Religion of Green

[istodolez]

Correct me on this then.

MAN IS IN CHARGE OF CLIMATE Yes or No

Your pedantic use of "in charge of" makes me uncomfortable since I have NEVER said that once. Not once. I will, however, note that the general consensus of the science is best summarized thusly:

"More than half of the observed increase in global mean surface temperature (GMST) from 1951-2010 is very likely due to the observed anthropogenic increase in greenhouse gas (GHG) concentrations". ("Very Likely" = 90-100%)

Source: https://www.ipcc.ch/report/ar5/wg1/...on-of-climate-change-from-global-to-regional/

 

[Robertinfremont]

Stupid is as stupid does.


Green recovery must end the reign of GDP, argue Cambridge and UN economists
Our fixation with Gross Domestic Product for over half a century as the primary indicator of economic health has rendered nature “invisible” from national finances, intensifying the biosphere’s destruction by omitting its value from the systems that govern us.
Continue reading →

We have long known that there are people who want to take over the United states, but they also want to take charge of the entire planet. A one world Government in other words. And this act by some at various nations digs into how we will be digging our own graves.

 

[Robertinfremont]

Your pedantic use of "in charge of" makes me uncomfortable since I have NEVER said that once. Not once. I will, however, note that the general consensus of the science is best summarized thusly:

"More than half of the observed increase in global mean surface temperature (GMST) from 1951-2010 is very likely due to the observed anthropogenic increase in greenhouse gas (GHG) concentrations". ("Very Likely" = 90-100%)

Source: https://www.ipcc.ch/report/ar5/wg1/...on-of-climate-change-from-global-to-regional/

I am pleased it makes you uncomfortable for to the true man is to blame group, that is what it boils down to. And your comments in dark confirm your own beliefs.

 

[istodolez]

I am pleased it makes you uncomfortable for to the true man is to blame group, that is what it boils down to. And your comments in dark confirm your own beliefs.

I'm not seeing why this is a problem for me. I'm using the science based on decades of work by countless thousands of experts across the globe.

 

[Robertinfremont]

I'm not seeing why this is a problem for me. I'm using the science based on decades of work by countless thousands of experts across the globe.

Ahh the old the mob says it so it has to be true form of argument.

 

[istodolez]

Ahh the old the mob says it so it has to be true form of argument.

No. It's the old "the experts generally agree" form of argument.

Science is not done by consensus, but good science can be expected to create a consensus around it.

 

[longview]

I'm not seeing why this is a problem for me. I'm using the science based on decades of work by countless thousands of experts across the globe.

But are you really? Yours is mostly an appeal to authority, not to actual science and data.
If one peels away the layers of the concept of AGW, it comes back to how the modelers attribute observed warming to observed CO2 increases.
It is a subtraction method, where they subtract all the know causes of warming, and then claim that 100% of what remains is from added CO2.
The flaw in this logic, is that as we learn more about what causes warming, the value of the CO2 attribution column, goes down.
Some recent work on changes in atmospheric aerosols (Man Made), could well have contributed as much warming as added CO2.
Global Dimming and Brightening
It should be worth noting the scale of energy imbalance between the 1980's and 2010's is nearly 11 W m-2,
while the CO2 attribution in that time period is only about .75 W m-2.

It should be stated that the research shows that large dimming and brightening was mostly in the northern Hemisphere,
but so was most of the "average" warming.

The point is the methodology of attribution in AGW is flawed, and may well be flawed beyond repair.

 

[istodolez]

But are you really?

Yes.

Yours is mostly an appeal to authority, not to actual science and data.

Incorrect.

I am going to go out on a limb here and say you probably have as much or less experience in the earth sciences than I do and as much or less experience in the physical sciences as I do in general. So in a sense we are BOTH relying on some appeal to an authority not our own.

For me when I read the science (after getting my BS, MS, and PhD in geology (geochemistry) and spending 25+ years as an R&D chemist) the science makes sense to me. While I am not a climate scientist I do have experience in how data is processed and a significant association with some of this science explicitly (earth systems and chemical systems).

BUT where you and I diverge is which authorities in the DETAILS we prefer. Given that the science that the majority of experts support makes sense to me I am prone to go with what these experts say.

If one peels away the layers of the concept of AGW, it comes back to how the modelers attribute observed warming to observed CO2 increases.

