Articles | Volume 24, issue 22
https://doi.org/10.5194/acp-24-12807-2024
© Author(s) 2024. 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-24-12807-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Diurnal variation in an amplified canopy urban heat island during heat wave periods in the megacity of Beijing: roles of mountain–valley breeze and urban morphology
Tao Shi
Department of Mathematics and Computer Science, Tongling University, Tongling, 244000, China
Yuanjian Yang
CORRESPONDING AUTHOR
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Ping Qi
Department 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, Lian Zong, Min Guo, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 25, 4989–5007, https://doi.org/10.5194/acp-25-4989-2025, https://doi.org/10.5194/acp-25-4989-2025, 2025
Short summary
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.
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.
Tao Shi, Yuanjian Yang, Lian Zong, Min Guo, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 25, 4989–5007, https://doi.org/10.5194/acp-25-4989-2025, https://doi.org/10.5194/acp-25-4989-2025, 2025
Short summary
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.
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.
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
Alonso, L. and Renard, F: A new approach for understanding urban microclimate by integrating complementary predictors at different scales in regression and machine learning models, Remote Sens., 12, 2434, https://doi.org/10.3390/rs12152434, 2020.
An, X., Chen, Y., and Lv, S.: Mesoscale simulations of winter low-level wind and temperature fields in Lanzhou city, Plateau Meteorol., 21, 2, 186–192, https://doi.org/10.3321/j.issn:1000-0534.2002.02.011, 2002.
Ao, X., Wang, L., Zhi, X., Gu, W., Yang, H., and Li, D.: Observed synergies between urban heat islands and heat waves and their controlling factors in Shanghai, China, J. Appl. Meteorol. Climatol., 74, 1789–1802, https://doi.org/10.1175/jamc-d-19-0073.1, 2019.
Bady, M., Kato, S., Takahashi, T., and Huang, H.: An experimental investigation of the wind environment and air quality within a densely populated urban street canyon, J. Wind Eng. Indust. Aerodynam., 99, 857–867, https://doi.org/10.1016/j.jweia.2011.06.005, 2011.
Berger, C., Rosentreter, J., Voltersen, M., Baumgart, C., Schmullius, C., and Hese, S.: Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature, Remote Sens. Environ., 193, 225–243, https://doi.org/10.1016/j.rse.2017.02.020, 2017.
Breiman, L.: Random forest, Mach. Learn., 45, 5–32, 2001.
Cai, H. and Xu, X.: Impacts of built-up area expansion in 2D and 3D on regional surface temperature, Sustainability, 9, 10, https://doi.org/10.3390/su9101862, 2017.
Cai, X., Guo, Y., Liu, H., and Chen, J.: Flow patterns of lower atmosphere over Beijing area, Acta Scientiarum Naturalium Universitatis Pekinensis, 38, 5, 698–704, https://doi.org/10.3321/j.issn:0479-8023.2002.03.015, 2002.
Cao, J., Liu, X., Li, G., and Zou, H.: Analysis of the phenomenon of lake-land breeze in Poyang Lake area, Plateau Meteorol., 34, 426–435, https://doi.org/10.7522/J.ISSN.1000-0534.2013.00197, 2015.
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.
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Kyle, T., Gibson, J., Lawler, J. J., Beard, H., and Hess, T.: Random forests for classification in ecology, Ecology, 88, 2783–2792, https://doi.org/10.1890/07-0539.12007, 2007.
Ding, Y.: Scientific questions and answers on climate change, Beijing:China Environmental Science Press, ISBN 9787511128805, 2018.
Dong, Q., Zhao, P., Wang, Y., Miao, S., and Gao, J.: Impact of mountain-valley wind circulation on typical cases of air pollution in Beijing, Environmental Science, 38, 6, 2218–2230, https://doi.org/10.13227/j.hjkx.201609231, 2017.
Dou, J., Wang, Y., and Miao, S.: Fine spatial and temporal characteristics of humidity and wind in Beijing urban area, J. Appl. Meteorol. Sci., 25, 559–569, https://doi.org/10.11898/1001-7313.20140505, 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.
