Climate Forecasting: The Big Gap – by The GWPF

Posted: April 9, 2019 by oldbrew in climate, modelling, Natural Variation, opinion, predictions, Uncertainty

Some might not agree with the claim here that ‘The basics of climate change are well known’, but the author spotlights the shortcomings of climate models that almost invariably over-predict warming that fails to occur – which strongly suggests a faulty basis for understanding climate patterns. Even the newest model shows signs of repeating these long-known errors.

There is a gap in climate predictions, says Dr. David Whitehouse.

It is between the annual and decadal.

I was once told by a very eminent climate scientist that he didn’t care what the observations of the real world were, he believed in models, and only models, and they were enough to work out what is going on.

But I wonder?

The basics of climate change are well known. Where the debate lies is in the important details — and the peer-reviewed scientific literature is full of such disagreements, frequently about the same sets of data. This is commonly ignored by the more enthusiastic alarmists.

Empirical data is one thing, but what is needed for developing policies are predictions. Predictions are based on models and their limitations are obviously: what we know about the real world, what we can measure, and how we can transpose them into computer code. Models are not, and never will be, the real world but they are never-the-less essential and seductive.

There is a gap in climate predictions. It is between the annual and decadal. If scientists could make seamless predictions from weather forecasts over a few days, through seasons to annual and decadal timescales they would be in a far better position to influence policy.

But as it is, it almost seems that weather forecasts and short-term seasonal forecasts are a different type of animal than decadal forecasts.

Establishing what some call a “seamless climate service delivery system,” has been called one of climate science’s grand challenges.

Some are optimistic that it can be done. According to a recent press release by the University of Exeter such near-term climate predictions are ‘coming of age.’ According to Professor Adam Scaife, jointly from the University of Exeter and Met Office and a lead author on the study in Nature Climate Change.

“There is a lot of work still to do, but just as weather forecasts became a regular operational activity in the 20th century, we are now approaching a similar point for near term climate predictions and these are now being made at a number of scientific institutes worldwide,” says Professor Adam Scaife, jointly from the University of Exeter and Met Office and a lead author on the study in Nature Climate Change.

Having read the paper and supporting literature I do not feel so optimistic. After all, on the one hand the study claims that prospects for success are good, but on the other hand that there are formidable challenges.

We are not yet able to even identify and constrain the key factors that will need to be understood let alone able to determine climate predictability on weekly to decadal scales when we don’t know the uncertainties. Indeed the paper talks of an “envelope of uncertainties” controlled by forced and internal climate variability.

Future Past

Some believe that near-term predictions have been vindicated by what are called retrospective predictions or hindcasts. Modelers of all disciplines will readily tell you that the past and the future are not the same, for the obvious reason you have to wait for the future.

Success in hindcasting is no guarantee of success in forecasting. The only thing that works is to make a prediction and wait to see how good it was.

I know I am not alone in thinking that optimism for near-term climate forecasting belies the difficulties, especially when one reads that it is hoped it will be as good as the seasonal forecasting currently available. You don’t have to have long memories to remember how such forecasts by the UK Met Office got the Government into a pickle after a ‘cold snap.’

Looking at the projects to compare climate models with real-world data shows that climate predictions are difficult, and that they are inaccurate. That is the lesson from the 5th round of the Climate Model Inter-comparison Project (CMIP) .

CMIP6 is not very encouraging. It will inform the AR6 report of the IPCC in 2021. It’s just at the start but so far it is repeating the problems of CMIP5 in running too warm.

Full report here.

  1. JB says:

    “…eminent climate scientist…” among whom? The most accurate forecaster in Kansas City rarely goes out beyond ten days, and every year he has a contest for the public to take a cash prize predicting the first snowfall day. Its just an article of common knowledge among the populace here that the weather can and will change at any time, and next year’s seasonal behavior is just as unlike the recent past, as is the daily weather predictions.. We live from day to day, and season to season. Over the last 20 years of life here I’ve seen the climate shift from warm winters and hot days in the peak of summer to erratic and extreme cold temperatures in winter, late Spring by 2-3 weeks, with an increase in peak flooding.

    I’d like to know how anyone can successfully model that.

  2. Scute says:

    The instrumental data in the graph appears to go up to 2009. I’ve seen a version going up to 2013 that uses Hadcrut3. We’re now a decade beyond 2009 so you really need to source an updated version or not use this 2009 version or use it and say it goes to 2009 with some indication of the instrumental record data after 2009.

    [reply] thanks – updated to 2017

  3. oldbrew says:

    Too many inter-dependent variables. Believing a minor trace gas matters more than anything else is absurd and foolish, but that’s where we are.

  4. oldbrew says:

    Failed Climate Models
    Posted on February 20, 2019 by tonyheller

    Several graphs + commentary.

  5. Phoenix44 says:

    If we understood the climate then a global temperature model is three lines:

    1. Change in CO2.
    2. ECS
    3. 1×2

    That wouldn’t work locally or in the short term but would be accurate over the long term. If we don’t know ECS then we cannot model the climate if CO2 plays any significant role.

  6. oldbrew says:

    They insist radiative factors are the biggest deal in the models, then wonder why they always come out too warm? Not hard to see where the main problem lies.

  7. hunterson7 says:

    The engineer’s at Boeing wish the science of aviation was as settled as the climate consensus believers claim climate science is.

  8. Oldbrew, agree with your point that the basics of climate are not well known but there are a couple of points which are known but the so-called “climate scientists” do not understand or deliberately ignore. 1/ CO2 in our atmosphere based on well known engineering formulae based on measurements has no significant temperature absorbing or holding affect. 2/ changes in CO2 concentrations in the atmosphere lag changes to atmospheric temperature ( therefore in practice can not be a cause) 3/ clouds made of water droplets and ice particles affect incoming radiation from the sun 4/ water vapour (H2O gas) unlike CO2 (due to its concentration and its wavelength absorption and emission ability) has an affect on atmospheric temperatures 5/ the evaporation of water from ocean, sea and lakes, and the condensation of water vapour to liquid and solid forms in clouds also has significant temperature effects.
    Just considering the 4 points above clearly shows so-called “climate change” due to CO2 especially resulting from human activities is a scam.

  9. Scute says:

    Thanks for updating the graph.

  10. oldbrew says:

    I was once told by a very eminent climate scientist that he didn’t care what the observations of the real world were, he believed in models, and only models, and they were enough to work out what is going on.

    Check the model results, then compare with the observations – the scientific method, no matter how ’eminent’ you are.

  11. oldbrew says:

    Falsified Hypotheses Are Rejected In Science. For Consensus Climate Science™, Failed Hypotheses Are Upheld.
    By Kenneth Richard on 11. April 2019

    In most scientific fields, hypotheses that fail to be verified by real-world observations 85% to 100% of the time are rejected immediately.

    In Consensus Climate Science™, when 126 of 126, 111 of 114, 42 of 49… modeled projections are wrong, or when the opposite sign of the modeled trend is observed, the climate models are still regarded as mechanistically correct, especially with regard to the CO2 climate influence.

    Those who disagree are dismissed as “denialists”.

    And this is exactly the problem. Who is really in denial?

    They keep getting the same wrong answers from the models but plough on regardless, or so it seems.

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