Science Friday: More Climate Model Innards and Critiques

This is an interesting article in Science Magazine leading up to the upcoming IPCC report.  This one looks at some of the reasons and the “poorly understoods” that are causing models to run hot.

The models were also out of step with records of past climate. For example, scientists used the new model from NCAR to simulate the coldest point of the most recent ice age, 20,000 years ago. Extensive paleoclimate records suggest Earth cooled nearly 6°C compared with preindustrial times, but the model, fed with low ice age CO2 levels, had temperatures plummeting by nearly twice that much, suggesting it was far too sensitive to the ups and downs of CO2. “That is clearly outside the range of what the geological data indicate,” says Jessica Tierney, a paleoclimatologist at the University of Arizona and a co-author of the work, which appeared in Geophysical Research Letters. “It’s totally out there.”

To find out why, modelers probed the guts of the simulations, focusing on their representation of clouds, long the wild card of climate change. The models can’t simulate clouds directly, so they rely on known physics and observations to estimate cloud properties and behavior. In previous models ice crystals made up more of the low clouds in the midlatitudes of the southern Pacific Ocean and elsewhere than satellite observations seemed to justify. Ice crystals reflect less sunlight than water droplets, so as these clouds heated and the ice melted, they became more reflective and caused cooling. The new models start with more realistic clouds containing more supercooled water, which allows other dynamics driven by warming—the penetration of dry air from above and a subduing of turbulence—to thin the clouds.

But that fix has allowed scientists to spy another bias previously countered by the faulty cooling trend. In both the old and new climate models, the patchy cumulus clouds that form in the tropics thin out in response to warming, allowing in more heat than satellite observations suggest, according to a study by Timothy Myers, a cloud scientist at Lawrence Livermore National Laboratory. “Even though one feature of the climate is now more realistic, another that’s persistently biased has been revealed,” Myers says.


But this part brought back a memory from the 70’s…

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. For 2100, in a worst-case scenario, that would reduce a raw 5°C of projected warming over preindustrial levels to 4.2°C.

Our silviculture professor at Yale, Dave Smith, had a skeptical view of forest vegetation models.  We were also, as graduate students, learning FORTRAN as a programming language (history, this was actually a graduate level course!). Dave told us something like “when trees grow too tall, they just put in a card that says “if the modeled tree height value is greater than 90, tree height equals 90″”. I don’t know if that was actually true about the JABOWA model, but this climate modeling intervention sounds a bit like the same thing.

The modelers hope to do better next time around. Lamarque says they may test new simulations against recent paleoclimates, not just historical warming, while building them. He also suggests that the development process could benefit from more time, with updates every decade or so rather than the current report interval of every 7 years. And it could be helpful to divide the modeling process in two, with one track focused on scientific experimentation—when a large range of climate sensitivities is helpful—and the other on providing a best estimate to policymakers. “It’s not easy to reconcile these two approaches under a single entity,” Lamarque says.

A cadre of researchers dedicated to the task of translating the models into useful projections could also help, says Angeline Pendergrass, a climate scientist at Cornell University who helped develop one technique for weighting the model results by their accuracy and independence. “It’s an actual job to go between the basic science and the tools I’m messing around with,” she says.

For now, policymakers and other researchers need to avoid putting too much stock in the unconstrained extreme warming the latest models predict, says Claudia Tebaldi, a climate scientist at Pacific Northwest National Laboratory and one of the leaders of CMIP’s climate projections. Getting that message out will be a challenge. “These issues don’t translate very well in practice,” she says. “It’s going to be hard for people looking to make some projection of a water basin in the West to make sense of it.”

Maybe people who manage resources like water just acknowledge “we don’t know, but things could be somewhat, very, or terribly much worse in a variety of ways due to climate change and a variety of other factors that may interact” and then see how the responses would play out, and what would be key decision points. It seems like that’s actually what they are doing, at least, as I recall Denver Water, ten or more years ago. Here are some examples.

8 thoughts on “Science Friday: More Climate Model Innards and Critiques”

  1. I thought climate science was settled, after all we are proposing trillions to mitigate it.. this is heresy to claim otherwise .. yet, we seem to follow ever changing CDC and WHO daily mandates on Covid which appears to be an unsettled science changing daily ..l am confused there an agenda embedded here? Just asking for a friend.

