Abstract
Climatic warming in the global mean has significantly increased the probability of occurrence of heat extremes on time scales ranging from months to seasons. As extreme heat events are most likely to become intense and frequent over the next decades, it is important to examine these events to mitigate its negative impacts on public health and society. This study focuses on Karachi heat extremes over the last 23 years. The power spectral analyses of Karachi heat extremes records have been carried out by two indices: heat index (HI) and effective temperature index (TEE), which are also found to be significantly correlated. The result indicates a regular cyclic pattern of 4.5 years which is estimated to face a heat index of more than 73.63 °C, associated with the El Niño–Southern Oscillation (ENSO). Other peaks are observed at 2.8 and 2.2 years with the expected value of the Karachi heat index of 70.53 and 68.71 °C, respectively. The probabilistic approach is also used to predict the future heatwave events of Karachi. Generalized extreme value (GEV) distribution is found to be the best-fitted probability distribution for the extreme heatwave events on the basis of goodness-of-fit test. Furthermore, the estimation of the return period of the heatwave event reveals that Karachi will be facing a maximum heat index of 84.37 °C or more in the coming 33 years, which suggests an urgent need for mitigation strategies in Karachi to overcome the effects of extreme heatwave events.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data and material used in this research are partly available in the body of the article; other data/material is also available to public and will be provided through writing to the corresponding author.
References
Aldriana E, Djamila YS (2008) Spatio-temporal climatic change of rainfall in East Java Indonesia. Int J Climatol 28:435–448. https://doi.org/10.1002/joc.1543
Ali J, Syed KH, Gabriel HF, Saeed F, Ahmad B, Bukhari SAA (2018) Centennial heat wave projections over Pakistan using ensemble NEX GDDP data set. Earth Syst Environ (2):437–454. https://doi.org/10.1007/s41748-018-0064-8
Allan R (2000) ENSO and climatic variability in the past 150 years. In: Diaz H, Markgraf V (eds) El Niño and the Southern Oscillation: multiscale variability and global and regional impacts. Cambridge University Press, Cambridge, pp 3–35
Anwar NH, Khan HF, Abdullah A, Macktoom S, Fatima A (2022) Designed to fail? Heat governance in urban South Asia: the case of Karachi a scoping study. Edinb Res Arch. https://doi.org/10.7488/era/2180
Arshad A, Ashraf M, Sundari RS, Qamar H, Wajid M, Hasan M (2020) Vulnerability assessment of urban expansion and modelling green spaces to build heat waves risk resiliency in Karachi. Int J Disaster Risk Reduct 46:101468
Blackman RB, Tukey JW (1959) The measurement of power spectra. Dover, New York, p 190
Chang TP (2011) Estimation of wind energy potential using different probability density functions. Appl Energy 88(5):1848–1856. https://doi.org/10.1016/j.apenergy.2010.11.010
Chatfield C (1989) The analysis of time series: an introduction, 4/e. Chapman and Hall, New York
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London. https://doi.org/10.1007/978-1-4471-3675-0
Conover WJ (1999) Practical nonparametric statistics, 3rd edn. Wiley, New York, pp 428–433
De US, Mukhopadhyay RK (1998) Severe heat wave over Indian subcontinent in 1998 in a perspective of global climate. Curr Sci 75(12):1308–1311
Della-Marta PM, Luterbacher J, Weissenfluh HV, Xoplaki E, Brunet M, Wanne H (2007) Summer heat waves over western Europe 1880–2003, their relationship to large-scale forcing and predictability. Clim Dyn 29:251–275. https://doi.org/10.1007/s00382-007-0233-1
Du S, Tang G, Li S (2019) Risk measurement of chinese stock market based on GARCH model and extreme value theory. Open J Bus Manag 7(2):963–997. https://doi.org/10.4236/ojbm.2019.72065
Glantz MH, Katz RW, Nicholls N (eds) (1991) Teleconnections linking worldwide climate anomalies: scientific basis and societal impact. Cambridge University Press, Cambridge
Goto-Maeda Y, Shin DW, O’Brien JJ (2008) Freeze probability of Florida in a regional climate model and climate indices. Geophys Res Lett 35:L11703. https://doi.org/10.1029/2008GL033720
Gregorczuk M (1968) Bioclimates of the world related to air enthalpy. Int J Biometeorol 12(1):35–39. https://doi.org/10.1007/BF01552976
Hasanean HM (2001) Fluctuations of surface air temperature in the Eastern Mediterranean. Theor Appl Climatol 68:75–87. https://doi.org/10.1007/s007040170055
