INSAM - International Society for Agricultural Meteorology
A recent editorial in “Nature” (19 September 2012) had the title “Extreme weather. Better models are needed before exceptional events can be reliably linked to global warming” [http://www.nature.com/news/extreme-weather-1.11428]. After some introduction it concludes first that lawyers, insurers and climate negotiators are watching with interest the emerging ability, arising from improvements in climate models, to calculate how anthropogenic global warming will change, or has changed, the probability and magnitude of extreme weather and other climate-related events. But to make this emerging science of “climate attribution” fit to inform legal and societal decisions will require enormous research effort.
“Attribution” is the attempt to deconstruct the causes of observable weather and to understand the physics of why extremes such as floods and heat waves occur. “But”, the editorial states, “extreme weather and changing weather patterns - the obvious manifestations of global climate change - (……) usually have complex causes, involving anomalies in atmospheric circulation, levels of soil moisture and the like. Solid understanding of these factors is crucial if researchers are to improve the performance of, and confidence in, the climate models on which event attribution and longer-term climate projections depend”.
“Event attribution”, it continues, “is one of the proposed “climate services” - seasonal climate prediction is another - that are intended to provide society with the information needed to manage (I prefer “cope with” KS) the risks and costs associated with climate change. Advocates of climate services see them as a counterpart to the daily weather forecast. But without the computing capacity of a well-equipped national meteorological office, heavily model-dependent services such as event attribution and seasonal prediction are unlikely to be as reliable (or even as unreliable, at times KS)”.
I wonder why we hear this kind of caution so much less than the opposite. In an earlier attempt to show the complications, I quoted the Asian Pacific Climate Centre (APCC) in http://www.agrometeorology.org/topics/accounts-of-operational-agrometeorology/apcc-guidance-questions-and-our-replies-related-to-handling-seasonal-climate-predictions-with-farmers saying: “Climate and agriculture are inextricably linked, as climate and weather are key factors in agricultural productivity. Advance climate forecasts allow agricultural producers to make better informed decisions, plan for contingencies, exploit favorable weather conditions, and minimize risk. Learning to adapt to present-day climate variability by integrating climate information into current decision-making processes lays a critical foundation for coping with long-term climate change”.
I noted: “This statement sounds a bit as if such advance climate predictions are easily made. But take the case of the isle of Java (Indonesia, one of the places I live and work each year KS). The advantage here is that there is a strong climate signal but we have influences from (i) ENSO. There are no two El-Nino’s the same. Cold and warm ENSO phases have asymmetric amplitudes and durations. There are big changes of these amplitudes with phases of two to three decades. But this Pacific Decadal Oscillation (PDO) can also be found in that part of the Pacific that is not in the tropics. And then there are (ii) the “Walker Circulation”, changing with the ENSO but having great influence on the ENSO, we have (iii) the “Trade Winds” of which the forces change during the ENSO, and we also have (iv) the “Madden-Julian Oscillation”, that brings large differences in intra-seasonal behavior. Add to this that there must be still some unknown teleconnections and the increasing indications that the ENSO is changing with global warming, and it is clear that seasonal climate predictions need a long way to go to become better, that is “made with more skill”, than they are at present”. This final conclusion is in line with the recent “Nature” discussion and both warn for the scientifically unacceptable forgetfulness of error limits and uncertainties in forecasts and predictions.
Another example of this uncritical “easy going” is in the way people talk about regional or even local downscaling of larger scale climate predictions. I would advise you to read Prof. Roger Pielke Sr. and Prof. Robert Wilby’s communication “Regional Climate Downscaling: What’s the Point?” in an Eos issue of last year [Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008. http://pielkeclimatesci.files.wordpress.com/2012/02/r-361.pdf]. This is no easy reading, at least not for this old man. Let’s give some conclusions.
“There is also an assumption that although global climate models cannot predict future climate change as an initial value problem, they can predict future climate statistics as a boundary value problem. However, for regional downscaling (and global) models to add value (beyond what is available to the impacts community via the historical, recent paleorecord and a worst-case sequence of days), they must be able to skillfully predict changes in regional weather statistics in response to human climate forcings. This is a greater challenge than even skillfully simulating current weather statistics. It is therefore inappropriate to present “type 4” results (shown as the best possible approach in that paper KS) to the impacts community as reflecting more than a subset of possible future climate risks. Alongside the special uses of “type 4” downscaling (noted above), we favor a bottom-up, resource-based vulnerability approach to assess the climate and other environmental and societal threats to critical assets. This framework considers the coping conditions and critical thresholds of natural and human environments beyond which external pressures (including climate change) cause harm to water resources, food, energy, human health, and ecosystem function. Such an approach could assist policy makers in developing more holistic mitigation and adaptation strategies that deal with the complex spectrum of social and environmental drivers over coming decades, beyond carbon dioxide and a few other greenhouse gases”. Interesting references on this last approach are given there.
Those of you working or planning to work (or just interested in the progress) in the regional or local climate downscaling practice, think about this proposed upscaling approach instead. Because it starts where the problems are or may occur, and it tries to reach the nearest cause and effect relationships and statistics to connect with atmospheric reality at the various scales, in the sequence from local to regional to global.