Climate cycles create California precipitation uncertainty

Posted: December 12, 2021 by oldbrew in climate, Cycles, ENSO, modelling, Natural Variation, predictions, Uncertainty
Tags:

Golden Gate Bridge from Fort Point, San Francisco

Inserting unnecessary theories into climate models, in order to invent ways of blaming human activities for the weather, seems to be making life more difficult for the modellers in terms of accuracy of results. Natural variation is getting in the way.
– – –
Over the past 40 years, winters in California have become drier, says Phys.org.

This is a problem for the region’s agricultural operations, as farmers rely on winter precipitation to irrigate their crops.

Determining if California will continue getting drier, or if the trend will reverse, has implications for its millions of residents.

But so far, climate models that account for changes in greenhouse gases and other human activities have had trouble reproducing California’s observed drying trends.

When climate models project the future or simulate the past, they can’t agree on long-term precipitation trends. Researchers at Pacific Northwest National Laboratory (PNNL) want to know why because these mixed results aren’t very useful for future water resource planning.

“When we see these large uncertainties in model simulations and projections, we have to ask whether or not the models are up for the task,” said Ruby Leung, a Battelle Fellow and atmospheric scientist at PNNL. “One challenge with modeling California is that long-term natural cycles heavily affect its precipitation.”

These cycles range from years long, like El Niño and La Niña, to decades long, like the Interdecadal Pacific Oscillation (IPO). They represent natural variability associated with sea surface temperature patterns in the Pacific Ocean and affect winter precipitation in California.

But how much of a role do they play in spawning uncertainty in California’s precipitation projections? A big one, it turns out. Results from Leung and a PNNL team show that natural cycles are responsible for >70 percent of the uncertainty in model simulations of precipitation trends over the past 40 years.

By isolating the effects of the natural cycles, scientists can focus on improving models to reduce the remaining uncertainty related to how greenhouse gases and other human activities affect climate.

The impact of ensembles

With more computing power, researchers can now run large sets of simulations called large ensemble simulations. To produce them, researchers run climate models from 40–100 times with minor differences in their starting conditions.

Because everything except for the starting conditions remains the same, these ensembles provide a unique representation of natural variability.

Modeling centers around the world also run simulations that contribute toward multi-model ensembles. These represent the total uncertainty due to both natural variability and model uncertainty.

Leung and her team analyzed three ensemble simulations generated by three different climate models and two multi-model ensembles of two recent climate model generations. They wanted to determine the sources of uncertainty in the projections of California precipitation. What they found surprised them.

The team found that natural climate cycles were responsible for roughly 70 percent of the total uncertainty in model simulations of California precipitation trends in the past 40 years. That leaves 30 percent of the uncertainty for how models represent human influence on climate.

“We know that natural cycles have major impacts on California’s climate, but we didn’t think that they would dominate the total uncertainty in climate simulations to this extent,” said Leung. “This result shows the importance of large ensemble simulations for isolating human influence on climate, which may be small compared to natural cycles in some regions.”

Natural cycles versus human impacts

Of the natural cycles that influence California’s climate, the IPO is one of the most important. Its decades-long phases help determine if California is in a wetting or drying trend. The team’s results point to its substantial role in California’s drying over the past 40 years.

Currently, climate models have limited skill in predicting the transition between the IPO phases—especially decades from now. Therefore, future projections of California precipitation have large uncertainty due to IPO cycles.

Full article here.
– – –
Nature article: Uncertainty in El Niño-like warming and California precipitation changes linked by the Interdecadal Pacific Oscillation

Comments
  1. oldbrew says:

    “Prediction is very difficult, especially about the future.”
    https://www.goodreads.com/quotes/23796-prediction-is-very-difficult-especially-about-the-future
    – – –
    Assuming unproven climate theories are correct doesn’t seem to be helping either.

  2. Philip Mulholland says:

    Here is another little recognised cause of rainfall variability:

    In conclusion, volcanic eruptions are a natural cause of climate change responsible for the second wettest year in Hong Kong’s instrumental record since records began in 1883. The 1982 year was notable for extreme weather events including disastrous floods and landslips. Based on the study of the observation record of the El Chichón eruption volcanic cloud, anthropogenic global warming may be ruled out as a cause. Further studies on the impact of other volcanic eruptions on rainfall are however needed to support this underestimated natural cause.

    Wyss W.-S. Yim 2021 Impact of a volcanic eruption in Mexico on Hong Kong rainfall. Imperial Engineer Autumn 2021

  3. oldbrew says:

    U.N. climate panel confronts implausibly hot forecasts of future warming
    For the first time, major IPCC report likely to use recent warming to predict future heat increase
    27 JUL 2021

    So the IPCC team will probably use reality—the actual warming of the world over the past few decades—to constrain the CMIP projections. Several papers have shown how doing so can reduce the uncertainty of the model projections by half, and lower their most extreme projections. [bold added]

    https://www.science.org/content/article/un-climate-panel-confronts-implausibly-hot-forecasts-future-warming
    – – –
    If all else fails, try reality 🤪
    It only leaves half the ‘uncertainties’.

  4. Philip Mulholland says:

    When all else fails, read the instructions.

  5. ivan says:

    So all they are proving is the old Garbage In = Garbage Out and the fact their climate models are totally useless.

    One would think that by now they would have given up trying to use models to predict anything with the climate.

  6. Gamecock says:

    ‘want to know why because these mixed results aren’t very useful for future water resource planning.

    “When we see these large uncertainties in model simulations and projections, we have to ask whether or not the models are up for the task’

    So average them all together, and call it “data.”

    ‘Because everything except for the starting conditions remains the same, these ensembles provide a unique representation of natural variability.’

    Not correct. ‘Unique.’ Which is worth what?

    This article reads like TPTB went to the climatologists and demanded something useful from their stupid, expensive models.

    The climatologists told them, “No.”

    Though they used several paragraphs of word salad to say it.

    ‘The team found that natural climate cycles were responsible for roughly 70 percent of the total uncertainty in model simulations’

    The strangest statistic I ever heard.

  7. […] Climate cycles create California precipitation uncertainty […]

  8. Phoenix44 says:

    So the models are usually wrong and they are wrong because they have assumed natural variability is much lower than it actually is.

    Exactly what we have been saying for years then.

    [reply] indeed

  9. jb says:

    “We used to think that if we put a million monkeys at a million keyboards, eventually they would produce the works of Shakespeare. Now that we have the Internet, we know that isn’t so.”–Unkown

  10. tom0mason says:

    “Climate cycles create California precipitation uncertainty”

    But so far, climate models that account for changes in greenhouse gases and other human activities have had trouble reproducing California’s observed drying trends.

    So the UN-IPCC approved models fail again, how not very surprising.

    “By isolating the effects of the natural cycles, scientists can focus on improving models to reduce the remaining uncertainty related to how greenhouse gases and other human activities affect climate.”
    Or more likely end up with a mathematical dog’s diner where all the error bands products overwhelm the results as more iterations are applied.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s