While it does not explicitly say so, an article in the May 2011 of the Bulletin of the American Meteorological Society has redefined the dominate climate issue. The authors might not admit that they have altered from the IPCC focus on global average surface temperature trends as the icon of climate change, but the reality of this change is obvious from their text. In the report
National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp.
it is written
“…..the traditional global mean TOA radiative forcing concept has some important limitations, which have come increasingly to light over the past decade. The concept is inadequate for some forcing agents, such as absorbing aerosols and land-use changes, that may have regional climate impacts much greater than would be predicted from TOA radiative forcing. Also, it diagnoses only one measure of climate change—global mean surface temperature response—while offering little information on regional climate change or precipitation. These limitations can be addressed by expanding the radiative forcing concept and through the introduction of additional forcing metrics. In particular, the concept needs to be extended to account for (1) the vertical structure of radiative forcing, (2) regional variability in radiative forcing, and (3) nonradiative forcing.”
Joe D’Aleo, Joe Bastardi, Judy Curry, Roy Spencer, Peter Webster and others who have been out front on this issue also need to be recognized for documenting a much more significant role of natural climate variability on seasonal, yearly, decadal and longer time scales.
The new article, which exemplifies where the broader climate community is finally starting to accept this more robust perspective on climate, is
Mehta, Vikram, and Coauthors, 2011: Decadal Climate Predictability and Prediction: Where Are We?. Bull. Amer. Meteor. Soc., 92, 637–640.doi: 10.1175/2010BAMS3025.1
Extracts from the paper read [highlight added]
“The importance of decadal climate variability (DCV) research is being increasingly recognized, including by the World Climate Research Program (WCRP) and the Intergovernmental Panel on Climate Change (IPCC). An improved understanding of DCV is very important because stakeholders and policymakers want to know the likely climate trajectory for the coming decades for applications to water resources, agriculture, energy, and infrastructure development. Responding to this demand, many climate modeling groups in the United States, Europe, Japan, and elsewhere are gearing up to assess the potential for decadal climate predictions. The magnitudes of regional DCV often exceed those associated with the trends resulting from anthropogenic changes.”
“PREDICTABILITY AND PREDICTION. Initial decadal prediction efforts in the last few years show predictive skill of global average temperature up to a decade in advance using both initial conditions and the climate change signal created by already emitted greenhouse gases.”
This claim of decade surface temperature prediction skill, of course, is not supported by their lack of skill since 1998. The next section of their paper highlights the wide range of uncertainties for decadal prediction and the movement away from the global average surface temperature as the icon of climate change. For multi-decadal climate predictions, these uncertainties are necessarily even higher.
“THEORY AND MODELING. Although global coupled models designed in the last 15 years are able to generate DCV patterns that resemble observed DCV patterns, the models tend to displace them spatially and temporally with respect to observed patterns. Also, it has not been obvious that the same mechanisms operate in both models and nature to produce similar DCV patterns. A much better understanding of the physical mechanisms of DCV in nature is required. Without this, the sources and skills used to make decadal predictions will remain unreliable.”
“Among the known, major problems in global coupled models are large systematic biases; the absence of eddies and nonlinear interactions in ocean components; incorrect/inaccurate representation of planetary wave dynamics, interactions with eddies, 3D basin modes, and forced responses of basin modes; air–sea interaction; representation of vertical mixing in the upper ocean; and subpolar ocean dynamics, including the relative importance of temperature,salinity, wind-driven and thermohaline circulations, weak vertical stratification, and interactions with sea ice. The atmospheric components of the global coupled models are also not complete; the most important required additions are a well-resolved stratosphere that includes its chemical makeup, the representation of ice in the water cycle, and a better parameterization of convection, cloud physics, and tropospheric chemistry. Because resolution appears to be one of the model attributes influencing DCV time scales, model resolution is another aspect that needs major improvement. Major biases, however, are not removed simply by increasing resolution; persistent problems such as poor representations of the Indian summer monsoon rainfall still remain even in high resolution models.”
The confessions that are listed in this article are an implicit admission of the bankruptcy of the approach on climate assessments such as the 2007 IPCC WG1 report with its assumption that the multi-decadal climate model predictions are skillful.