If a model comes along
with low frequency variability that is less polar concentrated and fits the century, or half - century, trend pattern better, that would be news.
DelSole et al 2011 provide a convenient figure (top of post) summarizing the spatial structure
of low frequency variability of sea surface temperature in an ensemble of GCMs.
«However, Fig. 15 and the associated uncertainties discussed in Section 3.4 show that long term estimates of time variable sea level acceleration in 203 year global reconstruction are significantly positive, which supports our previous finding (Jevrejeva et al., 2008a), that despite
strong low frequency variability (larger than 60 years) the rate of sea level rise is increasing with time.»
This is not the only way to summarize this information, but it serves my limited purpose here of illustrating
how low frequency variability tends to be concentrated in the subpolar oceans across the model ensemble.
The pattern in this particular 50 - yr trend in the control is qualitatively similar to the generic
very low frequency variability in this particular model, with maximum amplitudes in the subpolar oceans.
I mentioned this detail of
the low frequency variability in the control simulation of a GCM just to make the point that the strength of the radiative restoring on internal variability could be weaker than that for the forced response, making it harder to constrain the fraction of the trend due to internal variability from the sign of the heat flux alone.
Furthermore, SST variability (Seager et al. 2005, 2008; Meehl and Hu 2006; McCabe et al. 2008), as well as external forcings (Dai 2011, 2013; Coats et al. 2013), may contribute to
the low frequency variability and potential decadal - scale predictability of hydroclimate variability over North America.
The multivariate ENSO of Claus Wolter shows the same pattern of
low frequency variability.