Climate models: the limits in the sky

Posted: October 13, 2020 by oldbrew in climate, Clouds, modelling, Uncertainty

The debate about the role of clouds in climate — whether in isolation, or relative to other possible factors — rumbles on, and on, and adequate data is just not available. A rather large hole in the IPCC-claimed ‘settled science’, it seems.
– – –
Climate modellers hope machine learning can overcome persistent problems that still cloud their results, says E&T Magazine.

The discipline of climate modelling has entered its sixth decade. Large-scale analyses of Earth’s behaviour have evolved considerably but there remain significant gaps, some persistent.

One in particular helps illustrate challenges that are now being tackled by, almost inevitably, using artificial intelligence (AI) and machine learning (ML).

“How important the overall cloud effects are is, however, an extremely difficult question to answer. The cloud distribution is a product of the entire climate system, in which many other feedbacks are involved. Trustworthy answers can be obtained only through comprehensive numerical modelling of the general circulations of the atmosphere and oceans together with validation by comparison of the observed with the model-produced cloud types and amounts. Unfortunately, cloud observations in sufficient detail for accurate validation of models are not available at present.”

This passage comes from one of the cornerstone reports on climate change, the 1979 Woods Hall review by the US government’s Ad Hoc Study Group on Carbon Dioxide and Climate.

It was chaired by pioneering meteorologist Professor Jule Charney of MIT, the man who brought computers into weather forecasting with John von Neumann. Most of what Charney’s group said about clouds then stands today.

Clouds matter because more than 40 years later there is still scientific debate over the extent to which they sometimes warm and sometimes cool the planet, and what impact the balance has on global temperature.

Put simply, clouds that are higher in the atmosphere and thinner trap heat; those that are lower and thicker reflect the sun rays.

Research published in September from a joint team at the University of Liverpool, Imperial College London and the UK’s National Oceanography Centre highlighted that this lack of clarity is one leading reason why macro-scale models differ over what goals should be set for carbon emissions.

Moreover, the issue is today more pressing because as the Earth’s climate is already changing, so too are the proportions (high:low. thick:thin) and locations of the clouds and, by extension, their influence.

Clouds have proved hard to model because they defy the nature of the recognised macro-modelling strategies (as indeed do several other factors these models struggle to embrace, such as eddies in ocean currents).

The workhorse – used by the main contributors to the Assessment Reports released by The Intergovernmental Panel on Climate Change – is the general circulation model (GCM), a technique that pulls on fluid dynamics equations and thermodynamics, supplemented by parameterisation.

GCMs and their extensions are extremely complex, running to millions of lines of code. As an example of the 20 or so GCMs considered world-class, the HadCM3 coupled atmospheric and oceanic model (AO-GCM), developed by the Hadley Centre at the UK’s Met Office, can run simulations out across more than a thousand years.

In some other respects GCMs run at very low resolutions. They are based on imposing a 3D grid upon the sphere of the Earth. In earlier implementations, the grid’s boxes were several hundred kilometres square and had half a dozen or so vertical layers.

Some of the limitations were inherent in the complexity ceilings for the models as they evolved, but another major constraint has always been computational capacity. Climate modelling has tested and reached the limits of just about every generation of supercomputer, with every doubling in spatial resolution said to need a tenfold increase in processing.

As we move into the era of exascale supercomputers and quantum processing potentially moves out of the lab, resolutions are rising – as the Met Office notes, it is leveraging 256 times more crunching power today – but resolution maximums remain in a range between the lower hundreds-of-kilometres and upper tens.

Clouds, by contrast, are highly localised and comparatively brief events, requiring finer resolution to be addressed in the detail thought necessary. They still fall through the gaps. There is then a further complication.

We think of climate models in terms of the forecasts they produce. Alongside the high-profile targets such as keeping the rise in temperature below 2°C, every week seems to bring a new, more event-specific observation about sea levels, potential species extinction or migrations in population. And the need for this kind of more granular modelling is widely acknowledged.

Within the modelling community itself, another important task – particularly as models are refined and extended – involves looking into the past: does the model account for how the world’s climate has already behaved if you run it backwards? Of little interest to the public, this so-called hindcasting is very important for validation.

