Loading…
Internal Multidecadal and Interdecadal Climate Oscillations: Absence of Evidence Is No Evidence of Absence
Müller-Plath, Gisela
The present paper contributes a critical commentary on the recent finding by Mann, M. E., Steinman, B. A. and Miller, S. K (2020). Absence of internal multidecadal and interdecadal oscillations in climate model simulations. Nat. Commun. 11, 1–9.
Climate oscillations are recurring large-scale fluctuations in the surface temperatures of the oceans in connection with the atmosphere. This commentary focuses on the Pacific Decadal Oscillation (PDO, interdecadal timescale) and the Atlantic Multidecadal Oscillation (AMO, multidecadal timescale), which have been regarded as intrinsic climate drivers on the adjacent continents in numerous studies based on observations and paleoclimate reconstructions (Henley, 2017; O’Reilly et al., 2017). In a recent paper, Michael E. Mann and colleagues (Mann et al., 2020, hereafter M20) fail to find a PDO signal in global measured and modeled temperatures that is statistically different from noise. They further propose that the significant AMO-like signal is mainly due to anthropogenic aerosols in the 20th century, and to statistical artifacts before. Therefore they doubt the intrinsic nature of the two oscillations. The present paper shows that M20’s results are largely artifacts themselves with issues ranging from using inadequate data and referencing improper literature on anthropogenic aerosols with regards to the AMO to inappropriately interpreting the results with regards to the PDO.
After briefly sketching the rationale and method of M20, I will elaborate on these three points.
M20 (p. 3) argue that any truly oscillatory AMO or PDO signals should generate a spatially coherent and large-scale variability pattern in the climate system with a narrowband signature in the frequency domain. They search for such signals in global (observed and modeled) temperature grids of different time lengths with the multi-taper method of singular value decomposition (MTM-SVD), which was developed and widely applied by Mann and Park (Mann and Park, 1994; Mann et al., 1995; Mann and Park, 1999). Significance tests of the test statistic LFV (local fractional variance) are carried out with Monte Carlo simulations generated according to the null hypothesis of colored (red) noise. The method can generally be applied to reconstruct the time course and the spatial pattern of any potential oscillatory climate signal.
