Articles | Volume 25, issue 9
https://doi.org/10.5194/acp-25-4989-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-4989-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The modulation of synoptic weather patterns and human activities on the diurnal cycle of the summertime canopy urban heat island in the Yangtze River Delta Urban Agglomeration, China
Tao Shi
School of Mathematics and Computer Science, Tongling University, Tongling, 244000, China
Key Laboratory of Transportation Meteorology of the China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing, 210041, China
Yuanjian Yang
CORRESPONDING AUTHOR
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Lian Zong
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Min Guo
State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Ningxia Meteorological Information Centre, Ningxia, 750002, China
Ping Qi
School of Mathematics and Computer Science, Tongling University, Tongling, 244000, China
Simone Lolli
CNR-IMAA, Contrada S. Loja, 85050 Tito Scalo, PZ, Italy
Related authors
Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 9219–9234, https://doi.org/10.5194/acp-25-9219-2025, https://doi.org/10.5194/acp-25-9219-2025, 2025
Short summary
Short summary
The city significantly influences thunderstorm and lightning activity, yet the potential mechanisms remain largely unexplored. Our study has revealed that both city size and building density play pivotal roles in modulating thunderstorm and lightning activity. This research not only deepens our understanding of urban meteorology but also lays an important foundation for developing accurate and targeted urban thunderstorm risk prediction models.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2025-2785, https://doi.org/10.5194/egusphere-2025-2785, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Using Beijing’s Fifth Ring Road, the team combined data and models. Heatwave results: canopy heat island was 91.3 % stronger day/52.7 % night. Day heat relied on building coverage, night on sky visibility. Tall buildings block sun by day, trap heat at night. Night ventilation cools, day winds spread heat. Urban design must consider day-night cycles to fight extreme heat, guiding risk reduction.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
Short summary
Short summary
This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 9219–9234, https://doi.org/10.5194/acp-25-9219-2025, https://doi.org/10.5194/acp-25-9219-2025, 2025
Short summary
Short summary
The city significantly influences thunderstorm and lightning activity, yet the potential mechanisms remain largely unexplored. Our study has revealed that both city size and building density play pivotal roles in modulating thunderstorm and lightning activity. This research not only deepens our understanding of urban meteorology but also lays an important foundation for developing accurate and targeted urban thunderstorm risk prediction models.
Jialu Xu, Yingjie Zhang, Yuying Wang, Xing Yan, Bin Zhu, Chunsong Lu, Yuanjian Yang, Yele Sun, Junhui Zhang, Xiaofan Zuo, Zhanghanshu Han, and Rui Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3184, https://doi.org/10.5194/egusphere-2025-3184, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We conducted a year-long study in Nanjing to explore how the height of the atmospheric boundary layer affects fine particle pollution. We found that low boundary layers in winter trap pollutants like nitrate and primary particles, while higher layers in summer help form secondary pollutants like sulfate and organic aerosols. These findings show that boundary layer dynamics are key to understanding and managing seasonal air pollution.
Junhui Zhang, Yuying Wang, Jialu Xu, Xiaofan Zuo, Chunsong Lu, Bin Zhu, Yuanjian Yang, Xing Yan, and Yele Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-3186, https://doi.org/10.5194/egusphere-2025-3186, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We conducted a year-long study in Nanjing to understand how tiny airborne particles take up water, which affects air quality and climate. We found that particle water uptake varies by season and size, with lower values in summer due to more organic materials. Local pollution mainly influences smaller particles, while larger ones are shaped by air mass transport. These findings help improve climate models and support better air pollution control in fast-growing cities.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2025-2785, https://doi.org/10.5194/egusphere-2025-2785, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Using Beijing’s Fifth Ring Road, the team combined data and models. Heatwave results: canopy heat island was 91.3 % stronger day/52.7 % night. Day heat relied on building coverage, night on sky visibility. Tall buildings block sun by day, trap heat at night. Night ventilation cools, day winds spread heat. Urban design must consider day-night cycles to fight extreme heat, guiding risk reduction.
Tianwen Wei, Mengya Wang, Kenan Wu, Jinlong Yuan, Haiyun Xia, and Simone Lolli
Atmos. Meas. Tech., 18, 1841–1857, https://doi.org/10.5194/amt-18-1841-2025, https://doi.org/10.5194/amt-18-1841-2025, 2025
Short summary
Short summary
This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.
Bo Zheng, Jason Blake Cohen, Lingxiao Lu, Wei Hu, Pravash Tiwari, Simone Lolli, Andrea Garzelli, Hui Su, and Kai Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1446, https://doi.org/10.5194/egusphere-2025-1446, 2025
Short summary
Short summary
This study provides TROPOMI with a new methane emission estimation method that can accurately identify emission sources. Our results generate non-negative emission datasets using objective selection and filtering methods. The results include lower minimum emission thresholds for all power grids and fewer false positives. The new method provides more robust emission quantification in the face of data uncertainty, going beyond traditional plume identification and background subtraction.
Simone Lolli, Erica K. Dolinar, Jasper R. Lewis, Andreu Salcedo-Bosch, James R. Campbell, and Ellsworth J. Welton
EGUsphere, https://doi.org/10.5194/egusphere-2025-1237, https://doi.org/10.5194/egusphere-2025-1237, 2025
Short summary
Short summary
Clouds strongly influence Earth's climate by changing how sunlight is reflected or absorbed. We studied thin, high-altitude clouds using radar-laser measurements collected over twenty years at NASA GSFC. Our findings show these clouds increasingly trap heat, partly because of shrinking snow and ice cover. This trend could further accelerate warming locally, underlining the need for accurate cloud observations to improve climate forecasts and strategies to respond to climate change.
