ABSTRACT:In characterizing the patterns of climate change across Saudi Arabia, several extreme indices are calculated from station values daily maximum and minimum temperature data. The trend analyses are performed on 13 annual extreme indices for Saudi Arabia, using observations from 27 surface stations with high-quality data for the period 1981-2010. RClimDex is used to calculate the indices, and simple regression methods are employed for the trend analysis. The analyses of extreme temperature indices detected a significant increase in the majority of the stations, which further indicates that the country has experienced a warming trend. The findings show that 92/89% of the stations displayed a significant increase in the annual occurrence of warm days/nights and 96/93% revealed a significant decrease for the occurrence of cool days/nights. Time-series analysis has also shown an important feature of the climate change signal. When the dataset is divided into two sub-periods, the analysis reveals a distinctive long-term trend that clearly distinguishes the two periods. It was found that the temperature extremes (hot and cold) in Saudi Arabia have increased significantly with greater magnitude in the recent-past (1996-2010) compared to the previous period (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995). The most telling evidence for this is the heat-and cold-waves, where indices of both have occurred more frequently in the second part of the two sub-periods.

A climatic regionalization of Saudi Arabia that aims to classify the country into several homogenous groups is carried out, by grouping similar long-term climatological features for precipitation and air temperature. Twenty-seven stations are selected across Saudi Arabia for the analysis and 1985-2010 (26 years) is chosen as the common period. The employed methodology is a combination of mathematical and statistical techniques (principal components analysis and correlation); and a rather subjective physically detailed comparison with the long-term climatology and topography for Saudi Arabia. Five groups have been identified: group A (Northern), B (Red Sea Coastal), C (Interior), D (Highland) and E (Southern). Each of the climatic groups exhibits a unique feature (with strong similarities among stations that belong to the same group), especially in terms of inter-annual fluctuations and the degree of seasonality. This simplified overview of climatic characteristics is beneficial for assisting with economic planning, especially for any semi-arid or arid land that is naturally vulnerable to climatic variability and extremes. The classification method used in this study, however, would be applicable for similar studies on regionalization in other regions.

Background Projections of temperature and precipitation with low uncertainties are key parameters to climate changerelated studies. Purpose The projected temperature and precipitation and their uncertainties over the Arabian Peninsula for the 21st century for three CMIP5 multimodel ensembles under RCP4.5 and RCP8.5 are examined in this paper. Methods Analyzing the performance of 30 CMIP5 model individually, they are categorized into three groups for the present climate . By applying simple model averaging ensemble method, three multimodel ensemble means, namely: (i) all CMIP5 models ensemble (AME), (ii) selected CMIP5 models ensemble (SME), and (iii) best-performing CMIP5 models ensemble (BME) are developed. Results Over the Arabian Peninsula, a continuous rise in temperature is obtained in all three ensembles (i.e., AME, SME, and BME) in the 21st century. The BME shows enhanced changes in temperature at the end of 21st century as compared to AME and SME. Moreover, the BME shows a remarkable reduction in uncertainties for the projected temperature. The AME, SME, and BME show strong inter-annual variability for the projected precipitation over the peninsula. Compared to AME and SME, the BME revealed enhanced positive change in the annual mean precipitation by the end of 21st century. Conclusions Regionally, southern/northwestern areas of the peninsula receive enhanced/reduced future precipitation as compared to the present climate. The differences in the projected precipitation and temperature signals increase largely between the three ensembles towards the end of 21st century. Therefore, it is concluded that selecting the best-performing models may lead a better planning by the policy makers and stakeholder for the region.

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Vaccine hesitancy is a global health challenge in controlling the virulence of pandemics. The prevalence of vaccine hesitancy will put highly vulnerable groups, such as the elderly or groups with pre-existing health conditions, at a higher risk, as seen with the outbreak of the pandemic Covid-19. Based on the trends of vaccine hesitancy in the state of Sabah, located in East Malaysia, this study seeks to identify several variables that contribute to vaccine hesitancy. In addition to this, this study also determines which groups are affected by vaccine hesitancy based on their demographics. This study is based on a sampling of 1,024 Sabahan population aged 18 and above through an online and face-to-face questionnaire. The raw data was analysed using the K-Means Clustering Analysis, Principal Component Analysis (PCA), Mann-Whitney U Test, Kruskal-Wallis Test, and frequency. The K-Means Clustering found that more than half of the total number of respondents (Cluster 2 = 51.9%) tend to demonstrate vaccine hesitancy. Based on the PCA analysis, six main factors were found to cause vaccine hesitancy in Sabah: confidence (var(X) = 21.6%), the influence of local authority (var(X) = 12.1%), ineffectiveness of mainstream media (var(X) = 8.4%), complacency (var(X) = 7.4%), social media (var(X) = 6.4%), and convenience issues (var(X) = 5.8%). Findings from both Mann-Whitney U and Kruskal-Wallis tests demonstrate that several factors of group demographics, such as employment status, level of education, religion, gender, and marital status, may explain the indicator of vaccine hesitancy. In particular, specific groups tend to become vaccine hesitancy such as, unemployed, self-employed, students, male, single, level of education, and Muslim. Findings from this empirical study are crucial to inform the relevant local authorities on the level of vulnerability among certain groups in facing the hazards of COVID-19. The main contribution of this study is that it seeks to analyse the factors behind vaccine hesitancy and identifies which groups more likely hesitant toward vaccines based on their demographics.