Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 3
Abstract
Precipitation is one of the most complex weather phenomena; its successful spatiotemporal modeling provides substantial information for designing water management systems. This research aimed to determine the relationship of sixteen large-scale climate signals with weather types (WTs) as well as precipitation amounts over Iran during the winter season (January–March). Daily precipitation data covering the period 1991–2010 were collected from 130 weather stations across the country. In addition, four atmospheric variables affecting the precipitation process over Iran were derived from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets as predictors. Several nonhomogeneous hidden Markov models (NHMMs) were employed to identify the prevailing WTs governing the spatial distributions of daily precipitation in winter over Iran. A hidden state (i.e., WTs) in NHMMs was dependent on an immediately previous state of WT in the hidden layer and the reanalyzed atmospheric variables at the current time in the predictor layer. Daily WTs over the recording period were decoded using Viterbi’s algorithm. Results showed that the NHMM including the atmospheric variable of mean sea level pressure (Mslp) in the predictor layer had the minimum value (= 0) of Bayesian information criterion difference (BICD) among the other variables and was treated as the best model. The chosen model distinguished the eight spatially coherent WTs for winter that were in accordance with the eight synoptic patterns influencing the study area. Based on the Spearman’s rank method, amounts of winter precipitation at many stations in Iran were significantly correlated with four climate signals, including the atlantic multidecadal oscillation (AMO), East Atlantic/West Russia (EA/WR) pattern, Southern Oscillation Index (SOI), and Trans Polar Index (TPI). However, these station-based relationships could not exhibit well the spatial dependencies of the teleconnections and regional precipitation. It was also found that the unconditional and conditional frequencies of WTs were under the influence of a greater number of large-scale climate signals than the precipitation amounts, were under the influence of a greater number of large-scale climate signals. Moreover, the inherent nature of WTs in keeping spatial dependencies allowed for a better understanding of regional teleconnective relationships. These findings confirmed the advantage of regional rather than local assessments of teleconnective relationships.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Global datasets used in the current study are available at: https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html for reanalysis (NCEP/NCAR) and https://www.cpc.ncep.noaa.gov/data/teledoc/teleindcalc.shtml for large-scale climate indices (teleconnections).
Acknowledgments
The authors greatly appreciate the respectful reviewers and editors for their useful comments, which we believe have noticeably improved the paper’s presentation.
References
Ahmadi, M., S. Salimi, S. A. Hosseini, H. Poorantiyosh, and A. Bayat. 2019. “Iran’s precipitation analysis using synoptic modeling of major teleconnection forces (MTF).” Dyn. Atmos. Oceans 85 (Mar): 41–56. https://doi.org/10.1016/j.dynatmoce.2018.12.001.
Ailliot, P., C. Thompson, and P. Thompson. 2009. “Space–time modeling of precipitation by using a hidden Markov model and censored Gaussian distributions.” J. R. Stat. Soc. 58 (3): 405–426. https://doi.org/10.1111/j.1467-9876.2008.00654.x.
Araghi, A., M. Mousavi-Baygi, J. Adamowskib, and C. Martinez. 2017. “Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence.” Int. J. Climatol. 37 (6): 2809–2830. https://doi.org/10.1002/joc.4881.
Bazrafshan, J., N. Heidari, I. Moradi, and Z. Aghashariatmadary. 2014. “Simultaneous stochastic simulation of monthly mean daily global solar radiation and sunshine duration hours using copulas.” J. Hydrol. Eng. 20 (4): 04014061. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001051.
Bellone, E., J. P. Hughes, and P. Guttorp. 2000. “A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts.” J. Clim. Res. 15 (1): 1–12.
Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: A practical information-theoretical approach. 2nd ed. New York: Springer.
Cao, Q., Z. Hao, F. Yuan, R. Berndtsson, S. Xu, H. Cao, and J. Hao. 2019. “On the predictability of daily rainfall during rainy season over the Huaihe River Basin.” Water. 11 (5): 916–935. https://doi.org/10.3390/w11050916.
Casanueva, A., C. Rodríguez-Puebla, M. D. Frías, and N. González-Reviriego. 2014. “Variability of extreme precipitation over Europe and its relationships with teleconnection patterns.” Hydrol. Earth Syst. Sci. 18 (2): 709–725. https://doi.org/10.5194/hess-18-709-2014.
Chen, S., B. Huang, F. Yuan, R. Berndtsson, J. Qiu, X. Chen, and Z. Hao. 2019. “Spatiotemporal changes in precipitation and temperature in the Huaibei plain and the relation between local precipitation and global teleconnection patterns.” J. Hydrol. Eng. 24 (8): 05019019. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001790.
