A KAUST-led study published in Scientific Data provides updated global climate classification maps from 1901-2020 and projects future conditions up to 2099. Researchers used a refined selection of climate models, excluding those with unrealistic CO2-induced warming rates, to ensure accuracy. Projections indicate significant shifts in land surface climate, with large areas transitioning to warmer climate zones by the end of the century under moderate emission scenarios. Why it matters: The updated maps provide a crucial tool for understanding climate change impacts, ecological studies, and informing policy decisions in the face of global warming, especially for a region like the Middle East that is highly vulnerable to climate change.
This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.
Researchers have developed a scalable pre-screening framework that integrates climate and remote sensing data to identify cost-efficient sites for sustainable dryland restoration, using Saudi Arabia as a case study. The framework employs machine learning models to derive a Climate Suitability Score (CSS), which captures climatic dependencies on vegetation persistence. National-scale prediction maps were generated using multi-year ERA5-Land data for Saudi Arabia, leading to the identification of thirteen priority locations with an estimated potential for a 2.5-fold increase in vegetation coverage. Why it matters: This approach significantly reduces the search space and costs associated with restoration efforts, supporting more resilient and sustainable ecosystem recovery planning in water-limited regions of the Middle East.
KAUST's Atmospheric and Climate Modeling group, led by Georgiy Stenchikov, is using high-resolution global and regional climate models to predict climate change in the Middle East, focusing on local atmospheric and oceanic processes. The group developed coupled regional atmospheric and oceanic models for the Red Sea, accounting for the climate effect of aerosols, especially dust, which is significant in the region. They found that dust strongly affects the Red Sea, causing high optical depth and solar cooling effect, particularly in the southern part, impacting energy balance and circulation. Why it matters: Improving regional climate models with specific attention to dust and aerosols is crucial for predicting and mitigating the environmental impacts of climate change in arid regions like the Middle East.
KAUST researchers found a 25-30% increase in winter rainfall in the eastern Arabian Peninsula since 1981, with a 10-20% decrease in the south and northeast. This change correlates with a shifting El Niño pattern in the tropical Pacific Ocean, affecting sea surface temperatures and westerly winds. The study used rainfall data from the University of East Anglia and 39 stations across the peninsula from 1951-2010. Why it matters: Improved understanding of these climate drivers could enhance long-term rainfall predictions, benefiting agriculture and water resource management in this arid region.