CliMA in the News

Press & Media

New Climate Model to Be Built from the Ground Up

By Robert Perkins 12 December 2018

Scientists and engineers rethink how to model and predict climate

Facing the certainty of a changing climate coupled with the uncertainty that remains in predictions of how it will change, scientists and engineers from across the country are teaming up to build a new type of climate model that is designed to provide more precise and actionable predictions.

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See also: "A Model Climate", Caltech Magazine, Spring 2019

See also: "NPS Researchers Partner on Next Generation Climate Model", Naval Postgraduate School

The Earth Machine: Science Insurgents Plot a Climate Model Driven by Artificial Intelligence

By Paul Voosen 26 July 2018

Later this summer, an academic consortium will launch an ambitious project to create a new climate model. Taking advantage of breakthroughs in artificial intelligence (AI), satellite imaging, and high-resolution simulation, that as-yet-unnamed model—the Earth Machine is one candidate—aims to change how climate models render small-scale phenomena such as sea ice and cloud formation that have long bedeviled efforts to forecast climate.


Read a German version at Technology Review

Next-Generation Climate Models Could Learn, Improve on the Fly

By Sarah Stanley 21 March 2018

Even today’s most sophisticated Earth system models suffer from uncertainties that stem from the difficulty of simulating small-scale or complex processes, such as raindrop formation and carbon uptake by plants. Novel computational tools may hold the potential to address these uncertainties. In a new paper, Schneider et al. outline a blueprint for a next-generation climate model that would employ advancements in data assimilation and machine learning techniques to learn continuously from real-world observations and high-resolution simulations.


A Model Revolution

By Graham Simpkins 29 January 2018

While considerable progress has been made in our understanding of the climate system, projections of the future remain highly uncertain. Such relatively low confidence stems, in part, from uncertainties in the parameterization schemes of Earth system models (ESMs)—approximations of unresolved small-scale processes—including, for example, cloud dynamics. Tapio Schneider and colleagues from the California Institute of Technology, USA, envision a revolution in Earth system modelling using data assimilation and machine learning to improve parameterization schemes.