Türkiye’de Sıcak Hava Dalgalarının Mekânsal-Zamansal Eğilimleri ve Yükseltiye Bağlı Isınma (1980–2023)

Abstract

Bu çalışmada Türkiye’de 1980–2023 döneminde sıcak hava dalgalarının (SHD) mekânsal, zamansal ve yükselti bantlarına göre değişimleri araştırılmıştır. 239 meteoroloji istasyonunun günlük maksimum sıcaklık verileri kullanılmıştır. SHD olayları, TX90p ve TX95p yüzdelik eşik değerleriyle takvim günü bazlı kayan pencere yöntemiyle tanımlanmıştır. Uzun dönemli eğilimler Modifiye Mann-Kendall ve Sen eğim testleriyle; yükselti bantları arası ayrımlar Kruskal-Wallis ve Dunn sınamalarıyla değerlendirilmiştir. TX90p eşiğinde yıllık ortalama olay sayısı 1,27'den 2,29'a (1,8 kat), TX95p'de 0,48'den 0,99'a (2,1 kat) yükselmiştir. 44 yıllık dönemde olay sayısında 1–5 olay, sıklıkta 2– 20 gün/yıl, sürede 2–11 gün ve şiddette 2,1°C'ye varan artış eğilimleri saptanmıştır. Ege, Akdeniz ve Marmara kıyı kuşağındaki iller en yüksek olay sayılarına sahip bölgeler olarak öne çıkmıştır. SHD olaylı günlerde ortalama maksimum sıcaklıklar Güneydoğu Anadolu'da 44,1°C'ye ulaşmıştır. Eğilim artışları 500–1499 m yükselti kuşağında en güçlü düzeye ulaşmış; 1000–1499 m bandında medyan sıklık eğimi 0,237 gün/yıl iken 0–499 m bandında 0,185 gün/yıl düzeyinde kalmıştır. Şiddet değişkeni her iki eşikte de yükseltiyle istatistiksel olarak anlamlı sonuçlar üretmiştir. Kıyı kesimlerinde denizellik etkisi artışları sınırlarken, orta yükseltilerde belirgin bir ters-U örüntüsü ortaya çıkmıştır. Sonuçlar, sıcak hava dalgası eğilimlerinin bölgesel ölçeğin yanı sıra yükselti boyutunda da belirgin farklılıklar taşıdığını göstermekte; uyum stratejilerinde yükselti faktörünün de gözetilmesi gerektiğini ortaya koymaktadır.

Keywords

• Sıcak Hava Dalgaları
• Ekstrem Sıcaklıklar
• Yükseltiye Bağlı Isınma
• İklim Değişikliği
• Akdeniz Havzası

Spatiotemporal Trends of Heat Waves and Elevation-Dependent Warming in Türkiye (1980–2023)

Abstract

TThis study examines the spatial, temporal, and elevation-dependent changes in heat waves (HW) across Türkiye during the 1980–2023 period. Daily maximum temperature records from 239 meteorological stations were used. HW events were identified through a calendar-day sliding window method using TX90p and TX95p percentile thresholds. Long-term trends were assessed with the Modified Mann-Kendall and Sen's slope tests; differences among elevation bands were evaluated using Kruskal-Wallis and Dunn's tests. Mean annual HW events increased from 1.27 to 2.29 (1.8-fold) for TX90p and from 0.48 to 0.99 (2.1-fold) for TX95p. Over the 44-year period, event counts rose by 1–5 events, frequency by 2–20 days/year, duration by 2–11 days, and magnitude by up to 2.1°C. Provinces along the Aegean, Mediterranean, and Marmara coastal zones recorded the highest event counts. Mean maximum temperatures during HW days reached 44.1°C in southeastern Anatolia. The strongest increasing trends concentrated in the 500–1499 m elevation band; the median frequency slope reached 0.237 days/year in the 1000–1499 m band compared to 0.185 days/year at 0–499 m. The magnitude index yielded statistically significant results under both thresholds. Maritime influence constrained trends along coastal areas, while mid-elevations exhibited a distinct inverted-U pattern. The results indicate that HW trends differ significantly along the elevation gradient as well as regionally, underscoring the need to incorporate elevation as a factor in adaptation strategies.

