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Author Topic: 🚩 Global Climate Chaos ☠️  (Read 74958 times)

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AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1380 on: June 24, 2018, 03:44:03 pm »
Quote
RE: That graph only goes through 2004.  What do the latest real data points look like? ???

BP(2018) shows production in 2004 was 81.1 Mbpd and in 2017 was 92.6 mbpd - that's a 14% increase in 13 years. (1.01% / year, after 20 years of 1.6%).  That includes fracked oil , tar sands and Natural Gas Liquids.  The split between Crude and Condensate is unknown since nobody collects the data ( ! ) so we can't tell how much is fracked.  However we do know fracking only came on stream in 2006, just in time for the Peak in Crude Only in 2005.  Fracking has never made a profit and as soon as USG/Fed stops backing the losses, the whole lot will collapse in a heap overnight, causing a major financial shock, which will bring down the Dollar and Industrial Civilisation.

Amazon, Tesla, Facebook and numerous other Industrial Civilization ventures have never made a profit either, but that doesn't stop the  TBTF Banks from buying their corporate paper with money borrowed from Da Fed.  In fact, Steve's analysis shows no industrial enterprise from the railroads to carz to planes ever made a real profit, all these industries grew on credit and eventually went BK, to then be bailed out by Da Goobermint and the debt shifted (in theory) to the tapayer.  The taxpayer of course can't afford to retire this debt either, so the total debt keeps increasing as long as the enterprises are kept running.

Since as you say they must have an endless credit line to keep running, it's unlikely Da Fed will cut off that credit because then it means not just the end of Industrial Civilization but the end of their hegemony over the world, through the credit they issue to the MIC to buy planes, drones and bombs.  There will either have to be some sort of credit lockup or the resources become unavailable no matter how much credit you issue.  Another possibility is that the resource controllers who actually still have some oil left underground they can pump up stop taking the credit in payment, aka they abandon the Dollar.  However, since they hold so much of their reserves in Dollars. this amounts to cutting off your nose to spite your face.  It's why nobody has quit on the whole system yet.  It will of course inevitably occur, but the timeline on it still remains obscure.

RE

Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1381 on: June 24, 2018, 03:46:52 pm »
Agelbert NOTE: K-Dog lives in Seattle, Washington.

Coal Trains have joined the oil trains going north for export.  I have seen 3 coal trains passing through Seattle in the last week.   I saw the first one only two weeks ago.  It had three engines pulling and was a mile long easy.


I do not want to criticize or disrespect anybody here who cares about their carbon footprint but when I see megatons of coal and oil leaving the country to keep the fat cat gravy train going and nobody caring I wonder what is the point of it all. 

We import, so we export.  I get that.  I also know that our fracked oil is best blended with other stock because refineries just for fracked oil don't exist.  I get that.  Stop using the stuff tomorrow and we are all dead.  I get that.  It is not a black and white issue.  I get that.  Nobody cares about changing the equations so we survive.  I don't get that.

As too how much is left I'll make the observation that the conservative element of suit wearing forked tongued devils in American have digested peak oil dogma as well as we have here but from a completely different perspective.  The point of peak oil was only ever to show that the oil supply is finite.  The M. King Hubbert classic depletion curve is from a single field with a simple development and extraction profile.  All it was ever meant to do is tell everybody, see kiddies if you eat all your candy it will be all gone. 

The world was never going to follow that simple curve and even we advocates of understanding have been distracted by the 'theory' aspect of it all, as if the ragged edge of the real depletion curve disproved something.  The ragged edge reflects real world complexity.  Humans have wars and kill each other.  Economies crash.  People want to make America great again.  These things throw more variables in the picture than a single field has and the fact that the peak is ragged does not change what remains in the ground in any way.  Yet some are quick to say peak oil is dead and the theory wrong.

Ugo Bardi has pointed out that collapse can, most likely will, resemble Seneca's cliff.  In that scenario significant oil will be in the ground but it won't be easy to get to and it won't matter because we will be in a new stone age.  Fact is if we were not fracking now we would all be fu cked.  A minor detail it seems everybody has overlooked.  We found some more borrowed time.  That is all that has happened or America would already have crashed so hard that 2008 would be looking like a summer picnic right now.

That is what we have done again.  Technology which in modern times is mostly the hundreds of ways in which we use hydrocarbons has enabled us to innovate our way out of a corner to get more hydrocarbons to use up.  The can has been kicked down the road. But it can't last.  Our ditch awaits.


Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1382 on: June 24, 2018, 03:47:59 pm »
I do not wan't to criticize or disrespect anybody here who cares about their carbon footprint but when I see megatons of coal and oil leaving the country to keep the fat cat gravy train going and nobody caring I wonder what is the point of it all.

A few people care, like us Diners.

The point is to try and wake up a few of the slightly lower wattage bulbs out there  who might just grasp the truth if you explain it carefully, and then have some time to Prep Up!

Remember the Motto of the Diner...


"SAVE AS MANY AS YOU CAN"

RE

Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1383 on: June 24, 2018, 03:49:27 pm »
Interesting and informative comments. 🧐

I continue to believe that severe biosphere degradation will be the cause of the collapse of human civilization, not the lack of energy to run it.

Mankind is just too damned innovative in his ability to exploit nature. No matter how polluting, how destructive, how stupid and how short sighted, human technology refuses to trade short term comfort for long term preservation of the biosphere for the purpose of multi-species survival.

The Precautionary Principle is nothing but a joke to most people in industry. 


Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1384 on: June 25, 2018, 10:58:14 pm »


Solar Prices Nosedive After China Pullback Floods Global Market

June 21, 2018

By Christopher Martin, Bloomberg
         
Solar panels were already getting cheaper this year, and then China pulled the plug this month on about 20 GW of domestic installations. The result was a glut of global inventories, and now prices are plunging even faster.

China, the world’s biggest solar market, on June 1 slammed the brakes on new projects that would have had as much capacity as about 20 nuclear power plants. With a global panel glut it’s a buyer’s market and developers in other countries are delaying purchases, holding out for even lower prices.

The average price for a polysilicon module slumped 4.79 percent since May 30, reaching a record low of 27.8 cents a watt Wednesday, according to PVInsights. That’s on track to be the biggest monthly decline since December 2016, the last time the industry was facing a global oversupply. China manufactures about 70 percent of the world’s solar components.

The decline will hurt the largest manufacturers like JinkoSolar Holding Co. and is a boon for developers like Sunrun Inc., which are expected to benefit from lower costs.

“Chinese and international project developers are putting their orders on hold as modules get cheaper,” Yali Jiang, an analyst at Bloomberg New Energy Finance, said in a research note Tuesday. By the end of the year, she expects module prices will slide to 24 cents a watt 👀, down 35 percent from 37 cents at the end of 2017.

