LAUSR

AbstractTwo large ensembles (LEs) of historical climate simulations are used to compare how various statistical methods estimate the sea surface temperature (SST) changes due to anthropogenic and other external forcing, and how their removal affects the internally generated Atlantic multidecadal oscillation (AMO), Pacific decadal oscillation (PDO), and the SST footprint of the Atlantic meridional overturning circulation (AMOC). Removing the forced SST signal by subtracting the global mean SST (GM) or a linear regression on it (REGR) leads to large errors in the Pacific. Multidimensional ensemble empirical mode decomposition (MEEMD) and quadratic detrending only efficiently remove the forced SST signal in one LE, and cannot separate the short-term response to volcanic eruptions from natural SST variations. Removing a linear trend works poorly. Two methods based on linear inverse modeling (LIM), one where the leading LIM mode represents the forced signal and another using an optimal perturbation filter (LIM...