Correlation of NAO, IOD and ENSO with the sea surface temperature changes in the Persian Gulf

Document Type : Research


1 Ph.D. Student, Department of Marine and Atmospheric Science (Non-Biologic), Faculty of Marine Science and Technology, University of Hormozgan, Bandarabbas, Iran

2 Assistant Professor, Department of Marine and Atmospheric Science (Non-Biologic), Faculty of Marine Science and Technology, University of Hormozgan, Bandarabbas, Iran


Sea Surface Temperature (SST) variability, especially its slow variability, creates a potentially predictable source for climate fluctuations. Therefore, the SST variability study sheds light at climate changes, marine life, and prediction of short term and long term climate variation. In this research, the trend and interannual variability of the Persian Gulf SST were analyzed by employing monthly detrended Optimum Interpolation Sea Surface Temperature (OISST) data in 1982-2018. According to the effects of teleconnection patterns on atmospheric and oceanic parameters in different regions, the correlation between NAO, IOD, and ENSO with Persian Gulf SST anomaly is considered in this research. For this purpose, OISST data and MEI.V2, IOD, and NAO indices from 1982 to 2018 were analyzed. The Climatological mean of Persian Gulf SST during this period is shown in figure 5. According to figure 5, northwest of the Persian Gulf was found to be the coolest and southeast of the Persian Gulf was the warmest regions of the Persian Gulf. According to the investigation of this research on monthly variability of the Persian Gulf SST, there are two main seasons with four months, including Summer (June, July, August, September), and Winter (December, January, February, March), and two transition periods with two months, including Spring (April, May), and Autumn (October, November). Based on figure 6, February was the coldest month of winter and August was the warmest month of summer. In both of these months the minimum temperature was observed in the northeast, and the maximum temperature in the southeast of the Persian Gulf. The strongest and the weakest temperature gradient were calculated to be 5 ̊C in winter and 2 ̊C in summer, respectively. There was more than 13 ̊C difference between the spatial mean temperature of February and August. Evaluation of the SST anomaly variance indicated that the maximum variance belonged to the northwest of the Persian Gulf at the coast of Khuzestan province and Kuwait and also to the southwest of the Persian Gulf on the coast of Bahrain, Qatar, and east of Saudi Arabia. Sea surface temperature time series trend triggered by global warming from 1982 to 2018 was calculated to be 0.4 ̊C per decade using the least linear square method. Spatial distribution of trend implies that the maximum trend is observed in the northwest of the Persian Gulf in Khuzestan province and Kuwait coast and the minimum trend is observed in the east and southeast of the Persian Gulf. According to the Pearson correlation method, the maximum (minimum) correlation was calculated to be -0.23 (0.16) employing ENSO (IOD) index considering 4(13) months of delays. The spatial distribution of the correlation between teleconnection patterns indices and the Persian Gulf SST anomaly is demonstrated in figure 9. Results of the analysis pointed out that regarding IOD index, the maximum correlation (0.18) was found at the northwest of the Persian Gulf and the minimum correlation (0.12) was observed at the southeast of the Persian Gulf. Regarding ENSO index, the maximum correlation (-0.24) was at the central region of the Persian Gulf and the minimum correlation (-0.18) was at the south of the Persian Gulf. Concerning NAO index, the maximum correlation (-0.20) was seen at the northwest and the southwest of the Persian Gulf, and the minimum correlation (-0.16) was at the northwest and southeast of the Persian Gulf, near the strait of Hormuz. Therefore, the spatial distribution of correlation between the teleconnection patterns indices and SST anomaly, reveals that there is no center with significant maximum correlation which could give the possibility of distinguishing these areas from the others.


Main Subjects

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