Concerning #5, Nuclear power clearly lies in the future. But whether we should be investing now in gen 3 (e.g the Westinghouse AP 1000 design), or investing now in USC coal, CCGT, and nuclear research into Gen 4 designs for the future is an unresolved energy policy question. With the new observational estimates of TCR, it appears that China’s choice of primarily USC coal now plus gen 4 nuclear research ( they are building the world’s first pilot scale LFTR) is a wise one. Taking Xi’s commitment to Obama at face value (yes, a stretch) suggests China will be ‘going nuclear’ by 2030 after its gen 4 nuclear research matures.
For several gen 4 possibilities that appear underfunded in the US see essay Going Nuclear in Blowing Smoke: essays on energy and climate. They could all be funded from existing research budgets to at least pilot scale (including the Skunkworks high B modular fusion invention) by simply cancelling the funding for the NIF and ITER fusion boondoggles highlighted in the essay.

“Laissez les bon temps roules!!!” The world would be a very dull place if youse could agree.

JFP –

==> “Joshua, an example of why I say they aren’t measureable. Take the particulate matter emitted that causes asthma and other problems in large amounts. What can’t be said is how much that individual, who suffered, benefited from the goods and services in the manner that we can compute the cost of energy, the cost of cleaning particulates, the medical cost of treatment, the amount of profit generated from the emission of the particulates. This has not been measured, and is argued it cannot be measured.”

Again, we come to whether something can be measured as compared to whether it can be estimated. As with relevant to w/r/t global temps, we can always argue that our estimates are not sufficiently accurate. What is the objective exclusion criterion for accuracy?

If we just throw our hands up and say “It can’t be measured,” and then go on to project costs versus benefits ratios for various energy supply pathways, then we are assuming a net neutral or positive ratio for benefits versus costs for the status quo of fossil fuels for our energy supply. So then in making such an argument, you are essentially saying that you can make a valid estimate. But how can you say that we can’t estimate the cost verus benefit ratio of externalities and then say, with a sense of certainty, that there is a relatively higher benefits versus ratio of fossil fuels?

Seems to me it is better to either (1) try to estimate the cost vs. benefits ratio of externalities or, (2) say that they can’t be measured and, accordingly, stick with the view that this is about decision-making in the face of uncertainty.

==> ” This externality is just one, and as I show above, costs can be shown, but we do not measure externalities, and may not be able to give a “complete” enough picture.”

How do you determine what is “complete enough?”

==> “That is the basis of my comparison about temperatures. Assumption, not only to the amount, but to the worth, not only have to be made, these assumptions comprise a part of externalities not captured.”

Well, that is my point. Assumptions are being made. Then how can you say that there is no evidence-based methodology for estimating?

==> ” Without accounting for the social benefits, many of which are diffuse and only measured in the most general sense, such as the benefit that cheap available electricity has had on medical advances, and general health, it is a unattainable requirement.”

But I agree with this. The ratio of costs versus benefits is what I am focusing on.

==> “but rather that there is a limit to our knowledge.”

There, again, we are in agreement.

==> “The other point, that seems to be missed, is that with the intermittency of renewable derived energy which requires fossil fuel or more expensive back-up, not only are not all mega watts equal, but there is a legitimate question as to whether renewable derived energy is not a net increase in CO2 emissions.”

I accept all of that..

==> “The simplistic accounting that all have to use does not take into account the support of the manufacturing chain that is needed to develop, make, and sustain renewable derived energy, much less what is presently used for over and under production.”

Of course.

==> “I would just ask that if you would require a burden of proof, you require it for all”

Again, agreed. I thought I made that clear. It seems to me that both sides have a burden of proof. That is why I reject the argument that “your side has the burden of proof” – as I said above.