Fengjiao Chen, Yuanjian Yang, Lu Yu, Yang Li, Weiguang Liu, Yan Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 1587–1601, https://doi.org/10.5194/acp-25-1587-2025, https://doi.org/10.5194/acp-25-1587-2025, 2025
Short summary
Short summary
The microphysical mechanisms of precipitation responsible for the varied impacts of aerosol particles on shallow precipitation remain unclear. This study reveals that coarse aerosol particles invigorate shallow rainfall through enhanced coalescence processes, whereas fine aerosol particles suppress shallow rainfall through intensified microphysical breaks. These impacts are independent of thermodynamic environments but are more significant in low-humidity conditions.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
Short summary
Short summary
This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Chaman Gul, Shichang Kang, Yuanjian Yang, Xinlei Ge, and Dong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1144, https://doi.org/10.5194/egusphere-2024-1144, 2024
Preprint archived
Short summary
Short summary
Long-term variations in upper atmospheric temperature and water vapor in the selected domains of time and space are presented. The temperature during the past two decades showed a cooling trend and water vapor showed an increasing trend and had an inverse relation with temperature in selected domains of space and time. Seasonal temperature variations are distinct, with a summer minimum and a winter maximum. Our results can be an early warning indication for future climate change.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Simone Lolli, Michaël Sicard, Francesco Amato, Adolfo Comeron, Cristina Gíl-Diaz, Tony C. Landi, Constantino Munoz-Porcar, Daniel Oliveira, Federico Dios Otin, Francesc Rocadenbosch, Alejandro Rodriguez-Gomez, Andrés Alastuey, Xavier Querol, and Cristina Reche
Atmos. Chem. Phys., 23, 12887–12906, https://doi.org/10.5194/acp-23-12887-2023, https://doi.org/10.5194/acp-23-12887-2023, 2023
Short summary
Short summary
We evaluated the long-term trends and seasonal variability of the vertically resolved aerosol properties over the past 17 years in Barcelona. Results shows that air quality is improved, with a consistent drop in PM concentrations at the surface, as well as the column aerosol optical depth. The results also show that natural dust outbreaks are more likely in summer, with aerosols reaching an altitude of 5 km, while in winter, aerosols decay as an exponential with a scale height of 600 m.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
Short summary
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
Short summary
Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
Short summary
Short summary
We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Fan Wang, Gregory R. Carmichael, Jing Wang, Bin Chen, Bo Huang, Yuguo Li, Yuanjian Yang, and Meng Gao
Atmos. Chem. Phys., 22, 13341–13353, https://doi.org/10.5194/acp-22-13341-2022, https://doi.org/10.5194/acp-22-13341-2022, 2022
Short summary
Short summary
Unprecedented urbanization in China has led to serious urban heat island (UHI) issues, exerting intense heat stress on urban residents. We find diverse influences of aerosol pollution on urban heat island intensity (UHII) under different circulations. Our results also highlight the role of black carbon in aggravating UHI, especially during nighttime. It could thus be targeted for cooperative management of heat islands and aerosol pollution.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Lian Zong, Yuanjian Yang, Haiyun Xia, Meng Gao, Zhaobin Sun, Zuofang Zheng, Xianxiang Li, Guicai Ning, Yubin Li, and Simone Lolli
Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, https://doi.org/10.5194/acp-22-6523-2022, 2022
Short summary
Short summary
Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Taking Beijing as an example, three unfavorable synoptic weather patterns were identified to dominate the compound HW and O3 pollution events. Under the synergistic stress of HWs and O3 pollution, public mortality risk increased, and synoptic patterns and urbanization enhanced the compound risk of events in Beijing by 33.09 % and 18.95 %, respectively.
Shaohui Zhou, Yuanjian Yang, Zhiqiu Gao, Xingya Xi, Zexia Duan, and Yubin Li
Atmos. Meas. Tech., 15, 757–773, https://doi.org/10.5194/amt-15-757-2022, https://doi.org/10.5194/amt-15-757-2022, 2022
Short summary
Short summary
Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, https://doi.org/10.5194/amt-15-735-2022, 2022
Short summary
Short summary
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
Xinyan Li, Yuanjian Yang, Jiaqin Mi, Xueyan Bi, You Zhao, Zehao Huang, Chao Liu, Lian Zong, and Wanju Li
Atmos. Meas. Tech., 14, 7007–7023, https://doi.org/10.5194/amt-14-7007-2021, https://doi.org/10.5194/amt-14-7007-2021, 2021
Short summary
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
Short summary
Short summary
In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Gemine Vivone, Giuseppe D'Amico, Donato Summa, Simone Lolli, Aldo Amodeo, Daniele Bortoli, and Gelsomina Pappalardo
Atmos. Chem. Phys., 21, 4249–4265, https://doi.org/10.5194/acp-21-4249-2021, https://doi.org/10.5194/acp-21-4249-2021, 2021
Short summary
Short summary
We developed a methodology to retrieve the atmospheric boundary layer height from elastic and multi-wavelength lidar observations that uses a new approach based on morphological image processing techniques. The intercomparison with other state-of-the-art algorithms shows on average 30 % improved performance. The algorithm also shows excellent performance with respect to the running time, i.e., just few seconds to execute the whole signal processing chain over 72 h of continuous measurements.