Keywords

• Heatwaves
• Extreme Temperatures
• Elevation-Dependent Warming
• Climate Change
• Mediterranean Basin

References

1. Abbasnia, M., & Toros, H. (2020). Trend analysis of weather extremes across the coastal and non-coastal areas (case study: Turkey). Journal of Earth System Science, 129(1), 95. https://doi.org/10.1007/s12040-020-1359- 3
2. Aguilar, E., Auer, I., Brunet, M., Peterson, T.C. and Wieringa, J. (2003) Guidelines on Climate Metadata and Homogenization, WCDMP-No. 53, WMO-TD No. 1186. World Meteorological Organization, Geneva, 55 p.
3. Alexander, L., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J.,
4. Aguilar, E., Brunet, M., ... Vazquez- Aguirre, J. L. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5). https://doi.org/10.1029/2005JD006290
5. Atalay, İ. (1997). Türkiye Coğrafyası. Ege Üniversitesi Basımevi.
6. Baldwin, J. W., Dessy, J. B., Vecchi, G. A., & Oppenheimer, M. (2019). Temporally Compound Heat Wave Events and Global Warming: An Emerging Hazard. Earth’s Future, 7(4), 411-427. https://doi.org/10.1029/2018EF000989 Baltacı, H., Akkoyunlu, B. O., & Tayanç, M. (2018). Relationships between teleconnection patterns and Turkish climatic extremes. Theoretical and Applied Climatology, 134(3), 1365-1386. https://doi.org/10.1007/s00704-017-2350-z
7. Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., & García-Herrera, R. (2011). The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe. Science, 332(6026), 220-224. https://doi.org/10.1126/science.1201224
8. Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464-469. https://doi.org/10.1111/j.1467-842X.2001.tb00294.x

