The short-term rainfall climatology regime over Saudi Arabia is obtained from the Tropical Rainfall Measuring Mission (TRMM) data for the period 1998–2009. The TRMM rainfall amounts are calibrated ...with respect to the rain-gauge data recorded at 29 stations across the country. Day-to-day rainfall comparisons show that the TRMM rainfall trends are very similar to the observed data trends, even if a general overestimation in the satellite products must be highlighted. Besides, especially during the wet season, some of the TRMM algorithm runs tend to underestimate the retrieved rainfalls. The TRMM rainfall data also closely follow the observed annual cycle on a monthly scale. The correlation coefficient for rainfall between the TRMM and the rain-gauge data is about 0.90, with a 99% level of significance on the monthly scale.
The spatio-temporal distributions of rainfall over Saudi Arabia are analyzed. Besides the four conventional seasons, this analysis consider the wet (November–April) and dry (June–September) seasons, based on the rainfall amounts recorded. Spring is the highest and winter is the second highest rainfall-occurring season, resulting in large amounts of rainfall during the wet season over most of the country. Regional variations in the rainfall climatology over Saudi Arabia are studied through defining four regions. The false alarm ratio, probability of detection, threat score, and skill score are calculated to evaluate the TRMM performance. The country's average annual rainfall measured by the TRMM is 89.42mm, whereas the observed data is 82.29mm. Thus, the rainfall in Saudi Arabia is suggested as being the TRMM value multiplied by 0.93 plus 0.04. After this calibration, the TRMM-measured rainfall is almost 100% of the observed data, thereby confirming that TRMM data may be used in a variety of water-related applications in Saudi Arabia.
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► Rainfall was obtained from TRMM data for the period 1998 -2009 over Saudi Arabia. ► Between the TRMM and the rain-gauge data r is 0.90 with a 99% level of significance. ► TRMM overestimates rainfall over Saudi Arabia, particularly over the coastal areas. ► After calibration, TRMM-measured rainfall is almost close to the observed data.
The observed records of recent decades show increased economic damage associated with flash flooding in different regions of Saudi Arabia. An increase in extreme rainfall events may cause severe ...repercussions for the socio-economic sectors of the country. The present study investigated the observed rainfall trends and associated extremes over Saudi Arabia for the 42-year period of 1978–2019. It measured the contribution of extreme events to the total rainfall and calculated the changes to mean and extreme rainfall events over five different climate regions of Saudi Arabia. Rainfall indices were constructed by estimating the extreme characteristics associated with daily rainfall frequency and intensity. The analysis reveals that the annual rainfall is decreasing (5.89 mm decade−1, significant at the 90% level) over Saudi Arabia for the entire analysis period, while it increased in the most recent decade. On a monthly scale, the most significant increase (5.44 mm decade−1) is observed in November and the largest decrease (1.20 mm decade−1) in January. The frequency of intense rainfall events is increasing for the majority of stations over Saudi Arabia, while the frequency of weak events is decreasing. More extreme rainfall events are occurring in the northwest, east, and southwest regions of Saudi Arabia. A daily rainfall of ≥ 26 mm is identified as the threshold for an extreme event. It is found that the contribution of extreme events to the total rainfall amount varies from region to region and season to season. The most considerable contribution (up to 56%) is found in the southern region in June. Regionally, significant contribution comes from the coastal region, where extreme events contribute, on average, 47% of the total rainfall each month from October to February, with the largest (53%) in November. For the entire country, extreme rainfall contributes most (52%) in November and least (20%) in July, while contributions from different stations are in the 8–50% range of the total rainfall.
The present study analyzes the Survivability for a Fit Human Threshold (SFHT) maximum temperature during the summer (June–August) over the six Middle Eastern countries known as the Gulf Cooperation ...Council (GCC) in the twenty-first century. An ensemble of three dynamically downscaled global climate models available from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathways (RCPs) RCP4.5 and RCP8.5 emission scenarios is used to analyze the future climate (2006–2099) over the region. The ground-truth air temperature for ten major cities across the GCC countries is utilized for model evaluation and to estimate the model-simulated temperature biases. Both positive and negative biases found during the present climate (1976–2005) are used to adjust the future temperature changes. These adjustments show that the summer maximum temperature is likely to increase continuously for most cities in the GCC countries at the rate of about 0.2 °C (0.6 °C) per decade under RCP4.5 (RCP8.5) for the future period (2020–2099), which is significant at the 99% confidence level. For RCP8.5, the adjusted summer maximum temperature may exceed the SFHT limit of 42 °C in five capital cities of the GCC states and four major cities of Saudi Arabia. The projections based on adjusted values indicate that the average summer maximum temperature should not exceed 52 °C in any city investigated by the end of the twenty-first century. The daily maximum temperature is projected to exceed 55 °C in some cities in the GCC region by the end of the twenty-first century under a business-as-usual scenario that seems to be unrealistic if the biases are not taken into account. It is highly recommended that the GCC states should coordinate their efforts to respond appropriately to these projections using large ensembles of multimodel simulations while allowing for the associated uncertainty.