Again – and very likely because of the brevity and local nature of clouds – there is very little historical data available against which to compare cloud modelling.

The combination of a lack of resolution, knowledge and insight would appear to be fertile territory for machine learning, and a number of research projects are looking to leverage such techniques.

Full article here.

  1. Paul Vaughan says:

    No. 11√(Φ-φ)near e/11 laugh f(UN) the Room von [rama]nu[jan] [ra]man[ujan]

    Setting the boundary conditions is a number theory problem.
    There’s no limit to the truly monstrous computing capacity that will miss this.

    As Polya advised: Solve a simpler problem.

    “Mathematics is the queen of the sciences—and number theory is the queen of mathematics.” — Gauss

    Play yen left field shall O UNdoor the green monde f(UNweigh).

  2. stpaulchuck says:

    we’ve BEEN using artificial intelligence on this issue from the beginning – pols and sheeple, or at least the charlatans in science (fiction) and academia have been, anyway.

  3. oldbrew says:

    ‘The combination of a lack of resolution, knowledge and insight’


    “Unfortunately, cloud observations in sufficient detail for accurate validation of models are not available at present.”

  4. Paul Vaughan says:

    Fact Chuck Symbol Lies

    16 Figure$ type O: 73500 / 2 = 36750 left dawn seconds a way from the right roots.

    16 proof red. Call Cal. lace yen$. Sum won right.

  5. Gamecock says:

    ‘The discipline [sic] of climate modelling has entered its sixth decade. Large-scale analyses of Earth’s behaviour have evolved considerably but there remain significant gaps, some persistent.’

    In other words, it’s all been wrong. For over 50 years.

    But by saying you are applying AI and machine learning, you can get people to keep believing it’s real for a while longer.

  6. Gamecock says:

    Headline E&T Magazine 2023: “One in particular helps illustrate challenges that are now being tackled by, almost inevitably, using quantum computing.”

  7. oldbrew says:

    AI & ML = *computer says*

  8. Phoenix44 says:

    This is the classic mistake economic modelers made and continue to make. Its an extremely large, extremely complex non-linear system so let’s keep making models more and more detailed and use more and more computing power. But that approach CANNOT produce more accurate models unless the understanding of the actual processes is equally as well-detailed. It’s actually more likely to increase error. As with macroeconomists, climate scientists have convinced themselves that they understand it all so they can model it all, but they have barely scratched the surface.

    Better models would be smaller and much less detailed, relying on modelling the bigger principles we broadly understand.

  9. tom0mason says:

    Of course such AI and ML processes will not have any algorithmic bias, will they?
    All their AI & ML methods will tested to ensure that no bias exists, especially where due to the lack of the observations, invented ‘data’ is assumed?

    This article from 2 years ago seems prescient now, especially where they say …

    In fact, if not addressed, algorithmic bias can lead to the amplification of human biases. Under the illusion that software isn’t biased, humans tend to trust the judgement of AI algorithms, oblivious that those judgments are already reflecting their own prejudices. As a result, we will accept AI-driven decisions without doubting them and create more biased data for those algorithms to further “enhance” themselves on.

    IMHO All they will achieve is make an AI driven Machine Learning echo chamber for the complacent and self satisfied ‘scientific consensus’.

  10. A C Osborn says:

    Personally I think there is plenty of data available for the affects of Clouds.
    It just doesn’t tell them what they want hear.
    In fact it tells them the exact opposite, ie that cloud cover is responsible for heating and cooling on the decadal scale, less cloud = more heat in to the oceans, more cloud = less heat in to the oceans.
    CO2 no affect.
    There ares studies that show, even by NASA.

  11. Ron Clutz says:

    As AC says there is a lot of knowledge about clouds, especially the low cloud effects, but it is not welcomed by the CO2 faithful. Here is a summary of the natural sunscreen research into global dimming and brightening.

  12. pochas94 says:

    Climate models output politics, not science.

  13. oldbrew says:

    does the model account for how the world’s climate has already behaved if you run it backwards?

    In a word: No.

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