Archives par mot-clé : Model(s)

Flawed Models: New Studies Find Plants Take Up “More Than Twice As Much” CO2 Than Expected

by Fritz Vahrenholt, July 7, 2020 in NoTricksZone

First, the global mean temperature of satellite based measurements was surprisingly much higher in May 2020 than in April. In contrast, the global temperatures of the series of measurements on land and sea decreased. The difference can be explained by the fact that under warm El-Nino conditions the satellite measurements lag about 2-3 months behind the earth-based measurements.

From November 2019 to March 2020 a moderate El-Nino was observed, which has now been replaced by neutral conditions in the Pacific. Therefore, it is to be expected that also the satellite based measurements, which we use at this point, will show a decrease in temperatures within 2-3 months.

The average temperature increase since 1981 remained unchanged at 0.14 degrees Celsius per decade. The sunspot number of 0.2 corresponded to the expectations of the solar minimum.

The earth is greening

Hot Summer Epic Fail: New Climate Models Exaggerate Midwest Warming by 6X

by Dr Roy Spencer, July 3, 2020 in GlobalWarming

For the last 10 years I have consulted for grain growing interests, providing information about past and potential future trends in growing season weather that might impact crop yields. Their primary interest is the U.S. corn belt, particularly the 12 Midwest states (Iowa, Illinois, Indiana, Ohio, Kansas, Nebraska, Missouri, Oklahoma, the Dakotas, Minnesota, and Michigan) which produce most of the U.S. corn and soybean crop.

Contrary to popular perception, the U.S. Midwest has seen little long-term summer warming. For precipitation, the slight drying predicted by climate models in response to human greenhouse gas emissions has not occurred; if anything, precipitation has increased. Corn yield trends continue on a technologically-driven upward trajectory, totally obscuring any potential negative impact of “climate change”.

What Period of Time Should We Examine to Test Global Warming Claims?

Based upon the observations, “global warming” did not really begin until the late 1970s. Prior to that time, anthropogenic greenhouse gas emissions had not yet increased by much at all, and natural climate variability dominated the observational record (and some say it still does).

Furthermore, uncertainties regarding the cooling effects of sulfate aerosol pollution make any model predictions before the 1970s-80s suspect since modelers simply adjusted the aerosol cooling effect in their models to match the temperature observations, which showed little if any warming before that time which could be reasonably attributed to greenhouse gas emissions.

This is why I am emphasizing the last 50 years (1970-2019)…this is the period during which we should have seen the strongest warming, and as greenhouse gas emissions continue to increase, it is the period of most interest to help determine just how much faith we should put into model predictions for changes in national energy policies. In other words, quantitative testing of greenhouse warming theory should be during a period when the signal of that warming is expected to be the greatest.

50 Years of Predictions vs. Observations

Now that the new CMIP6 climate model experiment data are becoming available, we can begin to get some idea of how those models are shaping up against observations and the previous (CMIP5) model predictions. The following analysis includes the available model out put at the KNMI Climate Explorer website. The temperature observations come from the statewide data at NOAA’s Climate at a Glance website.

For the Midwest U.S. in the summer (June-July-August) we see that there has been almost no statistically significant warming in the last 50 years, whereas the CMIP6 models appear to be producing even more warming than the CMIP5 models did.

Models Can’t Accurately Predict Next Week’s Weather, So Why Should We Trust Them To Predict Climate Change?

by D. Turner, June 2, 2020 in WUWT

It’s curious … SpaceX has all the money in the world, and they didn’t hire someone who could have accurately predicted the afternoon weather in Florida on May 27, 2020.  Seems like a huge oversight, doesn’t it?  And to think there are scores of nonprofit leaders and academics in Washington, DC who can accurately predict global temperatures 10, 15, even 50 years into the future.

Oh, stop it with the “climate isn’t weather” rebuttal. It’s trite and silly. The guys who says “food isn’t cuisine” is a food critic, and by default, haughty and obnoxious.

How about this one: science isn’t semantics.

Cold Air Rises – How Wrong Are Our Global Climate Models?

by University of California Davis,  May 6, 2020 in WUWT

The lightness of water vapor buffers climate warming in the tropics.

Conventional knowledge has it that warm air rises while cold air sinks. But a study from the University of California, Davis, found that in the tropical atmosphere, cold air rises due to an overlooked effect — the lightness of water vapor. This effect helps to stabilize tropical climates and buffer some of the impacts of a warming climate.

The study, published today (May 6, 2020) in the journal Science Advances, is among the first to show the profound implications water vapor buoyancy has on Earth’s climate and energy balance.



