Are we using bad data?

Not my claim mate

 

When popular understanding of science takes a wrong turn.
What Sagan Got Wrong About the Human Brain - Ross Pomeroy, RCSci

 

Again, the irony runs deep in your posts. And the delusions are not worth any more of my time.

 

<yawn> I hold a degree with honors from, and studied planetary physics, including atmospheric physics at, an internationally respected one.

No. You need to learn the difference between "academic" and "scientific."

 

Yes: the role of aerosols is to provide a plausible rationalization when predictions of warming caused by CO2 are falsified.

Weaseling.

 

I judge by the way people post - not the claims they make and I will just say I cannot see alignment

 

Did you see the date the book was published?

 

Of course, but its influence continues.

 

Does it? The book was written before MRIs. Science has moved on since then. Just because new evidence emerges does not negate all of science

 

Nope. Read the article. Sagan's misinformation still is cited and believed.

 

Surprise: Hurricane Activity Reconstructions Show Greater Storm Frequency When Globe Was Cold
By P Gosselin on 4. June 2023

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Climate science gets violently shaken up! Sediment core analyses show hurricanes were more frequent when the globe was cool, during the Little Ice Age.

Germany’s “klimanachtrichten” (climate news) here reports on surprise findings concerning hurricanes frequency. It turns out hurricanes were more frequent during the Little Ice Age, when global temperatures were a degree colder, than they are today.

This finding contradicts the climate science claim that global warming cooks up more hurricanes.

The data show the opposite to be true.

The active Little Ice Age

Despite all the drama and hysteria we hear from the media every time a hurricane makes landfall, hurricane activity reconstructions using sediment cores show that hurricanes were indeed more frequent during the Little Ice Age and that their activity follows decadal cycles – as reported by The Conversation, November, 2022:



Image cropped at klimanachrichten.de here.

Hurricanes were more frequent during the Little Ice Age than they have been over the past 100 years:


Summary: Image cropped at klimanachrichten.de here.

Colder periods associated with more hurricanes.

This would tell us there’s much more complexity behind hurricane formation than simple the CO2 mechanism in the atmosphere. It’s much more complex than what alarmists scientists, governments and media claim.

In fact, the results contradict what we’ve been told all along. To the contrary, warmer periods don’t mean more hurricanes and it appears that colder periods are associated with greater hurricane frequency.

 

Your problem.

 

Oops:
Scientists Caught Inflating Antarctic Ice Losses 3000% More Than Observations
By Kenneth Richard on 5. June 2023

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A new study utilizing satellite observations determines Antarctic-wide ice shelves gained 661 Gt of mass from 2009 to 2019. An approach relying on assumptions of an unrealistic “steady state” or fixed calving flux (instead of real-world time-variable observations) estimates a net Antarctic ice shelf loss of -20,028 Gt over this same 11-year period – a more than 30-fold distortion of observed ice loss.
New research (Andreasen et al., 2023) uses observational evidence from MODIS to assess net ice losses, gains for 34 ice shelves across Antarctica from 2009-2019. These observed data show the mass gains from East Antarctica and the Ross and Ronne-Filchner ice shelves were larger on net than the mass losses in West Antarctica and the Peninsula. Consequently, Antarctica as a whole has been gaining mass since 2009.

“Overall, the Antarctic ice shelf area has grown by 5,305 km² since 2009, with 18 ice shelves retreating and 16 larger shelves growing in area.”

Most studies utilize an alarmism-friendly “steady-state assumption” approach to estimate ice losses “in the absence of observations.” This allows the agenda-driven facilitators of ice loss estimates to “overestimate ice loss on ice shelves that are advancing.”

For example, using the “steady-state assumption” method, a net loss of -20,028 Gt could be alleged for Antarctic ice shelves from 2009-2019. Satellite observations, in contrast, assess a +661 Gt mass gain during this same period.

Thus, assumption-based ice losses are artificially inflated over 3,000% more than observations, flagrantly misrepresenting ice shelf behavior across Antarctica.

The practice of distorting the numbers to drive a narrative has infiltrated another aspect of climate science.



Image Source: Andreasen et al., 2023

 

The greatest barrier to progress in climate science is now data falsified to conform to the CO2-controls-surface-temperature narrative.

 

New Study Shows We Are Now Worse At Modeling Cloud Climate Effects Than We Were In 1984
By Kenneth Richard on 15. June 2023

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Scientists have determined the error in calculating effects of shortwave cloud forcing on climate spans 82-132 W/m² since the mid-1990s. Total clear-sky climate forcing linked to CO2 since 1750 is 1.8 W/m². Therefore, there is no way to accurately determine anthropogenic CO2’s capacity to influence climate.
Even NASA acknowledges that for climate models to be capable of detecting an anthropogenic impact on climate – indeed, for the models to even be useful in long-term projections – their capacity to accurately measure radiation from clouds must be improved “about a hundredfold.”



