You’ll wonder where the warming went
when you brush your models with excrement
Climate models just aren’t good enough – yet.
As real observations increasingly diverge from model results, the global warming establishment is reacting in 2 ways:
- Denial by the Warmist orthodoxy who prefer model results to real data , and
- Real scientists who have begun to questions the assumptions on which these models are based.
Two articles have recently been published in the mainstream scientific literature which question climate models.
1. What Are Climate Models Missing?, Bjorn Stevens and Sandrine Bony, Science, 31 May 2013, Vol. 340 no. 6136 pp. 1053-1054 , DOI: 10.1126/science.1237554
Abstract: Fifty years ago, Joseph Smagorinsky published a landmark paper (1) describing numerical experiments using the primitive equations (a set of fluid equations that describe global atmospheric flows). In so doing, he introduced what later became known as a General Circulation Model (GCM). GCMs have come to provide a compelling framework for coupling the atmospheric circulation to a great variety of processes. Although early GCMs could only consider a small subset of these processes, it was widely appreciated that a more comprehensive treatment was necessary to adequately represent the drivers of the circulation. But how comprehensive this treatment must be was unclear and, as Smagorinsky realized (2), could only be determined through numerical experimentation. These types of experiments have since shown that an adequate description of basic processes like cloud formation, moist convection, and mixing is what climate models miss most.
2. Emerging selection bias in large-scale climate change simulations, Kyle L. Swanson, Geophysical Research Letters, online 16th May 2013, DOI: 10.1002/grl.50562
Abstract: Climate change simulations are the output of enormously complicated models containing resolved and parameterized physical processes ranging in scale from microns to the size of the Earth itself. Given this complexity, the application of subjective criteria in model development is inevitable. Here we show one danger of the use of such criteria in the construction of these simulations, namely the apparent emergence of a selection bias between generations of these simulations. Earlier generation ensembles of model simulations are shown to possess sufficient diversity to capture recent observed shifts in both the mean surface air temperature as well as the frequency of extreme monthly mean temperature events due to climate warming. However, current generation ensembles of model simulations are statistically inconsistent with these observed shifts, despite a marked reduction in the spread among ensemble members that by itself suggests convergence towards some common solution. This convergence indicates the possibility of a selection bias based upon warming rate. It is hypothesized that this bias is driven by the desire to more accurately capture the observed recent acceleration of warming in the Arctic and corresponding decline in Arctic sea ice. However, this convergence is difficult to justify given the significant and widening discrepancy between the modeled and observed warming rates outside of the Arctic.
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