[It’s been a while since we’ve had a good “It’s Worse Than We Thought” story~cr]
UNIVERSITY OF GOTHENBURG
CREDIT: PHOTO: CÉLINE HEUZÉ
Climate models used by the UN’s IPCC and others to project climate change are not accurately reflecting what the Arctic’s future will be. Researchers at the University of Gothenburg argue that the rate of warming will be much faster than projected.
Due to the Arctic´s sea ice cover and its harsh climate, relatively few observations are made in that part of world. This means that the climate models used for projecting the future of the Arctic have not been calibrated to the same extent there as in other parts of the world.
Two recent scientific studies involving researchers from the University of Gothenburg compared the results of the climate models with actual observations. They concluded that the warming of the Arctic Ocean will proceed at a much faster rate than projected by the climate models.
Climate models underestimate the consequences
“These climate models underestimate the consequences of climate change. In reality, the relatively warm waters in the Arctic regions are even warmer, and closer to the sea ice. Consequently, we believe that the Arctic sea ice will melt away faster than projected,” explains Céline Heuzé, climatologist at the University of Gothenburg and lead author of one of the studies.
Warm water flows into the Arctic Ocean via Fram Strait between Greenland and Svalbard. However, the volume of water in these ocean currents and its temperature in the climate models are too low, which is one of the reasons why the climate models’ projections will not be accurate. Even the stratification of the Arctic Ocean is incorrect. The researchers argue that since roughly half of the models project an increase and the other half a decrease in stratification, the consequences of global warming cannot be estimated accurately.
Acquiring hard data in the Arctic must be prioritised
“This is a serious situation. If governments and organisations all over the world are going to rely on these climate models, they must be improved. Which is why research and data acquisition in the Arctic ocean must be prioritised. At present, we cannot provide a useful prediction of how quickly the Arctic sea ice is melting,” Céline Heuzé explains.
The Arctic is an important region for projecting what the future intensity of global warming will be. Its sea ice contributes an albedo effect – a white surface that reflects sunlight away from the planet. If the ice were to disappear, more solar radiation would reach the Earth.
“We need a climate model that is tailored to the Arctic. In general, you can’t use the same model for the entire planet, as conditions vary considerably. A better idea would be to create a specific model for the Arctic that correctly factors in the processes occurring in the Arctic Ocean and surrounding land areas,” Céline Heuzé explains.
JOURNAL
Journal of Climate
DOI
METHOD OF RESEARCH
Meta-analysis
ARTICLE TITLE
The Deep Arctic Ocean and Fram Strait in CMIP6 Models
ARTICLE PUBLICATION DATE
4-Jan-2023
Basically they haven’t a clue but they’ll forecast disaster anyway. Pathetic.
If the article everything looks fine, no one would read it.
Claiming an emergency worse than worse than we thought gets the climate howlers interested, and even the climate realists here read it.
This strategy was learned in the mid-1970s when a small minority of climate scientists decided to predict a coming global cooling crisis. Scientists before then generally wrote serious studies that the general public never read. But after about 35 years of global cooling, from 1940 to 1975, in spite of CO2 emissions rising, 1974 that was a great time for scientists to approach the mass media.
So a small minority of scientists got a HUGE amount of mass media attention And the general public read aout climate change for the first time. That was 1974
.
As if to prove that humans could not predict the climate, nature gave us a new warming trend that began in 1975, and continued through 2015. So much for the coming global cooling predictions.
The new coming global warming crisis predictions began in 1979 — using the lessons of how to get mass media attention learned in 1974.
The 1940 to 1975 global cooling did not fit the new coming global warming crisis narrative, so it was “revised away”.
The eight charts at the link below showed how much average temperature “revising” was done. A lot. The global cooling from 1940 to 1975 was “warmed”, for one example. When Climate Howler Global Whiners talk about historical temperature revisions, they ALWAYS “forget” about 1940 to 1975, as if that never happened.
Here are eight relevant “temperature revision” charts:
Honest Climate Science and Energy Blog: Inconvenient historical temperature statistics get “revised” when they do not support the coming global warming narrative
The average temperature is whatever the governments tell you it is.
Do you trust governments?
Only UAH statistics are compiled by voluntdeer scientists with no agenda, acting like scientists acted in the goo old days. Not acting like political hacks..
Needed?: “Climate Howler Global Whiners”
They’re probably not reading, but what if?
Some day, probably during a Niut Zero blackout, the Climate Howlers just might be willing to read alternative views on climate science and Nut Zero. I remain optimistic.
Hopefully, by then, the minority of climate realists will get their acts together — specifically those science deniers who claim there is no greenhouse effect, manmade CO2 is not a climate change variable, and manmade CO2 only accounts for 3% to 5% of all atmospheric CO2. Hopefully, by then, those boozers and losers will have been will have been re-educated.
most climate skeptics don’t believe what you just said
Nor is everything he wrote accurate.
The small minority are Schneider’s “effective” scientists:
« On the other hand, we are not just scientists but human beings as well. And like most people we’d like to see the world a better place, which in this context translates into our working to reduce the risk of potentially disastrous climate change. To do that we need to get some broad based support, to capture the public’s imagination. That, of course, means getting loads of media coverage. So we have to offer up scary scenarios, make simplified, dramatic statements, and make little mention of any doubts we might have.»
Synopsis: “The climate models aren’t generating extremes large enough for us to use to generate the hype we need.”
I thought it had all melted back in 2017.
(attached photographic evidence)
How much can a polar bear?
Your jokes arer worse than mine
And that is a low standard.
But keep trying.
Polar bear surfing
In 2018, when she was younger and still somewhat foolish, Greta posted her prediction, all arctic ice would be gone by 2023.
She said a Harvard professor had told her. If I were Harvard’s President, he would be fired and blacklisted.
Of course, she had, and likely still has, no idea how much INCREASE IN SUMMER energy that would require, as ice tends not to melt in winter.
After 5 years of sleepless nights worrying about the sea ice not melting fast enough in SUMMER, and nevertheless scare-mongering about various causes dear to her long-suffering heart, she finally faced up to reality, the sea ice was here to stay for decades, similar to snow fall, and floods, and fires, and droughts, and failing electrical system in California, the No. 1 dysfunctional state.
She deleted her post, in the middle of the night, hoping no one would notice.
“She said a Harvard professor had told her.”
Harvard sas prestige but in no way superior thinkers. A friend of mine went to Northeastern Law School. He said a higher percent of his fellow grads passed the BAR- compared to the Harvard grads.
Legacy sons and daughters, those whose families donate copious amounts of money, take the LSAT, but fail to study for it, because they think legacy standards would still be applicable
The last Polar Bear!
I suppose that photo is supposed to make us think the bear is desperate. I suspect he’s just having a good time- relaxing from his swim and enjoying a sunny day.
