iowaclimatescienceeducation--2020-12-16--Rise of the Dominion Effect

Rise of the Dominion Effect

Background from Shocking History of Dominion Voting Excerpts in italics with my bolds .
Dominion Voting Systems Corp. is the Canadian company behind the ballot switching software .
Dominion was founded in 2003 , with a mission to provide electronic voting systems friendly for progressives .
Because of such partisanship , it languished with almost no customers for the next 5-6 years , until the Obama administration came to power .
In 2010 , the Obama administration confiscated electronic voting systems assets ( software , intellectual property , manufacturing tools , customer base , etc . )
from two established @ @ @ @ @ @ @ Dominion .
At the same time , Dominion got some employees and assets from a foreign EVS company , tied to Hugo Chavez .
Its software has been used by some 40 % of the voters in this election , mostly by Democrat-controlled states and election commissions .
Apparently , no protections were put in place against ballot switching , deletion , or creation .
According to Dominion ’ s own website , it software was used in “ battleground ” states and the largest Democrat states , including MI , GA , @ @ @ @ @ @ @ , AK , UT , NJ , CA , NY .
From Fraudspotters Statistical Evidence of Dominion Election Fraud ?
Time to Audit the Machines .
Excerpts in italics with my bolds .
Statistical analysis of past presidential races supports the view that in 2020 , in counties where Dominion Machines were deployed , the voting outcomes were on average ( nationwide ) approximately 1.5 % higher for Joe Biden and 1.5 % lower for Donald Trump after adjusting for other demographic and past voting preferences .
Upon running hundreds of models , @ @ @ @ @ @ @ appears to be somewhere between 1.0 % and 1.6 % .
For Dominion to have switched the election from Trump to Biden , it would have had to have increased Biden outcomes ( with a corresponding reduction in Trump outcomes ) by 0.3 % in Georgia , 0.6 % in Arizona , 2.1 % in Wisconsin , and 2.5 % in Nevada .
The apparent average “ Dominion Effect ” is greater than the margin in Arizona and Georgia , and close to the margin for Wisconsin and Nevada .
It is not @ @ @ @ @ @ @ actual effect in Wisconsin and Nevada was greater than the national average and would have changed the current reported outcome in those two states .
Assuming the “ Dominion Effect ” is real , it is possible that an audit of these machines would overturn the election .
These results are scientifically valid and typically have a p-value of less than 1 % , meaning the chances of this math occurring randomly are less than 1 in 100 .
This article , and its FAQ , shows many ways to model the “ @ @ @ @ @ @ @ restore faith in the system is to audit the Dominion voting machines in Arizona , Georgia , Nevada , and Wisconsin .
To do this study , we will link results from 2008 to 2020 by each county , parish , or in some cases city .
Since this is usually based on county , we will refer to it as county in this article .
By comparing the county to itself , we are constructing the test similar to how a drug company would test the effects of its proposed therapy .
@ @ @ @ @ @ @ counties that do not have Dominion in 2008 .
In 2020 , 657 of the counties have Dominion while 2,388 do not .
If we assume that the same societal forces are acting upon all of these counties equally , then in comparing the average change from 2008 to 2020 for Dominion counties versus non Dominion counties , we should have a similar change in voter share .
In this regard , it is as if Dominion is the proposed treatment , and non-Dominion is the placebo .
When doing this analysis , @ @ @ @ @ @ @ is constant across counties .
In fact , below are the results comparing 2008 to 2020 .
A verbal description is “ the average US county ’ s percentage of vote for the Democrat presidential candidate was 8.4 percentage points less Democrat in 2020 ( Biden vs. Trump ) than in 2008 .
( Obama vs. McCain ) .
However , despite this 8.4 % -point decrease , Dominion counties only decreased 6.4 % points , while the non-Dominion counties decreased 9.0 % points.
” Unlike a drug company ’ s test of @ @ @ @ @ @ @ not randomly selected to be “ treated ” by Dominion .
These counties chose to install Dominion .
Was there selection bias ?
We should control for other factors to see if the presence of Dominion still significantly affects results .
We can obtain demographic data on a county level basis from the U.S. department of agriculture .
By attaching this data on a county basis to our already existing dataset , and running multiple linear regression , we obtain the following results .
You ’ ll notice that Dominion ’ s p-value @ @ @ @ @ @ @ other variables .
In some cases Dominion is more significant than the control variables .
To provide a basic interpretation , look at the sign of the coefficient .
It is telling you whether the demographic factor increased or decreased Democratic presidential voter percentage .
So , from 2008 to 2020 : • The more rural , the less the Democratic share • The more manufacturing dependent , the less the Democratic share • The more a county is considered a “ high natural amenity , ” the more the Democratic share if @ @ @ @ @ @ @ if we give larger counties more weight .
Note this variable has a less significant p-value than some of the others .
• The more a county is considered “ high creative class , ” the more the Democratic share • The more a county is considered “ low education , ” the more the Democratic share • The more the population increased , the more the Democratic share • The more international immigration , the more the Democratic share , although one measure had this value with a questionable p-value .
• @ @ @ @ @ @ @ installed , there was approximately a 1.5 % -point increase in Democratic share which also corresponds to a 1.5 % -point Republican decrease , so a total swing of 3 % points .
If the “ Dominion Effect ” is real , would it have affected the election ?
This article showed a range of estimates for the “ Dominion Effect , ” the more persuasive being from the multiple linear regression analysis : I find the weighted least square model the most persuasive and refer to it often in the FAQ .
@ @ @ @ @ @ @ it adds that percentage to Democrat presidential vote and subtracts from Republican .
If the Dominion Effect is real , it may have affected this close election .
For Dominion to have switched the election from Trump to Biden , it would have had to increase Democratic presidential outcomes by 0.3 % and reduced Republican outcomes by 0.3 % in Georgia .
The factors for the other states are 0.6 % in Arizona , 2.1 % in Wisconsin , and 2.5 % in Nevada .
Click here to see the math .
If @ @ @ @ @ @ @ , it is not hard to believe that this effect would be greater in swing states and could have swung these four states into Biden ’ s column , putting the electoral college in his favor .

Background from Shocking History of Dominion Voting Excerpts in italics with my bolds.
Dominion Voting Systems Corp. is the Canadian company behind the ballot switching software.
Dominion was founded in 2003, with a mission to provide electronic voting systems friendly for progressives. Because of such partisanship, it languished with almost no customers for the next 5-6 years, until the Obama administration came to power. In 2010, the Obama administration confiscated electronic voting systems assets (software, intellectual property, manufacturing tools, customer base, etc.) from two established American companies, and gave them to Dominion. At the same time, Dominion got some employees and assets from a foreign EVS company, tied to Hugo Chavez.
Its software has been used by some 40% of the voters in this election, mostly by Democrat-controlled states and election commissions. Apparently, no protections were put in place against ballot switching, deletion, or creation. According to Dominion’s own website, it software was used in “battleground” states and the largest Democrat states, including MI, GA, AZ, NV, NM, CO, AK, UT, NJ, CA, NY.
The Dominion Effect on Vote Counting
From Fraudspotters Statistical Evidence of Dominion Election Fraud? Time to Audit the Machines. Excerpts in italics with my bolds.
Overview
Statistical analysis of past presidential races supports the view that in 2020, in counties where Dominion Machines were deployed, the voting outcomes were on average (nationwide) approximately 1.5% higher for Joe Biden and 1.5% lower for Donald Trump after adjusting for other demographic and past voting preferences. Upon running hundreds of models, I would say the national average effect appears to be somewhere between 1.0% and 1.6%.
For Dominion to have switched the election from Trump to Biden, it would have had to have increased Biden outcomes (with a corresponding reduction in Trump outcomes) by 0.3% in Georgia, 0.6% in Arizona, 2.1% in Wisconsin, and 2.5% in Nevada. The apparent average “Dominion Effect” is greater than the margin in Arizona and Georgia, and close to the margin for Wisconsin and Nevada. It is not hard to picture a scenario where the actual effect in Wisconsin and Nevada was greater than the national average and would have changed the current reported outcome in those two states.
Assuming the “Dominion Effect” is real, it is possible that an audit of these machines would overturn the election.
These results are scientifically valid and typically have a p-value of less than 1%, meaning the chances of this math occurring randomly are less than 1 in 100. This article, and its FAQ, shows many ways to model the “Dominion Effect.”
The best way to restore faith in the system is to audit the Dominion voting machines in Arizona, Georgia, Nevada, and Wisconsin.
Discussion
To do this study, we will link results from 2008 to 2020 by each county, parish, or in some cases city. Since this is usually based on county, we will refer to it as county in this article.
By comparing the county to itself, we are constructing the test similar to how a drug company would test the effects of its proposed therapy. In this case, we have 3,050 counties that do not have Dominion in 2008. In 2020, 657 of the counties have Dominion while 2,388 do not. If we assume that the same societal forces are acting upon all of these counties equally, then in comparing the average change from 2008 to 2020 for Dominion counties versus non Dominion counties, we should have a similar change in voter share. In this regard, it is as if Dominion is the proposed treatment, and non-Dominion is the placebo.
When doing this analysis, we do NOT see a change that is constant across counties. In fact, below are the results comparing 2008 to 2020. A verbal description is “the average US county’s percentage of vote for the Democrat presidential candidate was 8.4 percentage points less Democrat in 2020 (Biden vs. Trump) than in 2008. (Obama vs. McCain). However, despite this 8.4%-point decrease, Dominion counties only decreased 6.4% points, while the non-Dominion counties decreased 9.0% points.”
Unlike a drug company’s test of a new treatment, our counties were not randomly selected to be “treated” by Dominion. These counties chose to install Dominion. Was there selection bias? We should control for other factors to see if the presence of Dominion still significantly affects results.
We can obtain demographic data on a county level basis from the U.S. department of agriculture. By attaching this data on a county basis to our already existing dataset, and running multiple linear regression, we obtain the following results. You’ll notice that Dominion’s p-value became more significant as we controlled for other variables. In some cases Dominion is more significant than the control variables.
To provide a basic interpretation, look at the sign of the coefficient. It is telling you whether the demographic factor increased or decreased Democratic presidential voter percentage. So, from 2008 to 2020:
The more rural, the less the Democratic share
The more manufacturing dependent, the less the Democratic share
The more a county is considered a “high natural amenity,” the more the Democratic share if we consider counties equally weighted but not if we give larger counties more weight. Note this variable has a less significant p-value than some of the others.
The more a county is considered “high creative class,” the more the Democratic share
The more a county is considered “low education,” the more the Democratic share
The more the population increased, the more the Democratic share
The more international immigration, the more the Democratic share, although one measure had this value with a questionable p-value.
And most importantly, if Dominion was installed, there was approximately a 1.5%-point increase in Democratic share which also corresponds to a 1.5%-point Republican decrease, so a total swing of 3% points.
If the “Dominion Effect” is real, would it have affected the election?
This article showed a range of estimates for the “Dominion Effect,” the more persuasive being from the multiple linear regression analysis:
Multiple Linear Regress: Ordinary Least Squares: 1.65%
Multiple Linear Regress: Weighted Least Squares: 1.55%
I find the weighted least square model the most persuasive and refer to it often in the FAQ.
If there is a Dominion Effect, it adds that percentage to Democrat presidential vote and subtracts from Republican. If the Dominion Effect is real, it may have affected this close election. For Dominion to have switched the election from Trump to Biden, it would have had to increase Democratic presidential outcomes by 0.3% and reduced Republican outcomes by 0.3% in Georgia. The factors for the other states are 0.6% in Arizona, 2.1% in Wisconsin, and 2.5% in Nevada. Click here to see the math.
If you believe the Dominion Effect is real, it is not hard to believe that this effect would be greater in swing states and could have swung these four states into Biden’s column, putting the electoral college in his favor.
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December 16, 2020 at 01:32PM