Climate change is posing severe threats to human health through its impacts on food, water supply, and weather. Saudi Arabia has frequently experienced record-breaking climate extremes over the last ...decade, which have had adverse socioeconomic effects on many sectors of the country. The present study explores the changes in average temperature and temperature extremes over Saudi Arabia using an updated daily temperature dataset for the period 1978–2019. Also, changes in frequency and percentile trends of extreme events, as well as in absolute threshold-based temperature extremes, are analyzed at seasonal and annual time scales. The results are robust in showing an increase in both temperature trends and temperature extremes averaged over the second period (2000–2019) when compared to the first period (1980–1999). Over the period 1978–2019, the minimum temperature for the country increased (0.64°C per decade) at a higher rate than the maximum temperature (0.60°C per decade). The rate of increase in the minimum and maximum temperatures was reported as 0.48 and 0.71°C per decade, respectively, for the period 1978–2009. The minimum temperature increased by 0.81°C per decade for the second period compared to an increase of 0.47°C per decade for the first period. The significant increase in minimum temperature has resulted in a decreasing linear trend in the diurnal temperature range over recent decades. The maximum (minimum) temperature increased at a higher rate for Jan-Mar (Jun-Nov) with the highest increase of 0.82 (0.89)°C per decade occurring in March (August). The analysis shows a substantial increase (decrease) in the number of warm (cold) days/nights over the second period compared to the first period. The number of warm days (nights) significantly increased by about 13 (21) days per decade, and there is a significant decrease of about 11 (13) days per decade of cold days (nights). The seasonal analysis shows that this increase in warm days/nights is enhanced in boreal summer, with a reduction in the number of cold days/nights in winter. These results indicate that the warming climate of Saudi Arabia is accelerating in recent decades, which may have severe socioeconomic repercussions in many sectors of the country.
This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project ...Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.
The Saudi Arabia (SA) climate varies greatly, depending on the geography and the season. According to K ppen and Geiger, the climates of SA is “desert climate”. The analysis of the seasonal rainfall ...detects that spring and winter seasons have the highestrainfall incidence, respectively. Through the summer,small quantities of precipitation are observed, while autumn received more precipitation more than summer season considering the total annual rainfall. In all seasons, the SW area receives rainfall, with a maximum in spring, whereas in the summer season, the NE and NW areas receive very little quantities of precipitation. The Rub Al-Khali (the SE region) is almost totally dry. The maximum amount of annual rainfall does not always happen at the highest elevation. Therefore, the elevation is not the only factor in rainfall distribution.A great inter-annual change in the rainfall over the SA for the period (1978–2009) is observed. In addition, in the same period, a linear decreasing trend is found in the observed rainfall, whilst in the recent past (1994–2009) a statistically significant negative trend is observed. In the Southern part of the Arabian Peninsula (AP) and along the coast of the Red Sea, it is interesting to note that rainfall increased, whilst it decreased over most areas of SA during the 2000–2009 decade, compared to 1980–1989.Statistical and numerical models are used to predict rainfall over Saudi Arabia (SA). The statistical models based on stochastic models of ARIMA and numerical models based on Providing Regional Climates for Impact Studies of Hadley Centre (PRECIS). Climate and its qualitative character and quantified range of possible future changes are investigated. The annual total rainfall decreases in most regions of the SA and only increases in the south. The summertime precipitation will be the highest between other seasons over the southern, the southwestern provinces and Asir mountains, while the wintertime rainfall will remain the lowest.The climate in the SA is instructed by the El Niño Southern Oscillation (ENSO) and other circulations such as centers of high and low pressure, the North Atlantic Oscillation (NAO) and SOI. Strength and oscillation of subtropical jet stream play a big role in pulling hot, dry air masses of SA.