Moist air is lighter than dry air at the same temperature, pressure, and volume because the molecular weight of water is less than that of dry air. We call this the vapor buoyancy effect. Although this effect is well documented, its impact on Earth’s climate has been overlooked. Here, we show that the lightness of water vapor helps to stabilize tropical climate by increasing the outgoing longwave radiation (OLR). In the tropical atmosphere, buoyancy is horizontally uniform. Then, the vapor buoyancy in the moist regions must be balanced by warmer temperatures in the dry regions of the tropical atmosphere. These higher temperatures increase tropical OLR. This radiative effect increases with warming, leading to a negative climate feedback. At a near present-day surface temperature, vapor buoyancy is responsible for a radiative effect of 1 W/m2 and a negative climate feedback of about 0.15 W/m2 per kelvin.

Science team points out a new failure of climate models

by A. Watts, April 6, 2020 in WUWT

From Nature Climate Change:

Ill-sooted models by Baird Langenbrunner

Atmospheric black carbon (BC) or soot — formed by the incomplete combustion of fossil fuels, biofuel and biomass — causes warming by absorbing sunlight and enhancing the direct radiative forcing of the climate. As BC ages, it is coated with material due to gas condensation and collisions with other particles. These processes lead to variation in the composition of BC-containing particles and in the arrangement of their internal components — a mixture of BC and other material — though global climate models do not fully account for these heterogeneities. Instead, BC-containing particles are typically modelled as uniformly coated spheres with identical aerosol composition, and these simplifications lead to overestimated absorption.

Full article here

Here, the PNAS paper

Carbon soot in from industrial process in the air. Licensed from

Study: Computer Models Overestimate Observed Arctic Warming

by Craig Idso, February 26, 2020 in ClimateChageDispatch

Paper Reviewed:
Huang, J., Ou, T., Chen, D., Lun, Y. and Zhao, Z. 2019. The amplified Arctic warming in recent decades may have been overestimated by CMIP5 models. Geophysical Research Letters 46: 13,338-12,345.

Policies aimed at protecting humanity and the environment from the potential effects of CO2-induced global warming rely almost entirely upon models predicting large future temperature increases.

But what if those predictions are wrong? What if a comparison between model projections and observations revealed the models are overestimating the amount of warming?

Would climate alarmists admit as much and back away from promoting extreme policies of CO2 emission reductions?

Probably not — at least based upon the recent rhetoric of each of the candidates seeking the Democrat Party’s nomination for President of the United States, all of whom continue to call for the complete elimination of all CO2 emissions from fossil fuel use within the next three decades, or less.

But for non-ideologues who are willing to examine and accept the facts as they are, the recent work of Huang et al. (2019) provides reason enough to pause the crazy CO2 emission-reduction train.

In their study, the five researchers set out to examine how well model projections of Arctic temperatures (poleward of 60°N) compared with good old-fashioned observations.

More specifically, they used a statistical procedure suitable for nonlinear analysis (ensemble empirical mode decomposition) to examine secular Arctic warming over the period 1880-2017.

Observational data utilized in the study were obtained from the HadCRUT4.6 temperature database, whereas model-based temperature projections were derived from simulations from 36 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs).

Figure 1. Observed and model-predicted rates of nonlinear, secular warming in the Arctic (60-90°N) over the period 1880-2017. The black and red dashed lines indicate the 10th and 90th percentiles for temperature means. Adapted from Huang et al. (2019).

As indicated there, the model-estimated rate of secular warming (the solid red line) increased quite sharply across the 138 year period, rising from a value of around 0°C per decade at the beginning of the record to a value of 0.35°C per decade in the end.

Throwing More Cold Water On An Alarmist Ocean-Warming Paper

by Dr. D. Whitehouse, January 17, 2020 in ClimateChangeDispatch

It’s the usual story. It’s the beginning of the year and the statistics of the previous year are hurriedly collected to tell the story of the ongoing climate crisis.

First off, we have the oceans which, according to some, are living up to the apocalyptic narrative better than the atmosphere.

The atmosphere is complicated, subjected to natural variabilities, that make the temperature increase open to too much interpretation.

The oceans, however, are far more important than the air as they absorb most of the anthropogenic excess heat.

Looking at the literature reveals no one knows just how much excess heat (created in the atmosphere) it mops up or indeed exactly how or where it does it. Some say it is 60% which is a bit on the low side, most say 90% or 93%.

The real figure is unknown though it should be noted that a few percent errors translate to a lot of energy, about the same amount that is causing all the concern.

On 14 January the Guardian had the headline, “Ocean temperatures hit record high as the rate of heating accelerates.” The study that reached this conclusion was published in the journal Advances in Atmospheric Sciences.

It’s a badly written paper full of self-justifying statements and unwarranted assumptions that should have been stripped-out by the editor.