iowaclimatescienceeducation--2020-12-16--Project Veritas: Julian Assange’s recorded phone call with State Department

In this new recorded phone call , Julian Assange can be heard calling Hillary Clinton ’ s State Department trying @ @ @ @ @ could protect their agents and stop the releases .
strong Assange believed that there was @ @ @ @ @ States Agents and interests with the publication of unredacted documents stolen from @ Wikileaks by @ @ @ @ @ Domscheit-Berg .
This is a recording of State Department lawyer Cliff Johnson on August 26 @ @ @ @ @ that Assange went to great lengths to try to prevent the uncontrolled publication of the @ @ @ @ @ by third parties on the internet .
Notably , the audio from the call on @ @ @ @ @ makes clear that WikiLeaks only re-published the full US embassy cables after they were on @ @ @ @ @ sites and Twitter .
Specifically , the full cables were published on Cryptome.org and the @ @ @ @ @ they were published on WikiLeaks .
Johnson responds by asking , “ And who would @ @ @ @ @ Is this WikiLeaks ?
”

In this new recorded phone call, Julian Assange can be heard calling Hillary Clinton’s State Department trying to warn them so they could protect their agents and stop the releases. strong
James O’Keefe @JamesOKeefeIII
Assange believed that there was a great danger to United States Agents and interests with the publication of unredacted documents stolen from @Wikileaks by his rouge former employee Daniel Domscheit-Berg.
This is a recording of State Department lawyer Cliff Johnson on August 26, 2011
The audio shows that Assange went to great lengths to try to prevent the uncontrolled publication of the full US State Department cables by third parties on the internet.
Gateway Pundit:
Project Veritas Releases Shocking Never-Before-Heard Phone Call Between Julian Assange and Hillary Clinton’s State Department
Notably, the audio from the call on August 26, 2011, makes clear that WikiLeaks only re-published the full US embassy cables after they were on hundreds of websites, torrent sites and Twitter. Specifically, the full cables were published on Cryptome.org and the Pirate Bay — days before they were published on WikiLeaks.
Johnson responds by asking, “And who would be releasing these cables? Is this WikiLeaks?”
“No, […]
Rating: 7.0/10 (3 votes cast)
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December 16, 2020 at 12:45PM


iowaclimatescienceeducation--2020-12-16--Huge Success! German Draft Bill Provision Deeming Green Energies A Matter Of ‘Public Safety’ Gets De

Huge Success! German Draft Bill Provision Deeming Green Energies A Matter Of ‘Public Safety’ Gets Deleted!

A huge success has been achieved for nature and species conservation in Germany , reports the Naturschutzinitiative e.V .
( NI ) .
This means installing wind parks in nature-sensitive areas will remain extremely difficult .
Red kites will now be less threatened – photo : Ingo Kühl/NI The notorious § 1 paragraph 5 EEG 2021 ( Wind energy is in the “ public interest ” and serves the “ public safety ” ) will be deleted from the proposed German bill !
Not long ago I wrote about how the German government wanted to make the use of renewable @ @ @ @ @ @ @ and thus threatened to make the permitting of wind parks in nature sensitive areas almost free of obstacles .
The draft of a new German Renewable Energy Sources Act , stated : “ The use of renewable energies for electricity generation is in the public interest and serves public security.
” But this has now been rejected .
A study commissioned by the Naturschutzinitiative e.V .
( NI ) commissioned expert opinion by Prof. Dr. Gellermann as well as the ad campaign by the NI “ STOP THE EEG ” have contributed significantly @ @ @ @ @ @ @ power-related killing of European birds may currently not be permitted on the basis of § 45 para .
7 p. 1 No .
5 BNatSchG for reasons of EU law .
Exceptions to the prohibition of killing can also not be based on § 45 para .
7 p. 1 No .
4 BNatSchG ( ‘ public safety ’ ) in favor of wind power use , because wind turbines do not meet the requirements of these Union-based provisions , ” was the conclusion reached by the legal expert opinion of university lecturer @ @ @ @ @ @ @ Gellermann on behalf of the environmental association Naturschutzinitiative e.V .
( NI ) .

A huge success has been achieved for nature and species conservation in Germany, reports the Naturschutzinitiative e.V. (NI). This means installing wind parks in nature-sensitive areas will remain extremely difficult.
RESULT: Endangered species and biotopes will remain protected.
Red kites will now be less threatened – photo: Ingo Kühl/NI
The notorious § 1 paragraph 5 EEG 2021 (Wind energy is in the “public interest” and serves the “public safety”) will be deleted from the proposed German bill!
Not long ago I wrote about how the German government wanted to make the use of renewable energies a question of national security, and thus threatened to make the permitting of wind parks in nature sensitive areas almost free of obstacles.
The draft of a new German Renewable Energy Sources Act, stated: “The use of renewable energies for electricity generation is in the public interest and serves public security.”
But this has now been rejected.
A study commissioned by the Naturschutzinitiative e.V. (NI) commissioned expert opinion by Prof. Dr. Gellermann as well as the ad campaign by the NI “STOP THE EEG” have contributed significantly to this success.
“The wind power-related killing of European birds may currently not be permitted on the basis of § 45 para. 7 p. 1 No. 5 BNatSchG for reasons of EU law. Exceptions to the prohibition of killing can also not be based on § 45 para. 7 p. 1 No. 4 BNatSchG (‘public safety’) in favor of wind power use, because wind turbines do not meet the requirements of these Union-based provisions,” was the conclusion reached by the legal expert opinion of university lecturer and lawyer apl. Prof. Dr. Martin Gellermann on behalf of the environmental association Naturschutzinitiative e.V. (NI).
Story also at Die Welt.
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December 16, 2020 at 12:41PM


iowaclimatescienceeducation--2020-12-16--Communist China to run Biden climate policy?

Joe Biden ’ s climate czar will be Obama EPA chief , current Natural Resources Defense Council ( NRDC ) @ @ @ @ @ tool Gina McCarthy .
NRDC has a 20-year ( likely including financial ) relationship with @ @ @ @ @ NRDC admits : “ NRDC has been supporting China ’ s sustainable development by providing @ @ @ @ @ more than 20 … Continue reading Communist China to run Biden climate policy ?

Joe Biden’s climate czar will be Obama EPA chief, current Natural Resources Defense Council (NRDC) chief and possible Communist Chinese tool Gina McCarthy. NRDC has a 20-year (likely including financial) relationship with the Communist Chinese government. NRDC admits: “NRDC has been supporting China’s sustainable development by providing technical and legal expertise for more than 20 … Continue reading Communist China to run Biden climate policy?
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December 16, 2020 at 12:37PM


iowaclimatescienceeducation--2020-12-16--WUWT – big changes, returning to a ‘war footing’ on climate

With the likely installation of a Biden administration comes a renewed and likely vicious climate war .
I have inside information that Sen. Sheldon Whitehouse ( D-R.I. ) will be pushing the Biden administration , calling for prominent climate skeptics , and climate skeptical organizations to be put on investigations and “ show trials ” for daring to question the consensus .
There may be worse than that coming , as he seems to genuinely believe we ’ ve committed some sort of crime and should be punished for it .
Make no mistake , it ’ s coming .
@ @ @ @ @ @ @ targeted as well .
While it ’ s been an easy ride during the Trump administration with him pulling out of the Paris agreement , bringing the gestapo-like EPA back to sensibility , eliminating WOTUS rules , appointing prominent climate skeptics to NOAA , and many other things , we now face a renewed “ in your face ” series of actions by the Biden administration to make batting the invisible boogeyman of climate change not just a priority , but a declared war .
Hence , eliminating and/or neutralizing the climate skeptics @ @ @ @ @ @ @ – eliminate/neutralize competing voices and media outlets .
To that end , I ’ m not going down without a fight .
I hope you won ’ t either .
First , starting tomorrow , WUWT will be changing to a new look , something that I ’ ve been working on behind the scenes on for months .
Here is a preview of the new look .
Second , we are likely going to have to move our hosting again .
If you haven ’ t heard about it , the plight @ @ @ @ @ @ @ their hosting provider provides a glimpse of the likely future ahead for many climate skeptic websites if a Biden administration becomes a reality .
Third , facts are power , and to that end WUWT is launching a new , second website that will be a reference website for many climate topics , linked to the main WUWT site in the menu .
It won ’ t be a discussion or a blog , but more like a user friendly climate wiki .
Look for an announcement in the first week of January @ @ @ @ @ @ @ to need your help .
We ’ ll need you to share stories , to help get the word out , we ’ ll need guest authors , we ’ ll need programming help , and we ’ ll need donations .
“ The only thing necessary for the triumph of evil is for good men to do nothing.
” –Edmund Burke , Thoughts on the Cause of the Present Discontents 82-83 ( 1770 ) in : Select Works of Edmund Burke , vol .
1 , p. 146 ( Liberty Fund ed @ @ @ @

