Teleconnections and analysis of long-term wind speed variability in the UAE

Abstract

Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter‐annual wind variability. The objective of this paper is to examine the long‐term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Niño Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step‐wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.

Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics

“…The noticeable change between the mean number of fog events before and after 1999 could be associated with an ENSO event, as confirmed by other studies that tackled climatic variables in the UAE like precipitation and wind [48,51]. In their research, [51] explained the role of large-scale atmospheric teleconnection patterns in altering the surface energy balance and how they operate when the strong westerly jet streams persistent in the upper atmosphere are favorable for the propagation of Rossby waves (Section 3.2, Figure 6).…”

“…The formulation of a thermal low depression, resulting from high insolation over the Empty Quarter, and moving towards the UAE eventually reaching Al Hajar Mountain in Oman (to the northeast of the UAE-See Figure 1b), deepens the thermal low and accelerates the southerly wind over the mountain, carrying dust and sand towards the UAE [47]. Furthermore, as confirmed by Naizghi, and Ouarda [48] Al Ain city has the highest wind speed, which triggers more significant dust emission and transport. Figure 8 shows the total number of poor visibility events per city for the ten cities in relation to the four visibility classes.…”

“…This teleconnection mechanism shows that ENSO has an impact on the formulation of dust storms in the region during summer periods, and thus an effect on visibility. In their research, Naizghi, and Ouarda [48] prove that climate oscillations play a role in dust transportation in the UAE, hence affecting visibility. High temperature and wind speed improve visibility by enhancing the dispersive capability of the atmosphere via thermal and mechanical turbulence respectively and reducing aerosol concentration levels [13].…”

Recent trends in wind speed across Saudi Arabia, 1978–2013: a break in the stilling

“…Our analyses of the relationship of the observed wind speed anomalies with five teleconnection indices showed close agreement with the findings of Naizghi and Ouarda (), who analysed links between the long‐term wind speed variability in the neighbouring United Arab Emirates and teleconnection indices. For instance, we detected that atmospheric circulation has important relationship with the variability of terrestrial near‐surface wind speed trends across SA, with the NAOI strongly affecting winds annually, the SOI in winter, spring and autumn, and the EAI (also NAOI) in summer.…”

“…Following findings from Naizghi and Ouarda (), who examined the wind speed variability in the neighbouring United Arab Emirates (UAE) and its relationships with teleconnection indices, we selected five atmospheric circulation patterns to analyse the interplay between large‐scale atmospheric circulation and the wind speed variability. These are: (1) the North Atlantic Oscillation (NAO; Jones et al, ), as the station‐derived normalized pressure between the Gibraltar and Reykjavik as obtained from the Climate Research Unit (https://crudata.uea.ac.uk/cru/data/nao/; accessed 1 September 2017), (2) the East Atlantic (EA) teleconnection as the north–south dipole of anomaly centre spanning the North Atlantic from east to west, as shown in Barnston and Livezey () and retrieved from the National Oceanic and Atmospheric Administration‐National Centers for Environmental Prediction (NOAA‐NCEP) (http://www.cpc.ncep.noaa.gov/data/teledoc/ea.shtml; accessed 1 September 2017), (3) the Southern Oscillation (SO) defined as the normalized pressure difference between Tahiti and Darwin as calculated by Ropelewski and Jones () and retrieved from the Climate Research Unit (https://crudata.uea.ac.uk/cru/data/soi/; accessed 1 September 2017), (4) the Mediterranean Oscillation (MO) as the normalized sea level pressure (SLP) difference between 35°N and 5°W (Gibraltar) and that at 30°N and 35°E (Lod), similar to Palutikof (), and computed using SLP grids from the daily Northern Hemisphere sea level pressure grids data sets (http://rda.ucar.edu/datasets/ds010.0/; accessed 1 September 2017) and, finally, (5) the Western Mediterranean Oscillation (WEMO) as the pressure dipole of normalized SLP between San Fernando (Spain) and Padova (Italy) as defined by Martin‐Vide and Lopez‐Bustins () and downloaded from the Group of Climatology of the University of Barcelona (http://www.ub.edu/gc/en/2016/06/08/wemo/; accessed 1 September 2017).…”