Image Source: NASA
In a study published in 1984, scientists compared the total radiative surface effects of doubling CO2 from 300 to 600 ppm (“only 1-2 W/m²”) to the “much larger” variability in the radiative effects of clouds in longwave (~70 to 120 W/m²). . . . .

 

Is the Arctic September sea ice doomed to disappear in the 2030’s?
Posted on June 15, 2023 by curryja | 32 comments
by Frank Bosse

Short answer: NO

Continue reading →

 

Epic Fail in America’s Heartland: Climate Models Greatly Overestimate Corn Belt Warming
June 17th, 2023
For the last decade I’ve been providing long-range U.S. Corn Belt forecasts to a company that monitors and forecasts global grain production and market forces. My continuing theme has been, “don’t believe gloom and doom forecasts for the future of the U.S. Corn Belt”.

The climate models relied upon by the United Nations Intergovernmental Panel on Climate Change (IPCC) are known to overestimate warming compared to observations. Depending upon the region (global? U.S.?), temperature metric (surface? deep ocean? lower atmosphere?) and time period (last 150 years? last 50 years?) the average model over-estimate of warming can be either large or small.

But nowhere is it more dramatic than in the U.S. Corn Belt during the growing season (June, July, August).

The following plot shows the 50-year area-averaged temperature trend during 1973-2022 for the 12-state corn belt as observed with the official NOAA homogenized surface temperature product (blue bar) versus the same metric from 36 CMIP6 climate models (red bars, SSP245 emissions scenario, output here).


This kind of sanity check is needed because efforts to change U.S. energy policy are based upon climate model predictions, which are often wildly out of line with observed history. This is why environmentalists emphasize models (which can show dramatic change) over actual observations (which are usually unremarkable).

 

Exclusive: Three Typhoon Jets Landed Next to Thermometer When Britain’s ‘Record’ Temperature of 40.3°C Was Recorded
Guest Blogger
For the purpose of taking temperature measurements to build a picture of long-term climate change, there are few places more unsuitable than an airport runway. . . .

 

Tampering With CET
Guest Blogger
This all rather puts the Met Office in a bad light. . . .

 

The Urban Heat Island Effect in GHCN Station Temperatures: Urban Locations show Large Spurious Warming Effects
July 17th, 2023

It has been a while since I have posted progress on our DOE-funded research into the Urban Heat Island (UHI) effect in the GHCN station temperatures used to monitor land-based global warming. It should be remembered that everything I post on this subject is (as is usually the case) a work in progress.

What I am addressing is the existence of localized long-term warming associated with population increases which are over-and-above the large-scale warming due to humans or nature. These urban-influenced changes are very localized, and yet they influence large-scale area averages and make the land areas look like they are warming faster than they really are. The problem is pervasive because virtually all thermometer locations are where people live, and since the 1800s even most rural locations have experienced population growth.

The bottom line is that there are UHI-based trend (warming) effects in the GHCN station temperatures; the only question is, how much have they affected reported temperature trends? Most previously published research on the subject has suggested the effects are small (Hausfather et al., 2013; Wickham et al., 2013; Hansen et al., 2010; Parker, 2010; Jones et al., 2008; Parker, 2006; Peterson & Owen, 2005; Peterson, 2003; Peterson et al., 1999; Gallo et al., 1999; Karl et al., 198. As a result, you will find most who defend the “climate crisis” narrative will refer to one or more of those studies as showing the “science is settled”, and that GHCN-based land warming estimates are largely free of UHI warming effects.

I have argued that those studies involved methodologies that were not very good. Identifying the UHI effect is difficult. I’ve come up with a novel way of quantifying the average UHI effect, even at stations that would be considered “rural” with presumably no UHI effect. We have a paper in review in Nature Scientific Reports describing the methodology (my blog description of the methodology is here), but I have no idea what chance it has of being published.

I will get right to the results as they stand today. What I show below are for the all-station average of GHCN stations; they are NOT area averages, which are what is needed for climate monitoring. They just show how much the average GHCN station is influenced by spurious UHI warming. The stations cover the latitude bands from 20N to 80N, but are dominated by U.S. stations (about 80% of the total) due to the huge numbers of stations we have in this country.

The plots are for 4 classes of initial GHCN station population density (the first year those stations started operating) during the warm season (May/June/July), and give the cumulative year-on-year temperature increase averaged across all stations in each of the four initial station population classes. The adjusted (homogenized) GHCN station temperature changes are in green, and my calculated UHI effect is in red.


For the “wilderness to very rural” class (upper-left panel), the UHI effect on temperature trends turns out to be quite small, contrary to what I have recently argued. Since many of these low-population stations are at high northern latitudes, this would suggest that the UHI effects on the large warming trends reported there are small.