Shouldn’t suck his glacier mint so hard…
And before that, 2012, “much faster than previous predictions”, according to high and mighty Seth
AboBorenstein. These improved and updated prophets never quote those “previous predictions”, for some reason.“Climate Models” = Yawn..zzzzzzzzzzz – Wake me up when there is real science…
Models don’t match human timescales – e.g. written history seems limited to about 3000 years. Also quality and detail are moving targets.
Googling oldest bound pages:
Thought to be the oldest multi-page book in the world, dating to about 660 BC,…
“Consequently, we believe that the Arctic sea ice will melt away faster than projected”
Except it hasn’t. See Feynman about beautiful theories and data.
“We need a climate model that is tailored to the Arctic. In general, you can’t use the same model for the entire planet, as conditions vary considerably”
Special pleading. Current model ain’t working, so we need a “special” model.
Of course, never occurs to them that the underlying climate change hypothesis or the “physics” in the models might be wrong. See Feynman on fooling yourself.
“Of course, never occurs to them that the underlying climate change hypothesis or the “physics” in the models might be wrong.”
I think that’s the major problem.
Alarmists assume too much. They don’t follow the scientific method. If they did, they would discover that they have no science to back up their claims. They only have speculation, assumptions and unsubstantiated assertions. This is not science, it is a religion.
I agree if you are talking about climate science. But I would disagree if you were talking about science concerning radiation and heat transfer in the atmosphere, including the GHE.
That was actually Thomas Henry Huxley in 1870:
“the great tragedy of Science—the slaying of a beautiful hypothesis by an ugly fact—which is so constantly being enacted under the eyes of [scientists]”
So Feynman was quoting Huxley, or plagiarising?
Only the Shadow knows!
““We need a climate model that is tailored to the Arctic. In general, you can’t use the same model for the entire planet, as conditions vary considerably”” (bolding mine, tpg)
They condemn the entire GAT model with this statement.
It follows that:
“We need a climate model that is tailored to the 0 to +20 deg latitude”
“We need a climate model that is tailor to the +20 to +40 deg latitude”
“We need a climate model that is tailored to the +40 to +60 deg latitude”
-same for the minus latitudes-
Slice the latitude bands even finer and you will have co-invented integral calculus. 🙂
My mother told me never use the word believe, you either know or don’t know
My mother told me to not start fights, break windows or accidently start fires. I then did all three.
Me too, multiple times
They assume that melting sea ice is a positive feedback, when in fact the science says it’s a negative feedback.
When the basic science in your model is wrong, no amount of tweaking can save you.
“At present, we cannot provide a useful prediction of how quickly the Arctic sea ice is melting,” Céline Heuzé explains.”
But it’s worse that we thought. Right?
These scaremongers are pathetic.
It’s a good job the science was settled years ago, so we don’t need to believe this cr@p.
The problem is, that for the last 11 years, Arctic sea ice hasn’t been melting.
In some years it’s been increasing.
I’m not conceding the analysis, but IF the Arctic gets a little warmer, will that not mean an almost immediate increase in infrared radiation to space in the low humidity atmosphere? Thus, cooling the Earth back down.almost as soon as it warms up? Do climate activists think they’re being clever running tendentious climate models? Do they think they’re making a persuasive case with those stupid toys? I know that Gavin Schmidt does, but hey, he thinks the water cycle only operates because of the presence of CO2 in the first place.
CO2 is “dangerous”, because at high altitudes, with little water vapor and much less CO2 than at “ground” level, which is about 2000 meter above sea level, the low CO2 and high-up CO2 will absorb and radiate away to space some of the long wave radiation emitted by the cold arctic ice. What a bummer!
The Antarctic has not increased in temperature for 7 decades, except the calving Ross Ice Shelf area, which is in the news, ad infinitum, for scare-mongering purposes
That shelf, which points northward towards South America, for hundreds of miles, is influenced by “warm” sea currents, whereas the rest of the Antarctic shore line is not.
Not to mention the ‘newly’ discovered hydrothermal activity under the ice.
Warm ”Antarctic sea currents: should I pack a bathing suit?
“‘newly’ discovered”
Accidentally apt. We have yet to find significant sub ice hydro heating that we can confidently say is “new”.
You meant the peninsula which is affected by sea currents and underseas volcanoes.
Calving is normal for all glaciers, which are slow moving rivers of ice
The local warming of some Antarctica ice shelves from underseas volcanoes is more than offset by cooling of the rest of Antarctica, which cools from more CO2 in the atmosphere.
Thank you for the additional info.
At present, CO2 is at minimal levels of the past 600 MILLION years.
The cooling and enlargement of the Antarctic with higher CO2, during past eons, must have been significant, unless there were mitigating factors.
This “study” went over a cliff after the first sentence: “Climate models used by the UN’s IPCC and others to project climate change are not accurately reflecting what the Arctic’s future will be.”
I wrote “study” because this might be called a study.
But exactly what are they studying?
There are no observations of the future climate.
Strike One
There are no data for the future climate.
Strike Two
Prior long term climate predictions have been 100% wrong
Strike Three
We have a strikeout.
*********************************************************************
[It’s been a while since we’ve had a good “It’s Worse Than We Thought” story~cr]
Worse than we thought is so 2022.
It’s 2023 now.
— This is a “Worse than worse than we thought story”
It’s fortunate that polar bears are great swimmers.
CMIP3 vrs annual average temperatures
All climate change alarmists have to offer is bastardized Hockey Stick charts, better described as “Computer-generated Science Fiction”.
Climate change alarmists are one-trick ponies.
No, they’re surface observations compared to the CMIP3 model ensemble and 95% range.The forecast period starts in 2000. The multi-model ensemble runs very close to observations, as the IPCC says should be expected if the model range is skilful.
People here will ask for evidence of skilful forecasts then, when it’s presented to them, they will say the evidence is fabricated. Why ask for evidence that you know you will reject out of hand before it’s presented? It’s most odd.
CMIP6 models tell a different story.
So why should we time travel back to CMIP3 models?
Honest Climate Science and Energy Blog: If you hear that climate models are accurate, then someone is lying to you.
CMIP6 isn’t as promising as yet; but it’s still early days for it. It started in 2019 but has only been running its full ensemble of models since 2021 and I don’t think they’re all within the constrained TCR range (1.4-2.2 C). The ensemble of those that are is pretty close to observations.
However, even the fact that the CMIP3 ensemble has proved to be skilful thus far kind of scuppers the usual claim that ‘all the models have failed’.
You must have some special eyeglasses on to think that this graph “scuppers the claim that all the models have failed”.
No, the CMIP3 models. See above.
Any surface data that’s not USCRN is officially shit. That graph is UHI corrupted junk.
Entitled to your opinion.
”Any surface data that’s not USCRN is officially shit. That graph is UHI corrupted junk.”
Yes.
