which is in complete agreement with my view on this topic. Following is my Comment submission to BAMS
Comment On Mearns et al 2012
The Mearns et al 2012 BAMS paper with respect to downscaling from reanalyses it is an important new contribution. However, its claim of that these results can provide useful information about climate change is inappropriate and misleading to the impacts and policy communities.
The Mearns et al (2012) article provides documentation of the level of skill of one type of dynamic downscaling. Within that framework it is an important new contribution which will be widely cited. However, the paper only provides, at best, an upper bound of what is possible with respect to their goal to provide to provide
“uncertainties in regional scale projections of future climate and produce high resolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere ocean general circulation models (AOGCMs) forced with the A2 SRES scenario.”
The Mearns et al 2012 study concludes with the claim that
“….. We have shown that all the models can simulate aspects of climate well, implying that they all can provide useful information about climate change. In particular, the results from phase I of NARCCAP will be used to establish uncertainty due to boundary conditions as well as final weighting of the models for the development of regional probabilities of climate change.”
However, this conclusion significantly overstates the significance of their findings in terms of its application to the multi-decadal prediction of regional climate (i.e. “climate change”). The Mearns et al study uses observational data (from a reanalysis) to drive the regional models. Using the classification we have introduced in Castro et al (2005), Mearns et al is a Type 2 dynamic downscaling study.
As we wrote in Pielke and Wilby (2011)
“Type 2dynamic downscaling refers to regional weather (or climate) simulations…in which the regional model’s initial atmospheric conditions are forgotten…..but results still depend on the lateral boundary conditions from a global numerical weather prediction where initial observed atmospheric conditions are not yet forgotten or are from a global reanalysis.….Downscaling from reanalysis products (Type 2 downscaling) defines the maximum forecast skill that is achievable with Type 3 and Type 4 downscaling.”
“Type 4 dynamic downscaling takes lateral boundary conditions from an Earth system model in which coupled interactions among the atmosphere, ocean, biosphere, and cryosphere are predicted ……Other than terrain, all other components of the climate system are calculated by the model except for human forcings, including greenhouse gas emissions scenarios, which are prescribed. Type 4 dynamic downscaling is widely used to provide policy makers with impacts from climate decades into the future……. Type 4 downscaling has practical value but with the very important caveat that it should be used for model sensitivity experiments and not as predictions [e.g., Pielke, 2002; Prudhomme et al., 2010].”
As discussed in Pielke and Wilby, Type 1downscaling is used for short-term, numerical weather prediction, while Type 3 dynamic downscaling takes lateral boundary conditions from a global model prediction forced by specified real world surface boundary conditions such as seasonal weather predictions based on observed sea surface temperatures. Because real-world observational constraints diminish from Type 1 to Type 4 downscaling, uncertainty grows as more climate variables must be predicted by models, rather than obtained from observations.
One cannot, therefore, use Type 2 downscaling to make claims, as Mearns et al have, about the accuracy of Type 4 downscaling. Type 2 downscaling provides a real world observational constraint on how much the regional model can diverge from reality. This is not the case with Type 4 downscaling. A Type 4 downscaling cannot be more accurate than a Type 2 downscaling.
A more appropriate approach is to first assess what changes in climate statistics would have to occur in order to cause a negative impact to key resources, as we recommend in Pielke et al 2012. Only then assess what is plausibly possible and how to mitigate/adapt to prevent a negative effect from occurring.
The type of downscaling used in a study is a critically important point that needs to be emphasized when dynamic downscaling studies are presented. Mearns et al (2012) did not do this.
Indeed, Mearns et al 2012 is a study of the current climate, not of changes in climate statistics over the time period of the model runs. The Mearns et al 2012 study did not look at the issue of their skill to predict changes in climate statistics. Even reproducing the current regional climate in a hindcast mode when the results are not constrained by reanalyses is being shown to be a daunting challenge; e.g. Xu et al 2012; Fyfe et al 2011; van Oldenborgh et al 2012; Anagnostopoulos et al 2010; Stephens et al 2010; Sun et al 2012; van Haren et al 2012; Kundzewicz et al 2010; Goddard el al 2012; Driscoll et al 2012; Mauritsen et al 2012; Jiang et al 2012.