Jasper R. Lewis, James R. Campbell, Sebastian A. Stewart, Ivy Tan, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 13, 6901–6913, https://doi.org/10.5194/amt-13-6901-2020, https://doi.org/10.5194/amt-13-6901-2020, 2020
Short summary
Short summary
In this work, the authors describe a process to determine the thermodynamic cloud phase using the Micro Pulse Lidar Network volume depolarization ratio measurements and temperature profiles from the Global Modeling and Assimilation Office GEOS-5 model. A multi-year analysis and comparisons to supercooled liquid water fractions derived from CALIPSO satellite measurements are used to demonstrate the efficacy of the method.
Cited articles
Bahuguna, R. N., Solis, C. A., Shi, W., and Jagadish, K. S. V.: Post-flowering night respiration and altered sink activity account for high night temperature-induced grain yield and quality loss in rice (Oryza sativa L.), Physiol. Plantarum, 159, 59–73, https://doi.org/10.1111/ppl.12485, 2017.
Bai, K. and Li, K.: Daily 1-km gap-free PM2.5 grids in China, v1 (2000–2020), in: Earth System Science Data (Vol. 14, 907–927), Zenodo [data set], https://doi.org/10.5281/zenodo.5652265, 2021a.
Bai, K. and Li, K.: Daily 1-km gap-free PM10 grids in China, v1 (2000–2020), in: Earth System Science Data (Vol. 14, 907–927), Zenodo [data set], https://doi.org/10.5281/zenodo.5652263, 2021b.
Bansal, P. and Quan, S.: Examining temporally varying nonlinear effects of urban form on urban heat island using explainable machine learning: A case of Seoul, Build. Environ., 247, 110957, https://doi.org/10.1016/j.buildenv.2023.110957, 2024.
Cai, H. and Xu, X.: Impacts of built-up area expansion in 2D and 3D on regional surface temperature, Sustainability, 9, 1862, https://doi.org/10.3390/su9101862, 2017.
Cai, X.: Footprint Analysis in Micrometeorology and its Extended Applications, Chinese Journal of Atmospheric Sciences, 32, 123–132, https://doi.org/10.3724/SP.J.1148.2008.00288, 2008.
Chen, B., Dong, L., Liu, X., Shi, G., Chen, L., Nakajima, T., and Habib, A.: Exploring the Possible Effect of Anthropogenic Heat Release Due to Global Energy Consumption upon Global Climate: a Climate Model Study, Int. J. Climatol., 36, 15, 4790–4796, https://doi.org/10.1002/joc.4669, 2016.
Chen, G., Wang, D., Wang, Q., Li, Y., Wang, X., Hang, J., Gao, P., Ou, C., and Wang, K.: Scaled Outdoor Experimental Studies of Urban thermal Environment in Street canyon Models with Various Aspect Ratios and thermal Storage, Sci. Total Environ., 726, 138147, https://doi.org/10.1016/j.scitotenv.2020.138147, 2020.
Chen, S., Yang, Y., Deng, F., Zhang, Y., Liu, D., Liu, C., and Gao, Z.: A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations, Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, 2022.
China Meteorological Data Service Centre: Daily Timed Data From Automated Weather Stations In China, China Meteorological Data Service Centre, http://data.cma.cn/en, last access: 12 September 2024.
Dong, L., Jiang, Z., and Shen, S.: Urban heat island change and its relationship with urbanization of urban agglomerations in Yangtze River Delta in past decade, Trans. Atmos. Sci., 37, 146–154, https://doi.org/10.3969/j.issn.1674-7097.2014.02.003, 2014.
Drach, P., Krüger, E. L., and Emmanuel, R.: Effects of atmospheric stability and urban morphology on daytime intra-urban temperature variability for Glasgow, UK, Sci. Total Environ., 627, 782–791, https://doi.org/10.1016/j.scitotenv.2018.01.285, 2018.
Du, H., Wang, D., Wang, Y., Zhao, X., Qin, F., Jiang, H., and Cai, Y.: Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration, Sci. Total Environ., 571, 461–470, https://doi.org/10.1016/j.scitotenv.2016.07.012, 2016.
Duan, Z., Yang, Y., Zhou, S., and Yin, J.: Estimating Gross Primary Productivity (GPP) over Rice-Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product, Remote Sens., 13, 21, https://doi.org/10.3390/rs13214229, 2021.
Estoque, R. C., Murayama, Y., and Myint, S. W.: Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia, Sci. Total Environ., 577, 349–359, https://doi.org/10.1016/j.scitotenv.2016.10.195, 2017.
Filleul, L., Cassadou, S., Médina, S., Fabres, P., Lefranc, A., Eilstein, D., Tertre, A. L., Pascal, L., Chardon, B., and Blanchard, M.: The relation between temperature, ozone, and mortality in nine french cities during the heat wave of 2003, Environ. Health Persp., 114, 1344–1347, https://doi.org/10.1289/ehp.8328, 2006.
Fischer, E. M. and Schär, C.: Consistent geographical patterns of changes in high-impact European heatwaves, Nat. Geosci., 3, 398–403, https://doi.org/10.1038/NGEO866, 2010.
