Author: Ron Clutz

September Outlook Arctic Ice 2024

Posted on by Ron Clutz

Figure 1. Distribution of SIO contributors for August estimates of September 2024 pan-Arctic sea-ice extent. No Heuristic methods were submitted in August. “Sun” is a public/citizen contribution. Image courtesy of Matthew Fisher, NSIDC.

2024: August Report from Sea Ice Prediction Network

The August 2024 Outlook received 24 pan-Arctic contributions (Figure 1). This year’s median
forecasted value for pan-Arctic September sea-ice extent is 4.27 million square kilometers with
an interquartile range of 4.11 to 4.54 million square kilometers. This is lower than the 2022 (4.83
million square kilometers) and 2023 (4.60 million square kilometers) August median forecasts
for September. . .This reflects relatively rapid ice loss during the month of July, resulting in August
Outlooks revising estimates downward. The lowest sea-ice extent forecast is 3.71 million square
kilometers, from the RASM@NPS submission); the highest sea-ice extent forecast is 5.23
million square kilometers, submitted by BCCR.

These are predictions for the September 2024 monthly average ice extent as reported by NOAA Sea Ice Index (SII). This post provides a look at the 2024 Year To Date (YTD) based on monthly averages comparing MASIE and SII datasets. (18 year average is 2006 to 2023 inclusive).

The graph puts 2024 into recent historical perspective. Note how 2024 was slightly above the 18-year average for the first 5 months, then tracked slightly lower to average through August. The outlier 2012 provided the highest March maximum as well as the lowest September minimum, coinciding with the Great Arctic Cyclone that year.  2007 began the period with the lowest minimum except for 2012.  SII 2024 started slightly higher than MASIE the first 3 months, then ran the same as MASIE until dropping in August 400k km2 below MASIE 2024 and also lower than 2007 and 2012.

The table below provides the monthly Arctic ice extent averages for comparisons (all are M km2)

Monthly MASIE 2024 SII 2024 MASIE -SII MASIE 2024-18 YR AVE SII 2024-18 YR AVE MASIE 2024-2007
Jan 14.055 13.917 0.139 0.280 0.333 0.293
Feb 14.772 14.605 0.167 0.096 0.152 0.121
Mar 14.966 14.873 0.093 0.111 0.199 0.344
Apr 14.113 14.131 -0.018 0.021 0.118 0.418
May 12.577 12.783 -0.207 -0.038 0.123 0.150
June 10.744 10.895 -0.151 -0.072 0.024 -0.082
July 8.181 7.884 0.297 -0.107 -0.160 0.188
Aug 5.617 5.214 0.404 -0.267 -0.423 0.033

The first two data columns are the 2024 YTD shown by MASIE and SII, with the MASIE surpluses in column three.  Column four shows MASIE 2024 compared to MASIE 18 year averages, while column five shows SII 2024 compared to SII 18 year averages.  YTD August MASIE and SII are below their averages, SII by nearly half a Wadham. The last column shows MASIE 2024 holding surpluses over 2007 most of the months, and nearly the same in August.

Summary

The experts involved in SIPN are expecting SII 2024 September to be much lower than 2023 and 2022, based largely on the large deficits SII is showing in July and August. The way MASIE is going, this September looks to be lower than its average, but much higher than SII.  While the daily minimum for the year occurs mid September, ice extent on September 30 is typically slightly higher than on September 1.

Footnote:

Some people unhappy with the higher amounts of ice extent shown by MASIE continue to claim that Sea Ice Index is the only dataset that can be used. This is false in fact and in logic. Why should anyone accept that the highest quality picture of ice day to day has no shelf life, that one year’s charts can not be compared with another year? Researchers do this, including Walt Meier in charge of Sea Ice Index. That said, I understand his interest in directing people to use his product rather than one he does not control. As I have said before:

MASIE is rigorous, reliable, serves as calibration for satellite products, and continues the long and honorable tradition of naval ice charting using modern technologies. More on this at my post Support MASIE Arctic Ice Dataset

MASIE: “high-resolution, accurate charts of ice conditions”
Walt Meier, NSIDC, October 2015 article in Annals of Glaciology.

Acidification Alarmists Forced to Fake Findings

Posted on by Ron Clutz

The story of fake research findings was published at the journal Science entitled Star marine ecologist committed misconduct, university says.  Excerpts below in italics with my bolds.

Finding against Danielle Dixson vindicates whistleblowers
who questioned high-profile work on ocean acidification

A major controversy in marine biology took a new twist last week when the University of Delaware (UD) found one of its star scientists guilty of research misconduct. The university has confirmed to Science that it has accepted an investigative panel’s conclusion that marine ecologist Danielle Dixson committed fabrication and falsification in work on fish behavior and coral reefs. The university is seeking the retraction of three of Dixson’s papers and “has notified the appropriate federal agencies,” a spokesperson says.

Danielle Dixson, asistant professor at the University of Delaware, will explore coral reefs off Belize over the next three years. Here, she is diving on a reef in the Indo-Pacific. Courtesy of Danielle Dixson Source: delaware online

Dixson is known as a highly successful scientist and fundraiser. She obtained her Ph.D. at James Cook University (JCU),  Townsville in Australia, in 2012; worked as a postdoc and assistant professor at the Georgia Institute of Technology for 4 years; and in 2015 started her own group at UD’s marine biology lab in Lewes, a small town on the Atlantic Coast. She received a $1.05 million grant from the Gordon and Betty Moore Foundation in 2016 and currently has a $750,000 career grant from the National Science Foundation (NSF). She presented her research at a 2015 White House meeting and has often been featured in the media, including in a 2019 story in Science.

Together with one of her Ph.D. supervisors, JCU marine biologist Philip Munday, Dixson pioneered research into the effects on fish of rising CO2 levels in the atmosphere, which cause the oceans to acidify. In a series of studies published since 2009 they showed that acidification can disorient fish, lead them to swim toward chemical cues emitted by their predators, and affect their hearing and vision. Dixson’s later work focused on coral reef ecology, the subject of her Science paper.

The colorful diversity of coral found at One Tree Island. The structure and diversity of coral we see today is already at risk of dissolution from ocean acidification. Kennedy Wolfe University of Sydney

Among the papers is a study about coral reef recovery that Dixson published in Science in 2014, and for which the journal issued an Editorial Expression of Concern in February. Science—whose News and Editorial teams operate independently of each other—retracted that paper today.

The investigative panel’s draft report, which Science’s News team has seen in heavily redacted form, paints a damning picture of Dixson’s scientific work, which included many studies that appeared to show Earth’s rising carbon dioxide (CO2) levels can have dramatic effects on fish behavior and ecology. “The Committee was repeatedly struck by a serial pattern of sloppiness, poor recordkeeping, copying and pasting within spreadsheets, errors within many papers under investigation, and deviation from established animal ethics protocols,” wrote the panel, made up of three UD researchers.

Several former members of Dixson’s lab supported the whistleblowers’ request for an investigation. One of them, former postdoc Zara Cowan, was the first to identify the many duplications in the data file for the now-retracted Science paper. Another, former Ph.D. student Paul Leingang, first brought accusations against Dixson to university officials in January 2020. He left the lab soon after and joined the broader group of whistleblowers.

Leingang, who had been at Dixson’s lab since 2016, says he had become increasingly suspicious of her findings, in part because she usually collected her fluming data alone. In November 2019 he decided to secretly track some of Dixson’s activities. He supplied the investigation with detailed notes, chat conversations, and tweets by Dixson to show that she did not spend enough time on her fluming studies to collect the data she was jotting down in her lab notebooks.

