GW = AGW = CAGW?

As a non-native English speaker, I often encounter new words. One such word is “equivocation” (using the same word for different things or the use of such word in multiple senses throughout an argument, leading to a false conclusion). The first time I heard about it, I recognized it as something that is frequently used in global warming/climate change communication.

At the end of last week, when searching for something related to the consensus, I landed at the Skeptical Science page titled The 97% consensus on global warming (intermediate version). I am pretty sure that I must have read this before, but having “equivocation” at the back of my mind, gave it a new dimension.

As the title suggests, its subject is the 97% consensus. It starts from the statement of the Petition Project that “there is no convincing scientific evidence that human release of carbon dioxide will, in the foreseeable future, cause catastrophic heating of the Earth’s atmosphere”.

The Skeptical Science author calls this a myth and tackles it by explaining that a consensus of around 95% is found in papers like Cook et al 2013 & 2016, Oreskes 2004, Doran 2009 and Anderegg 2010. Also mentioned are the Vision Prize poll that basically found something similar and a list of scientific organizations that endorse the consensus.

I don’t know much about the Petition Project, but from the excerpt given in the Skeptical Science article, it is clear that the Petition Project statement is very specific. They claim that there is no consensus specifically on the catastrophic nature of global warming caused by human emissions.

Continue reading

Advertisements

The impact of that “exponentially growing” capacity of solar PV and wind on electricity generation in Australia

In previous posts, I several times made the remark that installed capacity is not a good measure to define the success of solar and wind energy. Those remarks were the reaction on the claims of Blakers and Stocks that solar PV and wind energy are “growing exponentially”, that they are “on the path of dominance” when it comes to new capacity and that they “are on track to entirely supplant fossil fuels worldwide within two decades”. The authors also claimed that other low-carbon energy sources would only play a minor supporting role.

The subject of this post will be the impact of this much celebrated new capacity of solar PV and wind when it comes to the actual production of electricity by those sources. I did something rather similar in another post with world data, my guess was that the outcome for Australia would be something rather similar.

Continue reading

When the relevant data doesn’t fit the narrative, just use other data that will and suggest that the relevant data fits even better

A graph that caught my attention in the “100% renewable electricity in Australia” paper by Blakers and Stocks was this one:

It shows growth of the installed capacity of solar PV and wind compared with other energy sources between 2014 and 2016. The remarkable increase of solar energy stood out, it almost doubled in three years time. Wind energy did not do bad either, the increase grew in 2015, dropped a bit in 2016, but nevertheless stayed above the 2014 value.

There is something weird about this graph: there is also an entry “nuclear” and, as far as I know, Australia doesn’t have any nuclear power plants. So this is obviously not the Australian situation.

The relevance of the graph was explained in the paper as (my emphasis):

PV and wind constitute half of the world‘s new generation capacity installed in 2014-16 (Fig. 1). In recent years, these sources provided nearly all new generation capacity installed in Australia.

That PV and wind constitute half of the new generation capacity is rather meaningless since they are comparing intermittent energy sources with dispatchable energy sources. But let’s assume, for the sake of the argument, that this comparison is somehow meaningful. What they apparently want to say is that the installed capacity of solar PV and wind did very well compared to other power sources worldwide (which explains the “nuclear” entry) and that solar PV and wind in Australia were responsible for nearly all of the growth. Therefor suggesting that there is a similar increase for Australia, only much better since solar PV and wind provided most of the new installations there.

The big question then is: why don’t they just use the Australian data to illustrate their case? The subject of the paper is renewables in Australia, yet they illustrate their claim with renewables in the world. More, since Australian solar PV and wind were almost the only generation capacity that increased between 2014 and 2016, the Australian situation should in theory be a much better illustration of what they want to prove.

Continue reading

100% renewable electricity at “low cost”: the more costs are not accounted for, the “cheaper” it will get

It is not unusual in alternative energy communication to ignore or minimize its negative sides. This is no different in the Conversation article on solar PV and wind being on track of replacing fossil-fuels within two decades as discussed in the last two posts. Halfway the article there is the only admission that there might be a negative side to solar PV and wind energy:

A renewable grid
PV and wind are often described as “intermittent” energy sources.

When I read this the first time, I had high hopes that real issues would be tackled. That hope was in vein, it was followed by this sentence in full cheering mode:

But stabilising the grid is relatively straightforward, with the help of storage and high-voltage interconnectors to smooth out local weather effects.

Relatively straightforward?!?!

Continue reading

The average of a decreasing trend extrapolated as an exponential growth in the future

While writing previous post, I got intrigued by the graph representing the electricity generation forecast until 2032, especially that yellow line representing the exponential growth of solar PV and wind. To recapitulate, this is the graph I am talking about:

The authors claim that this graph represents:

Current world electricity generation trends, extrapolated to 2032

They also state that the growth rate of solar PV was 28% and that of wind was 13% between 2012 and 2016. The yellow line, being a combination of solar PV and wind, will be somewhere between those two values.

The suggestion is that this growth of solar PV and wind is somehow established between 2012 and 2016, therefor could be used to extrapolate future values that give rise to that exponentially increasing yellow graph line. I wondered whether such trend really could been found between 2012 and 2016, so I looked at the values of electricity generation that I used for previous post:

Continue reading

Solar PV and wind are on track to replace all coal, oil and gas within two decades (define “all”)

Solar PV and wind are getting so cheap and more abundant that they are on track to entirely displace fossil fuels worldwide by 2032. This remarkable claim is made in The Conversation article titled Solar PV and wind are on track to replace all coal, oil and gas within two decades.

It is a remarkable claim because the last figures that I found show that solar PV plus wind generated only a tiny fraction of total energy compared to fossil fuels. So I would doubt that solar PV and wind suddenly could replace all coal, oil and gas in just a couple decades. Two decades seems like an awfully short time to go from (almost) zero to hero.

That made me really curious about the principle behind this claim. To clarify their case, the authors showed two graphs. This is the first one:

Continue reading

Finding unprecedented high resolution values in a low resolution dataset

In previous post, I discussed a graph that suggested that the CO2 and CH4 levels in the atmosphere are unprecedented in the last 800,000 years and proposed that it is misleading to compare high resolution data with low resolution data. After I published that post, I wondered whether I could illustrate this with an example. It should be possible if I had some detailed dataset. Then I could make a detailed graph, see how that looks like, then sample this dataset in the same way as a proxy dataset and again make a graph. Comparing both graphs should make clear what the effect is.

Continue reading