Also : Ocean Warming: Not As Simple As Headlines Say

Climate models continue to project too much warming

by Dr. J. Lehr & J. Taylor, January 6, 2020 in CFACT

A recently published paper, titled “Evaluating the Performance of Past Climate Model Projections,” mistakenly claims climate models have been remarkably accurate predicting future temperatures. The paper is receiving substantial media attention, but we urge caution before blindly accepting the paper’s assertions.

As an initial matter, the authors of the paper are climate modelers. Climate modelers have a vested self-interest in convincing people that climate modeling is accurate and worthy of continued government funding. The fact that the authors are climate modelers does not by itself invalidate the paper’s conclusions, but it should signal a need for careful scrutiny of the authors’ claims.

Co-author Gavin Schmidt has been one of the most prominent and outspoken persons asserting humans are creating a climate crisis and that immediate government action is needed to combat it. Again, Schmidt’s climate activism does not by itself invalidate the paper’s conclusions, but it should signal a need for careful scrutiny of the authors’ claims.

The paper examines predictions made by 17 climate models dating back to 1970. The paper asserts 14 of the 17 were remarkably accurate, with only three having predicted too much warming.

One of the paper’s key assertions is that global emissions have risen more slowly than commonly forecast, which the authors claim explains why temperatures are running colder than the models predicted. The authors compensate for this by adjusting the predicted model temperatures downward to reflect fewer-than-expected emissions. Yet fewer-than-expected greenhouse gas emissions undercut the climate crisis narrative.

The U.N. Intergovernmental Panel on Climate Change has already reduced its initial projection of 0.3 degrees Celsius of warming per decade to merely 0.2 degrees Celsius per decade. Keeping in mind that skeptics have typically predicted approximately 0.1 degree Celsius of warming per decade, the United Nations has conceded skeptics have been at least as close to the truth with their projections as the United Nations. Moreover, global temperatures are likely only rising at a pace of 0.13 degrees Celsius per decade, which is even closer to skeptic predictions.

Even after the authors adjusted the model predictions to reflect fewer-than-expected greenhouse gas emissions, there remains at least one very important problem, which immediately jumped out at us when carefully examining the paper’s findings: The paper’s assertion of remarkable model accuracy rests on a substantial temperature spike from 2015 through 2017. A strong, temporary El Niño caused the short-term spike in global temperatures from 2015 to 2017. The plotted temperature data in the paper, however, show that temperatures prior to the El Niño spike ran consistently colder than the models’ adjusted predicted temperatures. When the El Niño recedes, as they always do, temperatures will almost certainly resume running colder than the models predicted, even after adjusting for fewer-than-expected greenhouse gas emissions.

Another problem with the paper is that it utilizes controversial and dubiously adjusted temperature datasets rather than more reliable ones. The paper relies on temperature datasets that are not replicated in any real-world temperature measurements. Surface temperature measurements and measurements taken by highly precise satellite instruments show significantly less warming than the authors claim. The authors rely on temperature datasets that utilize controversial adjustments to claim more recent warming than what has actually been measured, which further undercuts their claim of remarkable model accuracy.

Contrary to what has been written in many breathless media reports, the most important takeaways from the paper are that greenhouse gas emissions are rising at a more modest pace than predicted, the modest pace of global temperature rise reflects the modest pace of rising emissions, and climate models have consistently predicted too much warming—even after accounting for fewer-than-expected greenhouse gas emissions. A temporary spike in global temperatures reflecting the recent El Niño does not save the models from their consistent inaccuracy.

Climate change and bushfires — More rain, the same droughts, no trend, no science

by JoNova, December 24, 2019

To Recap: In order to make really Bad Fires we need the big three: Fuel, oxygen, spark.Obviously getting rid of air and lightning is beyond the budget. The only one we can control is fuel. No fuel = no fire.   Big fuel = Fireball apocalypse that we can;t stop even with help from Canada, California, and New Zealand.

The most important weather factor is rain, not an extra 1 degree of warmth. To turn the nation into a proper fireball, we “need” a good drought.  A lack of rain is a triple whammy — it dries out the ground and the fuel — and it makes the weather hotter too. Dry years are hot years in Australia, wet years are cool years. It’s just evaporative cooling for the whole country. The sun has to dry out the soil before it can heat up the air above it.  Simple yes?  El Nino’s mean less rain (in Australia), that’s why they also mean “hot weather”.

So ask a climate scientist the right questions and you’ll find out what the ABC won’t say: That global warming means more rain, not less. Droughts haven’t got worse, and climate models are really, terribly, awfully pathetically bad at predicting rain.