With the likely installation of a Biden administration comes a renewed and likely vicious climate war.
I have inside information that Sen. Sheldon Whitehouse (D-R.I.) will be pushing the Biden administration, calling for prominent climate skeptics, and climate skeptical organizations to be put on investigations and “show trials” for daring to question the consensus. There may be worse than that coming, as he seems to genuinely believe we’ve committed some sort of crime and should be punished for it. Make no mistake, it’s coming.
I suspect I’ll be personally targeted as well.
While it’s been an easy ride during the Trump administration with him pulling out of the Paris agreement, bringing the gestapo-like EPA back to sensibility, eliminating WOTUS rules, appointing prominent climate skeptics to NOAA, and many other things, we now face a renewed “in your face” series of actions by the Biden administration to make batting the invisible boogeyman of climate change not just a priority, but a declared war.
Hence, eliminating and/or neutralizing the climate skeptics. It’s war psyops 101 – eliminate/neutralize competing voices and media outlets.
To that end, I’m not going down without a fight. I hope you won’t either.
First, starting tomorrow, WUWT will be changing to a new look, something that I’ve been working on behind the scenes on for months. Here is a preview of the new look.
NEW WUWT HOME PAGE
Second, we are likely going to have to move our hosting again. If you haven’t heard about it, the plight of the Conservative Treehouse being de-platformed by their hosting provider provides a glimpse of the likely future ahead for many climate skeptic websites if a Biden administration becomes a reality.
Third, facts are power, and to that end WUWT is launching a new, second website that will be a reference website for many climate topics, linked to the main WUWT site in the menu. It won’t be a discussion or a blog, but more like a user friendly climate wiki. Look for an announcement in the first week of January.
Finally, I’m going to need your help. We’ll need you to share stories, to help get the word out, we’ll need guest authors, we’ll need programming help, and we’ll need donations.
Tomorrow, we start.
“The only thing necessary for the triumph of evil is for good men to do nothing.”
–Edmund Burke, Thoughts on the Cause of the Present Discontents 82-83 (1770) in: Select Works of Edmund Burke, vol. 1, p. 146 (Liberty Fund ed. 1999).
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iowaclimatescienceeducation--2020-12-16--Sea-Surface Temperatures: Hadley Centre v. NOAA

My last post compared actual sea-surface temperature ( SST ) estimates to one another to see how well they agreed .
It was not a pretty sight ; the various estimates covered a range of global average SSTs from ~14°C to almost 20°C .
In addition , some SSTs were declining with time and others were increasing .
While I did check the latitude range of each of the grids I averaged , John Kennedy ( HadSST climate scientist in the UK MET Hadley Centre ) pointed out that I did not check the cell-by-cell areal coverage of the HadSST @ @ @ @ @ @ @ grid .
He suspected that the results I presented were mostly due to null grid cells in HadSST that were populated by interpolation and extrapolation in the ERSST dataset .
The original results were presented in Figure 6 of my previous post which is Figure 1 here .
Figure 1 .
The original comparison of ERSST and HadSST from my previous post .
I asked Kennedy if he had a mask of the populated area in his SST grid , but he didn ’ t , which was unfortunate .
A mask is @ @ @ @ @ @ @ be digitally overlain on a map and used to clip any data that falls outside it .
Further Kennedy pointed out that the mask changes month-to-month since much of the data are from ships , drifting buoys and floats .
Too bad , more work for me .
HadSST is a five-degree by five-degree latitude and longitude grid and ERSST is a two-degree grid .
They are not even multiples of one another , so creating a mask of null areas in HadSST that can be used to clip ERSST data is complicated @ @ @ @ @ @ @ programming two “ clever ” solutions that failed , I was successful in my third attempt , although my computer may never be the same again .
The “ clever ” methods would have saved computer time , but neither worked .
In the end I used a “ brute force ” method , which was an awful looking five-deep computer loop , comparing every grid-cell to every grid cell .
Ugly , but it worked , and it is relatively safe from errors .
The results of my ugly logic are show @ @ @ @ @ @ @ the original NOAA ERSST record , which is identical to the line in Figure 1 , the “ HadSST masked ” ERSST record , and HadSST itself , which is also the same as in Figure 1 .
At least HadSST falls in between the two NOAA lines .
Figure 2 .
The NOAA ERSST global average temperature before the HadSST mask is applied original ) and after it is applied .
HadSST is also shown .
We remember from the first post that HadSST is constructed by averaging the points within each @ @ @ @ @ @ @ insufficient points in a cell , the cell is left null .
NOAA starts with essentially the same data but use a different method to populate their cells .
They use a gridding algorithm that populates each of their 2-degree cells with interpolation and some extrapolation .
In this way they get a grid that has many fewer null cells and nearly global coverage .
Since most of the HadSST null cells are near the poles , the NOAA ERSST record has a lower temperature , as seen in Figures 1 and 2 @ @ @ @ @ @ @ is instructive to look at a portion of the original 2-degree ERSST grid , see Table 1 .
The area shown is in the Arctic ocean , north of Russia and Alaska .
Table 1 .
A portion of the ERSST 2-degree grid of 2018 SSTs in the Arctic Ocean , north of Alaska and Russia .
Notice the constant “ fill ” value of 1.8°C over much of the area .
Next , we will look at the HadSST grid over the same portion of the Arctic Ocean in Table 2 .
@ @ @ @ @ @ @ the number of 2018 null months for that cell .
Using our methodology of only allowing one null month in a year , only one of these cells , latitude = 62.5 and longitude = -177.5 , would be used in our average .
While the portion of the Arctic Ocean shown in Table 1 is fully populated in the ERSST grid prior to our applying the HadSST mask , all the values become null after applying the mask .
Table 2 .
Arctic Ocean HadSST grid cells .
The values are the @ @ @ @ @ @ @ .
Figure 3 is the original ERSST average temperature map for 2018 .
As you can see , it has values over most of the globe , the nulls are generally in the polar oceans under sea ice .
The blue patch in the far northwest , north of Alaska and Russia , is the area shown in Tables one and two .
Figure 3 is the map representing the lower ERSST line in Figures 1 and 2 .
The line shows an increasing temperature trend of about 1.6°C per century .
Figure @ @ @ @ @ @ @ longitude ERSST grid cells .
White regions are null .
Figure 4 is the same data shown in Figure 3 , but every HadSST cell that has more than one month with no value in 2018 has been nulled .
Notice that the blue patch north of Alaska and Russia , in the Arctic Ocean , is gone .
The null cells are shown in white .
We subjectively allowed one month to have no value , but two was considered to be too much .
The wide seasonal variations in temperature in @ @ @ @ @ @ @ than one null month to affect the average .
The uppermost line in Figure 2 is represented by Figure 4 .
The line lies between 20°C and 21.5°C .
By masking the exact cells where HadSST had more than one null month , the ERSST average jumped from 14°C to 21°C , an increase of 7° !
Further , the new line is more than 2°C warmer than HadSST .
The new ERSST line has also reversed its trend , it now shows a declining temperature trend of 3.5°C/century , almost exactly the @ @ @ @ @ @ @ Figure 4 .
The ERSST map with a HadSST null grid-cell mask applied .
White regions are null .
Figure 5 shows the mask used to null the HadSST null cells in the ERSST map in Figure 4 .
The colors in Figure 5 represent the number of HadSST missing monthly values .
We allowed one missing month , so some of the blue cells are nulled , those that represent “ 1 ” and some are not , those that represent “ 2.
” All other colors are nulled .
White regions @ @ @ @ @ @ @ aware that the shapes in Figure 4 will not match the shapes in Figure 5 exactly .
The ERSST grid is a 2-degree grid and we are applying a 5-degree grid mask to it .
To do this I had to search 2.5 degrees in all directions from every cell and populated a two-degree cell grid with the missing months from a five-degree grid .
This process is as exact as possible , but it can distort the null areas by up to one two-degree cell .
There is no more accurate way @ @ @ @ @ @ @ with matching grid-cell sizes .
Figure 5 .
The HadSST mask applied to Figure 3 to get Figure 4 .
We accepted cells that had one monthly null ( deep blue ) or no monthly null values ( white ) .
Two monthly nulls or more were rejected .
The colors represent the number of months in 2018 that have no values .
Thus , most of the colored boxes are nulled in Figure 4 .
Some of the deep blue boxes are accepted , those that represent “ 1 , ” some @ @ @ @ @ @ @ 2.
” John Kennedy was quite correct that applying the HadSST null mask to the NOAA ERSST record made a difference , the average global temperature jumped 7°C !
Perhaps more importantly , the trend reversed from a warming trend of 1.6°C/century to a cooling trend of 3.5°C/century .
Since the polar regions contain most of the newly nulled cells , one could conclude that the polar regions are warming quite a lot and the rest of the world ocean is cooling .
This is consistent with observations , especially in the region @ @ @ @ @ @ @ is not consistent with the “ CO control knob ” hypothesis ( Lacis , Schmidt , Rind , & Ruedy , 2010 ) .
If rising CO is causing recent warming , why would most of the world be cooling ?
We will not judge which estimate of global ocean surface temperature is superior , both have utility .
The HadSST estimate accurately reflects the underlying data , averages represent data better than interpolation and extrapolation .
Yet , if the goal is to try and provide the best global average temperature estimate @ @ @ @ @ @ @ then the ERSST grid is better .
Our goal was to compare the two grids without using anomalies , which are not needed here .
Kennedy ’ s comments helped us do that and we are grateful for the help .
The result shows that while the ERRST estimate , which falls on the University of Hamburg and the NOAA MIMOC multiyear estimates , is probably the best estimate of global ocean surface temperature , it is speculative .
The true ocean measurements , well represented by HadSST , do not support ERSST @ @ @ @ @ @ @ estimate is logical , it does not derive from the underlying data .
Just because a map looks like you think it “ should ” look , doesn ’ t make it right .
We can also conclude that we have no idea what the global average surface temperature of the oceans is or whether it is warming or cooling .
The data plotted in Figures 1 and 2 show a huge range of temperatures , and all based on essentially the same the raw data .
The error implied by the estimates @ @ @ @ @ @ @ The HadSST/ERSST estimated warming , via unnecessary and misleading anomalies , is 1.7°C/century for this 19-year period .
Yet both HadSST and ERSST actual temperatures show cooling of 3.5°C/century over the same period .
Anyone who thinks they know what the climate is doing today or will do ten years in the future , just isn ’ t paying attention .
It should be obvious to everyone that the oceans are the single most important driver of our long-term climate and the records that we rely upon to tell us what sea-surface and @ @ @ @ @ @ @ up to the task .
You might as well use dice to choose a temperature .
I processed a lot of data to make this post , I think I did it correctly , but I do make mistakes .
For those that want to check my work , you can find my R code here .
None of this is in my new book Politics and Climate Change : A History but buy it anyway .

