Archives par mot-clé : Model(s)

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.

https://www.nature.com/ngeo/journal/vaop/ncurrent/full/ngeo2973.html

http://climatechangedispatch.com/the-pause-in-global-warming-is-real-admits-climategate-scientist/

Read more at budbromley.blog

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.

 

Conclusions

 

  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


Abstract
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.

PUTTING CLIMATE CHANGE CLAIMS TO THE TEST

by John Christy, June 18, 2019 in GWPF


This is a full transcript of a talk given by Dr John Christy to the GWPF on Wednesday 8th May.

When I grew up in the world of science, science was understood as a method of finding information. You would make a claim or a hypothesis, and then test that claim against independent data. If it failed, you rejected your claim and you went back and started over again. What I’ve found today is that if someone makes a claim about the climate, and someone like me falsifies that claim, rather than rejecting it, that person tends to just yell louder that their claim is right. They don’t look at what the contrary information might say.

OK, so what are we talking about? We’re talking about how the climate responds to the emission of additional greenhouse gases caused by our combustion of fossil fuels. In terms of scale, and this is important, we want to know what the impact is on the climate, of an extra half a unit of forcing amongst total forcings that sum to over 100 units. So we’re trying to figure out what that signal is of an extra 0.5 of a unit.

Here is the most complicated chart I have tonight, and I hope it makes sense:

 

Why Climate Models Can’t Predict The Future (And Never Have)

by Jay Lehr, June 11, 2019 in Climate ChangeDispatch


SEE ALSO: Climate Models Of Incompetence

Consider the following: we do not know all the variables that control our climate, but we are quite sure they are likely in the hundreds.

Just take a quick look at ten obviously important factors for which we have limited understanding:

1- Changes in seasonal solar irradiation;

2- Energy flows between ocean and atmosphere;

3- Energy flow between air and land;

4- The balance between Earth’s water, water vapor, and ice;

5- The impacts of clouds;

6- Understanding the planet’s ice;

7- Mass changes between ice sheets, sea level and glaciers;

8- The ability to factor in hurricanes and tornadoes;

9- The impact of vegetation on temperature;

10- Tectonic movement on ocean bottoms.

Yet, today’s modelers believe they can tell you the planet’s climate decades or even a century in the future and want you to manage your economy accordingly.

Dr. Willie Soon of the Harvard-Smithsonian astrophysics laboratory once calculated that if we could know all the variables affecting climate and plugged them into the world’s largest computer, it would take 40 years for the computer to reach an answer.

Climatologist: Climate Models Are Predicting Too Much Warming

by Dr. Benny Peiser, May 23, 2019 in GWPF


A leading climatologist has said that the computer simulations that are used to predict global warming are failing on a key measure of the climate today and cannot be trusted.

Speaking to a meeting in the Palace of Westminster in London, Professor John Christy of the University of Alabama in Huntsville told MPs and peers that almost all climate models have predicted rapid warming at high altitudes in the tropics:

A paper outlining Dr. Christy’s key findings is published today by the Global Warming Policy Foundation.

Circular reasoning with climate models

by Dr. Wojick, March 1, 2018 in CFact


Climate models play a central role in the attribution of global warming or climate change to human causes. The standard argument takes the following form: “We can get the model to do X, using human causes, but not without them, so human causes must be the cause of X.” A little digging reveals that this is actually a circular argument, because the models are set up in such a way that human causes are the only way to get change.

The finding is that humans are the cause of global warming and climate change is actually the assumption going in. This is circular reasoning personified, namely conclude what you first assume.

This circularity can be clearly seen in what many consider the most authoritative scientific report on climate change going, although it is actually just the most popular alarmist report. We are talking about the Summary for Policymakers (SPM), of the latest assessment report (AR5), of the heavily politicized UN Intergovernmental Panel on Climate Change (IPCC). Their 29 page AR5 SPM is available here.

40387018 – the raging whirlpool

Global-scale multidecadal variability missing in state-of-the-art climate models

by S. Kravstov et al. 2018, in Nature


Reliability of future global warming projections depends on how well climate models reproduce the observed climate change over the twentieth century. In this regard, deviations of the model-simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model-simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood. Here we show that the discrepancies between the observed and simulated climate variability on decadal and longer timescale have a coherent structure suggestive of a pronounced Global Multidecadal Oscillation. Surface temperature anomalies associated with this variability originate in the North Atlantic and spread out to the Pacific and Southern oceans and Antarctica, with Arctic following suit in about 25–35 years. While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, none of the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.