©2018 Bloomberg News

https://www.renewableenergyworld.com/articles/2018/06/solar-prices-nosedive-after-china-pullback-floods-global-market.html

Agelbert NOTE: Why don't I think this news is actually good? Because, to anyone that reads between the lines, it means China is, rather than contiinuing the big push for total Renewable Energy, is finding it cheaper (when the pollution issue is ignored, of course -replacing coal with GAS cuts down on particulates, but does nothing to slow GHG pollution) to buy GAS for energy than to get it from Solar Panels.

They are probably suddenly getting GAS real cheap, some of which comes from the USA, not just the usual suspects like Russia and Iran. This "bridge fuel" means an INCREASE in global emissions in an already runaway GHG situation 🔥. China uses a LOT OF ENERGY!


I'm sure the Fossil Fuel funded Climate Change Deniers will tell us this is "no big deal"😈.


I think it is one more straw breaking the Biosphere Camel's Back. IOW, it's probably just about (see below) for Human civilization, even if it takes another two or three decades to feel the full brunt of Catastrophic climate Change.

« Last Edit: June 26, 2018, 11:54:17 am by AGelbert »
Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1385 on: June 26, 2018, 05:31:26 pm »
I don't expect this to be resolved completely, but I appreciate you being willing to keep it a bit more friendly. I see your point on ethanol, which makes sense in the context you're describing.

I'm not sure what Palloy's background is, because he never shares much personal info, but my guess is that he's coming from a teaching background in addition to having been an IT professional. His POV is one I'd expect from an experienced and knowledgable teacher who taught exceptional high school students or college students.

Although he likes to say the word mathematics, he is equally schooled on physics and physical chemistry (and general chemistry, including solutions). His arguments do tend to place emphasis on calculated theoretical thermodynamic results and ignore the sort of real world deviations from that (like incomplete combustion) that would be of more of interest to an engineer or someone actually building real engines.

But it's really good to have someone smart and well educated on the Diner who can debunk obvious bullshit, which he is good at. There are a bunch of people who want to believe in magic, when it comes to understanding FF's. (And renewables, unfortunately).

It is my studied opinion that the advocates of a 100% Renewable Energy Transition are far more reality based than the fossil fuel funded happy talkers, endlessly reminding us, with a litany of half-truths, how much we "owe" the fossil fuel welfare queen Industry for destroying our democracy by buying our politiicans giving us such a high standard of living. Never mind the "irrelevant" MASSIVE, biosphere DESTROYNG pollution issue. Belief in magic solutions, (See: Trust us, we'll think of something) is what the Fossil Fuel Fascists specialize in.  😈🦕

One salient point that must never be left out of the discussion about readership in the Doomstead Diner is the fact that it is based on collapse being triggered by peak oil.

When what actually materializes is a global multi-government fascist pollution producing monstrocity that prefers to see the entire biopshere go **** up than to allow a collapse, the interest in this forum will fade. As RE says, this is part of Collapsenicksphere, or something like that.

I see galloping fascism. I see increased human depravity and ruthless high tech cruelty like has never been seen before. I see massive inceases in pollution. I see more and more species we depend on being so depleted, even before they go extinct, that TPTB start engineering a human population "useless eater" die-off so that they can enjoy their Libertarian Wet Dream for another century or so (until they end up as crispy critters while trying to move to Mars).

I do not see a collapse. I entertained that idea for a while back in 2012. After much study, research and reading, I am convinced that collapse talk is "high energy density" hot air that makes fossil fuels appear "precious", rather than the biosphere killing poisons that they actually are. That is profit over planet stupidity.

I sincerely wish Palloy is right and I am wrong.

But, unlike Palloy, I live in the real world of cause and effect. Tesla understood people like Palloy. So do I.

Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1386 on: June 26, 2018, 07:38:28 pm »
 
Make Nexus Hot News part of your morning: click here to subscribe.

June 25, 2018



Despite Denial, Thirty Years of Climate Projections Still Match Observations

Saturday was the 30th anniversary of the day Dr. James Hansen famously testified to Congress on the dangers of climate change. A variety of great pieces that ran this week, from the AP, Guardian and others have shown how Dr. Hansen’s predictions back in 1988 have largely--and unfortunately--come true.

Deniers, of course, trumpeted otherwise. They’re wrong, but that didn’t stop the Wall Street Journal ran an op-ed on Friday from Cato’s Pat Michaels and Ryan Maue that claims because the Earth is “only modestly warmer,”  the “rapid warming [Hansen] predicted isn’t happening.”

Maue and Michaels point, obliquely, to the pause as an excuse, claiming that aside from the 2015-16 El Nino, temperature hasn’t increased since 2000. Not sure what data they’re looking at, because it definitely has. There’s also the fact that 2015 would have been record hot even without that year’s El Nino, and 2016 would not have been record hot without climate change. 

Pat Michaels, who once said he figures about 40% of his funding comes from fossil fuels, and Ryan Maue, who pretends not to be a denier but writes crap like this, make all sorts of other claims in the piece, with not much hard evidence.

Here, then, we’ll provide some. If you only click one link today, make it this post by blogger Tamino, who provides a very simple set of graphs showing Hansen’s forecast and observations. No big surprise, Hansen was right. (If you’d like something more technical about Hansen’s predictions, Real Climate has what you’re looking for.)

The AP’s Seth Borenstein talked with Hansen for a piece last week (part of a great AP series of stories) about how Hansen wishes he wasn’t so right. Borenstein offers some details Michaels and Maue conveniently ignore, along with some real-world implications of warming.

If you’d like a more qualitative judgement on Hansen’s prediction, or a video to watch, Yale’s Climate Connections talked to a number of climate experts who aren’t biased by fossil fuel funding like Michaels. (Spoiler alert: hey were all impressed with how “remarkably prescient” Hansen was.)

And finally, because Michaels and Maue also mention the IPCC models, it’s worth looking at Zeke Hausfather’s analysis last year for Carbon Brief of how well various models projected warming.

Take a look at all these thoughtful analyses, and then wonder why the Journal chose to include two different pictures of Hansen along with the op-ed, and not a single graph, chart, or visual comparison comparing and contrasting his forecast with what’s happened.

It’s almost like the Journal’s opinion editor knew such a graph would totally debunk the entire thrust of the op-ed. No doubt that was just an accidental oversight and not a sign that they’re being deliberately deceptive. It’s not like the Journal :evil4: would allow denial on its opinion page, right?  ;)




Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1387 on: June 26, 2018, 10:38:07 pm »
EcoWatch

By Olivia Rosane

Jun. 25, 2018 11:53AM EST

2,500 Forced to Flee as California Wildfire Season Heats Up 🔥

In 2017, the effects of drought and climate change led California to suffer its most destructive wildfire season in history, which caused $10 billion in damage and left 44 dead, according to The New York Times.

As summer begins, 2018 isn't starting out any easier for the Golden State. On Monday, authorities ordered the evacuation of 2,500 residents of a rural community in Spring Valley, California, out of fear a raging fire would cut it off, Reuters reported.

Spring Valley, about 100 miles northwest of Sacramento, is in danger from the Pawnee Fire, which jumped the only road leading to the small community, Lake County Sheriff's Office Emergency Services Manager Dale Carnathan told Reuters.

