Iran's Seasonal Precipitation Analysis Associated with El-Niño Events: Historical Analysis and the 2015/2016 El-Niño

Any potential predictability will be of great value to cope with climate extremes, variability and change. Although hydro-energy, agricultural and water resource system planners require this information at seasonal to inter-annual time ranges, they have proven reluctant to incorporate climate forecasts into the decision making process. One reason lies in the concept of the level of predictability at different spatiotemporal scales given the complexities associated with ocean-atmosphere nonlinear interactions, teleconnections pattern and their influences. The provision of quantitative, probabilistic outlooks of societally-relevant variables based on historical analysis of what has occurred in the past can be an effective tool in the context of hydroclimate risk management to overcome the issue. The aim of this study is I) to understand potential predictability of 3-month precipitation total for the rainy season over Iran associated with El Niño events. Taking the contingency tables approach instead of typical composite and correlation, probabilistic seasonal precipitation anomalies conditional upon the phase of strongest El Niño events are provided. Probabilities of wet/dry climate conditions are evaluated for two gridded monthly precipitation datasets, GPCC (Global Precipitation Climatology Centre) and CRU (Climate Research Unit). II) To see if historic analysis of previous El Niño events can be considered as an indication of what has been likely to occur (likelihood of wet/dry climate anomalies) during the 2015/2016 El Niño. When historical analyzes based on more than 60 years of GPCC and CRU over the whole country are compared to climate conditions experienced for the most recent event, the results suggest that there is more than 60 percent probability that above-normal conditions at the western (especially northwestern) Iran are associated with significant El Niño events. Moreover, wet precipitation anomalies are observed for fall 2015 as has been expected for western Iran. Once policymakers understand the level of predictability for different seasons and river basins from historical analysis, they can be prepared for the risks ahead when seasonal climate forecast models having skill in capturing the right ENSO signal (and its impacts at regional scale) suggest an ENSO extreme.