Why is the 2008/9 temp higher than 1999/2000? according to UAH it should be lower. You graph does not reflect reality. Therefore your graph must be garbage.
Can the forecast period start time be pushed back to 1923?
CMIP6 actually starts its forecasts in 1850. What you are seeing in the graphic is a 2088 month prediction of the global average temperature. You can download the full time range from the KNMI Climate Explorer.
Don’t know.
Although very unlikely for our planet, it’s possible average temperatures were relatively steady for a few thousand years.
The LOCAL climate reconstructions tend to show +/- 0.5 degree variations when combined to resemble a global average. Combining them tends to reduce the variations. So the averaged reconstructions in the past 5,000 years can not prove the straight line of the hockey stick is wrong.
From 5000 to 9000 years ago, the LOCAL reconstructions had variations large enough to exceed a reasonable margin of error of +/- 1 degree C.
The Hockey Stick is bad because it spliced surface measurements to proxies, without saying that, and used bad proxies too. The truncated section of the proxies did not match the surface measurements! The haphazard methodology created a very unlikely hockey stick shape on a chart. It doesn’t match historical climate anecdotes at all.
There are no climate models
Such models would require knowledge about EVERY climate cange variable and that knowledge does not exist, The so called models are not models of climate change on this planet. Maybe they would work on Uranis.
There are only climate confuser games, aka climate astrology
If the CMIP3 climate confuser games actually resemble reality, that is just a lucky guess.
I’s assuming your chart is honest.
Most leftists defending climate confuser games, such as Zeke H., present TCS with RCP 4.5, rather than the far more publicized ECS with RCP 8.5. Which cuts the warming rate in half. And TCS is a wild guess for the next 70 years while ECS is a wild guess for the next 200 to 400 years.
So even if the CMIP3 chart is honest:
(1) It is no longer relevant because we are now at CMIP6, which is even less accurate than CMIP5 was, and
(2) Maybe the climate confuser games were more reasonable at CMIP3, and fell off the wagon after that?
CMIP 5 and CMIP6 ECSs, on average, predict double the warming rate that actually happened since 1975. Even worse when you start in 1940, rather than cherry picking 1975 to 2023 to make the “models” look better.
TCS with RCP 4.5 using CMIP6 “models” is about the same as the actual 1975 to 2015 warming rate.
But not one “model” predicted no warming from 2015 to 2023, which had the largest eight-year period of total manmade CO2 emissions in history.
No model is perfect, you will probably say.
I say THERE ARE NO CLMATE MODELS at all
Just climate confuser games
Here are five charts of CMIP6 models versus reality.
The CMIP3 models are ancient history.
Honest Climate Science and Energy Blog: If you hear that climate models are accurate, then someone is lying to you.
Need color code key for 1st 2 charts.
How often does it need to be said that no individual model is expected to replicate observed temperature, ice loss, etc? The idea is that, as an ensemble, they cast a wide enough range such that the ensemble mean settles in close to observations over time.
You’re now dismissing CMIP3 because it’s ‘too old?’ Really it’s just too right for your liking. But let’s just discard accurate forecasts, based on an arbitrary age limit of our own choice, then say all forecasts are 100% wrong… until they’re not, again. Then we just declare them too old, or some other excuse. On it goes….
The ‘how never to be wrong’ method of ‘skepticism’!
Except the mean of the ensemble does *not* match observations.
What you are actually claiming is that newer models, which most would expect to get *BETTER* over time, are actually worse than the older models.
That’s pretty damning for the climate science cadre pushing the model forecasts.
The CMIP3 models ensemble mean is very close to observations, as is clear from the chart. The trend in observations over the forecast period, 22 years so far, is almost identical to the trend in the CMIP3 model ensemble average. It’s there for anyone to check. To say otherwise is just nonsense.
Re the newer model ranges, these are not expected to immediately replicate observations. Look at the early part of CMIP3; there is quite a divergence from observations in some years. This is to be expected due to natural variability (hence the need for multiple models).
But over time the multi-model mean gravitates towards observations.
Oh for Christ’s sake! When are you going to understand that any suggested correlation between models and reality is pure coincidence because the Earth’s climate cannot be modelled. Out by a inch – out by a mile. I mean what is it with some people that they just don’t get that?? Is it fear of embarrassment or what?
The slightest error propagation renders any model meaningless. In fact, worse than meaningless. Look at the mess the climastrology modelers have the Western world in at the moment.
“The slightest error propagation renders any model meaningless.”
The climate science elite always assume several things they never actually state.
There are probably more that I don’t remember at this point.
The annual average monthly Tmax in Las Vegas is 80F while in Miami it is 83F. Yet in Las Vegas the average highest temp is 104F in Las Vegas while in Miami it is 89F. Vastly different climates yet very close annual average Tmax.
Climate science uses the 80F and 83F to calculate its average temp. And that is supposed to compare climates?
I can’t imagine why anyone would use average annual temperatures for anything. I could see using average daily temperatures at times.
The average daily temps tells you little. Can you choose what clothes to pack based on the average daily temp when you are taking a trip?
Mike said: “Earth’s climate cannot be modelled.”
And yet there are many models of Earth’s climate available.
Mike said: “The slightest error propagation renders any model meaningless.”
Newtonian Mechanics has errors. Is it meaningless?
Quantum Mechanics has errors. Is it meaningless?
General Relativity has errors. Is it meaningless?
The Standard Model has errors. Is it meaningless?
Each of these has a range of applicability within which they provide fairly accurate results. You wouldn’t use QM to measure the acceleration of a Dodge Charger down the quarter mile. You wouldn’t use NM to predict how many electrons will tunnel through an energy barrier.
But NM provides pretty accurate results for the car. And QM provides pretty accurate answers for a transistor. Neither are perfect but are usable.
Climate models? Nope. Follow the linear projection of the climate models and the result is ultimately the heat death of the earth – just like the CAGW alarmists keep predicting. If you believe in the heat death of the earth from CO2 then there isn’t any hope for you.
”And yet there are many models of Earth’s climate available.”
Wrong. They are not models. How do you models something you cannot fully understand? Climate ”models” are not representational – obviously. So yes, they are utterly meaningless and utterly useless.
A model is just a set of equations, heuristics, and algorithms that accept inputs and produce outputs. You can create a model of anything even with little or no understanding of the thing being modeled. Obviously the more understanding you impart the more skillful the model will be. No model of reality will be perfect or embody full understanding. Nor will any model built with at least some amount of understanding exhibit zero skill. There is a spectrum of skill as measured by root sum squared error, anomaly correlation coefficient, Brier skill score, etc. Climate models are not any different in this regard.
“You can create a model of anything even with little or no understanding of the thing being modeled.”
How do you validate that model if you have little to no understanding of the thing being modeled and no way to compare the output to reality?
How do you calculate root-sum-square error for a Global Average Temperature that you can’t measure?
