Comments On The Article By Palmer et al. 2008 “Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts”

UPDATE: JULY 10 2008: Unfortunately, Tim Palmer elected not to consider writing a guest weblog in response to the weblogs on his paper until possibly the Fall. This is yet another example of how, when science issues are raised regarding papers or assessments, constructive scientific discussion is avoided.  

Tim Palmer of the European Centre for Medium Range Forecasting (ECMWF) is an excellent scientist. He is Head of the Probability and Seasonal Forecasting Division at ECMWF. A brief overview of his credentials are that he “is a fellow of the Royal Society and of the American Meteorological Society, and has received awards from both of these societies. He is currently chairman of the Scientific Steering Group of the U.N. World Meteorological Organization’s Climate Variability and Predictability Project, and was lead author of the most recent assessment report of the Intergovernmental Panel on Climate Change.”

 Thus, any publication that he authors is worthy of discussion.

There is a new paper led by Dr. Palmer;

 T. N. Palmer, F. J. Doblas-Reyes, A. Weisheimer, and M. J. Rodwell, 2008: Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts. Bulletin of the American Meteorological Society Volume 89, Issue 4 (April 2008) pp. 459–470 DOI: 10.1175/BAMS-89-4-459.

Professor Hendrik provided an excellent weblog on this paper yesterday (see), and I am adding my perspective today.

The Palmer et al paper is headlined with the text

“In a seamless prediction system, the reliability of coupled climate model forecasts made on seasonal time scales can provide useful quantitative constraints for improving the trustworthiness of regional climate change projections.”

 The paper uses a schematic to illustrate the subject which is reproduced below

Figure 1 A schematic figure illustrating that the link between climate forcing and climate impact involves processes acting on different time scales. The whole chain is as strong as its weakest link. The use of a seamless prediction system allows probabilistic projections of climate change to be constrained by validations on weather or seasonal forecast time scales [reproduced with the permission of Tim Palmer].

“The figure shows a chain. One end of this chain represents humanity’s forcing of climate through emissions of greenhouse gases into the atmosphere. The other end of the chain represents the impact of this forcing in terms of regional climate change (temperature, precipitation, wind, and so on).”

“This is where the notion of seamless prediction can play a key role. It will be decades before climate change projections can be fully verified. However, our basic premise, illustrated by the schematic in Fig. 1, is that there are fundamental physical processes in common to both seasonal forecast and climate change time scales. If essentially the same ensemble forecasting system can be validated probabilistically on time scales where validation data exist, that is, on daily, seasonal, and (to some extent) decadal time scales, then we can modify the climate change probabilities objectively using probabilistic forecast scores on these shorter time scales.”

However, the climate system has physical, biological and chemical interactions within and across each component of the system on all time scales (see Figures 1 and 2 below). As one seeks to predict, even probabilistically, further into the future, more of the slower feedbacks and forcings become more important (as Professor Tennekes noted in his guest weblog).  The forcings and nonlinear feedbacks that operate on the time scale longer than seasonal cannot be tested by the methodology proposed in the BAMS paper.

 
 
 

 

 

 

Figure 2 Conceptual framework of climate forcing, response, and feedbacks  Examples of human activities, forcing agents, climate system components, and variables that can be involved in climate response are provided in the lists in each box. ( from NRC, 2005: Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties. Board on Atmospheric Sciences and Climate (BASC), National Academy of Sciences, Washington DC)

 

 

 Therefore, while I agree that evaluating model prediction performance on seasonal time scale, by running the global models with initial conditions, is a very worthwhile and important goal (and deserving of support), it will not inform us of the skill at making longer term forecasts, where, for example, such aspects of the climate as sea surface temperature must be accurately predicted. With seasonal prediciton, the sea surface temperatures retain a signficant correlation with the values inserted in the initialization.

Seasonal prediction itself is a difficult problem. We have examined this issue in our paper

Castro, C.L., R.A. Pielke Sr., J. Adegoke, S.D. Schubert, and P.J. Pegion, 2007: Investigation of the summer climate of the contiguous U.S. and Mexico using the Regional Atmospheric Modeling System (RAMS). Part II: Model climate variability. J. Climate, 20, 3866-3887.

As we conclude in this paper

“In order for RCMs [regional climate models] to be successful in a seasonal weather prediction mode for the summer season, it is required that the GCM [general circulation model] provide a reasonable representation of the teleconnections and have a climatology that is comparable to a global atmospheric reanalysis.”

In other words, unless the global model is realistic,  as defined by comparing the model results to global reanalyses, it is not possible for a skillful regional scale prediction. 

This also means that longer term skillful predictions will be unable to produce skillful regional forecasts unless the global model accurately predicts the statistics of atmospheric and ocean circulation features (such as ENSO, the NAO, PDO, etc), as well as drought and other aspects of the climate system.

 While, I endorse the analysis of multimodel seasonal forecast reliability that Tim Palmer has been a pioneer in introducing, the claim that the verification of model skill on this time scale can provide confidence in the skill of longer term (e.g., decadal and multi-decadal) climate prediction is not a robust scientfic conclusion. If there is disagreement on this claim by Climate Science, than the only arbitrator is to perfofm these long term predictions for the coming years, and validate whether or not they are reliable.

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