==> “The research I did on the Wyoming and EU wind derived energy was that it appeared break even at best in CO2 reduction, but cost about 60% more for the same output at best scenario which is 7% penetration. The difference in my numbers and those put out by the EU study and the Wyoming study was to include the cost of non-existent software and hardware. Without these, the CO2 cost is much more for wind derived energy than fossil fuels due to economic loss of usable electric devices that cannot that the under or over voltage wind derived energy causes, or by putting the CO2 inefficiencies caused by ramping up and down, where they should be…on the wind derived source.”

I am not questioning specific estimation for specific contexts. I am certainly not able to evaluate those estimations.

==> “The rabbit hole PE opened up by a too general statement, I can’t defend.”

That was my point.

Thank you, Dr. Morton, for this learned commentary. A few months ago I reviewed an article on the same topic by Cato Institute scientists Patrick Michaels and Chip Knappenberger. They looked at how well IPCC models would match observations over the 80-year period from 1951 to 2030 under three scenarios of how global surface temperature might behave between now and 2030.

In Scenario 1, the “plateau” continues and the warming rate in 2014-2030 remains what it was in 2001-2013 (essentially zero). In Scenario 2, warming resumes at the long-term 1951-2012 rate (0.107ºC/decade). In Scenario 3, warming resumes at the 1977-1998 rate (0.17ºC/decade) — the rate from the start of recent global warming until the plateau. They found that even in the warmest scenario, fewer than 5% of model simulations of the long-term, 80-year trend agree with observations by 2020 and fewer than 2.5% agree by 2030. Their analysis is posted here: http://www.cato.org/blog/clear-example-ipcc-ideology-trumping-fact

What, I wondered, would be the result if warming resumes at 0.265ºC/decade — the rate during 1984-1998, which the IPCC identifies as the 15-year period with the most rapid warming?

I asked Mr. Knappenberger to test the models’ agreement with long-term observations using an alternative scenario in which warming resumes at 0.265ºC/decade. He kindly obliged.

Here’s what he found. If warming resumes at the 1984-1998 rate, the models never reach outright statistical failure (<2.5%). Nonetheless, they perform very poorly, matching observations less than 5% of the time in 2020 and continue to do so through 2030. My blog post provides a bit more detail and helpful graphics: http://www.globalwarming.org/2014/08/17/can-natural-variability-save-climate-models/

Thanks for the great essay Dr. Morton

John Pittman – please don’t be swayed by Joshua’s highlighting of my statement and stretching it well out of context.

==>…”your point should seem obvious, that huge gaps will not be swayed by vague and distant externalities” He seemed to think I was dismissing all externalities as well as claiming the ability to do it in my head.

I would not in any way ever characterize all externalities as vague and distant. Certainly there is a role for evaluating externalities and externalities that demand careful attention. But beyond the list of reasonable externalities there are always more vague and distant ones that will not be worth further pursuit. There has to come a point when you look at the gap and the remaining pool of “potential” externalities make some assumptions around their maximal impacts and say enough is enough.

Nice essay, good job hitting many of the big points.
Of course, even if we had a perfect model, that model would have to be discretized to solve, and the error in integrating such a nonlinear system forward in time would be extremely challenging, most likely impossible.
“But the averages are correct even if the solution isn’t,” the claim goes…. And the mathematical theory for why this is so? Nonexistent, because its not true.

PA – thanks for your detailed analysis. It has helped expand my knowledge and focus my thinking. This has been a good thread; at least for me.

Wagathon said

‘….and, the EU falling off a cliff and becoming a Russian puppet state by then will either be a sobering wake-up call to the rest of the West or simply be looked at as Eurocommie government planning ending up as planned: out of gas.’

What??!!!

tonyb

Joshua – I am very concerned by the way you twist things. That’s a big reason why I have to question your good faith efforts here. You take a quote and interpret it thusly:

Planning Engineer”…your point should seem obvious, that huge gaps will not be swayed by vague and distant externalities”

Joshua

My use of obvious obviously refereed to Peter’s point – not externalities. Further I am agreeing with Peter’s point, not endorsing his example, if that incorrectly had some weight in your mind. It’s s a stretch to think I’m referring to an and all possible externalities. Why do you go there?