Hasanean HM, Almazroui M, Jones PD, Alamoudi AA (2013) Siberian high variability and its teleconnections with tropical circulations and surface air temperature over Saudi Arabia. Clim Dyn 41:2003–2018. https://doi.org/10.1007/s00382-012-1657-9
Hong HP, Li SH, Mara TG (2013) Performance of the generalized least-squares method for the Gumbel distribution and its application to annual maximum wind speeds. J Wind Eng Ind Aerodyn 119:121–132
Hosking JRM (1990) L-moments: analysis and estimation of distributions using linear combinations of order statistics. J R Stat Soc B 52(1):105–124
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation, a special report of working groups I and II of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 582
Iqbal MJ, Ali M (2012) A probabilistic approach for estimating the return period of extreme annual rainfall in different cities of Punjab. Arab J Geosci 6:2599–2606. https://doi.org/10.1007/s12517-012-0548-z
Iqbal MJ, Quamar J, Yousufzai MAK (2011) Spectral analysis of local climatic fluctuations. Arab J Geosci 4:291–298. https://doi.org/10.1007/s12517-010-0154-x
Jenamani RK (2012) Analysis of Ocean-Atmospheric features associated with extreme temperature variation over east coast of India—a special emphasis to Orissa heat waves of 1998 and 2005. Mausam 63(3):401–422
Jewson S, Caballero R (2003) The use of weather forecasts in the pricing of weather derivatives. Meteorol Appl 10:377–389. https://doi.org/10.1017/S1350482703001099
Katz R, Brush G, Parlange M (2005) Statistics of extremes: modeling ecological disturbances. Ecology 86(5):1124–1134
Keellings D, Waylen P (2014) Increased risk of heat waves in Florida: characterizing changes in bivariate heat wave risk using extreme value analysis. Appl Geogr 46:90–97. https://doi.org/10.1016/j.apgeog.2013.11.008
Kenyon J, Hegerl GC (2008) Influence of modes of climate variability on global temperature extremes. J Clim 21:3872–3889. https://doi.org/10.1175/2008JCLI2125.1
Khan N, Shahid S, Ahmed K, Wang X, Ali R, Ismail T, Nawaz N (2020) Selection of GCMs for the projection of spatial distribution of heat waves in Pakistan. Atmos Res 233:104688. https://doi.org/10.1016/j.atmosres.2019.104688
Kharin VV, Zwiers FW (2000) Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere-ocean GCM. J Clim 13:3760–3788. https://doi.org/10.1175/1520-0442(2000)013%3c3760:CITEIA%3e2.0.CO;2
Lamb HH (1972) Climate: present, past, and future, I: fundamentals and climate now. Methuen, London
Lo Brano V, Orioli A, Ciulla G, Culotta S (2011) Quality of wind speed fitting distributions for the urban area of Palermo Italy. Renew Energy 36(3):1026–1039. https://doi.org/10.1016/j.renene.2010.09.00
Mandal S, Choudhury BU (2015) Estimation and prediction of maximum daily rainfall at Sagar Island using best-fit probability models. Theor Appl Climatol 121:87–97. https://doi.org/10.1007/s00704-014-1212-1
Morala L, Serrano A, Garcia GA (2003) Detecting quasi-oscillation in monthly precipitation regimes of Iberian Penusula. Ann Geophys 21:819–832
Mueller V, Gray C, Kosec K (2014) Heat stress increases long-term human migration in rural Pakistan. Nat Clim Change 4(3):182–185
Negrón-Juárez RI, Liu WT (2001) FFT analysis on NDVI annual cycle and climatic regionality in northeast Brazil. Int J Climatol 21:1803–1820. https://doi.org/10.1002/joc.639
Pal JS, Eltahir EAB (2015) Future temperature in Southwest Asia projected to exceed a threshold for human adaptability. Nat Clim Change 6:197–200. https://doi.org/10.1038/NCLIMATE2833
Parker TJ, Berry GJ, Reeder RJ, Nicholls N (2014) Modes of climate variability and heatwaves in Victoria, Southeastern Australia. Geophys Res Lett 41:6926–6934. https://doi.org/10.1002/2014GL061736
Perkins SE, Argüeso D, White CJ (2015) Relationships between climate variability, soil moisture, and Australian heatwaves. J Geophys Res Atmos 120:8144–8164. https://doi.org/10.1002/2015JD023592
Rizvi SH, Alam K, Iqbal MJ (2019) Spatio-temporal variations in urban heat island and its interaction with heat wave. J Atmos Sol Terr Phys 185(2019):50–57
Rizvi SH, Fatima H, Iqbal MJ, Alam K (2020a) The effect of urbanization on the intensification of SUHIs: analysis by LULC on Karach. J Atmos Sol Terr Phys 207:105374. https://doi.org/10.1016/j.jastp.2020.105374
Rizvi SH, Fatima H, Alam K, Iqbal MJ (2020b) The surface urban heat Island intensity and urban expansion: a comparative analysis for the coastal areas of Pakistan. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00828-5
Rothfusz LP (1990) The heat index “equation” NWS tech. Attach SR 2:90–23
Roulston MS, Kaplan DT, Hardenberg J, Smith LA (2003) Using medium-range weather forecasts to improve the value of wind energy production. Renew Energy 28:585–602. https://doi.org/10.1016/S0960-1481(02)00054-X
