Skip to main content

Advertisement

SpringerLink
Log in

Estimating the local predictability of heatwaves in south China using the backward nonlinear local Lyapunov exponent method

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Heatwaves can have significant negative socioeconomic impacts and damage ecosystems. Consequently, accurate forecasts of such events are of great importance. In this study, the backward nonlinear local Lyapunov exponent (BNLLE) is used to investigate the local predictability of summer (June, July, and August) heatwave events over south China (SC). The predictability of the summer 2-m temperature (T2m) was quantified first. The results show that the summer T2m has a high predictability, and its predictability limit can reach 12 days, which is close to the upper limit of atmospheric predictability. Nine major heatwave events that occurred over SC were analysed, and their error dynamics and local predictabilities were studied using the BNLLE method. The local predictability limits covered a wide range, varying from 4 to 12 days. Analysis of the forecast error growth and associated rates revealed that the nine major heatwave events showed different error dynamics, thereby leading to the different local predictability limits. In addition, the regional dynamical information associated with these nine heatwave events was investigated and the error dynamics were found to have a heterogeneous spatial distribution. That is, both the error growth and their rates in the northern regions were larger than those in the southern regions. The regional dynamical information associated with the summer T2m also showed the same pattern. Therefore, the northern regions of SC are sensitive to error growth and limit the heatwave forecast skill.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The TIGGE (The Interactive Grand Global Ensemble) (https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=pf/). The ERA-interim analysis is from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form and (https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/), respectively.

References

  • Barlow KM, Christy BP, O’leary G, Riffkin P, Nuttall J (2015) Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crop Res 171:109–119

    Google Scholar 

  • Bougeault P, Coauthors (2010) The THORPEX interactive grand global ensemble. B. Am. Meteorol. Soc. 91:1059–1072

    Google Scholar 

  • Craven M, Barnard A, Labuschagne MT (2007) The impact of cold temperatures during grain maturation on selected quality parameters of wheat. J Sci Food Agr 87:1783–1793

    Google Scholar 

  • Dee DP, Coauthors (2011) The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J Roy Meteor Soc 137:553–597

    Google Scholar 

  • Ding RQ, Li JP (2007) Nonlinear finite-time Lyapunov exponent and predictability. Phys Lett A 364:396–400

    Google Scholar 

  • Ding R, Li J, Ha K-J (2008) Trends and interdecadal changes of weather predictability during 1950s–1990s. J Geophys Res 113:D24112. https://doi.org/10.1029/2008JD01040

    Article  Google Scholar 

  • Ding T, Qian W, Yan Z (2010) Changes in hot days and heat waves in China during 1961–2007. Int J Climatol 30:1452–1462

    Google Scholar 

  • Ding RQ, Li JP, Seo KH (2011) Estimate of the predictability of boreal summer and winter intraseasonal oscillations from observations. Mon Wea Rev 139:2421–2438

    Google Scholar 

  • Ding RQ, Li JP, Zheng F, Feng J, Liu DQ (2016) Estimating the limit of decadal-scale climate predictability using observational data. Clim Dyn 46:1563–1580

    Google Scholar 

  • Duan WS, Mu M (2009) Conditional nonlinear optimal perturbation: applications to stability, sensitivity, and predictability. Sci China Ser D 52:883–906

    Google Scholar 

  • Farrell BF (1990) Small error dynamics and the predictability of atmospheric flows. J Atmos Sci 47:2409–2416

    Google Scholar 

  • Freychet N, Tett S, Wang J, Hegerl G (2017) Summer heat waves over eastern China: dynamical processes and trend attribution. Environ Res Lett 12:024015

    Google Scholar 

  • García-León D, Casanueva A, Standardi G, Burgstall A, Flouris AD, Nybo L (2021) Current and projected regional economic impacts of heatwaves in Europe. Nat Commun 12:1–10

    Google Scholar 

  • Gruber S, Hoelzle M, Haeberli W (2004) Permafrost thaw and destabilization of Alpine rock walls in the hot summer of 2003. Geophys Res Lett 31:L13504. https://doi.org/10.1029/2004GL020051

    Article  Google Scholar 

  • Ham Y-G, Kim J-H, Luo J-J (2019) Deep learning for multi-year ENSO forecasts. Nature 573:568–572

    Google Scholar 

  • He W, Feng GL, Dong W, Li J (2006) On the predictability of the Lorenz system. Acta Physica Sinica 55(2):969–977