And that is an appeal to "...but models!" type argument. It belies a real lack of any scientific expertise on your part since if you know science you know science utilizes models ALL THE TIME. And indeed while no model is perfect they get very close and they do a very good job of it! You cannot just say "....but MODELS!" and think you have made a valid point.

It is a subtraction method, where they subtract all the know causes of warming, and then claim that 100% of what remains is from added CO2.

That is such a gross oversimplification that it borders on meaninglessness. If you work with models you know how models operate. The models I normally used in my research were "statistical models" in which I varied the factors I had control of and looked for how well the model fit. I always knew there were unknowns and error in the data. That's the nature of the beast.

Climate models are a bit different. They are initially predicated on known physical laws and those things which lack a clear equation to describe are fitted empirically (more like a statistical model) but in the end they are checked against data that has already been collected. This is called "hindcasting" to see how effective the model is.

These models may be imperfect but they are a damn sight better than waving one's hands or hiding one's head in the sand and decreeing "Nothing to see here!"

The flaw in this logic, is that as we learn more about what causes warming, the value of the CO2 attribution column, goes down.

No it doesn't.

Some recent work on changes in atmospheric aerosols (Man Made), could well have contributed as much warming as added CO2.

This is not new really. The "Mid-Century Cooling" (1940's-1970's) was thought to be largely due to sulfate aerosols. And when we cleaned the air up in the 1970's the warming picked up again just as expected. So the solution would appear to be simply to pollute more. That's not a robust response.

The point is the methodology of attribution in AGW is flawed, and may well be flawed beyond repair.

Well, it's good that you and a couple of other anonymous folks on line have access to a few blogs and minority opinions to overturn the work of thousands of experts over the last 60 years.

And if that isn't a twisted appeal to some authority I don't know what is.

 

[longview]

Yes.



Incorrect.



For me when I read the science (after getting my BS, MS, and PhD in geology (geochemistry) and spending 25+ years as an R&D chemist) the science makes sense to me. While I am not a climate scientist I do have experience in how data is processed and a significant association with some of this science explicitly (earth systems and chemical systems).





And that is an appeal to "...but models!" type argument. It belies a real lack of any scientific expertise on your part since if you know science you know science utilizes models ALL THE TIME. And indeed while no model is perfect they get very close and they do a very good job of it! You cannot just say "....but MODELS!" and think you have made a valid point.



That is such a gross oversimplification that it borders on meaninglessness. If you work with models you know how models operate. The models I normally used in my research were "statistical models" in which I varied the factors I had control of and looked for how well the model fit. I always knew there were unknowns and error in the data. That's the nature of the beast.

Climate models are a bit different. They are initially predicated on known physical laws and those things which lack a clear equation to describe are fitted empirically (more like a statistical model) but in the end they are checked against data that has already been collected. This is called "hindcasting" to see how effective the model is.

These models may be imperfect but they are a damn sight better than waving one's hands or hiding one's head in the sand and decreeing "Nothing to see here!"



No it doesn't.



This is not new really. The "Mid-Century Cooling" (1940's-1970's) was thought to be largely due to sulfate aerosols. And when we cleaned the air up in the 1970's the warming picked up again just as expected. So the solution would appear to be simply to pollute more. That's not a robust response.



Well, it's good that you and a couple of other anonymous folks on line have access to a few blogs and minority opinions to overturn the work of thousands of experts over the last 60 years.

And if that isn't a twisted appeal to some authority I don't know what is.

I know you expect people to be intimidated by your claiming advanced degrees, but it doesn't matter much to me, I have been working in R&D for 40 years
and encountered plenty of people with advanced degrees that were only good in their specific area.
As to the subtraction method, here is a quote from NASA's scientific consensus page from the American Geophysical Union.
NASA scientific consensus
"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases,
are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019)5
" There is no alterative explanation supported by convincing evidence."
Wow, that sure sounds like if an alternative explanation were found, the new attribution would no longer be applied to greenhouse gasses.

The problem with the dimming and brightening, is that the models are based on only the brightening phase,
so they looked at warming from 1978 to 1998, and then expected that rate to continue.
Climate change: The case of the missing heat
"On a chart of global atmospheric temperatures, the hiatus stands in stark contrast to the rapid warming of the two decades that preceded it.
Simulations conducted in advance of the 2013–14 assessment from the Intergovernmental Panel on Climate Change (IPCC) suggest that the warming should have continued
at an average rate of 0.21 °C per decade from 1998 to 2012. Instead, the observed warming during that period was just 0.04 °C per decade,
as measured by the UK Met Office in Exeter and the Climatic Research Unit at the University of East Anglia in Norwich, UK. "
That sure sounds like the simulations (models) expected the warming that was observed between 1978 and 1998, would continue as projected.
The reality is that the observation included a warming rebound from clearing aerosols from the skies.
What we need is a real red team blue team approach, the science is not settled!

