There is a new paper which documents further the lack of skill of multi-decadal climate model predictions. This paper has also been commented on by Judy Curry in the post
and by Anthony Watts at
As I summarized in my post
these climate model predictions are failing to accurately simulate fundamental aspects of the climate system.
The paper is
McKitrick, Ross R. and Lise Tole (2012) “Evaluating Explanatory Models of the Spatial Pattern of Surface Climate Trends using Model Selection and Bayesian Averaging Methods” Climate Dynamics, 2012, DOI: 10.1007/s00382-012-1418-9
with the abstract [highlight added]
We evaluate three categories of variables for explaining the spatial pattern of warming and cooling trends over land: predictions of general circulation models (GCMs) in response to observed forcings; geographical factors like latitude and pressure; and socioeconomic influences on the land surface and data quality. Spatial autocorrelation (SAC) in the observed trend pattern is removed from the residuals by a well-specified explanatory model. Encompassing tests show that none of the three classes of variables account for the contributions of the other two, though 20 of 22 GCMs individually contribute either no significant explanatory power or yield a trend pattern negatively correlated with observations. Non-nested testing rejects the null hypothesis that socioeconomic variables have no explanatory power. We apply a Bayesian Model Averaging (BMA) method to search over all possible linear combinations of explanatory variables and generate posterior coefficient distributions robust to model selection. These results, confirmed by classical encompassing tests, indicate that the geographical variables plus three of the 22 GCMs and three socioeconomic variables provide all the explanatory power in the data set. We conclude that the most valid model of the spatial pattern of trends in land surface temperature records over 1979-2002 requires a combination of the processes represented in some GCMs and certain socioeconomic measures that capture data quality variations and changes to the land surface.
The text starts off with
General Circulation Models (GCMs) are the basis for modern studies of the effects of greenhouse gases and projections of future global warming. Reliable trend projections at the regional level are essential for policy guidance, yet formal statistical testing of the ability of GCMs to simulate the spatial pattern of climatic trends has been very limited. This paper applies classical regression and Bayesian Model Averaging methods to test this aspect of GCM performance against rival explanatory variables that do not contain any GCM-generated information and can therefore serve as a benchmark.
This paper supports the viewpoint of the 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.
where we showed that the multi-decadal trends in surface and lower tropospheric temperature trends are diverging from one another with much greater differences over land areas than over ocean areas. The socioeconomic influences on the land surface and data quality issues identified in the McKittrick and Tole 2012 paper are reasons such a divergence should be expected.
In a paper in press (which I am a co-author on) on the subject of the surface temperature trends, we docuement in depth why there is warm bias in the minumum temperature trends that are used to construct an annual, global average multi-decadal temperture trends. I will be posting on this paper as soon as it is posted on the journal website. It provides even more support on the findings of McKittrick and Tole 2012 on the importance of socioeconomic influences on the land surface and data quality as a factor in long term temperature trends.