Founda, D., Pierros, F., Petrakis, M., and Zerefos, C.: Interdecadal variations and trends of the urban heat island in Athens (Greece) and its response to heat waves, Atmos. Res., 161, 1–13, https://doi.org/10.1016/j.atmosres.2015.03.016, 2015.
Friedman, J.: Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Giridharan, R., Ganesan, S., and Lau, S. S. Y.: day urban heat island effect in high-rise and high-density residential developments in Hong Kong, Energ. Buildings, 36, 525–534, https://doi.org/10.1016/j.enbuild.2003.12.016, 2004.
Giridharan, R., Lau, S. S. Y., and Ganesan, S.: Nocturnal heat island effect in urban residential developments of Hong Kong, Energ. Buildings, 37, 964–971, https://doi.org/10.1016/j.enbuild.2004.12.005, 2005.
Gosling, S. N., Lowe, J. A., McGregor, G. R., Pelling, M., and Malamud, B. D.: Associations between elevated atmospheric temperature and human mortality: A critical review of the literature, Climatic Change, 92, 299–341, https://doi.org/10.1007/s10584-008-9441-x, 2009.
Guo, G., Zhou, X., Wu, Z., Xiao, R., and Chen, Y.: Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China, Environ. Model. Softw., 84, 427–439, https://doi.org/10.1016/j.envsoft.2016.06.021, 2016.
Guo, J., Li, Y., Cohen, J. B., Li, J., Chen, D., Xu, H., Liu, L., Yin, J., Hu, K., and Zhai, P.: Shift in the Temporal Trend of Boundary Layer Height in China Using Long-Term (1979–2016) Radiosonde Data, Geophys. Res. Lett., 46, 6080–6089, https://doi.org/10.1029/2019GL082666, 2019.
Guo, M., Zhang, M., Wang, H., Wang, L., and Li, Y.: Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China, Front. Environ. Sci., 9, 672295, https://doi.org/10.3389/fenvs.2021.672295, 2021.
Hang, J., Li, Y., and Sandberg, M.: Experimental and numerical studies of flows through and within high-rise building arrays and their link to ventilation strategy, J. Wind Eng. Ind. Aerodyn., 99, 1036–1055, https://doi.org/10.1016/j.envsoft.2016.06.021, 2011.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data mining, Inference, and Prediction, Springer Series in Statistics, Springer, New York, https://doi.org/10.1207/S15328007SEM1101_10, 2009.
He, Y., Miao, L., Gu, W., and Ju, L.: Analysis of PM2.5 concentration dynamics and its influencing factors in the Yangtze River Delta based on different city sizes, Scientia Geographica Sinica, 44, 1426–1436, https://doi.org/10.13249/j.cnki.sgs.20220878, 2024.
Herbel, I., Croitoru, A. E., Rus, A.V. Roca, C. F., Harpa, G. V., Ciupertea, A. F., and Rus, I.: The impact of heat waves on surface urban heat island and local economy in Cluj-Napoca city, Romania, Theor. Appl. Climatol., 133, 681–695, https://doi.org/10.1007/s00704-017-2196-4, 2018.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hoffmann, P. and Schlünzen, K. H.: Weather Pattern Classification to Represent the Urban Heat Island in Present and Future Climate, J. Appl. Meteorol. Clim., 52, 2699–2714, https://doi.org/10.1175/JAMC-D-12-065.1, 2013.
Hong, J. S., Yeh, S. W., and Seo, K. H.: Diagnosing physical mechanisms leading to pure heat waves versus pure tropical nights over the Korean peninsula, J. Geophys. Res.-Atmos., 123, 7149–7160, https://doi.org/10.1029/2018JD028360, 2018.
Huang, L., Miao, J., and Liu, Y.: Spatial and temporal variation characteristics of urban heat island in Tianjin, Transactions of Atmospheric Sciences, 35, 620–632, https://doi.org/10.1007/s11783-011-0280-z, 2012.
Huang, Q. and Lu, Y.: The Effect of Urban Heat Island on Climate Warming in the Yangtze River Delta Urban Agglomeration in China, Int. J. Env. Res. Pub. He., 12, 8773–8789, https://doi.org/10.3390/ijerph120808773, 2015.
Imran, H. M., Kala, J., Ng, A. W. M., and Muthukumaran, S.: Impacts of future urban expansion on urban heat island effects during heatwave events in the city of Melbourne in southeast Australia, Q. J. Roy. Meteor. Soc., 145, 2586–2602, https://doi.org/10.1002/qj.3580, 2019.
IPCC (Intergovernmental Panel on Climate Change): Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Reportof the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge and New York, ISBN 9781009157896, 2021.
Jiang, S., Lee, X., Wang, J., and Wang, K.: Amplified urban heat islands during heat wave periods, J. Geophys. Res.-Atmos., 124, 7797–7812, https://doi.org/10.1029/2018jd030230, 2019.
Khan, H. S., Paolini, R., Santamouris, M., and Caccetta, P.: Exploring the synergies between urban overheating and heatwaves (HWs) in Western Sydney, Energies, 13, 470, https://doi.org/10.3390/en13020470, 2020.
Krayenhoff, E. S. and Voogt, J. A.: Daytime Thermal Anisotropy of Urban Neighbourhoods: Morphological Causation, Remote Sens., 8, 108, https://doi.org/10.3390/rs8020108, 2016.