The investigative panel found Leingang’s account convincing and singled him out for praise. “It is very difficult for a young scholar seeking a Ph.D. to challenge their advisor on ethical grounds,” the draft report says. “The Committee believes it took great bravery for him to come forward so explicitly. The same is true of the other members of the laboratory who backed the Complainant’s action.”

UD “did a decent investigation. I think it’s one of the first universities that we’ve seen actually do that,” says ecophysiologist Fredrik Jutfelt of the Norwegian University of Science and Technology, one of the whistleblowers. “So that’s really encouraging.” But he and others in the group are disappointed that the committee appears to have looked at only seven of the 20 Dixson papers they had flagged as suspicious. They also had hoped UD would release the committee’s final report and detail any sanctions against Dixson. “That is a shame,” Jutfelt says.

Inventing Facts to Promote an Imaginary Crisis

Legacy and social media are awash with warnings about hydrocarbon emissions making the oceans acidic and threatening all ocean life from plankton up to whales.  For example:

Ocean acidification: A wake-up call in our waters – NOAA

Canada’s oceans are becoming more acidic – Pêches et Océans Canada

The Ocean Is Getting More Acidic—What That Actually Means– National Geographic

What Is Ocean Acidification? – NASA Climate Kids

Ocean acidification: why the Earth’s oceans are turning to acid – OA-ICC

Etc, etc., etc.

With the climatism hype far beyond any observations, marine biologists have stepped up to make an industry out of false evidence.  They are forced to do so because reality does not conform to their beliefs.  A good summary of acidification hoaxes comes from Jim Steele Un-refutable Evidence of Alarmists’ Ocean Acidification Misinformation in 3 Easy Lessons posted at WUWT.  Points covered include:

♦  The Undisputed Science

♦  The Dissolving Snail Shell Hoax

♦  The Reduced Calcification Hoax

More detail on the bogus fish behavior studies is also found at WUWT: James Cook University Researchers Refuted: “Ocean Acidification Does not Impair” Fish behaviour

A brief explanation debunking the notion of CO2 causing ocean “acidification” is here:

Background Post Shows Alarmist Claims Not Supported in IPCC WG1 References

Headlines Claim, But Details Deny

Update Sept. 9 Response to Brian Catt

Below I note that claimed %s of increasing acidity involve changes in parts per billion for H ion in water.  Further, the relation between atmospheric CO2 and ocean pH needs to be understood.

Figure 1: pH of ocean water and rain water versus concentration of CO2 in the atmosphere. Calculated with (20); Ocean alkalinity [A] = 2.3 × 10−3 M. Rain alkalinity [A] = 0. Temperature T = 25 C.

The source is Cohen and Happer (2015), where these conclusions are written:

This minimalist discussion already shows how hard it is to scare informed people with ocean acidification, but, alas, many people are not informed. For example:

• The oceans would be highly alkaline with a pH of about 11.4, similar to that of household ammonia, if there were no weak acids to buffer the alkalinity. Almost all of the buffering is provided by dissolved CO2, with very minor additional buffering from boric acid, silicic acid and other even less important species.

• As shown in Fig. 1, doubling atmospheric CO2 from the current level of 400 ppm to 800 ppm only decreases the pH of ocean water from about 8.2 to 7.9. This is well within the day-night fluctuations that already occur because of photosynthesis by plankton and less than the pH decreases with depth that occur because of the biological pump and the dissolution of calcium carbonate precipitates below the lysocline.

• As shown in Fig. 2, doubling atmospheric CO2 from the current level of 400 ppm to 800 ppm only decreases the carbonate-ion concentration, [CO2−3], by about 30%. Ocean surface waters are already supersaturated by several hundred per cent for formation of CaCO3 crystals from Ca2+ and CO2−3. So scare stories about dissolving carbonate shells are nonsense.

• As shown in Fig. 7, the ocean has only absorbed 1/3 or less of the CO2 that it would eventually absorb when the concentrations of CO2 in the deep oceans came to equilibrium with surface concentrations. Effects like that of the biological pump and calcium carbonate dissolution below the lysocline allow the ocean to absorb substantially more than the amount that would be in chemical-equilibrium with the atmosphere.

• Over most of the Phanerozoic, the past 550 million years, CO2 concentrations in the atmosphere have been measured in thousands of parts per million, and life flourished in both the oceans and on land. This is hardly surprising, given the relative insensitivity of ocean pH to large changes in CO2 concentrations that we have discussed above, and given the fact that the pH changes that do occur are small compared to the natural variations of ocean pH in space and time.

 

 

 

 

 

 

UAH August 2024: Most Regions Cooler, Offset by SH Land Spike

Posted on by Ron Clutz

The post below updates the UAH record of air temperatures over land and ocean. Each month and year exposes again the growing disconnect between the real world and the Zero Carbon zealots.  It is as though the anti-hydrocarbon band wagon hopes to drown out the data contradicting their justification for the Great Energy Transition.  Yes, there has been warming from an El Nino buildup coincidental with North Atlantic warming, but no basis to blame it on CO2.  

As an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  At year end 2022 and continuing into 2023 global temp anomaly matched or went lower than average since 1995, an ENSO neutral year. (UAH baseline is now 1991-2020). Now we have an usual El Nino warming spike of uncertain cause, unrelated to steadily rising CO2 and now moderating.

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~60 ppm, a 15% increase.

Furthermore, going back to previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

gmt-warming-events

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. And now in 2024 we have seen an amazing episode with a temperature spike driven by ocean air warming in all regions, along with rising NH land temperatures, now receding from its peak.

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

August 2024 Most Regions Cooler Offset by SH Land Spike
 banner-blog

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you heard a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino had fully dissipated with chilly temperatures in all regions. After a warming blip in 2022, land and ocean temps dropped again with 2023 starting below the mean since 1995.  Spring and Summer 2023 saw a series of warmings, continuing into October, followed by cooling. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for August 2024. Posts on their reading of ocean air temps this month are ahead of the update from HadSST4.  I posted last month on SSTs using HadSST4 Oceans Warming Uptick July 2024. These posts have a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Sometimes air temps over land diverge from ocean air changes. Last February 2024, both ocean and land air temps went higher driven by SH, while NH and the Tropics cooled slightly, resulting in Global anomaly matching October 2023 peak. Then in March Ocean anomalies cooled while Land anomalies rose everywhere. After a mixed pattern in April, the May anomalies were back down led by a large drop in NH land, and a smaller ocean decline in all regions. In June all Ocean regions dropped down, as well as dips in SH and Tropical land temps. In July all Oceans were unchanged except for Tropical warming, while all land regions rose slightly. Now in August we see a warming leap in SH land, slight Land cooling elsewhere, a dip in Tropical Ocean temp and slightly elswhere. End result is a small upward bump.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values changed with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus cooling oceans portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for August.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean air temps since January 2015.

 

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  After an upward spike in July, ocean air everywhere cooled in August and also in September.   

After sharp cooling everywhere in January 2023, all regions were into negative territory. Note the Tropics matched the lowest value, but since have spiked sharply upward +1.7C, with the largest increases in April to July, and continuing through adding to a new high of 1.3C January to March 2024.  In April and May that started dropping in all regions.   June showed a sharp decline everywhere, led by the Tropics down 0.5C. The Global anomaly fell to nearly match the September 2023 value. In July, the Tropics rose slightly while SH, NH and the Global Anomaly were unchanged. Now in August a drop in the Tropics, with little NH cooling and Global Ocean anomaly slightly lower.

Land Air Temperatures Tracking in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for August is below.

