Climate Change

Hacky McAxe

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It's a shame he didn't stick with the band. He'd have plenty of opportunity to attain more degrees from google university on his phone while playing Acadaca drum beats.
I can still remember when the game Rock Band ACDC came out. The drum sections for every song was the same boring beat.
 

Mr 95%

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The real evidence of climate change is in my pants.. I used to have a strong southerly front..now all it is an occasional mild draught.. :cry: Ironically now I have a strong northerly, which can cause a lot of hail damage..
 
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TwinTurbo

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That doesn't mean what you think it means.

In data science you have bias and variance. This happens because models are often too simple or too complex for the data inserted into the model, and there's variation based on the various data sources.

Bias isn't something that's added to a model. It's an effect that happens when the model can't be refined enough. You attempt to minimise bias but increasing or reducing the complexity of the model to provide more accurate data.

In this case the IPCC is saying that there's bias in the data modelling resulting in inaccurate data. So adjustments are made to reduce that bias using available allowance for variance while attempting to avoid increasing the bias in other aspects.

Anyone who has modelled data on scale knows how difficult it is to avoid high bias in modelling.
I relate to 3 types of bias in regards to the IPCC studies, being information (measurement) bias, selection bias and confounding bias. I think in the above post you are only referring to measurement bias (the IPCC very rarely publishes raw measurements and even more rarely promotes them). But I also believe that the IPCC is guilty of selection bias (they choose what information they publish) and confounding bias (they mix in extraneous factors) whenever the actual results don't align with their preconceived (modeled) results.

You seem to be of the opinion that whenever the IPCC changes measurement biases that it is always down to correcting incorrect data. Using your example;
Take your ruler example. It's more like if you're trying to measure the volume of the water in a deep tank using only cloth tape measure and you're on a boat during a storm. And you have to compare it to earlier measurements that were done blindfolded.
Even if that were the case the problem is the IPCC publishes comparisons of measurements with the recent biases adjustments but does not (probably because they can't) go back and correct for the previous biases. In the real world example, shout it from the roof tops "Oh look the Antarctic Ice Cap is shrinking" whilst in the fine print (or no print) because we no longer measure snow like we used to. The argument is not whether they should measure snow or not, but whether the comparison is in any way valid.

Climate Change is measured via comparisons (it is a "change" after all), not via absolutes, and it is impossible in many instances to produce any meaningful results when the comparisons are skewed by ever changing biases.

The IPCC literally says it in the excerpt you provided. Bias reduction will lead to more accurate projections..
Is that true though? There is plenty of evidence to the contrary, where more recent modeling is less accurate (when compared to the actual raw data) than previous iterations with supposedly inferior biases.


You're seeing the shadows of a tree and thinking it's a monster.
I think that description more accurately applies to the IPCC.



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Hacky McAxe

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I relate to 3 types of bias in regards to the IPCC studies, being information (measurement) bias, selection bias and confounding bias. I think in the above post you are only referring to measurement bias (the IPCC very rarely publishes raw measurements and even more rarely promotes them). But I also believe that the IPCC is guilty of selection bias (they choose what information they publish) and confounding bias (they mix in extraneous factors) whenever the actual results don't align with their preconceived (modeled) results.
And that's possible, but you would need to provide evidence of that as the biases in the excerpt you provided are the IPCC pointing out model bias and aiming to reduce it. Nothing to do with selection bias.

You seem to be of the opinion that whenever the IPCC changes measurement biases that it is always down to correcting incorrect data. Using your example;


Even if that were the case the problem is the IPCC publishes comparisons of measurements with the recent biases adjustments but does not (probably because they can't) go back and correct for the previous biases. In the real world example, shout it from the roof tops "Oh look the Antarctic Ice Cap is shrinking" whilst in the fine print (or no print) because we no longer measure snow like we used to. The argument is not whether they should measure snow or not, but whether the comparison is in any way valid.
That is the argument. If the comparison is not valid then it's a large positive bias, as pointed out by the IPCC.

