Tiny droplets of sea spray generated at the ocean surface can affect the intensity and evolution of hurricanes and other tropical storms.

Their impact, however, is not well understood because of the difficulty of measuring spray concentration and the size and velocity of individual droplets under high wind conditions.

At The University of Texas at Dallas, researchers are studying sea spray, particularly spume, or foam, droplets, in the lab to develop a model based on machine learning to improve hurricane forecasting. The model incorporates the effects of the spray generation function, which quantifies the rate at which droplets form.

Though scientists have long understood how lightning strikes, the precise atmospheric events that trigger it within thunderclouds remained a perplexing mystery. The mystery may be solved, thanks to a team of researchers led by Victor Pasko, professor of electrical engineering in the Penn State School of Electrical Engineering and Computer Science, that has revealed the powerful chain reaction that triggers lightning.

The Asian monsoon is one of the world's most influential climate systems, directly impacting the weather, water resources, agriculture, and livelihoods of billions of people across Asia. A recent review paper by an international team is expected to inspire new research and innovation to advance monsoon prediction further, ultimately supporting better risk management and adaptation across Asia

The global marine heatwaves (MHWs) of 2023 were unprecedented in their intensity, persistence, and scale, according to a new study. The findings provide insights into the region-specific drivers of these events, linking them to broader changes in the planet’s climate system. They may also portend an emerging climate tipping point. Marine heatwaves (MHWs) are intense and prolonged episodes of unusually warm ocean temperatures. These events pose severe threats to marine ecosystems, often resulting in widespread coral bleaching and mass mortality events. They also carry serious economic consequences by disrupting fisheries and aquaculture. It’s widely understood that human-driven climate change is driving a rapid increase in the frequency and intensity of MHWs. In 2023, regions across the globe, including the North Atlantic, Tropical Pacific, South Pacific, and North Pacific, experienced extreme MHWs. However, the causes underlying the onset, persistence, and intensification of widespread MHWs remain poorly understood.

 

To better understand the MHWs of 2023, Tianyun Dong and colleagues conducted a global analysis using combined satellite observations and ocean reanalysis data, including those from the ECCO2 (Estimating the Circulation and Climate of the Ocean-Phase II) high-resolution project. According to the findings, MHWs of 2023 set new records for intensity, duration, and geographic extent, lasting four times the historical average and covering 96% of the global ocean surface. Regionally, the most intense warming occurred in the North Atlantic, Tropical Eastern Pacific, North Pacific, and Southwest Pacific, collectively accounting for 90% of the oceanic heating anomalies. Dong et al. show that the North Atlantic MHW, which began as early as mid-2022, persisted for 525 days, while the Southwest Pacific event broke prior records with its vast spatial extent and prolonged duration. What’s more, in the Tropical Eastern Pacific, temperature anomalies peaked at 1.63 degrees Celsius during the onset of El Niño. Using a mixed-layer heat budget analysis, the authors discovered diverse regional drivers contributing to the formation and persistence of these events, including increased solar radiation due to reduced cloud cover, weakened winds, and ocean current anomalies. According to the authors, the 2023 MHWs may mark a fundamental shift in ocean–atmosphere dynamics, potentially serving as an early warning of an approaching tipping point in Earth’s climate system.

A novel deep learning–based framework that dramatically improves the accuracy of forecasts, even when data are limited. The task involved using past atmospheric variables to predict five key surface weather indicators—including temperature, wind, and precipitation—every 6 hours for the next 5 days.