 

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere.  After a summer 2022 NH spike, land temps dropped everywhere, and in January, further cooling in SH and Tropics offset by an uptick in NH. 

Remarkably, in 2023, SH land air anomaly shot up 2.1C, from  -0.6C in January to +1.5 in September, then dropped sharply to 0.6 in January 2024, matching the SH peak in 2016. Then in February and March SH anomaly jumped up nearly 0.7C, and Tropics went up to a new high of 1.5C, pulling up the Global land anomaly to match 10/2023. In April SH dropped sharply back to 0.6C, Tropics cooled very slightly, but NH land jumped up to a new high of 1.5C, pulling up Global land anomaly to its new high of 1.24C.

In May that NH spike started to reverse.  Despite warming in Tropics and SH, the much larger NH land mass pulled the Global land anomaly back down to the February value. In June, sharp drops in SH and Tropics land temps overcame an upward bump in NH, pulling Global land anomaly down to match last December. In July, all land regions rose slightly, and now in August a record spike up to 1.87 and pulling the Global land anomaly up by 0.17°C. Despite this land warming, the Global land and ocean combined anomaly rose only 0.03°C.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.04, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   An upward bump in 2021 was reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, later reversed

With the sharp drops in Nov., Dec. and January 2023 temps, there was no increase over 1980. Then in 2023 the buildup to the October/November peak exceeded the sharp April peak of the El Nino 1998 event. It also surpassed the February peak in 2016. After March and April took the Global anomaly to a new peak of 1.05C.  The cool down started with May dropping to 0.90C, and in June a further decline to 0.80C.  Despite an uptick to 0.85 in July,   it remains to be seen whether El Nino will weaken or gain strength, and it whether we are past the recent peak.

The graph reminds of another chart showing the abrupt ejection of humid air from Hunga Tonga eruption.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST4, but are now showing the same pattern. Despite the three El Ninos, their warming has not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Fishy Activists Destroying Hydro Dams

Posted on by Ron Clutz

AP Photo/Nicholas K. Geranios

John Stossel bring us up to date on the fishy case for removing hydroelectric dams on the Snake River in Washington state.  His Townhall article is A Dam Good Argument.  Excerpts in italics with my bolds.and added images.

Instead of using fossil fuels, we’re told to use “clean” energy: wind, solar or hydropower.  Hydro is the most reliable. Unlike wind and sunlight, it flows steadily.

But now, environmental groups want to destroy dams that create hydro power.

The Klamath River flows by the remaining pieces of the Copco 2 Dam after deconstruction in June 2023. |Located on Oregon/California border.Juliet Grable / JPR

“Breach those dams,” an activist shouts in my new video. “Now is the time, our fish are on the line!“

The activists have targeted four dams on the Snake River in Washington State. They claim the dams are driving salmon to extinction.

Walla Walla District Dams on the Snake & Columbia Rivers

It’s true that dams once killed lots of salmon. Pregnant fish need to swim upriver to have babies, and their babies swim downriver to the ocean.  Suddenly, dams were in the way. Salmon population dropped sharply.

But that was in the 1970s.Today, most salmon
make it past the dam without trouble.

How?  Fish-protecting innovations like fish ladders and spillways guide most of the salmon away from the turbines that generate electricity.

Lower Granite fish count station & ladder (left, bottom right); Lower Monumental fish ladder (top right)  Source: Fish Passage Thru the Lower Snake & Columbia Rivers

“Between 96% and 98% of the salmon successfully pass each dam,” says Todd Myers, Environmental Director at the Washington Policy Center.  Even federal scientific agencies now say we can leave dams alone and fish will be fine.

But environmental groups don’t raise money by acknowledging good news. “Snake River Salmon Are in Crisis,” reads a headline from Earthjustice.  Gullible media fall for it. The Snake River is the “most endangered in the country!” claimed the evening news anchor.

“That’s simply not true,” Myers explains. “All you have to do is look at the actual population numbers to know that that’s absurd.”  Utterly absurd. In recent years, salmon populations are higher than they were in the 1980s and 90s.

The fish passage report for 2023 (here) has many results like this for various species. Conversion refers to completing the Snake River run from Ice Harbor through Lower Granite.

“They make these claims,” Myers says, “because they know people will believe them … they don’t want to believe that their favorite environmental group is dishonest.”

But many are. In 1999, environmental groups bought an ad in the New York Times saying “salmon … will be extinct by 2017.” “Did the environmentalists apologize?” I ask Meyers. “No,” he says. “They repeat almost the exact same arguments today, they just changed the dates.

I invited 10 activist groups that want to destroy dams to come to my studio and defend their claims about salmon extinction. Not one agreed. I understand why. They’ve already convinced the public and gullible politicians.  Idaho’s Republican Congressman Mike Simpson says, “There is no viable path that can allow us to keep the dams in place.”

“We keep doing dumb things,” says Myers. “We put money into places where it doesn’t have an environmental impact, and then we wonder 10, 20, 30 years (later) why we haven’t made any environmental progress.”

Politicians and activists want to tear down Snake River dams
even though they generate tons of electricity.

“Almost the same amount as all of the wind and solar turbines in Washington state,” says Myers, “Imagine if I told the environmental community we need to tear down every wind turbine and every solar panel. They would lose their minds. But that’s essentially what they’re advocating by tearing down Snake River dams.”

I push back: “They say, ‘Just build more wind turbines.’”  “The problem is, several times a year, there’s no wind,” he replies. “You could build 10 times as many wind turbines, but if there’s no wind, there’s no electricity.”

Hydro, on the other hand, “can turn on and off whenever it’s needed. Destroying hydro and replacing it with wind makes absolutely no sense. It will do serious damage to our electrical grid.”

“It’s not their money,” I point out.”Exactly,” he says. “If you want to spend $35 billion on salmon, there’s lots of things we can do that would have a real impact.”  Like what?

“Reduce the population of) seals and sea lions,” he says, “The Washington Academy of Sciences says that unless we reduce the populations, we will not recover salmon.” “People used to hunt sea lions,” I note. “Yeah, that’s why the populations are higher today.”

But environmentalists don’t want people to hunt sea lions or seals. Instead, they push for destruction of dams. “Because it’s sexy and dramatic, it sells,” says Myers. “It’s more about feeling good than environmental results.”

PostScript

Of course there is a political dimension to this movement.  Left coast woke progressives are targeting Lower Snake River dams located in Eastern Washington state.  Folks there and in Eastern Oregon would rather be governed by common sense leaders like those in Idaho.

The case against the dams is actually about climatism.  The fish are not at risk, as shown by many scientific reports. But climatists do not include hydro in their definition of “renewable.”  And they promote fear of methane, claiming dam reservoirs increase methane emissions.

So here’s the political solution.  Keep the dams open and the fish running to their spawning grounds.  And to appease climatists ban any transmission of electricity from those dams to Seattle and Western Washington state.  Deal?

Background Post

Left Coast Closes the Dam Lights

Antidote for Radiation Myopia

Posted on by Ron Clutz

On a previous post a reader queried me about my position.  Taking him to be serious, I prepared a reply with resources that can serve anyone wanting to understand radiative GHG theory and reality.  The key is to escape radiation myopia, that is focusing on radiative energy transfers in earth’s climate system to the exclusion of the other transfers.  Energy in our world moves by conduction, convection and phase changes of H2O in addition to radiation.  And not surprisingly at any place and time, the most active mode is the one with the least resistance.

The post triggering the question was this one:

The Original Sin of GHG Theory

My Reply to Questioner

Thanks for your response. Your inital question sounded trollish, but I take your comment seriously.

