Abstract
Temporal trends and spatial patterns of six warm season (May–September) hot temperature indicators (WSHTIs) were developed and explored for Saudi Arabia. The indicators focus on the frequency and intensity of hot days and nights, and heat waves. Systematic upward trends in maximum (Tmax) and minimum (Tmin) temperatures were found at most of the stations, suggesting ongoing change in the climatology of the upper-tail of the frequency distribution. Taking into the account the observed effects of climate change on the country’s climate, hot temperature events were defined using a monthly and decadal, time-sensitive approach. Indicators of event frequency are count data; thus, different Poisson models were used for temporal analysis. Further, a novel method of time-series clustering was introduced to recognize spatiotemporal patterns of WSHTIs. Different patterns were observed over time and space not only across stations but also among WSHTIs. Generally, warming trends were detected in the upper limits for both Tmax and Tmin across the warm season months with a few surprising exceptions, mostly for Tmax. Results suggest that the impact of climate change on hot weather events was more pronounced at night at most of the stations in Saudi Arabia. The overall results suggest that not only local and regional factors, such as elevation, latitude, and distance from a large body of water, but also large-scale factors such as atmospheric circulation patterns are likely responsible for the observed temporal and spatial patterns.
Similar content being viewed by others
Change history
22 June 2019
The presentation of Fig. 4 was incorrect.
References
Aghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering—a decade review. Inf Syst 53:16–38
Alghamdi AS, Harrington J (2018) Time-sensitive analysis of a warming climate on heat waves in Saudi Arabia: temporal patterns and trends. Int J Climatol 38:3123–3139. https://doi.org/10.1002/joc.5489
Ali AH (1994) Wind regime of the Arabian gulf. In: El-Baz F, Makharita RM (eds) The Gulf war and the environment. Gordon and Breach Science Publishers, Switzerland, pp 31–48
Alkolibi FM (1995) Mid-tropospheric geopotential height patterns as related to temperature and precipitation in Saudi Arabia, ETD collection for University of Nebraska-Lincoln. Paper AAI9604395
Almazroui M, Islam MN, Athar H, Jones PD, Rahan MA (2012a) Recent climate change in the Arabian Peninsula: annual rainfall and temperature analysis of Saudi Arabia for 1978–2009. Int J Climatol 32:953–966
Almazroui M, Islam MN, Athar H, Jones PD, Rahan MA (2012b) Recent climate change in the Arabian Peninsula: seasonal rainfall and temperature climatology of Saudi Arabia for 1978–2009. Atmos Res 11:29–45
Almazroui M, Islam MN, Jones PD (2013) Urbanization effects on the air temperature rise in Saudi Arabia. Clim Chang 120:109–122
Almazroui M, Islam MN, Dambul R, Jones PD (2014) Trends of temperature extremes in Saudi Arabia. Int J Climatol 34:808–826
AlSarmi S, Washington R (2011) Recent observed climate change over the Arabian Peninsula. J Geophys Res 116:D11109. https://doi.org/10.1029/2010JD015459
AlSarmi S, Washington R (2013) Changes in climate extremes in the Arabian Peninsula: analysis of daily data. Int J Climatol 34:1329–1345
Ambaum MH (2010) Significance tests in climate science. J Clim 23:5927–5932
Athar H (2014) Trends in observed extreme climate indices in Saudi Arabia during 1979–2008. Int J Climatol 34:1561–1574
Bajat B, Blagojević D, Kilibarda M, Luković J, Tošić I (2015) Spatial analysis of the temperature trends in Serbia during the period 1961–2010. Theor Appl Climatol 121:289–301
Balling RC Jr, Skindlov JA, Phillips DH (1990) The impact of increasing summer mean temperatures on extreme maximum and minimum temperatures in Phoenix, Arizona. J Clim 3:1491–1494
Bao J, Li X, Yu C (2015) The construction and validation of the heat vulnerability index, a review. Int J Environ Res Public Health 12:7220–7234
Braga ALF, Zanobetti A, Schwartz J (2001) The time course of weather-related deaths. Epidemiology 12:662–667
Brandt PT, Williams JT, Fordham BO, Pollins B (2000) Dynamic models for persistent event count time series. Am J Polit Sci 44:823–843
Broxton PD, Zeng X, SullaMenashe D, Troch PA (2014) A global land cover climatology using MODIS data. J Appl Meteorol Climatol 53:1593–1605
Cameron AC, Trivedi PK (1998) Introduction. In: Regression analysis of count data. Cambridge University Press, Cambridge, pp 1–17
Chatterjee S, Simonoff JS (2013) Count regression. In: Handbook of regression analysis. Wiley, Hoboken, pp 191–213
Coelho CAS, Ferro CAT, Stephenson DB, Steinskog DJ (2008) Methods for exploring spatial and temporal variability of extreme events in climate data. J Clim 21:2072–2092
Donat MG et al (2014) Changes in extreme temperature and precipitation in the Arab region: long-term trends and variability related to ENSO and NAO. Int J Climatol 34:581–592
Easterling DR, Meehl GA, Parmesan C, Chagnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074
Ebi KL, Teisberg TJ, Kalkstein LS, Robinson L, Weiher RF (2004) Heat watch/warning systems save lives: estimated costs and benefits for Philadelphia 1995–1998. Am Meteorol Soc 85:1067–1073