Dannenberg, M. P., E. K. Wise, M. Janko, T. Hwang, and W. K. Smith. 2018. “Atmospheric teleconnection influence on North American land surface phenology.” Environ. Res. Lett. 13 (3): 034029. https://doi.org/10.1088/1748-9326/aaa85a.
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. “Maximum likelihood from incomplete data via the EM algorithm.” J. R. Stat. Soc. B. 39 (1): 1–38.
Deng, Y., W. Jiang, B. He, Z. Chen, and K. Jia. 2018. “Change in intensity and frequency of extreme precipitation and its possible teleconnection with large-scale climate index over the China from 1960 to 2015.” J. Geophys. Res. Atmos. 123 (4): 2068–2081. https://doi.org/10.1002/2017JD027078.
Duzenli E., H. Tabari, P. Willems, and M. T. Yilmaz. 2018. “Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns.” Hydrol. Processes 32 (23): 3513–3528. https://doi.org/10.1002/hyp.13275.
Gerlitz, L., E. Steirou, C. Schneider, V. Moron, S. Vorogushyn, and B. Merz. 2018. “Variability of the cold season climate in Central Asia—Part I: Weather types and their tropical and extra-tropical drivers.” J. Clim. 31 (18): 7185–7207. https://doi.org/10.1175/JCLI-D-17-0715.1.
Ghamghami, M., N. Ghahreman, P. Irannejad, and H. Pezeshk. 2020. “A parametric empirical Bayes (PEB) approach for estimating maize progress percentage at field scale.” Agric. For. Meteorol. 281 (Feb): 107829. https://doi.org/10.1016/j.agrformet.2019.107829.
Ghamghami, M., N. Ghahreman, H. Olya, and T. Ghasdi. 2019. “Comparison of three multi-site models in stochastic reconstruction of winter daily rainfall over Iran.” Model. Earth Syst. Environ. 5 (4): 1319–1332. https://doi.org/10.1007/s40808-019-00599-7.
Ghamghami, M., and P. Irannejad. 2019. “An analysis of droughts in Iran during 1988–2017.” SN Appl. Sci. 1 (10): 1217–1221. https://doi.org/10.1007/s42452-019-1258-x.
Ghanghermeh, A., G. R. Roshan, and S. Al-Yahyai. 2015. “The influence of Atlantic-Eurasian teleconnection patterns on temperature regimes in South Caspian Sea coastal areas: A study of Golestan Province, North Iran.” Pollution 1 (1): 67–83.
Ghasemi, A. R., and D. Khalili. 2008. “The association between regional and global atmospheric patterns and winter precipitation in Iran.” Atmos. Res. 88 (2): 116–133. https://doi.org/10.1016/j.atmosres.2007.10.009.
Ghil, M., and A. W. Robertson. 2002. “Waves” vs. “particles” in the atmosphere’s phase space: A pathway to long-range forecasting?” Proc. Natl. Acad. Sci. U.S.A. 99 (S1): 2493–2500. https://doi.org/10.1073/pnas.012580899.
Gonsamo, A., J. Chen, and D. Lombardozzi. 2016. “Global vegetation productivity response to climatic oscillations during the satellite era.” Global Change Biol. 22 (10): 3414–3426. https://doi.org/10.1111/gcb.13258.
Granato-Souza, D., E. Adenesky-Filho, and K. Esemann-Quadros. 2019. “Dendrochronology and climatic signals in the wood of Nectandra oppositifolia from a dense rain forest in southern Brazil.” J. For. Res. 30 (2): 545–553. https://doi.org/10.1007/s11676-018-0687-5.
Gupta, V., and M. K. Jain. 2019. “Impact of ENSO, global warming, and land surface elevation on extreme precipitation in India.” J. Hydrol. Eng. 25 (1): 05019032. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001872.
Hauke, J., and T. Kossowski. 2011. “Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data.” Quaestiones Geogrhaphicae 30 (2): 87–93. https://doi.org/10.2478/v10117-011-0021-1.
Hejazizade, Z., and E. Fattahi. 2005. “Synoptical analysis of Iran winter rainfall.” [In Persian.] J. Geogr. 2 (3): 89–107.
Hosseinzadeh Talaee, P., H. Tabari, and S. S. Ardakani. 2012. “Hydrological drought in the west of Iran and possible association with large-scale atmospheric circulation patterns.” Hydrol. Processes 28 (3): 764–773. https://doi.org/10.1002/hyp.9586.
Hughes, J. P., and P. Guttorp. 1994. “A class of stochastic models for relating synoptic atmospheric patterns to local hydrologic phenomenon.” Water Resour. Res. 30 (5): 1535–1546. https://doi.org/10.1029/93WR02983.