9. Burrough, P.A., McDonnell, R.A. and Lloyd, C.D. (2015). Principles of Geographical Information Systems (Third Edition). Oxford University Press.
10. Chimborazo, O., Minder, J. R., & Vuille, M. (2022). Observations and Simulated Mechanisms of Elevation- Dependent Warming over the Tropical Andes. Journal of Climate, 35(3), 1021-1044. https://doi.org/10.1175/JCLI-D-21-0379.1
11. Coumou, D., Di Capua, G., Vavrus, S., Wang, L., & Wang, S. (2018). The influence of Arctic amplification on mid- latitude summer circulation. Nature Communications, 9(1), 2959. https://doi.org/10.1038/s41467-018- 05256-8
12. Demirtaş, M. (2018). The high-impact 2007 hot summer over Turkey: Atmospheric-blocking and heat-wave episodes. Meteorological Applications, 25(3), 406-413. https://doi.org/10.1002/met.1708
13. Di Capua, G., Sparrow, S., Kornhuber, K., Rousi, E., Osprey, S., Wallom, D., van den Hurk, B., & Coumou, D. (2021). Drivers behind the summer 2010 wave train leading to Russian heatwave and Pakistan flooding. Npj Climate and Atmospheric Science, 4(1), 55. https://doi.org/10.1038/s41612-021-00211-9
14. Diffenbaugh, N. S., & Giorgi, F. (2012). Climate change hotspots in the CMIP5 global climate model ensemble. Climatic Change, 114(3), 813-822. https://doi.org/10.1007/s10584-012-0570-x
15. Durmuş, B., Bulut, İ., & Gönençgil, B. (2021). Antalya Bölümünde sıcaklık ve yağış indislerinin değişim analizleri. Türk Coğrafya Dergisi, (78), 91-108. https://doi.org/10.17211/tcd.1009270
16. Erlat, E., & Avsar, E. (2020). Türkiye’de Mayis 2020 Gözlenen Sicak Hava Dalgasinin Klimatolojik ve Sinoptik Değerlendirmesi. Ege Coğrafya Dergisi, 29(2), 201-215. https://izlik.org/JA84FB28FX
17. Erlat, E., Türkeş, M., & Aydin-Kandemir, F. (2021). Observed changes and trends in heatwave characteristics in Turkey since 1950. Theoretical and Applied Climatology, 145(1), 137-157. https://doi.org/10.1007/s00704-021-03620-1
18. Espinosa, L. A., Portela, M. M., Freitas, L. M. M., Gharbia, S. (2023). Addressing the Spatiotemporal Patterns of Heatwaves in Portugal with a Validated ERA5-Land Dataset (1980–2021). Water, 15(17). https://doi.org/10.3390/w15173102
19. Fischer, E. M., & Knutti, R. (2015). Anthropogenic contribution to global occurrence of heavy- precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560-564. https://doi.org/10.1038/nclimate2617
20. Founda, D., Varotsos, K. V., Pierros, F., & Giannakopoulos, C. (2019). Observed and projected shifts in hot extremes’ season in the Eastern Mediterranean. Global and Planetary Change, 175, 190-200. https://doi.org/10.1016/j.gloplacha.2019.02.012
21. Galanaki, E., Giannaros, C., Kotroni, V., Lagouvardos, K., Papavasileiou, G. (2022). Spatio-Temporal Analysis of Heatwaves Characteristics in Greece from 1950 to 2020. Climate, 11(1). https://doi.org/10.3390/cli11010005
22. Gao, Y., Chen, F., Lettenmaier, D. P., Xu, J., Xiao, L., & Li, X. (2018). Does elevation-dependent warming hold true above 5000 m elevation? Lessons from the Tibetan Plateau. Npj Climate and Atmospheric Science, 1(1), 19. https://doi.org/10.1038/s41612-018-0030-z
23. García-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J., & Fischer, E. M. (2010). A Review of the European Summer Heat Wave of 2003. Critical Reviews in Environmental Science and Technology, 40(4), 267- 306. https://doi.org/10.1080/10643380802238137
24. Giorgi, F. (2006). Climate change hot-spots. Geophysical Research Letters, 33(8). https://doi.org/10.1029/2006GL025734
25. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1), 113-129. https://doi.org/10.1016/S0022-1694(00)00144-X
26. Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1), 182-196. https://doi.org/10.1016/S0022-1694(97)00125-X
27. Hengl, T., Heuvelink, G. B. M., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers & Geosciences, Spatial Analysis, 33(10), 1301-1315. https://doi.org/10.1016/j.cageo.2007.05.001
28. Horton, D. E., Johnson, N. C., Singh, D., Swain, D. L., Rajaratnam, B., & Diffenbaugh, N. S. (2015). Contribution of changes in atmospheric circulation patterns to extreme temperature trends. Nature, 522(7557), 465-469. https://doi.org/10.1038/nature14550