Fenner, D., Meier, F., Bechtel, B., Otto, M., and Scherer, D.: Intra and inter “local climate zone” variability of air temperature as observed by crowdsourced citizen weather stations in Berlin, Germany, Meteorol. Z., 26, 525–547, https://doi.org/10.1127/metz/2017/0861, 2017.
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., 161–162, 1–13, https://doi.org/10.1016/j.atmosres.2015.03.016, 2015.
Friedman, J. H.: Greedy function approximation: a gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Fu, B.: A method for calculating the velocity and local circulation by wind observation data, J. Meteorol. Sci., 17, 258–267, 1997.
Gao, J., Sun, Y., Liu, Q., Zhou, M., Lu, Y., and Li, L.: Impact of extreme high temperature on mortality and regional level definition of heat wave: A multi-city study in China, Sci. Total Environ., 505, 535–544, https://doi.org/10.1016/j.scitotenv.2014.10.028, 2015.
Gemechu, F. G.: How the interaction of heatwaves and urban heat islands amplify urban warming, Adv. Environ. Eng. Res., 3, 2, https://doi.org/10.21926/aeer.2202022, 2022.
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, F., Hu, D., and Schlink, U.: A comprehensive metric scheme for characterizing the heterogeneity of urban thermal landscapes: A case study of 14-year evaluation in Beijing, Ecol. Indicator., 16, 112268–112268, https://doi.org/10.1016/j.ecolind.2024.112268, 2024.
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. Indust., 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, 2nd Edition, Springer Series in Statistics, Springer, New York, 66, 4, https://doi.org/10.1111/j.1541-0420.2010.01516.x, 2010.
He B.: Potentials of meteorological characteristics and synoptic conditions to mitigate urban heat island effects, Urban Clim., 24, 26–33, https://doi.org/10.1016/j.uclim.2018.01.004, 2018.
Hu, X., Liu, S., Liang, F.,Wang, J., Liu, H., Li, J., and Wang, Y.: Numerical simulation of features of surface boundary-layer over Beijing area, Acta Scientiarum Naturalium Universitatis Pekinensis, 41, 514–522, https://doi.org/10.3321/j.issn:0479-8023.2005.04.003, 2005.
IPCC (Intergovernmental panel on climate change): Climate Change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, Cambridge University Press, ISBN 9781009157896, 2023.
Jia, S., J., Wang, Y., Chen, L., and Bi, X.: A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models, Urban Clim., 47, 101390, https://doi.org/10.1016/j.uclim.2022.101390, 2023.
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.
Jiang, W., Xu, Y., and Yu, H.: Fundamentals of boundary layer meteorology, Nanjing: Nanjing University Press, ISBN 9787305025983, 1994.
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, 2, https://doi.org/10.3390/rs8020108, 2016.
Letcher, T. W. and Minder, J. R.: The simulated impact of the snow albedo feedback on the large-scale mountain-plain circulation east of the Colorado Rocky mountains, J. Atmos. Sci., 75, 755–774, https://doi.org/10.1175/JAS-D-17-0166.1, 2018.
Li, Q.: Statistical modeling experiment of land precipitation variations since the start of the 20th Century with external forcing factors, Chinese Sci. Bull., 65, 2266–2278, https://doi.org/10.1360/TB-2020-0305, 2020.
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. Climatol., 52, 2051–2064, https://doi.org/10.1175/JAMC-D-13-02.1, 2013.
Li, M., Wang, T., Xie, M., Zhuang, B., Li, S., Han, Y., and Cheng, N.: Modeling of urban heat island and its impacts on thermal circulations in the Beijing–Tianjin–Hebei region, China, Theor. Appl. Climatol., 128, 999–1013, https://doi.org/10.1007/s00704-016-1903-x, 2017.
Liu, S., Liu, Z., Li, J., Wang, Y,; Ma, Y., Sheng, L., Liu, H., Liang, F., Xin, G., and Wang, J.: Numerical simulation for the coupling effect of local atmospheric circulations over the area of Beijing, Tianjin and Hebei Province, Sci. China (Series D: Earth Sciences), 52, 382–392, https://doi.org/10.1007/s11430-009-0030-2, 2009.
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.