Fouillet A et al (2008) Has the impact of heat waves on mortality changed in France since the European heat wave of summer 2003? A study of the 2006 heat wave. Int J Epidemiol 37:309–317
Hajat S et al (2002) Impact of hot temperatures on death in London: a time series approach. J Epidemiol Community Health 56:367–372
Handl J, Knowles J, Kell DB (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21:3201–3212
Hansen J, Sato M (2016) Regional climate change and national responsibilities. Environ Res Lett 11:034009
Hyndman RJ, Fan Y (1996) Sample quantiles in statistical packages. Am Stat 50:361–365
Islam NM, Almazroui M, Dambul R, Jones PD, Alamoudi AQ (2015) Long-term changes in seasonal temperature extremes over Saudi Arabia during 1981–2010. Int J Climatol 35(7):1579–1592
Katz RW, Brown BG (1992) Extreme events in a changing climate: variability is more important than averages. Clim Chang 21:289–302
Liao TW (2005) Clustering of time series data—a survey. Pattern Recogn 38:1857–1874
Lord E, Willems M, Lapointe FJ, Makarenkov V (2017) Using the stability of objects to determine the number of clusters in datasets. Inf Sci 393:29–46
Mearns L, Katz R, Schneider S (1984) Extreme high-temperature events: changes in their probabilities with changes in mean temperature. J Clim Appl Meteorol 23:1601–1613
Medina-Ramon M, Schwartz J (2007) Temperature, temperature extremes, and mortality: a study of acclimatisation and effect modification in 50 US cities. Occup Environ Med 64:827–833
Mora C et al (2017) Global risk of deadly heat. Nat Clim Chang 7:501–506
Nasrallah HA, Nieplova E, Ramadan E (2004) Warm season extreme temperature events in Kuwait. J Arid Environ 56:357–371
Oke TR, Grimmond CS, Spronken RA (1998) On the confounding role of rural wetness in assessing urban effects on climate. Preprints 2nd Second Urban Environment Symposium, AMS, 59–62
Pal JS, Eltahir EA (2015) Future temperature in Southwest Asia projected to exceed a threshold for human adaptability. Nat Clim Chang 6:197–200
Perkins SE, Alexander LV (2013) On the measurement of heat waves. J Clim 26:4500–4517
Rehman S, Al-Hadhrami LM (2012) Extreme temperature trends on the west coast of Saudi Arabia. Atmos Clim Sci 2:351–361
Robeson SM (2002a) Increasing growing-season length in Illinois during the 20th century. Climate Change 52:219–238
Robeson SM (2002b) Relationships between mean and standard deviation of air temperature: implications for global warming. Clim Res 22:205–213
Robeson SM (2004) Trends in time-varying percentiles of daily minimum and maximum temperature over North America. Geophys Res Lett 31. https://doi.org/10.1029/2003GL019019
Robeson SM, Doty JA (2005) Identifying rogue air-temperature stations using cluster analysis of percentile trends. J Clim 18:1275–1287
Ryden J (2016) A statistical analysis of trends for warm and cold spells in Uppsala by means of counts. Geogr Ann 97:431–436
Schär C et al (2004) The role of increasing temperature variability for European summer heat waves. Nature 427:332–336. https://doi.org/10.1038/nature02300
Seneviratne SI et al (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB et al (eds) 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 (IPCC). Cambridge University Press, Cambridge, pp 109–230
Sokal RR, Rohlf FJ (1962) The comparison of dendrograms by objective methods. Taxon 11:33–40. https://doi.org/10.2307/1217208
Suseela V, Conant RT, Wallenstein MD, Dukes JS (2012) Effects of soil moisture on the temperature sensitivity of heterotrophic respiration vary seasonally in an old-field climate change experiment. Glob Chang Biol 18:336–348
Unal Y, Kindap T, Karaca M (2003) Redefining the climate zones of Turkey using cluster analysis. Int J Climatol 23:1045–1055
Vuong QH (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57:307–333
Wang X, Smith K, Hyndman R (2006) Characteristic-based clustering for time series data. Data Min Knowl Disc 13:335–364
Whan K et al (2015) Impact of soil moisture on extreme maximum temperatures in Europe. Weather Clim Extremes 9:57–67
Yang M, Cavanaugh JE, Zamba GK (2015) State-space models for count time series with excess zeros. Stat Model 15:70–90
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
Acknowledgments
The authors acknowledge the freely available statistical software: R (http://www.r-project.org/).
Funding
This work is a part of first author’s doctoral dissertation research at Kansas State University, where the first author was sponsored by King Saud University. The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Thesis Publication Fund No (TPF-009).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original version of this article was revised: The presentation of Fig. 4 was incorrect.
Rights and permissions
About this article
Cite this article
Alghamdi, A.S., Harrington, J. Trends and spatial pattern recognition of warm season hot temperatures in Saudi Arabia. Theor Appl Climatol 138, 793–807 (2019). https://doi.org/10.1007/s00704-019-02860-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-019-02860-6