Climate change: models and their limitations

Prominent climatologist Richard Lindzen, writing in Climate Science: Is it currently designed to answer questions? a paper he presented at the “Creativity and Creative Inspiration in Mathematics, Science, and Engineering: Developing a Vision for the Future” conference held in San Marino, August 2008, summarises the problem thus:

Data that challenges the [AGW] hypothesis are simply changed. In some instances, data that was thought to support the hypothesis is found not to, and is then changed … Bias can be introduced by simply considering only those errors that change answers in the desired direction. The desired direction in the case of climate is to bring the data into agreement with models, even though the models have displayed minimal skill in explaining or predicting climate.

Model projections, it should be recalled, are the basis for our greenhouse concerns. That corrections to climate data should be called for is not at all surprising, but that such corrections should always be in the “needed” direction is exceedingly unlikely.

Although the situation suggests overt dishonesty, it is entirely possible, in today’s scientific environment, that many scientists feel that it is the role of science to vindicate the greenhouse paradigm for climate change as well as the credibility of models. Comparisons of models with data are, for example, referred to as model validation studies rather than model tests. [Author’s emphasis.]

It needs to be kept in mind that computer climate models do not output data: their results are simply computations of the input data. Obviously then, the accuracy or otherwise of the computated output is dependent upon the accuracy of the input data. Furthermore, a climate model’s output is only reliable to the degree that the model’s performance can be validated, not necessarily by comparisons with other models but from raw data recorded or observed from the real world. Of course, tuned parameter corrections may be legitimate but only if they include both those corrections that bring observations into agreement with the model, and those that do not - to exclude the latter is to obfuscate the model’s outcome through omission.

In climate science the most notorious example of obfuscation through omission is what has become known as Mann’s Hockey Stick. Lindzen again:

In the first IPCC assessment (IPCC, 1990), the traditional picture of the climate of the past 100 years was presented. In this picture, there was a medieval warm period that was somewhat warmer than the present as well as the little ice age that was cooler. The presence of a period warmer than the present in the absence of any anthropogenic greenhouse gases was deemed an embarrassment for those holding that present warming could only be accounted for by the activities of man. Not surprisingly, efforts were made to get rid of the medieval warm period

... The most infamous effort was that due to Mann et al … which used primarily a few handfuls of tree ring records to obtain a reconstruction of Northern Hemisphere temperature going back eventually a thousand years that no longer showed a medieval warm period. Indeed, it showed a slight cooling for almost a thousand years culminating in a sharp warming beginning in the nineteenth century. The curve came to be known as the hockey stick, and featured prominently in the next IPCC report, where it was then suggested that the present warming was unprecedented in the past 1000 years. The study immediately encountered severe questions concerning both the proxy data and its statistical analysis.

The Mann Hockey Stick has since been discredited by two independent assessments, both statistically and by reference to historical and archaeological records, though his initial claim that the current (late 20th century) warming is unprecedented remains within the lexicon of adherents to the AGW hypothesis.

There is a problem here for the reliability of science when models fail, either through prediction or hindcasting, but are still given the same validity as observed or model input data. One could suspect that advocacy is overriding science in this instance. While advocates and politicians might think that the science of AGW is settled, scientists and climate modelers need to be able to, and be seen to, separate clearly what is science and what is advocacy otherwise their research may be subjected to political manipulation.

The computated output of climate models, often used in conjuction with models from outside the field of climate science, have been used to construct climate change scenarios, often abbreviated as SRES, an acronym for Special Report on Emission Scenarios. SRES was developed by the IPCC to develop scenarios with which to analyse, according to SRES:

… how driving forces may influence future [greenhouse gas] emission outcomes and to assess the associated uncertainties. They assist in climate change analysis, including climate modeling and the assessment of impacts, adaptation, and mitigation. The possibility that any single emissions path will occur as described in scenarios is highly uncertain … Any scenario necessarily includes subjective elements and is open to various interpretations. [Author’s emphasis.]

The output of SRES models, alternative views of how the future may unfold, are termed projections. Projections are often stated or implied erroneously, particularly in the media in connection with runaway climate change, as forecasts. This creates the impression that the SRES model output is new data, even proof, as opposed to being simply a projection of computated input data and parameters from a number of sources within and beyond the field of climate science.

Multi-model SRES climate change scenarios are said to create an essemble of climate change projections. Modellers then consider this spread of these SRES projections, upon which has been built the notion that if the spread is close together then they can have confidence in the forecast while if the spread significantly differs then there is uncertainty about the projections even though they may offer a range of possibilities.

The SRES approach is problematic: it is assumptive; prone to exaggerated errors; and unscientific.