The quote you “wanted” me to make, is that “All externalities are vague and distant and it is obvious that their impacts on cost benefit analysis can be summarily dismissed” . Read the two sentences-they are quite different. I disagree strongly with the second one and very much resent your disabuse of my original quote.

Joshuas quote got left out (guess it did not like the brackets)

So PE is saying that he can estimate the externalities, and know their impact on the cost/benefit analysis. In fact, he says, it is obvious.

Pin point accuracy ? What a straw man ! In any case correlation
with observations is waaaaay out. But never the less guvuhmints squander $$$$$$ on pseudo – science hockey stick models. Not
‘evidence – based – policy’ but’ policy – based – evidence.’

Tony,

I suspect there is a tendency for people to read too much into the results of models. Also, there may be a tendency to expect too much.

“1. Ocean heat content is extremely unresponsive to policy.

While the increase in global temperature could indeed be stopped within decades by reducing emissions, ocean heat content will continue to increase for at least a thousand years after we have reached zero emissions. Ocean heat content is one of the most inert components of the climate system, second only to the huge ice sheets on Greenland and Antarctica (hopefully at least – if the latter are not more unstable than we think).”

http://www.realclimate.org/index.php/archives/2014/10/ocean-heat-storage-a-particularly-lousy-policy-target/#sthash.WVQ7QVqT.dpuf

Something not bat crazy from realclimate, wow.
To say ocean content is extremely unresponsive to policy is precisely
correct. And to tie Ocean heat content to the other unmovable elements
of the polar caps, makes wonder if the guys have lowered their consumption of recreational drugs.
Polar caps are not only a similar “problem” but they tied to ocean content.
Or you can’t significantly affect one without affecting the other- and of course, neither can affected by the puny human influence.
Though I see they are still concerned by our future thousands of year in future, which is of course adorable.
And thousands of years is also good clue to remind some that it took thousands of years in last intergalactic to get the ocean warmer than they are today. It one thing to hope within a century or two that might get back to MWP type warm conditions- grapes growing in UK and farming in Greenland- but such warming would have little affect upon the ocean’s temperature.

Models have at their core a set of non-linear equations. Much as Lorenz’s convection model did. From there it is a modest step to the idea of sensitive dependence – perfectly deterministic but seemingly random regime shift in the words of Julia Slingo and Tim Palmer.

Or indeed James McWilliams.

‘Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable. They echo other famous limitations on scientist’s expectations, namely the undecidability of some propositions within axiomatic mathematical systems (Gödel’s theorem) and the uncomputability of some algorithms due to excessive size of the calculation.’

http://www.pnas.org/content/104/21/8709.full

So these models are chaotic in the sense of complexity theory – and unless we get beyond mere definitional issues to the widespread understanding that it is so – then there is nothing left to say.

Climate is chaotic – because it shifts abruptly. ‘What defines a climate change as abrupt? Technically, an abrupt climate change occurs when the climate system is forced to cross some threshold, triggering a transition to a new state at a rate determined by the climate system itself and faster than the cause. Chaotic processes in the climate system may allow the cause of such an abrupt climate change to be undetectably small.’ http://www.nap.edu/openbook.php?record_id=10136&page=14

Getting to an idea of what that means for the real system – is the problem.

‘‘Prediction of weather and climate are necessarily uncertain: our observations of weather and climate are uncertain, the models into which we assimilate this data and predict the future are uncertain, and external effects such as volcanoes and anthropogenic greenhouse emissions are also uncertain. Fundamentally, therefore, therefore we should think of weather and climate predictions in terms of equations whose basic prognostic variables are probability densities ρ(X,t) where X denotes some climatic variable and t denoted time. In this way, ρ(X,t)dV represents the probability that, at time t, the true value of X lies in some small volume dV of state space.’ (Predicting Weather and Climate – Palmer and Hagedorn eds – 2006)

Fundamentally – a probability density function of a family of solutions of a systematically perturbed model – rather than an ensemble of opportunity.

