Guest “Ya think?” by David Middleton

U.N. climate panel confronts implausibly hot forecasts of future warming
By Paul Voosen Jul. 27, 2021

Next month, after a yearlong delay because of the pandemic, the U.N. Intergovernmental Panel on Climate Change (IPCC) will begin to release its first major assessment of human-caused global warming since 2013. The report, the first part of which will appear on 9 August, will drop on a world that has starkly changed in 8 years, warming by more than 0.3°C to nearly 1.3°C above preindustrial levels. Weather has grown more severe, seas are measurably higher, and mountain glaciers and polar ice have shrunk sharply. And after years of limited action, many countries, pushed by a concerned public and corporations, seem willing to curb their carbon emissions.

But as climate scientists face this alarming reality, the climate models that help them project the future have grown a little too alarmist. Many of the world’s leading models are now projecting warming rates that most scientists, including the modelmakers themselves, believe are implausibly fast. In advance of the U.N. report, scientists have scrambled to understand what went wrong and how to turn the models, which in other respects are more powerful and trustworthy than their predecessors, into useful guidance for policymakers. “It’s become clear over the last year or so that we can’t avoid this,” says Gavin Schmidt, director of NASA’s Goddard Institute for Space Studies.

[…]

Science! (as in, “she blinded me with”)

These have to be the funniest, most internally self-contradicted two sentences ever written in the English language:

Many of the world’s leading models are now projecting warming rates that most scientists, including the modelmakers themselves, believe are implausibly fast. In advance of the U.N. report, scientists have scrambled to understand what went wrong and how to turn the models, which in other respects are more powerful and trustworthy than their predecessors, into useful guidance for policymakers. “It’s become clear over the last year or so that we can’t avoid this,” says Gavin Schmidt, director of NASA’s Goddard Institute for Space Studies.

Science! (as in, “she blinded me with”)

Apart from being wronger than past models, the new models “are more powerful and trustworthy than their predecessors” and the modelers “can’t avoid this.”

“Warming by more than 0.3°C to nearly 1.3°C above preindustrial levels”

Least squares trend line; slope = 0.0151445 per year. 2013: 0.611651 2021.42: 0.739116. 0.739116-0.611651 = 0.127645 (~0.1).
Wood For Trees

Considering that the “preindustrial levels” were during the Little Ice Age, the coldest climatic episode of the Holocene Epoch, 1.3 °C of warming should be welcomed. It certainly beats the Hell out of this:

That 70’s Climate Crisis Show

Particularly since all of the warming prior to “The Ice Age Cometh” can be explained by the natural forcing mechanisms the climate modelers are aware of.

Modeled human climate forcing compared to three instrumental records (see Terando for specifics)

The modelers can only account for the warming warming since 1975 by blaming fossil fuels. That said, the modelers only incorporate known, reasonably well-understood, forcing mechanisms. Judith Curry illustrated this concept quite well…

You only find what you’re looking for. (JC at the National Press Club)

“Weather has grown more severe, seas are measurably higher, and mountain glaciers and polar ice have shrunk sharply.”

Since 2013?

“Weather has grown more severe”?

Global Hurricane Frequency (all & major) — 12-month running sums. The top time series is the number of global tropical cyclones that reached at least hurricane-force (maximum lifetime wind speed exceeds 64-knots). The bottom time series is the number of global tropical cyclones that reached major hurricane strength (96-knots+). Adapted from Maue (2011) GRL.
Global Tropical Cyclone Activity Dr. Ryan N. Maue

Last 50-years+ of Global and Northern Hemisphere Accumulated Cyclone Energy: 24 month running sums. Note that the year indicated represents the value of ACE through the previous 24-months for the Northern Hemisphere (bottom line/gray boxes) and the entire global (top line/blue boxes). The area in between represents the Southern Hemisphere total ACE.
Global Tropical Cyclone ActivityDr. Ryan N. Maue

“Seas are measurably higher”

Than 2013?

Yes… It can be measured.

Ruler and US Quarter added for scale. NASA

Sea level has risen $0.25 since 2013.

We can also measure the standard deviation of those measurements.

Sea level data from NASA, ruler and US $1 bill added for scale.

Sea level has risen $0.25 +/- $1.00. The rate (GIA applied) is about 3.4 mm/yr. The standard deviation of the monthly GMSL altitude measurement is about 88 mm. The sea surface constantly moves up and down, they make ~40,000 measurements each month, hence the large standard deviation.

“Mountain glaciers and polar ice have shrunk sharply.”

I don’t have a handy measurement for global changes in mountain glaciers. However, it is better for them to retreat rather than advance.

THE LITTLE ICE AGE
By Alan Cutler August 13, 1997


The year was 1645, and the glaciers in the Alps were on the move. In Chamonix at the foot of Mont Blanc, people watched in fear as the Mer de Glace (Sea of Ice) glacier advanced. In earlier years, they had seen the slowly flowing ice engulf farms and crush entire villages.

