Document Type : Original Article
Authors
1 Ph.D. Student, Department of Irrigation and Drainage Engineering, Faculty of Engineering and Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Associate Professor, Department of Irrigation and Drainage Engineering, Faculty of Engineering and Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
Abstract
Teleconnection, the large-scale patterns of climate change that link distant regions, has long been recognized as an important factor in understanding and predicting weather patterns around the world. In Iran, these teleconnections have been shown to significantly affect the distribution and variability of rainfall, cloudiness, and temperature as a key driver affecting the region. One of the most important links in this field is the large-scale index of several decades’ atlas. In this research, the climatic component of cloudiness and the effects of several decades of the Atlas index on it have been analyzed. With the method of Principal Components Analysis, the climatic component of cloudiness and the effect of 12 teleconnections have been investigated and analyzed. that the greatest effect is obtained from the index of several decades of atlas. The highest percentage of correlation is calculated from Karaj station located in the western Alborz slopes and is -0.65%. Stations in the western and southwestern regions showed the highest percentage of correlation between the cloudiness parameter and the aforementioned index, followed by the central and southern regions. In general, the positive phase of the index will cause a decrease in cloudiness in the western and central regions of Iran, and the negative phase of the index will cause an increase in cloudiness in the mentioned areas. It is recommended to consider climate change and global warming in future studies.
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Ahmadi, F Mojard, M& Najafi. R. (2025). Analysis of the relationship between soil temperature sensors of meteorological stations in Kermanshah province and remote sensing patterns. Journal of Drought and Climate Change Research. https://doi.org/10.22077/jdcr.2025.8448.1085 [In Persian].
Akhtar‑Danesh, N. (2023). Impact of factor rotation on Q‑methodology analysis. PLOS ONE, 18(8), e0286587. https://doi.org/10.1371/journal.pone.0286587 [In Persian].
An, S.-I., Park, H. J., Kim, S. K., Cai, W., Santoso, A., Kim, D., & Kug, J.-S. (2023). Main drivers of Indian Ocean Dipole asymmetry revealed by a simple IOD model. npj Climate and Atmospheric Science, 6(1), 93. https://doi.org/10.1038/s41612-023-00422-2
Atkins, J. R. C., Tinker, J., Graham, J. A., Scaife, A. A., & Halloran, P. R. (2024). Seasonal forecasting of the European North‑West shelf seas. Climate Dynamics, 63, 1761–1777. https://doi.org/10.1007 / s00382-024-07439-0
Beranová, R. (2025). A multi‑dataset analysis of precipitation trends in Europe in the 21st century. Journal of Hydrometeorology, 26(7), 1313–1333. https://doi.org/10.1175/JHM-D-24-0114.1
Bueso, D., Piles, M., & Camps‑Valls, G. (2022). Let’s consider more general nonlinear approaches to study teleconnections of climate variables. arXiv preprint, arXiv:2212.07635. https://doi.org/10.48550/arXiv.2212.07635
Bueso, M.-C., Piles, M., & Camps‑Valls, G. (2020). Nonlinear PCA: ROCK approach. IEEE Transactions on Geoscience and Remote Sensing, arXiy 58(5), 3272–3284. https://doi.org/10.1109/TGRS.2020.2969813
Cape, J. (2024). On varimax asymptotic in network models and spectral methods. arXiv preprint, arXiv:2403.05461. https://doi.org/10.48550/arXiv.2403.05461
Caporaso, L., Duveiller, G., Giuliani, G., Giorgi, F., Stengel, M., Massaro, E., Piccardo, M., & Cescatti, A. (2024). Converging findings of climate models and satellite observations on global afforestation impacts on clouds. Journal of Geophysical Research: Atmospheres, 129, e2023JD039235. https://doi.org/10.1029/2023JD039235