Le Tertre, A., Lefranc, A., Eilstein, D., Declercq, C., Medina, S., Blanchard, M., Chardon, B., Fabre, P., Filleul, L., Jusot, J.-F., Pascal, L., Prouvost, H., Cassadou, S., and Ledrans, M.: Impact of the 2003 heatwave on all-cause mortality in 9 French cities, Epidemiology, 17, 75–79, https://doi.org/10.1097/01.ede.0000187650.36636.1f, 2006.
Li, D. and Bou-Zeid, E.: Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts, J. Appl. Meteorol. Clim., 52, 2051–2064, https://doi.org/10.1175/JAMC-D-13-02.1, 2013.
Li, Y., Schubert, S., Kropp, J. P., and Rybski, D.: On the influence of density and morphology on the Urban Heat Island intensity, Nat. Commun., 11, 2647, https://doi.org/10.1038/s41467-020-16461-9, 2020.
Lin, Z., Xu, H., Han, L., Zhang, H., Peng, J., and Yao, X.: Day and night: Impact of 2D/3D urban features on land surface temperature and their spatiotemporal non-stationary relationships in urban building spaces, Sustain. Cities Soc., 108, 105507, https://doi.org/10.1016/j.scs.2024.105507, 2024.
Liu, W., Ji, C., Zhong, J., Jiang, X., and Zheng, Z.: Temporal characteristics of the Beijing urban heat island, Theor. Appl. Climatol., 87, 213–221, https://doi.org/10.1007/s00704-005-0192-6, 2007.
Liu, W., Yang, P., You, H., and Zhang, B.: Heat island effect and diurnal temperature range in Beijing area, Climatic and Environmental Research, 18, 171–177, https://doi.org/10.3878/j.issn.1006-9585.2012.11147, 2013.
Liu, Y., Xu, Y., Zhang, Y., Han, X., Weng, F., Xuan, C., and Shu, W.: Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands, J. Meteorol. Res., 36, 882–7899, https://doi.org/10.1007/s13351-022-2045-y, 2022.
Marks, D. and Connell, J.: Unequal and unjust: The political ecology of Bangkok's increasing urban heat island, Urban Studies, 61, 2887–2907, https://doi.org/10.1177/00420980221140999, 2024.
Menon, S.: Climate effects of black carbon aerosols in China and India, Science, 297, 2250–2253, https://doi.org/10.1126/science.1075159, 2002.
Miao, Y., Guo, J., Liu, S., Liu, H., Li, Z., Zhang, W., and Zhai, P.: Classification of summertime synoptic patterns in Beijing and their associations with boundary layer structure affecting aerosol pollution, Atmos. Chem. Phys., 17, 3097–3110, https://doi.org/10.5194/acp-17-3097-2017, 2017.
Mirzaei, P. A. and Haghighat, F.: Approaches to study Urban Heat Island – Abilities and limitations, Build. Environ., 45, 2192–2201, https://doi.org/10.1016/j.buildenv.2010.04.001, 2010
Mora, C., Dousset, B., Caldwell, I., Powell, F., Geronimo, R., Bielecki, C., Counsell, C., Dietrich, B., Johnston, E., Louis, L., Lucas, M., McKenzie, M., Shea, A., Tseng, H., Giambelluca, T., Leon, L., Hawkins, E., and Trauernicht, C.: Global risk of deadly heat, Nature Climate Change, 7, 501–506, https://doi.org/10.1038/nclimate3322, 2017.
Muthers, S., Laschewski, G., and Matzarakis, A.: The Summers 2003 and 2015 in South-West Germany: Heat Waves and Heat-Related Mortality in the Context of Climate Change, Atmosphere 8, 224, https://doi.org/10.3390/atmos8110224, 2017.
Ngarambe, J., Nganyiyimana, J., Kim, I., Santamouris, M., and Yunid, G. Y.: Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics, PLoS ONE, 15, e0243571, https://doi.org/10.1371/journal.pone.0243571, 2020.
National Oceanic and Atmospheric Administration: Version 4 DMSP-OLS Nighttime Lights Time Series, National Oceanic and Atmospheric Administration [data set], http://ngdc.NOAA.gov/eog/dmsp/downloadV4composites.html, last access: 12 September 2024.
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban climates, Cambridge University Press, ISBN 9780521849500, 2017.
Park, S., Park, J., and Lee, S.: Unpacking the nonlinear relationships and interaction effects between urban environment factors and the urban night heat index, J. Clean. Prod., 428, 139407, https://doi.org/10.1016/j.jclepro.2023.139407, 2023.
Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A.: Impact of regional climate change on human health, Nature, 438, 310–317, https://doi.org/10.1038/nature04188, 2005.
Philipp, A., Beck, C., Esteban, P., Krennert, T., Lochbihler, K., Spyros, P., Pianko-Kluczynska, K., Post, P., Alvarez, R., Spekat, A., and Streicher, F.: Cost733class-1.2 User guide, http://cost733.met.no/ (last access: 7 June 2021), 2014.
Poupkou, A., Nastos, P., Melas, D., and Zerefos, C.: Climatology of discomfort index and air quality index in a large urban mediterranean agglomeration, Water Air Soil Poll., 222, 163–183, https://doi.org/10.1007/s11270-011-0814-9, 2011.
