Machine Learning and Climate Change Prediction

Current climate prediction models are markedly defective, even in reproducing the changes that have already occurred. Given the great importance of climate change, we must identify the causes of model errors and reduce the uncertainty of climate predictions [TM205 or search for “thatsmaths” at irishtimes.com].

Schematic diagram of some key physical processes in the climate system.

The Charney Report

In 1979, a study group led by Jule Charney, one of the greatest meteorologists of the twentieth century, submitted a report to the Climate Research Board of the US National Research Council. The task of the group was to assess the scientific basis for projection of possible future climatic changes resulting from man-made releases of carbon dioxide into the atmosphere. The report was entitled Carbon Dioxide and Climate: a Scientific Assessment. In the Foreword, Verner Suomi, Chairman of the Board wrote: “We now have incontrovertible evidence that the atmosphere is indeed changing and that we ourselves contribute to that change.”

The study group estimated the most probable global warming for a doubling of CO2 was 3°C, with an error of plus or minus 1.5°C. In 2014, the Fifth Assessment Report of the Intergovernmental Panel on Climate Change stated that warming due to doubling of CO2 is “likely between 1.5°C and 4.5°C”, the same uncertainty range as the Charney report some 35 years earlier. Thus, notwithstanding major improvements in climate modelling, large uncertainties remain about how much the Earth will warm in response to increasing CO2.

Causes of uncertainty

Climate models are complex computer programs with millions of lines of code. Some of the atmospheric and oceanic processes are represented very well, for example fluid flow, described accurately by the Navier-Stokes equations. Other processes are accounted for in a much cruder fashion.

What are the reasons for errors and uncertainties in climate predictions? The treatment of clouds, and their interaction with radiation from the Sun and the Earth, are the main culprits. To model the climate, we represent the continuous atmosphere and ocean by a set of values on a computational grid. The pressure, temperature, humidity and winds, which vary from place to place, are specified at points separated by tens of kilometres. Details at finer scales – so-called sub-grid processes – are poorly represented in the models, and lead to errors that grow with time.

As computer power increases, the grids are refined, leading to greater confidence in the predictions, but it will be decades before grids at a one-kilometre scale can be used for long-term climate simulations. In the meantime, we must seek other ways to narrow the confidence gap.

Using Weather Forecasts to train ML schemes

Short-range simulations, for days or weeks, using very high resolution modes are now in routine use for weather forecasting. Can we use information from these to improve the treatment of clouds in low-resolution models? Several climate modelling groups are now using machine learning (ML) to improve the representation of clouds and other sub-grid processes. Is it possible to train a machine learning scheme by using short-term, high-resolution data?

Early evidence indicates that this approach is feasible and enables low-resolution models to produce some key features of the high-resolution simulations. However, these experiments have been done only with simplified models. We are still far from being able to generate better climate simulations. There remain some significant problems, one of which is instability, where the simulations become completely unrealistic. Since ML schemes involve “black boxes” like neural networks, where the variables have no obvious physical interpretation, it is difficult to rectify these instabilities.

These problems are formidable and, as a result, the technique of marrying machine learning to deterministic models is far from trivial, and its potential is yet to be realised. However, there have been major advances in machine learning over the past decade, especially in computer vision and language translation. These give us hope that, as ML techniques improve, we will be able to produce climate predictions with reduced levels of uncertainty.

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That’s Maths II: A Ton of Wonders

by Peter Lynch now available.
Full details and links to suppliers at
http://logicpress.ie/2020-3/

>>  Review in The Irish Times  <<

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Sources

Matthew Chantry, Hannah Christensen, Peter Dueben and Tim Palmer (Editors), 2021:  Machine Learning for Weather and Climate Modelling. Special themed issue of Phil Trans A. Vol 379, Issue 2194. 5 April, 2021. https://royalsocietypublishing.org/toc/rsta/2021/379/2194


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