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Post by missouriboy on Feb 12, 2019 23:26:41 GMT
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Post by nautonnier on Feb 13, 2019 9:44:54 GMT
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Post by Ratty on Feb 13, 2019 11:23:18 GMT
Not going to let him fly my spaceship again. Ever ....
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Post by nautonnier on Feb 14, 2019 20:27:27 GMT
To quote the IPCC: "The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible." This is pure Lorentz. Lorentz showed that even a lack of precision in the input values of a system 'current state' was sufficient to cause models to wildly deviate from each other and reality. The IPCC CIMP approach of using 'ensembles' so called** of incorrect models to average out the errors when each model has various internal 'reality checks' that put a thumb heavily on the internal virtual scales based on what the modeler thinks; reduces these averages of erroneous models to biased random number generators. ** A real ensemble in meteorological modeling is the same model being run multiple times with slightly differing initial states (exercising the Lorentz chaotic effect). This provides a sensitivity test but also may show a median value that many runs follow providing a level of confidence in them. The wildly differing runs are discarded. The point at which most of the runs become widely distributed is the limit of 'reliable' prediction by that model. This is a totally different approach to using a multiplicity of incorrect models.
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Post by acidohm on Feb 15, 2019 17:05:27 GMT
To quote the IPCC: "The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible." This is pure Lorentz. Lorentz showed that even a lack of precision in the input values of a system 'current state' was sufficient to cause models to wildly deviate from each other and reality. The IPCC CIMP approach of using 'ensembles' so called** of incorrect models to average out the errors when each model has various internal 'reality checks' that put a thumb heavily on the internal virtual scales based on what the modeler thinks; reduces these averages of erroneous models to biased random number generators. ** A real ensemble in meteorological modeling is the same model being run multiple times with slightly differing initial states (exercising the Lorentz chaotic effect). This provides a sensitivity test but also may show a median value that many runs follow providing a level of confidence in them. The wildly differing runs are discarded. The point at which most of the runs become widely distributed is the limit of 'reliable' prediction by that model. This is a totally different approach to using a multiplicity of incorrect models. The reliability factor also shifts a great deal depending on the volatility of the system. When stable airmasses dominate, reliability extends to 7-10 days, when an ssw occurs for example, it can retract to 3-5 days. (Incredibly frustrating!)
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Post by Ratty on Feb 16, 2019 4:18:52 GMT
I might self-medicate and do this course; it could show me the errors of my ways: Making Sense of Climate Science DenialAbout this course
In public discussions, climate change is a highly controversial topic. However, in the scientific community, there is little controversy with 97% of climate scientists concluding humans are causing global warming.
. Why the gap between the public and scientists? . What are the psychological and social drivers of the rejection of the scientific consensus? . How has climate denial influenced public perceptions and attitudes towards climate change?
This course examines the science of climate science denial.
We will look at the most common climate myths from “global warming stopped in 1998” to “global warming is caused by the sun” to “climate impacts are nothing to worry about.”
We’ll find out what lessons are to be learnt from past climate change as well as better understand how climate models predict future climate impacts. You’ll learn both the science of climate change and the techniques used to distort the science.
With every myth we debunk, you’ll learn the critical thinking needed to identify the fallacies associated with the myth. Finally, armed with all this knowledge, you’ll learn the psychology of misinformation. This will equip you to effectively respond to climate misinformation and debunk myths.
This isn’t just a climate MOOC; it’s a MOOC about how people think about climate change.
What you'll learn
. How to recognise the social and psychological drivers of climate science denial . How to better understand climate change: the evidence that it is happening, that humans are causing it and the potential impacts . How to identify the techniques and fallacies that climate myths employ to distort climate science . How to effectively debunk climate misinformation
Check out the rogues' list of instructors.
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Post by missouriboy on Feb 16, 2019 5:51:36 GMT
I might self-medicate and do this course; it could show me the errors of my ways: Making Sense of Climate Science DenialAbout this course
In public discussions, climate change is a highly controversial topic. However, in the scientific community, there is little controversy with 97% of climate scientists concluding humans are causing global warming.
. Why the gap between the public and scientists? . What are the psychological and social drivers of the rejection of the scientific consensus? . How has climate denial influenced public perceptions and attitudes towards climate change?