Four reasons carbon emissions are irrelevant

1. Droughts are the same as they ever were.

In the 178 year record, there is no trend. All that CO2 has made no difference at all to the incidence of Australian droughts. Climate scientists have shown droughts have not increased in Australia. Click the link to see Melbourne and Adelaide. Same thing.

The List Grows – Now 100+ Scientific Papers Assert CO2 Has A Minuscule Effect On The Climate

by K. Richard, December 12, 2019 in NoTricksZone

Within the last few years, over 50 papers have been added to our compilation of scientific studies that find the climate’s sensitivity to doubled CO2 (280 ppm to 560 ppm) ranges from <0 to 1°C. When no quantification is provided, words like “negligible” are used to describe CO2’s effect on the climate. The list has now reached 106 scientific papers.

Link: 100+ Scientific Papers – Low CO2 Climate Sensitivity

A few of the papers published in 2019 are provided below:

CMIP5 Model Atmospheric Warming 1979-2018: Some Comparisons to Observations

by Roy Spencer, December 12, 2019 in WUWT

I keep getting asked about our charts comparing the CMIP5 models to observations, old versions of which are still circulating, so it could be I have not been proactive enough at providing updates to those. Since I presented some charts at the Heartland conference in D.C. in July summarizing the latest results we had as of that time, I thought I would reproduce those here.

The following comparisons are for the lower tropospheric (LT) temperature product, with separate results for global and tropical (20N-20S). I also provide trend ranking “bar plots” so you can get a better idea of how the warming trends all quantitatively compare to one another (and since it is the trends that, arguably, matter the most when discussing “global warming”).

From what I understand, the new CMIP6 models are exhibiting even more warming than the CMIP5 models, so it sounds like when we have sufficient model comparisons to produce CMIP6 plots, the discrepancies seen below will be increasing.

Global Comparisons

First is the plot of global LT anomaly time series, where I have averaged 4 reanalysis datasets together, but kept the RSS and UAH versions of the satellite-only datasets separate. (Click on images to get full-resolution versions).

Climate Models Have Not Improved in 50 Years

by David Middleton, December 6, 2019 in WUWT

The accuracy of the failed models improved when they adjusted them to fit the observations… Shocking.

The AGU and Wiley currently allow limited access to Hausfather et al., 2019. Of particular note are figures 2 and 3. I won’t post the images here due to the fact that it is a protected limited access document.

Figure 2: Model Failure

Figure 2 has two panels. The upper panel depicts comparisons of the rates of temperature change of the observations vs the models, with error bars that presumably represent 2σ (2 standard deviations). According to my Mark I Eyeball Analysis, of the 17 model scenarios depicted, 6 were above the observations’ 2σ (off the chart too much warming), 4 were near the top of the observations’ 2σ (too much warming), 2 were below the observations’ 2σ (off the chart too little warming), 2 were near the bottom of the observations’ 2σ (too little warming), and 3 were within 1σ (in the ballpark) of the observations.

Figure 2. Equilibrium climate sensitivity (ECS) and transient climate response

Scientists Cite Uncertainty, Error, Model Deficiencies To Affirm A Non-Detectable Human Climate Influence

by K. Richard, November 21, 2019 in NoTricksZone

Observational uncertainty, errors, biases, and estimation discrepancies in longwave radiation may be 100 times larger than the entire accumulated influence of CO2 increases over 10 years. This effectively rules out clear detection of a potential human influence on climate.

The anthropogenic global warming (AGW) hypothesis rides on the fundamental assumption that perturbations in the Earth’s energy budget – driven by changes in downward longwave radiation from CO2 — are what cause climate change.

According to one of the most frequently referenced papers advancing the position that CO2 concentration changes (and downward longwave radiation perturbations) drive surface temperature changes, Feldman et al. (2015) concluded there was a modest 0.2 W/m² forcing associated with CO2 rising by 22 ppm per decade.

Again, that’s a total CO2 influence of 0.2 W/m² over ten years.

In contrast, analyses from several new papers indicate the uncertainty and error values in downwelling (and outgoing) longwave radiation in cloudless environments are more than 100 times larger than 0.2 W/m².

In other words, it is effectively impossible to clearly discern a human influence on climate.


1.  Kim and Lee, 2019   Measurement errors of outgoing longwave radiation (OLR) reach 11 W/m², more than 50 times larger than total CO2 forcing over 10 years. Cloud optical thickness (COT) and water vapor have “the greatest effect” on OLR – an influence of 2.7 W/m². CO2 must rise to 800 ppm to impute an influence of 1 W/m².

