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Author Topic: Global Warming is WITH US  (Read 24759 times)

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AGelbert

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Re: Global Warming is WITH US
« Reply #1380 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. 


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AGelbert

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Re: Global Warming is WITH US
« Reply #1381 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 »
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AGelbert

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Re: Global Warming is WITH US
« Reply #1382 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.

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AGelbert

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Re: Global Warming is WITH US
« Reply #1383 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?  ;)




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AGelbert

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Re: Global Warming is WITH US
« Reply #1384 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...

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AGelbert

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Re: Global Warming is WITH US
« Reply #1385 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.
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AGelbert

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Re: Global Warming is WITH US
« Reply #1386 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

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AGelbert

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Re: Global Warming is WITH US
« Reply #1387 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


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AGelbert

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Re: Global Warming is WITH US
« Reply #1388 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 …
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Re: Global Warming is WITH US
« Reply #1389 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/



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Re: Global Warming is WITH US
« Reply #1390 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
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AGelbert

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Re: Global Warming is WITH US
« Reply #1391 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 ).  👎  👎  👎 
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Re: Global Warming is WITH US
« Reply #1392 on: June 28, 2018, 12:39:44 pm »
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 …


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/


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if it has not works, is dead, being alone.

AGelbert

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Re: Global Warming is WITH US
« Reply #1393 on: June 28, 2018, 03:32:33 pm »
CO2 traps heat by continually absorbing and releasing it until it finally gets to space.  The effect can be measured and a mathematical model could be built to show average warming to high accuracy.  On the earth other variables such as cloud cover will affect how warming manifests locally.  Anybody who would actually do the work would be a fool and likely assassinated so instead mountains of obfuscation hides a truth that could easily be modeled by a second order differential equation.

An analog computer from the fifties could do it.


The physics behind the problem have been known for a 100 years.  All some mathematicians have to do is make a model of the atmosphere and 'illuminate' it with a stimulus which models solar radiation inputs.  In an analog computer that would modeled by a series of voltages.  Several voltages would account for different frequencies of solar radiation.  A fixed percentage of solar radiation from each band is turned into heat by a resistive divider.  The heat signals are summed together and passed up thorough the atmosphere simulation.

Heat is continually caught and released as it travels through the atmosphere but eventually finds its way into space.  The more it bounces around before it gets a clear shot off the planet determines how much the atmosphere warms.  The method described produces a graph of retained heat as a function of CO2 concentration.  This graph of values could then be used in weather predictors to see what happens. 

A Monte Carlo analysis of predictions could produce a series of stepwise approximations to determine a new steady state.   This would allow us to know how bad thermogeddon will be.

https://barringtonstewart.wordpress.com/thermogeddon-when-the-earth-gets-too-hot-for-humans/

Parts of the Earth could start to become uninhabitable within a century. Surely it cannot be true?

Belong caught in trivia is the game 'they' want you to play because that cultivates doubt.  Yesterday's political production of fear has run its course.  Now a switch is being made to emphasize doubt until fear regains potency.  Doubt is divide and conquer so don't play! 

Or we can all pretend the problem is really complicated and have nothing to do with it on a personal level.  We can all talk **** about it if we all do that.  That being the easiest thing to do will be the way it rolls.

In my dreams I see a club of nerds and one of them has added a methane gas model to simulate the effect of arctic methane release.  He is going to release it into the model both slowly and all at once to see if there is a difference at Tuesdays meeting.  The final steady state being the same he won't see any once things settle down.  That will be true for the earth as well though for humans it makes a lot of difference it thermogeddon comes on quickly or slowly.

Doing such a simulation would be far too useful.  Consequently it must never be done!



Let's talk about what is applicable to this debate. I appreciate the fact that you are objective enough to not point out my frequent spelling errors. Anyone that does that, instead of arguing the issues, is deliberately creating an Ad Hominum distraction.

Since you are not into disingenuos distractions, I will debate the CO2 issue with you briefly.

As you know, CO2 is a tri-atomic molecule. Tri-atomic molecules (e.g. H2O), due to their covalent bond nature (varying electronegativity in the molecular electron cloud topography) to absorb energy in some frequencies in the Infrared Band.

