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INSAM homepage until December 2009

Last modified January 12, 2010 10:20

INSAM homepage until December 2009

Earlier this year, in volume 90, No 13, of the journal Eos, Stephan Harrison and Dave Stainforth have used a postulated principle difference in basic physics between particle physics (reductionism) and condensed matter physics (emergence) to argue that there exist system behaviours and structures that are not amenable to explanation or prediction by reductionist methodologies. Complex climate models must be viewed as a combination of the reductionist and emergence approaches.

The authors point towards the limitations of climate models in re-creating some aspects of past climates and indicate that we might have to do with aspects that are the result of emergence properties that simply cannot be captured by current models or potentially by any models likely to be developed. Lessons from other scientific disciplines appear to be that there exist principal limits to our understanding of complex systems. Climate prediction to the degree and the scale we would really need in agriculture might therefore just be impracticable.

In the same issue of Eos, Suraje Dessai, Mike Hulme, Robert Lempert and Roger Pielke Jr. argue that such predictions should indeed not be seen as indispensable and a prerequisite for effective decision making on adaptation to climate change. The search for objective constraints with which to reduce the uncertainty in regional predictions has proven elusive. However, they argue that in recent years a number of researchers have begun to use climate models to provide information that can help evaluate alternative responses to climate change, without necessarily relying on accurate predictions.

The authors explain that as one key step in the assessment process, such analyses use climate models to identify potential vulnerabilities of proposed adaptation strategies. These analyses do not require accurate predictions of future climate change from cutting-edge models. They require only a range of plausible representations of future climate that can be used to help organizations, such as water resources agencies, better understand where their climate change-related vulnerabilities may lie and how those vulnerabilities can be addressed. Even without accurate probability distributions over the range of future climate impacts, such information can prove very useful to decision makers.

These authors ultimately believe that the “predict-then-act” approach to science in support of climate change adaptation is significantly flawed. Efforts to justify renewed investments in climate models based on promises of guiding decisions are misplaced. They believe that society will benefit more from having a greater understanding of the vulnerability of climate-influenced decisions in the face of large irreducible uncertainties, and the various means of reducing such vulnerabilities, than from any plausible and foreseeable increase in the accuracy and precision of climate predictions.

In my book “Applied Agrometeorology”, that may be expected to appear in December 2009 or January 2010, Nathaniel Logar argues that since 1989 the US spent approximately 2 billion dollars per year on climate change research that purportedly aimed to support decisions. However, there is little evidence that it produced a commensurate amount of usable information.

In line with Dessai et al. above, also Logar argues that a large part of the possible complications is an artefact of inaccurate statements being made by modelers, but another contributing factor stems from policy makers funding predictive models, because they expect them to answer important policy questions, but not necessarily funding the right modeling to do so.

The author also reveals that after study of the climate research programs within the US Agricultural Research Service (ARS), it is evident, as one ARS staff member said, that “global climate change is a Washington DC policy maker issue, not a farm issue”. Part of the reason of this problem, Logar states, and for many problems in prioritization and execution of public science, is that views of the people who are supposed to be benefiting from the science are not sufficiently taken into account.

One should therefore also sceptically encounter the “World climate services framework” agreed at the recent World Climate Conference in Geneva, when for agricultural production the emphasis will be on classical climate forecasting. With strong climate signals such as ENSO and La Nina this may work in some parts of the world, when very high probabilities may be expected, but in other cases and on smaller scales there is no future. There are very often much more important and to the point agrometeorological services than climate forecasting, as we also show in “Applied Agrometeorology”. We make clear that only in a very wide interpretation of meteorological and climate services in agriculture would such a framework make sense.

A very convincing example of this was already given by early 2008 by the Institute of Science in Society in a lengthy paper with many examples, that is freely available on the website of “Doctors for the Environment Australia (DEA)” ( For years, many scientists have been making dire predictions of widespread irreversible desertification in the African Sahel. But recent findings have proven them wrong. Satellite images consistently show an increase in “greenness” since the 1980s over large areas, confirming evidence on the ground indicating that the Sahel is recovering from the great droughts of the 1980s, and that human factors have played a large role in reclaiming the desert.

That paper indicates with convincing examples from Africa that scientists, policy-makers and aid workers must recognize the overriding importance of local knowledge and ingenuity for innovation, as well as of cooperative community networks for solving problems of survival in times of climate change. The rural response to climate change that we are after (see my previous homepage) already started with the knowledge and initiatives of local farmers.

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