By Andy May
My last post compared actual sea-surface temperature (SST) estimates to one another to see how well they agreed. It was not a pretty sight; the various estimates covered a range of global average SSTs from ~14°C to almost 20°C. In addition, some SSTs were declining with time and others were increasing. While I did check the latitude range of each of the grids I averaged, John Kennedy (HadSST climate scientist in the UK MET Hadley Centre) pointed out that I did not check the cell-by-cell areal coverage of the HadSST grid, relative to the NOAA ERSST grid. He suspected that the results I presented were mostly due to null grid cells in HadSST that were populated by interpolation and extrapolation in the ERSST dataset. The original results were presented in Figure 6 of my previous post which is Figure 1 here.
Figure 1. The original comparison of ERSST and HadSST from my previous post.
I asked Kennedy if he had a mask of the populated area in his SST grid, but he didn’t, which was unfortunate. A mask is a latitude and longitude enclosure that can be digitally overlain on a map and used to clip any data that falls outside it. Further Kennedy pointed out that the mask changes month-to-month since much of the data are from ships, drifting buoys and floats. Too bad, more work for me.
HadSST is a five-degree by five-degree latitude and longitude grid and ERSST is a two-degree grid. They are not even multiples of one another, so creating a mask of null areas in HadSST that can be used to clip ERSST data is complicated and time consuming. Fortunately, after programming two “clever” solutions that failed, I was successful in my third attempt, although my computer may never be the same again. The “clever” methods would have saved computer time, but neither worked. In the end I used a “brute force” method, which was an awful looking five-deep computer loop, comparing every grid-cell to every grid cell. Ugly, but it worked, and it is relatively safe from errors.
The results of my ugly logic are show in Figure 2. This figure shows the original NOAA ERSST record, which is identical to the line in Figure 1, the “HadSST masked” ERSST record, and HadSST itself, which is also the same as in Figure 1. At least HadSST falls in between the two NOAA lines.
Figure 2. The NOAA ERSST global average temperature before the HadSST mask is applied original) and after it is applied. HadSST is also shown.
We remember from the first post that HadSST is constructed by averaging the points within each 5-degree grid cell. If there are insufficient points in a cell, the cell is left null. NOAA starts with essentially the same data but use a different method to populate their cells. They use a gridding algorithm that populates each of their 2-degree cells with interpolation and some extrapolation. In this way they get a grid that has many fewer null cells and nearly global coverage. Since most of the HadSST null cells are near the poles, the NOAA ERSST record has a lower temperature, as seen in Figures 1 and 2. To illustrate the difference, it is instructive to look at a portion of the original 2-degree ERSST grid, see Table 1. The area shown is in the Arctic ocean, north of Russia and Alaska.
Table 1. A portion of the ERSST 2-degree grid of 2018 SSTs in the Arctic Ocean, north of Alaska and Russia. Notice the constant “fill” value of 1.8°C over much of the area.
Next, we will look at the HadSST grid over the same portion of the Arctic Ocean in Table 2. These values are not temperatures, but the number of 2018 null months for that cell. Using our methodology of only allowing one null month in a year, only one of these cells, latitude = 62.5 and longitude = -177.5, would be used in our average. While the portion of the Arctic Ocean shown in Table 1 is fully populated in the ERSST grid prior to our applying the HadSST mask, all the values become null after applying the mask.
Table 2. Arctic Ocean HadSST grid cells. The values are the number of missing monthly values in 2018.
Figure 3 is the original ERSST average temperature map for 2018. As you can see, it has values over most of the globe, the nulls are generally in the polar oceans under sea ice. The blue patch in the far northwest, north of Alaska and Russia, is the area shown in Tables one and two. Figure 3 is the map representing the lower ERSST line in Figures 1 and 2. The line shows an increasing temperature trend of about 1.6°C per century.
Figure 3. The original 2-degree latitude and longitude ERSST grid cells. White regions are null.
Figure 4 is the same data shown in Figure 3, but every HadSST cell that has more than one month with no value in 2018 has been nulled. Notice that the blue patch north of Alaska and Russia, in the Arctic Ocean, is gone. The null cells are shown in white. We subjectively allowed one month to have no value, but two was considered to be too much. The wide seasonal variations in temperature in the polar regions could have caused more than one null month to affect the average. The uppermost line in Figure 2 is represented by Figure 4. The line lies between 20°C and 21.5°C. By masking the exact cells where HadSST had more than one null month, the ERSST average jumped from 14°C to 21°C, an increase of 7°! Further, the new line is more than 2°C warmer than HadSST. The new ERSST line has also reversed its trend, it now shows a declining temperature trend of 3.5°C/century, almost exactly the same as HadSST (3.48°C/century).
Figure 4. The ERSST map with a HadSST null grid-cell mask applied. White regions are null.
Figure 5 shows the mask used to null the HadSST null cells in the ERSST map in Figure 4. The colors in Figure 5 represent the number of HadSST missing monthly values. We allowed one missing month, so some of the blue cells are nulled, those that represent “1” and some are not, those that represent “2.” All other colors are nulled. White regions have no missing months. Please be aware that the shapes in Figure 4 will not match the shapes in Figure 5 exactly. The ERSST grid is a 2-degree grid and we are applying a 5-degree grid mask to it. To do this I had to search 2.5 degrees in all directions from every cell and populated a two-degree cell grid with the missing months from a five-degree grid. This process is as exact as possible, but it can distort the null areas by up to one two-degree cell. There is no more accurate way to do it without having two grids with matching grid-cell sizes.
Figure 5. The HadSST mask applied to Figure 3 to get Figure 4. We accepted cells that had one monthly null (deep blue) or no monthly null values (white). Two monthly nulls or more were rejected. The colors represent the number of months in 2018 that have no values. Thus, most of the colored boxes are nulled in Figure 4. Some of the deep blue boxes are accepted, those that represent “1,” some are not, those that represent “2.”
Discussions and Conclusions
John Kennedy was quite correct that applying the HadSST null mask to the NOAA ERSST record made a difference, the average global temperature jumped 7°C! Perhaps more importantly, the trend reversed from a warming trend of 1.6°C/century to a cooling trend of 3.5°C/century. Since the polar regions contain most of the newly nulled cells, one could conclude that the polar regions are warming quite a lot and the rest of the world ocean is cooling. This is consistent with observations, especially in the region around the North Pole. But it is not consistent with the “CO 2 control knob” hypothesis (Lacis, Schmidt, Rind, & Ruedy, 2010). If rising CO 2 is causing recent warming, why would most of the world be cooling?
We will not judge which estimate of global ocean surface temperature is superior, both have utility. The HadSST estimate accurately reflects the underlying data, averages represent data better than interpolation and extrapolation. Yet, if the goal is to try and provide the best global average temperature estimate possible with the data we have, then the ERSST grid is better. Our goal was to compare the two grids without using anomalies, which are not needed here. Kennedy’s comments helped us do that and we are grateful for the help. The result shows that while the ERRST estimate, which falls on the University of Hamburg and the NOAA MIMOC multiyear estimates, is probably the best estimate of global ocean surface temperature, it is speculative. The true ocean measurements, well represented by HadSST, do not support ERSST. Thus, while ERSST’s estimate is logical, it does not derive from the underlying data. Just because a map looks like you think it “should” look, doesn’t make it right.
We can also conclude that we have no idea what the global average surface temperature of the oceans is or whether it is warming or cooling. The data plotted in Figures 1 and 2 show a huge range of temperatures, and all based on essentially the same the raw data. The error implied by the estimates is, at least, ±3.5°C. The HadSST/ERSST estimated warming, via unnecessary and misleading anomalies, is 1.7°C/century for this 19-year period. Yet both HadSST and ERSST actual temperatures show cooling of 3.5°C/century over the same period.
Anyone who thinks they know what the climate is doing today or will do ten years in the future, just isn’t paying attention. It should be obvious to everyone that the oceans are the single most important driver of our long-term climate and the records that we rely upon to tell us what sea-surface and mixed layer temperatures are doing are not up to the task. You might as well use dice to choose a temperature.
I processed a lot of data to make this post, I think I did it correctly, but I do make mistakes. For those that want to check my work, you can find my R code here.
None of this is in my new book Politics and Climate Change: A History but buy it anyway.
Works Cited
Lacis, A., Schmidt, G., Rind, D., & Ruedy, R. (2010, October 15). Atmospheric CO 2 : Principal Control Knob Governing Earth’s Temperature. Science, 356-359. Retrieved from https://ift.tt/387M8wR
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iowaclimatescienceeducation--2020-12-17--New Research Shows The Oceans Can ‘Spontaneously’ Warm 8°C In Under 100 Years ‘Without External Trig

New Research Shows The Oceans Can ‘Spontaneously’ Warm 8°C In Under 100 Years ‘Without External Trigger’

Scientists are increasingly finding that abrupt , decadal-scale temperature changes of multiple degrees Celsius can arise unforced and spontaneously .
The surface temperatures for the Greenland ice sheet have been known to warm 8 to 16°C “ within decades or less ” – yes , fewer than 10 years – due to “ unforced oscillations.
” In other words , no external forcing mechanism is required for these abrupt warming events ( Li and Born , 2019 ) .
Scientists ( Gomes et al.
, 2020 ) examined the sea surface temperatures ( SSTs ) off the coasts of Lisbon @ @ @ @ @ @ @ addition to finding the SSTs were 5°C warmer ( 19.7°C vs. 14.6°C ) about 11,000 years ago compared to the last millennium , they record a “ sudden warming episode ( in < 100 years ) , of ~8 °C ” for this region .
That ’ s a warming of nearly 1°C per decade .
Modern summer sea surface temperatures in the Western North Pacific average 16°C ( 15-17°C ) ( Davis et al.
, 2020 ) .
Proxy evidence indicates peak SSTs reached 20-23°C ~8000 to ~6000 yrs ago , and @ @ @ @ @ @ @ or two during the Early Holocene .
Another new study finds Iceland was ~2°C warmer and the Arctic region had much less sea ice as recently as ~1400 to 1000 years ago ( Miles et al.
, 2020 ) .
What ’ s most interesting is that in the last 1400 years the Arctic experienced marked climate changes ( ~1.5°C in decades ) that may have occurred “ spontaneously , ” or without requiring “ an external trigger ” .
In other words , these warming events may arise due to unforced natural @ @ @ @ @ @ @ magnitudes can and do occur without an external forcing mechanism , there is little reason to assume that the much more modest changes in SSTs in the last century fall outside the range of internal variability either .