The Pawnee fire started Saturday in Clearlake Oaks, California and has burned 12 buildings and put another 600 at risk, The Associated Press reported Monday. It has been fanned by strong winds and high temperatures, according to Reuters.

"What we're stressing is that people, when they get the evacuation order, they heed it immediately and get out and stay out until it is safe to return," the state Department of Forestry and Fire Protection Battalion Chief Jonathan Cox told The Associated Press. "This is one of four large fires burning in Northern California. It's a good reminder that fire season is upon us."

As of Sunday, the Pawnee fire had not been contained and covered 12 square miles.

Firefighters have made more progress on the other three fires burning in the region. The Stoll Fire in Tehama County destroyed "multiple residential and commercial buildings," Cal Fire told The Associated Press. But it only burned across three-quarters of a mile and was halfway contained as of reporting Monday.

The Lane Fire, also in Tehama County, covered 5.5 square miles but did not burn any buildings, though it threatened 200 and prompted some evacuations. It was 10 percent contained. Another fire in Shasta County, the Creek Fire, prompted evacuations but did not cause any property damage. It covered 1.6 square miles and was 20 percent contained.

The fires come as California braces for another devastating fire season. There are 129 million dead trees in the state due to drought and a bark-beetle infestation that could provide fuel for fires, in addition to dry brush and other potential fuels, CNBC reported Friday. Wildfires in Southern California already forced thousands to evacuate Laguna Beach and Alison Viejo earlier in June.

A month ago, Cal Fire Deputy Chief Scott McLean told The New York Times his department had already fought 1,200 fires that burned 8,000 acres, whereas by the same time in 2017, they had fought 1,049 fires that had burned 2,200 acres.

"The new normal is already here. We don't even use 'new' anymore," McLean told The New York Times. "It's the reality. The fire season is expanding. The weather has changed. We don't even consider it a fire season anymore. In Southern California especially, it's year-round."

https://www.ecowatch.com/california-wildfires-summer-2018-2581218464.html

Agelbert NOTE: Do not hold your breath waiting for the Hydrocarbon Industry to be held, even partially, PAY for this forest wildlife and human community destoying EFFECT that THEY CAUSED by profiting from the burning of hydrocarbons. That isn't part of their "high energy density" ERoEI calculations...

Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1388 on: June 26, 2018, 10:42:58 pm »
Truthout

June 25, 2018

Area of Global Dead Zones Doubling Every 10 Years 😟

Dahr Jamail, Truthout: Oceanic dead zones -- areas of low oxygen that are caused by warming waters, human pollution and runoff from fertilizers used in industrial agriculture -- number more than 400, and are growing rapidly. These dead zones not only impact marine life but also fishing industries.



SNIPPET:

Impacts on Marine Life

Scavia described how organisms unable to swim away from the dead zones — like worms and other animals that many fish feed on — will die.

“Fish that can swim will avoid the dead zone, but that often forces them into habitats that are less suitable for them, resulting in slower growth,” Scavia said. “Sometimes the fish (especially shrimp) are forced into more confined areas, making them more vulnerable to predators, including human fishing nets.”

Rabalais added that the ocean’s ability to recover from dead zones can take time. “Improvement of oxygen conditions following excess nutrient flux may take years to decades,” she said.

Rota pointed out how the commercial fishing industry in the Gulf of Mexico is impacted negatively by the dead zone. “This has impacts on some of the key prey species in the gulf, such as shrimp, crabs and Atlantic croaker,” he said, while adding that the only dead zone larger than that in the Gulf of Mexico is one in the Baltic Sea.

Scavia said we should be concerned that thousands of square miles of water on Earth have low oxygen levels. “These regions are basically ‘taken out of production,’ and if this amount of land was taken out of production, there would be significant concern,” he explained. “These dead zones also put some of our most important fisheries at risk.

Full article:

https://truthout.org/articles/area-of-global-dead-zones-doubling-every-10-years/

This really, really does suck. It really makes me want to see it while it's still around. Even more, I mean. Perhaps if I knew the ocean was gonna be fine, I wouldn't want so badly to spend time in and around it. It's not like I don't fear it too. I do. I've seen the evidence now.


We are environmentally screwed, Eddie. I've got a few positive nuggets lined up for tomorrow but the negative stuff is there too and I feel compelled to post that tomorrow as well. Hasta Mañana, Compadre.
Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1389 on: June 27, 2018, 06:33:14 pm »

Without getting into the argument, because not enough of it is explained, I would just like to say that the above chart does NOT come from IPCC AR5 as the label on it suggests. It appears to have been photoshopped.  Why I don't know, but this is an article I wouldn't believe based on the photoshopping alone.

Standing by for the abuse.

The chart was not "photoshopped. That chart is a  screenshot I TOOK from the short 2 minutes plus 43 seconds video you failed to watch. ;D 

Here's the video. The discussion in the video is actually a short abstract from a recent paper by Brown and Caldeira. The scientist talking is Doctor Brown, but either one of them knows a bit more about the IPCC RCP pathways than you do, to put it mildly. Take it up with one of these esteemed scientists if you think they were "faking" that chart data you are so erroneously unable to accept as reality based. Shame on you. 👎


Here's an interesting post by Doctor Brown.

Quote
Patrick T. Brown, PhD
Global temperature: 2018 likely to be colder than 2017, record high possible in 2019
Posted on January 18, 2018 by ptbrown31

We are working on a new statistical method for predicting interannual variability in global mean surface air temperature (GMST). The method uses the preceding few years of globally gridded temperature anomalies and Partial Least Squares regression to predict the GMST of the following couple of years. See our recent Nature paper for information on applying Partial Least Squares regression in a different climate context.

The plot below shows our forecast for 2017 (using no data from 2017) compared to the just-released 2017 value for the NASA GISTEMP dataset. We also show our forecast for 2018 and 2019 with 68% confidence intervals. The method suggests that 2018 is likely to be colder than 2017 but record warmth is ‘more-likely-than-not’ in 2019.


Brown_GMST_Forecast_2018_2019

We are still in the midsts of sensitivity tests, the method is unpublished and it has not undergone peer review. Thus, these results should be considered to be part of a ‘beta version’ of our method.

Another caveat is that the method cannot possibly predict events like large volcanic eruptions which would drastically alter any annual GMST anomaly and invalidate our forecast.
 https://patricktbrown.org/]https://patricktbrown.org/
[/color]

Here's another post with a lengthy video (Stanford Presentation by Doctor Brown) that you can try to discredit at your peril. ;D  Doctor Brown is head and shoulders above you in scientific knowledge, education and experience in objectively comparing different IPCC RCP model scenarios with empirically measured climate data.

Here's a Screenshot I just made from the video included in his post. Don't you DARE claim it is a "Photoshopped" chart.