Quote
CO2 and other molecules with a similar structure (e.g. O3, H2O) insulate the earth. This greenhouse effect keeps the earth’s surface temperature at a habitable level of about 14 °C (57°F) instead of the temperature -19 °C (-2.2 °F) which it would have without the atmosphere absorbing the radiated IR. Thus, the greenhouse accounts for approximately a
33°C (59°F) warming (Le Treut et al., 2007). By trapping energy, the greenhouse heats the earth and its atmosphere by raising the radiative equilibrium.

http://jvarekamp.web.wesleyan.edu/public_htmlA/public_htmlA/CO2/FP-1.pdf

Your statement, "CO2 traps heat by continually absorbing and releasing it until it finally gets to space." omits 
a very import frequency changing aspect of CO2 gas. It absorbs energy in higher bands and emits energy at a much lower IR frequency. That heat energy, due to being of such a low frequency, cannot ever get into space. That is why we have such a huge global Warming problem with GHG pollution from the burning of hydrocarbons.

At the end of this post I will provide additonal quotes about CO2 atmospheric chemistry.

Other atmospheric variables that decrease surface heat that you mentioned, such as cloud cover, don't actually come into play when the overall average temperature of the atmosphere is figured, though they do affect surface temperatures, as global dimming caused by particulates from hydrocarbon burning pollution also do now. That is because virtually all the higher energy coming in the atmosphere, except for the very highest levels, where O3 sends some heat energy back into space (O3 at the surface sends nada back into space), has already been converted into an IR frequency without the power to get back into space. It is here.

Quote
Many people incorrectly assume that once we stop making greenhouse gas emissions, the CO2 will be drawn out of the air, the old equilibrium will be re-established and the climate of the planet will go back to the way it used to be; just like the way the acid rain problem was solved once scrubbers were put on smoke stacks, or the way lead pollution disappeared once we changed to unleaded gasoline. This misinterpretation can lead to complacency about the need to act now. In fact, global warming is, on human timescales, here forever. The truth is that the damage we have done—and continue to do—to the climate system cannot be undone.

And it is here for centuries, not a decade or so. The above quote and the following one, from a 2013 well referenced article, explains:

Quote
Since the Industrial Revolution, CO2 from our burning of fossil fuels has been building up in the atmosphere. The concentration of CO2 is now approaching 400 parts per million (ppm), up from 280 ppm prior to 1800. If we were to stop all emissions immediately, the CO2 concentration would also start to decline immediately, with some of the gas continuing to be absorbed into the oceans and smaller amounts being taken up by carbon sinks on land.

According to the models of the carbon cycle, the level of CO2  (the red line in Figure 1A) would have dropped to about 340 ppm by 2300, approximately the same level as it was in 1980. In the next 300 years, therefore, nature will have recouped the last 30 years of our emissions.


https://www.skepticalscience.com/global-warming-not-reversible-but-stoppable.html
 [/size]

As dire as the above quotes sound, the article is actually overly optimistic. As of 2013, it was believed that, IF we stopped TOTALLY any and all human industrial hydrocarbon burning caused addition of CO2 to the atmoshere, the temperature would remain where it was, due to natural processes that absorb CO2.

As of 2017, scientists like Dr. Brown (and others) have made clear that the heat in the Global Warming pipeline has been grossly underestimated.

Also, even if the "Collapse from ZERO availability of cheap hydrocarbons to burn will save us" meme was true, no IPCC scenario addresses that. They ALL count on a certain amount of hydrocarbons to burn. The rosy scenarios count on technology for capturing and sequestering CO2 that, as the International Space Station evidences, cannot get the ppm of CO2 back to 280 ppm. The best they can do in nuclear submarines and space is about 4,000 ppm CO2. Astronauts have headache issues. Ya don't say? The Planet's Biosphere is starting to have them too, to put it mildly.

Even the RCP 8.5 "business as Usual" scenario is too conservative, though RCP 8.5 (that 8.5 means radiative forcing in W/m2) is, according to the Brown and Caldeira peer reviewed paper published in Nature, the one that best fits empirical observations.