Pu, X., Wang, T., Huang, X., Melas, D., Zanis, P., Papanastasiou, D. K., and Poupkou, A.: Enhanced surface ozone during the heat wave of 2013 in Yangtze River Delta region, China, Sci. Total Environ., 603, 807–816, https://doi.org/10.1016/j.scitotenv.2017.03.056, 2017.
Ren, G., Chu, Z., Chen, Z., and Ren, Y.: Implications of temporal change in urban heat island intensity observed at Beijing and Wuhan stations, Geophys. Res. Lett., 34, L05711, https://doi.org/10.1029/2006GL027927, 2007.
Ren, G. Y.: Urbanization as a major driver of urban climate change, Advances in Climate Change Research, 6, 1–6, https://doi.org/10.1016/j.accre.2015.08.003, 2015.
Ren, Y. and Ren, G.: A remote-sensing method of selecting RS for evaluating urbanization effect on surface air temperature trends, J. Climate, 24, 3179–3189, https://doi.org/10.1175/2010JCLI3658.1, 2011.
Rizwan, A. M., Dennis, L. Y., and Chunho, L.: A review on the generation, determination and mitigation of urban heat island, J. Environ. Sci., 20, 120–128, https://doi.org/10.1016/S1001-0742(08)60019-4, 2008.
Roth, M.: Review of urban climate research in (sub) tropical regions. Int. J. Climatol., 27, 1859–1873, https://doi.org/10.1002/joc.1591, 2007.
Seidel, D. J., Zhang, Y., Beljaars, A., Golaz, J. C., Jacobson, A. R., and Medeiros, B.: Climatology of the planetary boundary layer over the continental United States and Europe, J. Geophys. Res.-Atmos., 117, 1–15, https://doi.org/10.1029/2012JD018143, 2012.
Shi, T., Huang, Y., Shi, C., and Yang, Y.: Influence of Urbanization on the Thermal Environment of Meteorological Stations: Satellite-observational Evidence, Advances in Climate Change Research, 1, 7–15, https://doi.org/10.1016/j.accre.2015.07.001, 2015.
Shi, T., Sun, D., Huang, Y., Lu, G., and Yang, Y.: A new method for correcting urbanization-induced bias in surface air temperature observations: Insights from comparative site-relocation data, Front. Environ. Sci., 9, 625418, https://doi.org/10.3389/fenvs.2021.625418, 2021.
Shi, T., Yang, Y., Qi, P., Ren, G., Wen, X., and Gul, C.: Adjustment of the urbanization bias in surface air temperature series based on urban spatial morphologies and using machine learning, Urban Climate, 55, 101991, https://doi.org/10.1016/j.uclim.2024.101991, 2024.
Singh, V. K., Mohan, M., and Bhati, S.: Industrial heat island mitigation in Angul-Talcher region of India: Evaluation using modified WRF-Single Urban Canopy Model, Sci. Total Environ., 858, 159949, https://doi.org/10.1016/j.scitotenv.2022.159949, 2023.
Taleghani, M., Sailor, D., and Ban-Weiss, G. A.: Micrometeorological simulations to predict the impacts of heat mitigation strategies on pedestrian thermal comfort in a Los Angeles neighborhood, Environ. Res. Lett., 11, 024003, https://doi.org/10.1088/1748-9326/11/2/024003, 2016.
Tan, J., Zheng, Y., Tang, X., Guo, C., Li, L., Song, G., Zhen, X., Yuan, D., Kalkstein, A. J., Li, F., and Chen, H.: The urban heat island and its impact on heat waves and human health in Shanghai, Int. J. Biometeorol., 54, 75–84, https://doi.org/10.1007/s00484-009-0256-x, 2010.
Tan, M., Liu, K., Liu, L.,Zhu, Y., and Wang, D.: Population Spatialization of 30 m Grid in Pearl River Delta Based on Stochastic Forest Model, Progress in Geography, 36, 10, 122–130, https://doi.org/10.18306/dlkxjz.2017.10.012, 2017.
Tian, G., Jiang, J., Yang, Z., and Zhang, Y.: The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China, Ecol. Model., 222, 865–878, https://doi.org/10.1016/j.ecolmodel.2010.09.036, 2011.
Tian, Y. and Miao, J.: Overview of Mountain-Valley Breeze Studies in China, Meteorological Science and Technology, 47, 41–51, https://doi.org/10.19517/j.1671-6345.20170777, 2019.
Tong, N. Y. O. and Leung, D. Y. C.: Effects of Building Aspect Ratio, Diurnal Heating Scenario, and Wind Speed on Reactive Pollutant Dispersion in Urban Street Canyons, J. Environ. Sci., 24, 2091–2103, https://doi.org/10.1016/S1001-0742(11)60971-6, 2012.
Tong, S., Wong, N., Jusuf, S. K., Tan, C., Wong, H., Ignatius, H., and Tan, E.: Study on correlation between air temperature and urban morphology parameters in built environment in northern China, Build. Environ., 127, 239–249, https://doi.org/10.1016/j.buildenv.2017.11.013, 2018.
Tysa, S. K., Ren, G., Qin, Y., Zhang, P., Ren, Y., Jia, W., and Wen, K.: Urbanization effect in regional temperature series based on a remote sensing classification scheme of stations, J. Geophys. Res.-Atmos., 124, 10646–10661, https://doi.org/10.1029/2019JD030948, 2019.
