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Spatiotemporal characteristics of clear-air turbulence (CAT) potential in China during 1979–2020

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Abstract

Air turbulence, especially the clear-air turbulence (CAT), always affects flight safety. Based on six CAT diagnostics and ERA5 reanalysis data, the spatiotemporal characteristics of CAT potential in the middle and upper troposphere in China during 1979–2020 and the possible mechanisms are analyzed in this study. The results show that CAT occurs the most frequently in winter and spring at 250–300 and 200–225 hPa, respectively. In general, CAT occurs relatively less in summer and autumn. Moreover, the CAT potential decreases with altitude in each season, and the high-magnitude centers gradually move northward and eastward accompanied with a rising motion from winter to summer. During 1979–2020, the CAT potential shows clear increasing trends in most of China in winter, while decreasing trends in most regions in summer. In the “high CAT frequency region (HCFR, 75–135° E, 25–45° N)”, the CAT potential has been significantly increasing in winter and decreasing in summer. Mechanism analysis indicates that the spatiotemporal distribution of East Asian subtropical westerly jet is well correlated with the CAT potential in the HCFR, with the spatial (temporal) correlation coefficients greater than 0.75 (0.54). In addition, the geopotential height pattern in various regions, the zonal circulation in the Eurasia and Atlantic, the Polar Vortex, the Subtropical High, and the sea surface temperature all have significant impact on the CAT in the HCFR.

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Availability of data and material

The ERA5 dataset used in this work is available at: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The CMA-NCC dataset used in this work is available at: http://cmdp.ncc-cma.net/Monitoring/cn_index_130.php.

Code availability

The analysis code is available on request from the corresponding author.

References

  • Barnston AG, Livezey RE (1987) Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon Weather Rev 115(6):1083–1126

    Google Scholar 

  • Brown R (1973) New indices to locate clear-air turbulence. Meteorol Mag 102(1217):347–360

    Google Scholar 

  • Chambers E (1955) Clear air turbulence and civil jet operations. Aeronaut J 59(537):613–628

    Google Scholar 

  • Chu JE, Ha KJ, Lee JY et al (2014) Future change of the Indian ocean basin-wide and dipole modes. Clim Dyn 43(1–2):535–551

    Google Scholar 

  • Dutton MJO (1980) Probability forecasts of clear-air turbulence based on numerical output. Meteorol Mag 109:293–310

    Google Scholar 

  • Eick D (2014) Turbulence related accidents and incidents. Presentation at NCAR turbulence impact mitigation workshop 2, 3–4 September 2014. Accessed 20 Jun, 2021

  • Ellrod GP, Knapp DI (1992) An objective clear-air turbulence forecasting technique: verification and operational use. Weather Forecast 7(1):150–165

    Google Scholar 

  • Ellrod GP, Knox JA (2010) Improvements to an operational clear air turbulence diagnostic index by addition of a divergence trend term. Weather Forecast 25:789–798

    Google Scholar 

  • Gultepe I, Sharman R, Williams PD et al (2019) A review of high impact weather for aviation meteorology. Pure Appl Geophys 176(5):1869–1921

    Google Scholar 

  • Hersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049

    Google Scholar 

  • Hu B, Tang J, Ding J et al (2021) Regional downscaled future change of clear-air turbulence over East Asia under RCP8.5 scenario within the CORDEX-EA-II project. Int J Climatol 41(10):5022–5035

    Google Scholar 

  • Hu B, Ding J, Liu G et al (2022) Objective verification of clear-air turbulence (CAT) diagnostic performance in china using in-situ aircraft observation. J Atmos Oceanic Tech 39(7):903–914

    Google Scholar 

  • Jaeger EB, Sprenger M (2007) A Northern Hemispheric climatology of indices for clear air turbulence in the tropopause region derived from ERA40 reanalysis data. J Geophys Res 112(36):D20106

