UHI intensity (UHII) during each stage is compared to that during the baseline period of 2016-2019 and 2021. Daytime, night-time, and daily UHIIs are reduced; the larger decrease is during EP_LS1 and EP_LS2, of which EP_LS1 with the strictest antivirus measures shows a significant decrease (<−0.12 C or <−36%), especially during night-time with −0.23 ± 0.05 C (or −47 ± 9%). The declined UHII persists in all stages (excluding EP_ALS), even after removing the natural variability of the air temperature; EP_LS1 still exhibits the largest and significant decrease (<−0.18 C or <−52%).To remove possible impacts of the local weather conditions, the UHII changes are recalculated by randomly sampling the 90, 80, 70, and 60% data points in each stage of EP with 1,000 repetitions. Results suggest limited influence of the local weather conditions, and then validate our findings. This study provides an important observational evidence of human-induced control on urban climate, and further confirms the necessity to include human dynamics in climate/earth system models for better simulating and predicting urban climate.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID‐19) lockdown in Wuhan, China: Observational evidence

Sun

Zhou

Chen

et al. 2022

Intl Journal of Climatology

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show abstract

“…In contrast to vegetation surfaces, urban surfaces tend to absorb and store more solar radiation energy during the day and release more longwave radiation at night, thus contributing to the urban heat island effect [ 49 , 50 , 51 ]. Moreover, the increased amount of heat released from industrial activities, transportation, electrical appliances, and other anthropogenic sources also heats the air during the urbanization process [ 52 , 53 ]. With urbanization comes the loss of green vegetation, especially trees; this means the dual loss of shading and cooling from evapotranspiration.…”

Section: Discussionmentioning

confidence: 99%

Compound Heat Vulnerability in the Record-Breaking Hot Summer of 2022 over the Yangtze River Delta Region

Jiang

2023

IJERPH

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Hourly meteorological data and multisource socioeconomic data collected in the Yangtze River Delta (YRD) region were used to analyze its heat vulnerability during the record-breaking hot summer of 2022 in both daytime and nighttime. Over forty consecutive days, daytime temperatures exceeded 40 °C, and 58.4% of the YRD region experienced 400 h with temperatures hotter than 26 °C during the nighttime. Only 7.5% of the YRD region was under low heat risk during both daytime and nighttime. Strong heat risk combined with strong heat sensitivity and weak heat adaptability led to strong heat vulnerability during both daytime and nighttime in most areas (72.6%). Inhomogeneity in heat sensitivity and heat adaptability further aggravated the heterogeneity of heat vulnerability, leading to compound heat vulnerability in most regions. The ratios of heat-vulnerable areas generated by multiple causes were 67.7% and 79.3% during daytime and nighttime, respectively. For Zhejiang and Shanghai, projects designed to decrease the urban heat island effect and lower the local heat sensitivity are most important. For Jiangsu and Anhui, measures aiming to decrease the urban heat island effect and improve heat adaptability are most important. It is urgent to take efficient measures to address heat vulnerability during both daytime and nighttime.

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“…(2018) used classic statistical approaches (Annual Maxima) to define the extreme temperature series and their trends globally. A plethora of regional studies have recently surfaced aiming to assess extreme temperature trends mainly in Asia (Ahmad et al., 2023; Ding et al., 2021; Ma et al., 2022; Om et al., 2022; Wang et al., 2021; Yatim et al., 2019; Yosef et al., 2019; S. Zhang et al., 2022), but also Latin America (Meseguer‐Ruiz et al., 2019; Ruiz‐Alvarez et al., 2020; Sanches et al., 2023), Europe (Tošić et al., 2023) Antarctica (Wei et al., 2019), and New Zealand (Caloiero, 2017), among other regions. Here we use an alternative method (H1‐L1) for obtaining the extreme value series and assess global changes in extreme temperature.…”

Section: Discussionmentioning

confidence: 99%

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

Nerantzaki,

Papalexiou,

Rajulapati

et al. 2023

Earth's Future

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We 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.

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Linkage of extreme temperature change with atmospheric and locally anthropogenic factors in China mainland

Cited by 14 publications

References 36 publications

Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID‐19) lockdown in Wuhan, China: Observational evidence

Decreases in the urban heat island effect during the Coronavirus Disease 2019 (COVID‐19) lockdown in Wuhan, China: Observational evidence

Compound Heat Vulnerability in the Record-Breaking Hot Summer of 2022 over the Yangtze River Delta Region

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

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