Quote
Combining Physical and Statistical Models in Order to Narrow Uncertainty in Projected Global Warming

Posted on January 19, 2018 by ptbrown31

Below is a presentation I gave on our recent research published in Nature titled “Greater future global warming inferred from Earth’s recent energy budget”. This was for the Stanford University Department of Electrical Engineering and Computer Systems Colloquium (EE380). Thus, it is intended for a very technically-savvy but non-climate scientist audience.


https://patricktbrown.org/

I don't need to abuse you, because you are too good at embarrassing yourself, "Professor" Palloy.

Not trying to bust your chops or question your personal bona fides. Thanks for setting me straight.

I'm not convinced that you're completely right about alcohol as a fuel. I'm listening to the argument with interest.

I KNOW you're much more widely read on the subject than I am, but I'm of the opinion that the EROEI for alcohol (from corn) is quite poor (as in just over 1).  I have read that alcohol from sugar cane has a much higher EROEI, maybe 8.

I wasn't aware of Edison's work on alcohol fuel, but remember, he's the guy who held out for DC transmission lines, so he wasn't always right.


You are welcome. I told all that about my edumecation ;D to RE back in 2012. I actually had three iterations of college fun and games. The first was mostly 2 years of pre-engineering followed by one year of Aviation.

In the second iteration I took Accounting and Business Adminsitration all the way up to, and including, Managerial Accounting. I know how to read corporate financial reports because of that those two years of accounting and other businees stuff. I KNOW GAAP. I KNOW how they have been thoroughly corrupted by Wall Street BULLSHIT.

I have the equivalent of an associate degree in computer science as well, from FAA lengthy computer science courses.

I don't genrally toot my horn like this but I was the only one in my Automation Department that could code in assembler language from memory, and without a mnemonics reference card.

So, why ain't I upper middle class or rich? The REASON I am not rolling in the doe is not something I wish to discuss, but my intelligence is not the reason I am not upper middle class. They had a nickname for me in the Automation Department. They used to call me lazer brain. Very few people on this planet are my equal in the cursed intelligence that forces me to think, think, analyze, and think some more about how and why this society is so SCREWED UP. I can see though bullshit, especially the pseudo-erudite brand that "Professor" Palloy specializes in.

I will never toot my horn here again. If people do not think I am credible, **** THEM. How's that for good Christian neighborly behavior?

I'll dig up some data on the ethanol issue and post it on my channel when I get to it. This is not the place for that discussion.

I have to tell you AG that my heart is with the animals and the remaining indigenous tribes. But it's not possible for me now to go and live in the Amazon. I think that I have already expressed my extreme dislike of the actions of the deep state and their corporatists. Also my disgust at living any longer in the US with all its lack of morality, civility, traditional virtues, etc. I mostly could never stand American popular culture, it's vulgarity, it's ignorance of anything truly beautiful. 

I don't have a clue as to how I could reduce my carbon footprint further. I haven't owned a motor vehicle for at least eight years. I use only public transportation run on electricity and not that often. I walk a lot for shopping. I buy local which is not under the aegis of the damned Kochs. I boycott anything Israeli. I did fly once more than a year ago to get here. I use as little utilities as possible in a land with abundant water. I am moving towards being vegan. The medical profession is now confronted with having to acknowledge that a plant based diet produces the healthiest life for humans not to mention reducing methane in the atmosphere . Not to say that everything here is perfect by any means, but it sure beats the hell out of California on every level. And I'm certainly NOT talking about Mexicans, legal or illegal, some of the sweetest people I have ever known. So what to do? What can be done? You said to me many years(by now) ago, "We do what we can". So I am!




As long as you continue to do what you can, anybody that says you are not "doing enough" is full of ****. And you can tell them that Christian Anthony said so.  ;D

Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1390 on: June 27, 2018, 08:17:25 pm »
Quote
The Nuts and Bolts of Arctic Methane 🚩


Paul Beckwith

Published on Jun 27, 2018

In this first of a series of videos on Arctic Methane, I get down to the nitty-gritty. I discuss natural and human-caused sources of methane, and how humans are even changing these natural sources with abrupt climate change. 

I highly recommend that you google “AMAP Arctic Methane” and download the comprehensive report to follow along as you watch this video and the ones to follow.

The risk of huge burps of methane in the Arctic are ever increasing from Arctic Temperature Amplification and accelerating sea ice loss.

Please support my work with a donation at http://paulbeckwith.net


Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1391 on: June 27, 2018, 09:35:03 pm »
Quote
AG: That chart is a  screenshot I TOOK from the short 2 minutes plus 43 seconds video you failed to watch.

I DID watch the Brown video and saw the photoshopped chart in it, so I wasn't suggesting YOU photoshopped it, Brown presumably did, hence my reluctance to believe what he says. For the record, here is the chart from IPCC AR5:


The part we should be interested in is the RCP-2.6 part, not the RCP-8.5 part. Note that the mean in 2100 is about +1.7 C (with 90% of modeled results being in the range +0.1 - +2.6), which may be bad, but is nowhere near as bad as for RCP-8.5. RCP-2.6 is way worse than a Peak Oil collapse scenario as it has to provide for a Total Energy Production that increases steadily to 2010:


Brown appears to be saying that MOST of the models gives results greater than the averages, which is absurd, so I don't think everything has been explained properly.

Anyway, my point was that that was not the IPCC AR5 chart, even though it was labeled as such, which is very suspicious.

You are insulting Doctor Brown, you grossly ignorant, sad excuse for an eductated person. You should be banned for disingenuous BULLSHIT!

Anyone reading this, please visit Doctor Brown's page and inform him of what Palloy is saying if possible.

This is an OUTRAGE by Palloy!

Quote
It’s easy to construct a persuasive argument, it’s much more difficult to figure out the truth… Patrick T. Brown, PhD

https://patricktbrown.org/

Patrick T. Brown, PhD

Curriculum vitae

 CURRENT POSITION

Carnegie Institution for Science, Stanford University
Postdoctoral Research Scientist (under Ken Caldeira)
EDUCATION

Duke University, Durham, North Carolina
Doctor of Philosophy, Earth and Ocean Science, 2016
San Jose State University, San Jose, California
Master of Science, Meteorology and Climate Science, 2012
University of Wisconsin – Madison, Madison, Wisconsin
Bachelor of Science, Atmospheric and Oceanic Sciences, 2008

Quote
Tweets by ‎@PatrickTBrown31
 Patrick T. Brown, PhD Retweeted

Max Roser

@MaxCRoser
Recently @BillGates asked me which statistics we should all know if we want to understand how the world is changing.

Just now he published my answer on his personal website:https://www.gatesnotes.com/Development/Max-Roser-three-facts-everyone-should-know …
Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1392 on: June 27, 2018, 09:36:22 pm »

Greater future global warming (still) inferred from Earth’s recent energy budget

Posted on December 21, 2017 by ptbrown31

SNIPPET:

We recently published a paper in Nature in which we leveraged observations of the Earth’s radiative energy budget to statistically constrain 21st-century climate model projections of global warming. We found that observations of the Earth’s energy budget allow us to infer generally greater central estimates of future global warming and smaller spreads about those central estimates than the raw model simulations indicate. More background on the paper can be obtained from our blog post on the research.