It is even more challenging to skillfully predict changes in regional climate which is what is required if the RCMs are to add any value for predicting climate in the coming decades beyond what could be extracted from reanalyses. The Mearns et al 2012 paper is, therefore, misleading the impacts communities by indicating that their results apply to regional climate change (i.e. Type 4 downscaling).
In summary, the Mearns et al 2012 BAMS paper with respect to Type 2 downscaling it is an important new contribution. However, its application to climate change runs (Type 4 downscaling) is inappropriate and is misleading to the impacts and policy communities on a level of predictive skill that does not yet exist.
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
Castro, C.L., R.A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res. – Atmospheres, 110, No. D5, D05108, doi:10.1029/2004JD004721.
Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.
Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn (2011), Regional climate models add value to global model data—A review and selected examples, Bull. Am. Meteorol. Soc., 92, 1181–1192, doi:10.1175/2011BAMS3061.1.
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
Goddard, A. Kumar, A. Solomon, D. Smith, G. Boer, P. Gonzalez, V. Kharin, W. Merryfield, C. Deser, S. J. Mason, B. P. Kirtman, R. Msadek, R. Sutton, E. Hawkins, T. Fricker, G. Hegerl, C. A. T. Ferro, D. B. Stephenson, G. A. Meehl, T. Stockdale, R. Burgman, A. M. Greene, Y. Kushnir, M. Newman, J. Carton, I. Fukumori, T. Delworth. (2012) A verification framework for interannual-to-decadal predictions experiments. Climate Dynamics Online publication date: 24-Aug-2012.
Jiang, J. H., et al. (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations, J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237. published 18 July 2012.
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.
Mauritsen, T., et al. (2012), Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, doi:10.1029/2012MS000154. published 7 August 2012.
Mearns, Linda O. , Ray Arritt, Sébastien Biner, Melissa S. Bukovsky, Seth McGinnis, Stephan Sain, Daniel Caya, James Correia, Jr., Dave Flory, William Gutowski, Eugene S. Takle, Richard Jones, Ruby Leung, Wilfran Moufouma-Okia, Larry McDaniel, Ana M. B. Nunes, Yun Qian, John Roads, Lisa Sloan, Mark Snyder, 2012: The North American Regional Climate Change Assessment Program: Overview of Phase I Results. Bull. Amer.Met Soc. September issue. pp 1337-1362.
Pielke, R. A., Sr., (2002), Overlooked issues in the U.S. national climate and IPCC assessments, Clim. Change, 52(1-2), 1–11, doi:10.1023/ A:1017473207687.
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.
Pielke, R. A., Sr., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku,J. Adegoke, G. Kallos, T. Seastedt, and K. Suding (2012), Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective, in Extreme Events and Natural Hazards: The Complexity Perspective, Geophys. Monogr. Ser., vol. 196, edited by A. S. Sharma et al. 345.359, AGU, Washington, D. C., doi:10.1029/2011GM001086.
Prudhomme, C., R. L. Wilby, S. Crooks, A. L. Kay, and N. S. Reynard (2010), Scenario-neutral approach to climate change impact studies: Application to flood risk, J. Hydrol., 390, 198–209, doi:10.1016/ j .jhydrol .2010.06.043.
Sakaguchi, K., X. Zeng, and M. A. Brunke (2012), The hindcast skill of the CMIP ensembles for the surface air temperature trend, J. Geophys. Res., 117, D16113, doi:10.1029/2012JD017765. published 28 August 2012
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.
Sun, Z., J. Liu, X. Zeng, and H. Liang (2012), Parameterization of instantaneous global horizontal irradiance at the surface. Part II: Cloudy-sky component, J. Geophys. Res., doi:10.1029/2012JD017557, in press.
van Haren, Ronald, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, 2012: SST and circulation trend biases cause an underestimation of European precipitation trends Climate Dynamics 2012, DOI: 10.1007/s00382-012-1401-5
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
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