Climate models: No warming for 30 years – possibly

Posted: July 15, 2020 by oldbrew in climate, modelling, Natural Variation, predictions, Temperature, Uncertainty

Instead of promoting meaningless climate thresholds, targets etc., alarmists might want to take a closer look at the neglected topic of natural factors.
– – –
A new study demonstrates how a prolonged warming pause or even global cooling may happen in coming years despite increasing levels of atmospheric greenhouse gases — caused by natural climatic variability, says The GWPF.

Natural climatic variability has always been a topic that contains a lot of unknowns, but it has been rarely explicitly stated just how little we know about it.

Such variability has been habitually underplayed as it was “obvious” that the major driver of global temperature was the accumulation of greenhouse gasses in the atmosphere, with natural variability a weaker effect.

But the global temperature data of this century demonstrate that natural variability has dominated in the form of El Ninos.

‘Doesn’t matter’, came the reply, ‘just wait and the signal of greenhouse warming will emerge out of the noise of natural climatic variability.’

How long will we have to wait for that signal? Quite a long time, according to some researchers as more papers acknowledge that natural climatic variability has a major, if not a dominant influence on global temperature trends.

With the usual proviso concerning climatic predictions there seems to be a growing number of research papers suggesting that the global average temperature of at least the next five years will remain largely unchanged. The reason: natural climatic variability.

Only last week the UK Met Office produced figures suggesting that there is only a 1 in 34 chance that the 1.5°C threshold will be exceeded for the next five year period.

Now a new paper by climate modellers extends such predictions, suggesting that because of natural variability the average global temperature up to 2049 could remain relatively unchanged – even with the largest increase in greenhouse gas emissions.

Continued here.

  1. Chaswarnertoo says:

    Grand solar minimum, anyone?

  2. Bloke back down the pub says:

    Strangely enough, natural variability is always accepted as an excuse for why the climate might not warm as much as expected in the future, but rarely as an explanation of why it warmed as much as it did in the past.

  3. oldbrew says:

    El Niño looks unlikely this year…

    July 2020 ENSO update: La Niña Watch!
    Author: Emily Becker
    July 9, 2020

    ENSO is still in neutral, and likely to continue so through the summer. However, the 50-55% chance of La Niña developing in the fall and lasting through winter means NOAA has hoisted a La Niña Watch.
    – – –
    After solar cycle 25 gets going there might be an El Niño of some sort.

  4. oldbrew says:

    Some old-time natural variability…

    *The deadly heat wave of July 1936 in the middle of arguably the hottest decade on record in the US*
    July 15, 2020

    Many of the all-time high temperature records that were set in the decade of the 1930’s still stand today. The heat wave experienced in 1936 began in late June, reached a peak in July, and didn’t really come to an end until September. This extreme heat wave was particularly deadly; especially, in high population areas where air conditioning was still in the early stages of development.

  5. tom0mason says:

    Hahahah, “internal variability” says Maher et al.’s paper.
    No. What they truly mean is natural chaotic variability! And it is something that is not accurately quantified or modeled, so instead climate catastrophists try to squeeze it into some sort of ‘known-unknowns’ box.
    CO2 warming ain’t making it (still in 20+years of temperature records there’s a ‘hiatus’), so the modeling maniacs resort to mere sophistry to try and say they do understand ‘climate change’ but don’t know about the operation of all it’s specifics.

    Model uncertainty is found to be the main driver of mid-term trends when we take a low estimate of internal variability, while with a high estimate, internal variability instead dominates. This result highlights the importance of using multiple SMILEs, with a range of estimates of internal variability in future studies investigating mid-term time-scales and underscores the importance of evaluating not just a model’s mean state or forced trend, but also its internal variability.

    IMO all that Maher et al.’s paper show is that climate models are as useful as chocolate rocking-horse excrement.

    Nature controls the climate (and atmospheric CO2 levels), not humans.

  6. nessimmersion says:

    Somewhat OT, but BBC 10 o’clock news has just headlined on hottest temperatures evah in Siberia, unprecedented global warming etc etc etc.
    Very partial reporting with an extremely biased reportage.
    Hopefully something can be done about the BBC not abiding by its charter.

  7. Phoenix44 says:

    I do love these probabilities from the Met Office and other Alarmists. How many sides on your dice? Don’t know. What numbers are on the sides? Don’t know. So you are calculating the probability of the next throw how?

  8. oldbrew says:

    The BBC specialises in cherrypicking isolated weather-related events and claiming, or implying, that they prove something.

  9. tom0mason says:

    Someone (Bo-Wen Shen, San Diego State University) has realized that all that weather chaos requires a bit more structured analysis …

    Is Weather Chaotic? Coexisting Chaotic and Non-Chaotic Attractors within Lorenz Models

    The pioneering study of Lorenz in 1963 and a follow-up presentation in 1972 changed our view on the predictability of weather by revealing the so-called butterfly effect, also known as chaos. Over 50 years since Lorenz’s 1963 study, the statement of “weather is chaotic’’ has been well accepted. Such a view turns our attention from regularity associated with Laplace’s view of determinism to irregularity associated with chaos. Here, a refined statement is suggested based on recent advances in high-dimensional Lorenz models and real-world global models. In this study, we provide a report to:
    (1) Illustrate two kinds of attractor coexistence within Lorenz models (i.e., with the same model parameters but with different initial conditions). Each kind contains two of three attractors including point, chaotic, and periodic attractors corresponding to steady-state, chaotic, and limit cycle solutions, respectively.
    (2) Suggest that the entirety of weather possesses the dual nature of chaos and order associated with chaotic and non-chaotic processes, respectively. Specific weather systems may appear chaotic or non-chaotic within their finite lifetime. While chaotic systems contain a finite predictability, non-chaotic systems (e.g., dissipative processes) could have better predictability (e.g., up to their lifetime). The refined view on the dual nature of weather is neither too optimistic nor pessimistic as compared to the Laplacian view of deterministic unlimited predictability and the Lorenz view of deterministic chaos with finite predictability

    Hopefully such ideas can filter into the ‘climate modelers’ thinking.

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