Firstly, you said “I’ve never seen anyone outside of the anti-GHG crowd ever talk about “back-radiation”. Actually references to that notion are readily found since it is the primary way global warming/ climate change is explained to the public. Some examples:

“However, GHGs, unlike other atmospheric gases such as oxygen and nitrogen, are opaque to outgoing infrared radiation. As the concentration of GHGs in the atmosphere increases due to human-caused emissions, energy radiated from the surface becomes trapped in the atmosphere, unable to escape the planet. This energy returns to the surface, where it is reabsorbed.” UNEP

“Greenhouses gases are atmospheric gases such as carbon dioxide (CO2), methane (CH4), and water vapor (H2O) that absorb and re-radiate heat, which warms the lower atmosphere and Earth’s surface. This process of absorption and re-radiation of heat is called the greenhouse effect. Although greenhouse gases only make up a small percentage of the atmosphere, small changes in the amount of greenhouse gases can greatly alter the strength of the greenhouse effect, which in turn, affects the Earth’s average temperature and climate. UCBerkeley

“As CO2 soaks up this infrared energy, it vibrates and re-emits the infrared energy back in all directions. About half of that energy goes out into space, and about half of it returns to Earth as heat, contributing to the ‘greenhouse effect.’ ColumbiaU

The favored term now is “re-radiation” and it is central in the narrative everywhere, including among others, NASA, MIT and of course multiple UN agencies. So it is necessary to debunk the notion.

I know as well as you that back- or re-radiation is a caricature, and climate scientists make a different claim, namely raising the ERL which slows the cooling. That theory is also wrong for different empirical reasons. See:

Refresher: GHG Theory and the Tests It Fails

Secondly, the root issue is the abuse of Stefan-Boltzman law to create a fictious downward energy transfer, such as seen in energy balance cartoons, misleading and not funny. The equation calculates the transfer from the difference in temperature between two bodies in thermal contact, it does not attribute thermal radiation to each of them. Full explanation here:

Experimental Proof Nil Warming from GHGs

And regarding the failed energy balance diagrams:

Fatal Flaw in Earth Energy Balance Diagrams

For extra credit and insight, look at a Sabine Hosenfelder video to understand how current GHG theory goes astray. Link includes excerpts and critique.

Sabine’s Video Myopic on GHG Climate Role

Summary

“The Earth, a rocky sphere at a distance from the Sun of ~149.6 million kilometers, where the Solar irradiance comes in at 1361.7 W/m2, with a mean global albedo, mostly from clouds, of 0.3 and with an atmosphere surrounding it containing a gaseous mass held in place by the planet’s gravity, producing a surface pressure of ~1013 mb, with an ocean of H2O covering 71% of its surface and with a rotation time around its own axis of ~24h, boasts an average global surface temperature of +15°C (288K).

Why this specific temperature? Because, with an atmosphere weighing down upon us with the particular pressure that ours exerts, this is the temperature level the surface has to reach and stay at for the global convectional engine to be able to pull enough heat away fast enough from it to be able to balance the particular averaged out energy input from the Sun that we experience.

It’s that simple.”  E. M. Smith

 

See Also

New Wholistic Paradigm of Climate Change

 

August 2024 Arctic Ice, NOAA Missing Nearly Half a Wadham

Posted on by Ron Clutz

The images above come from AARI (Arctic and Antarctic Research Institute) St. Petersburg, Russia. Note how the location of remaining ice at late August varies greatly from year to year.  The marginal seas are open water, including the Pacific basins, Canadian Bays (Hudson and Baffin), and the Atlantic basins for the most part.  Note ice extent fluctuations especially in Eurasian seas (lower right) and in Can-Am seas (upper right).  Notice the much greater ice extent in 2021 compared to 2018. As discussed later on, some regions retain considerable ice at the annual minimum, with differences year to year. [Note: Images prior to 2009 are in a different format.  AARI Charts are (here)

The annual competition between ice and water in the Arctic ocean is approaching the maximum for water, which typically occurs mid September.  After that, diminishing energy from the slowly setting sun allows oceanic cooling causing ice to regenerate. Those interested in the dynamics of Arctic sea ice can read numerous posts here.  This post provides a look at end of August from 2007 to yesterday as a context for anticipating this year’s annual minimum.  Note that for climate purposes the annual minimum is measured by the September monthly average ice extent, since the daily extents vary and will go briefly lowest on or about day 260. In a typical year the overall ice extent will end September slightly higher than at the beginning.

The melting season mid July to mid August shows 2024 melted at nearly the average rate, while retaining more ice extent at the end than some other recent years of note.

Firstly note that on average August shows ice declining 1.8M km2 down to 4.9M km2.  2024 started 288k km2 below average and on day 244 was only 98k km2 or 2% in deficit to average. The extents in Sea Ice Index in orange  were considerably lower during August, meaning that SII August 2024 monthly average will be ~400k km2 lower than MASIE., nearly half a Wadham.

The table for day 244 shows how large how the ice is distributed across the various seas comprising the Arctic Ocean.

Region 2024244 Day 244 ave 2024-Ave. 2007244 2024-2007
 (0) Northern_Hemisphere 4802455 4900416 -97962 4525136 277319
 (1) Beaufort_Sea 331017 568911 -237894 629454 -298437
 (2) Chukchi_Sea 508350 261504 246846 96232 412118
 (3) East_Siberian_Sea 476831 342187 134644 196 476635
 (4) Laptev_Sea 209967 163938 46029 245578 -35612
 (5) Kara_Sea 253 47999 -47746 74307 -74054
 (6) Barents_Sea 0 15867 -15867 11061 -11061
 (7) Greenland_Sea 101048 171695 -70647 288223 -187174
 (8) Baffin_Bay_Gulf_of_St._Lawrence 51428 26156 25272 32804 18624
 (9) Canadian_Archipelago 224943 301460 -76516 234389 -9445
 (10) Hudson_Bay 3868 19658 -15790 28401 -24533
 (11) Central_Arctic 2893622 2980244 -86622 2883200.58 10421

The largest deficit to average is in Beaufort Sea, followed by smaller losses in Greenland Sea, CAA and Central Arctic.   Hudson Bay and Barents Sea are mostly open water. The offsetting surpluses are in Chukchi, East Siberian and Laptev seas.

For context, note that the average maximum has been 15M, so on average the extent shrinks to 30% of the March high before growing back the following winter. Presently 2024 is at 32% of last March maximum.  In this context, it is foolhardy to project any summer minimum forward to proclaim the end of Arctic ice.

Resources:  Climate Compilation II Arctic Sea Ice

Warning! Trojan Horses Offshore (Wind Farms)

Posted on by Ron Clutz

Gordon Hughes explains the analogy in his Real Clear Energy article Offshore Trojan Horses.  Excerpts in italics with my bolds and added images.

In July, the U.S. Department of Interior greenlighted large offshore wind farms in New Jersey and Maryland. Once the financial agreements are in place, New Jersey’s Atlantic Shores and Maryland’s MarWin and Momentum will join the two large wind farms in New York approved in June. These projects will receive huge, multibillion-dollar subsidies from the federal government and electricity ratepayers. What benefits will New Jersey and Maryland enjoy from this flood of money?   

To answer this question, it is best to recall the classic warning of the Trojan Horse legend,  “Beware of Greeks bearing gifts”—in other words, the hidden dangers of accepting something that seems too good to be true. New York State ignored that warning when it agreed to pay very high prices for the electricity to be supplied from its new offshore wind farms—Empire Wind 1 and Sunrise Wind—located off the coast of Long Island.

In announcing the final agreements, New York Governor Kathy Hochul triumphantly claimed that the new projects would create more than 800 jobs during the construction phase and deliver more than $6 billion in economic benefits for the state over 25 years. 

