Spectral Analysis of Spatial Relationship between Surface Wind Speed (SWS) and Sea Surface Temperature (SST) in Oman Sea | ||
| پژوهش های جغرافیای طبیعی | ||
| Article 6, Volume 50, Issue 3, October 2018, Pages 473-489 PDF (1.52 M) | ||
| Document Type: Full length article | ||
| DOI: 10.22059/jphgr.2018.245219.1007137 | ||
| Authors | ||
| Younes Khosravi* 1; Ali Bahri2; Azadeh Tavakoli1 | ||
| 1Assistant Professor of Environmental Science, Environmental Science Research Laboratory, Department of Environmental Science, Faculty of Science, University of Zanjan, Iran | ||
| 2MA in Environmental Science, Department of Environmental Science, Faculty of Science, University of Zanjan, Iran | ||
| Abstract | ||
| Introduction Surface wind speed (SWS) and sea surface temperature (SST) are interacting as climatic, atmospheric and oceanic parameters. In such a way, variations in the SST are considered to be the factors in wind speed values and in the future weather forecasting model, monitoring SWS changes plays an important role in identifying the SST heating pattern. Over the past few years, wind speed has indicated a clear decrease in many areas. When wind speed decreases, urban air pollution does not stagnant. On the other hand, changes in SST can bring about various effects on marine environments. One of the most important effects in long term is reduction of the pattern of ocean cycles, which brings nutrients from the depths to the sea surface. This can carry dissolved oxygen from the surface into the deep ocean. Furthermore, due to the interaction between the atmosphere and oceans, SST can bring about dramatic effects on global climate. An important point in the SWS and SST studies is that simultaneous examination of these two parameters makes it possible to study the interactions between atmosphere and ocean. Accordingly, the present study aims to investigate the relationship between surface wind speed and sea surface temperatures in the Gulf of Oman using one of the most important instruments of spatial statistics (spatial autocorrelation techniques) from 2003 to 2015. Study area The Gulf of Oman is a watershed located in the northwest part of Arabian Sea and the Indian Ocean and in east part of the Strait of Hormuz and the Persian Gulf. Through this sea, the Persian Gulf is connected to the Indian Ocean. The gulf is relatively deep and has a depth of 3550 meters, which the depth is reduced in the west and reaches 72 meters near the Strait of Hormuz. Due to the passage of Tropic of Cancer from this watershed zone, this gulf is one of the warmest seas in Southwest Asia. The orientation of surface currents is along the coast of the Gulf of Oman from north-west to south-east during the winter, but during the winter general currents are from the Oman Sea towards the Persian Gulf and reverse in the summer. Iran and Pakistan are located in the northern areas of the Gulf and Oman, and a small part of the UAE in the south. The Gulf of Oman is located at coordinates 22°-27° of northern latitude and 56°-61° of Eastern longitude. Materials and methods According to Anselin, the place has two kinds of effects, spatial dependence and spatial heterogeneity. The first is the spatial correlation or spatial continuity that follows directly the first Law of Geography, Tobler law. This means that the values close to each other are more similar to each other and this leads to spatial aggregation. The second is the spatial impact belonging to regional or spatial differences that follow the inherent uniqueness of each place. Determination of the degree of scattering or clustering of complications in space is possible using Global Spatial Moran Autocorrelation, Global Moran’s I. In fact, this index is intended to describe the spatial characteristics of a variable in the whole region, and it can be used to determine the mean space difference between all spatial cells and their adjacent cells. In global Moran index, in addition to application of the arrangement of complications, remarkable attention is also paid to the characteristics of the complications and the status of spatial autocorrelation based on the location and the internal values of the complications. There are various spatial techniques to represent the statistical distribution of phenomena in space; one of the most authentic indices derived from the Anselin Local Moran's I. Using weighted spatial features and with the aid of this statistic, we can find points with small or high distribution represented in clusters or values with high difference (outliers). The Anselin local Moran’I explains the pattern of a spatial correlation of a spatial parameter in neighborhoods. This index was developed by Anselin in 1995 with the aim of identifying local sites and proposing effective individual sites in spatial links. Result and discussion In order to evaluate the relationship between SWS and SST and determine the type of spatial distribution, two variable Global Moran is calculated for monthly and yearly periods. The results of this study in monthly periods indicate that there is a positive relationship among evaluated parameters during cold months but, the relationship is negative and reverse during warm months. Previous surveys documented that from January, as a cold month of year, toward warm months the relationship become more negative and reverse. The most negative form of that is related to July. Then, with the start of cold season, relation of parameters is changed again to positive and direct, as the most positive case occurs on January. The values of two variables of Global Moran between SWS and SST which is examined for a period of 13 years show a negative number and represent a reverse relationship between them. In addition, Moran index values follow a decreasing and negative pattern over time. The surface wind speed and surface temperature of Gulf of Oman is being more reverse. Analysis of local Moran shows that cold months have the most number of spatial clusters (High-High and Low-Low) and warm months experience the most number of spatial outliers (High-Low and Low-High). It could be concluded that the greater number of spatial clusters in comparison to spatial outliers may lead to positive and direct relationship between surface wind speed statistics with sea surface temperature. The negative autocorrelation and reverse relation of these parameters are due to the greater number of spatial outliers. At the next step, the annual changes of High-High and Low-Low clusters is evaluated and it was found that spatial clusters formed in the Gulf of Oman during annual period were very small and the number of spatial outliers formed was much higher. This is consistent with the results indicated by negative values of Global Moran index. Also, there are rise and falls in the number of High-Low outliers during 13 years but in general, the formation of these outliers in the Oman Sea has been declining. On the other hand, changes in the timing of the diagram of Low-High outliers don’t show an increasing or decreasing pattern but closer looks show that from 2009, these outliers are increasing and decreasing by a periodic form. These points tend to increase. By this interpretation, the increasing pattern of negative autocorrelation and reverse relation, which was obtained by Global Moran analysis, could be attributed to Low-High outliers. Conclusion It can be concluded that two parameters of surface wind speed and surface sea temperature have a direct and positive relationship during cold months and a reverse and negative relation in warm months. The reason of these phenomena could be related to interaction of the factors such as latent heat flux and humidity changes. The effects of surface evaporation and Manson air masses are likely possible to create this situation. Therefore, it is necessary to study these parameters simultaneously. Annually changes in scales show that surface wind speed is gradually decreasing and sea surface temperature is increasing. It should be mentioned that the sea surface temperature in Oman Sea was evaluated by this technique and found that during a period of 13 years, the temperature variable follows an increasing pattern in this region. According to the results, the effects of climate change and global warming on surface wind speed and sea surface temperature in Oman Sea are very likely and possible and it is needed to continue monitoring of these parameters and the other climatic and oceanic factors which are affected by them. | ||
| Keywords | ||
| surface wind speed; sea surface temperature; spatial Autocorrelation; arcgis; Oman Sea | ||
| References | ||
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