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Ancient ocean slowdown warns of future climate chaos

When it comes to the ocean s response to global warming, we re not in entirely uncharted waters. A new study shows that episodes of extreme heat in Earth s past caused the exchange of waters from the surface to the deep ocean to decline.

Sandra-kirtland-turner
Kirtland-turner
Planetary-sciences
Department-of-earth
Proceedings-of-the-national-academy-sciences
National-academy
North-atlantic
Early-eocene

6,000 years ago, men and women had equal access to resources

Using isotope geochemistry, scientists have uncovered new information about the Barmaz necropolis in Valais (Switzerland): 14% of the people buried 6,000 years ago at this site were not locals. What s more, the study suggests that this Middle Neolithic agropastoral society one of the oldest known in the western part of Switzerland was relatively egalitarian. The isotope ratios of carbon, nitrogen and sulphur contained in the bones reveal that all members of the community, including people from elsewhere, had access to the same food resources.

France
Italy
Switzerland
Chablais
Rhôalpes
Barmaz
Switzerland-general
Swiss
Massimo-chiaradia
Laboratory-of-archaeology-africa
Swiss-national-science-foundation
Department-of-earth

Machine learning speeds up climate model simulations at finer resolutions, making them usable on local levels

Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth s climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city.

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Massachusetts
United-states
Massachusetts-institute-of-technology
Bangladesh
Chicago
Illinois
Denver
Colorado
Puerto-rico
Anamitra-saha
Department-of-earth

Making climate models relevant for local decision-makers

A climate modeling downscaling method developed by MIT scientists leverages a machine-learning technique called adversarial learning to produce simulations faster and at finer resolutions, making them relevant to use on local levels for assessing risks of extreme weather.

Chicago
Illinois
United-states
Bangladesh
Puerto-rico
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Massachusetts
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Colorado
Anamitra-saha
Department-of-earth
Planetary-sciences

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