There is another scientifically flawed claim of skillful multi-decadal regional climate predictions. This report by the US Department of Agriculture is another failure to assess what the scientific literature actually says with respect to these forecasts, as I summarized in the post
Kevin Trenberth Was Correct – “We Do Not Have Reliable Or Regional Predictions Of Climate”
and in the article
Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008.
The new report is
Daniels, A.E.; Morrison, J.F.; Joyce, L.A.; Crookston, N.L.; Chen, S.C.; McNulty, S.G. 2012. Climate projections FAQ. Gen. Tech. Rep. RMRS-GTR-277WWW. Fort Collins, CO: U.S. Forest Service, Rocky Mountain Research Station. 32 p.
The abstract reads [highlight added]
Climate scenarios offer one way to identify and examine the land management challenges posed by climate change. Selecting projections, however, requires careful consideration of the natural resources under study, and where and how they are sensitive to climate. Selection also depends on the robustness of different projections for the resources and geographic area of interest, and possibly on what climate projections are available for a region. Rather than a misguided attempt to identify the “most accurate” climate scenario, managers are strongly encouraged to explore variability through the use of multiple climate scenarios. Considering a range of possible future climates facilitates the identification of management strategies to help ensure resilience of natural resource systems across a broad set of potential conditions.Downscaling climate projections increases the spatial resolution of climate information and can make projections more relevant to natural resource managers by allowing decision-makers to better visualize what these different futures imply locally and regionally. The following series of questions describes key concepts that end-users of climate projection products should understand to appropriately interpret downscaled climate projections, including various sources of uncertainty. The selection used for each component of a downscaled climate projection has implications for interpreting the resulting climate scenario. Understanding the merits and limitations of the downscaling method employed is also important since downscaling approaches vary in their dependence on observed data availability, computational requirements, and in resultant uncertainty owed to biases of the method or the spatial scale of the downscaling.
Here is how the report addresses the reliability of the model predictions
7. How reliable are GCM-based climate projections?
The IPCC concluded that GCMs provide a credible range of quantitative estimates of future climate change, particularly at global and continental scales and over long time periods (Randall and others 2007). Extensive, rigorous multi-model intercomparisons underpin this conclusion. Over the many generations of climate models and across a range of emissions scenarios, models unanimously and unambiguously project warming over the next 2 decades in response to increasing atmospheric GHG concentrations.
My Comment: As shown in the post
Kevin Trenberth Was Correct – “We Do Not Have Reliable Or Regional Predictions Of Climate”
despite the claim made in the statement in the US Forest Service report
GCMs provide a credible range of quantitative estimates of future climate change, particularly at global and continental scales and over long time periods (Randall and others 2007).
this does not mean that regional predictions are skillful even if the global and continental scales are accurate [which they have shown not to be as summarized in this weblog post]. That the models and observations show warming over the last 100 years is correct, but this hardly translates into assuming the models have regional skill, as is required for the needs of the US Forest Service.
The report continues
The scientific credibility of climate models and resulting projections hinges on several lines of evidence. First, climate models are consistent with well-understood physical processes and physical laws (e.g., conservation of energy and Newton’s laws of motion). Second, current-generation climate models demonstrate a significant and increasing ability to simulate recent and past climate dynamics (e.g., Reichler and Kim 2008). Third, extensive comparisons of multiple models reveal that over the past 2 decades different models have converged toward similar results (Reichler and Kim 2008). GCM projections include uncertainties and they represent some climate elements better tha others. For example, confidence in the projections of temperatures is greater than for precipitation projections. As with all models, interpreting and applying results appropriately entails understanding models’ strengths and limitations.
My Comments: With respect to the three points listed above.
1. They write
First, climate models are consistent with well-understood physical processes and physical laws (e.g., conservation of energy and Newton’s laws of motion).
Actually the climate models only have a part that is basic physics (advection, pressure gradient force and gravity). The rest of the physics (e.g. subgrid scale mixing, cumulus parameterization stable clouds and precipitation, long and short wave radiation, vegetation dynamics, ice sheet dynamics, etc) is parametrized using engineering code which is tuned for individual modules that are developed from just a subset of idealized real world conditions. Those parameterization also often contain a framework of physics (such as conservation of energy) but always have tunable coefficients. I discuss the atmospheric part of climate models in my book
Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp.
The multi-decadal global climate models used by the US Forest Service are not basic physics code.
2. The report writes
Second, current-generation climate models demonstrate a significant and increasing ability to (e.g., Reichler and Kim 2008).
As summarized and report on in the post
Kevin Trenberth Was Correct – “We Do Not Have Reliable Or Regional Predictions Of Climate”
from the paper
Kundzewicz, Z. W., and E.Z. Stakhiv (2010) Are climate models “ready for prime time” in water resources management applications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.
“Simply put, the current suite of climate models were not developed to provide the level of accuracy required for adaptation-type analysis.”
The authors also ignore the fundamental conclusion with downscaling that we report on in
Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008
where we write
It is therefore inappropriate to present [mutli-decadal regional climate prediction] results to the impacts community as reflecting [at best] more than a subset of possible future climate risks.
The authors of the Forest Service Report are ignoring[and not refuting] evidence that documents a lack of ability to simulate recent and past climate dynamics.
3. The report than writes
Third, extensive comparisons of multiple models reveal that over the past 2 decades different models have converged toward similar results (Reichler and Kim 2008).
Model agreement is not a test of the accuracy of the models at replicating reality. Real world comparisons must be the basis for hypothesis testing (which is what models are). In this context, the multi-decadal climate models are not even accurately predicting in hindcast climate statistics over the last few decades, much less CHANGES in these statistics.
The authors of the report are making the mistake of assuming that intermodel agreement increases confidence in the model skill to accurately predict real world regional climate.
The Bottom Line Message
Users of these model results by the US Forest Service community are being misled into the actual value of the climate projections. The Forest Service climate projection FAQ is scientifically flawed.
Climate Science: Roger Pielke Sr. 
