I thought I might write about the new paper by Jochem Marotzke and Piers Forster called Forcing, feedback and internal variability in global temperature trends. It’s already been discussed in a Carbon Brief post called claims that climate models overestimate warming are unfounded.
Having read the paper, I’m not sure I quite agree with the Carbon Brief title. I think (although happy to be convinced otherwise) that a fairer assessment would be that the paper shows that internal variability can explain the discrepancy between forced model trends and observed trends for periods of 15 years. It also shows that for longer period (62 years) the impact of internal variability is small and the forced model trends are a good match to observations.
I’ll briefly try to explain. They use a very basic energy balance-like model
where is the climate sensitivity, is the ocean heat uptake efficiency, and is a term representing internal variability. I’ll be honest and say that I’m a little confused by this equation since, I think, should be time-dependent. I think, though, that for short enough timescales (when the system is not in equilibrium) it’s probably okay to assume it’s constant.What they do then is write the above in a perturbed form (relative to the means of each variable)
where the barred terms are the means, and the primed terms are perturbations from the mean. They then do a multiple linear regression of against , . If I understand what that means, they select from the range of , , and , and then try to fit the model trend to the observed trend, defining as the residual [Edit (6/2/15) : I think this may be wrong. I think they use this to determine the externally forced and internally forced () trends in the models, and then compare that to the observed trends, showing that the combined externally and internally forced trends in the models can explain the observed trends for all timescales, with internal forcing dominating for short time intervals.]
The results are shown in the figure on the right (Figure 2 from Marotzke & Forster 2015). The top panel shows then 15-years trends from observations (black line), the 15-year trends from models (red line), the range for the forced model trends (difficult to see coloured region around the red line), and the residual (bars). The second panel shows the range for the forced 15-year trends, relative to the mean. The third panel shows the range of the residual, representing internal variability. It’s clear that for these 15-year trends the magnitude of the internal variability is bigger than the range of the forced trend. The bottom panel shows the distribution of the internal variability trends for different start years.
The paper then repeats the above for trend lengths of 62 years, finding that the impact of internal variability is much smaller, with forced trends producing a good match to observed trends. Maybe the most interesting figure is the one below, which shows that if one considers different start years, the 15-year model trends can have a tendency to be both larger and smaller than the observed trend (depending on the start year), but if one considers all start years, the distributions are very similar.
So, as far as I can tell, this paper is illustrating that one can explain the discrepancy between observed and modelled trends as being primarily due to internal variability, with the impact of this variability decreasing as the time interval increases. I notice that there has also been quite a lot of interest in this recently. This press release from Duke University says
It just means the road to a warmer world may be bumpier and less predictable, with more decade-to-decade temperature wiggles than expected.
Ed Hawkins also has a post discussing variability in climate models. However, if I understand this properly, there is no suggestion that this variability will have a significant impact on long-term trends. It might be difficult to robustly predict decadal trends, because of this variability, but it will tend average out over many decades and hence the forced trend will dominate on these longer timescales. Additionally, as pointed out in Roe (2009),
If the temperature reconstructions reflect high natural variability of global mean temperature, then odds are that the climate system is even more sensitive to external forcing (i.e., the positive feedbacks are even larger).
So, if anything, if internal variability is large, it might suggest a higher, rather than lower, climate sensitivity.
Anyway, this post has got rather long and convoluted, which may be a good thing as it would be nice to have a break from moderating somewhat contentious comment threads 🙂 . Of course, if I have misunderstood something about this paper, feel free to point it out. I suspect this type of paper also has implications for the energy balance models (EBMs) favoured Nic Lewis, for example. If internal variability is as large as suggested by some of this work, then – given that EBMs essentially assume that there is no internal variability – climate sensitivity estimates from EBMs would depend strongly on the time interval considered.