Carvalho‑Oliveira, J., Di Capua, G., Borchert, L. F., Donner, R. V., & Baehr, J. (2024). Causal relationships and predictability of the summer East Atlantic teleconnection. Weather and Climate Dynamics, 5, 1561–1578. https://doi.org/10.5194/wcd-5-1561-2024
Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C., Hu, Z., & Qi, Y. (2024). A machine learning model that outperforms conventional numerical weather models at S2S timescales. Nature Communications, 15, 50714. https://doi.org/10.1038/s41467-024-50714-1
Cheng, S., Li, J., Zhang, P., & Zhang, R. (2024). Impact of summer North Atlantic Sea surface temperature on European precipitation. Journal of Climate, 37(19), —. https://doi.org/10.1175/JCLI-D-24-0072.1
Chtirkova, B., Folini, D., Correa, L. F., & Wild, M. (2023). Internal variability of the climate system mirrored in decadal‑scale trends of surface solar radiation. Journal of Geophysical Research: Atmospheres, 128(12), e2023JD038573. https://doi.org/10.1029/2023JD038573
Dalelane, C., Paxian, A., Senande, M., Sanfiz, S., Rodríguez Guisado, E., Wandel, J., & Tyagi, A. (2025). Targeted teleconnections and their application to the postprocessing of climate predictions. EGUsphere (preprint), —. https://doi.org/10.5194/egusphere-2025-3664
Diodato, N., Seftigen, K., & Bellocchi, G. (2025). Millennium‑scale Atlantic multidecadal oscillation and soil moisture in the Mediterranean. Research, 6, 606. https://doi.org/10.34133/research.0606
Doiteau, B., Pantillon, F., Plu, M., Descamps, L., & Rieutord, T. (2024). Systematic evaluation of the predictability of different cyclone types over the Mediterranean. Weather and Climate Dynamics, 5, 1409–1435. https://doi.org/10.5194/wcd-5-1409-2024
Fang, K., Tao, Q., Lv, K., He, M., Huang, X., & Yang, J. (2024). Kernel PCA for out‑of‑distribution detection. NeurIPS 2024 Proceedings, Proc. 38th NeurIPS.
Felsche, E., Böhnisch, A., Poschlod, B., & Ludwig, R. (2024). European hot and dry summers are projected to become more frequent and expand northwards. Communications Earth & Environment, 5, 410. https://doi.org/10.1038/s43247-024-01575-5
Foroozan, Z., Grießinger, J., Pourtahmasi, K., & Bräuning, A. (2020). 501 years of spring precipitation history for the semi‑arid Northern Iran derived from tree‑ring δ18O data. Atmosphere, 11(9), 889. https://doi.org/10.3390/atmos11090889 [In Persian].
Fourotan, Z., Zainali, —., & Betul, —. (2023). The simultaneous effect of NAO and AMO cycle indices on the variability of temperature and precipitation in the neighboring cities of Sablan. Environmental Science Studies, 8(1), 5857–5868. https:// doi.org/ 10.22034/jess.2022.345519.1802 [In Persian].
Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., & Eyring, V. (2023). Evaluating causal Arctic–midlatitude teleconnections in CMIP6. Journal of Geophysical Research: Atmospheres, 128, e2022JD037978. https://doi.org/10.1029/2022JD037978
Geng, X., Kug, J.-S., & Kosaka, Y. (2024). Future changes in the wintertime ENSO–NAO teleconnection under greenhouse warming. npj Climate and Atmospheric Science, 7, 81. https://doi.org/10.1038/s41612-024-00627-z
Gutiérrez‑Fernández, J., Miglietta, M. M., González‑Alemán, J. J., & Gaertner, M. Á. (2024). A new refinement of Mediterranean tropical‑like cyclones identification. Geophysical Research Letters, 51, e2023GL106429. https://doi.org/10.1029/2023GL106429
Halifa‑Marín, A., Woollings, T., Hernández‑Carrascal, A., Barriopedro, D., & Vicente‑Serrano, S. M. (2024). Too‑stable North Atlantic climate system in CMIP6 models limits precipitation teleconnections. Quarterly Journal of the Royal Meteorological Society, 150(753), 1193–1210. https://doi.org/10.1002/qj.4999