Last week, Nic Lewis published a critique of our work on several blogs titled A closer look shows global warming will not be greater than we thought. We welcome scientifically-grounded critiques of our work since this is the fundamental way in which science advances. In this spirit, we would like to thank Nic Lewis for his appraisal. However, we find Lewis’ central criticisms to be lacking merit. As we elaborate on below, his arguments do not undermine the findings of the study.

Full lengthy article:

https://patricktbrown.org/2017/12/21/greater-future-global-warming-still-inferred-from-earths-recent-energy-budget/



Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1393 on: June 27, 2018, 10:15:22 pm »

Greater future global warming (still) inferred from Earth’s recent energy budget


Posted on December 21, 2017 by Patrick T. Brown, PhD (ptbrown31)

We recently published a paper in Nature in which we leveraged observations of the Earth’s radiative energy budget to statistically constrain 21st-century climate model projections of global warming. We found that observations of the Earth’s energy budget allow us to infer generally greater central estimates of future global warming and smaller spreads about those central estimates than the raw model simulations indicate. More background on the paper can be obtained from our blog post on the research.

Last week, Nic Lewis published a critique of our work on several blogs titled A closer look shows global warming will not be greater than we thought. We welcome scientifically-grounded critiques of our work since this is the fundamental way in which science advances. In this spirit, we would like to thank Nic Lewis for his appraisal. However, we find Lewis’ central criticisms to be lacking merit. As we elaborate on below, his arguments do not undermine the findings of the study.

Brief background

Under the ‘emergent constraint’ paradigm, statistical relationships between model-simulated features of the current climate system (predictor variables), along with observations of those features, are used to constrain a predictand. In our work, the predictand is the magnitude of future global warming simulated by climate models.

We chose predictor variables that were as fundamental and comprehensive as possible while still offering the potential for a straight-forward physical connection to the magnitude of future warming. In particular, we chose the full global spatial distribution of fundamental components of Earth’s top-of-atmosphere energy budget—its outgoing (that is, reflected) shortwave radiation (OSR), outgoing longwave radiation (OLR) and net downward energy imbalance (N). We investigated three currently observable attributes of these variables—mean climatology, the magnitude of the seasonal cycle, and the magnitude of monthly variability. We chose these attributes because previous studies have indicated that behavior of the Earth’s radiative energy budget on each of these timescales can be used to infer information on fast feedbacks in the climate system. The combination of these three attributes and the three variables (OSR, OLR and N) result in a total of nine global “predictor fields”. See FAQ #3 of our previous blog post for more information on our choice of predictor variables.

We used Partial Least Squares Regression (PLSR) to relate our predictor fields to predictands of future global warming. In PLSR we can use each of the nine predictor fields individually, or we can use all nine predictor fields simultaneously (collectively). We quantified our main results with “Prediction Ratio” and “Spread Ratio” metrics. The Prediction Ratio is the ratio of our observationally-informed central estimate of warming to the previous raw model average and the Spread Ratio is the ratio of the magnitude of our constrained spread to the magnitude of the raw model spread. Prediction Ratios greater than 1 suggest greater future warming and Spread Ratios below 1 suggest a reduction in spread about the central estimate.

Lewis’ 🦖 criticism

Lewis’ post expresses general skepticism of climate models and the ‘emergent constraint’ paradigm. There is much to say about both of these topics but we won’t go into them here. Instead, we will focus on Lewis’ criticism that applies specifically to our study.

We showed results associated with each of our nine predictor fields individually but we chose to emphasize the results associated with the influence of all of the predictor fields simultaneously. Lewis suggests that rather than focusing on the simultaneous predictor field, we should have focused on the results associated with the single predictor field that showed the most skill: The magnitude of the seasonal cycle in OLR. Lewis goes further to suggest that it would be useful to adjust our spatial domain in an attempt to search for an even stronger statistical relationship. Thus, Lewis is arguing that we actually undersold the strength of the constraints that we reported, not that we oversold their strength.

This is an unusual criticism for this type of analysis. Typically, criticisms in this vein would run in the opposite direction. Specifically, studies are often criticized for highlighting the single statistical relationship that appears to be the strongest while ignoring or downplaying weaker relationships that could have been discussed. Studies are correctly criticized for this tactic because the more relationships that are screened, the more likely it is that a researcher will be able to find a strong statistical association by chance, even if there is no true underlying relationship. Thus, we do not agree that it would have been more appropriate for us to highlight the results associated with the predictor field with the strongest statistical relationship (smallest Spread Ratio), rather than the results associated with the simultaneous predictor field. However, even if we were to follow this suggestion, it would not change our general conclusions regarding the magnitude of future warming.

We can use our full results, summarized in the table below (all utilizing 7 PLSR components), to look at how different choices, regarding the selection of predictor fields, would affect our conclusions.


Lewis’ 🦖 post makes much of the fact that highlighting the results associated with the ‘magnitude of the seasonal cycle in OLR’, rather than the simultaneous predictor field, would reduce our central estimate of future warming in RCP8.5 from +14% to +6%. This is true but it is only one, very specific example. Asking more general questions gives a better sense of the big picture:

1) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we use the OLR seasonal cycle predictor field exclusively? It is 1.15, implying a 15% increase in the central estimate of warming.

2) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we always use the individual predictor field that had the lowest Spread Ratio for that particular RCP (boxed values)? It is 1.13, implying a 13% increase in the central estimate of warming.

3) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we just average together the results from all the individual predictor fields? It is 1.16, implying a 16% increase in the central estimate of warming.

4) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we always use the simultaneous predictor field? It is 1.15, implying a 15% increase in the central estimate of warming.

One point that is worth making here is that we do not use cross-validation in the multi-model average case (the denominator of the Spread Ratio). Each model’s own value is included in the multi-model average which gives the multi-model average an inherent advantage over the cross-validated PLSR estimate. We made this choice to be extra conservative but it means that PLSR is able to provide meaningful Prediction Ratios even when the Spread Ratio is near or slightly above 1. We have shown that when we supply the PLSR procedure with random data, Spread Ratios tend to be in the range of 1.1 to 1.3 (see FAQ #7 of our previous blog post, and Extended Data Fig. 4c of the paper). Nevertheless, it may be useful to ask the following question:

5) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we average together the results from only those individual predictor fields with spread ratios below 1? It is 1.15, implying a 15% increase in the central estimate of warming.

So, all five of these general methods produce about a 15% increase in the central estimate of future warming.

Lewis also suggests that our results may be sensitive to choices of standardization technique. We standardized the predictors at the level of the predictor field because we wanted to retain information on across-model differences in the spatial structure of the magnitude of predictor variables. However, we can rerun the results when everything is standardized at the grid-level and ask the same questions as above.


1b) What is the mean Prediction Ratio across the end-of-century RCPs if we use the OLR seasonal cycle predictor field exclusively? It is 1.15, implying a 15% increase in the central estimate of warming.