Root-sum-square error from one model to the mean of an ensemble of models is meaningless. All the models can be wrong and you are just finding the error between two wrong things. The same critique applies to anomaly correlation coefficient and BSS.
You are basically using an assumption that the models are correct but some are more correct than others and none are really wrong.
How often does it need to be said that no individual model is expected to replicate observed temperature, ice loss, etc? The idea is that, as an ensemble, they cast a wide enough range such that the ensemble mean settles in close to observations over time.
Surely you must be joking.
Why do the observed temperatures differ so much from the UAH satellite series?
Two possible reasons:
(1) Surface measurement Infilling for insufficient coverage, poor weather station siting, adjustments, re-adjustments, re-re-re-adjustments, homogenization, pasteurization and UHI.
(2) Because government bureaucrats who believe in CAGW predictions are in charge. They want to have as much warming in the historical temperature record as possible, because they predicted a lot of warming since 1979.
They want their hysterical temperature predictions to look good. And they have the power to do that. The historical average temperature is whatever government bureaucrats tell you it is, and don’t you forget it.
I’ll take number two.
UAH measures the atmosphere; GISS is surface temps. UAH also differs from other data sets that measure the atmosphere.
“UAH measures the atmosphere; GISS is surface temps.”
So the surface can warm without the atmosphere warming also?
“UAH also differs from other data sets that measure the atmosphere.”
So what’s your point? That UAH is wrong?
UAH is an outlier at present. RSS processes the same data sources as UAH and comes up with a trend that matches GISS and the other surface data sources. Are you saying RSS is wrong?
Who knows? The measurement uncertainty in *all* of it is wider than the differences they are trying to identify! It’s all based on the belief that somehow averaging data increases measurement resolution. If that were true I could measure crankshaft journals using a yardstick. I’d just have to take enough samples to increase the resolution!
It’s true that individual monthly values overlap statistically. But over time trends emerge. It so happens that the UAH trend is consistently lower than those of all the other global data sets, including the other satellite data sets.
Maybe UAH is right and everyone else is wrong. It just seems unlikely though. They have made several large adjustments to their TLT data in the past (much larger in scale than the much-critcised surface data adjustments); and, as mentioned, there is close agreement among all the other global temperature data producers.
“Maybe UAH is right and everyone else is wrong.”
That’s what I think. UAH has not manipulated its data for political purposes the way the temperature data mannipulators do with their “hottest year evah!” scam, for example.
The Data Mannipulators managed to squeeze 10 years of “hottest year Evah! out of the period from 1998 to 2016, claiming each succesive year was hotter than the last, in their efforts to scare people about CO2. Meanwhile, the UAH chart shows none of those years as being the “hottest year evah!”.
Climate Change Alarmists are lying to us about the climate and about CO2, and temperature data mannipulation is the way they carry out this climate change hoax.
None of these are MEASUREMENTS. They are metrics. Metrics with uncertainty although that uncertainty is never quantified. The standard deviation of the sample means is *NOT* the uncertainty unless you assume that all measurement uncertainty cancels – an assumption I have *NEVER* seen justified for temperature.
Adjustments are *NOT* an indication of something being wrong. It’s exactly what you do when you compensate for systematic bias in your measurement device.
Adjustment to temperature data without knowing the systematic bias associated with the measurement device is nothing more than trying to make the data “look better”. It’s a fraud.
The other metrics never being adjusted is actually a count against them. It means they think their measurements were, are, and will continue to be 100% accurate. In IMPOSSIBILITY.
If they are wrong it doesn’t matter how closely the other metrics match. Wrong is wrong.
“UAH is an outlier at present. RSS processes the same data sources as UAH and comes up with a trend that matches GISS and the other surface data sources.”
Nope. RSS uses data from one satellite that UAH does not use because the UAH guys think that satellite is giving spurious readings, so they don’t include it. GISS and the other surface data sources also use the data of the satellite giving spurious readings.
I guess the weather balloon data is an outlier, too? The Weather Balloon data correlates with the UAH data (97 percent).
TA said: “the UAH guys think”
Year / Version / Effect / Description / Citation
Adjustment 1: 1992 : A : unknown effect : simple bias correction : Spencer & Christy 1992
Adjustment 2: 1994 : B : -0.03 C/decade : linear diurnal drift : Christy et al. 1995
Adjustment 3: 1997 : C : +0.03 C/decade : removal of residual annual cycle related to hot target variations : Christy et al. 1998
Adjustment 4: 1998 : D : +0.10 C/decade : orbital decay : Christy et al. 2000
Adjustment 5: 1998 : D : -0.07 C/decade : removal of dependence on time variations of hot target temperature : Christy et al. 2000
Adjustment 6: 2003 : 5.0 : +0.008 C/decade : non-linear diurnal drift : Christy et al. 2003
Adjustment 7: 2004 : 5.1 : -0.004 C/decade : data criteria acceptance : Karl et al. 2006
Adjustment 8: 2005 : 5.2 : +0.035 C/decade : diurnal drift : Spencer et al. 2006
Adjustment 9: 2017 : 6.0 : -0.03 C/decade : new method : Spencer et al. 2017 [open]
That is 0.307 C/decade worth of adjustments with a net of +0.039 C/decade and that does not include the unknown magnitude of adjustments in the inaugural version A.
Pay particular attention to their infilling strategy.
15 grids representing 2.5° of longitude each at the equator is 4175 km. Compare this to GISTEMP which only interpolates to a maximum of 1200 km. GISTEMP does not perform any temporal interpolation.
TA said: “I guess the weather balloon data is an outlier, too?”
TA said: “The Weather Balloon data correlates with the UAH data (97 percent).”
Keep in mind that the [Christy et al. 2018] methodology does two interesting things.
First…they use IGRA. Note what IGRA says about their own dataset.
Second…they adjust IGRA to match the satellite data.
The data from UAH and RSS once agreed. RSS with help from NASA engineers changed the way they processed the raw data. UAH did not. For whatever that is worth.
DWM said: “UAH did not.”
This has to be a joke right?
That was my understanding when I was comparing the two. RSS would list all the process changes while UAH was pretty consistent. The RSS changes always made it warmer as I recall.
Look at my post above. I summarized all changes that UAH has made over the years.
When UAH made a change the resultant change in a value was insignificant For example when moving from v5.6 to v6 the TPW trend in %/decade changed about 0.1 %. Where as when RSS changed from v 4.0 to v7 the trend changed 5 times that value. That comparison seemed typical.
DWM said: “When UAH made a change the resultant change in a value was insignificant”
Changes totaled 0.307 C/decade including a single change from C to D that amounted to 0.10 C/decade is not what I would call insignificant. They also changed their attitude around this time on the accuracy of their product going from 0.01 C in the early 1990’s to 0.20 C in the early 2000’s.