This course examines the science of climate science denial.
We will look at the most common climate myths from “global warming stopped in 1998” to “global warming is caused by the sun” to “climate impacts are nothing to worry about.”
We’ll find out what lessons are to be learnt from past climate change as well as better understand how climate models predict future climate impacts. You’ll learn both the science of climate change and the techniques used to distort the science.
With every myth we debunk, you’ll learn the critical thinking needed to identify the fallacies associated with the myth. Finally, armed with all this knowledge, you’ll learn the psychology of misinformation. This will equip you to effectively respond to climate misinformation and debunk myths.
This isn’t just a climate MOOC; it’s a MOOC about how people think about climate change.
What you'll learn
. How to recognise the social and psychological drivers of climate science denial . How to better understand climate change: the evidence that it is happening, that humans are causing it and the potential impacts . How to identify the techniques and fallacies that climate myths employ to distort climate science . How to effectively debunk climate misinformation
Check out the rogues' list of instructors. Perhaps we should all sign up. It's free. Why deny ourselves the experience? But since it's online, will we be censored in our interactions ... or outright banned for our views? I would really like to look them in the eyeball. Yes, they probably only have one given the statements in their syllabus. I feel a case of GDS coming on, so I might be cranky. We could be the counter faculty, but I doubt they'll let us interact. What was that Colonel Custer guote about ... "Where the hell did all those f___ing Indians come from?" Since it seems to be a psychology course, it's more likely to try and show us the ways of our error. And 97% of the pseudo science instructors will probably agree, thus sealing the case. The Holy Sea would be pleased. Lord love a duck.
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Post by nautonnier on Feb 16, 2019 9:55:24 GMT
To quote the IPCC: "The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible." This is pure Lorentz. Lorentz showed that even a lack of precision in the input values of a system 'current state' was sufficient to cause models to wildly deviate from each other and reality. The IPCC CIMP approach of using 'ensembles' so called** of incorrect models to average out the errors when each model has various internal 'reality checks' that put a thumb heavily on the internal virtual scales based on what the modeler thinks; reduces these averages of erroneous models to biased random number generators. ** A real ensemble in meteorological modeling is the same model being run multiple times with slightly differing initial states (exercising the Lorentz chaotic effect). This provides a sensitivity test but also may show a median value that many runs follow providing a level of confidence in them. The wildly differing runs are discarded. The point at which most of the runs become widely distributed is the limit of 'reliable' prediction by that model. This is a totally different approach to using a multiplicity of incorrect models. The reliability factor also shifts a great deal depending on the volatility of the system. When stable airmasses dominate, reliability extends to 7-10 days, when an ssw occurs for example, it can retract to 3-5 days. (Incredibly frustrating!) Yet despite a reliability factor that extends out a whole 10 days, climate models iterate out to centuries. The only reason they don't go totally outside physical possibility is the 'parameterized thumbs on the scales' stopping them doing so. Total mathematical inventions.
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Post by acidohm on Feb 16, 2019 10:08:05 GMT
The reliability factor also shifts a great deal depending on the volatility of the system. When stable airmasses dominate, reliability extends to 7-10 days, when an ssw occurs for example, it can retract to 3-5 days. (Incredibly frustrating!) Yet despite a reliability factor that extends out a whole 10 days, climate models iterate out to centuries. The only reason they don't go totally outside physical possibility is the 'parameterized thumbs on the scales' stopping them doing so. Total mathematical inventions. Exactly so! To use the UK as a case study, expectations were consistant for cold in feb on medium range models, as end of jan approached, cold appeared to be 80% certain within 6 days (as stated by met professionals at the time). I think it was a thursday? One by one each model confirmed run by run the blocking signal, at the end of the day, the previously bullish ECM gave a milder run.....and that was that, all other models collapsed on subsequent runs too. And nobody has a clue why! Tho id love to have a chat with some of these model makers, they dont appear in the public sphere it seems......
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Post by nautonnier on Feb 16, 2019 12:04:52 GMT
" AUSTRALIAN MET OFFICE ACCUSED OF MAN-MADE CLIMATE CHANGE Date: 16/02/19 Graham Lloyd, The Australian The Bureau of Meteorology has rewritten Australia’s temperature records for the second time in six years, greatly increasing the rate of warming since 1910 in its controversial homogenised data set.