But as we progress to higher population stations, we find that UHI warming effect becomes larger. In the highest population density class (“suburban to urban”, lower-right panel) my calculation of UHI warming is virtually the entire GHCN-reported warming signal since 1880, but only a small part of the reported warming since 1980.

If these results stand, what will they mean for reported land warming trends?

I’m guessing that the UHI effect on area-average trends since 1980 (the period of most rapid temperature rise) will turn out to be relatively small. But before 1980 it looks like the UHI effect on GHCN temperatures could be substantial. This would change the nature of the global warming narrative, with little land-based warming for the first 100 years starting in 1880.

What could change these results? First, I do not account for increases in the UHI effect due to per-capita increases in infrastructure and energy use (buildings, vehicles, parking lots, electricity use and resulting waste heat). I assume the UHI effect is only a function of population density (partly because we have global gridpoint data on population extending back into the 1800s). Thus, my UHI warming estimates might be a little low for stations where population stopped growing but spurious sources of heat continued to increase, such as in Vienna, Austria (R. Bohm, Climatic Change, 199.

In any event, I feel like I am finally converging on useful results. One aspect of this is that the record high temperatures now being reported in major population centers in the southwest U.S. and southern Europe need to be revisited based upon the very large urban heat island temperature increases seen in the lower-right panel of the above plot at suburban-to-urban stations.

 

Population density is not a good proxy for non-CO2 artificial heating effects because it does not account for land use and other changes. The farm population has declined dramatically in most agricultural areas over the last century, while energy use has exploded. Heating of outbuildings and outdoor lighting are orders of magnitude more prevalent now than in the 19th century, and high-powered outdoor lighting makes night-time use of machinery possible. But even the simple paving of roads increases daytime energy absorption and consequently night-time energy release. It is no accident (to borrow the Marxists' phrase) that night-time temperatures have increased far more than daytime temperatures: daytime artificial energy emissions are insignificant compared to solar heating, but much more significant relative to the energy the earth receives at night from the moon (which is far greater than all the incident energy from the stars and planets). This increase in night-time heat emissions is almost independent of local population, and no doubt accounts for the majority of the temperature increase since the instrument record began. That is why there has been no acceleration in the rise of sea level, and arctic sea ice extent is the same as in the 1940s: the claimed "global" warming is mostly an artifact of human (especially night-time) energy use in the vicinity of the temperature measurement stations, rural as well as urban.

 

Bad models produce bad data.
New Study: Climate Models Mired In ‘Terra Incognita’ – Lack The Resolution To Model Climate Change
By Kenneth Richard on 3. August 2023

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“…models are pushing further and further into the domain of the ‘terra incognita.'” – Stephens et al., 2023
It is well established in climate science that water (1) “exerts a fundamental influence on the physical climate system and on climate change,” (2) clouds “control the planetary albedo and the amount of solar radiation reaching the surface,” and (3) “ocean circulation…determines and modulates the climate of many regions of the world.”

But there’s a rather large problem here. When it comes to climate models, the unknown, unexplored “terra incognita zone is what we experience today.”

“Existing climate models…cannot resolve the detailed structure and life cycles of systems such as tropical cyclones, depressions, and persistent high pressure systems, which are key in the coupling of the energy and water cycle.”

While ocean circulation determines the regional climate, “present day global models are also unable to resolve ocean currents that are fundamental to climate variability.”

At the surface-troposphere altitude, convection dominates heat transfer. While radiation only accounts for 8% of the heat transfer process here, convection accounts for 67%. But today’s climate models cannot “resolve convection.”

“There is now compelling evidence the lack of resolution of coarse global models and even coarsely resolved mesoscale cloud models and the inability to explicitly resolve convection specifically is a major obstacle in making the advances needed to confront important Earth science challenges of today.”

Assessing Earth’s top-of-atmosphere (TOA) energy imbalance (EEI) is fundamental to determining the extent and rate of global (ocean) warming. A measured accuracy of less than ±0.3 W/m² (±0.1 W/m²) is required for attribution (to, for example, human CO2 emissions), and yet the absolute accuracy of TOA measurements is ±2 W/m², and long-term estimates of global mean EEI are “not possible.”

“…none of the techniques available today enable us to estimate the EEI with the perceived required accuracy less than ±0.3 W/m², let alone with an aspirational accuracy of ±0.1 W/m².”

Claiming that we know it is humans controlling the climate today is thus a statement of belief. The claim has no scientific meaning or testability. Indeed, it is unfalsifiable.



Image Source: Stephens et al., 2023

 

The short answer is: Yes.
WINNING – Trillions Spent on ‘Climate Change’ Based on Faulty Temperature Data
Anthony Watts
This is something I’m proud of – an article in the nationally published Epoch Times covering my work on weather stations. If there is one article in your lifetime you…

 

One.
Just one.

 

A gripe on a blog - yep! Believe that over thousands of scientists worldwide