 

[istodolez]

I know you expect people to be intimidated by your claiming advanced degrees

I wish you could be honest with your analysis of my posts.

I've been MORE than openly honest of my limitations in this area. I really have. I haven't seen YOU ever indicate any lack of skill in this area as I have. So who is "intimidating" here?

but it doesn't matter much to me, I have been working in R&D for 40 years

I mean no offense but, sadly, I doubt that very highly. Otherwise you'd have more nuanced view of models in science. In fact as an "R&D Scientist" you should know better than MOST how intergral models are to science. You should also know how data is modeled and interepreted. Basic inferential stuff.

As to the subtraction method, here is a quote from NASA's scientific consensus page from the American Geophysical Union.
NASA scientific consensus
"Based on extensive scientific evidence, it is extremely likely that human activities, especially emissions of greenhouse gases,
are the dominant cause of the observed warming since the mid-20th century. There is no alterative explanation supported by convincing evidence." (2019)5
" There is no alterative explanation supported by convincing evidence."
Wow, that sure sounds like if an alternative explanation were found, the new attribution would no longer be applied to greenhouse gasses.

Like I said, I doubt seriously that you've been involved in R&D science in your career. Here's why: in any system in which you are attempting to explain the results based on input variables you are forced ALWAYS to be aware that there are unaccounted variables that COULD be there to explain the variance in the data that you don't know about. This is basic intro science and inference.

I've done a huge number of "design of experiments" in which I sought to vary formulations to see how it would impact my final performance of the material I was working with. I hoped to explain the MAJORITY of the variance in the data but I always knew that some of that would not be explained. There's random error in the data and there's unexplained variables. But the key is can we explain a MAJORITY of the data?

That's how science works. They NEVER seek to "prove" something, they merely present the best available evidence.

If this confuses you then you have a lot to learn about R&D.

 

[longview]

I wish you could be honest with your analysis of my posts.

I've been MORE than openly honest of my limitations in this area. I really have. I haven't seen YOU ever indicate any lack of skill in this area as I have. So who is "intimidating" here?



I mean no offense but, sadly, I doubt that very highly. Otherwise you'd have more nuanced view of models in science. In fact as an "R&D Scientist" you should know better than MOST how intergral models are to science. You should also know how data is modeled and interepreted. Basic inferential stuff.



Like I said, I doubt seriously that you've been involved in R&D science in your career. Here's why: in any system in which you are attempting to explain the results based on input variables you are forced ALWAYS to be aware that there are unaccounted variables that COULD be there to explain the variance in the data that you don't know about. This is basic intro science and inference.

I've done a huge number of "design of experiments" in which I sought to vary formulations to see how it would impact my final performance of the material I was working with. I hoped to explain the MAJORITY of the variance in the data but I always knew that some of that would not be explained. There's random error in the data and there's unexplained variables. But the key is can we explain a MAJORITY of the data?

That's how science works. They NEVER seek to "prove" something, they merely present the best available evidence.

If this confuses you then you have a lot to learn about R&D.

I do know better which is why I question the catastrophic predictions.
I understand that there are always plenty of unaccounted for variables on the bleeding edge, it is the nature of the beast!

On my first job out of University, I worked on the team that developed the worlds first fiber optic marine seismic system.
The technology was quite crude in the early 80's and we had to build and test the electronics from components, in addition to the harsh offshore environment
When you talk about the majority of the data, you are speaking of multiple control runs, where the variable assumptions are fixed, and the output varies,
in the case of AGW this is not the case, the spread is based on different assumptions used for the input.
The graphic you posted earlier attests to this. Each represents a different methodology and different variables.

In R&D we are usually tying to solve some problem, One of my projects had me looking for fiber optic jacket material
that would work over the full range of land seismic crews operating temperature, as opposed to warm weather and cold weather cables.
The top one inch of air, gets both very cold and very hot. Thermoplastics that are pliable at the cold temperature
loose integrity at high temperatures, and those that hold up at high temperatures, are brittle and cause excessive attenuation at low temperatures.
Alas, real R&D is 90% failure and 10% success.

