Comment Submitted To BAMS On The Mearns Et Al 2012 Paper

As discussed in my posts

Follow Up On My E-Mail Request To Linda Mearns Of NCAR

“The North American Regional Climate Change Assessment Program: Overview of Phase I Results” By Mearns Et Al 2012 – An Excellent Study But It Overstates Its Significance In The Multi-Decadal Prediction Of Climate

E-Mail To Linda Mearns On The 2012 BAMS Article On Dynamic Downscaling

there is a significant overstatement of the implications of the paper

Linda O. Mearns, 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.

Linda is not interested in discussing this outside of a formal Comment to BAMS in which she and her co-authors can reply.  Thus I have submitted a Comment and reproduced it below. BAMS usually takes quite a while to complete the Comment/Reply process and I will post (and respond on my weblog further if needed) when this publication process is complete. Before I post my Comment, however, I want to alert readers to Judy Curry’s post from today titled

RS Workshop on Handling Uncertainty in Weather & Climate
Prediction. Part I

where she wrote

Regional climate change:

  • Little to no skill here; increased  resolution not helping
  • Dynamical & statistical downscaling adds little value
  • Many extreme weather events not  explicitly simulated
  • Depends on poorly simulated modes of  natural internal variability

GCMs are currently incapable of simulating:

  • Regional climate variability and change
  • Network of teleconnection climate regimes on DEC-CEN timescales
  • Predictions of emergent phenomena, e.g. abrupt climate change

It is unlikely that the current path of development will improve this

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 Comment

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.


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