Examining the Diurnal Temperature Range (DTR) in Iran using the AgERA5 dataset

Document Type : Research Article


1 MSc in Climatology, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

2 Associate Professor in Climatology, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

3 Postdoctoral Research Associate in Climatology, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran


This research was conducted with the aim of investigating the day and night temperatures in Iran. For this purpose, the minimum and maximum temperatures during 40 years (1981-2020) were examined using the AgERA5 dataset. Then, the diurnal temperature range (DTR) was calculated. In order to evaluate the performance of the AgERA5 dataset, the data from 56 meteorological stations and RMSE and R2 metrics were used, and the Theil-Sen test was used to analyze the average trend. The results of the evaluation of the minimum and maximum temperatures showed that the AgERA5 dataset has high accuracy for temperature estimation. The trend showed that the monthly trend of minimum and maximum temperatures in Iran is increasing. The increasing trend of temperature over time is not constant and its rate varies in different months. However, the increasing trend of temperature during different months of the year is consistent for the two variables of minimum temperature and maximum temperature. In all months, the maximum temperature increase is observed in winter and March. The DTR index in Iran is a minimum of 0.48 and a maximum of 16.6 °C, which occurs in December and July, respectively. The maximum DTR occurs in the interior dry regions and the minimum occurs in northern and northwestern Iran. The maximum increasing trend of minimum and maximum temperatures is in March, which increases by 0.8 oC/decade and 1.2 oC/decade, respectively. In contrast to the maximum temperature, there is a decreasing trend of the minimum and maximum minimum temperature in November, which decreases by -0.1 oC/decade and -0.2 oC/decade, respectively.