Hughes, J. P., P. Guttorp, and S. P. Charles. 1999. “A non-homogeneous hidden Markov model for precipitation occurrence.” J. R. Stat. Soc. Ser. C (Appl. Stat.) 48 (1): 15–30.
Kim, T., J. Y. Shin, S. Kim, and J. H. Heo. 2018. “Identification of relationships between climate indices and long-term precipitation in South Korea using ensemble empirical mode decomposition.” J. Hydrol. 557 (Feb): 726–739. https://doi.org/10.1016/j.jhydrol.2017.12.069.
Kirshner, S. 2005. Modeling of multivariate time series using hidden Markov models. Berkeley, CA: University of California Press.
Lee, J. H., J. A. Ramirez, T. W. Kim, and P. Y. Julien. 2019. “Variability, teleconnection, and predictability of Korean precipitation in relation to large scale climate indice.” J. Hydrol. 568 (Jan): 12–25. https://doi.org/10.1016/j.jhydrol.2018.08.034.
Lorenzo, M. N., J. J. Taboada, and L. Gimeno. 2008. “Links between circulation weather types and teleconnection patterns and their influence on precipitation patterns in Galicia (NW Spain).” Int. J. Climatol. 28 (11): 1493–1505. https://doi.org/10.1002/joc.1646.
Lorrey, A. M., and N. C. Fauchereau. 2017. “Southwest Pacific atmospheric weather regimes: Linkages to ENSO and extra-tropical teleconnections.” Int. J. Climatol. 38 (4): 1893–1909. https://doi.org/10.1002/joc.5304.
Mares, C., I. Mares, H. Huebener, M. Mihailescu, U. Cubasch, and P. Stanciu. 2014. “A hidden Markov model applied to the daily spring precipitation over the Danube Basin.” Adv. Meteorol. 237247. https://doi.org/10.1155/2014/237247.
Messie, M., and F. Chavez. 2011. “Global modes of sea surface temperature variability in relation to regional climate indices.” J. Clim. 24 (16): 4314–4331. https://doi.org/10.1175/2011JCLI3941.1.
Molavi-Arabshahi, M., K. Arpe, and S. A. G. Leroy. 2016. “Precipitation and temperature of the southwest Caspian Sea region during the last 55 years: Their trends and teleconnections with large-scale atmospheric phenomena.” Int. J. Climatol. 36 (5): 2156–2172. https://doi.org/10.1002/joc.4483.
Nazemosadat, M. J., and I. Cordery. 2000. “On the relationships between ENSO and autumn rainfall in Iran.” Int. J. Climatol. 20 (1): 47–61. https://doi.org/10.1002/(SICI)1097-0088(200001)20:1%3C47::AID-JOC461%3E3.0.CO;2-P.
Nazemosadat, M. J. 2001. “The impact of the Caspian Sea surface temperature on rainfall over northern parts of Iran.” In Proc., 2nd National Conf. of the Royal Meteorological Society, 47–61. London: Royal Meteorological Society.
NCARS (National Center for Atmospheric Research Staff). 2015. “The climate data guide: Overview: Climate indices.” Accessed September 22, 2018. https://climatedataguide.ucar.edu/climate-data/overview-climate-indices.
Neykov, N., P. Neytchev, W. Zucchini, and H. Hristov. 2012. “Linking atmospheric circulation to daily precipitation patterns over the territory of Bulgaria.” Environ. Ecol. Stat. 19(2), 249–267. https://doi.org/10.1007/s10651-011-0185-9.
Oldenborgh, G. V., G. Burgers, and A. Tank. 2000. “On the El-Nino teleconnection to spring precipitation in Europe.” Int. J. Climatol. 20 (5): 565–574. https://doi.org/10.1002/(SICI)1097-0088(200004)20:5%3C565::AID-JOC488%3E3.0.CO;2-5.
Oliver, C. I. 2009. Markov processes for stochastic modeling. New York: Wiley.
Rabiner, L. R. 1989. “A tutorial on hidden Markov models and selected applications in speech recognition.” Proc. IEEE 77 (2): 257–286. https://doi.org/10.1109/5.18626.
Raziei, T. 2018a. “An analysis of daily and monthly precipitation seasonality and regimes in Iran and the associated changes in 1951–2014.” Theor. Appl. Climatol. 134 (3–4): 913–934. https://doi.org/10.1007/s00704-017-2317-0.
Raziei, T. 2018b. “A precipitation regionalization and regime for Iran based on multivariate analysis.” Theor. Appl. Climatol. 131 (3–4): 1429–1448. https://doi.org/10.1007/s00704-017-2065-1.
Robertson, A. W., S. Kirshner, and P. Smyth. 2003. Hidden Markov models for modeling daily rainfall occurrence over Brazil. San Francisco: Univ. of California.