Climate Change is measured via comparisons (it is a "change" after all), not via absolutes, and it is impossible in many instances to produce any meaningful results when the comparisons are skewed by ever changing biases.
Nope. It's adjustments to counteract bias. It doesn't make previous data invalid and ignored. It means that bias adjustments need to be made so the data accurately models against the new data. As I said, this isn't an IPCC thing. This is a data science thing. It's done in data modelling to minimise the faulty data.

Is that true though? There is plenty of evidence to the contrary, where more recent modeling is less accurate (when compared to the actual raw data) than previous iterations with supposedly inferior biases.
You are going to have to provide evidence of that.


I think that description more accurately applies to the IPCC.
Because that's what you want to believe due to your own (non-modelled) bias
 

TwinTurbo

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And that's possible, but you would need to provide evidence of that as the biases in the excerpt you provided are the IPCC pointing out model bias and aiming to reduce it. Nothing to do with selection bias.
Sorry, I'm confused, if there is bias in the selection then that automatically means there is bias in the modeling. Conversely, if there is bias in the modeling that could easily have come from bias in the selection of the input to the modeling.

That is the argument. If the comparison is not valid then it's a large positive bias, as pointed out by the IPCC.
Why does it have to be positive bias, it could just as easily be a negative bias. For example leaving snow out is a negative bias when compared to the previous measurements which left the snow in.

Nope. It's adjustments to counteract bias. It doesn't make previous data invalid and ignored. It means that bias adjustments need to be made so the data accurately models against the new data. As I said, this isn't an IPCC thing. This is a data science thing. It's done in data modelling to minimise the faulty data.
I'm not sure how you counteract bias by adding even more bias.

When I do modeling I publish the actual results, unfiltered, with any biases. I then explain and list the biases and then publish their effect side by side That way the reader can understand what, why and by how much I have adjusted the results. The IPCC never does this, they simply publish the biased results and then publish the usual plethora of pages full of jargon words of explanation, but they never (not that I have seen anyway) publish a side by side revelation of what they changed from the actuals. They do occasionally publish graphs that show both corrected and uncorrected data but I haven't seen any data on the numerical value of what the corrections are.

I honestly don't see how that is too much to ask.

You are going to have to provide evidence of that.
We have been here before;
.... when it comes to CC the evidence is that the modeling is getting more inaccurate, since the most accurate in the period from 2007 to 2017 was the Hansen modelling from 1981. Which in that period the IPCC, with millions of actual data points has not been able to better, despite 41 years of actuals to improve the accuracy of later models.
Because that's what you want to believe due to your own (non-modelled) bias
I have always been more than happy to look at any unfiltered, unsmoothed, unbiased evidence, or at the very least the data on how much and the reasons why filtering, smoothing and biasing has been applied.


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TwinTurbo

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An example of filtering, smoothing and biasing https://climate.nasa.gov/ask-nasa-climate/3071/the-raw-truth-on-global-temperature-records/

NASA’s GISTEMP Analysis
GISTEMP uses a statistical method that produces a consistent estimated temperature anomaly series from 1880 to the present. A “temperature anomaly” is a calculation of how much colder or warmer a measured temperature is at a given weather station compared to an average value for that location and time, which is calculated over a 30-year reference period (1951-1980).

Why do they use 1951 to 1980? Because there was no global warming (corrected or uncorrected) in that period? In fact in the 45 years from 1935 to 1980 there was next to zero global warming (corrected or uncorrected). How is that possible? Did it just stop for some reason right in the middle of the largest increase in industrial activity post WW2? Hansen, yes that Hansen, proposed that it was due to the cooling effects of aerosols (air pollution) generated by factories, power plants, and motor vehicles in those years of rapid economic growth.

Which basically means by reducing air pollution we have increased the temperature, no doubt a good thing reducing pollution but that resulted in a bad thing increasing the temperature. The result is the more rapid increase in temperature since we reduced pollution is partially caused by that very reduction in pollution. If we had kept on polluting then there is a valid argument that says we wouldn't have global warming currently. Obviously we all would prefer a lot less pollution for what is a tiny increase in the temperature, at least I would anyway.