Hajjarian, Ahmad. (2025). Drought risk monitoring and zoning using the random forest model (case study: Ardabil province). Journal of Drought and Climate Change Research. https://doi.org/10.22077/jdcr.2025.8551.1093 (In Persian)
Hersbach, H., Bell, B., Berrisford, P., et al. (2024). The ERA5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150, 3392–3430. https://doi.org/10.1002/qj.4803
Hou, Y., Man, K., Yu, S., & Li, X. (2025). Record‑high precipitation over Eastern Europe induced by atmospheric rivers in late fall 2023. Geophysical Research Letters, 52, e2024GL114309. https://doi.org/10.1029/2024GL114309
Hussain, A., Cao, J., Hussain, I., Begum, S., Akhtar, M., Wu, X., Guan, Y., & Zhou, J. (2021). Observed trends and variability of temperature and precipitation and their global teleconnections in the Upper Indus Basin, Hindukush‑Karakoram‑Himalaya. Atmosphere, 12(8), 973. https://doi.org/10.3390/atmos12080973
Ioniță, M., Vaideanu, P., & Nagavciuc, V. (2025). Breaking records under clear skies: The impact of sunshine duration and soil moisture on Eastern European heat in 2024. npj Natural Hazards, 1, 9. https://doi.org/10.1038/s44304-025-00137-9
Javorskyj, I., Yuzefovych, R., Lychak, O., & Matsko, I. (2024). Hilbert transform for covariance analysis of periodically nonstationary random signals with high‑frequency modulation. ISA Transactions, 144, 452–481. https://doi.org/10.1016/j.isatra.2023.10.025
Jin, S., Yu, L., Cao, J., Zhang, L., Zhao, Y., Zhou, W., & Jiang, J. H. (2024). ENSO disrupts the effectiveness of the cloud‑radiative‑effect feedback perturbations on precipitation. Journal of Climate, 37(6), 2553–2572. https://doi.org/10.1175/JCLI-D-23-0282.1
Kambezidis, H. D. (2024). Atmospheric Processes over the Broader Mediterranean Region: Effect of the El Niño–Southern Oscillation? Atmosphere, 15(3), 268. https://doi.org/10.3390/atmos15030268
Liu, Y., Zhu, L., Yu, Z., Wang, Z., Sillmann, J., & Li, C. (2023). Opposing trends of cloud coverage over land and ocean under global warming. Atmospheric Chemistry and Physics, 23(10), 6559–6577. https://doi.org/10.5194/acp-23-6559-2023
Llanes‑Cárdenas, O., Norzagaray‑Campos, M., Gaxiola, A., & González, G. E. G. (2020). Regional precipitation tele‑connected with PDO–AMO–ENSO in northern Mexico. Theoretical and Applied Climatology, 140, 667–681. https://doi.org/10.1007 / s00704-019-03003-7
Luo, H., Quaas, J., & Han, Y. (2024). Diurnally asymmetric cloud cover trends amplify daytime warming and nighttime moistening. Science Advances, 10, eado5179. https://doi.org/10.1126/sciadv.ado5179
Malik, A., Stenchikov, G., Mostamandi, S., Parajuli, S., Lelieveld, J., Zittis, G., Ahsan, M. S., Atique, L., & Usman, M. (2024). Accelerated historical and future warming in the Middle East and North Africa. Journal of Geophysical Research: Atmospheres, 129(19), e2024JD041625. https://doi.org/10.1029/2024JD041625
Marukatat, S. (2023). Tutorial on PCA and approximate kernel PCA. Artificial Intelligence Review, 56, 5445–5477. https://doi.org/10.1007/s10462-022-10297-z
Matsuki, A., Kori, H., & Kobayashi, R. (2023). An extended Hilbert transform method for reconstructing the phase from an oscillatory signal. Scientific Reports, 13, 3535. https://doi.org/10.1038/s41598-023-30405-5
Maycock, A. C. (2025). North Atlantic seasonal climate variability significantly modulates European windstorm hazards. EGUsphere (preprint), EGUsphere 2025‑1131. https://doi.org/10.5194/egusphere-2025-1131
Meidani, E., & Araghinejad, S. (2014). Long‑lead streamflow forecasting in the southwest of Iran by sea surface temperature of the Mediterranean Sea. Journal of Hydrologic Engineering, 19(8), 05014005. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000965 [In Persian].