 

[istodolez]

I do know better which is why I question the catastrophic predictions.
I understand that there are always plenty of unaccounted for variables on the bleeding edge, it is the nature of the beast!

On my first job out of University, I worked on the team that developed the worlds first fiber optic marine seismic system.
The technology was quite crude in the early 80's and we had to build and test the electronics from components, in addition to the harsh offshore environment
When you talk about the majority of the data, you are speaking of multiple control runs, where the variable assumptions are fixed, and the output varies,
in the case of AGW this is not the case, the spread is based on different assumptions used for the input.
The graphic you posted earlier attests to this. Each represents a different methodology and different variables.

In R&D we are usually tying to solve some problem, One of my projects had me looking for fiber optic jacket material
that would work over the full range of land seismic crews operating temperature, as opposed to warm weather and cold weather cables.
The top one inch of air, gets both very cold and very hot. Thermoplastics that are pliable at the cold temperature
loose integrity at high temperatures, and those that hold up at high temperatures, are brittle and cause excessive attenuation at low temperatures.
Alas, real R&D is 90% failure and 10% success.

when you did your thermoplastic polymer study did you systematically vary aspect of the polymer used? Those variables probably explained some significant portion of the variability in the final performance. There were probably other factors you didn’t account for which might explain a smaller amount of the variability. But the fact that the unknown variable wasn’t factored into your experimental design doesn’t make the dominant explanatory variable any less meaningful.

Those of us who have done actual science understand that this is part of the limitation of science.

This is true for all science regardless of how many control experiments you use or how strictly you control the variables.

The citation I gave for the estimate of climate sensitivity is a GREAT example of how real science works. If you look at the graph you see that the estimates all converge on similar estimates with obvious distributions but central tendencies that are all relatively close! FROM MULTIPLE INDEPENDENT METHODS!

That’s how science works.

 

[longview]

when you did your thermoplastic polymer study did you systematically vary aspect of the polymer used? Those variables probably explained some significant portion of the variability in the final performance. There were probably other factors you didn’t account for which might explain a smaller amount of the variability. But the fact that the unknown variable wasn’t factored into your experimental design doesn’t make the dominant explanatory variable any less meaningful.

Those of us who have done actual science understand that this is part of the limitation of science.

This is true for all science regardless of how many control experiments you use or how strictly you control the variables.

The citation I gave for the estimate of climate sensitivity is a GREAT example of how real science works. If you look at the graph you see that the estimates all converge on similar estimates with obvious distributions but central tendencies that are all relatively close! FROM MULTIPLE INDEPENDENT METHODS!

That’s how science works.

I used the modern plastics encyclopedia, that being 1985 without any online resources.
Mind you my memory from 35 years ago, says the best looking one was Santoprene, but I seem to recall, that it had
some consistency issues with extrusion, but it also could have been limitations on our extrusion equipment.
My choices were limited, because if successful, we would require enough product for many thousand kilometres.

What you are missing is that output variables, and in looking at their range, it is important to keep the inputs as close as possible.
The range with the climate models has none of that, some used only instrumental data, some used only theoretical, or a combination.
What matters is that the inputs were not consistent, so the range of outputs is not meaningful.
If the science is so solid, why is there resistance to a red team blue team approach?

 

[istodolez]

I used the modern plastics encyclopedia, that being 1985 without any online resources.
Mind you my memory from 35 years ago, says the best looking one was Santoprene, but I seem to recall, that it had
some consistency issues with extrusion, but it also could have been limitations on our extrusion equipment.
My choices were limited, because if successful, we would require enough product for many thousand kilometres.

What you are missing is that output variables, and in looking at their range, it is important to keep the inputs as close as possible.
The range with the climate models has none of that, some used only instrumental data, some used only theoretical, or a combination.
What matters is that the inputs were not consistent, so the range of outputs is not meaningful.
If the science is so solid, why is there resistance to a red team blue team approach?

The fact of the matter is that the vast majority of experts in the field agree that AGW is real. There is no red team blue team. There’s the vast majority and a tiny minority

 

[longview]

The fact of the matter is that the vast majority of experts in the field agree that AGW is real. There is no red team blue team. There’s the vast majority and a tiny minority

No one is contesting that AGW is real! The red team blue team approach would be for both sides to argue for what sensitivity is best supported by all the observed data.
IS 2XCO2 sensitivity closer to 1.5 C of 4.5C, or is it something else.