We provide an assessment of future daily characteristics of African precipitation by explicitly comparing the results of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate ...models, specifically highlighting the similarities and inconsistencies between them. Results for seasonal mean precipitation are not always consistent amongst ensembles: in particular, global models tend to project a wetter future compared to regional models, especially over the Eastern Sahel, Central and East Africa. However, results for other precipitation characteristics are more consistent. In general, all ensembles project an increase in maximum precipitation intensity during the wet season over all regions and emission scenarios (except the West Sahel for CORE) and a decrease in precipitation frequency (under the Representative Concentration Pathways RCP8.5) especially over the West Sahel, the Atlas region, southern central Africa, East Africa and southern Africa. Depending on the season, the length of dry spells is projected to increase consistently by all ensembles and for most (if not all) models over southern Africa, the Ethiopian highlands and the Atlas region. Discrepancies exist between global and regional models on the projected change in precipitation characteristics over specific regions and seasons. For instance, over the Eastern Sahel in July–August most global models show an increase in precipitation frequency but regional models project a robust decrease. Global and regional models also project an opposite sign in the change of the length of dry spells. CORE results show a marked drying over the regions affected by the West Africa monsoon throughout the year, accompanied by a decrease in mean precipitation intensity between May and July that is not present in the other ensembles. This enhanced drying may be related to specific physical mechanisms that are better resolved by the higher resolution models and highlights the importance of a process-based evaluation of the mechanisms controlling precipitation over the region.
The potential predictability and skill of boreal winter (December to February: DJF) precipitation over central-southwest Asia (CSWA) is explored in six models of the North American Multimodel ...Ensemble project for the period 1983–2018. The seasonal prediction data for DJF precipitation initialized at Nov. (Lead-1) observed initial condition is utilized. The potential skill is estimated by perfect model correlation (PMC) method, while observed real skill is calculated by the temporal anomaly correlation coefficient (TCC). The main focus is over the Northern Pakistan (NP: 68°–78°E, 31°–37°N), which is a dominant winter precipitation sub-region in CSWA. All participating models generally capture the observed climatological pattern and variation in winter precipitation over the region. However, there are some systematic biases in the prediction of the climatological mean DJF precipitation, specifically an overestimation of precipitation over the foothills of the Himalayas in all models. The substantial internal atmospheric variability (noise) in the seasonal mean (signal) means that the regional winter precipitation is poorly predictable. The NCEP climate forecast system (CFSv2) and two Geophysical Fluid Dynamics Laboratory models (FLOR-A and FLOR-B) show the lowest potential and real skill. The COLA and NASA models show moderate but statistically significant PMC and TCC values. Each model captures the observed relationship between spatially averaged DJF precipitation over NP, with sea surface temperature (SST) and 200 hPa geopotential height (Z200), in varying details. The COLA and NASA models skillfully matched the observed teleconnection patterns, which could be a reason for their good performance as compared to other models. It also found that SSTs in the tropical oceans are relatively well predicted by NASA model when compared with other models. A critical outcome of the predictive analysis is that the multimodel ensemble (MME: A combination of six models and 79 members) does not show many advantages over the individual models in predicting boreal winter precipitation over the region of interest. Together, these results indicate that reliable prediction of the boreal winter precipitation over CSWA remains a big challenge in initialized models.
This study compares the precision of the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection–51 (C–51) and Collection–06 (C–06) Aerosol Optical Depth (AOD, ...at 550 nm) products with surface-based aerosol robotic network (AERONET) observations for the period 2002–2013 at the Solar Village, Saudi Arabia. In general, MODIS captures the patterns of AERONET AOD although C−51 tends to underestimate them while C−06 overestimates them. We found a slightly higher correlation for C–06 (0.79) than for C–51 (0.74) over the Solar Village. The C–06 retrievals are typically of better quality than those of C−51 with a smaller root mean square error (RMSE) and mean absolute error (MAE) and more AODs fall within the expected error range and relative mean bias. Overall, both the C–51 and C–06 MODIS DB algorithms show significant uncertainties and errors. The errors in AOD measurements arise due to imperfect aerosol model schemes and underestimation of surface reflectance over the Solar Village. This study suggests that further quantitative research is required to provide better estimates of satellite-based AOD over Saudi Arabia.
•An inter-comparison of MODIS DB C−51 and –06 AOD has been studied.•The AOD data have been taken from 2002 to 2013 over the Solar Village and KAUST.•Both the C–51 and –06 show the significant uncertainties and errors.•The inappropriate aerosol model selection is responsible for the error.•The underestimation of surface reflectance is also responsible for the error.
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