    Google Scholar 

  • Kaplan ML, Charney JJ, Waight KT (2006) Characterizing the severe turbulence environments associated with commercial aviation accidents A real-time turbulence model (RTTM) designed for the operational prediction of hazardous aviation turbulence environments. Meteorol Atmos Phys 94(1–4):235–270

    Google Scholar 

  • Kim JH, Chun HY (2010) A numerical study of clear-air turbulence (CAT) encounters over South Korea on 2 April 2007. J Appl Meteorol Climatol 49(12):2381–2403

    Google Scholar 

  • Kim JH, Chun HY, Sharman RD et al (2011) Evaluations of upper-level turbulence diagnostics performance using the graphical turbulence guidance (GTG) system and pilot reports (PIREPs) over East Asia. J Appl Meteorol Climatol 50:1936–1951

    Google Scholar 

  • Kim JH, Chan WN, Sridhar B et al (2016) Impact of the north Atlantic oscillation on transatlantic flight routes and clear-air turbulence. J Appl Meteorol Climatol 55(3):763–771

    Google Scholar 

  • Kim JH, Sharman RD, Strahan M et al (2018) Improvements in non-convective aviation turbulence prediction for the world area forecast system (WAFS). Bull Am Meteor Soc 99(11):2295–2311

    Google Scholar 

  • Kim JH, Kim D, Lee DB et al (2020) Impact of climate variabilities on trans-oceanic flight times and emissions during strong NAO and ENSO phases. Environ Res Lett 15:105017

    Google Scholar 

  • Kuang XY, Zhang YC (2006) The seasonal variation of the east Asian sub-tropical westerly jet and its thermal mechanism. Acta Meteor Sin 64(5):564–575

    Google Scholar 

  • Lane TP, Sharman RD, Trier SB et al (2012) Recent advances in the understanding of near-cloud turbulence. Bull Am Meteor Soc 93(4):499–515

    Google Scholar 

  • Lee D-B, Chun H-Y (2018) A numerical study of aviation turbulence encountered on 13 February 2013 over the yellow sea between China and the Korean Peninsula. J Appl Meteorol Climatol 57(4):1043–1060

    Google Scholar 

  • Lee SH, Williams PD, Frame THA (2019) Increased shear in the North Atlantic upper-level jet stream over the past four decades. Nature 572:639–642

    Google Scholar 

  • Lee DB, Chun HY, Kim SH et al (2022) Development and evaluation of global Korean aviation turbulence forecast systems with outputs of an operational numerical weather prediction model and in situ flight turbulence observation data. Weather Forecast 37(3):371–392

    Google Scholar 

  • Lee JH, Kim JH, Sharman RD et al (2023) Climatology of Clear-Air Turbulence in upper troposphere and lower stratosphere in the Northern Hemisphere using ERA5 reanalysis data. J Geophys Res Atmos 128(1):e2022

    Google Scholar 

  • Liman A, Jun W, Jinming F et al (2016) Spatiotemporal characteristics of high altitude turbulence over eastern China and their relationship with the equatorial central and eastern Pacific sea surface temperature. Chin J Atmos Sci 40(5):1073–1088

    Google Scholar 

  • Miles JW (1961) On the stability of heteorogeneous shear flows. J Fluid Mech 10:496–508

    Google Scholar 

  • Miles JW (1986) Richardson’s criterion for the stability of stratified shear flow. Phys Fluids 29:3470–3471

    Google Scholar 

  • Park SH, Kim JH, Sharman RD et al (2016) Update of upper level turbulence forecast by reducing unphysical components of topography in the numerical weather prediction model. Geophys Res Lett 43:7718–7724

    Google Scholar 

  • Pearson JM, Sharman RD (2017) Prediction of energy dissipation rates for aviation turbulence. Part II: nowcasting convective and nonconvective turbulence. J Appl Meteorol Climatol 56:339–351

    Google Scholar 

  • Peng X, She Q, Long L et al (2017) Long-term trend in ground-based air temperature and its responses to atmospheric circulation and anthropogenic activity in the Yangtze River Delta, China. Atmos Res 195:20–30