There is no way in God's good Earth that this planet's increasingly over-heated atmosphere is going to recover for at least 300 years, even if we had stopped buring hydrocarbons as of 2013. It is now 2018. It is now a lot worse. Three hundred years is a long time, K-Dog.

More technical stuff on Carbon Dioxide Gas:

Quote
Much attention has been given to the CO2 absorption band at 15 microns because this absorption peak has high absorptivity placed in the far-IR region, and the paramitization of its effects have been comparably easy to quantify (Fomichev et al., 1993; Fomichev & Turner, 1998; Kiehl & Briegleb, 1991).

Carbon dioxide, though, also absorbs radiation in near-IR (NIR) bands between 1.05-4.3 µm. When a CO2 molecule absorbs NIR radiation it is excited to the v3 (anti-symmetric) mode. Using the more realistic model of non-local thermodynamic equilibrium (non-LTE), the energy is redistributed by emission in the NIR bands, and converted into thermal energy by colliding with other molecules, or de-excited to the v2 (bending) mode. The v2 energy is either emitted as a photon of lower frequency or it is converted to thermal energy by colliding with other molecules (Figure 3).

In lower altitudes where pressure is higher, more energy is converted to thermal energy due to more frequent collisions with other molecules such as N2. (López-Puertas & López-Valverde, 1990; Fomichev et al., 2004;
Ogibalov & Fomichev, 2003)

Quote
There are three general vibrations for a CO2 molecule:

a symmetric mode,
 
a bending mode,

and an asymmetric mode (Figure 1).

Each mode is able to absorb certain bands of wavelengths, with the bending mode absorbing longer wavelengths (667 cm-1) and the asymmetric absorbing shorter wavelengths, 2349 cm-1 (Figure 2). (Kverno)

Ultimately, the energy from the photon has two pathways.

It can either be converted into thermal energy by the conversion of the internal kinetic energy of the CO2 molecule to the kinetic energy of a different, inert molecule such as N2. Or the molecule can reemit a photon at a lower frequency.

CO2 and other molecules with a similar structure (e.g. O3, H2O) insulate the earth.

http://jvarekamp.web.wesleyan.edu/public_htmlA/public_htmlA/CO2/FP-1.pdf

Leges         Sine    Moribus     Vanae   
Faith,
if it has not works, is dead, being alone.

AGelbert

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Re: Global Warming is WITH US
« Reply #1394 on: June 28, 2018, 03:39:08 pm »
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.

Anybody who's been around the block knows damn well that affluence is not a function of intelligence. Sometimes I think it's a major impediment. Not that affluence was ever your goal (I know it wasn't).

Affluenza is not my disease either, although some people seem to think so. Actually, my goal is simply to prosper in the coming hard times and hopefully help my kids and grandkids (hope springs eternal). I hope for the best and don't at all expect it. Like GO, I've been poor. I can do poor if I need to, and I'm not expecting that I won't need to, at some point.

I never expected anybody to help me in life. But several people did, and I give them a lot of thanks, even though most of them are long gone now. I've always been lucky.

Karma of Luck. Dharma of Hard Work. I don't think it's an accident that our little Diner group is here, watching the world end.

I don't believe in coincidences. Where some people see nothing but randomness, I see elegant patterns. I don't always understand them.

Thank you. I too do not believe in coincidences.

Eddie, I appeal to you, a person who survived the gruelling mess of 23 plus credit hours of college punishment, to carefully read what Dr. Brown has said. If you are unfamiliar with some term just please look it up.

That's what I did with some of the terms in the article until I figured out exactly where the clever scientist trying undermine Brown's paper was coming from.

The whole point, which I have tried to make for YEARS on this forum, to moslty deaf ears, is that the IPCC RCP 8.5 is the closest one to reality, even if it is too conservative. I held this view long before Dr. Brown did, but not have the credentials or the scientific tools to make my case, I would post items from those who did.

RCP 8.5 is ANATHEMA to the fossil fuel industry and the denier shills they fund BECAUSE it forces TPTB to DO something about Global Warming. So, I understand why all shills, like the scientist Lewis, go ballistic every time RCP 8.5 is presented, in a peer reviewed paper, as the best fit.
Leges         Sine    Moribus     Vanae   
Faith,
if it has not works, is dead, being alone.

 

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