The Difference Between Prediction and Predictability – Recommendations For Research Funding Related to These Distinctly Different Concepts

Summary–  While the quantiative assessment of predictability is worthy of funding by the National Science Foundation, and other such funding agencies in the United States and elsewhere,  the production of multi-decadal climate predictions of regional impacts, whose skill cannot be verified until decades from now, are a waste of the available funds for research. A bottom-up, resource assessment of vulnerabilities, even without predictive skill  (i.e. see

A Way Forward In Climate Science Based On A Bottom-Up Resourse-Based Perspective)

is a much more scientifically robust approach.

The Terms Prediction and Predictability

The terms “prediction” and “predictability” are quite similar in their spelling, but in geophysics, they have distinctly (and important) different meanings.

In geophysics the  term “prediction” is used for a “forecast” [such as a weather forecast] or for a “projection” [such as the forecasts by the IPCC reports which mean, if certain scenarios of greenhouse gas emissions occur in the coming decades, this “projection” is a prediction). The term “prediction” is also a “hypothesis” as I discuss later in this post.

Muliti-decadal climate predictions (which are than provided to policymakers and the impacts community, as given in my posts on Monday and Tuesday of this week), have been vigorously promoted.  This advocacy as also been reported, for example, in my recent post

Climate Prediction Advocacy By Shukla Et Al 2010 “Toward a New Generation Of World Climate Research And Computing Facilities”

where I report on a BAMS paper (see) that has the abstract

“To accelerate progress in understanding and predicting regional climate change, national climate research facilities must be enhanced and dedicated.”


“Soon the societal demand for policy-relevant climate predictions will be so great that the most advanced technology and the best available talent must be brought to bear to address this great challenge. The time to begin that process is now!”

The term “prediction” is also a hypothesis, as I reported in my post

Hypothesis Testing – A Failure In The 2007 IPCC Reports

The steps of hypothesis testing that I reported in that post from  the website can be rephrased (without a loss of meaning) in the following with respect to climate predictions (of Type 1, 2, 3, and 4).

  • Make a Prediction
  • Quantitatively Compare the Prediction With Real World Observations [i.e. Test the Hypothesis]
  • Communicate The Assessment the Skill of the Prediction

The term “predictability”, as applied in geophysics, is thus accurately defined as

Predictability–  The capability of a prediction to make skillful forecast of the future of observed geophysical variables. Skill is defined as an improvement over a clearly defined benchmark.  For example, the predictability of a geophysical feature (such as the occurrence of an El Niño next year) by a dynamic numerical model prediction  can be assessed by comparing with a statistical model prediction (such as performed by Landsea and Kanff, 2000  for El Niños).

The proposal by Judah Cohen, that I discussed in my post

Further Comment On The Posts At Dot Earth On Judah Cohen’s Research

to predict atmospheric features in the coming years is a good example of assessing the predictability.

However, the 2007 and the new IPCC reports, the CCSP reports, and  the WCRP CMIP3 project (and other such studies and assessments)  fail to present an adequate plan to assess the predictability of their predictions of the climate system decades from now. Research funding by the NSF and others which provide predictions of climate impacts decades from now, but present no way to test the skill of these forecasts is a waste of funding. Even more importantly, it misleads policymakers in terms of the spectrum of risks we will face from climate, but also other social and environmental threats, in the coming decades, as we discussed in

Pielke, R.A. Sr., and L. Bravo de Guenni, 2004: Conclusions. Chapter E.7 In: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Global Change – The IGBP Series, P. Kabat et al., Eds., Springer, 537-538. [See Table E.7];

Pielke Sr., R., K. Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D. Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E. Philip Krider, W. K.M. Lau, J. McDonnell,  W. Rossow,  J. Schaake, J. Smith, S. Sorooshian,  and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases. Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American Geophysical Union;

McAlpine, C.A., W.F. Laurance, J.G. Ryan, L. Seabrook, J.I. Syktus, A.E. Etter, P.M. Fearnside, P. Dargusch, and R.A. Pielke Sr. 2010: More than CO2: A broader picture for managing climate change and variability to avoid ecosystem collapse. Current Opinion in Environmental Sustainability, 2:334-336, DOI10.1016/j.cosust.2010.10.001.


A Way Forward In Climate Science Based On A Bottom-Up Resourse-Based Perspective

Comments Off on The Difference Between Prediction and Predictability – Recommendations For Research Funding Related to These Distinctly Different Concepts

Filed under Q & A on Climate Science

Comments are closed.