Colloquium: Manabe’s legacy of simulating and understanding global warming
Rev. Mod. Phys. 98, 031003 – Published 28 September, 2026
DOI: https://doi.org/10.1103/mfpg-3th9
Abstract
In 2021 the Nobel Prize in Physics was awarded in part to Syukuro Manabe, a climate physicist, “for the physical modeling of Earth’s climate, quantifying variability and reliably predicting global warming.” The Colloquium introduces the audience of Reviews of Modern Physics to climate as a complex system, Manabe’s research, and his legacy of using a hierarchy of climate models across a range of physical complexity to simulate and understand global warming. The Colloquium reviews Manabe’s work with his colleagues from the late 1960s to the early 1990s. During this time Manabe and his colleagues developed a hierarchy of climate models across a range of physical complexity and used them to simulate and understand the temperature response to increased carbon dioxide () concentration in the atmosphere. The Colloquium discusses Manabe’s approach of (1) applying physical constraints to large-spatial scales while making simplifying assumptions about small-scale physics, (2) predicting the temperature response to increased as physical complexity was added through dimensionality, and (3) understanding the emergent temperature responses by connecting them to the underlying physics that was added. It highlights that this approach was key to reliably predicting—before they were observed—global-mean surface warming, upper-level cooling, that the Arctic is warming the most, that land is warming more than the ocean, and delayed Southern Ocean warming. Furthermore, Manabe and his colleagues showed how uncertainty due to assumptions about small-scale physics could be quantified by holding physical processes (water vapor, clouds, etc.) fixed, allowing for a clear understanding of their impact. Manabe’s legacy is more important than ever as we enter a unique time in climate science where additional signals have emerged from the noise in observations, including both successful predictions and discrepancies. Furthermore, complex new tools have emerged, such as machine learning emulators and kilometer-scale climate models, that benefit from Manabe’s reductionist, hierarchical approach.