Scientists are increasingly finding that abrupt, decadal-scale temperature changes of multiple degrees Celsius can arise unforced and spontaneously.
The surface temperatures for the Greenland ice sheet have been known to warm 8 to 16°C “within decades or less” – yes, fewer than 10 years – due to “unforced oscillations.” In other words, no external forcing mechanism is required for these abrupt warming events (Li and Born, 2019).
Scientists (Gomes et al., 2020) examined the sea surface temperatures (SSTs) off the coasts of Lisbon in a newly published study. In addition to finding the SSTs were 5°C warmer (19.7°C vs. 14.6°C) about 11,000 years ago compared to the last millennium, they record a “sudden warming episode (in <100 years), of ~8 °C” for this region. That’s a warming of nearly 1°C per decade.
Modern summer sea surface temperatures in the Western North Pacific average 16°C (15-17°C) (Davis et al., 2020). Proxy evidence indicates peak SSTs reached 20-23°C ~8000 to ~6000 yrs ago, and SSTs twice warmed 4-5°C in a century or two during the Early Holocene.
Image Source: Davis et al., 2020 Another new study finds Iceland was ~2°C warmer and the Arctic region had much less sea ice as recently as ~1400 to 1000 years ago (Miles et al., 2020). What’s most interesting is that in the last 1400 years the Arctic experienced marked climate changes (~1.5°C in decades)that may have occurred “spontaneously,” or without requiring “an external trigger”. In other words, these warming events may arise due to unforced natural variability. If climate changes of these magnitudes can and do occur without an external forcing mechanism, there is little reason to assume that the much more modest changes in SSTs in the last century fall outside the range of internal variability either.
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December 17, 2020 at 04:26AM


iowaclimatescienceeducation--2020-12-17--CLAIM: Expect fewer, but more destructive landfalling tropical cyclones

A study based on new high-resolution supercomputer simulations , published in this week ’ s issue of the journal Science Advances , reveals that global warming will intensify landfalling tropical cyclones of category 3 or higher in the Indian and Pacific Oceans , while suppressing the formation of weaker events .
Tropical cyclones ( including typhoons and hurricanes ) , are the most fatal and costliest weather disasters on our planet .
Millions of people are affected every year by the destructive power of these extreme weather systems , but how tropical cyclone properties – in particular in coastal areas @ @ @ @ @ @ @ warming has long remained a mystery .
To address this question , scientists for over two decades have used the world ’ s largest supercomputers to run climate model simulations that show important aspects of these destructive storms .
However , until recently the computing power has been insufficient to capture both atmospheric details and resolve the full interaction with the ocean on a global scale .
A team of researchers from South Korea ’ s IBS Center for Climate Physics ( ICCP ) at Pusan National University recently completed one of the @ @ @ @ @ @ @ so far .
The global climate model records small-scale atmospheric and oceanic processes with a horizontal scale of 25 km and 10 km , respectively .
This unprecedented resolution is adequate to simulate tropical cyclones and oceanic cold wakes ( Fig .
1 ) , which are generated when a strong slowly moving tropical cyclone brings cold deep water up to the ocean surface , further influencing the track and intensity of tropical cyclones .
To determine the sensitivity of tropical cyclones to global warming , the research team ran computer model simulations @ @ @ @ @ @ @ doubled and quadrupled CO2 concentrations .
The simulations ran for 13 months on one of South Korea ’ s fastest academic supercomputers named Aleph ( Fig .
1 ) , generating an equivalent of about two-thousand 1TB hard disks of data .
This climate computer model is also unique in other aspects .
For example , it exhibits much weaker ocean temperature errors than the previous generation of climate models .
“ This improvement was important for a realistic simulation of typhoons in the Indian and Pacific Ocean ” says Dr. Jung-Eun Chu @ @ @ @ @ @ @ a project leader at the ICCP .
Over the warm tropical oceans , air rises , flowing polewards at high altitude and sinking in the subtropical regions .
The surface-return-flow of this so-called Hadley circulation feeds the global trade wind system .
According to the new study , the summer-time Hadley circulation is expected to weaken in future due to the accelerated atmospheric warming at an elevation of 5-15 km , relative to the ground .
“ A future reduction of rising motion in the tropical atmosphere will make it more difficult for @ @ @ @ @ @ @ the projected future suppression in tropical cyclone seeds and overall numbers in the Pacific and Indian Ocean ( Fig .
2 ) ” , explains Dr. Sun-Seon Lee from the ICCP , who conducted the simulations on Aleph .
“ Interestingly the simulated pattern of future tropical cyclone changes is quite similar to the recent observed trends , which supports the notion that global warming is already altering global extreme weather ” adds Dr. Lee .
However , the story how global warming will alter tropical cyclones is more complex .
Although for @ @ @ @ @ @ @ tropical cyclones is expected to decrease in future , developing events will have much higher chance to intensify beyond category 3 due to the higher humidity and energy levels in the atmosphere .
“ This result confirms previous studies which used less-detailed global climate models .
By representing coastal processes more accurately than ever before in a global model , we now have a much higher confidence in these robust model projections , in particular for landfalling tropical cyclones.
” , says Prof. Axel Timmermann , co-author of the study and Director of @ @ @ @ @ @ @ gas concentrations ( CO2 quadrupling ) , the research team discovered an important , previously unknown , saturation effect : beyond ~5 ?
global warming , the suppression of tropical cyclone seeds is so strong that also the number of strong landfalling events ( category 3 or higher ) will begin to level off .
“ Nevertheless , the rainfall associated with each event will continue to increase , amplifying the risk for extreme coastal flooding .
Our study provides crucial policy-relevant information for countries that are affected by the impact of tropical @ @ @ @ @ @ @ ICCP research team will continue to analyze the vast amount of data generated by these computer simulations to better understand the interaction between ocean heat content and tropical cyclones under present day and future conditions .

INSTITUTE FOR BASIC SCIENCE
Research News
IMAGE: RAINBANDS (WHITE) OF A TROPICAL CYCLONE AND RELATED OCEAN COLD WAKE (COLORED SHADING) SIMULATED BY THE ULTRA-HIGH RESOLUTION CLIMATE MODEL SIMULATION FOR PRESENT-DAY CONDITIONS (LEFT). THE SIMULATION WAS CONDUCTED WITH… view more
CREDIT: IBS
A study based on new high-resolution supercomputer simulations, published in this week’s issue of the journal Science Advances, reveals that global warming will intensify landfalling tropical cyclones of category 3 or higher in the Indian and Pacific Oceans, while suppressing the formation of weaker events.
Tropical cyclones (including typhoons and hurricanes), are the most fatal and costliest weather disasters on our planet. Millions of people are affected every year by the destructive power of these extreme weather systems, but how tropical cyclone properties – in particular in coastal areas – will change in response to global warming has long remained a mystery. To address this question, scientists for over two decades have used the world’s largest supercomputers to run climate model simulations that show important aspects of these destructive storms. However, until recently the computing power has been insufficient to capture both atmospheric details and resolve the full interaction with the ocean on a global scale.
A team of researchers from South Korea’s IBS Center for Climate Physics (ICCP) at Pusan National University recently completed one of the most computing-intensive and detailed global warming simulations so far. The global climate model records small-scale atmospheric and oceanic processes with a horizontal scale of 25 km and 10 km, respectively. This unprecedented resolution is adequate to simulate tropical cyclones and oceanic cold wakes (Fig. 1), which are generated when a strong slowly moving tropical cyclone brings cold deep water up to the ocean surface, further influencing the track and intensity of tropical cyclones.
To determine the sensitivity of tropical cyclones to global warming, the research team ran computer model simulations for present-day atmospheric greenhouse gas composition and doubled and quadrupled CO2 concentrations. The simulations ran for 13 months on one of South Korea’s fastest academic supercomputers named Aleph (Fig. 1), generating an equivalent of about two-thousand 1TB hard disks of data.
This climate computer model is also unique in other aspects. For example, it exhibits much weaker ocean temperature errors than the previous generation of climate models. “This improvement was important for a realistic simulation of typhoons in the Indian and Pacific Ocean” says Dr. Jung-Eun Chu, lead author of the study and a project leader at the ICCP.
Over the warm tropical oceans, air rises, flowing polewards at high altitude and sinking in the subtropical regions. The surface-return-flow of this so-called Hadley circulation feeds the global trade wind system. According to the new study, the summer-time Hadley circulation is expected to weaken in future due to the accelerated atmospheric warming at an elevation of 5-15 km, relative to the ground. “A future reduction of rising motion in the tropical atmosphere will make it more difficult for tropical cyclones to develop, which explains the projected future suppression in tropical cyclone seeds and overall numbers in the Pacific and Indian Ocean (Fig. 2)”, explains Dr. Sun-Seon Lee from the ICCP, who conducted the simulations on Aleph. “Interestingly the simulated pattern of future tropical cyclone changes is quite similar to the recent observed trends, which supports the notion that global warming is already altering global extreme weather” adds Dr. Lee.
However, the story how global warming will alter tropical cyclones is more complex. Although for a CO2 doubling the total number of tropical cyclones is expected to decrease in future, developing events will have much higher chance to intensify beyond category 3 due to the higher humidity and energy levels in the atmosphere. “This result confirms previous studies which used less-detailed global climate models. By representing coastal processes more accurately than ever before in a global model, we now have a much higher confidence in these robust model projections, in particular for landfalling tropical cyclones.”, says Prof. Axel Timmermann, co-author of the study and Director of the ICCP.
For even higher greenhouse gas concentrations (CO2 quadrupling), the research team discovered an important, previously unknown, saturation effect: beyond ~5? global warming, the suppression of tropical cyclone seeds is so strong that also the number of strong landfalling events (category 3 or higher) will begin to level off. “Nevertheless, the rainfall associated with each event will continue to increase, amplifying the risk for extreme coastal flooding. Our study provides crucial policy-relevant information for countries that are affected by the impact of tropical cyclones” says Dr. Chu.
The ICCP research team will continue to analyze the vast amount of data generated by these computer simulations to better understand the interaction between ocean heat content and tropical cyclones under present day and future conditions.
###
From EurekAlert!
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iowaclimatescienceeducation--2020-12-17--Government launches long-awaited Energy White Paper