29. IPCC. (2023). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009157896
30. Ji, H., Feng, A., Zhao, Y., Liao, J., Zhang, Z., Gu, C., Feng, A., Ji, H., Feng, A. (2023). Characteristics of Heat Waves in Mainland China since 1961 Based on Absolute and Relative Methods. Atmosphere, 14(3). https://doi.org/10.3390/atmos14030544
31. Junk, J., Goergen, K., Krein, A. (2019). Future Heat Waves in Different European Capitals Based on Climate Change Indicators. International Journal of Environmental Research and Public Health, 16(20). https://doi.org/10.3390/ijerph16203959
32. Katavoutas, G., & Founda, D. (2019). Response of Urban Heat Stress to Heat Waves in Athens (1960–2017). Atmosphere, 10(9). https://doi.org/10.3390/atmos10090483
33. Kendall, M. G. (1975). Rank Correlation Methods (4th bs.).
34. Kruskal, W. H., & Wallis, W. A. (1952). Use of Ranks in One- Criterion Variance Analysis. Journal of the American Statistical Association, 47(260), 583-621. https://doi.org/10.2307/2280779
35. Kuglitsch, F. G., Toreti, A., Xoplaki, E., Della-Marta, P. M., Zerefos, C. S., Türkeş, M., & Luterbacher, J. (2010). Heat wave changes in the eastern Mediterranean since 1960. Geophysical Research Letters, 37(4). https://doi.org/10.1029/2009GL041841
36. Lelieveld, J., Hadjinicolaou, P., Kostopoulou, E., Chenoweth, J., El Maayar, M., Giannakopoulos, C., Hannides, C.,
37. Lange, M. A., Tanarhte, M., Tyrlis, E., & Xoplaki, E. (2012). Climate change and impacts in the Eastern Mediterranean and the Middle East. Climatic Change, 114(3), 667-687. https://doi.org/10.1007/s10584-012- 0418-4
38. Li, L., & Zha, Y. (2020). Population exposure to extreme heat in China: Frequency, intensity, duration and temporal trends. Sustainable Cities and Society, 60, 102282. https://doi.org/10.1016/j.scs.2020.102282
39. Lionello, P. (2012). The climate of the Mediterranean region: From the past to the future (1st ed). Elsevier. Lionello, P., Malanotte-Rizzoli, P., Boscolo, R., Alpert, P., Artale, V., Li, L., Luterbacher, J., May, W., Trigo, R., Tsimplis,
40. M., Ulbrich, U., & Xoplaki, E. (2006). The Mediterranean climate: An overview of the main characteristics and issues. Developments in Earth and Environmental Sciences, 4(C), 1-26. https://doi.org/10.1016/S1571-9197(06)80003-0
41. Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse- distance weighting spatial interpolation technique. Computers and Geosciences, 34, 1044-1055. https://doi.org/10.1016/j.cageo.2007.07.010
42. Ma, Y., Huang, Y., Chen, X., Li, Y., & Bao, A. (2013). Modelling Snowmelt Runoff under Climate Change Scenarios in an Ungauged Mountainous Watershed, https://doi.org/10.1002/wea.3296
43. Meehl, G. A., & Tebaldi, C. (2004). More Intense, More Frequent, and Longer Lasting Heat Waves in the 21st Century. Science, 305(5686), 994-997. https://doi.org/10.1126/science.1098704
44. Miralles, D. G., Gentine, P., Seneviratne, S. I., & Teuling, A. J. (2019). Land–atmospheric feedbacks during droughts and heatwaves: State of the science and current challenges. Annals of the New York Academy of Sciences, 1436(1), 19-35. https://doi.org/10.1111/nyas.13912
45. Navarro-Serrano, F., López-Moreno, J. I., Azorin-Molina, C., Alonso-González, E., Aznarez-Balta, M., Buisán, S. T., Revuelto, J. (2020). Elevation Effects on Air Temperature in a Topographically Complex Mountain Valley in the Spanish Pyrenees. Atmosphere, 11(6). https://doi.org/10.3390/atmos11060656
46. Oliveira, A., Lopes, A., & Soares, A. (2022). Excess Heat Factor climatology, trends, and exposure across European Functional Urban Areas. Weather and Climate Extremes, 36, 100455. https://doi.org/10.1016/j.wace.2022.100455
47. Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M. Z., Liu, X. D., Miller, J. R., Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schöner, W., Severskiy, I., Shahgedanova, M.,
48. Wang, M. B., ... Mountain Research Initiative EDW Working Group. (2015). Elevation-dependent warming in mountain regions of the world. Nature Climate Change, 5(5), 424-430. https://doi.org/10.1038/nclimate2563
49. Perkins, S. E., & Alexander, L. V. (2013). On the Measurement of Heat Waves. Journal of Climate, 26(13), 4500-4517. https://doi.org/10.1175/JCLI-D-12- 00383.1