Rather less emphasis was given to the fact that New York will pay an average price of over $150 per MWh (megawatt hour) for the electricity generated by Empire Wind 1 and Sunrise Wind.That’s more than four times the average wholesale price of electricity in New York during 2023–24, $36 per MWh. The total annual premium over the wholesale market price for the power from these wind farms will be about $520 million per year at 2024 prices. Over 25 years, New York ratepayers will be paying about $13 billion for alleged benefits of $6 billion.

That is not all. Thanks to tax credits, U.S. taxpayers will cover at least 40% of the costs of constructing the wind farms. At a minimum cost of $5.5 million per MW (million watts) of capacity, the total federal subsidy for New York’s two wind farms will be at least $3.8 billion.

What about jobs and other economic benefits?  A study prepared for Equinor, the owner of Empire Wind 1, and submitted to the federal Bureau of Ocean Energy Management (BOEM) claimed that it would directly generate 180 annual jobs in New York during the six-year construction phase. The study estimated another 60 annual jobs due to the indirect employment effect, i.e., extra employment in the supply chain for the project. 

A more reasonable estimate for the two projects together would be 515 annual jobs, not 800. The total contribution to New York State’s gross value added (the equivalent of GDP at the state level) during the construction of both projects would be less than $450 million, based on the report submitted to BOEM. Similar calculations for annual operating and maintenance (O&M) costs suggest an annual contribution of about $24 million to gross value-added or about $600 million over 25 years.

Rather than the benefits of $6 billion over 25 years touted by Governor Hochul, a realistic assessment would be closer to $1.1 billion at 2024 prices. In any event, residents will be paying a cumulative premium of $13 billion for  the electricity these projects will generate. 

Moreover, the additional jobs claimed for the project are concentrated heavily in the final year of construction—and the largest share (47%) consists of professional services. Overwhelmingly, these are jobs for people who would otherwise be working on other assignments.

The economic benefits of the two offshore wind farms are much lower than claimed by the governor and the jobs are, in large part, temporary assignments for professional services staff. Promoting business for consulting firms may be considered a desirable outcome by Ms. Hochul. Still, the very high financial burden will be borne by almost the entire population of the state.

Stepping back from the New York projects, the Biden administration’s overall goal is to reach a target of 30 GW (billion watts) of offshore electricity generation capacity by 2030 or shortly thereafter. That is equivalent to 17 times the capacity of the combined Empire Wind 1 and Sunrise Wind projects. Detailed costs and financial arrangements vary, but the figures above suggest that the recurring premium paid by electricity ratepayers in states with offshore wind farms will be about $9 billion per year. The benefits of new job creation and incomes from capital and O&M expenditures are likely to be less than $800 million per year. 

In addition to the very large subsidies paid for from ultra-high electricity bills, federal taxpayers will contribute about $65 billion via tax credits if the Biden administration’s offshore wind target is met. While the subsidies for individual projects may not seem outrageous, the commitment of money to subsidize offshore generation is about $870 for every member of the country’s population. This may be spread over 25 years, but it is a huge liability for one very small element of U.S. programs to support renewable energy. 

PS  And it’s doubtul how many wind turbines will last 25 years

The Short Lives of Wind Turbines

Latest INM Climate Model Projections Triggered by Scenario Inputs

Posted on by Ron Clutz

The latest climate simulation from the Russian INM was published in April 2024: Simulation of climate changes in Northern Eurasia by two versions of the INM RAS Earth system model. The paper includes discussing how results are driven greatly by processing of cloud factors.  But first for context readers should be also aware of influences from scenario premises serving as model input, in this case  SSP3-7.0.

Background on CIMP Scenario  SSP3-7.0

A recent paper reveals peculiarities with this scenario.  Recognizing distinctiveness of SSP3-7.0 for use in impact assessments by Shiogama et al (2024).  Excerpts in italics with my bolds and added images.

Because recent mitigation efforts have made the upper-end scenario of the future GHG concentration (SSP5-8.5) highly unlikely, SSP3-7.0 has received attention as an alternative high-end scenario for impacts, adaptation, and vulnerability (IAV) studies. However, the ‘distinctiveness’ of SSP3-7.0 may not be well-recognized by the IAV community. When the integrated assessment model (IAM) community developed the SSP-RCPs, they did not anticipate the limelight on SSP3-7.0 for IAV studies because SSP3-7.0 was the ‘distinctive’ scenario regarding to aerosol emissions (and land-use land cover changes). Aerosol emissions increase or change little in SSP3-7.0 due to the assumption of a lenient air quality policy, while they decrease in the other SSP-RCPs of CMIP6 and all the RCPs of CMIP5. This distinctive high-aerosol-emission design of SSP3-7.0 was intended to enable climate model (CM) researchers to investigate influences of extreme aerosol emissions on climate.

SSP3-7.0 Prescribes High Radiative Forcing

SSP3-7.0 Presumes High Aerosol Emissions

Aerosol Emissions refer to Black Carbon, Organic Carbon, SO2 and NOx.

•  Aerosol emissions increase or change little in SSP3-7.0 due to the assumption of a lenient air quality policy, while they decrease in the other SSP-RCPs of CMIP6 and all the RCPs of CMIP5.

• This distinctive high-aerosol-emission design of SSP3- 7.0 was intended to enable AerChemMIP to investigate the consequences of continued high levels of aerosol emissions on climate.

SSP3-7.0 Supposes Forestry Deprivation

• Decreases in forest area were also substantial in SSP3- 7.0, unlike in the other SSP-RCPs.
• This design enables LUMIP to analyse the climate influences of extreme land-use and land-cover changes.

SSP3-7.0 Projects High Population Growth in Poorer Nations

Global population (left) in billions and global gross domestic product (right) in trillion US dollars on a purchasing power parity (PPP) basis. Data from the SSP database; chart by Carbon Brief using Highcharts.

SSP3-7.0 Projects Growing Use of Coal Replacing Gas and Some Nuclear

My Summary:  Using this scenario presumes high CO2 Forcing (Wm2), high aerosol emissions and diminished forest area, as well as much greater population and coal consumption. Despite claims to the contrary, this is not a “middle of the road” scenario, and a strange choice for simulating future climate metrics due to wildly improbable assumptions.

How Two Versions of a Reasonable INM Climate Model Respond to SSP3-7.0

The preceding information regarding the input scenario provides a context for understanding the output projections from INMCM5 and INMCM6.  Simulation of climate changes in Northern Eurasia by two versions of the INM RAS Earth system model. Excerpts in italics with my bolds and added images.

Introduction

The aim of this paper is the evaluation of climate changes during last several decades in the Northern Eurasia, densely populated region with the unprecedentedly rapid climate changes, using the INM RAS climate models. The novelty of this work lies in the comparison of model climate changes based on two versions of the same model INMCM5 and INMCM6, which differ in climate sensitivities ECS and TCR, with data from available observations and reanalyses. By excluding other factors that influence climate reproduction, such as different cores of GCM components, major discrepancies in description of physical process or numerical schemes, the assessment of ECS and TCR role in climate reproduction can be the exclusive focus. Also future climate projections for the middle and the end of 21st century in both model versions are given and compared.

After modification of physical parameterisations, in the model version INMCM6 ECS increased from 1.8K to 3.7K (Volodin, 2023), and TCR increased from 1.3K to 2.2K. Simulation of present-day climate by INMCM6 Earth system model is discussed in Volodin (2023). A notable increase in ECS and TCR is likely to cause a discrepancy in the simulation of climate changes during last decades and the simulation of future climate projections for the middle and the end of 21st century made by INMCM5 and INMCM6.