New climate models – even more wrong

by P. Matthews, Nov. 5, 2019 in ClimateScepticsim

The IPCC AR5 Report included this diagram, showing that climate models exaggerate recent warming:

If you want to find it, it’s figure 11.25, also repeated in the Technical Summary as figure TS-14. The issue is also discussed in box TS3:

“However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006–2012 by RCP4.5 simulations) reveals that 111 out of 114 realizations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble (Box TS.3, Figure 1a; CMIP5 ensemble mean trend is 0.21°C per decade). This difference between simulated and observed trends could be caused by some combination of (a) internal climate variability, (b) missing or incorrect RF, and (c) model response error.”

Well, now there is a new generation of climate models, imaginatively known as CMIP6. By a remarkable coincidence, two new papers have just appeared, from independent teams, giving very similar results and published on the same day in the same journal. One is UKESM1: Description and evaluation of the UK Earth System Model, with a long list of authors, mostly from the Met Office, also announced as a “New flagship climate model” on the Met Office website.  The other is Structure and Performance of GFDL’s CM4.0 Climate Model, by a team from GFDL and Princeton. Both papers are open-access.

Now you might think that the new models would be better than the old ones. This is mathematical modelling 101: if a model doesn’t fit well with the data, you improve the model to make it fit better. But such elementary logic doesn’t apply in the field of climate science.

Does the Climate System Have a Preferred Average State? Chaos and the Forcing-Feedback Paradigm

by Roy Spencer, October 25, 2019 in GlobalWarming

The UN IPCC scientists who write the reports which guide international energy policy on fossil fuel use operate under the assumption that the climate system has a preferred, natural and constant average state which is only deviated from through the meddling of humans. They construct their climate models so that the models do not produce any warming or cooling unless they are forced to through increasing anthropogenic greenhouse gases, aerosols, or volcanic eruptions.

This imposed behavior of their “control runs” is admittedly necessary because various physical processes in the models are not known well enough from observations and first principles, and so the models must be tinkered with until they produce what might be considered to be the “null hypothesis” behavior, which in their worldview means no long-term warming or cooling.

What I’d like to discuss here is NOT whether there are other ‘external’ forcing agents of climate change, such as the sun. That is a valuable discussion, but not what I’m going to address. I’d like to address the question of whether there really is an average state that the climate system is constantly re-adjusting itself toward, even if it is constantly nudged in different directions by the sun.


1575 Winter Landscape with Snowfall near Antwerp by Lucas van Valckenborch.Städel Museum/Wikimedia Commons

La science classique s’arrête où commence le chaos…

Prof. Igr. H. Masson, 25 octobre 2019 in ScienceClimatEnergie

1. Un nouveau paradigme : les systèmes chaotiques

« Depuis les premiers balbutiements de la Physique, le désordre apparent qui règne dans l’atmosphère, dans la mer turbulente, dans les fluctuations de populations biologiques, les oscillations du cœur et du cerveau ont été longtemps ignorées ».

 « Il a fallu attendre le début des années soixante-dix, pour que quelques scientifiques américains commencent à déchiffrer le désordre, il s’agissait surtout de mathématiciens, médecins, biologistes, physiciens, chimistes cherchant tous des connections entre diverses irrégularités observées. Le syndrome de la mort subite fut expliqué, les proliférations puis disparitions d’insectes furent comprises et modélisées, et de nouvelles méthodes d’analyse de cours boursiers virent le jour, après que les traders aient dû se rendre à l’évidence que les méthodes statistiques conventionnelles n’étaient pas adaptées. Ces découvertes furent ensuite transposées à l’étude du monde naturel : la forme des nuages, les trajectoires de la foudre, la constitution de galaxies. La science du chaos (« dynamical systems » pour les anglo-saxons) était née et allait connaître un développement considérable au fil des années ».


Figure 4. L’effet papillon : analogie entre les ailes d’un papillon et l’attracteur étrange découvert par E. Lorenz.

The Great Failure Of The Climate Models

by Tyler Durden, 26 August 2019 in ZeroHedge


Christy is not looking at surface temperatures, as measured by thermometers at weather stations. Instead, he is looking at temperatures measured from calibrated thermistors carried by weather balloons and data from satellites. Why didn’t he simply look down here, where we all live? Because the records of the surface temperatures have been badly compromised.

Globally averaged thermometers show two periods of warming since 1900: a half-degree from natural causes in the first half of the 20th century, before there was an increase in industrial carbon dioxide that was enough to produce it, and another half-degree in the last quarter of the century.

The latest U.N. science compendium asserts that the latter half-degree is at least half manmade. But the thermometer records showed that the warming stopped from 2000 to 2014. Until they didn’t.

In two of the four global surface series, data were adjusted in two ways that wiped out the “pause” that had been observed.