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., and Wilkes, J.: Large-scale cluster management at Google with Borg, Proceedings of the Tenth European Conference on Computer Systems, Bordeaux, France, 21–24 April 2015, ACM, New York, USA, https://doi.org/10.1145/2741948.2741964, 2022.
Wang, H., Li, J., Gao, Z., Yim, S. H. L., Shen, H., Ho, H. C., Li, Z., Zeng, Z., Liu, C., Li, Y., Ning, G., and Yang, Y.: High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal V ariation in the Yangtze River Delta, China in 2013, Remote Sens., 11, 2724, https://doi.org/10.3390/rs11232724, 2019.
Wang, K., Jiang, S., Wang, J., Zhou, C., Wang, X., and Lee, X.: Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network, J. Geophys. Res.-Atmos., 122, 2131–2154, https://doi.org/10.1002/2016jd025304, 2017.
Wang, W., Zhou, W., Li, X., Wang, X., and Wang, D.: Synoptic-scale characteristics and atmospheric controls of summer heat waves in China, Clim. Dynam., 46, 2923–2941, https://doi.org/10.1007/s00382-015-2741-8, 2015.
Wen, K., Ren, G., Li, J., Zhang, A., Ren, Y., Sun, X., and Zhou, Y.: Recent Surface Air Temperature Change Over Mainland China Based on an Urbanization-Bias Adjusted Dataset, J. Climate, 32, 2691–2705, https://doi.org/10.1175/JCLI-D-18-0395.1, 2019.
Wu, H., Wang, T., Wang, Q., Cao, Y., Qu, Y., and Nie, D.: Radiative effects and chemical compositions of fine particles modulating urban heat island in Nanjing, China, Atmos. Environ., 247, 118201, https://doi.org/10.1016/j.atmosenv.2021.118201, 2021.
Xia, Y., Li, Y., Guan, D., Tinoco, D.M., Xia, J., Yan, Z., Liu, Q., and Huo, H.: Assessment of the economic impacts of heat waves: a case study of Nanjing, China, J. Clean. Prod., 171, 811–819, https://doi.org/10.1016/j.jclepro.2017.10.069, 2018.
Xie, J., Sun, T., Liu, C., Li, L., Xu, X., Miao, S., Lin, L., Chen, Y., and Fan, S.: Quantitative evaluation of impacts of the steadiness and duration of urban surface wind patterns on air quality, Sci. Total Environ., 850, 157957, https://doi.org/10.1016/j.scitotenv.2022.157957, 2022.
Xu, W. H., Li, Q. X., Wang, X. L., Yang, S., Cao, L., and Feng, Y.: Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices, J. Geophys. Res.-Atmos., 118, 9708–9720, https://doi.org/10.1002/jgrd.50791, 2013.
Xu, Z., Fitzgerald, G., Guo, Y., Jalaludin, B., and Tong, S.: Impact of heatwave on mortality under different heatwave definitions: A systematic review and meta-analysis, Environ. Int., 89–90, 193–203, https://doi.org/10.1016/j.envint.2016.02.007, 2016.
Xue, J., Zong, L., Yang ,Y., Bi, X., Zhang, Y., and Zhao, M.: Diurnal and interannual variations of canopy urban heat island (CUHI) effects over a mountain-valley city with a semi-arid climate, Urban Climate, 48, 101425, https://doi.org/10.1016/j.uclim.2023.101425, 2023.
Yan, Z. and Zhou, D.: Rural agriculture largely reduces the urban heating effects in China: A tale of the three most developed urban agglomerations. Agr. Forest Meteorol., 331, 109343, https://doi.org/10.1016/j.agrformet.2023.109343, 2023.
Yang, G., Ren, G., Zhang, P., Xue, X., Tysa, S. K., Jia, W., Qin, Y., Zheng, X., and Zhang, S.: PM2.5 influence on urban heat island (UHI) effect in Beijing and the possible mechanisms, J. Geophys. Res.-Atmos., 126, e2021JD035227, https://doi.org/10.1029/2021JD035227, 2021.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yang, J. and Huang, X.: The 30 m annual land cover datasets and its dynamics in China from 1990 to 2021, in: Earth System Science Data (1.0.1, Vol. 13, 3907–3925), Zenodo [data set], https://doi.org/10.5281/zenodo.5816591, 2022.
Yang, J., Liu, H. Z., Ou, C. Q., Lin, G. Z., Zhou, Q., Shen, G. C., Chen, P. Y., and Guo, Y.: Global Climate Change: Impact of Diurnal Temperature Range on Mortality in Guangzhou, China, Environ. Pollut., 175, 131–136, https://doi.org/10.1016/j.envpol.2012.12.021, 2013.
Yang, J., Dong, J., Xiao, X., Dai, J., Wu, C., Xia, J., Zhao, G., Zhao, M., Li, Z., Zhang, Y., and Ge, Q.: Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens. Environ., 233, 111395, https://doi.org/10.1016/j.rse.2019.111395, 2019.
Yang, P., Liu, W., Zhong, J., and Yang, J.: Evaluating the Quality of Temperature Measured at Automatic Weather Stations in Beijing, Journal of Applied Meteorological Science, 22, 706–715, https://doi.org/10.3969/j.issn.1001-7313.2011.06.008, 2011.
Yang, Y., Zheng, X., Gao, Z., Wang, H., Wang, T., Li, Y., Lau, G. N. C., and Yim, S. H. L.: Long-Term Trends of Persistent Synoptic Circulation Events in Planetary Boundary Layer and Their Relationships With Haze Pollution in Winter Half Year Over Eastern China, J. Geophys. Res.-Atmos., 123, 10991–11007, https://doi.org/10.1029/2018JD028982, 2018.