About 20% of the Earth’s land surface and 60% of the terrestrial land cover north of 40N refer to Northern Eurasia (Groisman et al, 2009). The Hoegh-Guldberg et al (2018) states that the topography and climate of the Eurasian region are varied, encompassing a sharply continental climate with distinct summer and winter seasons, the northern, frigid Arctic environment and the alpine climate on Scandinavia’s west coast. The Atlantic Ocean and the jet stream affect the climate of western Eurasia, whilst the Mediterranean region, with its hot summers, warm winters, and often dry spells, influences the climate of the southwest. Due to its location, the Eurasian region is vulnerable to a variety of climate-related natural disasters, including heatwaves, droughts, riverine floods, windstorms, and large-scale wildfires.

Historical Runs

One of the most important basic model experiments conducted within the CMIP project in order to control the model large-scale trends is piControl (Eyring et al, 2016). With 1850 as the reference year, PiControl experiment (Eyring et al, 2016) is conducted in conditions chosen to be typical of the period prior to the onset of large-scale industrialization. Perturbed state of the INMCM model at the end of the piControl is taken as the initial condition for historical runs. The historical experiment is conducted in the context of changing external natural and anthropogenic forcings. Prescribed time series include:

♦  greenhouse gases concentration,
♦  the solar spectrum and total solar irradiance,
♦  concentrations of volcanic sulfate aerosol in the stratosphere, and
♦  anthropogenic emissions of SO2, black, and organic carbon.

The ensemble of historical experiments consists of 10 members for each model version. The duration of each run is 165 model years from 1850 to 2014.

SSP3-7.0 Scenario

Experiments are designed to simulate possible future pathways of climate evolution based on assumptions about human developments including: population, education, urbanization, gross domestic product (GDP), economic growth, rate of technological developments, greenhouse gas (GHG) and aerosol emissions, energy supply and demand, land-use changes, etc. (Riahi et al, 2016). Shared Socio-economic Pathways or “SSP” vary from very ambitious mitigation and increasing shift toward sustainable practices (SSP1) to fossil-fueled development (SSP5) (O’Neill et al, 2016).

Here we discuss climate changes for scenario SSP3-7.0 only, to avoid presentation large amount of information. The SSP3-7.0 scenario reflects the assumption on the high GHG emissions scenario and priority of regional security, leading to societies that are highly vulnerable to climate change, combined with relatively high forcing level (7.0 W/m2 in 2100). On this path, by the end of the century, average temperatures have risen by 3.0–5.5◦C above preindustrial values (Tebaldi et al, 2021). The ensembles of historical runs with INMCM5 and INMCM6 were prolonged for 2015-2100 using scenario SSP3-7.0.

Observational data and data processing

Model near surface temperature and specific humidity changes were compared with ERA5 reanalysis data (Hersbach et al, 2020), precipitation data were compared with data of GPCP (Adler et al, 2018), sea ice extent and volume data were compared with satellite obesrvational data NSIDC (Walsh et al, 2019) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) (Schweiger et al, 2011) respectively, land snow area was compared with National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR) of Snow Cover Extent (SCE) reanalysis (Robinson et al, 2012) based on the satellite observational dataset Estilow et al (2015). Following Khan et al (2024) Northern Eurasia is defined as land area lying within boundaries of 35N–75N, 20E–180E. Following IPCC 6th Assessment Report (Masson-Delmotte et al, 2021), the following time horizons are distinguished: the recent past (1995– 2014), near term (2021–2040), mid-term (2041–2060), and long term (2081–2100). To compare observed and model temperature and specific humidity changes in the recent past, data for years 1991–2020 were compared with data for years 1961–1990.

Near surface air temperature change

Fig. 1 Annual near surface air temperature change in Northern Eurasia with respect to 1995–2014 for INMCM6 (red), INMCM5 (blue) and ERA5 reanalysis (Hersbach et al, 2020)(black), K. Orange and lightblue lines show ensemble spread.

Despite different ECS, both model versions show (Fig. 1) approximately the same warming over Northern Eurasia by 2010–2015, similar to observations. However, projections of Northern Eurasia temperature after year 2040 differ. By 2100, the difference in 2-m air temperature anomalies between two model versions reaches around 1.5 K. The greater value around 6.0 K is achieved by a model with higher sensitivity. This is consistent with Huusko et al (2021); Grose et al (2018); Forster et al (2013), which confirmed that future projections show a stronger relationship than historical ones between warming and climate sensitivity. In contrast to feedback strength, which is more important in forecasting future temperature change, historical warming is more associated with model forcing. Both INMCM5 and INMCM6 show distinct seasonal warming patterns. Poleward of about 55N the seasonal warming is more pronounced in winter than in summer (Fig. 2). That means the smaller amplitude of the seasonal temperature cycle in 1991– 2020 compared to 1961–1990. The same result was shown in Dwyer et al (2012) and Donohoe and Battisti (2013). The opposite situation is observed during the hemispheric summer, where stronger warming is observed over the Mediterranean region (Seager et al, 2014; Kr¨oner et al, 2017; Brogli et al, 2019), subtropics and midlatitudinal regions of the Pacific Ocean, leading to an amplification of the seasonal cycle. The spatial patterns of projected warming in winter and summer in model historical experiments for 1991-2020 relative to 1961-1990 are in a good agreement with ERA5 reanalysis data, although for ERA5 the absolute values of difference are greater.

East Atlantic/West Russia (EAWR) Index

The East Atlantic/West Russia (EAWR) pattern is one of the most prominent large-scale modes of climate variability, with centers of action on the Caspian Sea, North Sea, and northeast China. The EOF-analysis identifies the EAWR pattern as the tripole with different signs of pressure (or 500 hPa geopotential height) anomalies encompassing the aforementioned region.

In this study, East Atlantic/ West Russia (EAWR) index was calculated as the projection coefficient of monthly 500 hPa geopotential height anomalies to the second EOF of monthly reanalysis 500 hPa geopotential height anomalies over the region 20N–80N, 60W–140E.

Fig. 5 Time series of June-July-August 5-year mean East Atlantic/ West Russia (EAWR) index. Maximum and minimum of the model ensemble are shown as a dashed lines. INMCM6 and INMCM5 ensemble averaged indices are plotted as a red and blue solid lines, respectively.  The ERA5 (Hersbach et al, 2020) EAWR index is shown in green.

[Note: High EAWR index indicates low pressure and cooler over Western Russia, high pressure and warmer over Europe. Low EAWR index is the opposite–high pressure and warming over Western Russia, low pressure and cooling over Europe.]

East Atlantic/ West Russia (EAWR) index Time series of EAWR index can be seen in Fig. 5. Since the middle of 1990s the sign of EAWR index has changed from positive to negative according to reanalysis data. Both versions of the INMCM reproduce the change in the sign of EAWR index. Therefore, the corresponding climate change in the Mediterranean and West Russia regions should be expected. Actually, the difference in annual mean near-surface temperature and specific humidity between 2001–2020 and 1961–1990 shows warmer and wetter conditions spreading from the Eastern Mediterranean to European Russia both for INMCM6 and INMCM5 with the largest difference being observed for the new version of model.

Fig. 6 Annual mean near surface temperature, K (left) and specific humidity, kg/kg (right) in 2001– 2020 with respect to 1961–1990 for INMCM6 (a,b) and INMCM5 (c,d).

Fig. 7 Annual precipitation change (% with respect to 1995–2014) in Northern Eurasia for INMCM6 (red), INMCM5 (blue) and GPCP analysis (Adler et al, 2018) (black). Orange and lightblue lines show ensemble spread.

