Earlier this week, I made the presentation
Pielke Sr., R.A. 2011: Vulnerability and adaptation to climate change in water hazard assessments using regional climate scenarios in the Tokyo region. The International Workshop on Downscaling, Tuskuba, Japan, January 19, 2011
where I raised fundamental flaws in the use of regional downscaling of multi-decadal IPCC-type forecasts to provide impact information to policymakers and other users who want this information.
In today’s post, I want to briefly summarize why dynamic and statistical regional downscaling of multi-decadal IPCC-type forecasts are misleading the impacts community and policymakers.
The reasons this engineering technique does not work for multi-decadal climate predictions are briefly presented as follows:
- Statistical downscaling from the parent global model should be used as the benchmark (control) with which dynamic downscaling would have to improve on. An excellent example of this type of testing is given in the paper Landsea, C.W., Knaff, J.A., 2000: “How much skill was there in forecasting the very strong 1997-98 El Niño?” Bulletin of the American Meteorological Society, 81. Among their insight conclusions from this seminal paper is “…..the use of more complex, physically realistic dynamical models does not automatically provide more reliable forecasts. Increased complexity can increase by orders of magnitude the sources for error, which can cause degradation in skill.”
- IF the statistical relationship changes in the future, this method will not provide the actual real world response.
- accurate (regional resolution) lateral boundary conditions require regional scale Information from a global forecast model which, however, does not have regional scale information! This Is A classic “Catch-22”. A Catch-22 a logical paradox arising from a situation in which the regional model needs something that can only be acquired by a regional model (or regional observations); therefore, the acquisition of this lateral boundary conditions with the needed spatial resolution becomes logically impossible.
- The parent global multi-decadal predictions are unable to skillfully simulate major atmospheric circulation features such the PDO, NAO, El Niño, La Niña etc. Such observed regional atmospheric features explain the recent extreme cold and snow in western Europe, for example. However, the regional climate models are slaves of the lateral boundary conditions and of interior nudging from their parent models.
- If the global multi-decadal climate model predictions cannot accurately predict the larger scale circulation features of PDO, NAO, El Niño, La Niña etc, there is no way they can provide accurate lateral boundary conditions and interior nudging to the regional climate models. The regional models themselves do not have the domain scale (or two-way interaction) to skillfully predict these larger scale atmospheric features.
- There is only one-way interaction between the regional and global models. This 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.
- As a necessary condition for skillful predictions, the multi-decadal global climate model predictions must include all first-order climate forcings and feedbacks that impact lateral boundary conditions of the regional model. However, they do not (e.g. see NRC 2005; Pielke et al 2009).
- The advocates of the multi-decadal climate predictions state that, while they recognize that they cannot predict future climate change as an initial value problem, they assume they can predict future climate statistics as a boundary value problem. 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, they must be able to skillfully predict the changes in the regional weather statistics. There is only value for predicting climate change 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 models can predict the changes in these climate statistics even in hindcast.
- The need for regional climate models (RCMs) themselves will shortly become irrelevant, as the global models themselves achieve the same spatial resolution as the RCMs
Thus neither dynamic downscaling or statistical downscaling from multi-decadal global model projections add spatial or temporal accuracy of value to the impacts community. The global and regional climate models are providing a level of confidence in forecast skill of the coming decades that does not exist.