Spatio-temporal variability of the atmospheric boundary layer depth over the Paris agglomeration: An assessment of the impact of the urban heat island intensity

被引:101
|
作者
Pal, S. [1 ,2 ]
Xueref-Remy, I. [1 ]
Ammoura, L. [1 ]
Chazette, P. [1 ]
Gibert, F. [2 ]
Royer, P. [1 ,3 ]
Dieudonne, E. [1 ,4 ]
Dupont, J. -C. [5 ]
Haeffelin, M. [5 ]
Lac, C. [6 ]
Lopez, M. [1 ]
Morille, Y. [2 ,7 ]
Ravetta, F. [4 ]
机构
[1] IPSL UVSQ CNRS CEA, Lab Sci Climat & Environm, F-91191 Gif Sur Yvette, France
[2] Ecole Polytech, CNRS, Meteorol Dynam Lab, F-91128 Palaiseau, France
[3] LEOSPHERE, Paris, France
[4] Univ Paris 06, Lab Atmospheres Milieux Observat Spatiales LATMOS, Paris, France
[5] Ecole Polytech, CNRS, Inst Pierre Simon Laplace, F-75230 Paris, France
[6] CNRM GAME CNRS Meteo France, Toulouse, France
[7] Univ Angers, Angers, France
关键词
Atmospheric boundary layer depth; Entrainment zone thickness; Lidar; Nocturnal boundary layer; Urban heat island; Wavelet analysis; ENTRAINMENT ZONE THICKNESS; OPTICAL-PROPERTIES; LIDAR; HEIGHT; MODEL;
D O I
10.1016/j.atmosenv.2012.09.046
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Within the framework of a French nationally funded project (CO2-MEGAPARIS) for quantifying the CO2 emissions of the Paris area, a lidar-based experimental investigation of the variability of the atmospheric boundary layer (ABL) depths was performed over four days in March 2011 under clear sky conditions. The prevailing synoptic settings were mainly characterized by anti-cyclonic situations with low wind. The key aim of this paper is to assess the impact of the urban heat island intensity (UHII) on the spatio-temporal variability of the ABL depths over the Paris megacity. A network of fixed aerosol lidars was deployed inside the city and in the vicinity of sub-urban and rural areas. Additionally, the spatial heterogeneity of the nocturnal boundary layer (NBL) depths over greater Paris area is addressed, thanks in particular, to the deployment of a 355-nm elastic lidar in a mobile van to measure the aerosol distributions. Radiosonde-derived profiles (twice a day) of thermodynamic variables over the sub-urban site helped investigate the temperature inversion above ground and hence to compare the lidar-derived ABL depths. Comparing these two results, an excellent concordance was found with a correlation coefficient of 0.994. Five important factors closely related to the ABL circulation, namely, spatio-temporal variability of the ABL depths, growth rate of the ABL depths, entrainment zone thickness, and near-surface temperature fields including resultant UHII were considered to infer the urban rural contrasts. The mean NBL depth over the urban area was on average 63 m (45%) higher than its adjacent sub-urban area which was, on occasion, as much as (74 m) 58% higher mainly due to the effect of UHII. Daytime well-mixed convective boundary layer and associated strong turbulent mixing near its top over the urban area showed higher entrainment zone thickness (326 m) than over sub-urban (234 m) and rural (200 m) areas. Temperature growth rates during sunrise increased up to more than 3 degrees C h(-1) over the sub-urban area while over the urban region it was 2.5 degrees C h(-1) or even less. The ABL depths over the urban site decayed more slowly (500 m h(-1)) than over the sub-urban area (600 m h(-1)) during the late afternoon transition period suggesting an impact of the UHII on the ABL dynamics over the urban area. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:261 / 275
页数:15
相关论文
共 50 条
  • [1] Spatio-temporal variability of the atmospheric boundary layer depth over the Paris agglomeration: An assessment of the impact of the urban heat island intensity
    Pal, S.
    Xueref-Remy, I.
    Ammoura, L.
    Chazette, P.
    Gibert, F.
    Royer, P.
    Dieudonné, E.
    Dupont, J.-C.
    Haeffelin, M.
    Lac, C.
    Lopez, M.
    Morille, Y.
    Ravetta, F.
    [J]. Atmospheric Environment, 2012, 63 : 261 - 275
  • [2] Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
    Yuan, Debao
    Zhang, Liuya
    Fan, Yuqing
    Sun, Wenbin
    Fan, Deqin
    Zhao, Xurui
    [J]. APPLIED SCIENCES-BASEL, 2024, 14 (12):
  • [3] Urban heat island in the atmospheric boundary layer
    Kamardin, A. P.
    Gladkikh, V. A.
    Nevzorova, I., V
    Odintsov, S. L.
    [J]. 27TH INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS, ATMOSPHERIC PHYSICS, 2021, 11916
  • [4] A Spatio-Temporal Assessment and Prediction of Surface Urban Heat Island Intensity Using Multiple Linear Regression Techniques Over Ahmedabad City, Gujarat
    Mohammad, Pir
    Goswami, Ajanta
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (05) : 1091 - 1108
  • [5] A Spatio-Temporal Assessment and Prediction of Surface Urban Heat Island Intensity Using Multiple Linear Regression Techniques Over Ahmedabad City, Gujarat
    Pir Mohammad
    Ajanta Goswami
    [J]. Journal of the Indian Society of Remote Sensing, 2021, 49 : 1091 - 1108
  • [6] Spatio-temporal characteristics of urban heat Island of Jakarta metropolitan
    Siswanto, Siswanto
    Nuryanto, Danang Eko
    Ferdiansyah, Muhammad Rezza
    Prastiwi, Agita Devi
    Dewi, Ova Candra
    Gamal, Ahmad
    Dimyati, Muhammad
    [J]. REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2023, 32
  • [7] Investigating the spatio-temporal correlation between urban heat island and atmospheric pollution island interaction over Delhi, India using geospatial techniques
    Manish Kumar Mishra
    Aneesh Mathew
    [J]. Arabian Journal of Geosciences, 2022, 15 (20)
  • [8] Exploring the Spatio-temporal pattern of regional heat island (RHI) in an urban agglomeration of secondary cities in Eastern India
    Dutta, Ipsita
    Das, Arijit
    [J]. URBAN CLIMATE, 2020, 34
  • [9] Spatio-temporal distribution of atmospheric chloride deposition over a small island
    Teixeira, Paula Campos
    Ordens, Carlos M.
    McIntyre, Neil
    Pagliero, Liliana
    Crosbie, Russell
    [J]. HYDROLOGICAL PROCESSES, 2023, 37 (12)
  • [10] Assessment of spatio-temporal intra-rural heat island variability based on IoT monitoring
    Guo, Guanhua
    Wu, Zhifeng
    Cao, Zheng
    Li, Shaoying
    Chen, Yingbiao
    [J]. URBAN CLIMATE, 2023, 52