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Relevancy of Machine Learning to climatic models

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AbruptSLR:
The first link leads to a website on "Climate Informatics", with up-dated information on machine learning and climate change:

http://www.climateinformatics.org/

The second link leads to a pdf of the "Proceedings of the 6th International Workshop on Climate Informatics: CI 2016"

https://opensky.ucar.edu/islandora/object/technotes:543

Abstract: "Climate informatics is an emerging research area that combines the fields of climate science and data science (specifically machine learning, data mining and statistics) to accelerate scientific discovery in climate science. The annual climate informatics workshop, held at NCAR's Mesa Lab since 2012, promotes new collaborations and discusses new methods and directions for this emerging field. This year's proceedings contain 34 peer-reviewed short papers presented at the workshop, which describe many new methods and advances in the field. Making these papers available to all interested researchers is essential to maximize further advances in this important field."


The third link leads to a book based on 2014 information entitled: "Machine Learning and Data Mining Approaches to Climate Science"

http://www.springer.com/us/book/9783319172194

Summary: "This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014."


The fourth link leads to a 2014 article entitled: "What Machine Learning Can Do For Climate Science"

http://www.planetforward.org/2014/05/12/what-machine-learning-can-do-for-climate-science

6roucho:

--- Quote from: AbruptSLR on December 13, 2016, 04:40:35 PM ---I provide the following reference, co-authored by Murray Gell-Mann, that could be used to better address climate change issues including:  risk, insurance, revenue neutral carbon pricing, and other topics.  This reference makes it very clear that most humans (even most experts) have a very weak intuitive understanding of their own ignorance (which results in a poor understanding of gambles/risk that we are all exposed to w.r.t. climate consequences).
--- End quote ---
Absolutely.

I had the opportunity in the 1990s to work on the project at Lloyd’s to calculate the premium for reinsuring the bad long-tailed risk that was threatening the existence of the corporation. As part of that, we tried to understand why it had happened.

We highlighted three main causes:

•   Competition for profits
•   Consequences that played out in the future, beyond the likely incumbency of underwriters
•   A systematic underestimation of catastrophe risk

The irony is that insurance is well-served with excellent models, whose development budgets can often be many times more than pure science has to play with, but in this case underwriters with no mathematical knowledge chose to ignore them.

[Another is that if you substitute politician for underwriter, this relatively small business crisis (Lloyd's survived, even if all the Names who financed the insurance didn’t) played out much the same as the much larger catastrophe of climate change.]

I think that one area where machine learning has a lot to offer is in the interpretation of science by policymakers. If we go back to finance, systems that learn about markets can be exceptionally effective traders, because what humans tend to do in uncertain situations is throw away the mathematics, and instead bet by instinct, which black swan theory (and recent history) suggests is systematically optimistic when it comes to catastrophic events. The human instinct is to double down on optimistic bets when no good can come of the worst-case scenario.

Which is what some politicians are doing right now with climate change.

DrTskoul:
Machine leaning, data analytics etc etc is used to detect relationships and patterns in massive amounts of data. Data can be real ( measurements ) or modeled ( climate system simulations ). 

I hear about it a lot in chem eng  research circles ( e.g. catalyst development or process simulation, optimization and contol). A few years ago we had high throughput experimentation.

Analysing satellite images for patterns is a perfect example of ML application.

ML won't find a better model for arctic ice dynamics.

gerontocrat:
https://www.bbc.co.uk/news/science-environment-47267081

Machine learning might not be all its cracked up to be.

AAAS: Machine learning 'causing science crisis'
Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.

--- Quote ---Dr Genevera Allen from Rice University in Houston said that the increased use of such systems was contributing to a “crisis in science”.

She warned scientists that if they didn’t improve their techniques they would be wasting both time and money. Her research was presented at the American Association for the Advancement of Science in Washington.

A growing amount of scientific research involves using machine learning software to analyse data that has already been collected. This happens across many subject areas ranging from biomedical research to astronomy. The data sets are very large and expensive.

'Reproducibility crisis'
But, according to Dr Allen, the answers they come up with are likely to be inaccurate or wrong because the software is identifying patterns that exist only in that data set and not the real world.
“Often these studies are not found out to be inaccurate until there's another real big dataset that someone applies these techniques to and says ‘oh my goodness, the results of these two studies don't overlap‘," she said.

“There is general recognition of a reproducibility crisis in science right now. I would venture to argue that a huge part of that does come from the use of machine learning techniques in science.” The “reproducibility crisis” in science refers to the alarming number of research results that are not repeated when another group of scientists tries the same experiment. It means that the initial results were wrong.
--- End quote ---

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