In a new study in Science Advances, researchers uncovered a new relationship between Saharan dust plumes and hurricane rainfall. The findings show that these dust plumes can influence hurricane precipitation, impacting weather prediction and climate change mitigation.
For years, scientists focused on sea surface temperatures and atmospheric humidity as key factors influencing hurricane formation and precipitation. However, a new study led by Yuan Wang, an assistant professor of Earth system science at the Stanford Doerr School of Sustainability, shifts the focus to Saharan dust. “The leading factor controlling hurricane precipitation is not sea surface temperature or atmospheric humidity. It’s Sahara dust,” said Wang.
The research highlights the interplay between Saharan dust and hurricane rainfall. Previous studies suggested that Saharan dust transport might decline due to human-caused climate change, leading to increased hurricane rainfall. However, uncertainty remained regarding the mechanisms through which dust influences hurricane behavior.
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Saharan dust can have competing effects on tropical cyclones, classified as hurricanes in the North Atlantic, central North Pacific, and eastern North Pacific when maximum sustained wind speeds reach 74 miles per hour or higher. Dust particles can enhance precipitation by facilitating the formation of ice clouds within the hurricane’s core, a process known as microphysical enhancement. However, dust can also block solar radiation, cooling sea surface temperatures around a storm’s core and weakening the tropical cyclone.
Dust particles can make ice clouds form more efficiently in the hurricane core, leading to more precipitation, explained Wang. Conversely, high dust loading can shield the ocean surface from sunlight, creating the “radiative suppression effect,” according to Wang.
To unravel complex relationships, Wang and colleagues developed a machine learning model to predict hurricane rainfall. Using 19 years of meteorological data and hourly satellite precipitation observations, they trained their model to identify mathematical and physical relationships driving hurricane precipitation.
The results showed that dust optical depth predicts rainfall. There’s a boomerang-shaped relationship: rainfall increases with dust optical depths between 0.03 and 0.06, then sharply decreases. In other words, at high concentrations, dust switches from boosting to suppressing rainfall.
The study’s findings have significant implications for weather prediction and climate change mitigation. Traditionally, weather predictions, especially concerning hurricanes, have not considered the role of dust. “Hurricanes are among the most destructive weather phenomena on Earth,” said Wang. Even weak hurricanes can cause heavy rains and inland flooding. Understanding the influence of Saharan dust on hurricane precipitation is crucial for improving predictive models and preparing for future storms.
The study raises important questions about the future. How will climate change affect the outflows of dust from the Sahara? And how much more rainfall can we expect from future hurricanes? These questions remain unresolved, but the study provides a crucial piece of the puzzle.
Saharan dust, carried across the Atlantic by trade winds, is the main aerosol during summer and early fall over the tropical Atlantic. It can efficiently change atmospheric radiative fluxes and participate in cloud formation as cloud condensation nuclei (CCN) and/or ice nuclei (IN). This ability to influence cloud formation and precipitation makes Saharan dust critical in weather and climate systems.
The study is part of a broader effort to understand the global impact of Saharan dust. Previous research has shown that it can suppress tropical cyclone formation by cooling sea surface temperatures, reducing the energy supply for cyclones. This was evident during the peak of European air pollution in the 1970s and 1980s, coinciding with intensified dust transport and a downturn in Atlantic hurricane activity.
Machine learning in this study highlights its potential to revolutionize climate science. Traditional statistical methods, like linear regression, have struggled to model the complex and nonlinear relationships between environmental factors and hurricane precipitation. Machine learning offers a more sophisticated approach.
Wang and his team used Extreme Gradient Boosting (XGBoost), a machine-learning technique based on an ensemble of decision trees, to build their models. They developed two distinct models: one including traditional meteorological factors and geoinformation, and another adding dust optical depth as a predictor. The results showed that the model incorporating dust optical depth outperformed the traditional model, emphasizing the importance of considering dust in hurricane predictions.
The study provides valuable insights but leaves questions unanswered. For example, how will climate change affect Saharan dust plumes? And what are the mechanisms through which dust influences hurricane formation and precipitation?
Addressing these questions will require further research and advanced models. Current climate models lack sufficient spatial resolution to capture microphysical processes in cloud and precipitation formation. The combination of big data and machine learning offers a promising path forward.
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