Global Warming: Your opinion ....

No. I just when it comes to science am accurate. Now as well....I posted scientific data and scientific conclusions are without emotions or at least should be.
I can feel comfortable and use scientific means strictly there where they apply and anyway leaving the territory and then change my perspective completely.
Even close friends have sometimes a problem with that. .
It seems I live the "anything is relative" fact more vividly, lol.

As example seemingly two (different) opinions I'd say. Feelings and emotions have no place at statistical analyses. But at the same time I say analytical science has no clue about the meaning of 'feeling/presence' and relation to scientific progress.

Yes, therefore the regression analysis and not the left to right comparison.
But all one can expect are probabilities ('trends') as result.

 

I am not familiar there where scientists do forecast. I am familiar with statistics/maths, though.

My profession is practical research.... I apply established methods (also company know-how) which can be suitable to realize my (our) project. Therefore firstly we do literature research...then we think about a sequence of work steps.
If one step shouldn't led to the intended (inter)goal we replace it with another step.
(For instance the idea of separation of group of substances A from group B, an intellectual idea based on established works and know-how has to be tested if it is successful there, too)

I write down anything what I do (lab journal).
Then when I could achieve my goal practically in the lab I publish it internally.
Anything what hasn’t worked stays in the lab journal.
That what was uncertain has become reproducible following my paper (that what works and that what doesn’t)

That is the huge difference to climate forecast. What I have to do has not much room for personal interpretations. And I mostly produce 'materials' no values.

At other research branches for instance pharmacological research one can ‘bend’ the obtained values to color the results that they match personal interests.

The same applies to climate data as well everywhere there where predictions about future events play a role. It’s all about to be right and to take (personal) benefit from the interpretation of data. This is an emotional aspect that always plays a role more or less when it comes to scientific prognosis.
Usable data are free of 'color', though.
And interpretation of them should only be about probabilities and no attempt to perform a drama out of them.

I personally rely on 'reasonable' mechanisms, like renewability.
I think human behavior accelerates the present warming. But I don't actually waste much time going into details.

To realize measures to decrease fossile CO2 is important, but the argument 'climate' is not the best.
Humans generally produce and consume too much and the joke is many of us do not get what humans need anyway.

 

Forecasts are relatively true based on the current set of available data since the forecast is derived from them.
If the set of data changes then the forecast does as well.

Scientist do not know how many percent of the warming is natural.....already this uncertainty opens too much room for opponents. If the major part would be natural then it means the propagated measures against would not help stop warming either way.

When it comes to 'Reason' then Science should clearly state that to stop warming is not the major goal to be achieved.
And point to there where it 'hurts' namely exploitation of nature and disturbing of natural balances by pollution. One don't have to think about a complex ecosystem to get that when I take something that is limited it will come to an end! And when illustrating with pictures how sea dweller take up micro particles of plastics and how it comes onto our plates / body and the action on hormones....

Besides of that all we should consider how people make an opinion about a prognosis.

What does people make saying 'something' is legit and something isn't? Other words. 'Where' is science legit and how 'scientific' is your way to come to your own conclusion?
Do you think for instance Günethal's prognosis about Contergan has been legit...science of big pharma is legit? Finally 'belief' plays a role as well. Retrospectively it's no problem at all!

And finally the topic affects far more....it's about efforts which are contrary to the self-identification of a capitalistic person and institution....the most said con argument is that those politics do want more money (capital) from us that's all and there is no real reason of damage...

But environmental protection and responsibility demands to give up 'things' of which we have thought we need them to 'be' somebody.....we need to change our 'identity' closer to nature.

 

It's actually quite simple.
If we recognize 'capital' as preserving natural balances we can stay being capitalist, lol.
If environmental protection becomes as natural as getting new 'stuff' all the time, then it doesn't hurt anymore.
Time will change when it becomes a part in our society which is as 'cool' as to be identified with new stuff.
Will there be a time where using whatsapp or an Iphone is as cool as to do something for nature, lol?

ATM it doesn't seem to be.....new available stuff always distracts from it and the younger people -at least it seems to me that way- has no awareness on it (yet)...the industry/marketing has to do their job as well to make a change possible.

 

Yes.
We are basically in agreement. The red function of your graph is obtained by linear regression, the blue lines are no function (no interconnected relations). When you apply other than a linear regression you probably will find a function that 'describes' your current set of data (pairs of values) even more accurately. The value of accuracy is the correlation coefficient.

When you now extend your set of data (longer time line) then you include longer cycles. From those you can now make conclusions to future cycles which are also 'longer'. (presumed that they will go on as well)

But one fact remains: Finally the goal is always to answer the question: How will be the correlation time to temperature in the future? Besides of the set of data there are different statistical models for prognosis.

The job of a function is just to calculate (predict) temperatures from future time values.

All of them try to conclude from data from the past to future relations. The last measurable value always ends with the 'now'.
I had posted that I am not familiar there where science makes statistical predictions for future correlations.
I am familiar there where 'time' is no dimension of a correlation.

