Relationships between Large-Scale Climate Signals and Winter Precipitation Amounts and Patterns over Iran

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.