Rusticucci M, Tencer B (2008) Observed changes in return values of annual temperature extremes over Argentina. J Clim 21:5455–5467
Saleem F, Zeng X, Hina S, Omer A (2021) Regional changes in extreme temperature records over Pakistan and their relation to Pacific variability. Atmos Res 250:105407. https://doi.org/10.1016/j.atmosres.2020.105407
Sankarasubramanian A, Lall U, Devineni N, Espinueva S (2009) The role of monthly updated climate forecasts in improving intraseasonal water allocation. J Appl Meteorol Clim 48:1464–1482. https://doi.org/10.1175/2009JAMC2122.1
Sherwood SC, Huber M (2010) An adaptability limit to climate change due to heat stress. Proce Natl Acad Sci USA 107(21):9552–9555. https://doi.org/10.1073/pnas.0913352107
Soukissian T (2013) Use of multi-parameter distributions for offshore wind speed modeling: the Johnson SB distribution. Appl Energy 111:982–1000. https://doi.org/10.1016/j.apenergy.2013.06.050
Sprott JC (2003) Chaos and time series analysis. Oxford University Press, Oxford, pp 226–291
Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Frequency analysis of extreme events. Handbook of hydrology. McGraw-Hill, New York
Stevenson S, Capotondi A, Fasullo J, Otto-Bliesner BL (2017) An ensemble approach to understanding the ENSO response to climate change. American Geophysical Union, Fall Meeting 2017, abstract #GC32B-04
Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19:57–70. https://doi.org/10.1016/S0169-2070(01)00123-6
Teodoreanu E, Bunescu I (2007) Thermal comfort. Present Environ Sustain Dev 1:135–142
Thompson RORY (1979) Notes and correspondence: coherence significance levels. J Atmos Sci 36:2020–2021
Tošic I, Unkaševic M (2005) Analysis of precipitation series for Belgrade. Theor Appl Climatol 8:67–77. https://doi.org/10.1007/s00704-004-0076-1
Trenberth KE, Stepaniak DP (2001) Indices of El Nino evolution. J Clim 14:1697–1701. https://doi.org/10.1175/1520-0442(2001)014%3c1697:LIOENO%3e2.0.CO;2
Ullah W, Wang G, Ali G, Hagan DFT (2019) Comparing multiple precipitation products against in-situ observations over different climate regions of Pakistan. Remote Sens 11(6). https://doi.org/10.3390/rs11060628
Umar MA, Saeed F (2018) The role of heat stress in migration decisions: a case study of Faisalabad Islamabad. Sustainable Development Policy Institute, Islamabad
Van Oldenborgh GJ, Philip S, Collins M (2005) El Niño in a changing climate: a multimodel study. Eur Geosci Union 1(2):81–95 (hal-00298272)
Vivekanandan N, Roy SK, Jagtap RS (2012) Assessment of rainfall and temperature using osa estimators of extreme value distributions. Bonfring Int J Softw Eng Soft Comput 2(3):16–21
Waylen PR, Keellings D, Qiu Y (2012) Climate and health in Florida: changes in risks of annual maximum temperatures in the second half of the twentieth century. Appl Geogr 33:73–81. https://doi.org/10.1016/j.apgeog.2011.06.007
Whan K, Zscheischler J, Orth R, Shongwe M, Rahimi M, Asare EO, Seneviratne SI (2015) Impact of soil moisture on extreme maximum temperatures in Europe. Weather Clim Extreme 9:57–67. https://doi.org/10.1016/j.wace.2015.05.001
White CJ, Hudson D, Alves O (2014) ENSO, the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2. Clim Dyn 43:1791–1810
Wilks DS (2006) Statistical methods in the atmospheric sciences. Cornell University, 2nd edn. Elsevier, Amsterdam
Wilks DS (2011) Parametric probability distributions, Statistical methods in the atmospheric sciences. International geophysics series, vol 100, 3rd edn. Elsevier, Amsterdam
Zahid M, Rasul G (2012) Changing trends of thermal extremes in Pakistan. Clim Change 113(3):883–896
Zeng L (2000) Weather derivatives and weather insurance: concept, application, and analysis. Bull Am Meteorol Soc 81:2075–2082. https://doi.org/10.1175/15200477(2000)081%3c2075:WDAWIC%3e2.3.CO;2
Zhang X et al (2005) Trends in Middle East climate extreme indices from 1950 to 2003. J Geophys Res 110:D22104. https://doi.org/10.1029/2005JD006181
Zhou J, Erdem E, Li G, Shi J (2010) Comprehensive evaluation of wind speed distribution models: a case study for North Dakota sites. Energy Convers Manag 51(7):1449–1458. https://doi.org/10.1016/j.enconman.2010.01.020
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Clemens Simmer.
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 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
Rizvi, S.H., Iqbal, M.J. & Ali, M. Probabilistic modeling and identifying fluctuations in annual extreme heatwave regimes of Karachi. Meteorol Atmos Phys 134, 89 (2022). https://doi.org/10.1007/s00703-022-00927-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00703-022-00927-0