Jos de Laat of KNMI altered us to the paper
Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W.-S. Lee, and K. von Salzen (2011),Skillful predictions of decadal trends in global mean surface temperature, Geophys. Res. Lett.,38, L22801, doi:10.1029/2011GL049508
which is an example of the overstatment of model predictive skill.
The abstract reads
We compare observed decadal trends in global mean surface temperature with those predicted using a modelling system that encompasses observed initial condition information, externally forced response (due to anthropogenic greenhouse gases and aerosol precursors), and internally generated variability. We consider retrospective decadal forecasts for nine cases, initiated at five year intervals, with the first beginning in 1961 and the last in 2001. Forecast ensembles of size thirty are generated from differing but similar initial conditions. We concentrate on the trends that remain after removing the following natural signals in observations and hindcasts: dynamically induced atmospheric variability, El Niño-Southern Oscillation (ENSO), and the effects of explosive volcanic eruptions. We show that ensemble mean errors in the decadal trend hindcasts are smaller than in a parallel set of uninitialized free running climate simulations. The ENSO signal, which is skillfully predicted out to a year or so, has little impact on our decadal trend predictions, and our modelling system possesses skill, independent of ENSO, in predicting decadal trends in global mean surface temperature.
There are key admissions in the article which should clearly alert a reader to be skeptical about this observational/model comparison. A most revealing comment is that [highlight added]
Since observation-based and model-based climates tend to differ, hindcasts which are initialized to be near the observations tend to drift towards the model climate. For short term hindcasts this is accounted for by removing the mean bias. However, for longer term decadal hindcasts a linear trend correction may be required if the model does not reproduce long-term trends. For this reason, we correct for systematic long-term trend biases following a procedure detailed in the auxiliary material. We process the three sets of hindcasts using the different initialization techniques separately, but combine the predicted anomalies into one thirty-member ensemble in the following analysis. The ten-member ensemble of freecasts are also trend corrected in this way.
The authors define “freecasts” as
These are climate simulations (referred to here as “freecasts”) which evolve freely based on the specified external forcing.
This is quite an amazing admission. They write that the “model does not reproduce long-term trends” than “a linear trend correction may be required” and “we correct for systematic long-term trend biases.” The model results are tuned. They are not “freecasts“.
The authors also fail to compare their results with other temperature data sets such as the lower tropospheric temperature anomalies and trends. As we show in our papers
Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., J.R. Christy, and R.T. McNider, 2009: An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841.
Klotzbach, P.J., R.A. Pielke Sr., R.A. Pielke Jr., J.R. Christy, and R.T. McNider, 2010: Correction to: “An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J. Geophys. Res., 114, D21102, doi:10.1029/2009JD011841″, J. Geophys. Res., 115, D1, doi:10.1029/2009JD013655.
there is a warm bias in the surface temperature data. The authors chose to ignore this finding in their study. Instead they tuned their model results to the observed temperature data.
As an editor, at a minimum I would have insisted on this comparison before this paper would have been accepted. As written, however, the Fyfe et al article adds no robust evidence that the models have skill at predicting temperature changes over decadal and longer time periods. Indeed, they provide further evidence of the lack of skill of the multi-decadal climate model predictions.