Abstract
Cold wave (CW) events over India are usually observed during the boreal winter months, November–February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large scale features associated with these CW events are also discussed. Utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades, the CW prone region has been identified. By calculating the standardized area-averaged (over the CW prone region) Tmin anomalies time series, the CW events are identified over the period 1951–2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events even under the general warming scenarios. It is found that the long CW events (> 7 days) are favoured by the La-Nina condition and short CW events (\(\le\) 7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the north-west of Indian longitude with very slow movement of westerly trough to the east is found to be associated with the long CW events, whereas in case of short events the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW prone region up to 2–3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time in spite of its uncertainties in space and time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The IMD observation minimum temperature datasets are available at “https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html”. ERA5 reanalysis can be downloaded following https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5. NCEP reanalysis is available on https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html. The ERP model datasets will be made available upon request to the corresponding author. The other products generated during this study are available from the corresponding author upon reasonable request.
Code availability
Analytical scripts and programming used in this study are available from the corresponding author upon reasonable request.
References
Abhilash S, Sahai AK, Borah N et al (2014a) Does bias correction in the forecasted SST improve the extended range prediction skill of active-break spells of Indian summer monsoon rainfall? Atmos Sci Lett 15:114–119. https://doi.org/10.1002/asl2.477
Abhilash S, Sahai AK, Pattnaik S et al (2014b) Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP Climate Forecast System. Int J Climatol 34:98–113. https://doi.org/10.1002/joc.3668
Abhilash S, Sahai AK, Borah N et al (2015) Improved spread-error relationship and probabilistic prediction from the CFS-based grand ensemble prediction system. J Appl Meteorol Climatol 54:1569–1578. https://doi.org/10.1175/JAMC-D-14-0200.1
Arora M, Goel NK, Singh P (2005) Evaluation of temperature trends over India / Evaluation de tendances de température en Inde. Hydrol Sci J. https://doi.org/10.1623/hysj.50.1.81.56330
Bedekar VC, Dekate M V, Banerjee AK (1974) Heat and cold waves in India forcasting manual part IV (FMU Rep. No. IV-6). 63
Bedi HS, Parthasarathy B (1967) Cold Waves over northwest India and neighbourhood. Indian J Meteorol Geophys 18:371–378
Chand R, Singh C (2015) Movements of western disturbance and associated cloud convection. J Indian Geophys Union 19:62–70
Chattopadhyay S (2016) A fortnightly online magazine on weather, water and hazards, vol 2. IRIS Publ Pvt Ltd
Cohen J, Entekhabi D (1999) Corrections to “Eurasian snow cover variability and Northern Hemisphere climate predictability.” Geophys Res Lett 26:1051. https://doi.org/10.1029/1999GL900200
De US, Dube RK, Prakasa Rao GS (2005) Extreme Weather Events over India in the last 100 years. J Indian Geophys Union 9:173–187
Dimri AP (2013) Interannual variability of Indian winter monsoon over the Western Himalayas. Glob Planet Change 106:39–50. https://doi.org/10.1016/j.gloplacha.2013.03.002
Dimri AP, Niyogi D, Barros AP et al (2015) Western disturbances: a review. Rev Geophys 53:225–246. https://doi.org/10.1002/2014RG000460
Ferro CAT, Stephenson DB (2011) Extremal dependence indices: Improved Verification measures for deterministic forecasts of rare binary events. Weather Forecast 26:699–713. https://doi.org/10.1175/WAF-D-10-05030.1