    • I don’t mean to be hard on climate modelers but there are many things lost in translation… in a way economists have paved this way before.
      The future is uncertain. We should have models that help us understand what’s happening (for scientific understanding. Whoops! We can’t really believe them because we didn’t account for some things and unexpected things happen.
      So they developed a bunch of strategies to deal with uncertainty.

      That’s what the water managers are doing. That’s what forest practitioners are doing.

  2. “far too sensitive to the ups and downs of CO2”
    YUP! and ice cores from way back show things were at least as hot with half the CO2!!!

    “when trees grow too tall, they just put in a card that says “if the modeled tree height value is greater than 90, tree height equals 90″”
    YUP! Been there, Done that.

  3. Here is what I posted, slightly edited, in another blog dealing with this issue:

    It continues to be debatable as to whether taxpayer-funded “modeling” — whether for “climate catastrophe,” “critical habitat,” or the current pandemic — is actually a form of “science” at all, or just a really expensive computer-based technical tool useful to scientists in some fields. Widely circulated computerized predictions of Biblical catastrophes by government modelers over the past several decades have proven unnecessarily costly to millions, and perhaps primarily to our rural populations.

    Whether “science” (modeling) is somehow authoritatively quoted as providing “mitigating strategies” for “combatting” climate change; for “preserving” habitat “critical” for the survival of an obscure chosen species; or for being held responsible by government decree to close our schools, cover our faces with cloth masks and/or pay 5-cents for a plastic bag at the grocery story, the predicted results have uniformly been the same: arguably wrong. And way wrong.

    Binary decision-making machines programmed by a limited number of trained humans cannot predict the future. That fact has become ever more apparent with each failure of such-predicted catastrophes over time, dating back to the late 1980s and before. These failures also include missed predictions of actual catastrophes during the same time period, which is equally significant.

    Eisenhower clearly warned us of this potential situation: public policies generated by expensive taxpayer-funded computers controlled by a chosen few, in lieu of funding traditional scientific research with needed reasonable challenges. Florida still exists, there is still snow in Minnesota, and “critical habitat” has been going up in flames for years in the western US — all as clearly predicted by traditional scientific methods and missed entirely by the government modelers.

  4. I guess I’m missing the point here. Understanding model limitations is part of understanding how to use them and their products. Why does it matter whether modeling is “science?” As a (long-ago) former modeler, this all (particularly Bob’s innuendos about “failures”) sounds to me like efforts to discredit results by people who don’t like them.

    • Here’s an interesting essay: “How Climate Scenarios Lost Touch With Reality,” by Roger Pielke Jr. and Justin Ritchie.

      “Our research (and that of several colleagues) indicates that the scenarios of greenhouse gas (GHG) emissions through the end of the twenty-first century are grounded in outdated portrayals of the recent past. Because climate models depend on these scenarios to project the future behavior of the climate, the outdated scenarios provide a misleading basis both for developing a scientific evidence base and for informing climate policy discussions. The continuing misuse of scenarios in climate research has become pervasive and consequential—so much so that we view it as one of the most significant failures of scientific integrity in the twenty-first century thus far. We need a course correction. ”

    • Hi Jon: In my field, forestry, the constant failure of models in making predictive results regarding “old-growth preservation,” “critical habitat” and “climate change” have been notable. Yes, I don’t like those results, but no, I never thought they were likely to occur in the first place. My problem for many years — dating back to the early 1990s — is that predictions of future conditions made by university and agency modelers have been consistently referred to as “science” and “according to science.” By contrast, modeling work done by engineers has generally been very accurate. Scientists envisioned putting a man on the moon and engineering modelers made that possible. Meantime, spotted owl populations continue to decline and their “critical habitat” designations continue to go up in flames — as scientifically predicted, and in opposition to modeled projections and related regulations. Here is what I wrote about the topic — and including the Eisenhower reference — five years ago:

  5. I appreciate that background on your resentment of the “scientific technological elite:” “Most of the current policies, laws, and regulations governing our federal, state, tribal, private and municipal lands, waters, and resources are based upon the dictates of these elites.” I’m not qualified to judge those you accuse, but it does remind me of Trumpism’s tendency to blame “elites” for their problems.


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