The first adjustment changed how the temperature of the ocean surface is calculated, by replacing satellite data with drifting buoys and temperatures in ships’ water intake. The size of the ship determines how deep the intake tube is, and steel ships warm up tremendously under sunny, hot conditions. The buoy temperatures, which are measured by precise electronic thermistors, were adjusted upwards to match the questionable ship data. Given that the buoy network became more extensive during the pause, that’s guaranteed to put some artificial warming in the data.

The second big adjustment was over the Arctic Ocean, where there aren’t any weather stations. In this revision, temperatures were estimated from nearby land stations. This runs afoul of basic physics.


NASA: We Can’t Model Clouds, So Climate Model Projections Are 100x Less Accurate

by K. Richard, August 30, 2019 in ClimateChangeDispatch

NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds.

Clouds have the capacity to dramatically influence climate changes in both radiative longwave (the “greenhouse effect”) and shortwave.

Cloud cover domination in longwave radiation

In the longwave, clouds thoroughly dwarf the CO2 climate influence. According to Wong and Minnett (2018):

  • The signal in incoming longwave is 200 W/m² for clouds over the course of hours. The signal amounts to 3.7 W/m² for doubled CO2 (560 ppm) after hundreds of years.

  • At the ocean surface, clouds generate a radiative signal 8 times greater than tripled CO2 (1120 ppm).

  • The absorbed surface radiation for clouds is ~9 W/m². It’s only 0.5 W/m² for tripled CO2 (1120 ppm).

  • CO2 can only have an effect on the first 0.01 mm of the ocean. Cloud longwave forcing penetrates 9 times deeper, about 0.09 mm.


Climate Scientists Admit Their Models Are Wrong

by Bud Bromley, August 30, 2019 in PrincipiaScientificInternational

Climate scientists who support human-caused global warming, for example Ben Santer and Michael Mann, authored a peer reviewed paper which acknowledges that their climate models are wrong, although their admission is buried in weasel words and technical jargon:

In the scientific method it is not the obligation or responsibility of skeptics or “deniers” to falsify or disprove hypotheses and theories proposed by climate scientists.  It is the obligation and responsibility of climate scientists to present evidence and to defend their hypothesis.  Alarmist climate scientists have failed to do so despite the expense of billions of dollars of taxpayer money.


The pause in global warming shows CO2 may be *more* powerful! Say hello to Hyperwarming Wierdness.

by JoNova, July 24, 2019

It’s all so obvious. If researchers start with models that don’t work, they can find anything they look for — even abject nonsense which is the complete opposite of what the models predicted.

Holy Simulation! Let’s take this reasoning and run with it  — in the unlikely event we actually get relentless rising temperatures, that will imply that the climate sensitivity of CO2 is lower. Can’t see that press release coming…

Nature has sunk so low these days it’s competing with The Onion.

The big problem bugging believers was that global warming paused, which no model predicted, and which remains unexplained still, despite moving goal posts, searching in data that doesn’t exist, and using error bars 17 times larger than the signal. The immutable problem is that energy shalt not be created nor destroyed, so The Pause still matters even years after it stopped pausing. The empty space still shows the models don’t understand the climate — CO2 was supposed to be heating the world, all day, everyday. Quadrillions of Joules have to go somewhere, they can’t just vanish, but models don’t know where they went. If we can’t explain the pause, we can’t explain the cause, and the models can’t predict anything.

In studies like these, the broken model is not a bug, it’s a mandatory requirement — if these models actually worked, it wouldn’t be as easy to produce any and every conclusion that an unskeptical scientist could hope to “be surprised” by.

The true value of this study, if any, is in 100 years time when some psychology PhD student will be able to complete an extra paragraph on the 6th dimensional flexibility of human rationalization and confirmation bias.

Busted climate models can literally prove anything. The more busted they are, the better.

More sensitive climates are more variable climates

University of Exeter

A decade without any global warming is more likely to happen if the climate is more sensitive to carbon dioxide emissions, new research has revealed.

Climate: about which temperature are we talking about?

by S. Furfari and H. Masson, July 26, 2019 in ScienceClimateEnergie

Is it the increase of temperature during the period 1980-2000 that has triggered the strong interest for the climate change issue? But actually, about which temperatures are we talking, and how reliable are the corresponding data?

1/ Measurement errors

Temperatures have been recorded with thermometers for a maximum of about 250 years, and by electronic sensors or satellites, since a few decades. For older data, one relies on “proxies” (tree rings, stomata, or other geological evidence requiring time and amplitude calibration, historical chronicles, almanacs, etc.). Each method has some experimental error, 0.1°C for a thermometer, much more for proxies. Switching from one method to another (for example from thermometer to electronic sensor or from electronic sensor to satellite data) requires some calibration and adjustment of the data, not always perfectly documented in the records. Also, as shown further in this paper, the length of the measurement window is of paramount importance for drawing conclusions on a possible trend observed in climate data. Some compromise is required between the accuracy of the data and their representativity.