Ghil M, Allen MR, Dettinger MD et al (2002) Advanced spectral methods for climatic time series. Rev Geophys 40:3-1–3-41. https://doi.org/10.1029/2000RG000092
Gupta AK, Chopde S, Nair SS et al (2021) Mapping climatic and biological disasters in India: study of spatial & temporal patterns and lessons for strengthening resilience. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH India
Haiden T, Magnusson L, Richardson D (2014) Statistical evaluation of ECMWF extreme wind forecasts. ECMWF Newsl. No. 139–Spring 2014
Herring SC, Hoerling MP, Peterson TC, Stott PA (2014) Explaining extreme events of 2013 from a climate perspective. Bull Am Meteorol Soc 95:S1–S96. https://doi.org/10.1175/1520-0477-95.9.s1.1
Herring SC, Hoell A, Hoerling MP et al (2016) Explaining extreme events of 2015 from a climate perspective. Bull Am Meteorol Soc 97:S1–S145. https://doi.org/10.1175/bams-explainingextremeevents2015.1
Huang B, Liu C, Banzon V et al (2021) Improvements of the daily optimum interpolation sea surface temperature (DOISST) Version 2.1. J Clim 34:2923–2939. https://doi.org/10.1175/JCLI-D-20-0166.1
India Meteorological Department (2020) Precipitation over Western Himalayan Region and adjoining plains of Northwest India during 27th–28th December and fresh Cold wave spell over the plains of Northwest and adjoining Central India during subsequent days [Press Release]. https://internal.imd.gov.in/press_release/20201227_pr_967.pdf
Joseph S, Sahai AK, Phani R et al (2019) Skill evaluation of extended-range forecasts of rainfall and temperature over the meteorological subdivisions of India. Weather Forecast 34:81–101. https://doi.org/10.1175/WAF-D-18-0055.1
Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. https://doi.org/10.1175/1520-0477(1996)077%3c0437:TNYRP%3e2.0.CO;2
Kashyapi A, Natu JC, Shinde AS, Chopade PN (2019) Weather in India: winter season (January-February 2018). Mausam 70:181–194
Kaur M, Sahai AK, Phani R et al (2022) Multi-physics schema for sub-seasonal prediction of Indian summer monsoon. Clim Dyn 58:669–690. https://doi.org/10.1007/s00382-021-05926-2
Kodra E, Steinhaeuser K, Ganguly AR (2011) Persisting cold extremes under 21st-century warming scenarios. Geophys Res Lett 38:1–5. https://doi.org/10.1029/2011GL047103
Krishnamurti TN (1961) The subtropical jet stream of winter. J Meteorol 18:172–191
Li J, Zhu Z, Dong W (2017a) A new mean-extreme vector for the trends of temperature and precipitation over China during 1960–2013. Meteorol Atmos Phys 129:273–282. https://doi.org/10.1007/s00703-016-0464-y
Li J, Zhu Z, Dong W (2017b) Assessing the uncertainty of CESM-LE in simulating the trends of mean and extreme temperature and precipitation over China. Int J Climatol 37:2101–2110. https://doi.org/10.1002/joc.4837
Mandal R, Joseph S, Sahai AK et al (2019) Real time extended range prediction of heat waves over India. Sci Rep. https://doi.org/10.1038/s41598-019-45430-6
Nair SA, Pai DS, Rajeevan M (2016) Climatology and trend of cold waves over India during 1971–2010. Mausam 67:651–658. https://doi.org/10.54302/mausam.v67i3.1384
North R, Trueman M, Mittermaier M, Rodwell MJ (2013) An assessment of the SEEPS and SEDI metrics for the verification of 6h forecast precipitation accumulations. Meteorol Appl 20:164–175. https://doi.org/10.1002/met.1405
Pai DS, Thapliyal V, Kokate PD (2004) Decadal variation in the heat and cold waves over India during 1971–2000. Mausam 55:281–292. https://doi.org/10.54302/mausam.v55i2.1083
Peterson TC, Hoerling MP, Stott PA, Herring SC (2013) Explaining extreme events of 2012 from a climate perspective. Bull Am Meteorol Soc 94:S1–S74
Ratnam JV, Behera SK, Annamalai H et al (2016) ENSO’s far reaching connection to Indian cold waves. Sci Rep 6:1–12. https://doi.org/10.1038/srep37657
Reynolds RW, Smith TM, Liu C et al (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496. https://doi.org/10.1175/2007JCLI1824.1