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TwinTurbo

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Some trivia related to Peter Clack, he was a member ACDC during the recording of the debut album High Voltage but most of the drum parts were recorded by session musician Tony Currenti, who is not credited on the album. Tony actually owns a pizza restaurant in Penshurst called Toninos, which is worth a visit if you like Italian style pizzas.

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Hacky McAxe

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Sorry, I'm confused, if there is bias in the selection then that automatically means there is bias in the modeling. Conversely, if there is bias in the modeling that could easily have come from bias in the selection of the input to the modeling.
Basically put, the bias in the modelling isn't a selective or deliberate bias. It's just an incompatibility of data.

Why does it have to be positive bias, it could just as easily be a negative bias. For example leaving snow out is a negative bias when compared to the previous measurements which left the snow in.
Scientific terminology. A more accurate terminology is to call it "high bias" and "low bias". Basically it means that the data set doesn't match the training model.

When you create a projection model, you train it against past data. The complexity of the model is set based on past data. If you make the model too complex then the data set is going to see trends where there are none. If you make the model too simple then you miss vital. Data points and miss trends. So the model is based on past data and given the correct level of complexity. If the new data matches the same level of complexity then you have low bias, or neutral bias. If the new data set doesn't match the level of complexity then you can end up with high bias.

It just means that the data projections lean toward being less accurate. That's what it means by positive or high bias.

I'm not sure how you counteract bias by adding even more bias.
Again. You're thinking about a different type of bias. You're still looking at selective bias, or bias of the human. We're talking about bais of the model with no human influence. Weakness in the model due to humans not being psychic.

When I do modeling I publish the actual results, unfiltered, with any biases. I then explain and list the biases and then publish their effect side by side That way the reader can understand what, why and by how much I have adjusted the results. The IPCC never does this, they simply publish the biased results and then publish the usual plethora of pages full of jargon words of explanation, but they never (not that I have seen anyway) publish a side by side revelation of what they changed from the actuals. They do occasionally publish graphs that show both corrected and uncorrected data but I haven't seen any data on the numerical value of what the corrections are.

I honestly don't see how that is too much to ask.
Again, different biases. You seem to be talking about live bias which is referenced as either conflict of interest bias, or limitations in modelling.

The difference with the IPCC is that they are referencing bias in past models in this case. When modelling earth systems there's a certain level of guess work because people aren't psychic. This will result in variation in models which results in model bias. Future models look at this so they can refine the models to be more accurate


We have been here before;
You are going to have to be a bit more specific, and provide data to back up the claim. Are we talking just warming or something else?

If we're talking about warming, only one IPCC report has been less accurate than the 1981 Hansen model. Even the 1st IPCC report was more accurate.

And yes, we have been here before. I provided data from Hausfather showing that the other models have become more accurate over time and you criticised Hausfather but never provided data that contests his conclusions.
 

Hacky McAxe

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An example of filtering, smoothing and biasing https://climate.nasa.gov/ask-nasa-climate/3071/the-raw-truth-on-global-temperature-records/

NASA’s GISTEMP Analysis
GISTEMP uses a statistical method that produces a consistent estimated temperature anomaly series from 1880 to the present. A “temperature anomaly” is a calculation of how much colder or warmer a measured temperature is at a given weather station compared to an average value for that location and time, which is calculated over a 30-year reference period (1951-1980).

Why do they use 1951 to 1980? Because there was no global warming (corrected or uncorrected) in that period? In fact in the 45 years from 1935 to 1980 there was next to zero global warming (corrected or uncorrected). How is that possible? Did it just stop for some reason right in the middle of the largest increase in industrial activity post WW2? Hansen, yes that Hansen, proposed that it was due to the cooling effects of aerosols (air pollution) generated by factories, power plants, and motor vehicles in those years of rapid economic growth.