2/ Time averaging errors

If one considers only “reliable” measurements made using thermometers, one needs to define daily, weekly, monthly, annually averaged temperatures. But before using electronic sensors, allowing quite continuous recording of the data, these measurements were made punctually, by hand, a few times a day. The daily averaging algorithm used changes from country to country and over time, in a way not perfectly documented in the data; which induces some errors (Limburg, 2014) . Also, the temperature follows seasonal cycles, linked to the solar activity and the local exposition to it (angle of incidence of the solar radiations) which means that when averaging monthly data, one compares temperatures (from the beginning and the end of the month) corresponding to different points on the seasonal cycle. Finally, as any experimental gardener knows, the cycles of the Moon have also some detectable effect on the temperature (a 14 days cycle is apparent in local temperature data, corresponding to the harmonic 2 of the Moon month, Frank, 2010); there are circa 13 moon cycle of 28 days in one solar year of 365 days, but the solar year is divided in 12 months, which induces some biases and fake trends (Masson, 2018).

3/ Spatial averaging

Figs. 12, 13 and 14 : Linear regression line over a single period of a sinusoid.




  1. IPCC projections result from mathematical models which need to be calibrated by making use of data from the past. The accuracy of the calibration data is of paramount importance, as the climate system is highly non-linear, and this is also the case for the (Navier-Stokes) equations and (Runge-Kutta integration) algorithms used in the IPCC computer models. Consequently, the system and also the way IPCC represent it, are highly sensitive to tiny changes in the value of parameters or initial conditions (the calibration data in the present case), that must be known with high accuracy. This is not the case, putting serious doubt on whatever conclusion that could be drawn from model projections.

  2. Most of the mainstream climate related data used by IPCC are indeed generated from meteo data collected at land meteo stations. This has two consequences:(i) The spatial coverage of the data is highly questionable, as the temperature over the oceans, representing 70% of the Earth surface, is mostly neglected or “guestimated” by interpolation;(ii) The number and location of theses land meteo stations has considerably changed over time, inducing biases and fake trends.

  3. The key indicator used by IPCC is the global temperature anomaly, obtained by spatially averaging, as well as possible, local anomalies. Local anomalies are the comparison of present local temperature to the averaged local temperature calculated over a previous fixed reference period of 30 years, changing each 30 years (1930-1960, 1960-1990, etc.). The concept of local anomaly is highly questionable, due to the presence of poly-cyclic components in the temperature data, inducing considerable biases and false trends when the “measurement window” is shorter than at least 6 times the longest period detectable in the data; which is unfortunately the case with temperature data

  4. Linear trend lines applied to (poly-)cyclic data of period similar to the length of the time window considered, open the door to any kind of fake conclusions, if not manipulations aimed to push one political agenda or another.

  5. Consequently, it is highly recommended to abandon the concept of global temperature anomaly and to focus on unbiased local meteo data to detect an eventual change in the local climate, which is a physically meaningful concept, and which is after all what is really of importance for local people, agriculture, industry, services, business, health and welfare in general.

CO2 Is So Powerful It Can Cause Global Warming To Pause For Decades

by Joanna Nova, July 24, 2019 in ClimateChangeDispatch

It’s all so obvious. If researchers start with models that don’t work, they can find anything they look for — even abject nonsense which is the complete opposite of what the models predicted.

Holy Simulation! Let’s take this reasoning and run with it  — in the unlikely event, we actually get relentless rising temperatures, that will imply that the climate sensitivity of CO2 is lower. Can’t see that press release coming…

Nature has sunk so low these days it’s competing with The Onion.

The big problem bugging believers was that global warming paused, which no model predicted, and which remains unexplained still, despite moving goalposts, searching in data that doesn’t exist, and using error bars 17 times larger than the signal.

The immutable problem is that energy shalt not be created nor destroyed, so The Pause still matters even years after it stopped pausing.

The empty space still shows the models don’t understand the climate — CO2 was supposed to be heating the world, all day, every day.

Quadrillions of Joules have to go somewhere, they can’t just vanish, but models don’t know where they went. If we can’t explain the pause, we can’t explain the cause, and the models can’t predict anything.

In studies like these, the broken model is not a bug, it’s a mandatory requirement — if these models actually worked, it wouldn’t be as easy to produce any and every conclusion that an unskeptical scientist could hope to “be surprised” by.

The true value of this study, if any, is in 100 years time when some psychology Ph.D. student will be able to complete an extra paragraph on the 6th-dimensional flexibility of human rationalization and confirmation bias.

