There is a very large, well-funded research community (by the NSF and others) developing impact studies for decades from now based on multi-decadal global climate model predictions, often dynamically or statistically downscaled to reigons and local areas. However, as I have posted and published on numerous times, this is a scientifically fatally flawed approach; e.g. see
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
In that article, we list a set of reasons that regional downscaling of multi-decadal are not skillful, and, that [at best]
“It is therefore inappropriate to present …. multi-decadal climate prediction….results to the impacts community as reflecting more than a subset of possible future climate risks.”
I have interesting company in such a conclusion [at least unless he has changed his mind since 2007]. In my post
Comment on the Nature Weblog By Kevin Trenberth Entitled “Predictions of climate”
I reported on an essay that Kevin Trenberth wrote for Nature entitled
In this essay, Kevin wrote
“…..we do not have reliable or regional predictions of climate.”
In this post, I am summarizing conclusions from several peer-reviewed publications that show that Kevin Trenberth, on this issue, is correct. The significance is that multi-decadal regional impact studies, based on predictions of changes in climate statistics, are not only a waste of time and money, but are misleading policymakers.
Following are extracts from the peer-reviewed papers:
1. 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
who concluded that
“….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.”
2. Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1
who find that without tuning from real world observations, the model predictions are in significant error. For example, they found that
“…the traditional dynamic downscaling (TDD) [i.e. without tuning) overestimates precipitation by 0.5-1.5 mm d-1…..The 2-year return level of summer daily maximum temperature simulated by the TDD is underestimated by 2-6°C over the central United States-Canada region.”
3. van Oldenborgh, G.J., F.J. Doblas-Reyes, B. Wouters, W. Hazeleger (2012): Decadal prediction skill in a multi-model ensemble. Clim.Dyn. doi:10.1007/s00382-012-1313-4
who report quite limited predictive skill in two regions of the oceans on the decadal time period, but no regional skill elsewhere, when they conclude that
“A 4-model 12-member ensemble of 10-yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation. The main source of skill in temperature is the trend, which is primarily forced by greenhouse gases and aerosols. This trend contributes almost everywhere to the skill. Variation in the global mean temperature around the trend do not have any skill beyond the first year. However, regionally there appears to be skill beyond the trend in the two areas of well-known low-frequency variability: SST in parts of the North Atlantic and Pacific Oceans is predicted better than persistence. A comparison with the CMIP3 ensemble shows that the skill in the northern North Atlantic and eastern Pacific is most likely due to the initialisation, whereas the skill in the subtropical North Atlantic and western North Pacific are probably due to the forcing.”
4. Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110
who report that
“…. local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale.”
5. Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
who wrote
“models produce precipitation approximately twice as often as that observed and make rainfall far too lightly…..The differences in the character of model precipitation are systemic and have a number of important implications for modeling the coupled Earth system …….little skill in precipitation [is] calculated at individual grid points, and thus applications involving downscaling of grid point precipitation to yet even finer‐scale resolution has little foundation and relevance to the real Earth system.”
There is an important summary of the limitations in multi-decadal regional climate predictions in
Kundzewicz, Z. W., and E.Z. Stakhiv (2010) Are climate models “ready for prime time” in water resources managementapplications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.
who conclude that
“Simply put, the current suite of climate models were not developed to provide the level of accuracy required for adaptation-type analysis.”
This succinct, accurate statement should be highlighted in the next IPCC report [although they will probably ignore this, as they have in the past]. If they ignore or do not refute, however, when they have been alerted to this fundamental shortcoming, it would show clearly that they are either incompetent or willfully subverting the scientific process.
Climate Science: Roger Pielke Sr. 