Which basically means by reducing air pollution we have increased the temperature, no doubt a good thing reducing pollution but that resulted in a bad thing increasing the temperature. The result is the more rapid increase in temperature since we reduced pollution is partially caused by that very reduction in pollution. If we had kept on polluting then there is a valid argument that says we wouldn't have global warming currently. Obviously we all would prefer a lot less pollution for what is a tiny increase in the temperature, at least I would anyway.


Always a Bulldog
Ahh. The Global Warming Pause/Hiatus myth. That has been debunked heavily.

There was temperature variance due to changes in sun activity, ocean carbon sink, and other factors, but the trend never stopped.

This is once again coming back to the point of why I tell people that if they are going to read scientific research, read the conclusion, not the data. Unless you are an expert on the data they are researching, you're always going to misinterpret the data.
 

TwinTurbo

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You are going to have to be a bit more specific, and provide data to back up the claim. Are we talking just warming or something else?

If we're talking about warming, only one IPCC report has been less accurate than the 1981 Hansen model. Even the 1st IPCC report was more accurate.

And yes, we have been here before. I provided data from Hausfather showing that the other models have become more accurate over time and you criticised Hausfather but never provided data that contests his conclusions.
My comments in that post were derived from a number of sources, I don't have time today to post them all, so I'll just start off with 2.


Hansen 1981 very accurate.

1675320315570.png



Compared to the IPCC models which are all overstated' particularly the 1990 TAR which was the "scare everyone" prediction from the IPCC.

1675319225928.png



Notice, 1990 highest (0.80degrees by 2008) , 2007 lowest (0.50 degrees by 2013) and 1995 (0.55 by 2013) lower than 2001 (0.65 by 2013) and they aren't small variations. They are all inaccurate compared to the actual of 0.35 in 2011.


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TwinTurbo

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Ahh. The Global Warming Pause/Hiatus myth. That has been debunked heavily.

There was temperature variance due to changes in sun activity, ocean carbon sink, and other factors, but the trend never stopped.

This is once again coming back to the point of why I tell people that if they are going to read scientific research, read the conclusion, not the data. Unless you are an expert on the data they are researching, you're always going to misinterpret the data.
This is my problem I don't trust the interpretations, seriously is the IPCC going say "shit we stuffed it up"? Hardly, they obfuscate and introduce a raft of biases to explain why they are actually right but other factors made them wrong, not that they ever admit that they were wrong.

Please feel free to post up where they told everyone that their 1990 predictions were miles of the mark? Why didn't we get to 0.8 degrees in 2008?


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Hacky McAxe

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My comments in that post were derived from a number of sources, I don't have time today to post them all, so I'll just start off with 2.


Hansen 1981 very accurate.

View attachment 64679


Compared to the IPCC models which are all overstated' particularly the 1990 TAR which was the "scare everyone" prediction from the IPCC.

View attachment 64677


Notice, 1990 highest (0.80degrees by 2008) , 2007 lowest (0.50 degrees by 2013) and 1995 (0.55 by 2013) lower than 2001 (0.65 by 2013) and they aren't small variations. They are all inaccurate compared to the actual of 0.35 in 2011.


Always a Bulldog
You are going to need to provide your sources as there isn't enough data there to check the validity of the sources.

For example, the top graph and paragraph are from Hausfather's analysis of accuracy of climate change projections (something that you have criticised in the past for being alarmist), and the bottom graph I couldn't find anywhere. My immediate guess is that the IPCC 1990 projection is based on worst possible scenario, but I can't tell because I can't find the graph or source, and based on the fact that the graph says, "Reality Verses Alarm", my guess is that it's an inaccurate chart posted by a climate denier on a blog somewhere.

But I could be wrong. I will need to see the source.

As I said though, the top graph and summary are from Hausfather's analysis of climate models. And that report found that:

1) Hansen 1981 project accuracy = 20% off

2) IPCC 1990 project accuracy = 17% off

 

Hacky McAxe

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This is my problem I don't trust the interpretations, seriously is the IPCC going say "shit we stuffed it up"? Hardly, they obfuscate and introduce a raft of biases to explain why they are actually right but other factors made them wrong, not that they ever admit that they were wrong.