    Google Scholar 

  • He W, Xie X, Mei Y, Wan S, Zhao S (2021) Decreasing predictability as a precursor indicator for abrupt climate change. Clim Dyn 56:3899–3908

    Google Scholar 

  • Hochman A, Scher S, Quinting J, Pinto JG, Messori G (2021) A new view of heat wave dynamics and predictability over the eastern Mediterranean. Earth Syst Dynam 12:133–149

    Google Scholar 

  • Hou W, Coauthors, (2014) Climatic characteristics over China in 2013. Meteorol Mon (in Chinese) 40:482–493

    Google Scholar 

  • Hu K, Huang G, Qu X, Huang R (2012) The impact of Indian Ocean variability on high temperature extremes across the southern Yangtze River valley in late summer. Adv Atmos Sci 29:91–100

    Google Scholar 

  • Hu L, Huang G, Qu X (2017) Spatial and temporal features of summer extreme temperature over China during 1960–2013. Theor Appl ClimatoL 128:821–833

    Google Scholar 

  • Katsafados P, Papadopoulos A, Varlas G, Papadopoulou E, Mavromatidis E (2014) Seasonal predictability of the 2010 Russian heat wave. Nat Hazard Earth Sys 14:1531–1542

    Google Scholar 

  • Lacarra JF, Talagrand O (1988) Short-range evolution of small perturbations in a barotropic model. Tellus A 40:81–95

    Google Scholar 

  • Lee HJ, Lee WS, Yoo JH (2016) Assessment of medium-range ensemble forecasts of heat waves. Atmos Sci Lett 17:19–25

    Google Scholar 

  • Lee H-J, Lee W-S, Chun JA, Lee HW (2020) Probabilistic heat wave forecast based on a large-scale circulation pattern using the TIGGE data. Wea Forecasting 35:367–377

    Google Scholar 

  • Leutbecher, and T. N. Palmer, (2008) Ensemble forecasting. J Comput Phys 227:3515–3539

    Google Scholar 

  • Li JP, Ding RQ (2011) Temporal–spatial distribution of atmospheric predictability limit by local dynamical analogs. Mon Wea Rev 139:3265–3283

    Google Scholar 

  • Li X, Ding R (2022) The backward nonlinear local Lyapunov exponent and its application to quantifying the local predictability of extreme high-temperature events. Clim Dyn. https://doi.org/10.1007/s00382-022-06469-w

    Article  Google Scholar 

  • Li X, Ding R, Li J (2019) Determination of the backward predictability limit and its relationship with the forward predictability limit. Adv Atmos Sci 36:669–677

    Google Scholar 

  • Li X, Ding R, Li J (2020) Quantitative study of the relative effects of initial condition and model uncertainties on local predictability in a nonlinear dynamical system. Chaos Soliton Fract 139:110094

    Google Scholar 

  • Li X, Ding R, Li J (2022) A New technique to quantify the local predictability of extreme events: the backward nonlinear local lyapunov exponent method. Front Env Sci-Switz. https://doi.org/10.3389/fenvs.2022.825233

    Article  Google Scholar 

  • Li D, Hu S, He W et al (2022) The area prediction of western North Pacific Subtropical High in summer based on Gaussian Naive Bayes. Clim Dyn. https://doi.org/10.1007/s00382-022-06252-x

    Article  Google Scholar 

  • Lorenz EN (1963) Deterministic Nonperiodic Flow. J Atmos Sci 20:130–141

    Google Scholar 

  • Lorenz EN (1969a) Atmospheric predictability as revealed by naturally occurring analogues. J Atmos Sci 26:636–646

    Google Scholar 

  • Lorenz EN (1969b) Three approaches to atmospheric predictability. Bull Am Meteorol Soc 50:345–351

    Google Scholar 

  • Lu C, Coauthors, (2020) An unusual heat wave in North China during midsummer, 2018. Front Earth Sc-Switz 8:238

    Google Scholar 

  • Matsueda M (2011) Predictability of Euro‐Russian blocking in summer of 2010. Geophys Res Lett 38:L06801. https://doi.org/10.1029/2010GL046557

    Article  Google Scholar 

  • Mohammed AR, Tarpley L (2009) Impact of high nighttime temperature on respiration, membrane stability, antioxidant capacity, and yield of rice plants. Crop Sci 49:313–322

    Google Scholar 

  • Mu M, Duan W (2003) A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation. Chin Sci Bull 48:1045–1047

