Climate variability in Iran in response to the diversity of the El Niño‐Southern Oscillation

Abstract

Through emitting large-scale planetary waves, the El Niño-Southern Oscillation (ENSO) contributes to the inter-annual climate variability in extra-tropical latitudes, although the impact may be intervened by the chaotic extra-tropical atmosphere. Using meteorological records of 45 synoptic stations across Iran during the period 1980-2016, impacts of La Niña and the eastern Pacific (EP) and central Pacific (CP) El Niño events on the inter-annual climate variability of Iran are investigated. The data for four EP El Niño, six CP El Niño and nine La Niña events are averaged to produce a composite view of ENSO. On average, annually Iran is anomalously wet during the EP El Niño and anomalously dry during La Niña, and the anomalies in both phases of ENSO are statistically significant. The impact of ENSO depends not only on the strength of the ENSO event in the tropical Pacific but also on the background climate state of the affected regions such that it is found that the impacts of La Niña and the EP El Niño are generally stronger over the warm and arid regions of Iran. It is also found that the ENSO signal changes with season in many climate zones of Iran, indicating complex interactions between ENSO and other seasonally different features of the climate system. In spite of the seasonal variation of the ENSO signal, most regions of Iran have experienced statistically significant anomalously dry conditions during winter and spring of La Niña events. The most noticeable difference between the two types of El Niño is observed over the Makran coastal plain of Iran where the EP and CP El Niño events have been associated with positive and negative precipitation anomalies, respectively.

Dynamical downscaling of CSIRO‐Mk3.6 seasonal forecasts over Iran with the regional climate model version 4

“…The impacts of ENSO on the climate of Iran have been fully discussed by Alizadeh‐Choobari et al . () and Alizadeh‐Choobari and Najafi (). It can be observed that large errors in the simulated 2‐m temperatures (both in RegCM4 and the driving CSIRO‐Mk3.6) are associated with one of the contrasting phases of ENSO.…”

“…As ENSO is the main process that contributes to interannual climate variability in many regions of the globe (McPhaden et al, ; Alizadeh‐Choobari, ), including Iran (Alizadeh‐Choobari et al, ; Alizadeh‐Choobari and Najafi, ), quality of seasonal climate forecasting also strongly depends on the skill of the driving GCM in prediction of ENSO (Doblas‐Reyes et al, ). A better inclusion of processes that lead to ENSO events and teleconnections associated with them in driving GCMs will certainly improve seasonal climate forecasting.…”

“…Climate variability in the Iranian region is influenced by both local features and remote forcings such as NAO in the Atlantic (Syed et al, ) and ENSO in the Pacific (Alizadeh‐Choobari et al, ; Alizadeh‐Choobari and Najafi, ). The CSIRO‐Mk3.6 GCM was used as initial and boundary conditions to drive a nested RegCM4 over Iran for seasonal climate forecasting, with the aim to provide more detailed information on the regional scale and asses the hindcast skill of RegCM4.…”

“…To identify possible sources of errors in the downscaled simulations by RegCM4, SST anomalies in the Niño 3.4 region in the tropical Pacific during the same period (which can be used to identify different phases of ENSO) are also depicted in Figure 7. The impacts of ENSO on the climate of Iran have been fully discussed by Alizadeh-Choobari et al (2018) and Alizadeh-Choobari and Najafi (2018). It can be observed that large errors in the simulated 2-m temperatures (both in RegCM4 and the driving CSIRO-Mk3.6) are associated with one of the contrasting phases of ENSO.…”

Annual flood damage influenced by El Niño in the Kan River basin, Iran

“…Climate change, encroaching on floodplains, land use changes, diversion of the waterways, destructive effects of human activities, degradation of forests and pastures, and the construction of dysfunctional and vulnerable hydraulic structures can be mentioned as the reasons for increasing flood risks (Wang et al, 2019;Tang, 2020). The magnitude and frequency of flood events in each region depends on several factors: (i) physiographical features of the catchment such as shape, slope, and river network density, (ii) hydrological features such as precipitation, storage and initial losses, evapotranspiration, and permeability, (iii) human activities, (iv) large-scale atmospheric signals, and (v) climate change (Noori et al, 2011;Ward et al, 2014a, b;Alizadeh-Choobari and Najafi, 2017;Hooshyaripor and Yazdi, 2017;Hooshyaripor et al, 2017;Saghafian et al, 2017;Hao et al, 2018). These factors affect the frequency and intensity of floods and consequently the amount of damage costs.…”