There is an article in the Economist that provides a summary of yet another climate forcing (in this case a natural climate forcing) h/t to Don Bishop
The article reads [highlight added]
Those unconvinced—and those seeking to unconvince others—of the reality of man-made global warming often point to the changeable behaviour of the sun as an alternative hypothesis. A new study showing how the severity of winters in Europe, and warming in the Arctic, might be linked to changes in solar activity might seem to add to this case. In itself, it does not, for the heat (or, rather, the cold) in question is being redistributed, not retained. But it does point to two other lessons about climate change: that hard data on the factors which affect it are sometimes difficult to come by; and that computer models of the climate can be quite impressive tools for working out what is going on.
The sun’s activity waxes and wanes on an 11-year cycle, and over this cycle the amount of ultraviolet (UV) light the sun emits changes a lot more than does the total amount of energy. The stratosphere, the part of the Earth’s atmosphere which does most to absorb UV, might thus be expected to be particularly sensitive to the cycle.
In a paper just published in Nature Geoscience, Sarah Ineson of Britain’s Meteorological Office and her colleagues compared the way that the Met Office’s new and putatively improved climate model dealt with winters at times of high UV and at times of low UV, using data on the amount of ultraviolet the sun gives off that were collected by a satellite called SORCE.
Dr Ineson found that at low UV levels the stratosphere in the tropics was cooler, because there was less UV for it to absorb, which meant the difference in temperature between the tropical stratosphere and the polar stratosphere shrank. That changed the way the atmosphere circulated, and as those changes spread down into the lower atmosphere they made it easier for cold surface air from the Arctic to come south in winter, freezing chunks of northern Europe. These conditions looked similar to those seen in the past two cold European winters—which occurred at a time of low solar activity. The Arctic itself, in models and in real life, was warmer than usual, as were parts of Canada. In contrast, northern Europe, swathes of Russia and bits of America were colder.
Why had this solar effect not been seen before? To some extent it had. Earlier modelling of a period of prolonged low solar activity in the 17th and 18th centuries showed similar patterns.That models of today’s climate had not was, in part, because they used much lower estimates of the amount of UV variation over the solar cycle than those derived from the SORCE data, the most precise to be taken from a satellite looking at the sun. It may just be that working with more realistic data made the model work better.
This does not mean the question is settled. Some scientists suspect the SORCE data may be exaggerating the sun’s variability, and if they were revised the link might go away. There are other theories around seeking to explain the recent cold winters, too. Improving predictions of future cold winters on the basis of this work, as the researchers say they would like to do, may thus prove hard.
But though global warming has made people look to models as predictors of the future, that is not their strongest suit. Something they can do much better is look at what happens when a variable such as UV is altered, compare that with the data, and thus gain insight into the mechanisms by which climate works. This new research provides a good example of what such an approach can achieve.
This study also highlights the distintion between the use of the global climate models in process studies as contrasted with their use to provide multi-decadal predictions. I discuss this in my post
where I wrote
Climate models are comprised of fundamental concepts and parameterizations of physical, biological, and chemical components of the climate system, expressed as mathematical formulations, and then averaged over grid volumes. These formulations are then converted to a programming language so that they can be solved on a computer and integrated forward in discrete time steps over the chosen model domain. A global climate model needs to include component models to represent the oceans, atmosphere, land, and continental ice and the interfacial fluxes between each other. Weather models are clearly a subset of a climate model (a discussion of mesoscale weather models is given in Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp), where the basic framework of all scales of weather models is presented). On the global scale, it is very important to distinguish global atmospheric-ocean circulation models (AOGCMs) from global climate models. Global climate models need to include all important components of the climate system as discussed in a 2005 National Research Council report, while AOGCMs up the present have not.
There are three types of applications of these models: for process studies, for diagnosis and for forecasting.
Process studies: The application of climate models to improve our understanding of how the system works is a valuable application of these tools. In an essay, I used the term sensitivity study to characterize a process study. In a sensitivity study, a subset of the forcings and/or feedback of the climate system may be perturbed to examine its response. The model of the climate system might be incomplete and not include each of the important feedbacks and forcings.
Diagnosis: The application of climate models, in which observed data is assimilated into the model, to produce an observational analysis that is consistent with our best understanding of the climate system as represented by the manner in which the fundamental concepts and parameterizations are represented. Although not yet applied to climate models, this procedure is used for weather reanalyses (see the NCEP/NCAR 40-Year Reanalysis Project).
Forecasting: The application of climate models to predict the future state of the climate system. Forecasts can be made from a single realization, or from an ensemble of forecasts which are produced by slightly perturbing the initial conditions and/or other aspects of the model. Mike MacCracken, in his very informative response to my Climatic Change essay seeks to differentiate between a prediction and a projection.
With these definitions, the question is where does the IPCC and US National Assessment Models fit? Since the General Circulation Models do not contain all of the important climate forcings and feedbacks (as given in the aforementioned 2005 NRC report) the models results must not be interpreted as forecasts. Since they have been applied to project the decadal-averaged weather conditions in the next 50-100 years and more, they cannot be considered as diagnostic models since we do not yet have the observed data to insert into the models. The term projection needs to be reserved for forecasts, as recommended in Figure 6 in R-225.
Therefore, the IPCC and US National Assessments appropriately should be communicated as process studies in the context that they are sensitivity studies. It is a very convoluted argument to state that a projection is not a prediction. The specification to periods of time in the future (e.g., 2050-2059) and the communication in this format is very misleading to the users of this information. This is a very important distinction which has been missed by impact scientists who study climate impacts using the output from these models and by policymakers.
This Economist article thus not only documents another climate forcing, but shows the value of the climate models when used to better understand climate processes, rather than making flawed multi-decadal predictions for policymakers.