Rather than the nation’s temperature having increased by 1C over the past century, the bureau’s updated homogenised data set, known as ACORN-SAT, now shows mean temperatures have risen by 1.23C.
Bureau data shows the rate of mean warming since 1960 has risen to 0.2C a decade, putting the more ambitious IPCC target of limiting future warming to 1.5C close to being broken.
Homogenisation of temperature records is considered necessary to account for changes in instrumentation, changes in site locations and changes in the time at which temperatures were taken. But the bureau’s treatment of historical data has been controversial. In recent years there have been claims that the organisation was treating temperature records in such a way that left it exposed to accusations that ideological pursuits had trumped good scientific practice.........www.thegwpf.com/australian-met-office-accused-of-man-made-climate-change-again/
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Post by missouriboy on Feb 16, 2019 15:11:35 GMT
Yet despite a reliability factor that extends out a whole 10 days, climate models iterate out to centuries. The only reason they don't go totally outside physical possibility is the 'parameterized thumbs on the scales' stopping them doing so. Total mathematical inventions. Exactly so! To use the UK as a case study, expectations were consistant for cold in feb on medium range models, as end of jan approached, cold appeared to be 80% certain within 6 days (as stated by met professionals at the time). I think it was a thursday? One by one each model confirmed run by run the blocking signal, at the end of the day, the previously bullish ECM gave a milder run.....and that was that, all other models collapsed on subsequent runs too. And nobody has a clue why! Tho id love to have a chat with some of these model makers, they dont appear in the public sphere it seems...... After a decade in artificially lit environments visually subsumed by rapidly moving imagery, you'll understand the hesitation. They're not in Kansas anymore.
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Post by sigurdur on Feb 16, 2019 17:33:22 GMT
agupubs.onlinelibrary.wiley.com/doi/full/10.1002/grl.50563 Geophysical Research Letters Volume 40, Issue 12 Regular Article Free Access Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments Randall J. Donohue Michael L. Roderick Tim R. McVicar Graham D. Farquhar First published: 15 May 2013 doi.org/10.1002/grl.50563Cited by: 153 About Sections Abstract [1] Satellite observations reveal a greening of the globe over recent decades. The role in this greening of the “CO2 fertilization” effect—the enhancement of photosynthesis due to rising CO2 levels—is yet to be established. The direct CO2 effect on vegetation should be most clearly expressed in warm, arid environments where water is the dominant limit to vegetation growth. Using gas exchange theory, we predict that the 14%
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Post by missouriboy on Feb 17, 2019 1:48:06 GMT
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Post by missouriboy on Feb 17, 2019 14:32:10 GMT
Failed Climate Predictions of the BorgSig posted this on Picture Thread, but it seemed to deserve a place of special honor. I don't remember #3, but the Borg seem to be working that one now. Expect that extreme flooding events (a modification of #5) will be hit upon again ... perhaps it's already happening. humansarefree.com/2018/01/al-gores-10-global-warming-predictions.html?fbclid=IwAR3CdSDQJ1iBSiF_aXNT999i5TbRucirXpWtdPilwIh1FJnIFHb7QMO5Zf4&m=0Gore’s Predictions Fall Flat (there are probably more) 13 years after Al Gore’s “Inconvenient Truth” guilt/fear producing predictions, let’s close by examining just how accurate his “science” proved to be on his way to the bank. 1. Rising Sea Levels – inaccurate and misleading. Al was even discovered purchasing a beachfront mansion! 2. Increased Tornadoes – declining for decades. 3. New Ice Age in Europe – they’ve been spared; it never happened. 4. South Sahara Drying Up – completely untrue. 5. Massive Flooding in China and India – again didn’t happen. 6. Melting Arctic – false – 2015 represents the largest refreezing in years. 7. Polar Bear Extinction – actually they are increasing! 8. Temperature Increases Due to CO2 – no significant rising for over 18 years. 9. Katrina a Foreshadow of the Future – false – past 10 years, no F3 hurricanes; “longest drought ever!” 10. The Earth Would be in a “True Planetary Emergency” Within a Decade Unless Drastic Action Taken to Reduce Greenhouse Gasses – never happened.
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Post by nautonnier on Feb 18, 2019 11:19:05 GMT
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