RSS is currently on v4; not v7. And the largest change was from 3 to 4 which increased the trend by 0.04 C/decade. Note that RSSv4 has a better match to multiple radiosonde datasets than does RSSv3, UAHv5.6, and UAHv6.
[Mears & Wentz 2017]
Which scenario is that chart based on? The linked RealClimate article doesn’t say.
It’s all scenarios, as far as I know.
The bounds look far too narrow for that. It looks like the ensemble run on Scenario B / RCP 4.5, which I think Zeke Hausfather based his paper on.
I used the KNMI multi-model data for CMIP3 which seem to cover all the model runs and it’s very similar to the RC chart.
The point is, the multi-model mean is very close to the surface observations over the 22-year forecast period. That’s an indication of skilful modelling
You have to know which GHG concentration pathways they’re using. If all concentrations give the same results it isn’t particularly useful.
In the early decades they all tend to overlap, according to the spaghetti charts. I agree that the CO2 scenarios are important longer term.
There is quite a difference in concentrations between Scenarios A, B and C/D (RCP 8.5, 4.5 an 2.6, near enough) even in the early years.
For all we know, the graph includes the control case of 0 increase in GHG concentrations. Without that information, it’s just a pretty picture.
It may well be correct, but we just can’t tell from the information provided.
AR1 uses scenarios A, B, C, and D
AR2 uses IS92 scenarios
AR3 uses SRES scenarios
AR4 uses SRES scenarios
AR5 uses RCP scenarios
AR6 uses SSP scenarios
There isn’t an exact 1-to-1 mapping between scenario frameworks.
The graph does not contain the control case or “constant composition commitment” scenario.
SRES A1B. I think the uncertainty envelope is based on all scenarios though. Note that the Special Report on Emission Scenarios (SRES) do not diverge significantly until after 2050.
In a sense, your first quote is correct. Models are not accurately predicting what is happening in the Arctic. They are predicting way too much warming. Much like they predict too much warming for the rest of the world.
Accurate predictions would not scare the general public.
We can’t have that.
In the early 1990s there was a popular children’s book about a polar bear making its way from Antarctica to the Arctic. Didn’t bother getting it for my kids as I knew the premise was ridiculous. Wonder though how many children may still think polar bears live at both poles.
By way of illustration there was an article in the UK i newspaper some time back that claimed the Adelie penguins were dying out in the Arctic!
hmmm… I wonder if they could survive in the Antarctica?
Yes, but luring climate tourists into their habitat is much harder than it looked like when Professor Chris Turney was the best source of amusement on internet.
I showed very clearly in a post over a decade ago that the CMIP5 climate models didn’t/couldn’t simulate polar amplification properly. The post at my blog is here:
https://bobtisdale.wordpress.com/2012/04/24/polar-amplification-observations-versus-ipcc-climate-models/
The WUWT cross post is here:
Tisdale on Polar Amplification | Watts Up With That?
Regards,
Bob
So then CMIP3 and CMIP6?
“…relatively warm waters in the Arctic region…”, just jump on in and swim around for a while, wait a minute, quora.com says “death would likely occur in less than 15 minutes”. Go for it.
The world record is 15’3″
https://www.euronews.com/travel/2022/06/20/ice-mermaid-breaks-two-world-records-while-swimming-the-worlds-most-feared-waters
“”We need a climate model that…””
…works
The climate confuser games work PERFECTLY for scaring people
That is their goal
Not accurate predictions (those would not scare anyone).
The least inaccurate model, the Russian INM, is practically ignored.
It should be getting 99% of the attention
Those pesky Russians, whose INM in out of the desired
+2.5 to +4.0 degree C. IPCC ECS range. are troublemakers.
I predict the INM model will get sanctioned and deleted from CMIP7
here is what models look like with different CO2 growth rate scenarios. The radical RCP 8.5 versus us the more reasonable RCP 4.5 makes a big difference. These are CMIP5 models in the chart at the link below
CMIP5:
CMIP3:
“The scenario represented by the red trend line (IPCC Scenario A2) assumes humans will continue to accelerate the rate at which we emit carbon dioxide. This is consistent with a global economy that continues to rely mainly on coal, oil, and natural gas to meet energy demands. In this scenario, our carbon emission increases steadily from today’s rate of about 9 billion tons per year to about 28 billion tons per year in 2100. The middle trend (green, IPCC Scenario A1b) assumes humans will roughly balance their use of fossil fuels with other, non-carbon emitting sources of energy.”
SOURCE OF QUOTE:
File:Projected global warming over the 21st century using three SRES greenhouse gas emissions scenarios. Data from CMIP3 (2007).png – Wikimedia Commons
It is another way of saying models don’t work, so I guess we all agree on that.
Can you point out where this model/observation update is wrong please?
I downloaded the CMIP3 multi-model mean data myself and compared it to GISS surface observation data and got a chart that looks very similar to the one in the link.
I used land and ocean in both cases as I don’t have GISS land only data, so there are slight differences, but the trends over the forecast period (since 2000) are almost identical (CMIP3 +0.21 C/dec; GISS +0.22C/dec).
What were the CO2 projections used in the CMIP3 model? It is very hard to find those assumptions in links discussing past models.
P.S. I am working on my models to predict the results of a series of three coin flips. I have made 8 different models. I am pretty sure that an analysis from the future will show that one of my models made a perfect prediction!
I think they run a range of CO2 emission scenarios. Your coin flip analogy doesn’t quite work here because you’d need a lot more coins than three.
According to KNMI, the CMIP3 multi-model assembly has 30 models with a total of 286 runs between them. None of these runs is expected to replicate observations; but the multi-model ensemble, which covers a wide range of possible conditions, is expected to come close (as you can see, it does).
The purpose of the ensamble was to take into account unknown (then-) future conditions. It’s the future, What happens if we eliminate the least accurate inputs without watching the output?
I think that’s the idea of the later CMIP models. It’s an ongoing process.
Did you ask the researchers he quoted the same questions?
Yep. CMIP3 predicted +0.21 C/decade of warming. The 10 dataset composite is +0.20 ± 0.06 C/decade. That’s pretty close.
It’s a bullseye!
What about this?
That is a graph of the ‘tas’ parameter; not the ‘tos’ parameter. I told Dr. Spencer about the error. Apparently he never bothered fixing it.
That’s because the difference between TAS and TOS is about 0.05ºC between 1950-2000. It wouldn’t affect much the 0.7ºC we are seeing in that figure.
The difference between tas and tos amounts to more than 0.05 C.
https://moyhu.blogspot.com/2021/05/cmip6-comparing-tos-with-observed-sst.html
And remember that Dr. Spencer did not equally weight the CMIP6 members in his graph. He gave CanESM5 74% of the weight and all others combined only 26%. Note that CanESM5 shows the most warming by far so much so that it falls outside the 95% CI. So even if Dr. Spencer did fix the tax/tos error the graph is still woefully misleading.