Merckx, T., Souffreau, C., Kaiser, A., Baardsen, L. F., Backeljau, T.,; Bonte, D., Brans, K. I., Cours, M., Dahirel, M., Debortoli, N.,Wolf, K, D., Engelen, J. M. T., Fontaneto, D., Gianuca, A. T., Govaert, L., Hendrickx, F., Higuti, J., Lens, L., Martens, K., Matheve, H., Matthysen, E., Piano, E., Sablon, R., Schön, L., Doninck, K. V., Meester, L. D., and Dyck, H. V.: Body-size shifts in aquatic and terrestrial urban communities, Nature, 558, 7708, https://doi.org/10.1038/s41586-018-0140-0, 2018.
Miao, Y., Liu, S., Chen, B., Zhang, B., Wang, S., and Li, S.: Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model, Adv. Atmos. Sci., 30, 1663–1678, https://doi.org/10.1007/s00376-013-2234-9, 2013.
Ng, E.: Policies and technical guidelines for urban planning of high-density cities-air ventilation assessment (AVA) of Hong Kong, Build. Environ., 44, 1478–1488, https://doi.org/10.1016/j.buildenv.2008.06.013, 2009.
Ngarambe, J., Nganyiyimana, J., Kim, I., Santamouris, M., and Yun, G. Y.: Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics, PLoS ONE, 15, 12, https://doi.org/10.1371/journal.pone.0243571, 2020.
NSTI: Daily Timed Data from automated weather stations in China, China Meteorological Data Service Centre, NSTI [data set], http://data.cma.cn/en/?r=data/detail&dataCode=A.0012.0001 (last access: 1 April 2024), 2024.
Oke, T. R.: Initial guidance to obtain representative meteorological observations at urban sites, University of British Columbia, IOM Rep. 81, WMO/TD-No. 1250, 2004.
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates, Cambridge University Press, ISBN 9780521849500, 2017.
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.
Peng, F., Wong M. S., Ho, H. C., Nichol, J., and Chan, P. W.: Reconstruction of historical datasets for analyzing spatiotemporal influence of built environment on urban microclimates across a compact city, Build. Environ., 123, 649–660, https://doi.org/10.1016/j.buildenv.2017.07.038, 2017.
Radfar, M.: Urban microclimate, designing the spaces between buildings. Housing Stud., 27, 2, 293–294, https://doi.org/10.1080/02673037.2011.615987, 2012.
Rafiee, A., Dias, E., and Koomen, E.: Urban forestry & urban greening Local impact of tree volume on nocturnal urban heat island: a case study in Amsterdam, Urban For Urban Green, 16, 50–61, https://doi.org/10.1016/j.ufug.2016.01.008, 2016.
Rao, K. S. and Snodgrass, H. F.: A nonstationary nocturnal drainage flow model, Bound.-Lay. Meteorol., 20, 309–320, https://doi.org/10.1007/BF00121375, 1981.
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, 5, https://doi.org/10.1029/2006GL027927, 2007.
Ryu, Y. H. and Baik, J. J.: Quantitative analysis of factors contributing to urban heat island intensity, J. Appl. Meteorol. Climatol., 51, 842–854, https://doi.org/10.1175/JAMC-D-11-098.1, 2012.
Scarano, M. and Mancini, F.: Assessing the relationship between sky view factor and land surface temperature to the spatial resolution, International J. Remote Sens., 38, 6910–6929, https://doi.org/10.1080/01431161.2017.1368099, 2017.
Seto, K. C., Guneralp, B., and Hutyra, L. R.: Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools, P. Natl. Acad. Sci. USA, 109, 16083–16088, https://doi.org/10.1073/pnas.1211658109, 2012.
Shi, T., Huang, Y., Shi, C., and Yang, Y.: Influence of urbanization on the thermal environment of meteorological stations: Satellite-observational evidence, Adv. Clim. Change Res., 1, 7–15, https://doi.org/10.1016/j.accre.2015.07.001, 2015.
Shiroyama, R. and Yoshimura, C.: Assessing bluegill (Lepomis macrochirus) habitat suitability using partial dependence function combined with classification approaches, Ecol. Inform., 35, 9–18, https://doi.org/10.1016/j.ecoinf.2016.06.005, 2016.
