When Is A Model a Good Model?

I am reading the book The Grand Design by Stephen Hawking and Leonard Mlodinow. While the book involves a non-mathematical discussion of quantum physics and general relatively, among other topics, there is a concise summary on page 51 as to what is a “good model”.

They write

A model is a good model if it:

  1. Is elegant
  2. Contains few arbitrary or adjustable elements
  3. Agrees with and explains all existing observations
  4. Makes detailed predictions about future observations that can disprove or falsify the model if they are not borne out.

With respect to the mult-decadal global climate models, it is clear they fail these requirements to be a “good model”. As candidly summarized, for example, by Kevin Trenberth in 2007 [an IPCC WG1 author] [highlighting added]

“…there are no predictions by IPCC at all. And there never have been. The IPCC instead proffers “what if” projections of future climate that correspond to certain emissions scenarios. There are a number of assumptions that go into these emissions scenarios. They are intended to cover a range of possible self consistent “story lines” that then provide decision makers with information about which paths might be more desirable. But they do not consider many things like the recovery of the ozone layer, for instance, or observed trends in forcing agents. There is no estimate, even probabilistically, as to the likelihood of any emissions scenario and no best guess.Even if there were, the projections are based on model results that provide differences of the future climate relative to that today. None of the models used by IPCC are initialized to the observed state and none of the climate states in the models correspond even remotely to the current observed climate. In particular, the state of the oceans, sea ice, and soil moisture has no relationship to the observed state at any recent time in any of the IPCC models. There is neither an El Niño sequence nor any Pacific Decadal Oscillation that replicates the recent past; yet these are critical modes of variability that affect Pacific rim countries and beyond. The Atlantic Multidecadal Oscillation, that may depend on the thermohaline circulation and thus ocean currents in the Atlantic, is not set up to match today’s state, but it is a critical component of the Atlantic hurricanes and it undoubtedly affects forecasts for the next decade from Brazil to Europe. Moreover, the starting climate state in several of the models may depart significantly from the real climate owing to model errors. I postulate that regional climate change is impossible to deal with properly unless the models are initialized.

The current projection method works to the extent it does because it utilizes differences from one time to another and the main model bias and systematic errors are thereby subtracted out. This assumes linearity. It works for global forced variations, but it can not work for many aspects of climate, especially those related to the water cycle. For instance, if the current state is one of drought then it is unlikely to get drier, but unrealistic model states and model biases can easily violate such constraints and project drier conditions. Of course one can initialize a climate model, but a biased model will immediately drift back to the model climate and the predicted trends will then be wrong. Therefore the problem of overcoming this shortcoming, and facing up to initializing climate models means not only obtaining sufficient reliable observations of all aspects of the climate system, but also overcoming model biases. So this is a major challenge.”

The obvious answer to the questions posed regarding a “good model” in the Hawking and Mlodinow 2010 book is that the models used in the 2007 IPCC report are not “good models” as they fail all four of the requirements.

This failure does not mean we should not be concerned about the human addition of greenhouse gases (or other human and natural climate forcings), but it should cause policymakers and funders of climate model researchers to realize that they have been oversold on the scientific rigor of the IPCC models. The funding of model predictions decades into the future using these tools is not money well spent.

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