 

[istodolez]

No one is contesting that AGW is real!

-sigh- I get so tired of trying to keep track of the flavors of "skepticism".

The red team blue team approach would be for both sides to argue for what sensitivity is best supported by all the observed data.

That's why it is important to look at estimates that come from different independent methods. Which is what Knutti and Hegerl did. And which you think is somehow meaningless because it is from different methods.

If I have a bunch of different test methods that all converge on similar values that is evidence that that value is more likely correct.

I don't see what your point is here other than the fact that you REALLY want it to be lower than what the scientists are finding repeatedly.

 

[istodolez]

I used the modern plastics encyclopedia, that being 1985 without any online resources.
Mind you my memory from 35 years ago, says the best looking one was Santoprene, but I seem to recall, that it had
some consistency issues with extrusion, but it also could have been limitations on our extrusion equipment.
My choices were limited, because if successful, we would require enough product for many thousand kilometres.

Yeah, yeah, yeah...but let's look at what your experimental design was doing.

I assume that in the plastics encyclopedia you referenced you chose thermoplastics that had a variety of molecular weight, cross-linking, etc etc. All aspects (measurable) of the polymer you used and those were your INDEPENDENT VARIABLES.

You then made up cladding with these plastics and tested its performance (I dunno, instron strength, hardness, flexibility, degradation in certain chemical environments) and that resulted in your DEPENDENT VARIABLES, your outcomes.

At the end of it you MODELED the DEPENDENT VARIABLES based on the INDEPENDENT VARIABLES. You probably used a second order fit to the data and you checked to see what your "lack of fit" was. You looked at the regression line of your model to see how much of the variability in the DEPENDENT VARIABLE you could explain by the INDEPENDENT VARIABLES you had chosen.

Likely you could explain up to about 90% of the variability, but maybe not that much. Still more than 50% of the variability. So the majority of what you need to know about the proper selection of polymers came from the independent variables you originally selected. If you couldn't explain a majority of the dependent variable data you would have to go back to the drawing board and figure out what the missing variables were.

But there will ALWAYS be noise in the data that is a combination of both random error and missing variables. If you could explain 90% of your data with the independent variables you had chosen you wouldn't gain a HUGE amount more by finding the missing variable that would take you to 95%. Sure it would be valuable data and could be of importance but it's not like that next 5% wipes out the value and impact of the other 90% of the data that was explained by your model.

And this is where we are at with climate science. The models are a bit different (physical vs statistical) but we are able to account for a great deal of the temperature data over the last 50-100 years if we factor in both natural and human forcings and that is why the models are valuable and they point to a significant role for humans.

If we could find some other variable that explained an addition 5% of the warming that wasn't human we'd still be stuck with humans having a majority impact on the climate over the last 50 years.

 

[longview]

-sigh- I get so tired of trying to keep track of the flavors of "skepticism".



That's why it is important to look at estimates that come from different independent methods. Which is what Knutti and Hegerl did. And which you think is somehow meaningless because it is from different methods.

If I have a bunch of different test methods that all converge on similar values that is evidence that that value is more likely correct.

I don't see what your point is here other than the fact that you REALLY want it to be lower than what the scientists are finding repeatedly.

The different approaches need to be based on observations. The ones only based on theory need additional support.
I think the sensitivity is set too high for several reasons, the first being the 667 cm-1 dipole moment
related to 15 um, is a very low energy state change.
In addition, the high sensitivities all depend on strong feedbacks that do not seem to exists in the empirical data.

 

[Quaestio]

We have long known that there are people who want to take over the United states, but they also want to take charge of the entire planet. A one world Government in other words. And this act by some at various nations digs into how we will be digging our own graves.

Oh you mean the lizard people.

 

[istodolez]

Oh you mean the lizard people.

SHHHH! On't-day alk-tay about-ay izard-lay eople-pay!

 

[longview]

-sigh- I get so tired of trying to keep track of the flavors of "skepticism".



That's why it is important to look at estimates that come from different independent methods. Which is what Knutti and Hegerl did. And which you think is somehow meaningless because it is from different methods.

If I have a bunch of different test methods that all converge on similar values that is evidence that that value is more likely correct.

I don't see what your point is here other than the fact that you REALLY want it to be lower than what the scientists are finding repeatedly.