Discussion and conclusions

Climate changes during the last several decades and possible climate changes until 2100 over Northern Eurasia simulated with climate models INMCM5 and INMCM6 are considered. Two model versions differ in parametrisations of cloudiness, aerosol scheme, land snow cover and atmospheric boundary layer, isopycnal diffusion discretisation and dissipation scheme of the horizontal components of velocity. These modifications in atmosphere and ocean blocks of the model have led to increase of ECS to 3.7 K and TCR to 2.2 K, mainly due to modification of cloudiness parameterisation.

Comparison of model data with available observations and reanalysis show that both models simulate observed recent temperature and precipitation changes consistently with observational datasets. The decrement of seasonal temperature cycle amplitude poleward of about 55N and its increase over the Mediterranean region, subtropics, and mid-latitudinal Pacific Ocean regions are two distinct seasonal warming patterns that are displayed by both INMCM5 and INMCM6. In the long-term perspective, the amplification of difference in projected warming during June-JulyAugust (JJA) and December-January-February (DJF) increases. Both versions of the INMCM reproduce the observed change in the sign of EAWR index from positive to negative in the middle of 1990s, that allows to expect correct reproduction of the corresponding climate change in the Mediterranean and West Russia regions.

Specifically, the enhanced precipitation in the North Eurasian region since the mid-1990s has led to increased specific humidity over the Eastern Mediterranean and European Russia, which is simulated by the INMCM5 and INMCM6 models. Both versions of model correctly reproduce the precipitation change and continue its increasing trend onwards.

Both model versions simulate similar temperature, precipitation, Arctic sea ice extent in 1990–2040 in spite of INMCM5 having much smaller ECS and TCR than INMCM6. However, INMCM5 and INMCM6 show differences in the long-term perspective reproduction of climate changes. After 2040, model INMCM6 simulated stronger warming, stronger precipitation change, stronger Arctic sea ice and land snow extent decrease than INMCM5.

My Comment

So both versions of the model replicate well the observed history.  And when fed the SSP3-7.0 inputs, both project a warmer, wetter world out to 2100; INMCM5 reaches 4.5C and INMCM6 gets to 6.0C.  The scenario achieves the desired high warming, and the cloud enhancements in version 6 amplify it.  I would like to see a similar experiment done with the actual medium scenario SSP2-4.5.

Signs of Sun Setting on Renewables

Posted on by Ron Clutz

News come from renewables trailblazing Australia including signs there and around the world that wind and solar power are losing their momentum. From the Australian by way of John Ray is the article The sun is setting the renewables ‘superpower’ fantasy of the Australian Left. Excerpts in italics with my bolds and added images.

Renewable energy superpower status is supposedly in Australia’s grasp now the government has given Mike Cannon-Brookes the green light to export solar power to Singapore.

Sky News Business Editor Ross Greenwood says Australia’s largest solar farm to date has been given the “green light” by the Environment Minister Tanya Plibersek.  Plibersek announced environmental approval for the tech billionaire’s eccentric proposal last week, taking a swipe at Peter Dutton’s “expensive nuclear fantasy that may never happen”.

By contrast, the Environment Minister would have us believe Cannon-Brookes’s plan to siliconise the NT Outback is a done deal. All that’s left to do is:

♦  raise $35bn in capital,
♦  install 120 square kilometres of solar panels,
♦  build a modest 788km transmission line to Darwin, and
♦  lay a 4200km high-voltage cable on the seabed, and we’re good to go.

The Sun Cable AAPowerLink project feels like it was stolen from a Heath Robinson cartoon: a convoluted, unnecessarily elaborate, and impractical contraption designed to accomplish a mundane task. It may mark the beginning of the end of the renewable romance, the point at which the transition to wind, solar and hydropower collapses under the weight of its own absurdity.

There is increasing evidence the US has reached the point of peak renewables, as the pool of private investors shrinks and winning community approval becomes harder. Research by the Lawrence Berkley National Laboratory showed roughly one-third of utility-scale wind and solar applications submitted over the past five years were cancelled, while about half of wind and solar projects experienced significant delays.

The US Department of Energy says the national electricity network needs to grow by 57 per cent by 2035, the equivalent of approximately 21,000 km a year. Last year’s total was around 200 km, downfrom just over 1000 in 2022.

Meanwhile, the challenges of grid synchronisation and storage remain unresolved, and the technical problems for offshore wind turbines, in particular, are mounting. Last week, turbine manufacturer GE Vernova announced an investigation into a blade failure in the 3.6GW Dogger Bank project in the North Sea off the coast of the UK. It is the third blade failure this year.

In July, a newly installed blade crumbled at the Vineyard Wind offshore plant, creating debris that washed up on Nantucket Island, Massachusetts. At 107 metres long and weighing 55 tonnes, they are the most enormous blades deployed commercially. The failure of three in quick succession suggests the quest to increase output by installing ever-larger blades has reached its natural limits.

Yet the imperative of expanding generating capacity is hardening.

The principal driving force is not electric vehicles but the rapid growth of artificial intelligence. AI requires at least 10 times the power of conventional computing programs.

In the US, data centres account for about 2.5 per cent of power and demand could rise to 7.5 per cent by 2030, according to Boston Consulting Group. In Ireland, data processing and storage use 12 per cent of electricity produced, forcing the authorities to limit the number of connections to the grid.

Silicon Valley has long abandoned the notion it can be powered by silicon photovoltaic panels while burying stray emissions in the Amazon forests.

In April, the tech giant Amazon paid the best part of $1bn ($US650m) for a sizeable block of land next to Pennsylvania’s Susquehanna nuclear power station. It will be the site for a data centre powered by up to 480MW of carbon-free electricity delivered reliably around the clocky.

Shares in US nuclear power companies such as Consolidated Energy, Talem and Vistra have soared by between 80 per cent and 180 per cent in the past year. So-called green energy stocks, on the other hand, are static or falling, while coal is making an unexpected comeback.

In May, the Financial Times reported that the retirement dates for coal-fired power stations are being pushed back as operators become concerned about grid security. Allianz Energy has delayed the conversion of its Wisconsin plant from coal to gas for three years to 2028. Ohio-based FirstEnergy announced in February that it was scrapping its 2030 target to phase out coal, citing “resource adequacy concerns”.

The effect of AI on electricity demand was largely unanticipated at the beginning of the decade. AI chips will undoubtedly become more efficient, but there is no telling how much further the demand for AI will grow since the technology is in its infancy. Nor can we begin to guess what other power-hungry forms of technology might be developed by 2050.

What we do know, however, is that if Australia’s demand for electricity exceeds 313 TWH a year in a 2050, we’re in trouble. That’s the target the Australian Energy Market Operator has set in its updated blueprint for the great electricity transition.

As Chris Bowen points out, that’s going to take a lot solar panels and wind turbines. The Energy Minister says we need 22,000 new solar panels a day and a new 7MW wind turbine every 18 hours just to meet our 2030 target of a mere 202 TWH. For the record, the speed of the rollout in the first two years of Labor government is less than a tenth of that.

One of the hallmarks of the anointed is an unwavering conviction
in the integrity of their analysis and
the effectiveness of their proposed solutions.

They feel no need to hedge their bets by factoring in contingency arrangements should their predictions turn out to be wrong. Nothing in AEMO’s Integrated System Plan indicates its experts have given any thought to scaling up electricity production in line with actual demand, which may well be considerably higher than they’ve anticipated.