Mitevski, I., Lee, S. H., Vecchi, G. A., Orbe, C., & Polvani, L. M. (2025). More positive and less variable North Atlantic Oscillation at higher CO₂ forcing. npj Climate and Atmospheric Science, 8, 51. https://doi.org/10.1038/s41612-025-01051-7
Molteni, F., Corti, S., & Ferranti, L. (2023). Early‑ and late‑winter ENSO teleconnections to the Euro–Atlantic sector from C3S seasonal forecasts. Climate Dynamics, —. https://doi.org/10.1007 / s00382-023-06698-7
O’Reilly, C. H. (2025). Signal‑to‑noise errors in early winter Euro‑Atlantic teleconnections. Quarterly Journal of the Royal Meteorological Society, 151, e4952. https://doi.org/10.1002/qj.4952
Park, M., Johnson, N. C., & Delworth, T. L. (2024). The driving of North American climate extremes by wave interference over the North Pacific. Nature Communications, 15, 7455. https://doi.org/10.1038/s41467-024-51601-5
Pi, Y., Yu, Y., Zhang, Y., Xu, C., & Yu, R. (2020). Extreme temperature events during 1960–2017 in the arid region of Northwest China: Spatiotemporal dynamics and associated large‑scale atmospheric circulation. Sustainability, 12(3), 1198. https://doi.org/10.3390/su12031198
Portal, A., Raveh‑Rubin, S., Catto, J. L., Givon, Y., & Martius, O. (2024). Linking compound weather extremes to Mediterranean cyclone types. Weather and Climate Dynamics, 5, 1043–1066. https://doi.org/10.5194/wcd-5-1043-2024
Post, P., & Aun, M. (2024). Changes in cloudiness contribute to changing seasonality of marine climate variability. Progress in Oceanography, 217, 103134. https://doi.org/10.1016/j.pocean.2023.103134
Ramezani Edelali, Hadi, & Kouhi. (2024). Investigating the impact of climate change on drought in Iran using a population exposure approach. Journal of Drought and Climate Change Research. https://doi.org/10.22077/jdcr.2024.8258.1079 [In Persian].
Rantanen, M., Lindfors, A. V., Räisänen, P., & Laaksonen, A. (2025). Summer 2024 in northern Fennoscandia was very likely the warmest on record. npj Climate and Atmospheric Science, 8, 46. https://doi.org/10.1038/s41612-025-01046-4
Rashedi, S., Sorooshian, A., Kazemi, Z., Karimi, N., & Namazi, M. (2024). On the characteristics and long‑term trend of total cloud cover in Iran. Acta Geophysica, Advance online publication. https://doi.org/10.1007/s11600-024-01351-1(In Persian).