The difference between TOS and TAS was shown by Stefen MacIntyre to be about 0.05ºC.
https://climateaudit.org/2009/06/15/tas-vs-tos/
Via KNMI I see a tas-tos difference of 0.2 C from 1979-2022 in the 60S-60N band. The overweighting of CanESM5 adds another 0.3 C of discrepancy.
That’s CMIP6, which is the latest iteration. I asked you about CMIP3.
It is really quite simple.
-Do you think models get everything right?
-If you don’t, how many things do you think models get wrong? 1, 10, 100, 1000?
-If you think models get 10s or 100s of things wrong, or more, why do you think we should trust what they say?
-Would you fly a plane that has only been tested on computer models? If you wouldn’t why do you think we should fly the world economy on climate models that we do know have lots of things wrong?
Stupid is as stupid does.
1. No model gets everything right. That’s why they use ensembles.
2. Hard to put a number on it. But the things they get wrong tend to cancel over time, so the multi-model average tends to aggregate towards the observations.
3. We should put provisional trust in the model ensemble mean if it agrees with observations over the long term. In this case, 22 years.
4. Models, whether they be for aircaft performance or climate projections, should stand comparison with observations. 22 years of skilful projections in the CMIP3 ensemble mean so far…
You still haven’t answered my question: in what way do the CMIP3 ensemble models ‘not work’? It’s not a trick question.
“But the things they get wrong tend to cancel over time, so the multi-model average tends to aggregate towards the observations.”
Pure, total malarky! If that were the case the models could all be used to drive to *one* model that gets it all right and which matches observations.
That is the actual idea! Seems to be working.
”That is the actual idea! Seems to be working.”
I can’t believe what I’m reading.
CMIP3 is an assortment of old computer models from 2006. They are deprecated. Lots of things have been fixed and added in the 17 years since. The latest iteration is CMIP6. If I were using HadCRUT3 you would be saying the same to me.
Funny how all these ‘old’ and ‘depricated’ models, when averaged, come up with a very close replication of observations.
Gives hope to those of us who are old and depricated.
The models don’t seem to be replicating the current global cooling that is going on. The temperatures have cooled by about 0.6C since the 2016 highpoint.
So you think CMIP3 is better than CMIP5 and 6 despite all the improvements of 17 years of work? Most curious.
CMIP3 has a root mean squared error of 0.14 C wrt to GISTEMP monthly global average temperatures and is only off by 0.02 C/decade in terms of the trend. Not only does CMIP3 have skill, but its skill is significantly better than contrarian predictions.
The prediction with the most skill is that the warming is going to continue at the same rate, and sea level rise is going to continue at the same rate as they had in the past 70 years, i.e., lack of the acceleration prescribed by models due to the continuing increase in CO2. In 1990, the IPCC predicted an acceleration of the warming for the following 35 years based on model predictions. It hasn’t taken place.
The rate of Arctic sea ice extent decrease since 2007 is down to almost zero, much to the chagrin of affirmationists. Their predictions suck.
JV said: “In 1990, the IPCC predicted an acceleration of the warming for the following 35 years based on model predictions. It hasn’t taken place.”
No they didn’t. Here are the scenarios considered in the 1990 FAR report.
Notice that humans chose something closest to scenario C or perhaps even B.
And here is the temperature prediction for each scenario.
Notice for both B and C the IPCC predicted about 0.55 C of warming. Compare that with HadCRUT which shows about 0.65 C of warming over the prediction period. If anything the IPCC underestimated the warming.
The last diagram is the reason I asked which scenario was used for the CMIP3 diagram that thefinalnail posted. It didn’t seem consistent with Scenario A
The CMIP3 graph is for SRES A1B.
Okay. Thanks.
It seems they all give about the same CO2 concentrations up until at least 2030, which doesn’t provide a great deal of explanatory power. (https://www.e-education.psu.edu/meteo469/node/145)
JV said: “The rate of Arctic sea ice extent decrease since 2007 is down to almost zero, much to the chagrin of affirmationists. Their predictions suck.”
That I can agree with. They were far too conservative. In 2001 the IPCC (TAR pg. 446) predicted that the Arctic sea ice would not decline below 10.5e6 km2 for annual mean until 2040. It actually occurred in 2007 and then again in 2011, 2012, 2016, 2017, 2018, 2019, and 2020. 2022 saw the highest annual sea ice extent in 8 years ending at 10.7e6 km2. Yet that is still 5% below the expectation of 11.3e6 km2 which is itself a 10% decline. Even with only a -0.05e6 km2/decade trend since 2007 Arctic sea ice extent is still far below IPCC predictions.
”No model gets everything right.”
Then they get nothing right.
” the things they get wrong tend to cancel over time”
How by making adjustments? If they know what adjustments to make they would have it right in the first place. Or is it putting a million models together and picking the middle one? Is that how wrongs get cancelled out?
God, this would be funny if it wasn’t so important.
Quantum Mechanics and General Relativity get a lot of things wrong. By your logic that means they “get nothing right”. In fact, QM makes the worst prediction in all of science regarding the vacuum catastrophe being off by an astonishing 120 orders of magnitude and yet countless people literally make life and death decisions based on that model of reality every single day.
The two biggest issues I’ve seen with QM are 1) measurement resolution and 2) understanding that probability events don’t always happen with the “expected value”.
1) If you can’t measure what is actually happening then it is impossible the formulate accurate theories. The climate models are perfect examples.
2) Two transistors made side-by-side on the same substrate with exactly the same doping will have different leakage currents and gains. QM will give you an answer as to what is expected but it simply can’t actually predict what *will* happen. Part of that is measurement resolution and uncertainty at the time of manufacture but some of it just the fact that QM is a probability prediction. It’s why, when you roll a six-sided dice multiple times, you will “probably” never get *exactly* the same number of occurrences for each number. You might get close but it will “probably” never be exact.
I would also add that the “vacuum catastrophe” is being worked on to obtain a resolution. See “Resolving the Vacuum Catastrophe: A Generalized Holographic Approach” by Haramein & Baker.
They didn’t get anything wrong they are just not able to relate the QM vacuum energy with the cosmological energy density, the dark energy of the universe. That and many other questions are unknowns not errors.
General Relativity is a remarkable piece of work that only has one fault I know of namely it has a singularity at the center of a blackhole which puts no one in danger..
Who is making life and death decisions based on what model of reality every day that you think may be wrong?
DWM said: “That and many other questions are unknowns not errors.”
It is straight up an error. The QM prediction is about 1e72 GeV. The observed value is 1e-48 GeV. 1e72 GeV / 1e-48 GeV = 1e120. Some QM variations can get the discrepancy down to “only” 1e60.
DWM said: “Who is making life and death decisions based on what model of reality every day that you think may be wrong?”