Riaz SMF, Iqbal MJ, Baig MJ (2018) Influence of Siberian High on temperature variability over northern areas of South Asia. Meteorol Atmos Phys 130:441–457. https://doi.org/10.1007/s00703-017-0531-z
Sahai AK, Sharmila S, Abhilash S et al (2013) Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Curr Sci 104:1394–1408
Sahai AK, Abhilash S, Chattopadhyay R et al (2015) High-resolution operational monsoon forecasts: an objective assessment. Clim Dyn 44:3129–3140. https://doi.org/10.1007/s00382-014-2210-9
Sahai AK, Kaur M, Joseph S et al (2021) Multi-model multi-physics ensemble: a futuristic way to extended range prediction system. Front Clim 3:1–11. https://doi.org/10.3389/fclim.2021.655919
Samra JS, Singh G, Ramakrishna YS (2003) Cold wave of 2002–03—Impact on agricultural. Information Bulletin ICAR
Sandeep A, Prasad VS (2020) On the variability of cold wave episodes over northwest india using an NGFS retrospective analysis. Pure Appl Geophys 177:1157–1166. https://doi.org/10.1007/s00024-019-02335-9
Saranya Ganesh S, Abhilash S, Sahai AK et al (2019) Genesis and track prediction of pre-monsoon cyclonic storms over North Indian Ocean in a multi-model ensemble framework. Nat Hazards 95:823–843. https://doi.org/10.1007/s11069-018-3522-6
Screen JA, Deser C, Sun L (2015) Reduced risk of North American cold extremes due to continued arctic sea ice loss. Bull Am Meteorol Soc 96:1489–1503. https://doi.org/10.1175/BAMS-D-14-00185.1
Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 10:249–254. https://doi.org/10.1002/asl.232
Subbaramayya I, Rao D (1976) Heat wave and cold wave days in different States of India. Mausam 27:436–440. https://doi.org/10.54302/mausam.v27i4.2631
Zhang K, Li J, Zhu Z, Li T (2021) Implications from subseasonal prediction skills of the prolonged heavy snow event over Southern China in early 2008. Adv Atmos Sci 38:1873–1888. https://doi.org/10.1007/s00376-021-0402-x
Zhu Z, Li T (2017) Statistical extended-range forecast of winter surface air temperature and extremely cold days over China. Q J R Meteorol Soc 143:1528–1538. https://doi.org/10.1002/qj.3023
Acknowledgements
IITM is fully supported by the Ministry of Earth Sciences, Govt. of India, New Delhi. We thank National Centre for Medium Range Weather Forecasting (NCMRWF) and Indian National Centre for Ocean Information Services (INCOIS) for providing the analysis datasets. We acknowledge NCEP for technical support on CFS model. We would like to express our sincere gratitude to India Meteorological Department (IMD) for preparing the minimum temperature observation datasets over Indian region, ECMWF and NOAA/OAR/ESRL PSL for making available the ERA5 and NCEP reanalysis. The technical support of HPC (High Performance Computing facility installed at IITM, named AADITYA) support team during the analysis is highly appreciated.
Funding
No separate funding has been utilized for this work.
Author information
Authors and Affiliations
Contributions
SJ and RM conceptualized the study. RM performed the data analysis with the help of SJ and led the writing. The original manuscript is prepared by RM with the significant help of DRP, SJ and NK. The whole work is supervised by A.K.S. Contributions are made in performing the model runs by RM, AD, RP, MK and DRP. All authors contributed to the writing and interpretations of the results.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing financial and non-financial interests.
Ethics approval
The manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published elsewhere previously.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
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.
About this article
Cite this article
Mandal, R., Joseph, S., Sahai, A.K. et al. Diagnostics and real-time extended range prediction of cold waves over India. Clim Dyn 61, 2051–2069 (2023). https://doi.org/10.1007/s00382-023-06666-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-023-06666-1