    Google Scholar 

  • Sharman R, Pearson JM (2017) Prediction of energy dissipation rates for aviation turbulence. Part I: Forecasting nonconvective turbulence. J Appl Meteorol Climatol 56:317–337

    Google Scholar 

  • Sharman R, Tebaldi C, Wiener G et al (2006) An integrated approach to mid-and upper-level turbulence forecasting. Weather Forecast 21(3):268–287

    Google Scholar 

  • Sharman R, Trier SB, Lane TP et al (2012) Sources and dynamics of turbulence in the upper troposphere and lower stratosphere: a review. Geophys Res Lett 39(12):L12803

    Google Scholar 

  • Sharman R, Cornman L, Meymaris G et al (2014) Description and derived climatologies of automated in situeddy-dissipation-rate reports of atmospheric turbulence. J Appl Meteorol Climatol 53:1416–1432

    Google Scholar 

  • Storer LN, Williams PD, Joshi MM (2017) Global response of clear-air turbulence to climate change. Geophys Res Lett 44(19):9976–9984

    Google Scholar 

  • Storer LN, Williams PD, Gill PG (2019) Aviation turbulence: dynamics, forecasting, and response to climate change. Pure Appl Geophys 176(5):2081–2095

    Google Scholar 

  • Thompson DWJ, Wallace JM (1998) The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophys Res Lett 25(9):1297–1300

    Google Scholar 

  • Wallace JM, Gutzler DS (1981) Teleconnections in the geopotential height field during the northern hemisphere winter. Mon Weather Rev 109(4):784–812

    Google Scholar 

  • Wang P, Hui P, Xue D et al (2019) Future projection of heat waves over China under global warming within the CORDEX-EA-II project. Clim Dyn 53:957–973

    Google Scholar 

  • Watkins CD, Browning KA (1973) The detection of clear air turbulence by radar. Phys Technol 4(1):28–61

    Google Scholar 

  • Williams JK (2014) Using random forests to diagnose aviation turbulence. Mach Learn 95:51–70

    Google Scholar 

  • Williams PD (2017) Increased light, moderate, and severe clear-air turbulence in response to climate change. Adv Atmos Sci 34(5):576–586

    Google Scholar 

  • Williams PD, Joshi MM (2013) Intensification of winter transatlantic aviation turbulence in response to climate change. Nat Clim Chang 3(7):644–648

    Google Scholar 

  • Xu J, Wang D, Gong Y et al (2018) Quantitative diagnostic and distribution characteristics of aircraft turbulence in China. J Chengdu Univ Inf Technol 33(6):704–712

    Google Scholar 

  • Zhao H, Pan X, Wang Z et al (2019) What were the changing trends of the seasonal and annual aridity indexes in northwestern China during 1961–2015? Atmos Res 222:154–162

    Google Scholar 

  • Zhou W, Leung LR, Lu J (2022) Seasonally and regionally dependent shifts of the atmospheric westerly jets under global warming. J Clim 35:5433–5447

    Google Scholar 

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Acknowledgements

This study is supported by the National Key Research and Development Program of China (2018YFA0606003) and the National Natural Science Foundation of China (41905045). We would like to express our gratitude to the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Climate Center (NCC) of China Meteorological Administration (CMA) for providing the valuable research data. We also thank Meiyu Chen for her suggestion and feedback about the grammatical issues in this work.

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BH and JT contributed to the study conception and design, material preparation, and data collection. Analysis were performed by BH and PH. JD, XS, and JT helped perform the analysis with constructive discussions. The first draft of the manuscript was written by BH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jianping Tang.

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Hu, B., Hui, P., Ding, J. et al. Spatiotemporal characteristics of clear-air turbulence (CAT) potential in China during 1979–2020. Clim Dyn 61, 2339–2353 (2023). https://doi.org/10.1007/s00382-023-06684-z

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  • DOI: https://doi.org/10.1007/s00382-023-06684-z

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