Ensemble-based analysis of the pollutant spreading intensity induced by climate change

被引:11
|
作者
Haszpra, Timea [1 ,2 ]
Herein, Matyas [1 ,2 ]
机构
[1] Eotvos Lorand Univ, Inst Theoret Phys, H-1117 Budapest, Hungary
[2] MTA ELTE Theoret Phys Res Grp, H-1117 Budapest, Hungary
关键词
NCEP-NCAR REANALYSIS; CYCLONE ACTIVITY; VARIABILITY; INTENSIFICATION; DISPERSION; ERA-40;
D O I
10.1038/s41598-019-40451-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The intensity of the atmospheric large-scale spreading can be characterized by a measure of chaotic systems, called topological entropy. A pollutant cloud stretches in an exponential manner in time, and in the atmospheric context the topological entropy corresponds to the stretching rate of its length. To explore the plethora of possible climate evolutions, we investigate here pollutant spreading in climate realizations of two climate models to learn what the typical spreading behavior is over a climate change. An overall decrease in the areal mean of the stretching rate is found to be typical in the ensembles of both climate models. This results in larger pollutant concentrations for several geographical regions implying higher environmental risk. A strong correlation is found between the time series of the ensemble mean values of the stretching rate and of the absolute value of the relative vorticity. Here we show that, based on the obtained relationship, the typical intensity of the spreading in an arbitrary climate realization can be estimated by using only the ensemble means of the relative vorticity data of a climate model.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Accelerating logical analysis of data using an ensemble-based technique
    Elfar, Osama
    Yacout, Soumaya
    Osman, Hany
    Engineering Letters, 2021, 29 (04) : 1616 - 1625
  • [22] Twentieth century global glacier mass change: an ensemble-based model reconstruction
    Malles, Jan-Hendrik
    Marzeion, Ben
    CRYOSPHERE, 2021, 15 (07): : 3135 - 3157
  • [23] ISSUES AND CHALLENGES WITH USING ENSEMBLE-BASED PREDICTION TO PROBE THE WEATHER-CLIMATE INTERFACE
    Cornuelle, Bruce
    Hansen, James
    Kirtman, Benjamin
    Sandgathe, Scott
    Warren, Steve
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2014, 95 (11) : 213 - 215
  • [24] Ensemble-Based Analysis of the May 2010 Extreme Rainfall in Tennessee and Kentucky
    Lynch, Samantha L.
    Schumacher, Russ S.
    MONTHLY WEATHER REVIEW, 2014, 142 (01) : 222 - 239
  • [25] Relativistic Effects in Platinum Nanocluster Catalysis: A Statistical Ensemble-Based Analysis
    Nair, Akhil S.
    Anoop, Anakuthil
    Ahuja, Rajeev
    Pathak, Biswarup
    JOURNAL OF PHYSICAL CHEMISTRY A, 2022, 126 (08): : 1345 - 1359
  • [26] Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
    Lussana, Cristian
    Nipen, Thomas N.
    Seierstad, Ivar A.
    Elo, Christoffer A.
    NONLINEAR PROCESSES IN GEOPHYSICS, 2021, 28 (01) : 61 - 91
  • [27] An Ensemble-Based Analysis of a Liminal Extreme Rainfall Event near Taiwan
    Cole, Alexandra S.
    Bell, Michael M.
    DeHart, Jennifer C.
    ATMOSPHERE, 2022, 13 (07)
  • [28] Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
    Tay, Wei-Liang
    Chui, Chee-Kong
    Ong, Sim-Heng
    Ng, Alvin Choong-Meng
    EXPERT SYSTEMS WITH APPLICATIONS, 2013, 40 (02) : 811 - 819
  • [29] Ensemble-based markov random field clustering for colony image analysis
    Yang, Linyun
    Zhuo, Qing
    Wang, Wenyuan
    Journal of Computational Information Systems, 2009, 5 (03): : 1309 - 1315
  • [30] Performance Analysis of Single- and Ensemble-Based Classifiers for Intrusion Detection
    Hariharan, R.
    Thaseen, I. Sumaiya
    Devi, G. Usha
    SOFT COMPUTING FOR PROBLEM SOLVING, SOCPROS 2018, VOL 2, 2020, 1057 : 759 - 770