By J. Richard Wakefield
20 June, 2016.
In part two of this report we will look into the assumptions used in other reports to justify what the Council of Middlesex Center used to make their decision of a user fee for storm water infrastructure funding.
When asked of Council what they based their decision on, I was sent this link. That report is about justifying having user fees pay for storm water infrastructure:
In 2007, the Federation of Canadian Municipalities estimated that the stormwater management infrastructure deficit across Canada stood at approximately $31 billion.
This paper evaluates the financial tools available to fund stormwater infrastructure (property taxes, development charges or cash-in-lieu payments, grants, borrowing, and user charges), and proposes user charges as the most appropriate. User charges are fees earmarked to specific projects or services. They are based on a benefits-received principle, and are considered a fair form of revenue, because the beneficiaries of a service are directly charged for their consumption of that service. Further, user charges are a dedicated and stable funding source based on clear objectives related to the city’s stormwater infrastructure needs. None of the alternative funding tools offers the same combination of stable and predictable revenues and fair pricing.
On page 3 they make this claim:
A report by the Intergovernmental Panel on Climate Change states that, on average, temperatures in Canada increased by more than 1.3 degrees Celsius between 1948 and 2007 – a rate of warming twice the global average.30 Warmer temperatures have led to more violent weather, such as more frequent and intense rainstorms.31 Floods and storm surges represent some of the most costly climate change-related weather events.32
Later we will examine the first claim, that Canada temperatures have increased. But what we will look at first is reference 31 cited, which contends that there is more violent weather, more intense storms. We have already seen in Part One that claim is absolutely false. But what we need to do is to look in detail at the reference itself. What is the underlying assumptions used to justify its suggestion of a user fee?
This is the reference cited:
G.R.A Richardson, Adapting to Climate Change: And Introduction for Canadian Municipalities, Natural Resources Canada, 2010, 3; Angela Peck, Pat Prodanovic, and Slobodan P. Simonovic, “Rainfall intensity duration frequency curves under climate change: City of London, Ontario, Canada,” Canadian Water Resources Journal 37(3), 2012, 179.
Unfortunately, the reference is behind a pay wall. But this is what is in the abstract:
A non-parametric K-Nearest Neighbour weather generator (WG) algorithm is used to synthetically create long time series of weather data. Nine daily maximum rainfall datasets (5, 10, 15, 30 minutes, 1, 2, 6, 12, and 24 hour) collected from the London Airport station for the period 1961–2002 are used as input into the WG. The WG uses sophisticated shuffling and perturbation mechanisms to generate synthetic rainfall records similar (but not identical) to the observed historic record.
So what is a “sophisticated shuffling and perturbation mechanisms to generate synthetic rainfall records?” It’s a computer model. Here is another example of a “sophisticated” computer model:
Looks real doesn’t it. Computers have gotten so powerful that in the film industry they have completely replaced special effects. But this virtual world exists only in computer programs. There aren’t any dinosaurs on an island.
The point is, just because a computer program shows something to appear realistic doesn’t mean it is modeling the real world. Sure, simulators are all over today, especially in the airline industry. Pilots “get their wings” using sophisticated flight simulators.
But aeronautical physics is no where near climate physics.
The climate is a chaotic system. In this 2010 science reference the non-linear and chaotic nature of the climate system is studied:
Atmospheric flows, an example of turbulent fluid flows, exhibit fractal fluctuations of all space-time scales ranging from turbulence scale of mm -sec to climate scales of thousands of kilometers – years and may be visualized as a nested continuum of weather cycles or periodicities, the smaller cycles existing as intrinsic fine structure of the larger cycles. The power spectra of fractal fluctuations exhibit inverse power law form signifying long – range correlations identified as self – organized criticality and are ubiquitous to dynamical systems in nature and is manifested as sensitive dependence on initial condition or ‘deterministic chaos’ in finite precision computer realizations of nonlinear mathematical models of real world dynamical systems such as atmospheric flows. Though the selfsimilar nature of atmospheric flows have been widely documented and discussed during the last three to four decades, the exact physical mechanism is not yet identified. There now exists an urgent need to develop and incorporate basic physical concepts of nonlinear dynamics and chaos into classical meteorological theory for more realistic simulation and prediction of weather and climate. A review of nonlinear dynamics and chaos in meteorology and atmospheric physics is summarized in this paper.
Notice the cycles referred to, both short term and long term cycles dominate the climate system. In fact, this paper shows that:
The above observational and modeling results suggest the following intrinsic mechanism of the climate system leading to major climate shifts. First, the major climate modes tend to synchronize at some coupling strength. When this synchronous state is followed by an increase in the coupling strength, the network’s synchronous state is destroyed and after that climate emerges in a new state.
What this means is that the future climate is unknowable, and unpredictable. As different cycles converge, it can shift the climate system in a new direction. This paper suggests that the “global warming” we have seen since the 1970’s is just one of these climate shifts. Once it reaches another set of coinciding cycles, it will shift again into a different direction (global cooling?).
Everything goes in cycles. Nothing is linear and non-cyclic.
Computer programs, no matter how sophisticated they are in their programming, can’t in any way predict the future for the simple reason that the climate is cyclic and chaotic. As we saw in the first Jurassic Park movie, any slight change in initial conditions can have profound and radical differences in the end result in the future.
Observations Now Inconsistent with Climate Model Predictions for 25-35 Years; ‘Basically, the models don’t work’
And this damning report that climate scientists don’t even understand the difference between variation in their models and error of measurement:
So, once again, climate modelers:
- neither respect nor understand the distinction between accuracy and precision.
- are entirely ignorant of propagated error.
- think the ± bars of propagated error mean the model itself is oscillating.
- have no understanding of physical error.
- have no understanding of the importance or meaning of a unique result.
No working physical scientist would fall for any one of those mistakes, much less all of them. But climate modelers do.
This author says that climate modelers are not scientists.
This study is alarming in its self:
To summarize, it looks like something like 55% of the modeling done in all of science is done in climate change science, even though it is a tiny fraction of the whole of science. Moreover, within climate change science almost all the research (97%) refers to modeling in some way.
This means that climate science exists only in computer models. It is absolutely important to understand that climate models are not evidence. They are “what if” computer scenarios that have nothing to do with the real world.
At least in the movie industry, if a dinosaur’s shadow doesn’t look right, they can change the programming to line up with what people know what the real world is. But climate modelers can’t because, deep down, they really don’t know what the climate is supposed to do. Year after year, new information is being discovered about what the climate does, which is not captured in their models. See here, here and here.
For a government to make any policies that rely on computer simulations of the climate will find that, when the future becomes the present, they were completely duped.
In Part Three we will look more into the climate change scenarios, and other references this municipality used to make their decision.