50. Perkins-Kirkpatrick, S. E., & Gibson, P. B. (2017). Changes in regional heatwave characteristics as a function of increasing global temperature. Scientific Reports, 7(1), 12256. https://doi.org/10.1038/s41598-017-12520-2
51. Perkins-Kirkpatrick, S. E., & Lewis, S. C. (2020). Increasing trends in regional heatwaves. Communications, 11(1), https://doi.org/10.1038/s41467-020-16970-7 Nature 3357.
52. Northwest China. Mathematical Engineering, 2013(1), https://doi.org/10.1155/2013/808565 Problems in 808565.
53. Raymond, C., Waliser, D., Guan, B., Lee, H., Loikith, P., Massoud, E., Sengupta, A., Singh, D., & Wootten, A. (2022). Regional and elevational patterns of extreme heat stress change in the US. Environmental Research Letters, 17(6), 064046. https://doi.org/10.1088/1748- 9326/ac7343
54. Russo, E., & Domeisen, D. I. V. (2023). Increasing Intensity of Extreme Heatwaves: The Crucial Role of Metrics. Geophysical Research Letters, 50(14), e2023GL103540. https://doi.org/10.1029/2023GL103540
55. Russo, S., Dosio, A., Graversen, R. G., Sillmann, J., Carrao, H., Dunbar, M. B., Singleton, A., Montagna, P., Barbola, P., & Vogt, J. V. (2014). Magnitude of extreme heat waves in present climate and their projection in a warming world. Journal of Geophysical Research: Atmospheres, 119(22), 12,500-12,512. https://doi.org/10.1002/2014JD022098 Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
56. Serrano-Notivoli, R., Lemus-Canovas, M., Barrao, S., Sarricolea, P., Meseguer-Ruiz, O., & Tejedor, E. (2022). Heat and cold waves in mainland Spain: Origins, characteristics, and trends. Weather and Climate Extremes, 37, 100471. https://doi.org/10.1016/j.wace.2022.100471
57. Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 23rd ACM National Conference On -, 517- 524. https://doi.org/10.1145/800186.810616
58. Tahir, A. A., Chevallier, P., Arnaud, Y., Neppel, L., & Ahmad, B. (2011). Modeling snowmelt-runoff under climate scenarios in the Hunza River basin, Karakoram Range, Northern Pakistan. Journal of Hydrology, 409(1), 104- 117. https://doi.org/10.1016/j.jhydrol.2011.08.035
59. Tatli, H., & Serkendiz, H. (2025). Heatwave dynamics in Türkiye: A long-term spatiotemporal analysis of frequency, duration, and intensity (1970–2022). Environmental Monitoring and Assessment, 197(7), 752. https://doi.org/10.1007/s10661-025-14246-5
60. Theil, H. (1950). A Rank-Invariant Method of Linear and Polynomial Regression Analysis, Part I–III. 173-177, 386-392, 497-505.
61. Trigo, R. M., Pereira, J. M. C., Pereira, M. G., Mota, B., Calado, T. J., Dacamara, C. C., & Santo, F. E. (2006). Atmospheric conditions associated with the exceptional fire season of 2003 in Portugal. International Journal of Climatology, 26(13), 1741- 1757. https://doi.org/10.1002/joc.1333
62. Türkeş, M. (2024). Klimatoloji ve Meteoroloji. Kriter Basım Yayın Dağıtım.
63. Unal, Y. S., Tan, E., & Mentes, S. S. (2013). Summer heat waves over western Turkey between 1965 and 2006. Theoretical and Applied Climatology, 112(1), 339-350. https://doi.org/10.1007/s00704-012-0704-0
64. Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., Meinshausen, N., & Frieler, K. (2019). The effects of climate extremes on global agricultural yields. Environmental Research Letters, 14(5), 054010. https://doi.org/10.1088/1748- 9326/ab154b
65. Wang, X., & Feng, Y. (2013). RHtestsV4 User Manual. https://doi.org/10.13140/RG.2.2.17309.17125
66. Wang, X. L. (2008). Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change. Journal of Atmospheric and Oceanic Technology, 25(3), 368-384. https://doi.org/10.1175/2007JTECHA982.1
67. Wang, X., Wen, Q., & Wu, Y. (2007). Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series. Journal of Applied Meteorology and Climatology, 46, 916-931. https://doi.org/10.1175/JAM2504.1
68. WMO. (2017). WMO guidelines on the calculation of climate normals.
69. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9), 1807-1829. https://doi.org/10.1002/hyp.1095
70. Yue, S., & Wang, C. (2004). The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management, 18(3), 201-218. https://doi.org/10.1023/B:WARM.0000043140.61082. 60