Robertson, A. W., S. Kirshner, and P. Smyth. 2004. “Downscaling of daily rainfall occurrence over northeast Brazil using a hidden Markov model.” J. Clim. 17 (22): 4407–4424. https://doi.org/10.1175/JCLI-3216.1.
Roller, C. D., J. Qian, L. Agel, M. Barlow, and V. Moron. 2016. “Winter weather regimes in the Northeast United States.” J. Clim. 29 (8): 2963–2980. https://doi.org/10.1175/JCLI-D-15-0274.1.
Romanic, D., H. Hangan, and M. Ćurić. 2018. “Wind climatology of Toronto based on the NCEP/NCAR reanalysis 1 data and its potential relation to solar activity.” Theor. Appl. Climatol. 131 (1–2): 827–843. https://doi.org/10.1007/s00704-016-2011-7.
Roozitalab, M. H., H. Siadat, and A. Farshad. 2018. The soils of Iran. New York: Springer.
Sagarika, S., A. Kalra, and S. Ahmad. 2015. “Interconnections between oceanic–atmospheric indices and variability in the U.S. streamflow.” J. Hydrol. 525 (Jun): 724–736. https://doi.org/10.1016/j.jhydrol.2015.04.020.
Schwing, F. B., R. Mendelssohn, S. J. Bograd, J. E. Overland, M. Wang, and S. Ito. 2010. “Climate change, teleconnection patterns, and regional processes forcing marine populations in the Pacific.” J. Mar. Syst. 79 (3–4): 245–257. https://doi.org/10.1016/j.jmarsys.2008.11.027.
Shen, Y., L. Wu, L. Di, G. Yu, H. Tang, G. Yu, and Y. Shao. 2013. “Hidden Markov models for real-time estimation of corn progress stages using MODIS and meteorological data.” Remote Sens. 5 (4): 1734–1753. https://doi.org/10.3390/rs5041734.
Sheridan, S. 2003. “North American weather-type frequency and teleconnection indices.” Int. J. Climatol. 23 (1): 27–45. https://doi.org/10.1002/joc.863.
Shirvani, A., A. Sabetan Fadaei, and W. A. Landman. 2019. “The linkage between geopotential height and monthly precipitation in Iran.” Theor. Appl. Climatol. 136 (1–2): 221–236. https://doi.org/10.1007/s00704-018-2479-4.
Soltani, A., and M. Gholipoor. 2006. “Teleconnections between El Nino/Southern oscillation and rainfall and temperature in Iran.” Int. J. Agric. Res. 1 (6): 603–608. https://doi.org/10.3923/ijar.2006.603.608.
Spearman, C. E. 1904. “The proof and measurement of association between two things.” Am. J. Psychol. 15 (1): 72–101. https://doi.org/10.2307/1412159.
Steirou, E., L. Gerlitz, H. Apel, and B. Merz. 2017. “Links between large-scale circulation patterns and streamflow in Central Europe: A review.” J. Hydrol. 549 (Jun): 484–500. https://doi.org/10.1016/j.jhydrol.2017.04.003.
Sun, X., M. Thyer, B. Renard, and M. Lang. 2014. “A general regional frequency analysis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall.” J. Hydrol. 512 (May): 53–68. https://doi.org/10.1016/j.jhydrol.2014.02.025.
Tan, X., and D. Shao. 2016. “Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China.” Int. J. Climatol. 37 (3): 1510–1525. https://doi.org/10.1002/joc.4794.
Wang, H., Y. Chen, Y. Pan, and W. Li. 2015. “Spatial and temporal variability of drought in the arid region of China and its relationships to teleconnection indices.” J. Hydrol. 523 (Apr): 283–296. https://doi.org/10.1016/j.jhydrol.2015.01.055.
Wise, E. K., M. L. Wrzesien, M. P. Dannenberg, and D. L. McGinnis. 2015. “Cool-season precipitation patterns associated with teleconnection interactions in the United States.” J. Appl. Meteorol. Climatol. 54 (2): 494–505. https://doi.org/10.1175/JAMC-D-14-0040.1.
Yuan, F., R. Berndtsson, C. B. Uvo, L. Zhang, and P. Jiang. 2016. “Summer precipitation prediction in the source region of the Yellow River using climate indices.” Hydrol. Res. 47 (4): 847–856. https://doi.org/10.2166/nh.2015.062.
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Journal of Hydrologic Engineering
Volume 26 • Issue 3 • March 2021
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© 2021 American Society of Civil Engineers.
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Received: Feb 4, 2020
Accepted: Nov 24, 2020
Published online: Jan 6, 2021
Published in print: Mar 1, 2021
Discussion open until: Jun 6, 2021
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