3Q: Machine learning and climate modeling
As machine learning expands into climate modeling, EAPS Associate Professor Paul O’Gorman answers what that looks like and why it's important now.
As machine learning expands into climate modeling, EAPS Associate Professor Paul O’Gorman answers what that looks like and why it's important now.
Firms learn from experience in the measurement, reporting, and verification of carbon emissions under China’s emissions trading systems.
Machine learning could help improve the accuracy of long-term forecasts, MIT climatologist argues.
Study shows the Sahara swung between lush and desert conditions every 20,000 years, in sync with monsoon activity.
New research finds a unique component of cell membranes in an archaea species conveys protection against acidic surroundings.
At UN Climate Change Conference, MIT researchers share knowledge and tools to help nations meet Paris Agreement targets.
Scientists and engineers will collaborate in a new Climate Modeling Alliance to advance climate modeling and prediction.
Experts gather at MIT to share insights, techniques, and strategies for building resilient urban water systems.
MIT Joint Program on the Science and Policy of Global Change workshop explores risks and opportunities for the agriculture sector.
Toxin will accumulate in the environment, particularly in remote regions, as countries delay implementing emissions controls.
The AGAGE network celebrates 40 years of measuring ozone-depleting and climate-warming gases.
MIT Energy Initiative Director Robert Armstrong offers his perspective on the takeaways from MITEI’s annual research conference.
A faster, cheaper modeling method could improve our understanding of long-term atmospheric chemistry and provide a powerful tool for risk assessment.
Long-term melting may lead to release of huge volumes of cold, fresh water into the North Atlantic, impacting global climate.
She will investigate the early history of complex life and the environments that supported it, both in the field and lab.