If they had, they would have to acknowledge that there are limits to the renewable energy frontier determined by energy density, the demand for land and the requirement for firming. The silicification of northern Australia cannot continue forever, nor can we expect to rely on China for most of the hardware and pretend there are no geopolitical consequences.

As for our nuclear-phobic Prime Minister’s dream of turning Australia into the Saudi Arabia of green hydrogen, while simultaneously sitting at the cutting edge of quantum computing, forget it. In 2006, as the shadow minister for the environment, Anthony Albanese gave a speech at the Swansea RSL on avoiding dangerous climate change.

“Why on Earth would we want to take the big health and economic risk of nuclear energy when we have a ready-made power source hovering peacefully in the sky every day?” he asked.

If Albanese doesn’t know the answer to that question 18 years later, he probably never will.

Being Properly Skeptical of Expert Consensus

Posted on by Ron Clutz

Solvay conference 1927, probably the most intelligence ever photographed. 17 of the 29 attendees were or became Nobel Prize winners.

Miriam Solomon writes at  iai news Scientific consensus needs dissent.  Excerpts in italics with my bolds and added images

We place high epistemic value on expert opinion and when it reaches a consensus, we may view this as settled science. But, writes Miriam Solomon, we should not equate expert opinion with certainty. While expertise is a valuable guide to decision-making, experts can be prone to human error too. Laypeople can, and should, critically evaluate how expert consensus is reached.

We live in an immense, complex world, and frequently benefit from guidance from those with more information and experience—people we regard as experts—to make sense of it. Along these lines, we often use expert consensus as an indicator of what is known, and expert disagreement as an indicator of what is uncertain. So, for example, earth scientist and historian Naomi Oreskes appeals to the record of peer-reviewed scientific publications on climate change to argue that the public should listen to the expert consensus that there is anthropogenic climate change. Oreskes identified that those who publicly disagree with this consensus have not contributed to the peer-reviewed scientific literature on climate science, and in this way they are not experts with regard to the relevant subject matter, although they may have PhDs and even university positions in unrelated sciences. [Note that position ignores many expert climatologists who disagree, but who are dismissed as “deniers” because they dissent.]

Survey starting the narrative “97% consensus of experts agree humans are causing global warming.”

Oreskes dissuades us from taking such non-expert disagreement seriously, especially since she also finds that it is politically motivated. The appeal to expertise encourages us to trust those who know more than we do about a particular matter and invites us to pay attention to reliable markers of expertise, such as publication in relevant peer-refereed journals. In traditional epistemological terms, it recommends deference to epistemic authority (or authorities).

However, even experts are fallible. Expert consensus
should not be equated with certainty or truth.

But experts are more likely to be correct than non-experts, and the agreement of experts with one another can provide additional evidence for the robustness of their conclusions.  Oreskes’ approach implicitly relies on the trustability of the relevant experts, not only on their expertise. We need to know not only that experts are knowledgeable but that they are acting in the best interests of furthering knowledge. The integrity of science—its commitment to norms such as openness of inquiry, responsiveness to criticism, disinterestedness, etc. (see Merton (1942) and Longino (1990))—is vital for its trustability.

Sometimes, this trust can be eroded. Philosopher of medicine Maya Goldenberg has explored what is needed for laypersons to build justified trust in vaccine research, mentioning concerns about Big Pharma producing biased research and concerns about the historical record of medical, scientific, and governmental communities’ willingness to use untested medical technologies on marginalized groups.

When experts disagree—a common occurrence in science—deference to expertise yields conflicting results. Laypersons are apt to respond to such disagreements, such as which sorts of diets are best for long-term health, or which vaccines should be mandated and for whom, with statements such as “even the experts don’t know.”

Knowing this, experts are aware of the need for a public
face of consensus on matters they wish to influence.

They have become savvy about disseminating any publicly relevant consensus that is achieved. This thinking is behind established institutions such as the United States’ NIH Consensus Development Conference Program (1977-2013), which issued regular reports on new clinical interventions, and the Intergovernmental Panel on Climate Change (1988-present), which issues regular updates on climate science.

Forcing a consensus when the science is not there rarely works.

Deferring to the consensus of trustable experts is one of the best kinds of argument based on epistemic authority. It is certainly better than the Scholastic practice of referring to the writings of just one “great man,” such as Aristotle or Aquinas. Several experts coming to the same conclusion about a matter is usually more convincing than one expert coming to that conclusion. However, as many have pointed out, the strength of the argument depends on:

(1) the degree of independence of these experts from each other, and
(2) the individual and collective interests of these experts.

Scientists, like the rest of us, come to their knowledge in social context and, generally speaking, scientists are neither independent of each other nor completely interest-free. They are often trained similarly—by the same schools, people, and educational materials—and feel pressured towards group conformity as well as towards deference to uber-experts. Scientists have individual biases, such as confirmation bias, that can be magnified when one scientist influences another. There are many well-known cases in the history of science and medicine in which expert consensus has turned out to be incorrect and harmful. Some examples in medicine are the traditional practice of blood-letting as a general cure-all, the use of surgery and antacids for stomach ulcers, and the practice of radical mastectomy for early stage breast cancer.

Thus, while deferring to the consensus of experts is often a good practice, it is defeasible: there are circumstances in which that deference is not ultimately justified. It is worth spelling out what those circumstances are. Here are some questions to ask of any purported expert consensus.

♦  Who agrees? Is there any dissent—if so, is it between particular groups of experts (say, family medicine practitioners disagreeing with radiologists about the effectiveness of screening mammography) or between experts and non-experts?

♦  What do the dissenters say? It is necessary to get at least a little “into the weeds” of dissent to decide whether or not dissenters are worth taking seriously.

♦  How long has there been agreement? If agreement is new, what brought it about? In particular, how much of a role did new evidence play?

♦  If agreement is longstanding, would counterevidence be sought, noticed and responded to?

 

These kinds of questions are a check on the processes that led to consensus. There will always be some social processes such as peer pressure and graduate school training that are unrelated to relevant evidence yet play a role in expert belief formation. This does not mean that we should distrust all consensus that has any sources in “bias.” That is too idealistic. Instead, we should look at the complete picture of what played a role in consensus formation and try to assess whether new evidence had a deciding role.

It is also worth reflecting that consensus is not the general end goal of science. Scientific communities tolerate—even benefit from—lack of consensus. Already in the nineteenth century, the philosopher John Stuart Mill put things especially well in On Liberty (1859) when he argued that consensus is an obstacle to progress, rationality and truth because it eliminates points of view that may turn out to be partially or wholly correct, or at least useful for criticism and consequent refinement of the correct view.

Dissent is strategically valuable when it leads to the distribution of cognitive labour over a variety of perspectives, hypotheses, and methods. While individual experts are often over-confident about their own views, this does little harm when it does not get in the way of other experts exploring alternatives. It is best to have the scientific community pursue all promising lines of inquiry.

Achieving consensus on a scientific matter becomes important only when there is a need for cooperative communal action and there needs to be agreement on steps to take to achieve a policy goal, such as health or sustainability. Even in such cases, there need not be agreement on all issues. The publications of the Intergovernmental Panel on Climate Change are quite clear that there is plenty of disagreement between scientists on some of the details of climate science. What is emphasized is that there is sufficient consensus on basic matters to guide important policy decisions. 

Expert consensus is an important, but not infallible, guide for laypeople and decision-makers. The strength of a consensus depends on the independence of the experts involved and the processes that shaped their agreement. While deference to experts is often justified, it should be accompanied by critical scrutiny, particularly when consensus is used to guide public policy. Dissent within the scientific community remains essential, not only for advancing knowledge but also for ensuring that consensus, when achieved, is robust and reliable.