‘In each of these model–ensemble comparison studies, there are important but difficult questions: How well selected are the models for their plausibility? How much of the ensemble spread is reducible by further model improvements? How well can the spread can be explained by analysis of model differences? How much is irreducible imprecision in an AOS?

Simplistically, despite the opportunistic assemblage of the various AOS model ensembles, we can view the spreads in their results as upper bounds on their irreducible imprecision. Optimistically, we might think this upper bound is a substantial overestimate because AOS models are evolving and improving. Pessimistically, we can worry that the ensembles contain insufficient samples of possible plausible models, so the spreads may underestimate the true level of irreducible imprecision (cf., ref. 23). Realistically, we do not yet know how to make this assessment with confidence.’ http://www.pnas.org/content/104/21/8709.full

Of course if we just listened to Maxy’s cr@pola – we wouldn’t need to understand actual science.

Max

I think we agree that people read too much into the precision of temperature reconstructions and the accuracy of models.

However, on the output from those we are spending many billions on trying to change the world and as many billions in payments to third world countries to follow our lead.

I can think of very many better things to do with that money although one of those better things would be to create a more robust and secure alterative energy system that meant we did not have to rely on suppliers that hated us or wanted to directly influence us or manipulate us i.e Russia and many of the Arab States.

Tonyb

It remains to ask what rate of warming there is to return to. The following shows the results of numerical analysis of breakpoints in atmospheric and oceanic indices.

‘Synchronization as measured by the root-mean-square correlation coefficient between all pairs of modes over a 7-year running window. Note the reversed ordinate; synchronization increases downward in the plot. High synchronization at the p = 0.95 level is denoted by shading, tested by generation of surrogate data as described by Tsonis et al. [2007]. (middle) Coupling as measured by the fraction of consistently increasing or decreasing mode time series as described in the text. The shaded region denotes coupling at the p = 0.95 level as calculated from the surrogate data used for the confidence intervals in Figure 1 (top). (bottom) HadCRUT3g global mean temperature over the 20th century, with approximate breaks in temperature indicated. The cross-hatched areas indicated time periods when synchronization is accompanied by increasing coupling.’ http://onlinelibrary.wiley.com/doi/10.1029/2008GL037022/full

It is no coincidence that shifts in ocean and atmospheric indices occur at the same time as changes in the trajectory of global surface temperature. Our ‘interest is to understand – first the natural variability of climate – and then take it from there. So we were very excited when we realized a lot of changes in the past century from warmer to cooler and then back to warmer were all natural,’ Tsonis said.

Four multi-decadal climate shifts were identified in the last century coinciding with changes in the surface temperature trajectory. Warming from 1912 to 1944, cooling to 1976, warming to 1998 and declining – or at least not rising – since.

It provides the beginnings of a rational starting point to disentangle anthropogenic forcing from natural variability. The temperature rise between 1944 and 1998 was 0.4K at 0.07K/decade. Starting elsewhere – outside of the regimes of natural warming and cooling – seems a trifle arbitrary.

Nor is it likely – all other things being equal – that the warming will continue – eventually perhaps – at this extreme rate as the Sun cools from a 1000 year high. And the ENSO shifts – coincidentally – from a 1000 year El Nino frequency and intensity peak to the more common La Nina dominance.

But you have to have some basis for planning, and though the future can’t be predicted with absolute certainty, a range of possibilities can help steer you in the right direction.

Max

I would plan against the possibility of warming AND the much greater threat to civilisation of cooling.

I would also have as my priority other unrelated matters which warrant much greater attention such as the threat from a natural Carrington event or the possibility of concerted cyber terrorist attacks. Both of these could knock out our highly vulnerable electronic infrastructure on which we totally rely, which in turn would cause collapse in a few days.

tonyb