People seeking medical treatment and diagnosis based at least in some part on QM. One example would be that of nuclear magnetic resonance.
You didn’t read what I said. They are not claimed to be equivalent.
You are confusing Quantum Physicists who totally understand nuclear magnetic resonance with those who implement the physics.
Implementations are based on models. Scientists do the modeling and engineers do the implementing. No QM model…no MRI.
“Implementations are based on models. Scientists do the modeling and engineers do the implementing. No QM model…no MRI.”
Really? What model did the Wright brothers implement? What model did Alexander Graham Bell implement? What model did the first person to make a wagon wheel implement?
The MRI is an example of APPLIED science, not of model making.
“…a white surface that reflects sunlight away from the planet. If the ice were to disappear, more solar radiation would reach the Earth.”
_________________________________________________
The incident angle of sunlight as it moves poleward produces less and less absorption of sunlight. Think of the glare from open water when the sun is low in the morning or late afternoon.
Just one of the many pieces of basic science that the so called climate models don’t handle correctly.
A very declarative statement about something that they only slightly earlier claim declares is faulty or non-existent. “The arctic model is wrong, but we know exactly how” would imply that the author is too lazy or incompetent to fix it.
With only inadequate models, how could one tell?
At least these Göteborgers are firmly on the side of climate denialism, it is good to recognize that the models don’t work. Let the cancellations commence, and as they said in Casablanca (the movie) welcome to the fight..
A good climate model would work at any point on the planet.
The fact that the admit the need to have different models for different places is an admission on their part that there are factors the current models do not handle correctly.
Worse, they don’t know what these factors are. If they did, they would fix them.
Agreed. Not arguing, but expanding-
A good model of any complex process should be constantly validated and verified against observation, and changed whenever execution of the model fails to produce natural results.
These people at least acknowledge that observations of nature disagree with the models they’ve used. They are so far unwilling to examine the working theory. I still presume that as idealogues in good standing that the will not be cancelled, as so many so-called deniers have been, for stating the obvious. Whether or not they deserve a dunce cap for presuming that any model can predict weather accurately more than about a week in advance, or for confusing weather with an arbitrary definition of climate, I leave to smarter, or more religious, people than I am.
“A good climate model would work at any point on the planet.”
Ahhh the good old “unless they get everything right all else must be wrong too” theory. Pity you don’t use that on yourself Mark.
Huh? “Climate models separate Earth’s surface into a three-dimensional grid of cells. The results of processes modeled in each cell are passed to neighboring cells to model the exchange of matter and energy over time.”
Climate models *are* based on working everywhere. If they are wrong then it has to be because they get everything else wrong too.
Let’s completely ignore the fact that models have over predicted warming for the last 50 years.
Take our word for it, in the future they are under predicting what is going to happen.
I just wish that was sarcasm.
Models are doing pretty well in regards to the global average temperature. It’s the Arctic sea ice extent where there are serious problems.
That appears to be not as good as a linear extrapolation of the hindcast observation tempeatures. It isn’t compelling evidence for the skill of the models. With a range of uncertainty of about 2 deg F (and growing), and a forecast of about 4 deg rise in 100 years, it isn’t the kind of precision that would win a Nobel in physics.
Yep. That is a large uncertainty envelop for sure. I do wonder if the envelope is too wide though. With 53 data points you’d expect at least 2 of them to fall outside the envelop yet 0 did. The closet appears to be 1976.
You still haven’t figured out uncertainty, have you? It isn’t an issue of how many annual data points you have in determining the uncertainty. The uncertainty interval is per year.
It looks to me like the relative uncertainty in 1980 is about 150%, in 2000 about 100% and in 2003 about 60%. The relative uncertainty is actually getting smaller over time.
It’s not an uncertainty range, it’s a spread of model outputs.
The graph says quite clearly, “95% CI.” Last time I checked, “CI” was the commonly accepted abbreviation for “confidence interval” when paired with 68% or 95%.
It’s not an uncertainty range in the usual sense, as I understand it. Each model run uses a discreet set of inputs, so the resulting output produces a definite set of values. There are no error margins in an individual model run, as such.
Instead, multiple runs of each model are carried out, each with different inputs, mostly to reflect natural variability. This produces a range of values (a range of uncertainty, if you like). If the models are skilful, then their average value over the longer term should be more likely to resemble observations than any single model run. This is what we see in CMIP3 models.
Yep. It’s the same way uncertainty is quantified with day-to-day weather forecasts as well. They run the operational models in ensemble suites with initialization, parametrization, and stochastic perturbations to form the forecast envelope. It is analogous to a type A quantification of uncertainty since mathematically speaking it is the same.
“Instead, multiple runs of each model are carried out, each with different inputs, mostly to reflect natural variability.”
This is the EXACT definition of “uncertainty”!
“(a range of uncertainty, if you like)”
It’s not “if you like”. It’s a realization of the uncertainty.
“ If the models are skilful, then their average value over the longer term should be more likely to resemble observations than any single model run.”
Why? You can make all the measurements you want, the average value of those measurements will be inaccurate if the ruler is actually 13″ long!
The average of wrong models will be wrong!
If the uncertainty bars of each of the annual ensemble means, calculated from both the expected value spread and from the uncertainty of every annual model point, were displayed, then perhaps some of the minima and maxima would poke out. The criticism that these are usually not offered in the data, or displayed, is valid for both temp and sea level data.
Now, of course, those intervals would be disputed by the rabbit hole intonements of “uncertainty of uncertainty of uncertainty…….”. But the technical backup for that, the repetition of the tale of 6 and 8 foot boards in the back of pick up trucks, will, thankfully and properly, stay relegated to subterranea, here…
Measurement uncertainty resulting from measuring different things never cancels. Never has, never will. Uncertainty in the inputs to models grows with each iteration of the model. If it didn’t the ensemble members would provide results with much less variation.
Inconvenient truths are still truths, whether you like it or not.
Why 2004?
Interesting timing. Gavin Schmidt discussed Dr. Christy’s misleading graphs from years past this month.
https://www.realclimate.org/index.php/archives/2023/03/how-not-to-science/
Oh btw I see that both NE and NW Passages were iced up all year for the first time since 2009. Tell Greta
In 2001 the IPCC (TAR pg. 446) predicted that the Arctic sea ice would not decline below 10.5e6 km2 for annual mean until 2040. It actually occurred in 2007 and then again in 2011, 2012, 2016, 2017, 2018, 2019, and 2020. 2022 saw the highest annual sea ice extent in 8 years ending at 10.7e6 km2. Yet that is still 5% below the expectation of 11.3e6 km2 which is itself a 10% decline. The underestimation of Arctic sea ice decline has a been a recurring problem for 4 decades now. This should cast some doubt on the IPCC’s most recent projection of the first summer where the minimum is < 1e6 km2 around 2050 (AR6 SPM pg. 16).
