Climate Science Myths And Misconceptions – Post #4 On Climate Prediction As An Boundary Value Problem

I was alerted to a paper on climate as an initial and boundary value problem (h/t  Jos de Laat). The paper inappropriately uses a model to make their (incorrect) conclusions. As has been discussed numerous times on this weblog, models are hypotheses.  Only real world observations can be used to test the skill of the models.

The paper is

Grant Branstator and Haiyan Teng, 2010: Two Limits of Initial-Value Decadal Predictability in a CGCM. Journal of Climate Volume 23, Issue 23 (December 2010) pp. 6292-6311 doi: 10.1175/2010JCLI3678.1

is another example of the misuse of the scientific method.

The abstract reads of their paper is [highlight added]

When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results ofthis study are sensitive and insensitive to are also discussed.

The text starts with

“The scientific community is now taking on the challenge of using initialized models to produce time-evolving climate predictions for the next 10–30 yr (Smith et al. 2007; Keenlyside et al. 2008; Pohlmann et al. 2009). Such predictions will be a key component of the next Intergovernmental Panel on Climate Change (IPCC) assessment report (Taylor et al. 2009). Compared with traditional climate change experiments, the fundamental difference in these forecasts is that the initial ocean state is determined from observations, and the hypothesis is that the resulting forecasts will substantially benefit from this added information. But the duration of the influence of the ocean initial conditions remains unknown. Since the climate system is chaotic, inevitable errors in the initial conditions growwith time causing the initial signals to fade (Lorenz 1963). Eventually, the impact of the initial conditions become undetectable, placing a fundamental limit on its influence. If one considers a situation where the forcing of the climate system is changing, a second limit on initial condition influence should be introduced. For, if, as in the case with forcing by the ongoing changes in greenhouse gas (GHG) and aerosol concentrations, the system response increases with time, then at some point the influence of the initial conditions becomes of secondary importance compared to the forced response. In this paper, we quantify the forecast range at which these two limits are reached. Our results should help to determine the feasibility and value of decadal predictions (Meehl et al. 2009; Hurrell et al. 2009; Solomon et al. 2011).”

The method to study this question is described as

Here, we have analyzed several Community Climate Model version 3 (CCSM3) ensemble experiments specifically designed to make it possible to address this issue.”

This paper uses a modeling approach, in which the models have not been able to show skill at regional multi-decadal climate prediction in order to make a statement regarding the real climate system. These models do not accurately represent atmospheric/ocean features such as ENSO, PDO, NAO etc as well as longer term changes in deep ocean circulations.

Thus

Misconception #4:  Multi-Decadal Climate Prediction Is A Boundary Value Problem

I have discussed this subject in my paper

Pielke, R.A., 1998: Climate prediction as an initial value problem. Bull. Amer. Meteor. Soc., 79, 2743-2746.

where I wrote

“weather prediction is a subset of climate prediction and that both are, therefore, initial value problems in the context of nonlinear geophysical flow.”

In our paper

Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s climate system. Climatic Change, 65, 11-38

we concluded that

“The Earth’s climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm.”

Excerpts from our paper are

“Past records of climate change are perhaps the most frequently cited examples of nonlinear dynamics, especially where certain aspects of climate, e.g., the thermohaline circulation of the North Atlantic ocean, suggest the existence of thresholds, multiple equilibria, and other features that may result in episodes of rapid change (Stocker and Schmittner, 1997). As described in Kabat et al. (2003), the Earth’s climate system includes the natural spheres (e.g., atmosphere, biosphere, hydrosphere and geosphere), the anthrosphere (e.g., economy, society, culture), and their complex interactions (Schellnhuber, 1998). These interactions are the main source of nonlinear behavior, and thus one of the main sources of uncertainty in our attempts to predict the effects of global environmental change. In sharp contrast to familiar linear physical processes, nonlinear behavior in the climate results in highly diverse, usually surprising and often counterintuitive observations…”

There is a paper

F. Giorgi, 2005 : Climate Change Prediction: Climatic Change (2005) 73: 239. DOI: 10.1007/s10584-005-6857-4

which discussed this subject, but its implications were ignored by Branstator and Teng in 2010. Girogi writes

“….because of the long time scales involved in ocean, cryosphere and biosphere processes a first kind predictability component also arises. The slower components of the climate system (e.g. the ocean and biosphere) affect the statistics of climate variables (e.g. precipitation) and since they may feel the influence of their initial state at multi decadal time scales, it is possible that climate changes also depend on the initial state of the climate system (e.g. Collins, 2002; Pielke, 1998). For example, the evolution of the THC in response to GHG forcing can depend on the THC initial state, and this evolution will in general affect the full climate system. As a result, the climate change prediction problem has components of both first and second kind which are deeply intertwined.”

 If the climate system is both a boundary value and an initial value problem (which I agree with), it is an initial value problem!

The  Branstator and Teng 2010 paper is an example of a study that has failed to properly follow the scientific method as was discussed in

Short Circuiting The Scientific Process – A Serious Problem In The Climate Science Community

where I wrote

There has been a development over the last 10-15 years or so in the scientific peer reviewed literature that is short circuiting the scientific method.

The scientific method involves developing a hypothesis and then seeking to refute it. If all attempts to discredit the hypothesis fails, we start to accept the proposed theory as being an accurate description of how the real world works.

A useful summary of the scientific method is given on the website sciencebuddies.org.where they list six steps

  • Ask a Question
  • Do Background Research
  • Construct a Hypothesis
  • Test Your Hypothesis by Doing an Experiment
  • Analyze Your Data and Draw a Conclusion
  • Communicate Your Results

Unfortunately, in recent years papers have been published in the peer reviewed literature that fail to follow these proper steps of scientific investigation. These papers are short circuiting the scientific method.”

All the  Branstator and  Teng 2010 paper does is to tell us how the models that they used behaved. This is an interesting classroom study, but does not advance our understanding of the climate system as their study is not properly evaluated against observed real world data. This study should  not have appeared in a peer reviewed journal.

The reality is that Climate Prediction Is An Initial Value Problem.

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