Please feel free to post up where they told everyone that their 1990 predictions were miles of the mark? Why didn't we get to 0.8 degrees in 2008?


Always a Bulldog
The important thing to note is the IPCC projections are not based on research studies they carry out. To the most part they are not based on measurements they carry out. The primary work of the IPCC is that thousands of scientists from over 190 countries read all available scientific research on the subject that is available and base their reports on that (with variance based on which work group they are in)

The sources of this data is research from 100's of thousands of research groups. So when you say that you don't trust the conclusions. You are saying that you don't trust the conclusions of 100's of thousands of research groups, all of which have members that have spent their lives studying the subject.

That's why I say, if you are going to say that they are wrong, then you need to provide your credentials. And not just your credentials. You are going against 100's of thousands of experts so you are going to need to provide a good reason why we should believe your analysis over the experts, and back that up with many peers of equal scientific validity that agree.

We can debate until the cows come home. But if you want to say that the vast majority of the experts on the subject are wrong, then it's going to take a really strong case, and really strong qualifications to prove that.
 

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The important thing to note is the IPCC projections are not based on research studies they carry out. To the most part they are not based on measurements they carry out. The primary work of the IPCC is that thousands of scientists from over 190 countries read all available scientific research on the subject that is available and base their reports on that (with variance based on which work group they are in)

The sources of this data is research from 100's of thousands of research groups. So when you say that you don't trust the conclusions. You are saying that you don't trust the conclusions of 100's of thousands of research groups, all of which have members that have spent their lives studying the subject.

That's why I say, if you are going to say that they are wrong, then you need to provide your credentials. And not just your credentials. You are going against 100's of thousands of experts so you are going to need to provide a good reason why we should believe your analysis over the experts, and back that up with many peers of equal scientific validity that agree.

We can debate until the cows come home. But if you want to say that the vast majority of the experts on the subject are wrong, then it's going to take a really strong case, and really strong qualifications to prove that.
The vast majority of scientists are never wrong, right?
 

Hacky McAxe

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The vast majority of scientists are never wrong, right?
Definitely wouldn't say that. But when it comes to probability I would say that it's much, much more likely that the vast majority of experts are right, as oppose to random internet people or people who are literally paid to lie in court.
 

TwinTurbo

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The important thing to note is the IPCC projections are not based on research studies they carry out. To the most part they are not based on measurements they carry out. The primary work of the IPCC is that thousands of scientists from over 190 countries read all available scientific research on the subject that is available and base their reports on that (with variance based on which work group they are in)

The sources of this data is research from 100's of thousands of research groups. So when you say that you don't trust the conclusions. You are saying that you don't trust the conclusions of 100's of thousands of research groups, all of which have members that have spent their lives studying the subject.

That's why I say, if you are going to say that they are wrong, then you need to provide your credentials. And not just your credentials. You are going against 100's of thousands of experts so you are going to need to provide a good reason why we should believe your analysis over the experts, and back that up with many peers of equal scientific validity that agree.

We can debate until the cows come home. But if you want to say that the vast majority of the experts on the subject are wrong, then it's going to take a really strong case, and really strong qualifications to prove that.
I don't believe that I have ever said that "the vast majority of the experts on the subject are wrong", my point is their predictions are far from accurate and since climate science is a long way from absolute that is hardly surprising. I also strongly believe that when it comes to climate change there is a lot of argumentum ad verecundiam (argument from authority) which has let to even more argumentum ad populum (where individuals analyse, edit and modify their beliefs and behaviors based on majority opinion).

An example, just one, there are countless, Theophilus Painter published a paper stating that humans had 24 pairs of chromosomes. For almost 36 years (1920 to 1956) scientists propagated this "fact" based on Painter's authority. This established number generated confirmation bias among researchers with most expecting to detect Painter's number always did so. Painter's influence was so great that many scientists preferred to believe his count over the actual evidence and scientists who obtained the accurate number (which BTW is 23 pairs) modified or discarded their data to agree with Painter.