Rieger, N., Corral, Á., Olmedo, E., & Turiel, A. (2021). Lagged teleconnections of climate variables identified via complex rotated maximum covariance analysis. Journal of Climate, 34(24), 9861–9878. https://doi.org/10.1175/JCLI-D-21-0244.1
Rivosecchi, A., Bollasina, M. A., & Colfescu, I. (2024). Future changes in the influence of the NAO on Mediterranean winter precipitation extremes in the EC‑Earth3 large ensemble: The prominent role of internal variability. SSRN Preprint, SSRN 4705279. https://doi.org/10.2139/ssrn.4705279
Rohe, K., & Zeng, M. (2023). Vintage factor analysis with Varimax performs statistical inference. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 85(4), 1037–1066. https://doi.org/10.1093/jrsssb/qkad029
Sandler, D., Saaroni, H., Ziv, B., Tamarin‑Brodsky, T., & Harnik, N. (2024). The connection between North Atlantic storm track regimes and eastern Mediterranean cyclonic activity. Weather and Climate Dynamics, 5, 1103–1116. https://doi.org/10.5194/wcd-5-1103-2024
Schilliger, L., Bourgeois, Q., Correa, L. F., & Wild, M. (2024). An investigation on causes of the detected surface solar radiation brightening in Europe. Journal of Geophysical Research:
Atmospheres, 129(18), e2024JD041101. https://doi.org/10.1029/2024JD041101
Shaw, T. A., Baldwin, M., Barnes, E. A., Hassanzadeh, P., Li, C., O’Gorman, P. A., Simpson, I. R., Son, S.‑W., Ting, M., & Voigt, A. (2024). Emerging climate‑change signals in atmospheric circulation. AGU Advances, 5(5), e2024AV001297. https://doi.org/10.1029/2024AV001297
Spensberger, C. (2024). Teleconnections through weather rather than stationary waves. Weather and Climate Dynamics, 5, 659–669. https://doi.org/10.5194/wcd-5-659-2024
Strnad, F. M., Chen, Z., Tan, G., Wang, C., & Whitehouse, L. (2022). Teleconnection patterns of different El Niño types revealed by a large‑ensemble full‑field GCM experiment. Geophysical Research Letters, 49(17), e2022GL098571. https://doi.org/10.1029/2022GL098571
Sundararajan, R. R. (2021). Principal component analysis using frequency components of multivariate time series. Computational Statistics & Data Analysis, 157, 107164. https://doi.org/10.1016/j.csda.2020.107164
Svennevik, H., Hicks, S. A., Riegler, M. A., Storelvmo, T., & Hammer, H. L. (2024). A dataset for predicting cloud cover over Europe. Scientific Data, 11, 245. https://doi.org/10.1038/s41597-024-03062-0
Vázquez, M., Nieto, R., Drumond, A., & Gimeno, L. (2023). Influence of teleconnection patterns on global moisture transport. International Journal of Climatology, 43(18), 7855–7878. https://doi.org/10.1002/joc.7843
Wang, G., Yuan, X., Jing, C., Hamdi, R., Ochege, F. U., Dong, P., Shao, Y., & Qin, X. (2024). The decreased cloud cover dominated the rapid spring temperature rise in arid Central Asia over the period 1980–2014. Geophysical Research Letters, 51(2), e2023GL107523. https://doi.org/10.1029/2023GL107523
Weylandt, M., & Swiler, L. P. (2024). Beyond PCA: Additional dimension reduction techniques to consider in the development of climate fingerprints. Journal of Climate, 37(5), 1723–1735. https://doi.org/10.1175/JCLI-D-23-0267.1
Yang, R., & Xing, B. (2022). Teleconnections of large‑scale climate patterns to regional drought in mid‑latitudes: A case study in Xinjiang, China. Atmosphere, 13(2), 230. https://doi.org/10.3390/atmos13020230
Yu, L., Wu, B., Jiang, Z., & Li, J. (2024). Seasonal phase change of the North Atlantic triple drivers shaping European winter climate. npj Climate and Atmospheric Science, 7, 73. https://doi.org/10.1038/s41612-024-00882-0
Zhang, G., Rantanen, M., & Clancy, P. (2024). SCAND: An index for the Scandinavian pattern. Weather and Climate Dynamics, 5(3), 893–907. https://doi.org/10.5194/wcd-5-893-2024
Zhang, R., Zhang, L., Wu, L., & Johns, W. E. (2019). Sensitivity of the Atlantic meridional overturning circulation to the Atlantic Multidecadal Oscillation. Nature, 565(7739), 367–372. https://doi.org/10.1038/s41586-018-0822-7
Zhu, Z., Piao, S., Xu, Y., Bastos, A., Ciais, P., & Peng, S. (2017). The effects of teleconnections on carbon fluxes of global terrestrial ecosystems. Geophysical Research Letters, 44(7), 3209–3218. https://doi.org/10.1002/2016GL072296
Zohrabi, N., Bavani, A. M., Goodarzi, E., & Eslamian, S. (2014). Attribution of temperature and precipitation changes to greenhouse gases in northwest Iran. Quaternary International, 345, 130–137 (In Persian). https://doi.org/10.1016/j.quaint.2014.01.026 [In Persian].
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