Smola, A. J. and Schölkopf, B.: A tutorial on support vector regression, Stat. Comput., 14, 199–222, https://doi.org/10.1023/B:STCO.0000035301.49549.88, 2004.
Srivanit, M. and Kazunori, H.: The influence of urban morphology indicators on summer diurnal range of urban climate in Bangkok metropolitan area, Thailand, Int. J. Civil Environ. Eng., 11, 34–46, 2011.
Stewart, I. D. and Oke T. R.: Local climate zones for urban temperature studies, B. Am. Meteorol. Soc., 93, 1879–1900, https://doi.org/10.1175/BAMS-D-11-00019.1, 2012.
Stewart, I. D., Oke, T. R., and Krayenhoff, E. S.: Evaluation of the 'local climate zone' scheme using temperature observations and model simulations, Int. J. Climatol., 34, 1062–1080, https://doi.org/10.1002/joc.3746, 2014.
Sun, J., Wang, H., and Yuan, W.: Decadal variability of the extreme hot event in China and its association with atmospheric circulations, Clim. Environ. Res., 16, 199–208, 2011.
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, 2, https://doi.org/10.1088/1748-9326/11/2/024003, 2016.
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, Prog. Geogr., 36, 122–130, https://doi.org/10.18306/dlkxjz.2017.10.012, 2017.
Tian, Y. and Miao, J.: Overview of mountain-valley breeze studies in China, Meteorol. Sci. Technol., 47, 11, https://doi.org/10.19517/j.1671-6345.20170777, 2019.
Tian, Y., Zhou, W., Qian, Y., Zheng, Z., and Yan, J.: The effect of urban 2D and 3D morphology on air temperature in residential neighborhoods, Landscape Ecol., 34, 1161–1178, https://doi.org/10.1007/s10980-019-00834-7, 2019.
Tompalski, P. and Wężyk, P.: LiDAR and VHRS Data for Assessing living quality in cities-an approach based on 3D spatial indices, International Archives of the Photogrammetry, Remote Sens. Spatial Inf. Sci., XXXIX-B6, 173–176, https://doi.org/10.5194/isprsarchives-XXXIX-B6-173-2012, 2012.
Unger, J.: Intra-urban relationship between surface geometry and urban heat island: review and new approach, Clim. Res., 27, 253–264, https://doi.org/10.3354/cr0272532004, 2004.
Unger, J., Sümeghy, Z., and Zoboki, J.: Temperature cross-section features in an urban area, Atmos. Res., 58, 117–127, https://doi.org/10.1016/S0169-8095(01)00087-4, 2001.
Wang, X., Wang, C., and Li, Q.: Wind regimes above and below a temperate deciduous forest canopy in complex terrain: Interactions between slope and valley winds, Atmosphere, 6, 60–87, https://doi.org/10.3390/atmos6010060, 2015.
Wang, Y., Zheng, D., and Li, Q.: Urban meteorological disasters. Beijing: China Meteorological Press, ISBN 9787502947163, 2009.
Wei, J. and Sun, J.: The analysis of summer heat wave and sultry weather in North China, Clim. Environ. Res., 12, 453–463, https://doi.org/10.1175/1520-0442(1998)011<3030:acrtai>2.0.co;2, 2007.
Whiteman, C. D. and Doran, J. C.: The relationship between overlying synoptic-scale flows and winds within a valley, J. Appl. Meteorol., 32, 1669–1682, https://doi.org/10.1175/1520-0450(1993)0322.0.CO;2, 1993.
Whiteman, C. D. and Zhong, S.: Downslope flows on a low-angle slope and their interactions with valley inversions, Part I: Observations, J. Appl. Meteorol. Climatol., 47, 2023–2038, https://doi.org/10.1175/2007JAMC1669.1, 2008.
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 Clim., 48, 101425, https://doi.org/10.1016/j.uclim.2023.101425, 2023.
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, Number 1, 3907–3925 pp., Zenodo [data set], https://doi.org/10.5281/zenodo.5816591, 2022.