2b) What is the mean Prediction Ratio across the end-of-century RCPs if we always use the single predictor field that had the lowest Spread Ratio (boxed values)? It is 1.12, implying a 12% increase in the central estimate of warming.

3b) What is the mean Prediction Ratio across the end-of-century RCPs if we just average together the results from all the predictor fields? It is 1.14, implying a 14% increase in the central estimate of warming.

4b) What is the mean Prediction Ratio across the end-of-century RCPs if we always use the simultaneous predictor field? It is 1.14, implying a 14% increase in the central estimate of warming.

5b) What is the mean Prediction Ratio across the end-of-century RCP predictands if we average together the results from only those individual predictor fields with Spread Ratios below 1? It is 1.14, implying a 14% increase in the central estimate of warming.

Conclusion

There are several reasonable ways to summarize our results and they all imply greater future global warming in line with the values we highlighted in the paper. The only way to argue otherwise is to search out specific examples that run counter to the general results.

Appendix: Example using synthetic data 

Despite the fact that our results are robust to various methodological choices, it is useful to expand upon why we used the simultaneous predictor instead of the particular predictor that happened to produce the lowest Spread Ratio on any given predictand. The general idea can be illustrated with an example using synthetic data in which the precise nature of the predictor-predictand relationships are defined ahead of time. For this purpose, I have created synthetic data with the same dimensions as the data discussed in our study and in Lewis’ blog post:

1) A synthetic predictand vector of 36 “future warming” values corresponding to imaginary output from 36 climate models. In this case, the “future warming” values are just 36 random numbers pulled from a Gaussian distribution.

2) A synthetic set of nine predictor fields (37 latitudes by 72 longitudes) associated with each of the 36 models. Each model’s nine synthetic predictor fields start with that model’s predictand value entered at every grid location. Thus, at this preliminary stage, every location in every predictor field is a perfect predictor of future warming. That is, the across-model correlation between the predictor and the “future warming” predictand is 1 and the regression slope is also 1.

The next step in creating the synthetic predictor fields is to add noise in order to obscure the predictor-predictand relationship somewhat. The first level of noise that is added is a spatially correlated field of weighing factors for each of the nine predictor maps. These weighing factor maps randomly enhance or damp the local magnitude of the map’s values (weighing factors can be positive or negative). After these weighing factors have been applied, every location for every predictor field still has a perfect across-model correlation (or perfect negative correlation) between the predictor and predictand but the regression slopes vary across space according to the magnitude of the weighing factors. The second level of noise that is added are spatially correlated fields of random numbers that are specific for each of the 9X36=324 predictor maps. At this point, everything is standardized to unit variance.

The synthetic data’s predictor-predictand relationship can be summarized in the plot below which shows the local across-model correlation coefficient (between predictor and predictand) for each of the nine predictor fields. These plots are similar to the type of thing that you would see using the real model data that we used in our study. Specifically, in both cases, there are swaths of relatively high correlations and anti-correlations with plenty of low-correlation area in between. All these predictor fields were produced the same way and the only differences arise from the two layers of random noise that were added. Thus, we know that any apparent differences between the predictor fields arose by random chance.


Next, we can feed this synthetic data into the same PLSR procedure that we used in our study to see what it produces. The Spread Ratios are shown in the bar graphs below. Spread Ratios are shown for each of the nine predictor fields individually as well for the case where all nine predictor fields are used simultaneously. The top plot shows results without the use of cross-validation while the bottom plot shows results with the use of cross-validation.


In the case without cross-validation, there is no guard against over-fitting. Thus, PLSR is able to utilize the many degrees of freedom in the predictor fields to create coefficients that fit predictors to the predictand exceptionally well. This is why the Spread Ratios are so small in the top bar plot. The mean Spread Ratio for the nine predictor fields in the top bar plot is 0.042, implying that the PLSR procedure was able to reduce the spread of the predictand by about 96%. Notably, using all the predictor fields simultaneously results in a three-orders-of-magnitude smaller Spread Ratio than using any of the predictor fields individually. This indicates that when there is no guard against over-fitting, much stronger relationships can be achieved by providing the PLSR procedure with more information.

However, PLSR is more than capable of over-fitting predictors to predictands and thus these small Spread Ratios are not to be taken seriously. In our work, we guard against over-fitting by using cross-validation (see FAQ #1 of our blog post). The Spread Ratios for the synthetic data using cross-validation are shown in the lower bar graph in the figure above. It is apparent that cross-validation makes a big difference. With cross-validation, the mean Spread Ratio across the nine individual predictor fields is 0.8, meaning that the average predictor field could help reduce the spread in the predictand by about 20%. Notably, a lower Spread Ratio of 0.54, is achieved when all nine predictor maps are used collectively (a 46% reduction in spread). Since there is much redundancy across the nine predictor fields, the simultaneous predictor field doesn’t increase skill very drastically but it is still better than the average of the individual predictor fields (this is a very consistent result when the entire exercise is re-run many times).

Importantly, we can even see that one particular predictor field (predictor field 2) achieved a lower Spread Ratio than the simultaneous predictor field. This brings us to the central question: Is predictor field 2 particularly special or inherently more useful as a predictor than the simultaneous predictor field? We created these nine synthetic predictor fields specifically so that they all contained roughly the same amount of information and any differences that arose, came about simply by random chance. There is an element of luck at play because the number of models (37) is small. Thus, cross-validation can produce appreciable Spread Ratio variability from predictor to predictor simply by chance. Combining the predictors reduces the Spread Ratio, but only marginally due to large redundancies in the predictors.

We apply this same logic to the results from our paper. As we stated above, our results showed that the simultaneous predictor field for the RCP 8.5 scenario shows a Spread Ratio of 0.67. Similar to the synthetic data case, eight of the nine individual predictor fields yielded Spread Ratios above this value but a single predictor field (the OLR seasonal cycle) yielded a smaller Spread Ratio. Lewis’ post argues that we should focus entirely on the OLR seasonal cycle because of this. However, just as in the synthetic data case, our interpretation is that the OLR seasonal cycle predictor may have just gotten lucky and we should not take its superior skill too seriously.  ;D

https://patricktbrown.org/2017/12/21/greater-future-global-warming-still-inferred-from-earths-recent-energy-budget/

Smileys added by Agelbert. Don't blame Dr. Brown! ;D
Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

AGelbert

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Re: 🚩 Global Climate Chaos ☠️
« Reply #1394 on: June 28, 2018, 12:36:40 pm »
Quote
AG: You are insulting Doctor Brown, you grossly ignorant, sad excuse for an eductated person. You should be banned for disingenuous BULLSHIT!

Anyone reading this, please visit Doctor Brown's page and inform him of what Palloy is saying if possible.

This is an OUTRAGE by Palloy!

Unfortunately you don't say WHY this is outrageous.

I have demonstrated that the image in the video is NOT from IPCC AR5, even though it is labeled as such.  You are now trying to drag me into an argument over whose models are "better" than others, when IPCC does that and presents them a
ll.

