I am posting below an e-mail I sent in late June to Jay Fein and Margaret Cavanaugh at the National Science Foundation regarding what I view as fatal problems with impact studies in the coming decades based on multi-decadal global climate predictions. These impact studies are based on an assumed skill at downscaling to regional and local regions where these impacts would occur.
from: Roger A Pielke Sr to”Fein, Jay S.” <xxxxxx> with a cc to “Cavanaugh, Margaret A.” <xxxxxx>
Wed, Jun 29, 2011 at 11:04 AM
subject: NSF
Dear Jay
I have not heard further from you or Margaret on the major issues I have raised with the funding by NSF of regional impact studies based on multi-decadal global climate model predictions. I would appreciate your (and/or Margaret’s or other NSF program manager responses) to the substantive concerns (one could consider these as hypotheses which need to be tested) raised in my post
The Failure Of Dynamic Downscaling As Adding Value to Multi-Decadal Regional Climate Prediction
https://pielkeclimatesci.wordpress.com/2011/06/15/the-failure-of-dynamic-downscaling-as-adding-value-to-multi-decadal-regional-climate-prediction/which 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. [2009] 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].”
These issues have passed peer review in our paperPielke 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.
https://pielkeclimatesci.wordpress.com/wp-content/uploads/2011/05/r-365.pdfI have also submitted comments to the National Science Board as I report in my post
My Comments On “NSB/NSF Seeks Input on Proposed Merit Review Criteria Revision and Principles”
https://pielkeclimatesci.wordpress.com/2011/06/28/my-comments-on-nsbnsf-seeks-input-on-proposed-merit-review-criteria-revision-and-principles/This is based on my documented negative experiences with the NSF program with respect to climate research in
My Experiences With A Lack Of Proper Diligence And Bias In The NSF Review Process For Climate Proposals
https://pielkeclimatesci.wordpress.com/2011/05/26/my-experiences-with-a-lack-of-proper-diligence-in-the-nsf-review-process-for-climate-proposals/
I plan to post on my request for information from you on my science issues regarding downscaling in order to obtain multi-decadal regional climate impacts. While, as you requested, I will not reproduce your e-mails but I will report that I have contacted you on this.Quite frankly, in my view, this is a waste of large amounts of NSF funding on climate. However, I welcome responses, which I can post, that seek to refute this conclusion in order to facilitate a much overdue debate on these questions.
Sincerely
Roger
The failure of Jay Fein and Margaret Cavanaugh of the National Science Foundation to even have the courtesy of a reply to the issues I am raising illustrates a failure in accountability of this US federal agency. They hold the authority over funding these multi-decadal climate prediction impact studies without any oversight over the scientific robustness of this methodology. Huge amounts of money are being wasted in this misuse of modeling.
My recommendation is that a Congressional subcommittee examine whether their expenditures of funds for these impact studies is an effective use of federal tax dollars. In my view, it is not only a waste of money but is misleading policymakers on the actual spectrum of risks that society and the environment face in the future, as outlined in our 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.
Climate Science: Roger Pielke Sr. 