Example: Calibration of scientific device.
One measures a defined solution with given concentration of a compound. The device responds with a corresponding value.
Now one has got the first pair of values. Then I measure some lower concentrations and some higher concentrations and get their pairs of values. I do that 6 times with 6 different concentrations means level =6
Lets say the lowest concentration I have measured is 1 the greatest is 10 and their response values are between 100 and 1000.
I can now illustrate them using a diagram and I can find their function f(x) by linear regression.
The calibration is done.

Now it comes to the part where I want to have information about an UNKNOWN value (in that case concentration of a solution I don't know). But this unknown value is related to the response of the device and not to a time in the future, because the device's response does not change now except it stops to work.
What I have to make sure though is that the formerly unknown concentration is within the calibrated boundary to get a reliable result.

Short: At my job I never leave the range of measured pairs of values to obtain unknown values, the unknown is in between and hence far more significant.

 

I just wanted to point to there where linear correlation is used to illustrate a trend. (There where you can find straight lines in a diagram, for instance your red line or on the linked wiki page "long time climate trend" )
It depends on what one wants to illustrate...to 'linearise' is nothing but making averages from averages (smoothing), the time frame actually doesn't matter it's just to show a trend using its average slope.

When one has a set of data one gets a maximum resolution, but the correlation of the value pair one to value pair two is actually unknown (no data in between) and assumed to be linear. (is connected with a line).

For instance when you know the outside temperature at nine o’Clock and at elven o’clock you don't know any temperature in between...what would have been the most probable temperature at 10 o’clock? What else should one do but to connect both with a straight line?

A mathematical function is made to calculate each corresponding x value to a given y value, though and can be an approximation only. The same happens when you have a graph in a diagram especially when the time frame 'extends the now'.
All we can get are probabilities.

I have not posted that as a pro or con argument for global warming.
I just wanted to illustrate the attributes of statistics.

 

Yes, but 'they' did that (it turned out) by changing the set of data and analysis..... not the way to illustrate them. (linear or plateau)

The pause AKA warming “hiatus” 1998-2012 is a real result (real values) and the climate researcher failed badly with their prognosis because they presumed an increase of temperature ALSO predicted from the same set of data.


What they have retrospectively differentiated is the origin of temperature values (used a more differentiated set of data) and the hiatus ‘vanished’…

After all it has left back a bitter taste. Bad tongues could say they change the origin of data until they get their favorite result!

http://science.sciencemag.org/content/348/6242/1469.full

 

One has to be careful making such a general statement.
I am actually a person that is critical about the ‘establishment’.

I could open a new thread about ‘what’s wrong at science’ and post about my own experiences, but general statements are not helpful.

Just a brief differentiation, a few examples and remarks.

It depends on the origin of research. Is it from University or University of applied science (governmental so to say) or from industry?
Or is it eventually an academic work of a person?
Who is the principal so to say, what’s the individual purpose?

Not 'science' it's humans/individual

Money plays a role yes but also individual interest just as appreciation.
And competition, better said ideas how to harm a competitor.

Some brief examples from own experience / personally important remarkable to me (each one easily could have its own thread).


-One (a competitor) takes benefit of the new analytical technology to detect a substance which shows toxic effects in rats in a successful and established product. Goal is to create bad press / bad reputation to decrease market share.
From the perspective of science (human action of present concentration) it’s purely out of context, though.

Counter measure: To develop a process that removes that compound completely.

Result: The inventor of the product can sell the one with additional process as ‘far improved and new’. The attack of the competitor backfired.

From the perspective of the customer and science there is nothing, though. The product wasn’t ‘poisonous’ before and the new one isn’t in a way better.

Or the general idea is: One declares something as harmful and later then removes it to have a reason to present a better product.


-Coloring of scientific results: One only notes down the results which matches the personal scientific idea…and lets other data vanish (I know one doctor -a Pharmacologist- so far…we made sure that he got fired)


-Established therapies and their sales. Example: Chemotherapy and the supportive action of Methadone (not approved by 'established' science to fight cancer, especially denied and unwanted by those who get benefits from the big pharma). A yearly dose of additional Methadone is 150 bucks and Chemotherapy without it around 25000!


-Established therapies and ‘laws’.
For instance laws which prevent further research on the so called ‘drugs’.
Example: Depression (brain pacemaker instead of 5-HT actions just like LSD)

-QM (Quality management and their ‘methods’)
One tries to establish more and more scientific methods to make scientific statements about a product. The QM people then equal it with the safety of a product.
The more paper/reports/studies I produce about a product the safer is the product. This is illusionary science, though. It’s there to justify their existence (QM), but the safety of a product is dependent on the manufacturing know how and quality and not on quantity of extra analyses.

The QM people create requirements and strict procedures based on an idea of more analyses/control=safety (the same issue surveillance=safety).

In reality the safety is ‘paper-made’ and not real (there is no evidence of a better product compared to it before..it does not change because of analyses).
One can buy 'safety' via certificate.