On the error factor, the 1990 IPCC predicted temp increase to 2007 was 0.8 degrees and the actual increase was 0.4 degrees, that's a 100% error. I have tried and I can not find in the millions of pages spewed out by the IPCC where they adequately explain a 100% error in their published predictions.


"The sources of this data is research from 100's of thousands of research groups. So when you say that you don't trust the conclusions. You are saying that you don't trust the conclusions of 100's of thousands of research groups, all of which have members that have spent their lives studying the subject."

I have quoted the above as it warrants a separate response.

That's not what I am saying, where I am at today is that the "100's of thousands of pieces of data" is what I refer to (not literally of course), the numbers themselves, not the interpretations that others apply to their selection of that data. I have absolutely no doubt that there is an element of expectation bias at work, it would be impossible to believe otherwise. Then I have even more scepticism in regards to the predictions (proven to be inaccurate) that they make based on that selection of data.

I also have trouble imagining the outrage if one scientist dared to go against the popular opinion and I do not for one second believe that his/her funding would not be cut immediately and they would be escorted off the university premises. What would their recourse be to pay the rent and feed their families? I'm pretty sure that they could obtain funding from, say, an oil company but that would immediately discredit any work they would produce, no matter how accurate it was. "Oh you can't believe him/her, they work for big oil".

On your last point about debating the subject, I am very open minded and I'm not a climate scientist, but I am OK with numbers.


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Hacky McAxe

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I don't believe that I have ever said that "the vast majority of the experts on the subject are wrong", my point is their predictions are far from accurate and since climate science is a long way from absolute that is hardly surprising. I also strongly believe that when it comes to climate change there is a lot of argumentum ad verecundiam (argument from authority) which has let to even more argumentum ad populum (where individuals analyse, edit and modify their beliefs and behaviors based on majority opinion).

An example, just one, there are countless, Theophilus Painter published a paper stating that humans had 24 pairs of chromosomes. For almost 36 years (1920 to 1956) scientists propagated this "fact" based on Painter's authority. This established number generated confirmation bias among researchers with most expecting to detect Painter's number always did so. Painter's influence was so great that many scientists preferred to believe his count over the actual evidence and scientists who obtained the accurate number (which BTW is 23 pairs) modified or discarded their data to agree with Painter.

On the error factor, the 1990 IPCC predicted temp increase to 2007 was 0.8 degrees and the actual increase was 0.4 degrees, that's a 100% error. I have tried and I can not find in the millions of pages spewed out by the IPCC where they adequately explain a 100% error in their published predictions.


"The sources of this data is research from 100's of thousands of research groups. So when you say that you don't trust the conclusions. You are saying that you don't trust the conclusions of 100's of thousands of research groups, all of which have members that have spent their lives studying the subject."

I have quoted the above as it warrants a separate response.

That's not what I am saying, where I am at today is that the "100's of thousands of pieces of data" is what I refer to (not literally of course), the numbers themselves, not the interpretations that others apply to their selection of that data. I have absolutely no doubt that there is an element of expectation bias at work, it would be impossible to believe otherwise. Then I have even more scepticism in regards to the predictions (proven to be inaccurate) that they make based on that selection of data.

I also have trouble imagining the outrage if one scientist dared to go against the popular opinion and I do not for one second believe that his/her funding would not be cut immediately and they would be escorted off the university premises. What would their recourse be to pay the rent and feed their families? I'm pretty sure that they could obtain funding from, say, an oil company but that would immediately discredit any work they would produce, no matter how accurate it was. "Oh you can't believe him/her, they work for big oil".

On your last point about debating the subject, I am very open minded and I'm not a climate scientist, but I am OK with numbers.


Always a Bulldog
Painter's Number wasn't a case of group think. He used a method of measurement to count 24. A crude method of measurement but the best they had at the time. Others repeated the same measurement and came to the same number (24). It wasn't until a new method of measurement was developed in 1955 that they could figure out that the count of 24 was wrong. That's how science works. New, better techniques are developed to correct past errors.

