Archives par mot-clé : Model(s)

Climate Scientists Admit Clouds are Still a Big Unknown

by E. Worrall, Sep 12, 2020 in WUWT


The authors assert that if we had a better understanding clouds, the spread of model predictions could be reduced. But there is some controversy about how badly cloud errors affect model predictions, and that controversy is not just limited to climate alarmists.

Pat Frank, who produced the diagram at the top of the page in his paper “Propagation of Error and the Reliability of Global Air Temperature Projections“, argues that climate models are unphysical and utterly unreliable, because they contain known model cloud physics errors so large the impact of the errors dwarfs the effect of rising CO2. My understanding is Pat believes large climate model physics errors have been hidden away via a dubious tuning process, which adds even more errors to coerce climate models into matching past temperature observations, without fixing the original errors.

Climate skeptic Dr. Roy Spencer disagrees with Pat Frank; Dr. Spencer suggests the cloud error biases hilighted by Pat Frank are cancelled out by other biases, resulting in a stable top of atmosphere radiative balance. Dr. Spencer makes it clear that he also does not trust climate model projections, though for different reasons to Pat Frank.

Other climate scientists like the authors of the study above, Paulo Ceppi and Ric Williams, pop up from time to time and suggest that clouds are a significant problem, though Paulo and Ric’s estimate of the scale of the problem appears to be well short of Pat Frank’s estimate.

Whoever is right, I think what is abundantly clear is the science is far from settled.

New confirmation that climate models overstate atmospheric warming

by Dr. Judith Curry, August 27, 2020 in WUWT


Reposted from Dr. Judith Curry’s Climate Etc.

Posted on August 25, 2020

by Ross McKitrick

Two new peer-reviewed papers from independent teams confirm that climate models overstate atmospheric warming and the problem has gotten worse over time, not better.

The papers are Mitchell et al. (2020) “The vertical profile of recent tropical temperature trends: Persistent model biases in the context of internal variability” Environmental Research Letters, and McKitrick and Christy (2020) “Pervasive warming bias in CMIP6 tropospheric layers” Earth and Space Science. John and I didn’t know about the Mitchell team’s work until after their paper came out, and they likewise didn’t know about ours.

Mitchell et al. look at the surface, troposphere and stratosphere over the tropics (20N to 20S). John and I look at the tropical and global lower- and mid- troposphere. Both papers test large samples of the latest generation (“Coupled Model Intercomparison Project version 6” or CMIP6) climate models, i.e. the ones being used for the next IPCC report, and compare model outputs to post-1979 observations. John and I were able to examine 38 models while Mitchell et al. looked at 48 models. The sheer number makes one wonder why so many are needed, if the science is settled. Both papers looked at “hindcasts,” which are reconstructions of recent historical temperatures in response to observed greenhouse gas emissions and other changes (e.g. aerosols and solar forcing). Across the two papers it emerges that the models overshoot historical warming from the near-surface through the upper troposphere, in the tropics and globally.

Mitchell et al. 2020

Mitchell et al. had, in an earlier study, examined whether the problem is that the models amplify surface warming too much as you go up in altitude, or whether they get the vertical amplification right but start with too much surface warming. The short answer is both.

Scientists: It’s ‘Impossible’ To Measure Critical Cloud Processes…Observations 1/50th As Accurate As They Must Be

by K. Richard, August 20, 2020 in NoTricksZone


Clouds dominate as the driver of changes in the Earth’s radiation budget and climate. A comprehensive new analysis suggests we’re so uncertain about cloud processes and how they affect climate we can’t even quantify our uncertainty. 

According to scientists (Song et al., 2016), the total net forcing for Earth’s oceanic atmospheric greenhouse effect (Gaa) during 1992-2014 amounted to -0.04 W/m² per year. In other words, the trend in total longwave forcing had a net negative (cooling) influence during those 22 years despite a 42 ppm increase in CO2. This was primarily due to the downward trend in cloud cover that overwhelmed or “offset” the longwave influence from CO2.

Cloud impacts on climate are profound – but so are uncertainties

The influence of clouds profoundly affects Earth’s radiation budget, easily overwhelming CO2’s impact within the greeenhouse effect. This has been acknowledged by scientists for decades.

Despite the magnitude of clouds’ radiative impact on climate, scientists have also pointed out that our limited capacity to observe or measure cloud effects necessarily results in massive uncertainties.

For example, Stephens et al. (2012) estimated the uncertainty in Earth’s annual longwave surface fluxes is ±9 W/m² (~18 W/m²) primarily due to the uncertainties associated with cloud longwave radiation impacts.

An Industry Out of Control: 13 Major Climate Reports in 2020, and 42 Minor Reports

by E. Worrall, August 21, 2020 in WUWT


Yale Climate Connections has listed 13 major climate reports published this year, like it is a good thing. But at least 6 of the major reports received funding from US taxpayers.

The reports listed by Yale:

State of the Climate 2019: Special Supplement to the Bulletin of the American Meteorological Society, edited by J. Blunden and D.S. Arndt (BAMS 2020, 435 pages, free download available here; a 10-page executive summary is also available) – paid for by taxpayers via NOAA

The First National Flood Risk Assessment: Defining America’s Growing Risk, by Flood Modelers (First Street Foundation 2020, 163 pages, free download available here) – not sure who pays for First Street Foundation

World Water Development Report 2020: Water and Climate Change, by UN Water (UN Educational, Scientific, and Cultural Organization 2020, 235 pages, free download available here) – paid for by taxpayers via the United Nations.