One of its claims is : ‘ we will make the UK the home of green ships and planes ’ .
Easy – just ban their engines from being started .
Publishing wish lists doesn ’ t guarantee engineering feasibility .
These policies will cost a fortune but energy bills will be ‘ affordable ’ , they claim .
Who ’ s paying for all the subsidies – Father Christmas ?
– – – The government has launched its long-awaited Energy White Paper to clean up the nation ’ s energy systems and ensure the journey to net zero by @ @ @ @ @ @ @ Energy Live News .
The white paper expands on Prime Minister Boris Johnson ’ s recently announced Ten Point Plan for a Green Industrial Revolution and sets out the steps needed to cut emissions from industry , transport and buildings by 230 million metric tonnes .
The Energy White Paper commits to supporting up to 220,000 jobs in the new , greener economy over the next decade , with positions ranging from employment in major power generation , carbon capture storage and hydrogen projects , in addition to a nationwide programme to retrofit @ @ @ @ @ @ @ clean heat technologies .
Shifting from fossil fuels to a net zero system The plan promises to overhaul the entire energy system and aims to retrofit millions of homes , electrify transport with renewables , double electricity use instead of oil and gas and bolster renewable energy supply .
The government has promised to make the energy retail market “ truly competitive ” by offering customers more simple methods of switching to cheaper energy tariffs , and tackling “ loyalty penalties ” by automatically switching consumers to fairer , cheaper deals – this @ @ @ @ @ @ @ effectiveness in the market .
The Energy White Paper commits to all electricity being emission-free by 2050 and sets the country on target for an “ overwhelmingly decarbonised ” energy system by 2030 to ensure it remains on the right trajectory for the final goal .
It states low carbon power is a “ key enabler ” of the more sustainable economy of the future and notes demand for clean electricity is forecast to double in order to decarbonise the transport and heating sectors .

One of its claims is: ‘we will make the UK the home of green ships and planes’. Easy – just ban their engines from being started. Publishing wish lists doesn’t guarantee engineering feasibility. These policies will cost a fortune but energy bills will be ‘affordable’, they claim. Who’s paying for all the subsidies – Father Christmas?
– – –
The government has launched its long-awaited Energy White Paper to clean up the nation’s energy systems and ensure the journey to net zero by 2050 is achievable and affordable, says Energy Live News.
The white paper expands on Prime Minister Boris Johnson’s recently announced Ten Point Plan for a Green Industrial Revolution and sets out the steps needed to cut emissions from industry, transport and buildings by 230 million metric tonnes.
Green jobs
The Energy White Paper commits to supporting up to 220,000 jobs in the new, greener economy over the next decade, with positions ranging from employment in major power generation, carbon capture storage and hydrogen projects, in addition to a nationwide programme to retrofit homes and buildings with energy efficiency and clean heat technologies.
Shifting from fossil fuels to a net zero system
The plan promises to overhaul the entire energy system and aims to retrofit millions of homes, electrify transport with renewables, double electricity use instead of oil and gas and bolster renewable energy supply.
Keep bills affordable
The government has promised to make the energy retail market “truly competitive” by offering customers more simple methods of switching to cheaper energy tariffs, and tackling “loyalty penalties” by automatically switching consumers to fairer, cheaper deals – this system will be trialled to test its effectiveness in the market.
Emission-free electricity by 2050
The Energy White Paper commits to all electricity being emission-free by 2050 and sets the country on target for an “overwhelmingly decarbonised” energy system by 2030 to ensure it remains on the right trajectory for the final goal.
It states low carbon power is a “key enabler” of the more sustainable economy of the future and notes demand for clean electricity is forecast to double in order to decarbonise the transport and heating sectors.
Full article here.
Government White Paper here.
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December 17, 2020 at 03:51AM


