A main tool for the impacts community with respect to climate risks in the coming decades is the use of dynamic downscaling from global multi-decadal climate predictions. The concept is that by using the higher spatial resolution of regional climate models, along with fine scale terrain and other landscape information, more accurate detailed impact information can be achieved.
Many millions of dollars are being spent on this approach; e.g. see
Is The NSF Funding Untestable Climate Predictions – My Comments On A $6 Million Grant To Fund A Center For Robust Decision–Making On Climate And Energy Policy”
Our new paper
Pielke Sr., R.A., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku, J. Adegoke, G. Kallos, T. Seastedt, and K. Suding, 2011: Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective. AGU Monograph on Complexity and Extreme Events in Geosciences, in press
summarizes why dynamic downscaling fails to add accurate information beyond what would be achieved just by interpolating the global model predictions onto a finer scale terrain/landscape map.
The main issues, as I summarize from our paper are:
1. As a necessary condition for an accurate prediction, the multi-decadal global climate model simulations must include all first-order climate forcings and feedbacks. However, they do not [see for example: NRC, 2005; Pielke Sr. et al., 2009].
2. These global multi-decadal predictions are unable to skillfully simulate major atmospheric circulation features such the Pacific Decadal Oscillation [PDO], the North Atlantic Oscillation [NAO], El Niño and La Niña, and the South Asian monsoon [Pielke Sr., 2010; Annamalai et al., 2007].
3. While dynamic regional downscaling yield higher spatial resolution, the regional climate models are strongly dependent on the lateral boundary conditions and interior nudging by their parent global models [e.g., see Rockel et al., 2008]. Large-scale climate errors in the global models are retained and could even be amplified by the higher spatial resolution regional models.
4. Since as reported, the global multi-decadal climate model predictions cannot accurately predict circulation features such as the PDO, NAO, El Niño, and La Niña [Compo et al., 2011] they cannot provide accurate lateral boundary conditions and interior nudging to the regional climate models.
5. The regional models themselves do not have the domain scale (or two-way interaction) to skillfully predict these larger-scale atmospheric features.
6. There is also only one-way interaction between regional and global models which is not physically consistent. If the regional model significantly alters the atmospheric and/or ocean circulations, there is no way for this information to alter the larger-scale circulation features which are being fed into the regional model through the lateral boundary conditions and nudging.
7. When higher spatial analyses of land use and other forcings are considered in the regional domain, the errors and uncertainty from the larger model still persists thus rendering the added complexity and details ineffective [Ray et al. 2010; Mishra et al. 2010].
8. The lateral boundary conditions for input to regional downscaling require regional-scale information from a global forecast model. However the global model does not have this regional-scale information due to its limited spatial resolution. This is, however, a logical paradox since the regional model needs something that can only be acquired by a regional model (or regional observations). Therefore, the acquisition of lateral boundary conditions with the needed spatial resolution becomes logically impossible.
Finally, There is sometimes an incorrect assumption that although global climate models cannot predict future climate change as an initial value problem, they can predict future climate statistics as a boundary value problem [Palmer et al., 2008]. With respect to weather patterns, for the downscaling regional (and global) models to add value over and beyond what is available from the historical, recent paleo-record, and worse case sequence of days, however, they must be able to skillfully predict the changes in the regional weather statistics.
There is only value for predicting climate change, however, if they could skillfully predict the changes in the statistics of the weather and other aspects of the climate system. There is no evidence, however, that the model can predict changes in these climate statistics even in hindcast. As highlighted in Dessai et al.  the finer and time-space based downscaled information can be “misconstrued as accurate”, but the ability to get this finer-scale information does not necessarily translate into increased confidence in the downscaled scenario [Wilby, 2010].
As also discussed in my post on June 9 2011
Dynamic downscaling from multi-decadal global model projections do not add proven value to spatial or temporal accuracy that can assist the impacts community in ways beyond what is already available from historical, paleo- or analogue records. I welcome guest posts from climate scientists colleagues who feel they can refute this conclusion.