Yang, J., Su, J., Xia, J., Jin, C., Li, X., and Ge, Q.: The impact of spatial form of urban architecture on the urban thermal environment: A case study of the Zhongshan District, Dalian, China, IEEE J. Select. Top. Appl. Earth Observ. Remote Sens., 11, 2709–2716, https://doi.org/10.1109/JSTARS.2018.2808469, 2018.
Yang, P., Liu, W., Zhong, J., and Yang, J.: Evaluating the quality of temperature measured at automatic weather stations in Beijing, J. Appl. Meteorol. Sci., 22, 706–715, https://doi.org/10.1016/j.buildenv.2023.110180, 2011 (in Chinese).
Yang, P., Ren, G., and Liu, W.: Spatial and temporal characteristics of Beijing urban heat island intensity, J. Appl. Meteorol. Climatol., 52, 1803–1816, https://doi.org/10.1175/JAMC-D-12-0125.1, 2013.
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, Adv. Earth Sci., 39, 1–16, https://doi.org/10.11867/j.issn.1001-8166.2024.032, 2024.
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.
You, C., Cai, X., Song, Y., and Guo, H.: Local atmospheric circulations over Beijing-Tianjin Area in summer, Acta Scientiarum Naturalium Universitatis Pekinensis, 42, 779–783, https://doi.org/10.3321/j.issn:0479-8023.2006.06.015, 2006.
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.
Zakšek, K., Oštir, K., and Kokalj, Ž.: Sky-view factor as a relief visualization technique, Remote Sens., 3, 398–415, https://doi.org/10.3390/rs3020398, 2011.
Zängl, G.: The impact of weak synoptic forcing on the valley-wind circulation in the Alpine Inn valley, Meteorol. Atmos. Phys., 105, 37–53, https://doi.org/10.1007/s00703-009-0030-y, 2009.
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, J. Appl. Meteorol. Sci., 27, 249–256, https://doi.org/10.11898/1001-7313.20160213, 2016.
Zhang, N., Zhu, L. F., and Zhu, Y.: Urban heat island and boundary layer structures under hot weather synoptic conditions: A case study of Suzhou City, China, Adv. Atmos. Sci., 28, 855–865, https://doi.org/10.1007/s00376-010-0040-1, 2011.
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, 2018a.
Zheng, Z., Ren, G., and Gao, H.: Analysis of the local circulation in Beijing area, Meteorol. Monthly, 44, 425–433, https://doi.org/10.7519/j.issn.1000-0526.2018.03.009, 2018b.
Zheng, Z., Ren, G., Gao, H., and Yang, Y.: Urban ventilation planning and its associated benefits based on numerical experiments: A case study in beijing, China, Build. Environ., 222, 109383, https://doi.org/10.1016/j.buildenv.2022.109383, 2022.
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.
Zhou, X., Okaze, T., Ren, C., Cai, M., and Mochida, A.: Evaluation of urban heat islands using local climate zones under the influences of sea-Land breeze, Sustain. Cities Soc., 55, 102060, https://doi.org/10.1016/j.scs.2020.102060, 2020.
Zinzi, M., Agnoli, S., Burattini, C., and Mattoni, B.: On the thermal response of buildings under the synergic effect of heat waves and urban heat island, Solar Energy, 211, 1270–1282, https://doi.org/10.1016/j.solener.2020.10.050, 2020.
Zong, L., Liu, S., Yang, Y., Ren, G., Yu, M., Zhang, Y., and Li, Y.: Synergistic influence of local climate zones and wind speeds on the urban heat island and heat waves in the Megacity of Beijing, China, Front. Earth Sci., 9, 673786, https://doi.org/10.3389/feart.2021.673786, 2021.
Zong, L., Yang, Y., Xia, H., Gao, M., Sun, Z., Zheng, Z., Li, X., Ning, G., Li, Y., and Lolli, S.: Joint occurrence of heatwaves and ozone pollution and increased health risks in Beijing, China: role of synoptic weather pattern and urbanization, Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, 2022.
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.
This paper explored the formation mechanisms of the amplified canopy urban heat island intensity...
Altmetrics
Final-revised paper
Preprint