iowaclimatescienceeducation--2020-12-17--It’s a fix – part 1

It’s a fix – part 1

Local electricity pricing isn ’ t all it ’ s cracked up to be “ Market failure ” often means “ market outcomes I don ’ t like ” , and “ market improvement ” often means “ skewing markets to try to make them produce outcomes that I approve of [ /in my interest ] ” .
Unless the market improvement is removing obstacles to voluntary exchange , it reduces efficiency by definition , because no institutional change would have been required to make markets gravitate towards outcomes that are more cost-effective and not institutionally-inhibited .
The justice of @ @ @ @ @ @ @ an increase in utility .
Typically , it consists of the circular logic of ( a ) calculating the “ efficient ” solution , ( b ) finding that the market doesn ’ t agree , so ( c ) trying to make the market deliver it in order to promote efficiency , which is morally-desirable .
Costs to customers and taxpayers ( including the costs to encourage these “ efficient ” outcomes ) often rise , but that is no discouragement to people who know that their calculations must be more accurate @ @ @ @ @ @ @ PX ) have a number of ways they think we should improve the electricity market : This is the first of three posts looking at each of these claims in detail .
In quantifying that cost , PX ( not Aurora ) resort to the classic device to make small numbers look bigger .
It will save £50bn , they say , so long as you add up all the prospective savings between 2026 and 2050 .
Yes , and I earn over £1,000,000 , so long as you add up my earnings @ @ @ @ @ @ @ .
It ’ s an annoying little propaganda technique that is a reliable pointer to people trying to spin a narrative .
£50bn isn ’ t even justified by the Aurora report .
They estimate an average of £2.1bn/year between 2030 and 2050 , i.e .
£42bn .
PX have to move the boundary to that rather odd starting point of 2026 to get their nice , big , round number .
But that assumes the costs can be backdated like that .
Given the scenario modelled by Aurora , it ’ s @ @ @ @ @ @ @ back-end loaded , and the savings at the start of the period are lower .
Still , savings sound great ; but relative to what ?
It ’ s a reduction in the projected average electricity system spend between 2030 and 2050 , from £53.9bn to £51.8bn p.a .
Aurora do not define electricity system spend .
Does it include taxes , for example ?
Like most British statistics that actually matter , it is surprisingly hard to find a figure for current electricity system spend .
But if we apply average prices @ @ @ @ @ @ @ figures , we get a rough figure of £45bn .
Aurora ’ s figures appear to be in constant £ ( though it is not clear ) .
£45bn in £ would be more like £40bn in £ .
And we know the £45bn includes tax , margins , etc , which we don ’ t know for sure about Aurora ’ s figures .
In other words , this reduction of £2.1bn/year is probably an increase in real terms of at least £10bn/year .
But at least it ’ s less than @ @ @ @ @ @ @ otherwise occur .
The report would have had a different tone if expressed that way .
How can local pricing save cost , you might well wonder ?
If all it did was substitute local prices for national prices , with no change to the underlying distribution of supply and demand , then all it could do would be to redistribute cost , not reduce it .
One effect of “ local pricing ” has nothing to do with its localism .
They have wrapped a hotter political potato in a cosy communitarian @ @ @ @ @ @ @ include time-of-day pricing ( “ Half-hourly demand within each zone is flexible , as EVs , heat pumps and electrolysers respond to regional pricing ” ) .
In other words , electricity will be cheap when demand is low and there ’ s plenty of supply ( e.g .
summer nights ) and expensive when demand is high and supply is tight ( e.g .
winter evenings , though that becomes much less predictable with a lot of intermittent generation on the system ) .
The expectation is that customers will reduce their @ @ @ @ @ @ @ to concentrate their demand on cheap periods .
Persuading people to cut their consumption when it ’ s cold , dark and still outside would undoubtedly save cost .
There ’ s no reason this wouldn ’ t be equally true for time-of-day pricing under a national pricing model , but that does not seem to be one of their modelled scenarios .
Time-of-day pricing is the right approach .
The current approach to pricing is inefficient .
But people are generally not keen on being exposed to reality through the pricing mechanism @ @ @ @ @ @ @ That is presumably why a change that depends on “ demand-side management ” , as it is known in the industry , is being dressed up as localism .
There is a problem with disguising the truth .
It might help to sneak legislation through with less opposition , but it can ’ t disguise the effect when implemented .
It treats the public as fools to be hoodwinked rather than intelligent people owed an honest explanation by their representatives .
It diminishes trust , inhibits reasoned discussion , and reduces the chance @ @ @ @ @ @ @ uncomfortable truth rather than reaching for comfortable fallacies .
It is classic British Establishment .
It is the reason why our governance is so dysfunctional .
It reveals the intellectual insecurity of the people who think themselves sufficiently superior to take this course – if they could justify their choices , why wouldn ’ t they declare them openly ?
In Aurora ’ s explanation , almost all of the saving comes from “ Balancing spending decreases ” .
Demand-side management driven by time-of-day pricing will have that effect .
But they focus @ @ @ @ @ @ @ as regional pricing introduces a signal for RES , particularly in Scotland , to curtail.
” There are reasons to be sceptical that this is as significant as demand responses .
Curtailment means reducing the output below the level that could be produced at that moment .
The curtailment of wind and solar saves almost no cost .
It moves it .
Their marginal operating cost is close to zero , so curtailment has a minimal impact on their costs .
It reduces the number of MWh over which their mostly-fixed costs can @ @ @ @ @ @ @ have to achieve higher prices at other times , or reduce their returns , neither of which is a real saving .
There is no particular reason why national pricing should not also send signals to RES to curtail .
It already does .
In fact , one of the drivers for PX ’ s report was the increase in system balancing costs under Covid , which highlighted ( through a fall in demand ) the challenges and costs of balancing demand with increasing levels of intermittent power .
A key part of @ @ @ @ @ @ @ increasing cost has been highlighted by the GWPF .
In fact , curtailment incurs cost .
Almost all the cost of wind and solar is incurred regardless of how much they are curtailed .
But the more they are curtailed , the less they are contributing to total annual supply .
Assuming total supply remains constant , that means that other sources must be contributing more in total than they would have been had intermittents not been curtailed .
This is the inverse of the argument often made for intermittents : they may @ @ @ @ @ @ @ that we need on the system , but they reduce their utilisation .
If the intermittents are curtailed , we must be using the fossil-fired capacity more than would have been the case without curtailment .
It is not persuasive that the main cause of reduced balancing spending is increased curtailment in Scotland , especially when we have a credible explanation for it in the unheralded inclusion of time-of-day pricing .
When we look at the modelled effect on the deployment of intermittents , it seems even less likely that the cost saving @ @ @ @ @ @ @ of the effects of regional pricing is to increase the capacity of solar PV .
Almost all ( > 10 GW ) of that extra PV is in Scotland ( North , as they categorise it ) .
We all know how sunny Scotland is , especially in winter .
One of the main drivers of the increased demand for electricity is the electrification of heat .
Demand for heat is very seasonal , particularly in Scotland .
Solar output is very seasonal , especially in Scotland .
Unfortunately , that is an @ @ @ @ @ @ @ Scotland would be singularly useless for meeting the electrified heat demand .
Admittedly , it would be quite useful for powering air-conditioning in the tropical parts of Scotland , but that is sadly a limited opportunity .
It is not obvious that this is a cost-saving , efficiency-improving effect of regional pricing .
If the model says it is , that says more about the model than about the potential for solar heating in Scotland .
Another key effect of regional pricing is the promotion of floating offshore wind capacity .
It doesn @ @ @ @ @ @ @ pricing , which may be news to the developers of floating offshore projects .
It seems likely that the addition of floating capacity under regional pricing was prompted by the need to explain how all the additional offshore capacity in the South will occur , rather than as a direct result of regional pricing .
In the South , the floating capacity is additive .
In the Central ( N England ) and North ( Scotland ) zones , it is substitutive , i.e .
displacing fixed offshore wind installations .
In total @ @ @ @ @ @ @ of additional offshore wind capacity as a result of regional pricing .
But the increase in the South is double that , so there is a net reduction in offshore wind capacity in the other regions , and a larger reduction of fixed offshore wind , as some of it is substituted by floating systems .
The GWPF has reported on the high cost of floating offshore wind .
It is ( again ) not obvious that the addition of ( say ) 20 GW of floating offshore wind , mostly in the @ @ @ @ @ @ @ say ) 7 GW of fixed offshore wind in the North of England and Scotland is a cost-saving , efficiency-improving measure .
Scotland may not have much sun in winter , but it has more wind than England .
If the model indicates that it is cost-effective to increase offshore wind capacity and switch it from fixed installations in Scotland to floating installations in the South , then that ( again ) says more about the credibility of the model than about the efficiency of this option .
The last of the main @ @ @ @ @ @ @ ( according to Aurora ’ s model ) is to reduce the total capacity of onshore wind , and to relocate it from Scotland , mainly to the Southern region .
That effect is so strong that the model suggests at best no increase at all in onshore wind in Scotland between 2030 and 2040 , and apparently a slight reduction , while installation goes like gangbusters in the South .
One might freeze capacity for certain constraints , such as a combination of demand and network limitations .
That is somewhat undermined @ @ @ @ @ @ @ capacity increases , but it is conceivable .
Why one would reduce capacity is hard to explain .
Only bad modellers think the job consists simply of constructing and running the model and reporting the outputs .
Good modellers have the ability to do a subjective “ sanity check ” or “ smell test ” on preliminary runs of the model .
This is a vital point at which one can spot an error in the design of the model .
It is tempting to speculate that Aurora has too many PhDs , @ @ @ @ @ @ @ and common sense to spot when their models are not credible .
Onshore wind is the cheapest of the three intermittent technologies covered in Aurora ’ s chart above .
Increasing solar and offshore wind whilst reducing onshore wind will not obviously save cost , any more than the previous measures .
Scotland has more wind than England , onshore as well as offshore .
Load factors are a key component of the economics of wind .
Wind projects located in the South will have materially lower load factors than those in Scotland @ @ @ @ @ @ @ be higher on average .
There may be fewer obstacles getting the power to customers , but the electricity will not be cheaper , quite the reverse .
And then there is the politics of a Conservative government encouraging a quadrupling of wind in the South whilst reducing its deployment in Scotland .
Models have their limitations… The charts say offshore wind will be around 30 GW in 2030 .
Government policy is 40 GW .
The Conservative manifesto and government policy includes no matching ambition for the expansion of onshore wind and @ @ @ @ @ @ @ and solar contribute around 55 of the 100 GW of extra wind and solar by 2050 in the default ( national pricing ) scenario , and more like 60 GW in the regional pricing scenario , most of it in the South , quadrupling the capacity of both in that region .
Which Minister is going to volunteer to give the good news to the Conservative heartlands ?
The reason that Aurora believe these changes will reduce cost is that “ capacity will build closer to demand centres to minimise constraints ” .
@ @ @ @ @ @ @ begs the question of how offshore wind will build close to demand centres .
Presumably , they will still be offshore , but perhaps technically closer , in the sense that the floating windfarms will connect into the grid further south than they otherwise would have done .
Is the avoidance of an undersea backbone really worth the negatives described above of moving the windfarms further south and onto floating platforms ?
It ’ s an odd conflation anyway , because the system problems created by an extra 100 GW of intermittent generation @ @ @ @ @ @ @ close those GW are to demand .
Supply and demand will be just as misaligned , wherever the windfarms are located .
That means large amounts of storage will be required .
If a lot of the output has to be taken into storage and then fed into the grid on demand , is the entry point to the grid the critical constraint ?
Cost of production will surely be more important , because the round-trip losses of storage exacerbate any cost differentials .
Is it really credible that this is about network @ @ @ @ @ @ @ wind and solar by region doesn ’ t change that much ?
The effect is not to move capacity south to reduce strains on the network .
The effect is to move solar capacity north and wind capacity south .
Presumably , the Aurora model has calculated that spreading the technologies more evenly is easier on the grid than concentrating the wind in the north and the solar in the south , even though that reflects the nature of the resources .
Does Aurora ’ s model value correctly the geopolitical realities and @ @ @ @ @ @ @ factors ?
The most credible elements of cost-saving in this aspect of the model are ( a ) the demand responsiveness of time-of-day pricing , and ( b ) a swing from wind ( primarily offshore ) to solar in the balance of intermittent capacity .
But that is not what it says on the tin .
It ’ s a low blow , but deserved : if you had a good idea for electricity , you probably wouldn ’ t promote it on the basis that it ’ s how the Californians @ @ @ @ @ @ @ family company ( Summerleaze ) in 1988 , the same year that they commissioned their first renewable power station .
He has spent most of the time since then developing renewable-energy businesses .
Those businesses included landfill-gas power generation and supplying renewable heating fuel .
Summerleaze holds the increasingly unfashionable view that the risk of climate change does not supersede the need for our energy to be produced when we need it , as cheaply as possible .
Bruno ’ s experiences of a nationalised energy sector , and then ( since privatisation @ @ @ @ @ @ @ corporatism , inept policy design , and regime uncertainty , lit a passion for classical-liberal economics , which led him to the Institute of Economic Affairs , where he is now a trustee .
He is writing here in a personal capacity .
The post It ’ s a fix – part 1 appeared first on The Global Warming Policy Forum ( GWPF ) .

