Chart comparing the maximum relative error obtained from various millennia long CMIP models using different estimation techniques.
Comparison of the maximum relative error obtained from various millennia long CMIP models using different estimation techniques. The new technique (multicomponent linear regression model or MC-LR) is given in brown tones, the raw simulation values in red and two versions of the Gregory method are given in blue and green tones (ignoring the first 20 years of the simulation). The different shades correspond to different number of years used in the estimation, namely 150 years (lightly shaded), 300 years (moderately shaded), and 500 years (dark shades). Credit: Bastiaansen et al. [2021], Figure 4
Source: Geophysical Research Letters

Climate sensitivity is an important measure of how much the Earth’s climate warms as the amount of CO2 increases in the atmosphere. However, estimations of climate sensitivity have a wide uncertainty range. A recent review by Sherwood et al. [2020] discussed various lines of evidence to reduce the uncertainty of this measure and stressed the need for additional research on this topic.

Now Bastiaansen et al. [2021] propose a new technique to estimate climate sensitivity using multiple variables in a multicomponent linear regression model. They applied this novel technique to conceptual models, as well as CMIP models with millennia long simulations. They showed that the new method leads to better estimates for climate sensitivity, as well as the value of using multivariate metrics for climate sensitivity, instead of temperature only, as is traditionally done.

Citation: Bastiaansen, R., Dijkstra, H. A., & von der Heydt, A. S. [2021]. Multivariate estimations of equilibrium climate sensitivity from short transient warming simulations. Geophysical Research Letters, 48, e2020GL091090. https://doi.org/10.1029/2020GL091090

—Suzana Camargo, Editor, Geophysical Research Letters

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