The Relations between North Atlantic Oscillation and Minimum Temperature in Turkey

Bozyurt, Okan; Özdemir, Mehmet Ali

doi:10.1016/j.sbspro.2014.02.133

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The NAO is especially influential on the temperature at the regions where continental climate prevails. In contrast, at the regions where and oceanic climate prevails, this effect is either too weak or creates an insignificant (Bozyurt and Ozdemir 2014).…”

Section: Introductionmentioning

confidence: 97%

Statistical analysis and temporal trend of annual maximum temperatures of Abadan in Southwestern of Iran

Rahimi

Ahmadi

2015

Arab J Geosci

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Human and natural physical caused climate change can affect climate extremes, as well as maximum temperatures. The variations of the interannual and interdecadal hot temperatures have been studied between 1960 and 2012 (53 years) based on Abadan annual maximum temperatures (AAMT) data. The decadal analysis of the time variations of AAMT showed a positive and ascending trend in 60d, 70d and 90d, the neutral trend in 80d, and the descending trend in the last decade of the twentieth century. The positive time variations of AAMT in the 60d and 70d are too severe and more significant than that of other decades. The analysis of relationship between the absolute AAMT and the factors such as the global annual mean land-ocean temperature index (GAMLOTI), the annual concentration of atmospheric carbon dioxide (ACD), and the summer June-July-August (JJA) index of North Atlantic Oscillation (NAO) teleconnection pattern in AAMT showed that there is a significant correlation between AAMT and the mentioned factors. Thus, the relationship between the AAMT and the GAMLOTI is significant with a correlation coefficient of 0.602 at the significant level of 0.01. ACD had a negative significant correlation (r=0.60), and the JJA NAO index had a negative significant correlation (r=−0.34) with AAMT. The results of multiple regression analysis showed that the overall effect of the GAMLOTI, the annual concentration of ACD, and the JJA NAO index on the temporal variability of AAMT during a 53-year period is 46.2 %. The relationship between the AAMT and the GAMLOTI and the annual concentration of ACD represents the effects of global warming on the variability of the AAMT.

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Section: Introductionmentioning

confidence: 97%

Statistical analysis and temporal trend of annual maximum temperatures of Abadan in Southwestern of Iran

Rahimi

Ahmadi

2015

Arab J Geosci

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“…The NAO is particularly influencing the temperature where the interior climate is in excess. In the areas where oceanic climate regulates, the outcome is so weak or trivial (Bozyurt and Özdemir 2014). The analysis and management of temperature rise and fall are essential to understand the continuous temperature changes.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis and temporal trend of annual maximum temperature with teleconnection patterns of different stations in Pakistan

et al. 2021

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Human and natural physical-caused climate variation can affect climate extremes, also called maximum high temperature. The change of the inter-annual maximum temperatures has been studied between1981 to 2016. Annual Maximum Temperatures (AMT) data consists of the temperature study of 36 years. The analysis of the correlation between the Badin, Karachi, Quetta, Lahore, and Peshawar, Islamabad temperature data and the explanatory variable such as Global Annual Mean-Land-Ocean Temperature Index (GAMLOTI), annual carbon dioxide (ACD), and North Atlantic Oscillation (NAO) teleconnection arrangement in annual maximum temperature indicated between explanatory variable is significant. Therefore, the correlation between the Badin (AMT) and the GAMLOTI is significant with the relationship of correlation coefficient 0.127 at the level of significance 0.05, and the index of NAO is negative significant correlation (r =−0.151), and the correlation between Islamabad and Karachi's annual maximum temperature and the explanatory variable is insignificant. The results of multiple regression analysis showed that the whole effect of global annual change means land-ocean temperature index GAMLOTI, the annual concentration of atmospheric carbon dioxide ACD, and the north Atlantic oscillation NAO index on the temporal variabilities of Badin, Quetta, Islamabad, Karachi, and Peshawar during the 36-year period. The variations occur R 2 45.7%, 51.3%, 43.1%, 46%, and 48.1%. The relationship between the AMT and the GAMLOTI and the annual concentration of ACD represent the global warming, mostly effects on the variability of AMT.

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“…The results showed the increasing trend of the daily temperature range (DTR) throughout the region. Other researchers also investigated on temperature extremes, including: Alijani et al Borna and Igin (2015) , David (1997), Vincent et al (2000), Grieser et al (2002), Gadgil and Dhorde (2004), Alexander et al (2006), Bozyurt and Ozdemir (2014), Rao et al (2014). Considering the conducted studies on the importance of temperature extremes and experiments on the study method can be declared that the temperature parameter is important in different parts of agriculture, economic and social.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of the effects of temperature extremes on the environment heating in the Central Part of Iran, Iran, Southwest Asia

Safarianzengir

Sobhani

Kianian

2020

Preprint

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Investigation of temperature extremes is very important as one of the most important climate parameters in different parts. The purpose of this study is to analyze and predict the temperature extremes in central Iran. Therefore, the minimum and maximum data of 15 synoptic stations in the study area for the period (1988–2018) using Hybrid Artificial Neural Network (HANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used. Finally, Multi-Criteria Decision-Making (MCDM) models TOSIS and SAW were used to prioritize the areas exposed to rising temperature. The results showed that according to ANFIS modeling for predicting extreme temperatures, the lowest mean training error and the mean error of validation for the minimum temperature was equal 0.10 for Yazd Station and 1.66% for Damghan Station. The lowest mean training error and the mean error of validation for the maximum extreme temperature obtained 0.016 for Garmsar Station and 9.39% for Shahroud station. The maximum extreme temperature of two stations of Garmsar and Bafgh (1 and 0.9689, respectively) were more exposed to extreme temperatures based on the TOPSIS model. Garmsar and Salafchegan Stations (1 and 0.9873, respectively) were more exposed to extreme temperatures based on the SAW model.

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The Relations between North Atlantic Oscillation and Minimum Temperature in Turkey

Cited by 7 publications

References 11 publications

Statistical analysis and temporal trend of annual maximum temperatures of Abadan in Southwestern of Iran

Statistical analysis and temporal trend of annual maximum temperatures of Abadan in Southwestern of Iran

Statistical analysis and temporal trend of annual maximum temperature with teleconnection patterns of different stations in Pakistan

Analysis and prediction of the effects of temperature extremes on the environment heating in the Central Part of Iran, Iran, Southwest Asia

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