Local electricity pricing isn’t all it’s cracked up to be
“Market failure” often means “market outcomes I don’t like”, and “market improvement” often means “skewing markets to try to make them produce outcomes that I approve of [/in my interest]”. Unless the market improvement is removing obstacles to voluntary exchange, it reduces efficiency by definition, because no institutional change would have been required to make markets gravitate towards outcomes that are more cost-effective and not institutionally-inhibited.
The justice of the improvement lies in something other than an increase in utility. Typically, it consists of the circular logic of (a) calculating the “efficient” solution, (b) finding that the market doesn’t agree, so (c) trying to make the market deliver it in order to promote efficiency, which is morally-desirable. Costs to customers and taxpayers (including the costs to encourage these “efficient” outcomes) often rise, but that is no discouragement to people who know that their calculations must be more accurate than market discovery.
Policy Exchange (PX) have a number of ways they think we should improve the electricity market:
“Local pricing”
“Floor-price Contracts for Differences”
A “low-carbon quota” for the Capacity Market
This is the first of three posts looking at each of these claims in detail.
Local pricing
In quantifying that cost, PX (not Aurora) resort to the classic device to make small numbers look bigger. It will save £50bn, they say, so long as you add up all the prospective savings between 2026 and 2050. Yes, and I earn over £1,000,000, so long as you add up my earnings over the course of my working life. It’s an annoying little propaganda technique that is a reliable pointer to people trying to spin a narrative.
£50bn isn’t even justified by the Aurora report. They estimate an average of £2.1bn/year between 2030 and 2050, i.e. £42bn. PX have to move the boundary to that rather odd starting point of 2026 to get their nice, big, round number. But that assumes the costs can be backdated like that. Given the scenario modelled by Aurora, it’s a fair bet that the costs are back-end loaded, and the savings at the start of the period are lower.
Still, savings sound great; but relative to what? It’s a reduction in the projected average electricity system spend between 2030 and 2050, from £53.9bn to £51.8bn p.a. Aurora do not define electricity system spend. Does it include taxes, for example?
Like most British statistics that actually matter, it is surprisingly hard to find a figure for current electricity system spend. But if we apply average prices to the UK’s sectoral electricity-consumption figures, we get a rough figure of £45bn. Aurora’s figures appear to be in constant £ 2012 (though it is not clear). £45bn in £ 2019 would be more like £40bn in £ 2012 . And we know the £45bn includes tax, margins, etc, which we don’t know for sure about Aurora’s figures.
In other words, this reduction of £2.1bn/year is probably an increase in real terms of at least £10bn/year. But at least it’s less than the increase of over £12bn that would otherwise occur. The report would have had a different tone if expressed that way.
How can local pricing save cost, you might well wonder? If all it did was substitute local prices for national prices, with no change to the underlying distribution of supply and demand, then all it could do would be to redistribute cost, not reduce it.
One effect of “local pricing” has nothing to do with its localism. They have wrapped a hotter political potato in a cosy communitarian blanket. They assume local pricing will include time-of-day pricing (“Half-hourly demand within each zone is flexible, as EVs, heat pumps and electrolysers respond to regional pricing”). In other words, electricity will be cheap when demand is low and there’s plenty of supply (e.g. summer nights) and expensive when demand is high and supply is tight (e.g. winter evenings, though that becomes much less predictable with a lot of intermittent generation on the system).
The expectation is that customers will reduce their demand when prices are high and try to concentrate their demand on cheap periods. Persuading people to cut their consumption when it’s cold, dark and still outside would undoubtedly save cost. There’s no reason this wouldn’t be equally true for time-of-day pricing under a national pricing model, but that does not seem to be one of their modelled scenarios.
Time-of-day pricing is the right approach. The current approach to pricing is inefficient. But people are generally not keen on being exposed to reality through the pricing mechanism. So it is politically unpopular. That is presumably why a change that depends on “demand-side management”, as it is known in the industry, is being dressed up as localism.
There is a problem with disguising the truth. It might help to sneak legislation through with less opposition, but it can’t disguise the effect when implemented. It treats the public as fools to be hoodwinked rather than intelligent people owed an honest explanation by their representatives. It diminishes trust, inhibits reasoned discussion, and reduces the chance that people will come to understand the uncomfortable truth rather than reaching for comfortable fallacies. It is classic British Establishment. It is the reason why our governance is so dysfunctional. It reveals the intellectual insecurity of the people who think themselves sufficiently superior to take this course – if they could justify their choices, why wouldn’t they declare them openly?
In Aurora’s explanation, almost all of the saving comes from “Balancing spending decreases”. Demand-side management driven by time-of-day pricing will have that effect. But they focus on “the reduction in constraint costs as regional pricing introduces a signal for RES, particularly in Scotland, to curtail.” There are reasons to be sceptical that this is as significant as demand responses.
Curtailment means reducing the output below the level that could be produced at that moment. The curtailment of wind and solar saves almost no cost. It moves it. Their marginal operating cost is close to zero, so curtailment has a minimal impact on their costs. It reduces the number of MWh over which their mostly-fixed costs can be spread, which means they would have to achieve higher prices at other times, or reduce their returns, neither of which is a real saving.
There is no particular reason why national pricing should not also send signals to RES to curtail. It already does. In fact, one of the drivers for PX’s report was the increase in system balancing costs under Covid, which highlighted (through a fall in demand) the challenges and costs of balancing demand with increasing levels of intermittent power. A key part of that cost is constraint payments, whose increasing cost has been highlighted by the GWPF.
In fact, curtailment incurs cost. Almost all the cost of wind and solar is incurred regardless of how much they are curtailed. But the more they are curtailed, the less they are contributing to total annual supply. Assuming total supply remains constant, that means that other sources must be contributing more in total than they would have been had intermittents not been curtailed. This is the inverse of the argument often made for intermittents: they may not reduce the capacity of fossil-fired generation that we need on the system, but they reduce their utilisation. If the intermittents are curtailed, we must be using the fossil-fired capacity more than would have been the case without curtailment.
It is not persuasive that the main cause of reduced balancing spending is increased curtailment in Scotland, especially when we have a credible explanation for it in the unheralded inclusion of time-of-day pricing. When we look at the modelled effect on the deployment of intermittents, it seems even less likely that the cost saving stems from their utilisation levels.
One of the effects of regional pricing is to increase the capacity of solar PV. Almost all (> 10 GW) of that extra PV is in Scotland (North, as they categorise it). We all know how sunny Scotland is, especially in winter. One of the main drivers of the increased demand for electricity is the electrification of heat. Demand for heat is very seasonal, particularly in Scotland. Solar output is very seasonal, especially in Scotland. Unfortunately, that is an inverse correlation. The extra PV in Scotland would be singularly useless for meeting the electrified heat demand. Admittedly, it would be quite useful for powering air-conditioning in the tropical parts of Scotland, but that is sadly a limited opportunity. It is not obvious that this is a cost-saving, efficiency-improving effect of regional pricing. If the model says it is, that says more about the model than about the potential for solar heating in Scotland.
Another key effect of regional pricing is the promotion of floating offshore wind capacity. It doesn’t exist at all under national pricing, which may be news to the developers of floating offshore projects. It seems likely that the addition of floating capacity under regional pricing was prompted by the need to explain how all the additional offshore capacity in the South will occur, rather than as a direct result of regional pricing.
In the South, the floating capacity is additive. In the Central (N England) and North (Scotland) zones, it is substitutive, i.e. displacing fixed offshore wind installations. In total, there seems to be 5-10 GW of additional offshore wind capacity as a result of regional pricing. But the increase in the South is double that, so there is a net reduction in offshore wind capacity in the other regions, and a larger reduction of fixed offshore wind, as some of it is substituted by floating systems. The GWPF has reported on the high cost of floating offshore wind.
It is (again) not obvious that the addition of (say) 20 GW of floating offshore wind, mostly in the South, and the removal of (say) 7 GW of fixed offshore wind in the North of England and Scotland is a cost-saving, efficiency-improving measure. Scotland may not have much sun in winter, but it has more wind than England. If the model indicates that it is cost-effective to increase offshore wind capacity and switch it from fixed installations in Scotland to floating installations in the South, then that (again) says more about the credibility of the model than about the efficiency of this option.
The last of the main effects of regional pricing on intermittent capacity (according to Aurora’s model) is to reduce the total capacity of onshore wind, and to relocate it from Scotland, mainly to the Southern region. That effect is so strong that the model suggests at best no increase at all in onshore wind in Scotland between 2030 and 2040, and apparently a slight reduction, while installation goes like gangbusters in the South.
One might freeze capacity for certain constraints, such as a combination of demand and network limitations. That is somewhat undermined by preceding and subsequent periods where the capacity increases, but it is conceivable. Why one would reduce capacity is hard to explain.
Only bad modellers think the job consists simply of constructing and running the model and reporting the outputs. Good modellers have the ability to do a subjective “sanity check” or “smell test” on preliminary runs of the model. This is a vital point at which one can spot an error in the design of the model. It is tempting to speculate that Aurora has too many PhDs, and not enough people with practical experience and common sense to spot when their models are not credible.
Onshore wind is the cheapest of the three intermittent technologies covered in Aurora’s chart above. Increasing solar and offshore wind whilst reducing onshore wind will not obviously save cost, any more than the previous measures.
Scotland has more wind than England, onshore as well as offshore. Load factors are a key component of the economics of wind. Wind projects located in the South will have materially lower load factors than those in Scotland, on average. Land values will be higher on average. There may be fewer obstacles getting the power to customers, but the electricity will not be cheaper, quite the reverse. And then there is the politics of a Conservative government encouraging a quadrupling of wind in the South whilst reducing its deployment in Scotland. Models have their limitations…
The charts say offshore wind will be around 30 GW in 2030. Government policy is 40 GW. The Conservative manifesto and government policy includes no matching ambition for the expansion of onshore wind and solar. The model assumes onshore wind and solar contribute around 55 of the 100 GW of extra wind and solar by 2050 in the default (national pricing) scenario, and more like 60 GW in the regional pricing scenario, most of it in the South, quadrupling the capacity of both in that region. Which Minister is going to volunteer to give the good news to the Conservative heartlands?
The reason that Aurora believe these changes will reduce cost is that “capacity will build closer to demand centres to minimise constraints”. This reinforces the political challenge. It begs the question of how offshore wind will build close to demand centres. Presumably, they will still be offshore, but perhaps technically closer, in the sense that the floating windfarms will connect into the grid further south than they otherwise would have done. Is the avoidance of an undersea backbone really worth the negatives described above of moving the windfarms further south and onto floating platforms?
It’s an odd conflation anyway, because the system problems created by an extra 100 GW of intermittent generation are not primarily to do with how close those GW are to demand. Supply and demand will be just as misaligned, wherever the windfarms are located. That means large amounts of storage will be required. If a lot of the output has to be taken into storage and then fed into the grid on demand, is the entry point to the grid the critical constraint? Cost of production will surely be more important, because the round-trip losses of storage exacerbate any cost differentials.
Is it really credible that this is about network constraints, when the combined capacity of wind and solar by region doesn’t change that much? The effect is not to move capacity south to reduce strains on the network. The effect is to move solar capacity north and wind capacity south. Presumably, the Aurora model has calculated that spreading the technologies more evenly is easier on the grid than concentrating the wind in the north and the solar in the south, even though that reflects the nature of the resources. Does Aurora’s model value correctly the geopolitical realities and the relative costs of transmission versus load factors?
The most credible elements of cost-saving in this aspect of the model are (a) the demand responsiveness of time-of-day pricing, and (b) a swing from wind (primarily offshore) to solar in the balance of intermittent capacity. But that is not what it says on the tin.
It’s a low blow, but deserved: if you had a good idea for electricity, you probably wouldn’t promote it on the basis that it’s how the Californians do it.
About the author
Bruno Prior joined his family company (Summerleaze) in 1988, the same year that they commissioned their first renewable power station. He has spent most of the time since then developing renewable-energy businesses. Those businesses included landfill-gas power generation and supplying renewable heating fuel. Summerleaze holds the increasingly unfashionable view that the risk of climate change does not supersede the need for our energy to be produced when we need it, as cheaply as possible.
Bruno’s experiences of a nationalised energy sector, and then (since privatisation) of the descent into ever greater corporatism, inept policy design, and regime uncertainty, lit a passion for classical-liberal economics, which led him to the Institute of Economic Affairs, where he is now a trustee. He is writing here in a personal capacity.
The post It’s a fix – part 1 appeared first on The Global Warming Policy Forum (GWPF).
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December 17, 2020 at 03:30AM


iowaclimatescienceeducation--2020-12-17--Wrong Again: 2020’s Failed Climate Doomsaying

2020 has been the wildest and most unpredictable year in the memory of most people .
But did the climate @ @ @ @ @ occur in or by 2020 materialize ?
What follows are 10 predictions made for 2020 @ @ @ @ @ As it turns out , climate doomsayers weren ’ t seeing so 20-20 when it @ @ @ @ @ Again : 2020 ’ s Failed Climate Doomsaying

2020 has been the wildest and most unpredictable year in the memory of most people. But did the climate doom that was predicted to occur in or by 2020 materialize? What follows are 10 predictions made for 2020 and what really happened. As it turns out, climate doomsayers weren’t seeing so 20-20 when it came … Continue reading Wrong Again: 2020’s Failed Climate Doomsaying
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December 17, 2020 at 01:32AM