Yang, Y., Zheng, Z., Yim, S. Y. L., Roth, M., Ren, G., Gao, Z., Wang, T., Li, Q., Shi, C., and Ning, G.: PM2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the BeijingTianjin-Hebei Megalopolis, China, Geophys. Res. Lett., 47, 1–12, https://doi.org/10.1029/2019GL084288, 2020.
Yang, Y., Guo, M., Ren, G., Liu, S., Zong, L., Zhang, Y., Zheng, Z., Miao, Y., and Zhang, Y.: Modulation of wintertime canopy urban heat island (CUHI) intensity in Beijing by synoptic weather pattern in planetary boundary layer, J. Geophys. Res.-Atmos., 127, e2021JD035988, https://doi.org/10.1029/2021JD035988, 2022.
Yang, Y., Guo, M., Wang, L., Zong, L., Liu, D., Zhang, W., Wang, M., Wan, B., and Guo, Y.: Unevenly spatiotemporal distribution of urban excess warming in coastal Shanghai megacity, China: Roles of geophysical environment, ventilation and sea breeze, Build. Environ., 235, 110180, https://doi.org/10.1016/j.buildenv.2023.110180, 2023.
Yang, Y., Luo, F., Xue, J., Zong, L., Tian, W., and Shi, T.: Research progress and perspective on synergy between urban heat waves and canopy urban heat island, Advances in Earth Science, 39, 1–16, https://doi.org/10.11867/j.issn.1001-8166.2024.032, 2024.
Yu, Z., Chen, S., Wong, N., Ignatius, M., Deng, J., He, Y., and Hii, D. J. C.: Dependence between urban morphology and outdoor air temperature: A tropical campus study using random forests algorithm, Sustain. Cities Soc., 61, 1–12, https://doi.org/10.1016/j.scs.2020.102200, 2020.
Zeng, Z., Wang, Z., Gui, K., Yan, X., Gao, M., Luo, M., Geng, H., Liao, T., Li, X., and An, J.: Daily Global Solar Radiation in China Estimated from High-density Meteorological Observations: A Random Forest Model Framework, Earth and Space Science, 7, e2019EA001058, https://doi.org/10.1029/2019EA001058, 2020.
Zhang, A., Ren, G., Zhou, J., Chu, Z., Ren, Y., and Tang, G.: Urbanization effect on surface air temperature trends over China, Acta Meteorol. Sin., 68, 957–966, https://doi.org/10.11676/qxxb2010.090, 2010.
Zhang, H., Zhu, S., Gao, Y., and Zhang, G.: The Relationship Between Urban Spatial Morphology Parameters and Urban Heat Island Intensity Under Fine Weather Condition, Journal of Applied Meteorological Science, 27, 249–256, https://doi.org/10.11898/1001-7313.20160213, 2016.
Zhang, K., Cao, C., Chu, H., Zhao, L., Zhao, J., and Lee, X.: Increased heat risk in wet climate induced by urban humid heat, Nature, 617, 7962, https://doi.org/10.1038/s41586-023-05911-1, 2023.
Zhang, M., Yang, Y., Zhan, C., Zong, L., Gul, C., and Wang, M.: Tropical cyclone-related heatwave episodes in the Greater Bay Area, China: synoptic patterns and urban-rural disparities, Weather and Climate Extremes, 44, 100656, https://doi.org/10.1016/j.wace.2024.100656, 2024.
Zhang, X., Chen, L., Jiang, W., and Jin, X.: Urban heat island of Yangtze River Delta urban agglomeration in China: Multi-time scale characteristics and influencing factors, Urban Climate, 43, 101180, https://doi.org/10.1016/j.uclim.2022.101180, 2022.
Zheng, Z., Ren, G., Wang, H., Dou, J., Gao, Z., Duan, C., Li, Y., Ngarukiyimana, J. P., Zhao, C., Cao, C., Jiang, M., and Yang, Y.: Relationship between fine-particle pollution and the urban heat island in Beijing, China: Observational evidence, Bound.-Lay. Meteorol., 169, 93–113, https://doi.org/10.1007/s10546-018-0362-6, 2018.
Zheng, Z., Zhao, C., Lolli, S., Wang, X., Wang, Y., Ma, X., Li, Q., and Yang, Y.: Diurnal variation of summer precipitation modulated by air pollution: Observational evidences in the Beijing metropolitan area, Environ. Res. Lett., 15, 094053, https://doi.org/10.1088/1748-9326/ab99fc, 2020.
Zhou, D., Zhao, S., Liu, S., Zhang, L., and Zhu, C.: Surface urban heat island in China's 32 major cities: Spatial patterns and drivers, Remote Sens. Environ., 152, 51–61, https://doi.org/10.1016/j.rse.2014.05.017, 2014.
Short summary
Our study explored the daily temperature patterns in urban areas of the Yangtze River Delta, focusing on how weather and human activities impact these patterns. We found that temperatures were higher at night, and weather patterns had a bigger impact during the day, while human activities mattered more at night. This helps us understand and address urban overheating.
Our study explored the daily temperature patterns in urban areas of the Yangtze River Delta,...
Altmetrics
Final-revised paper
Preprint