Multivariate Estimations of Equilibrium Climate Sensitivity From Short Transient Warming Simulations.

One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long‐term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks—spanning a wide range of temporal scales—it is hard to extract long‐term behavior from short‐time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long‐term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multicomponent linear regression model. This way, not only the dominant but also the next‐dominant eigenmodes of the climate system are captured, leading to better long‐term estimates from short, nonequilibrated time series. Plain Language Summary: Although it is clear that the atmospheric CO2 concentration influences the Earth's climate, it is difficult to quantify its long‐term effects accurately. Scientific efforts in this direction focus on idealized experiments carried out in global climate models. In these experiments, atmospheric CO2 is (instantaneously) doubled, and the long‐term temperature increase this causes is recorded. This resulting temperature increase is called the (equilibrium) climate sensitivity; accurately knowing its value helps to better quantify the effects of different emission scenarios on the future climate. However, it takes a very long time before all processes in a climate model are fully settled—especially in state‐of‐the‐art, more and more detailed models—and, in practice, settling all is simply not feasible. Hence, climate sensitivity needs to be estimated from limited model data. This is particularly difficult as the climate system consists of many processes that behave on vastly different time scales. Here, we present a new estimation technique that is better capable of capturing the very slow processes than conventional techniques, and hence leads to a more accurate quantification of (equilibrium) climate sensitivity. Key Points: A new and improved equilibrium climate sensitivity estimation technique is introduced that is intrinsically multieigenmodalThis new estimation technique better captures long‐term model behavior from short‐term forcing experiments compared to conventional methodsThe method uses multiple observables and can also estimate their equilibrium values, expediting multivariate sensitivity metrics [ABSTRACT FROM AUTHOR]