Ja. Ja. I told you. The heat is coming from the north. Strange. Look at table 2.
https://breadonthewater.co.za/2022/03/08/who-or-what-turned-up-the-heat/
In other words, a battle of the models. And these authors are convinced that their mental model is superior to the existing formal computer models. It is good to question the status quo and I’m in favor of trying to improve the existing the models.
However, the statement, “Its sea ice contributes an albedo effect – a white surface that reflects sunlight away from the planet” suggests that we have another group of climatologists that don’t understand the physics of reflection, and are probably unfamiliar with Fresnel’s equation for specular reflection, and I would venture to guess are similarly unfamiliar with the Bi-directional Reflectance Distribution Function for the diffuse reflector, snow. At glancing angles, water can have a reflectivity higher than the average albedo of snow, and that reflected light is reflected away from Earth.
https://wattsupwiththat.com/2016/09/12/why-albedo-is-the-wrong-measure-of-reflectivity-for-modeling-climate/
Clyde, I strongly suspect that the extra greening of earth due to more CO2 does cause some warming by changing albedo.
When I visited Norway a number of years ago I found everything teeming with live but we did not go much further clnorth than Bodo.
Let me show findings by Christie et al 2006 and my own findings as well.
https://breadonthewater.co.za/2022/01/10/global-warming-due-to-ehhh-global-greening/
Henry, Happer remarks, “Such human engineering of the environment has changed a high-albedo desert into a darker, moister, vegetated plain, …”
He may be generalizing a bit. Much of the Great Valley was actually quite swampy, and in some years had large lakes. Those swamps had to be drained before the land could be used for agriculture. While there are plenty of light tan soils (typical of deserts) to be found, particularly on the higher margins, I suspect that the former swampy areas have a high humus content, contributing to their fertility and desirability for farming. Also, any soil that is wet has a lower albedo. Thus, irrigation will darken the soils. The Santa Clara Valley (AKA Silicon Valley), while only receiving about 15-20″ of rain typically, had Artesian wells and swampy areas that created rich soils that were first used to grow wheat. Later, the whole valley was covered by various fruit orchards.
The problem is that most climatologists treat everything as a diffuse reflector with a characteristic albedo, approximated as a Lambertian reflector. That works well for clouds, which have to be handled with parameterization, unfortunately. Vegetation is typically a diffuse-dominant reflector, albeit it has a higher specular component if the leaves are very smooth and waxy. Specular reflection is accentuated if the plants are the type whose leaves track the sun. Open ocean water tends to be an almost exclusively specular reflector, except for white caps in rough water. Ocean water can develop a significant secondary diffuse component from plankton blooms and/or suspended river sediments, which contribute a random albedo on top of the directional specular component.
The bottom line is that most treatments that restrict themselves to “albedo” are too simplistic and lead to a lower-bound estimate of the amount of light leaving the surface. This can contribute to overestimating the warming that takes place. Funny, how most models run too warm, as they claim to be based on physics. Might this be explained by modelers ignoring specular reflection?
Agreed that it is definitely not ‘albedo only’. But recently, it seems there is evidence that not only the areas that are getting greener are getting warmer; it seems that also the oceans are getting greener, no doubt due to more algae growth. That is another problem. I once worked on a problem of algae growth in the cooling towers of an anodizing plant. Unbelievable.
I recently saw a report on algae growth near the coral reefs of one of the Dutch Antilles. Good luck with that. My finding is that once things grow, it is most difficult to stop it. The greening of the seas and oceans might also change the albedo. Which is evident. Let me look it up for you.
help me out here, but isn’t that -(minus) 1W/m2 due to changing albedo?
https://breadonthewater.co.za/wp-content/uploads/2023/01/albedo1674915190404.webp
We need climate models tailored to the scientific method where falsification is the most important step after proposing a hypothesis. There is no need to waste time on validating experiments if the hypothesis can be proven to be wrong. e.g.(CO2 as the control knob for climate)
In today’s Orwellian reality, people who point out flaws in assumptions are called science deniers by those who refuse to examine contrary data. It’s important for this abandonment of the scientific method to be pointed out to the misinformed. This exposes the climate charlatans as the actual science deniers.
Fair enough – they’re just asking for money.
(i.e. saying things they need to say to pay their bills)
“Which is why research and data acquisition in the Arctic ocean must be prioritised.”
Getting better current data in no way helps improve the models.
What about the sea ice pause since 2006?
This is mind numbingly stupid. I’m going out on a limb here and am going to suggest that anyone who lives north of the ArcticCircle lives in the Arctic. We have people from seven nations living and working in the Arctic. Including people from Sweden! How can these knucklehead academics think a computer model can tell you more than the people living there. It just gets dumber and dumber, it is embarrassing.
“We need a climate model that is tailored to the Arctic. In general, you can’t use the same model for the entire planet, as conditions vary considerably.“.
They seem to be saying that global climate models can’t work.
They are certainly saying that an Arctic model that ignores the Arctic’s surroundings can work. But their main claim is that the effect of those surroundings is not being treated properly – “Warm water flows into the Arctic Ocean via Fram Strait between Greenland and Svalbard.“.
What they have done is to come up against one of the major failings of the climate models:- the more they try to tweak the models to get the Arctic right, the worse the models get across the rest of the planet. From A Test of the Tropical 200- to 300-hPa Warming Rate in Climate Models by McKitrick and Christie: “Swanson (2013) noted that the changes in model output between CMIP3 and CMIP5 improved the fit to Arctic warming but worsened it everywhere else, raising the possibility that the models were getting the Arctic right for the wrong reasons. …“
Nice link! Thanks!
For others here is the abstract:
“Overall climate sensitivity to CO2doubling in a general circulation model results from a complex system of parameterizations in combination with the underlying model structure. We refer to this as the model’s major hypothesis, and we assume it to be testable. We explain four criteria that a valid test should meet: measurability, specificity, independence, and uniqueness. We argue that temperature change in the tropical 200- to 300-hPa layer meets these criteria. Comparing modeled to observed trends over the past 60 years using a persistence-robust variance estimator shows that all models warm more rapidly than observations and in the majority of individual cases the discrepancy is statistically significant. We argue that this provides informative evidence against the major hypothesis in most current climate models.”
My bolding and underlining.
All belief, a failure for science.
I presume “climatologist’ is a liberal arts degree that doesn’t require a lot of hard mathematics, scientific method or rigor?
And they can determine this from “uncalibrated” models. 😉
The key words are “we believe”. In other words, religion, no science.
“we believe that the Arctic sea ice will melt away faster than projected”
This is what they call science. Ignoring the results of models that, to date, can’t predict anything of consequence in climate with accuracy, their belief system motivates them to create even more alarming models that are also not validated. We should not spend a penny of taxpayer funds on these idiot fraudsters.