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Politics : Politics of Energy -- Ignore unavailable to you. Want to Upgrade?

To: Wharf Rat who wrote (50819)	5/6/2014 11:42:46 PM
From: Bilow	1 Recommendation Recommended By Brumar89 Read Replies (1) \| Respond to of 86356

Hi Wharf Rat; Re: "I think it [presumably sea level rise by 2100 as that's what the topic is] will be 4-6 feet."; Failing their 1998 to 2014 air temperature predictions has pretty much left climate science bankrupt. As I pointed out on the NASA funded website, current sea level rise is about 3mm per year and extending that linearly gives about 1 foot by 2100. But that's assuming that the growth remains linear. In fact, the previous history of planet warming is one of long cycles with periods of 60 years or much longer. And we're now at the end of a 60-year cycle. So the 3mm/year rise we've seen recently is about half caused by the most recent bump in the pacific decadal oscillation and we will return to the average growth seen over the last 100 years of around half that. This predicts that sea level rise would be about 6 inches by 2100. It's a matter of "how do you make predictions?" Your side makes predictions assuming quadratic (or even exponential) growth. My side believes in linear or even sinusoidal predictions. You don't believe that my side is represented in the literature. In fact you're unaware of it because your side ignores all evidence to the contrary. So here's a paper that compares the sinusoidal and quadratic extrapolations of sea level rise: Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO and NAO indexes N. Scafetta April 2013, Climate Dynamics ... Because of the presence of a quasi 60-year oscillation, a background secular acceleration (e.g. one that could be caused by the twentieth century anthropogenic warming) can be properly evaluated only for tide gauge records longer than about 110 years, as Fig. 10 a2 implicitly shows. If only shorter periods are available, separating a background acceleration from a 60-year oscillation is quite difficult because the record would not be sufficiently long to determine the correct amplitude and phase of the oscillation, which are required to separate the oscillation from a background quadratic polynomial term. More precisely, if the time interval to be fitted is significantly shorter than about 2 periods of the oscillation, a quadratic polynomial constructor would be too collinear with a sinusoidal oscillation, and in collinearity cases the linear regression algorithm becomes highly unreliable. The test depicted in Fig. 11 demonstrates that the minimum length that a record must have for making a quadratic polynomial constructor fully orthogonal to a sinusoidal curve is k = 1.8335 the period of the oscillation: that is, a 110-year long record is necessary for fully filtering out a 60-year periodic cycle from a background quadratic polynomial trend. In general, as Fig. 11 d implies, in the case of a record characterized by a 60-year cycle, using 100-year or longer records would be sufficiently fine, but using, for example, 60-year long or shorter records (e.g. as done in Sallenger et al. 2012 and in Boon 2012) can be highly misleading because the quadratic fit would interpret the bending of the 60-year oscillation as a strongly accelerating trend. link.springer.com Now your side already screwed up with its quadratic predictions by missing the "pause" in air temperatures. This proves that your models don't work; the best you can provide is a curve fit. But the pause fits nicely into a sinusoidal curve fit based on the long oscillations seen in the pacific and atlantic oceans. As time goes on, the difference between quadratic and sinusoidal grows and your side will be even more wrong than you are now. From the above paper, a graph shows the problem well: It's like your side has just noticed that temperatures are a lot warmer now in, for example, Seattle, than they were 3 months ago. From this you predict that temperatures in Seattle will be vastly warmer 3 months from now. And you'd be right! Your predictions were perfect! But then, inevitably, the fall and winter arrive and you're seen to be an idiot who used a quadratic function to predict the behavior of a sinusoidal function. -- Carl