The recent slowdown in global warming has brought into question the reliability of climate model projections of future temperature change and has led to a vigorous debate over whether this slowdown is the result of naturally occurring, internal variability or forcing external to Earth's climate system. To address these issues, we applied a semi-empirical approach that combines climate observations and model simulations to estimate Atlantic- and Pacific-based internal multidecadal variability (termed "AMO" and "PMO," respectively). Using this method, the AMO and PMO are found to explain a large proportion of internal variability in Northern Hemisphere mean temperatures. Competition between a modest positive peak in the AMO and a substantially negative-trending PMO are seen to produce a slowdown or "false pause" in warming of the past decade.

The level of agreement between climate model simulations and observed surface temperature change is a topic of scientific and policy concern. While the Earth system continues to accumulate energy due to anthropogenic and other radiative forcings, estimates of recent surface temperature evolution fall at the lower end of climate model projections. Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures. This work quantifies a systematic bias in model‐observation comparisons arising from differential warming rates between sea surface temperatures and surface air temperatures over oceans. A further bias arises from the treatment of temperatures in regions where the sea ice boundary has changed. Applying the methodology of the HadCRUT4 record to climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975–2014.

Persistent episodes of extreme weather in the Northern Hemisphere summer have been shown to be associated with the presence of high-amplitude quasi-stationary atmospheric Rossby waves within a particular wavelength range (zonal wavenumber 6–8). The underlying mechanistic relationship involves the phenomenon of quasi-resonant amplification (QRA) of synoptic-scale waves with that wavenumber range becoming trapped within an effective mid-latitude atmospheric waveguide. Recent work suggests an increase in recent decades in the occurrence of QRA-favorable conditions and associated extreme weather, possibly linked to amplified Arctic warming and thus a climate change influence. Here, we isolate a specific fingerprint in the zonal mean surface temperature profile that is associated with QRA-favorable conditions. State-of-the-art (“CMIP5”) historical climate model simulations subject to anthropogenic forcing display an increase in the projection of this fingerprint that is mirrored in multiple observational surface temperature datasets. Both the models and observations suggest this signal has only recently emerged from the background noise of natural variability.

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Separating low frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here we use both synthetic time series and CMIP5 historical simulations to examine several methods of performing this separation. We find that linear detrending, as is commonly used in studies of low frequency climate variability, introduces large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing.Applying the methods to observations of the Atlantic Multidecadal Oscillation leads to different estimates of the phase of this mode of variability in recent decades. 14 15 16 17 18 19 20 21 22 23 24 25 26 27In this paper we compare various methods for separating the forced signal from the background 51 of internal variability and examine the biases that may result from the different methods. We focus 52 on the specific example of multidecadal North Atlantic sea surface temperature (SST) variability, 53 but the results have broader implications for the problem of separating forced and internal climate 54 variability. 55Enhanced variability on multidecadal time scales centred in the North Atlantic has been found 56 in modern, observational climate data (Folland et al. 1984(Folland et al. , 1986Kushnir 1994; Mann and Park 57 1994; Delworth and Mann 2000) and in long-term climate proxy data (Mann et al. 1995; Delworth 58 and Mann 2000, for example). Such variability is also generated in a range of models, from 59 idealised ocean models to full GCMs (Delworth et al. 1993, 1997Huck et al. 1999; Knight et al. 60 2005; Parker et al. 2007; Ting et al. 2011; Zhang and Wang 2013). The variability has been named 61 the Atlantic Multidecadal Oscillation (AMO; Kerr 2000) or, alternatively, Atlantic Multidecadal 62 Variability (AMV) since it is unclear whether or not it truly constitutes a narrow-band oscillatory 63 climate signal. In this study, we do not attempt to address the mechanisms causing the variability; 64 we instead focus on North Atlantic SST variability as a case study in the application of competing 65 statistical approaches to separating internal and external variability.66The rest of this paper is divided as follows: we first describe the data used in the study (section 67 2) and then describe the various competing methods for separating forced and internal variabil-68 ity (section 3). The methods are tested on synthetic data, where the true internal and external 69 signals are known (section 4), and then applied to CMIP5 historical simulations (section 5) and 70 observational data (section 6). We then discuss the results of our analyses (section 7) and finally 71 s...