If the vast majority of the tests are based on nothing more than poor assumptions of a very short period of warming, of what use are they?
We now know that the highest level of warming 1978 to 1998, is the basis for many of the assumptions, yet that high warming was only in the Northern Hemisphere,
and more than likely most of if could be from more insolation from reductions of aerosols, Still human caused.
If we wanter to get a more accurate picture of CO2 caused warming, the Southern Hemisphere would be a better model,
the problem with that is that there is little alarming about the observed warming in the Southern Hemisphere.
The difference cannot be CO2, since it is the same in both hemispheres.

 

[longview]

Yeah, yeah, yeah...but let's look at what your experimental design was doing.

I assume that in the plastics encyclopedia you referenced you chose thermoplastics that had a variety of molecular weight, cross-linking, etc etc. All aspects (measurable) of the polymer you used and those were your INDEPENDENT VARIABLES.

You then made up cladding with these plastics and tested its performance (I dunno, instron strength, hardness, flexibility, degradation in certain chemical environments) and that resulted in your DEPENDENT VARIABLES, your outcomes.

At the end of it you MODELED the DEPENDENT VARIABLES based on the INDEPENDENT VARIABLES. You probably used a second order fit to the data and you checked to see what your "lack of fit" was. You looked at the regression line of your model to see how much of the variability in the DEPENDENT VARIABLE you could explain by the INDEPENDENT VARIABLES you had chosen.

Likely you could explain up to about 90% of the variability, but maybe not that much. Still more than 50% of the variability. So the majority of what you need to know about the proper selection of polymers came from the independent variables you originally selected. If you couldn't explain a majority of the dependent variable data you would have to go back to the drawing board and figure out what the missing variables were.

But there will ALWAYS be noise in the data that is a combination of both random error and missing variables. If you could explain 90% of your data with the independent variables you had chosen you wouldn't gain a HUGE amount more by finding the missing variable that would take you to 95%. Sure it would be valuable data and could be of importance but it's not like that next 5% wipes out the value and impact of the other 90% of the data that was explained by your model.

And this is where we are at with climate science. The models are a bit different (physical vs statistical) but we are able to account for a great deal of the temperature data over the last 50-100 years if we factor in both natural and human forcings and that is why the models are valuable and they point to a significant role for humans.

If we could find some other variable that explained an addition 5% of the warming that wasn't human we'd still be stuck with humans having a majority impact on the climate over the last 50 years.

Your statement shows your bias, as you assume that 90% of the observed warming is form human causes, implying CO2, without any supporting evidence.
Consider that of the 1.015C of observed warming, estimates of natural warming are as high as .5C, but without question .28C is pre 1950 warming and considered natural.
By the way where is the amplified feedback for that Per 1950 warming, it has had 70 years of latency, and remember that an ECS of 3 C demands a gain of 2.72?

 

[istodolez]

Your statement shows your bias, as you assume that 90% of the observed warming is form human causes,

I never said that. I NEVER said 90% of the warming was form humans. I was using a made up example of a designed experiment. Please do pay attention.

 

[istodolez]

We now know that the highest level of warming 1978 to 1998, is the basis for many of the assumptions,

Citation please. I have no idea what you are claiming here.

If we wanter to get a more accurate picture of CO2 caused warming, the Southern Hemisphere would be a better model,

I don't get this claim. You act as if the S. Hemisphere is being ignored. It isn't. Sure more temperature stations may be in the more populous N. Hemisphere but trust me, the AGW scientists actually KNOW to include estimates based on S. Hemisphere data in the analysis.

the problem with that is that there is little alarming about the observed warming in the Southern Hemisphere.

What? I don't think that's accurate at all. We are dealing with GLOBAL warming. OF COURSE people are concerned with warming in the S. Hemisphere. I mean half of the stories of negative impacts of sea level rise are coming from S. Pacific island nations!

The difference cannot be CO2, since it is the same in both hemispheres.

Well if you start from the flawed assumption that no one is paying attention to the S. Hemisphere you are probably able to draw all manner of conclusions that don't necessarily comport with fact.

 

[longview]

I never said that. I NEVER said 90% of the warming was form humans. I was using a made up example of a designed experiment. Please do pay attention.

But your made up example shows your bias by the ranges selected.
The same problem the modelers have.
We do not have a good idea of the ratio of natural warming vs co2 warming.
Many models simply assumed that 100% of the post 1950 warming was from co2.