    Google Scholar 

  • Mu M, Duan W, Wang B (2003) Conditional nonlinear optimal perturbation and its applications. Nonlinear Proc Geoph 10:493–501

    Google Scholar 

  • Mu M, Zhou F, Wang H (2009) A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: conditional nonlinear optimal perturbation. Mon Weather Rev 137(5):1623–1639

    Google Scholar 

  • Nese JM (1989) Quantifying local predictability in phase space. Physica D 35:237–250

    Google Scholar 

  • Poumade`re M, Mays C, Le Mer S, and B. R, (2005) The 2003 heat wave in France: dangerous climate change here and now. Risk Anal 25:1483–1494

    Google Scholar 

  • Qian C, Yan Z, Wu Z, Fu C, Tu K (2011) Trends in temperature extremes in association with weather-intraseasonal fluctuations in eastern China. Adv Atmos Sci 28:297–309

    Google Scholar 

  • Ratnam J, Dijkstra H, Behera SK (2020) A machine learning based prediction system for the Indian Ocean Dipole. Sci Rep UK 10:1–11

    Google Scholar 

  • Ren Y, Parker D, Ren G, Dunn R (2016) Tempo-spatial characteristics of sub-daily temperature trends in mainland China. Clim Dyn 46:2737–2748

    Google Scholar 

  • Russo S, Coauthors, (2014) Magnitude of extreme heat waves in present climate and their projection in a warming world. J Geophys Res-Atmos. https://doi.org/10.1002/2014JD022098

    Article  Google Scholar 

  • Schaller N, Sillmann J, Anstey J, Fischer EM, Grams CM, Russo S (2018) Influence of blocking on Northern European and Western Russian heatwaves in large climate model ensembles. Environ Res Lett 13:054015

    Google Scholar 

  • Su X, Yuan H, Zhu Y, Luo Y, Y. J. J. o. G. R. A. Wang, (2014) Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008–2012. J Geophys Res-Atmos 119:7292–7310

    Google Scholar 

  • Swinbank R, Coauthors, (2016) The TIGGE project and its achievements. B Am Meteorol Soc 97:49–67

    Google Scholar 

  • Van den Dool HM (1994) Searching for analogues, how long must we wait? Tellus 46A:314–324

    Google Scholar 

  • Wang Q, Zhong W, Yang S et al (2022) Southern China winter rainfall modulated by South China Sea warming. Geophys Res Lett 49(5):e2021GL097181

    Google Scholar 

  • Wang Q, Zhang B, Zeng L et al (2022) Properties and drivers of marine heat waves in the Northern South China Sea. J Phys Oceanogr 52(5):917–927

    Google Scholar 

  • Xu H, Chen L, Duan W (2021) Optimally growing initial errors of El Niño events in the CESM. Clim. Dyn 56:3797–3815

    Google Scholar 

  • Yeo SR, Yeh SW, Lee WS (2019) Two types of heat wave in Korea associated with atmospheric circulation pattern. J Geophys Res-Atmos 124:7498–7511

    Google Scholar 

  • Yoden S, Nomura M (1993) Finite-time Lyapunov stability analysis and its application to atmospheric predictability. J Atmos Sci 50:1531–1543

    Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Jie Feng for the discussion. This work is based on TIGGE data. TIGGE (The Interactive Grand Global Ensemble) is an initiative of the World Weather Research Programme (WWRP). This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 42005054, 42288101 and 41975070), China Postdoctoral Science Foundation (2020M681154) and the Science and Technology Commission of Shanghai Municipality (20dz1200700).

Funding

This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 42005054,42288101 and 41975070), China Postdoctoral Science Foundation (2020M681154) and the Science and Technology Commission of Shanghai Municipality (20dz1200700).

Author information

Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China

    Xuan Li

  2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Ruiqiang Ding

  3. Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES)/Key Laboratory of Physical Oceanography/Institute for Advanced Ocean Studies, Ocean University of China, Qingdao, China

    Jianping Li

Authors
  1. Xuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruiqiang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first draft of the manuscript was written by XL. RD and JL commented on initial versions of the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Ruiqiang Ding.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Ding, R. & Li, J. Estimating the local predictability of heatwaves in south China using the backward nonlinear local Lyapunov exponent method. Clim Dyn 61, 3605–3618 (2023). https://doi.org/10.1007/s00382-023-06757-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-023-06757-z

Keywords

Search

Navigation