They turned to the Bishop of Geneva for help, and he made the journey to Chamonix. At the ice front he performed a rite of exorcism.

Little by little, the glacier receded.

But before long the threatening ice returned, and once again the bishop was summoned. The struggle against the glacier continued for decades.

Similar dramas unfolded throughout the Alps and Scandinavia during the late 1600s and early 1700s, as many glaciers grew farther down mountain slopes and valleys than they had in thousands of years. Sea ice choked much of the North Atlantic, causing havoc with fisheries in Iceland and Scandinavia. Eskimos paddled their kayaks as far south as Scotland. At the same time in China, severe winters in Jiang-Xi province killed the last of the orange groves that had thrived there for centuries.

[…]

The Washington Post… Back when it was a rea newspaper.

But we do have pretty good measurements of recent polar ice changes.

Polar summer sea ice extent: 2021 vs 2013. NSIDC

It gets even better!

In the past, most models projected a “climate sensitivity”—the warming expected when atmospheric carbon dioxide (CO2) is doubled over preindustrial times—of between 2°C and 4.5°C. Last year, a landmark paper that largely eschewed models and instead used documented factors including ongoing warming trends calculated a likely climate sensitivity of between 2.6°C and 3.9°C. But many of the new models from leading centers showed warming of more than 5°C—uncomfortably outside these bounds.

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.”

Science! (as in, “she blinded me with”)

It’s unclear as to whether or not Mr. Voosen is discussing equilibrium climate sensitivity (ECS) or transient climate response (TCR). It’s an important distinction.

Transient and Equilibrium Climate Sensitivity

Projections of the severity of anthropogenic climate change are strongly dependent on our estimates of climate sensitivity, traditionally defined as the global average warming at the Earth’s surface due to a doubling of the carbon dioxide from pre-industrial levels. This importance arises not because global temperature change directly causes all of the impacts of major concern, but because many effects of climate change are predicted to increase in severity with larger global warming.

An important distinction is made between the equilibrium sensitivity — the temperature change realized after allowing the climate system to equilibrate with a higher value of CO2 — and the response on shorter time scales, before the deep oceans have had time to equilibrate, that is of more direct relevance to the changes we are likely to see in the 21st century. The latter is often quantified by raising the carbon dioxide in a model at the rate of 1% per year and examining the response at the time when carbon dioxide concentration has doubled, referred to as the transient climate sensitivity or response. (At a rate of 1% per year, doubling requires 70 years.)

Equilibrium sensitivities in global climate models typically range from 2 to 5K, while the transient climate responses are smaller, in range of 1.0-2.5 K, due to the cooling influence of ocean heat uptake. 

[…]

The ratio of transient to equilibrium sensitivity varies from 1/3 to 1/2 in this group of GFDL models indicating significant variation of the transient cooling influence of the ocean. The relationship of the ocean’s cooling influence to ocean heat uptake and circulation changes has been an ongoing thread of GFDL research. For example, He et al (2017) noted that stronger deep ocean circulation prior to forcing reduced the magnitude of transient warming in a GFDL model.

Cloud feedbacks are widely considered to contribute the largest uncertainty to climate sensitivity. Simulated climate sensitivity varies considerably with choices made about cloud parameterizations that are not well constrained by observations (Zhao et al 2016). Simulated cloud responses depend on the pattern of surface temperature change, not just its global magnitude (Silvers et al 2018). Because of the importance and complexity of the interactions of clouds and climate GFDL is focusing effort on a cloud climate initiative.

[…]

GFDL

Here’s a graphical illustration from IPCC TAR, 2001:

ECS vs TCR (IPCC AR1, 2001)

TCR occurs simultaneously with the rise in atmospheric CO2. While, the difference between ECS and TCR occurs over the next several hundred years and would likely be indistinguishable from background noise.

I am not claiming that this is correct or empirically verifiable. I’m just explaining the concept. These are the sensitivities of the GFDL climate models:

ModelTransient Climate Response Equilibrium Climate Sensitivity
CM2.11.5 K (Randall et al 2007)3.4 K (Stouffer et al 2006)
ESM2M1.3 K (Flato et al 2013)3.3 K (Paynter et al 2018)
ESM2G1.1 K (Flato et al 2013)3.3 K (Krasting et al 2018)
CM32.0 K (Flato et al 2013)4.8 K (Paynter et al 2018)
CM42.1 K (Winton et al submitted)5.0 K (Winton et al submitted)
ESM41.6 K (Dunne et al in prep)3.2 K (Dunne et al in prep)

Table 1. GFDL model climate sensitivities.

In the ESM4 model, the average temperature would rise 1.6 °C over a period of about 70 years as atmospheric CO2 and then another 1.6 °C over the subsequent 430 years. This doesn’t strike me as particularly catastrophic, considering that the first 1.0 °C or so has already occurred.