Two thoughts: @Palloy, as I recall, and unless I missed the post, you asserted that the graph in question was not from IPCC. Have you in fact demonstrated that? I recall finding that assertion interesting, but I don’t recall seeing any proof. If you have demonstrated it, please point me to the post, because I missed it.

@Agelbert: please argue the point not the man. No more ad hom.  Name calling makes the color look positively Trumpian.   I really enjoy the debate, although I don’t fully understand them, but can clearly do without the “your sister fucks goats“ portion of the program.

👍 👍 👍


Greater future global warming (still) inferred from Earth’s recent energy budget


Posted on December 21, 2017 by Patrick T. Brown, PhD (ptbrown31)

We recently published a paper in Nature in which we leveraged observations of the Earth’s radiative energy budget to statistically constrain 21st-century climate model projections of global warming. We found that observations of the Earth’s energy budget allow us to infer generally greater central estimates of future global warming and smaller spreads about those central estimates than the raw model simulations indicate. More background on the paper can be obtained from our blog post on the research.

Last week, Nic Lewis published a critique of our work on several blogs titled A closer look shows global warming will not be greater than we thought. We welcome scientifically-grounded critiques of our work since this is the fundamental way in which science advances. In this spirit, we would like to thank Nic Lewis for his appraisal. However, we find Lewis’ central criticisms to be lacking merit. As we elaborate on below, his arguments do not undermine the findings of the study.

Brief background

Under the ‘emergent constraint’ paradigm, statistical relationships between model-simulated features of the current climate system (predictor variables), along with observations of those features, are used to constrain a predictand. In our work, the predictand is the magnitude of future global warming simulated by climate models.

We chose predictor variables that were as fundamental and comprehensive as possible while still offering the potential for a straight-forward physical connection to the magnitude of future warming. In particular, we chose the full global spatial distribution of fundamental components of Earth’s top-of-atmosphere energy budget—its outgoing (that is, reflected) shortwave radiation (OSR), outgoing longwave radiation (OLR) and net downward energy imbalance (N). We investigated three currently observable attributes of these variables—mean climatology, the magnitude of the seasonal cycle, and the magnitude of monthly variability. We chose these attributes because previous studies have indicated that behavior of the Earth’s radiative energy budget on each of these timescales can be used to infer information on fast feedbacks in the climate system. The combination of these three attributes and the three variables (OSR, OLR and N) result in a total of nine global “predictor fields”. See FAQ #3 of our previous blog post for more information on our choice of predictor variables.

We used Partial Least Squares Regression (PLSR) to relate our predictor fields to predictands of future global warming. In PLSR we can use each of the nine predictor fields individually, or we can use all nine predictor fields simultaneously (collectively). We quantified our main results with “Prediction Ratio” and “Spread Ratio” metrics. The Prediction Ratio is the ratio of our observationally-informed central estimate of warming to the previous raw model average and the Spread Ratio is the ratio of the magnitude of our constrained spread to the magnitude of the raw model spread. Prediction Ratios greater than 1 suggest greater future warming and Spread Ratios below 1 suggest a reduction in spread about the central estimate.

Lewis’ 🦖 criticism

Lewis’ post expresses general skepticism of climate models and the ‘emergent constraint’ paradigm. There is much to say about both of these topics but we won’t go into them here. Instead, we will focus on Lewis’ criticism that applies specifically to our study.

We showed results associated with each of our nine predictor fields individually but we chose to emphasize the results associated with the influence of all of the predictor fields simultaneously. Lewis suggests that rather than focusing on the simultaneous predictor field, we should have focused on the results associated with the single predictor field that showed the most skill: The magnitude of the seasonal cycle in OLR. Lewis goes further to suggest that it would be useful to adjust our spatial domain in an attempt to search for an even stronger statistical relationship. Thus, Lewis is arguing that we actually undersold the strength of the constraints that we reported, not that we oversold their strength.

This is an unusual criticism for this type of analysis. Typically, criticisms in this vein would run in the opposite direction. Specifically, studies are often criticized for highlighting the single statistical relationship that appears to be the strongest while ignoring or downplaying weaker relationships that could have been discussed. Studies are correctly criticized for this tactic because the more relationships that are screened, the more likely it is that a researcher will be able to find a strong statistical association by chance, even if there is no true underlying relationship. Thus, we do not agree that it would have been more appropriate for us to highlight the results associated with the predictor field with the strongest statistical relationship (smallest Spread Ratio), rather than the results associated with the simultaneous predictor field. However, even if we were to follow this suggestion, it would not change our general conclusions regarding the magnitude of future warming.

We can use our full results, summarized in the table below (all utilizing 7 PLSR components), to look at how different choices, regarding the selection of predictor fields, would affect our conclusions.


Lewis’ 🦖 post makes much of the fact that highlighting the results associated with the ‘magnitude of the seasonal cycle in OLR’, rather than the simultaneous predictor field, would reduce
our central estimate of future warming in RCP8.5 from +14% to +6%.
This is true but it is only one, very specific example. Asking more general questions gives a better sense of the big picture:

1) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we use the OLR seasonal cycle predictor field exclusively? It is 1.15, implying a 15% increase in the central estimate of warming.

2) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we always use the individual predictor field that had the lowest Spread Ratio for that particular RCP (boxed values)? It is 1.13, implying a 13% increase in the central estimate of warming.

3) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we just average together the results from all the individual predictor fields? It is 1.16, implying a 16% increase in the central estimate of warming.

4) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we always use the simultaneous predictor field? It is 1.15, implying a 15% increase in the central estimate of warming.

One point that is worth making here is that we do not use cross-validation in the multi-model average case (the denominator of the Spread Ratio). Each model’s own value is included in the multi-model average which gives the multi-model average an inherent advantage over the cross-validated PLSR estimate. We made this choice to be extra conservative but it means that PLSR is able to provide meaningful Prediction Ratios even when the Spread Ratio is near or slightly above 1. We have shown that when we supply the PLSR procedure with random data, Spread Ratios tend to be in the range of 1.1 to 1.3 (see FAQ #7 of our previous blog post, and Extended Data Fig. 4c of the paper). Nevertheless, it may be useful to ask the following question:

5) What is the mean Prediction Ratio across the end-of-century RCP predictands, if we average together the results from only those individual predictor fields with spread ratios below 1? It is 1.15, implying a 15% increase in the central estimate of warming.

So, all five of these general methods produce about a 15% increase in the central estimate of future warming.

Lewis also suggests that our results may be sensitive to choices of standardization technique. We standardized the predictors at the level of the predictor field because we wanted to retain information on across-model differences in the spatial structure of the magnitude of predictor variables. However, we can rerun the results when everything is standardized at the grid-level and ask the same questions as above.


1b) What is the mean Prediction Ratio across the end-of-century RCPs if we use the OLR seasonal cycle predictor field exclusively? It is 1.15, implying a 15% increase in the central estimate of warming.

2b) What is the mean Prediction Ratio across the end-of-century RCPs if we always use the single predictor field that had the lowest Spread Ratio (boxed values)? It is 1.12, implying a 12% increase in the central estimate of warming.