Busted climate models can literally prove anything. The more busted they are, the better.

La croissance du CO2 dans l’atmosphère est-elle exclusivement anthropique? (3/3)

by J.C. Maurin, 19 juillet 2019 in ScienceClimatEnergie

Effet Bombe et Modèles du GIEC

Les prévisions du climat sont générées par des modèles informatiques. Leurs concepteurs pensent pouvoir décrire l’état moyen de l’atmosphère en 2100, en prenant comme principale donnée d’entrée, le taux futur de CO2 qui constituerait donc le ‘bouton de commande’ du climat.

Il y a deux étages de modélisation : on commence par prévoir le taux de CO2 en 2100 avec des modèles sélectionnés par le GIEC (ces modèles « IRF » du GIEC sont l’objet de l’article).
Cette prévision constitue ensuite l’entrée du second étage, à savoir les modèles types « échanges radiatifs » ou « effet de serre » qui ne sont pas traités ici (mais on peut consulter ceci).
Le présent article ( qui est la suite de deux autres ici et ici) compare la réponse impulsionnelle théorique de ces modèles « IRF » avec la réponse impulsionnelle observée du 14CO2(effet Bombe).

What Humans Contribute to Atmospheric CO2: Comparison of Carbon Cycle Models with Observations

by Herman Harde, April 3, 2019 in Earth Sciences

Abstract: The Intergovernmental Panel on Climate Change assumes that the inclining atmospheric CO2 concentration over

recent years was almost exclusively determined by anthropogenic emissions, and this increase is made responsible for the rising

temperature over the Industrial Era. Due to the far reaching consequences of this assertion, in this contribution we critically

scrutinize different carbon cycle models and compare them with observations. We further contrast them with an alternative

concept, which also includes temperature dependent natural emission and absorption with an uptake rate scaling proportional

with the CO2 concentration. We show that this approach is in agreement with all observations, and under this premise not really

human activities are responsible for the observed CO2 increase and the expected temperature rise in the atmosphere, but just

opposite the temperature itself dominantly controls the CO2 increase. Therefore, not CO2 but primarily native impacts are

responsible for any observed climate changes.

Keywords: Carbon Cycle, Atmospheric CO2 Concentration, CO2 Residence Time, Anthropogenic Emissions,

Fossil Fuel Combustion, Land Use Change, Climate Change


Human CO2 Emissions Have Little Effect on Atmospheric CO2

by Edwin X Berry , June, 2019 in JAtmOceanSciences

The United Nations Intergovernmental Panel on Climate Change (IPCC) agrees human CO2 is only 5 percent and natural CO2 is 95 percent of the CO2 inflow into the atmosphere. The ratio of human to natural CO2 in the atmosphere must equal the ratio of the inflows. Yet IPCC claims human CO2 has caused all the rise in atmospheric CO2 above 280 ppm, which is now 130 ppm or 32 percent of today’s atmospheric CO2. To cause the human 5 percent to become 32 percent in the atmosphere, the IPCC model treats human and natural CO2 differently, which is impossible because the molecules are identical. IPCC’s Bern model artificially traps human CO2 in the atmosphere while it lets natural CO2 flow freely out of the atmosphere. By contrast, a simple Physics Model treats all CO2 molecules the same, as it should, and shows how CO2 flows through the atmosphere and produces a balance level where outflow equals inflow. Thereafter, if inflow is constant, level remains constant. The Physics Model has only one hypothesis, that outflow is proportional to level. The Physics Model exactly replicates the 14C data from 1970 to 2014 with only two physical parameters: balance level and e-time. The 14C data trace how CO2 flows out of the atmosphere. The Physics Model shows the 14 CO2 e-time is a constant 16.5 years. Other data show e-time for 12CO2 is about 4 to 5 years. IPCC claims human CO2 reduces ocean buffer capacity. But that would increase e-time. The constant e-time proves IPCC’s claim is false. IPCC argues that the human-caused reduction of 14C and 13C in the atmosphere prove human CO2 causes all the increase in atmospheric CO2. However, numbers show these isotope data support the Physics Model and reject the IPCC model. The Physics Model shows how inflows of human and natural CO2 into the atmosphere set balance levels proportional to their inflows. Each balance level remains constant if its inflow remains constant. Continued constant CO2 emissions do not add more CO2 to the atmosphere. No CO2 accumulates in the atmosphere. Present human CO2 inflow produces a balance level of about 18 ppm. Present natural CO2inflow produces a balance level of about 392 ppm. Human CO2 is insignificant to the increase of CO2 in the atmosphere. Increased natural CO2 inflow has increased the level of CO2 in the atmosphere.