Attribution of a Record-Breaking Heatwave Event in Summer 2017 over the Yangtze River Delta

Zhou, Chunlüe; Wang, Kaicun; Qi, Dexing; Tan, Jianwei

doi:10.1175/bams-d-18-0134.1

Cited by 39 publications

(23 citation statements)

References 27 publications

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“…Li and Bou‐Zeid () analyzed the heat waves in Baltimore, Maryland, and Washington DC and concluded that the heat stress in urban areas was stronger than the sum of UHI effect and the heat wave effect. Zhou et al () studied the heat wave in 2017 in the Yangtze River Delta and concluded that the UHI could explain 58% of the record‐breaking heat stress. Perkins () reviewed previous studies on heat waves and noted that UHIs not only intensified heat stress on the first day but also weakened the ability of humans in urban areas to handle heat stress on the following day.…”

Section: Introductionmentioning

confidence: 99%

Amplified Urban Heat Islands during Heat Wave Periods

et al. 2019

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Heat waves and urban heat islands (UHIs) may interact together, but the dependence of their interaction on background climate is unclear. Hourly meteorological observations in June to August from 2013 to 2015 collected in the megacities of Beijing (temperate semihumid monsoon climate), Shanghai (subtropical humid monsoon climate), and Guangzhou (marine subtropical monsoon climate) in China were used to study the interaction. At each megacity, eight rural stations and eight urban stations, respectively, were selected to study the UHI. Although under different background climates, UHIs in Beijing and Guangzhou shared a similar diurnal variability, that is, higher in the nighttime. However, the diurnal cycle is opposite for Shanghai if rural coastal stations were selected as rural reference stations. During heat wave periods, daytime (10:00–16:00) UHIs were intensified by 0.9 ± 0.13 (mean ± 1 standard deviation) °C in Shanghai, nighttime (22:00–4:00) UHIs were intensified by 0.9 ± 0.36 and 0.8 ± 0.20 °C in Beijing and Guangzhou, respectively. The surface solar radiation during the heat wave period was approximately 1.5 times to that under normal conditions in each city. The enhanced solar radiation during heat waves, which was absorbed by the urban canopy in the daytime and released at night, was closely related to nighttime UHIs in Beijing and Guangzhou and daytime UHIs in Shanghai. Additionally, changes in wind direction were observed in Shanghai under heat waves, that is, with more than 63% (wind direction) of the wind originating from neighboring hot cities in the southwest instead of the cool sea breeze from the southeast, which led to a significant increase in daytime UHIs during heat wave periods.

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Section: Introductionmentioning

confidence: 99%

Amplified Urban Heat Islands during Heat Wave Periods

et al. 2019

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“…Hansen et al (2001) corrected the urbanization bias of one typical station by utilizing the two-stage linear trend based on the assumption that the SAT increased linearly in two periods. Zhou et al (2019) pointed occurrence probability of the heatwave events in summer over the Yangtze River Delta is closely related to the contribution of urbanization effect. These imply that the correction method of urbanization bias is very crucial to explore accurately the regional climate change.…”

Section: Introductionmentioning

confidence: 99%

“…However, in reality, the urbanization processes at different times and in different regions are variable, so it is impossible to subdivide the specific degree of contribution of the urbanization bias to the SAT series on temporal and spatial scales. In addition, there are considerable differences in the mechanisms and magnitudes of the impact of urbanization on different temperature elements (Li et al, 2014), despite the possibly limited contribution to regional warming (Chao et al, 2020), while its impact on extreme temperatures are huge (Li and Huang, 2013;Li et al, 2014;Zhou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

Tao

Huang

Sun³

et al. 2021

Front. Environ. Sci.

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The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

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“…On the other hand, reliable daily or sub-daily temperature data are needed for studying weather and climate extremes (Zhou and Wang 2016b;Zhou et al 2019;Sippel et al 2020) that greatly impact natural and social systems. Homogenized sub-daily upper air temperature data are also needed for input into reanalysis assimilation systems and for calculating water vapor variables (Dai et al 2011).…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Homogenize Global Subdaily Radiosonde Temperature Data from 1958 to 2018

et al. 2021

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This study develops an innovative approach to homogenize discontinuities in both mean and variance in global sub-daily radiosonde temperature data from 1958-2018. First, temperature natural variations and changes are estimated using reanalyses and removed from the radiosonde data to construct monthly and daily difference series. A Penalized Maximal F test and an improved Kolmogorov-Smirnov test are then applied to the monthly and daily difference series to detect spurious shifts in the mean and variance, respectively. About 60% (40%) of the changepoints appear in the mean (variance), and ∼56% of them are confirmed by available metadata. The changepoints display a country-dependent pattern likely due to changes in national radiosonde networks. Mean segment length is 7.2 (14.6) years for the mean (variance)- based detection. A mean (quantile)-matching method using up to 5-years of data from two adjacent mean (variance)-based segments is used to adjust the earlier segments relative to the latest segment. The homogenized series is obtained by adding the two homogenized difference series back to the subtracted reference series. The homogenized data exhibit more spatially coherent trends and temporally consistent variations than the raw data, and lack the spurious tropospheric cooling over North China and Mongolia seen in several reanalyses and raw datasets. The homogenized data clearly show a warming maximum around 300hPa over 30oS- 30oN, consistent with model simulations, in contrast to the raw data. The results suggest that spurious changes are numerous and significant in the radiosonde records and our method can greatly improve their homogeneity.

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Attribution of a Record-Breaking Heatwave Event in Summer 2017 over the Yangtze River Delta

Cited by 39 publications

References 27 publications

Amplified Urban Heat Islands during Heat Wave Periods

Amplified Urban Heat Islands during Heat Wave Periods

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

A New Approach to Homogenize Global Subdaily Radiosonde Temperature Data from 1958 to 2018

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