This graph is a favorite of mine:

The decline in estimates of ECS from 2000 to 2015. Source: Scafetta, Mirandola, and Bianchini, 2017.

Our buddy Zeke Hausfather was kind enough to “debunk” it on the Carbon Brief blog:

Stable climate sensitivities from Carbon Brief.

He also kindly provided a link to the Excel file for his graph. I downloaded the file and plotted only the instrumental estimates of ECS (I “eschewed models and instead used documented factors”).

ECS studyyear min  max  ECS mean 
Harvey and Kaufmann 20022002  1.0         3.0             2.0
Gregory et al. 20022002  1.6       10.0             2.1
Kaufmann and Stern 20022002  2.0         2.8             2.6
Knutti et al. 20022002  2.0         9.2             4.8
Frame et al. 20052005  1.2         5.2             2.3
Tsushima et al. 20052005  3.1         4.7             3.8
Forster and Gregory 20062006  1.0         4.1             1.6
Forest et al. 20062006  2.1         8.9             4.1
Stern et al. 20062006  4.4         4.5             4.4
Chylek et al. 20072007  1.1         1.8             1.6
Schwartz 20072007  0.9         2.9             1.9
Lindzen and Choi 20092009  0.4         0.5             0.5
Murphy et al. 20092009  0.9       10.0             3.0
Lin et al. 20102010  2.8         3.7             3.1
Lindzen and Choi 20112011  0.5         1.1             0.7
Aldrin et al. 20122012  1.2         3.5             2.0
Schwartz 20122012  1.5         6.0             3.0
Lewis 20132013  1.0         3.0             1.6
Otto et al. 20132013  0.9         5.0             1.9
Bengtsson and Schwartz 20132013  1.5         2.5             2.0
Otto et al. 20132013  1.2         3.9             2.0
Skeie et al. 20142014  0.9         3.2             1.8
Loehle 20142014  1.8         2.3             2.0
Lewis 20142014  1.2         4.5             2.2
Kummer and Dessler 20142014  1.6         4.1             2.3
Lovejoy 20142014  2.5         3.7             3.1
Donohoe et al. 20142014  3.1         3.2             3.1
Urban et al. 20142014  2.1         4.6             3.1
Monckton et al. 20152015  0.8         1.3             1.0
Loehle 20152015  1.5         1.6             1.5
Lewis and Curry 20152015  1.1         4.1             1.6
Cawley et al. 20152015  1.8         4.4             2.0
Johansson et al. 20152015  2.0         3.2             2.5
Johansson et al. 20152015  1.6         7.8             3.1
Bates 20162016  1.0         1.1             1.0
Lewis 20162016  0.7         3.2             1.7
Loeb et al. 20162016  0.8       10.0             2.0
Forster 20162016  1.1         5.3             3.0
Armour 20172017  1.7         7.1             2.9
Lewis and Curry 20182018  1.2         3.1             1.8
 Average              2.3
 σ              0.9
 -2σ              0.4
 +2σ              4.2

Instrumental ECS estimates

The average ECS was 2.3 °C. This would translate to a TCR of 1.2-1.6 °C.

Declining ECS from instrumental estimates, stock plot.

Multiple estimates published in the same year is why several years appear multiple times.

Scatter plots of the other methods demonstrate that the problem is models, “all the way down”…

Garbage In, Garbage Out

Paleo methods produce a higher sensitivity because, almost invariably, the CO2 proxies are of much lower resolution than the temperature proxies. This is particularly true of ice core data.

What’s the solution to the hot model problem?

The climate scientists say, “Put more PhD’s on the payroll!”

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.

Science! (as in, “she blinded me with”)

At least Gavin Schmidt sees that there might be a problem…

Already scientific papers are appearing using CMIP’s unconstrained worst-case scenarios for 2100, adding fire to what are already well-justified fears. But that practice needs to change, Schmidt says. “You end up with numbers for even the near-term that are insanely scary—and wrong.”

Science! (as in, “she blinded me with”)

However, with the prospects of even more “insanely scary—and wrong” climate “science” papers on the horizon and the even more insanely scary and wronger media reporting of these papers, the second half of 2021 will be a target rich environment…

Closing Notes

  • Some of this post was directly copied from a previous post of mine: Climate Sensitivity Estimates: Declining or Not? Is it possible to plagiarize yourself?
  • Yes. I realize that the instrumental climate sensitivity estimates rely on models. All measurements rely on models.

References

Terando, A., Reidmiller, D., Hostetler, S.W., Littell, J.S., Beard, T.D., Jr., Weiskopf, S.R., Belnap, J., and Plumlee, G.S., 2020, Using information from global climate models to inform policymaking—The role of the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1058, 25 p.,
https://doi.org/10.3133/ofr20201058.

via Watts Up With That?

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July 30, 2021