Graphical Abstract

Examining the Diurnal Temperature Range (DTR) in Iran using the AgERA5 dataset


احمدی، محمود؛ داداشی رودباری، عباسعلی؛ 1398. پایش روند دمای ماهیانه ایران مبتنی بر برونداد پایگاه داده مرکز پیش‌بینی میان‌مدت هواسپهر اروپایی (ECMWF) نسخه ERA Interim. جغرافیا. دوره 17. شماره 60. صص 103-86. https://rimag.ricest.ac.ir/fa/Article/8873
احمدی، محمود؛ داداشی رودباری، عباسعلی؛ احمدی، حمزه؛ علی‌بخشی، زهرا؛ 1397. واکاوی ساختار دمای ایران مبتنی بر برون‌داد پایگاه دادۀ مرکز پیش‌بینی میان‌مدت هواسپهر اروپایی (ECMWF) نسخۀ ERA Interim. پژوهش‌های جغرافیای طبیعی. دوره 50، شماره 2. صص 353-372.
زرین، آذر؛ داداشی رودباری، عباسعلی؛1400. پیش‌نگری دمای ایران در آینده نزدیک (2040-2021) بر اساس رویکرد همادی چند مدلی CMIP6. پژوهش‌های جغرافیای طبیعی. دوره 53. شماره 1. صص 75-90.
سبزی­پرور، علی­اکبر؛ سیف، زهرا؛ فرشته، قیامی؛ 1392. تحلیل روند دما در برخی از ایستگاه‌های مناطق خشک و نیمه‌خشک کشور. جغرافیا و توسعه. دوره 11. شماره 30. صص 117-137.
مسعودیان، سیدابوالفضل؛ 1383. بررسی روند دمای ایران در نیم سده گذشته. جغرافیا و توسعه. دوره 2، شماره 3. صص 106-89. https://doi.org/10.22111/gdij.2004.3831
مسعودیان، سیدابوالفضل؛ زینالی، حمید؛ حجتی­زاده، رحیم؛ 1387. نواحی دمایی ایران. تحقیقات جغرافیایی. دوره 23. شماره 2. صص 18-3. https://www.sid.ir/paper/30060/fa
منتظری، مجید؛ 1393. واکاوی زمانی مکانی دماهای سالانۀ ایران طیّ دورۀ 2008-1961. فصلنامه جغرافیا و توسعه. دوره 12. شماره 36. صص 209-228. https://doi.org/10.22111/gdij.2014.1719.
نظری‌‌پور، حمید؛ دوستکامیان، مهدی؛ علیزاده، سارا؛ 1394. بررسی الگوهای توزیع فضایی دما، بارش و رطوبت با استفاده از تحلیل اکتشافی زمین‌آمار (بررسی موردی: نواحی مرکزی ایران). فیزیک زمین و فضا. دوره 41. شماره 1. صص 99-117. https://doi.org/10.22059/jesphys.2015.53438.
Ahmadi, F., Nazeri Tahroudi, M., Mirabbasi, R., Khalili, K., & Jhajharia, D., 2018. Spatiotemporal trend and abrupt change analysis of temperature in Iran. Meteorological Applications, 25(2): 314-321.  https://doi.org/10.1002/met.1694.
Albergel, C., Dutra, E., Munier, S., Calvet, J. C., Munoz-Sabater, J., Rosnay, P. D., & Balsamo, G., 2018. ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?. Hydrology and Earth System Sciences, 22(6): 3515-3532. https://doi.org/10.5194/hess-22-3515-2018.
Beranová, R., & Huth, R., 2008. Time variations of the effects of circulation variability modes on European temperature and precipitation in winter. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(2): 139-158.
Betts, A. K., & Beljaars, A. C., 2017. Analysis of near‐surface biases in ERA‐I nterim over the C anadian P rairies. Journal of Advances in Modeling Earth Systems, 9(5): 2158-2173. https://doi.org/10.1002/2017MS001025.
Bromwich, D. H., Nicolas, J. P., Hines, K. M., Kay, J. E., Key, E. L., Lazzara, M. A., ... & Van Lipzig, N. P., 2012. Tropospheric clouds in Antarctica. Reviews of Geophysics, 50(1). https://doi.org/10.1029/2011RG000363.
Byass, P., 2020. Eco-epidemiological assessment of the COVID-19 epidemic in China, January–February 2020. Global health action, 13(1): 1760490.
Correa, C. S., Guedes, R. L., Rocha, A. M. M. D., & Corrêa, K. A. B., 2020. Multidecadal Cycles of the Climatic Index Atlantic Meridional Mode: Sunspots that Affect North and Northeast of Brazil. Journal of Aerospace Technology and Management, 12. https://doi.org/10.5028/jatm.v12.1101.
Dutra, E., Johannsen, F., & Magnusson, L., 2021. Late Spring and Summer Subseasonal forecasts in the Northern Hemisphere midlatitudes: biases and skill in the ECMWF model. Monthly Weather Review. https://doi.org/10.1175/MWR-D-20-0342.1.
Ehard, B., Malardel, S., Dörnbrack, A., Kaifler, B., Kaifler, N., & Wedi, N., 2018. Comparing ECMWF high‐resolution analyses with lidar temperature measurements in the middle atmosphere. Quarterly Journal of the Royal Meteorological Society, 144(712): 633-640. https://doi.org/10.1002/qj.3206.
Gleixner, S., Demissie, T., & Diro, G. T., 2020. Did ERA5 improve temperature and precipitation reanalysis over East Africa?. Atmosphere, 11(9): 996.
González‐Hidalgo, J. C., Beguería, S., Peña‐Angulo, D., & Sandonis, L., 2022. Variability of maximum and minimum monthly mean air temperatures over mainland Spain and their relationship with low‐variability atmospheric patterns for period 1916–2015. International Journal of Climatology, 42(3): 1723-1741. https://doi.org/10.1002/joc.7331.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., ... & Thépaut, J. N., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999-2049. https://doi.org/10.1002/qj.3803.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., ... & Wright, J. S., 2019. From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations. Atmospheric Chemistry and Physics, 19(5): 3097-3124. https://doi.org/10.5194/acp-19-3097-2019.
Janis, M. J., Hubbard, K. G., & Redmond, K. T., 2004. Station density strategy for monitoring long-term climatic change in the contiguous United States. Journal of climate, 17(1): 151-162. https://doi.org/10.1175/1520-0442(2004)017%3C0151:SDSFML%3E2.0.CO;2.
Johannsen, F., Ermida, S., Martins, J., Trigo, I. F., Nogueira, M., & Dutra, E., 2019. Cold Bias of ERA5 summertime daily maximum land surface temperature over Iberian Peninsula. Remote Sensing, 11(21): 2570. https://doi.org/10.3390/rs11212570.
Kozubek, M., Krizan, P., & Lastovicka, J., 2020. Homogeneity of the Temperature Data Series from ERA5 and MERRA2 and Temperature Trends. Atmosphere, 11(3): 235.
Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G., 2021. A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments. Data in Brief, 35, 106900. https://doi.org/10.1016/j.dib.2021.106900.
Rahmstorf, S., Foster, G., & Cahill, N., 2017. Global temperature evolution: recent trends and some pitfalls. Environmental Research Letters, 12(5): 054001.
Rao, Y., Liang, S., & Yu, Y., 2018. Land Surface Air Temperature Data Are Considerably Different Among BEST‐LAND, CRU‐TEM4v, NASA‐GISS, and NOAA‐NCEI. Journal of Geophysical Research: Atmospheres, 123(11): 5881-5900.
Royé, D., Íñiguez, C., & Tobías, A., 2020. Comparison of temperature–mortality associations using observed weather station and reanalysis data in 52 Spanish cities. Environmental research, 183, 109237. https://doi.org/10.1016/j.envres.2020.109237.
Tang, G., Clark, M. P., Newman, A. J., Wood, A. W., Papalexiou, S. M., Vionnet, V., & Whitfield, P. H., 2020. SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018. Earth System Science Data, 12(4): 2381-2409. https://doi.org/10.5194/essd-12-2381-2020.
Yu, C., Li, Z., & Blewitt, G., 2021. Global comparisons of ERA5 and the operational HRES tropospheric delay and water vapor products with GPS and MODIS. Earth and Space Science, 8(5): e2020EA001417. https://doi.org/10.1029/2020EA001417.
Zhu, J., Xie, A., Qin, X., Wang, Y., Xu, B., & Wang, Y., 2021. An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature. Atmosphere, 12(2): 217. https://doi.org/10.3390/atmos12020217.
Volume 12, Issue 1 - Serial Number 45
February 2023
Pages 189-208
  • Receive Date: 05 September 2021
  • Revise Date: 07 December 2021
  • Accept Date: 17 December 2021
  • First Publish Date: 17 December 2021