Con ayuda de un modelo de circulación oceánica, un equipo de investigadores logró etiquetar y rastrear el carbono emitido antropogénicamente para determinar si su destino es la atmósfera o el océano.
Aaron Sidder is a freelance writer based out of Denver, Colo. He has a master’s degree in ecology from Colorado State University. Aaron was an AGU-sponsored AAAS Mass Media Science & Engineering Fellow at National Geographic in 2016, and he has been writing for Eos ever since. In addition to Eos and National Geographic, he has written for National Geographic Kids Magazine, Smithosonian Smart News, 5280 Magazine, and the Santa Fe Institute. In his free time, he cultivates an extensive—and growing—collection of field guides from around the country.
Iron Is at the Core of This Earth Science Debate
A new study investigates iron’s form at the planet’s interior. The findings have repercussions for understanding the inner core’s structure.
A Deeper Dive into Wintry, Carbon-Absorbing Antarctic Waters
Cold surface water in the Southern Ocean is a critical component in ocean carbon uptake. A new study profiles it using state-of-the-art research techniques.
Jet-Propelled Tunicates Pump Carbon Through the Oceans
New research reveals that blooms of the widespread gelatinous zooplankton—along with their feces, daily vertical migrations, and carcasses—increase marine carbon export.
Dissecting Ocean Dynamics in Greenland Fjords
Researchers explored the patterns and drivers of variability in fjords linking the Greenland Ice Sheet and the Atlantic Ocean using numerical simulations and in situ observations.
Tracing Anthropogenically Emitted Carbon Dioxide into the Ocean
Researchers labeled anthropogenically emitted carbon and tracked it with an ocean circulation model to determine whether it winds up in the sky or sea.
Food Deficits in Africa Will Grow in a Warmer World
Under the combined stress of growing populations and current warming trends, many African nations will face increasing shortfalls in food production in the coming decades.
Machine Learning Could Revolutionize Mineral Exploration
Using a global data set of zircon trace elements, new research demonstrates the power of machine learning algorithms to accurately identify and locate porphyry copper deposits.