3b) What is the mean Prediction Ratio across the end-of-century RCPs if we just average together the results from all the predictor fields? It is 1.14, implying a 14% increase in the central estimate of warming.

4b) What is the mean Prediction Ratio across the end-of-century RCPs if we always use the simultaneous predictor field? It is 1.14, implying a 14% increase in the central estimate of warming.

5b) What is the mean Prediction Ratio across the end-of-century RCP predictands if we average together the results from only those individual predictor fields with Spread Ratios below 1? It is 1.14, implying a 14% increase in the central estimate of warming.

Conclusion

There are several reasonable ways to summarize our results and they all imply greater future global warming in line with the values we highlighted in the paper. The only way to argue otherwise is to search out specific examples that run counter to the general results.

Appendix: Example using synthetic data 

Despite the fact that our results are robust to various methodological choices, it is useful to expand upon why we used the simultaneous predictor instead of the particular predictor that happened to produce the lowest Spread Ratio on any given predictand. The general idea can be illustrated with an example using synthetic data in which the precise nature of the predictor-predictand relationships are defined ahead of time. For this purpose, I have created synthetic data with the same dimensions as the data discussed in our study and in Lewis’ blog post:

1) A synthetic predictand vector of 36 “future warming” values corresponding to imaginary output from 36 climate models. In this case, the “future warming” values are just 36 random numbers pulled from a Gaussian distribution.

2) A synthetic set of nine predictor fields (37 latitudes by 72 longitudes) associated with each of the 36 models. Each model’s nine synthetic predictor fields start with that model’s predictand value entered at every grid location. Thus, at this preliminary stage, every location in every predictor field is a perfect predictor of future warming. That is, the across-model correlation between the predictor and the “future warming” predictand is 1 and the regression slope is also 1.

The next step in creating the synthetic predictor fields is to add noise in order to obscure the predictor-predictand relationship somewhat. The first level of noise that is added is a spatially correlated field of weighing factors for each of the nine predictor maps. These weighing factor maps randomly enhance or damp the local magnitude of the map’s values (weighing factors can be positive or negative). After these weighing factors have been applied, every location for every predictor field still has a perfect across-model correlation (or perfect negative correlation) between the predictor and predictand but the regression slopes vary across space according to the magnitude of the weighing factors. The second level of noise that is added are spatially correlated fields of random numbers that are specific for each of the 9X36=324 predictor maps. At this point, everything is standardized to unit variance.

The synthetic data’s predictor-predictand relationship can be summarized in the plot below which shows the local across-model correlation coefficient (between predictor and predictand) for each of the nine predictor fields. These plots are similar to the type of thing that you would see using the real model data that we used in our study. Specifically, in both cases, there are swaths of relatively high correlations and anti-correlations with plenty of low-correlation area in between. All these predictor fields were produced the same way and the only differences arise from the two layers of random noise that were added. Thus, we know that any apparent differences between the predictor fields arose by random chance.


Next, we can feed this synthetic data into the same PLSR procedure that we used in our study to see what it produces. The Spread Ratios are shown in the bar graphs below. Spread Ratios are shown for each of the nine predictor fields individually as well for the case where all nine predictor fields are used simultaneously. The top plot shows results without the use of cross-validation while the bottom plot shows results with the use of cross-validation.


In the case without cross-validation, there is no guard against over-fitting. Thus, PLSR is able to utilize the many degrees of freedom in the predictor fields to create coefficients that fit predictors to the predictand exceptionally well. This is why the Spread Ratios are so small in the top bar plot. The mean Spread Ratio for the nine predictor fields in the top bar plot is 0.042, implying that the PLSR procedure was able to reduce the spread of the predictand by about 96%. Notably, using all the predictor fields simultaneously results in a three-orders-of-magnitude smaller Spread Ratio than using any of the predictor fields individually. This indicates that when there is no guard against over-fitting, much stronger relationships can be achieved by providing the PLSR procedure with more information.

However, PLSR is more than capable of over-fitting predictors to predictands and thus these small Spread Ratios are not to be taken seriously. In our work, we guard against over-fitting by using cross-validation (see FAQ #1 of our blog post). The Spread Ratios for the synthetic data using cross-validation are shown in the lower bar graph in the figure above. It is apparent that cross-validation makes a big difference. With cross-validation, the mean Spread Ratio across the nine individual predictor fields is 0.8, meaning that the average predictor field could help reduce the spread in the predictand by about 20%. Notably, a lower Spread Ratio of 0.54, is achieved when all nine predictor maps are used collectively (a 46% reduction in spread). Since there is much redundancy across the nine predictor fields, the simultaneous predictor field doesn’t increase skill very drastically but it is still better than the average of the individual predictor fields (this is a very consistent result when the entire exercise is re-run many times).

Importantly, we can even see that one particular predictor field (predictor field 2) achieved a lower Spread Ratio than the simultaneous predictor field. This brings us to the central question: Is predictor field 2 particularly special or inherently more useful as a predictor than the simultaneous predictor field? We created these nine synthetic predictor fields specifically so that they all contained roughly the same amount of information and any differences that arose, came about simply by random chance. There is an element of luck at play because the number of models (37) is small. Thus, cross-validation can produce appreciable Spread Ratio variability from predictor to predictor simply by chance. Combining the predictors reduces the Spread Ratio, but only marginally due to large redundancies in the predictors.

We apply this same logic to the results from our paper. As we stated above, our results showed that the simultaneous predictor field for the RCP 8.5 scenario shows a Spread Ratio of 0.67. Similar to the synthetic data case, eight of the nine individual predictor fields yielded Spread Ratios above this value but a single predictor field (the OLR seasonal cycle) yielded a smaller Spread Ratio. Lewis’ post argues that we should focus entirely on the OLR seasonal cycle because of this. However, just as in the synthetic data case, our interpretation is that the OLR seasonal cycle predictor may have just gotten lucky and we should not take its superior skill too seriously.  ;D

https://patricktbrown.org/2017/12/21/greater-future-global-warming-still-inferred-from-earths-recent-energy-budget/

Smileys added by Agelbert. Don't blame Dr. Brown! ;D

Surly, it is ourageous and insulting, not to me, but to Dr. Brown, for Palloy to claim his analysis of the IPCC RCP various scenarios is "absurd".

As to cotinuing this quixotic debate with Confirmation Bias Palloy,  I'll think about, it, but I believe I have already made my points clear enough to those who are objective about which of the two threats is greater, "Collapse" from Peak Hydrocarbons or death dealing massive pollution from their continued profit over planet use.

I am done with "Professor" Palloy. I am sure he is not done with me, as his latest bit of mockery (He said he is going to use my old nickname, "lazer brain", IN QUOTES of course ).  👎  👎  👎 
Rob not the poor, because he is poor: neither oppress the afflicted in the gate:
For the Lord will plead their cause, and spoil the soul of those that spoiled them. Pr. 22:22-23

 

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