Abstract
Abstract. A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901–2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.
Highlights
The ultimate goal in developing Earth system models (ESM) is to enable exploitation of the inherent Earth system predictability, and reduce weather- and climate-related uncertainties in our daily life, and guide societies in making sustainable choices (e.g. Slingo and Palmer, 2011; Meehl et al, 2014)
The aim of this study is to decompose the 20th century climate variability into its multi-annual modes, and to assess how these modes are represented by the contemporary climate models
Multi-channel singular spectrum analysis (MSSA) eigenvectors are called space–time empirical orthogonal functions (STEOFs), and the projections of the data set onto those STEOFs space–time principal components (ST-PCs)
Summary
The ultimate goal in developing Earth system models (ESM) is to enable exploitation of the inherent Earth system predictability, and reduce weather- and climate-related uncertainties in our daily life, and guide societies in making sustainable choices (e.g. Slingo and Palmer, 2011; Meehl et al, 2014). For the predictions to be useful and usable, the expectation is that the climate mean state and climate variability are well simulated by these tools. Representation of climate variability among models participating in climate model inter-comparisons, such as CMIP5, has been studied by, for example, Bellenger et al (2014), Knutson et al (2013), Ba et al (2014) and Fredriksen and Rypdal (2016). We will add to this literature by interfacing a representative set of contemporary coupled climate models with reanalysis data focusing on spatiotemporal modes of climate variability. One century of global reanalysis data is a very short period for this purpose and severely constrains inter-comparison studies Time series should cover a sufficient number of recurring “events” for Published by Copernicus Publications on behalf of the European Geosciences Union
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