The State of Food Security and Nutrition in the World 2020: Transforming Food Systems for Affordable Healthy Diets, by FAO, IFAD, UNICEF, WFP and WHO (United Nations 2020, 320 pages, free download available here) – paid for by taxpayers via United Nations.

WHO Global Strategy on Health, Environment, and Climate Change: The Transformation Need to Improve Lives and Wellbeing through Healthy Environments, by WHO (UN-WHO 2020, 36 pages, free download available here) – paid for by taxpayers via United Nations

Cooling Emissions and Policy Synthesis Report: Benefits of Cooling Efficiency and the Kigali Amendment, by UNEP-IEA (UNEP and IEA 2020, 50 pages, free download available here) – paid for by taxpayers via the United Nations

The 2035 Report: Plummeting Solar, Wind, and Battery Costs Can Accelerate Our Clean Electricity Future, by Sonia Aggarwal and Mike O’Boyle (Goldman School of Public Policy 2020, 37 pages, free download available here) – Goldman school was started by a charitable donation, so may still be privately funded.

Addressing Climate as a Systemic Risk: A Call to Action for U.S. Financial Regulators, by Veena Ramani (Ceres 2020, 68 pages, free download available here, registration required). Not sure who paid. Ceres Foundation is a tax exempt group based in Switzerland, who appear to function as a meta charity – they provide a vehicle for people who want to create a charitable fund without having to set everything up themselves.

Gender, Climate & Security: Sustaining Inclusive Peace on the Frontlines of Climate Change, by UN Women (UN Environment & Development Programs 2020, 52 pages, free download available here) – paid for by taxpayers via the United Nations.

Evicted by Climate Change: Confronting the Gendered Impacts of Climate-Induced Displacement, by Care International (Care International 2020, 33 pages, free download available here) – Care International receives a lot of funding from taxpayers via the EU and the United Nations.

EARTH’S ATMOSPHERE HAS NO “WALLS” OR “LID” — GREENHOUSE GAS THEORY IS BOTH MATHEMATICALLY AND PHYSICALLY WRONG

by Cap Allon, July 30, 2020 in Electroverse


“The CO2 greenhouse effect of the Earth’s atmosphere is a pure fiction of people who like to use large computers, without physical bases.” — Gerhard Gerlich ph.D.

Over the years, scientific paper after scientific paper has contended the entire foundation of the man-made global-warming theory is wrong. However, those in control of the agenda selectively choose which papers/theories the public can hear about, and, in turn, which get swept under the rug.

One such paper the ill-informed street-sheep have likely never heard of is that published in the journal “Environment Pollution and Climate Change” back in 2017–the “door-opener to a new paradigm,” former IPCC reviewer Nils-Axel Mörner is quoted as calling it (Mörner left the UN after realizing it was not truly interested in science).

New Insights on the Physical Nature of the Atmospheric Greenhouse Effect Deduced from an Empirical Planetary Temperature Model” argues that concentrations of CO2 and other supposed “greenhouse gases” in the atmosphere have virtually no effect on the earth’s temperature — it concludes the entire greenhouse gas theory is incorrect.

As reported by wnd.com, the prevailing theory on the earth’s temperature is that heat from the Sun enters the atmosphere, and then greenhouse gases such as CO2, methane, and water vapor trap part of that energy by preventing it from escaping back into space.

That theory, which underpins the anthropogenic global-warming hypothesis and the climate models used by the United Nations, was first proposed and developed in the 19th century.

Climate Predictions “Worse Than We Thought”

by P.J. Michaels, July 14, 2020 in RealClearEnergy


As the temperature of the eastern U.S. normally reaches its summer maximum around the last week of July, every year at this time we are bombarded with tired “climate change is worse than we thought” (WTWT) stories. These stories take time to produce, from imagination to final copy to editing to publication, so they have usually been submitted well in advance of the summer peak. Hence, orchestrated fear.

For once, I’m in agreement about the WTWT meme, but it’s about the climate models, not the climate itself.

Climate Models: No Warming For 30 Years – Possibly

by Maher et al., May 12, 2020 in GWPF


A new study demonstrates how a prolonged warming pause or even global cooling may happen in coming years despite increasing levels of atmospheric greenhouse gases — caused by natural climatic variability.

Natural climatic variability has always been a topic that contains a lot of unknowns, but it has been rarely explicitly stated just how little we know about it. Such variability has been habitually underplayed as it was “obvious” that the major driver of global temperature was the accumulation of greenhouse gasses in the atmosphere, with natural variability a weaker effect.

But the global temperature data of this century demonstrate that natural variability has dominated in the form of El Ninos. ‘Doesn’t matter’, came the reply, ‘just wait and the signal of greenhouse warming will emerge out of the noise of natural climatic variability.’ How long will we have to wait for that signal? Quite a long time, according to some researchers as more papers acknowledge that natural climatic variability has a major, if not a dominant influence on global temperature trends.

With the usual proviso concerning climatic predictions there seems to be a growing number of research papers suggesting that the global average temperature of at least the next five years will remain largely unchanged. The reason: natural climatic variability.

Only last week the UK Met Office produced figures suggesting that there is only a 1 in 34 chance that the 1.5°C threshold will be exceeded for the next five year period. Now a new paper by climate modellers extends such predictions, suggesting that because of natural variability the average global temperature up to 2049 could remain relatively unchanged – even with the largest increase in greenhouse gas emissions.

Using two types of computer models in a first of its kind study, Nicola Maher of the Max Planck Institute for Meteorology, Hamburg, Germany, and colleagues writing in Environmental Research Letters looked at the 2019-2034 period concluding that,

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.

 

Abstract

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 123rf.com

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.