Also, group think isn't really a thing in the scientific community. It's often used by people who don't agree with the vast majority of experts. People get into science because they find it interesting. It pays bugger all. But they do it because they love it and they hope that one day they will publish something that changes the world. You don't change the world or become famous by agreeing with everyone else. You do it by proving everyone else wrong. Scientists don't like agreeing on things. They want to prove that they are better than their peers and they do that by proving their peers wrong.

And science is never going to be 100% accurate. Especially climate change. There's way too many systems to give full accuracy. But you can get close enough.

As for the 1990 IPCC report, it was off but I'm not sure where you get those stats from. It estimated an average increase of 3 degrees per decade based on a business as usual scenario. That means a small temperature increase at the start, big toward the end (2100). Business as usual predicted a constant increase in CO2 emissions and roughly 420ppm by 2007 (we were much lower than that due to restrictions on CO2 emissions). They then used a number of scenarios that resulted in reduced emissions and they based this on the temperature rise since the start of the industrial revolution.

Here's the projection chart from the 1990 report

1990IPCCprojections.jpg


Business as usual would have resulted in 420ppm CO2 by 2007. At 2020 we were at 412ppm, so we never got near that. Instead we were closer to scenarios B, C and D, which estimated around 1.3 - 1.6 degree increase since the industrial revolution by 2020. We ended up with a 1.3 degree increase by 2020, and this is before the IPCC started incorporating margins of error. So the report average wasn't accurate, but the temperature increase wasn't that far off, and definitely not a 100% difference.

I think the biggest issue with the IPCC 1990 report was that it wasn't highly descriptive. It was a large report and covered many things, but as far as temperature rises goes, it was never very descriptive. It never says what temperature it will be at what year. That's why future reports were more descriptive.

This is all in comparison to Hansen's projections that predicted lower warming than seen, but based on trends may end up accurate in the long run

Hansen1981.jpg


To me it just seems like you're creating points to backup your argument then suggesting that you are unbiased.

For example:

"I have absolutely no doubt that there is an element of expectation bias at work" - If you make that claim and don't back it up with evidence then it's just a biased claim to support your pre-conceived beliefs. It's not looking at the data. There may be bias involved but you can't automatically jump to wide spread bias as a base point when there's no data to support your claim

"I also have trouble imagining the outrage if one scientist dared to go against the popular opinion and I do not for one second believe that his/her funding would not be cut immediately and they would be escorted off the university premises" - Again, another claim that you're going to need to backup with evidence. There have been very few incidents where this has happened. People like to reference Peter Ridd, but Peter Ridd won his case against the University, and he never provided opposing research. He made claims he couldn't backup with evidence, and taught these claims to his students. And these were claims of things that he wasn't technically an expert on. They were just anecdotal claims sold as actual science. Even if it happened on mass, Universities aren't the only source of research. There's think tanks and scientific organisations, many primarily funded by groups that hope the researchers will find evidence that climate projections are false. Not to mention that if you proved the projections wrong, you don't get disowned by the community. You end up being nominated for a Nobel prize

Look, there's definitely corruption in the scientific community. I have personally seen peer reviews passed because the board of reviewers were friends with the researchers. And there's vested interest at the personal level and higher. Just look at something like vaping. The US government constantly moved to block vaping until the Tobacco companies introduced their own vapes. Then government officials tried to legalise only the Tobacco industry vapes. This stuff happens.

But, the rule of conspiracy comes in. The more involved, then less likely the conspiracy is to be true. When you have so many scientific organisations, universities, think tanks, research groups, all over the world with hundreds of thousands of scientists all researching these topics. The idea that the scientists will not do their work, but instead just nod and agree with what the masses say. That's not only ridiculous, it's insulting to anyone who ever got into science.
 

CrittaMagic69

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If the oceans get warm enough will Megalodons return to explored waters? You just know there's some lunatics that can't wait to swim with them :tearsofjoy:
 
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