Archives par mot-clé : Model(s)

How good have climate models been at truly predicting the future?

by Gavin, Dec 4, 2019 in RealClimate


A new paper from Hausfather and colleagues (incl. me) has just been published with the most comprehensive assessment of climate model projections since the 1970s. Bottom line? Once you correct for small errors in the projected forcings, they did remarkably well.

Climate models are a core part of our understanding of our future climate. They also have been frequently attacked by those dismissive of climate change, who argue that since climate models are inevitably approximations they have no predictive power, or indeed, that they aren’t even scientific.

In an upcoming paper in Geophysical Research Letters, Zeke Hausfather, Henri Drake, Tristan Abbott and I took a look at how well climate models have actually been able to accurately project warming in the years after they were published. This is an extension of the comparisons we have been making on RealClimate for many years, but with a broader scope and a deeper analysis. We gathered all the climate models published between 1970 and the mid-2000s that gave projections of both future warming and future concentrations of CO2 and other climate forcings – from Manabe (1970) and Mitchell (1970) through to CMIP3 in IPCC 2007.

We found that climate models – even those published back in the 1970s – did remarkably well, with 14 out of the 17 projections statistically indistinguishable from what actually occurred.

We evaluated these models both on how well modeled warming compared with observed warming after models were published, and how well the relationship between warming and CO2 (and other climate forcings) in models compares to observations (the implied transient climate response) (see Figure). The second approach is important because even if an old model had gotten all the physics right, the future projected warming would be off if they assumed we would have 450 ppm CO2 in 2020 (which some did!). Future emissions depend on human societal behavior, not physical systems, and we can usefully distinguish evaluation of climate models physics from paths of future concentrations.

Figure 2 from Hausfather et al (2019) showing the comparisons between model predictions and observations for a) the temperature trends (above) and b) the implied Transient Climate Response (TCR) which is the trend divided by the forcing and scaled to an equivalent 2xCO2 forcing.

Critical Solar Factors Ignored…IPCC AR6 Covers Up Scientific Flaws In Climate Models

by P. Gosselin, Aug 22, 2021 in NoTricksZone


According to the latest IPCC Assessment Report 6 (AR6), the observed temperature increase and the calculated temperature increase according to climate models have been almost the same 1.3 °C from 1750 to 2020.  The report shows a strong positive trend in solar shortwave radiation from 9/2000 to  6/2017, but its impact has been omitted in post-2000 warming calculations which explains the high temperatures since El Nino of 2015-2016.

For example, the temperature effect in 2019 is about 0.7 °C according to the AR6 science. Actually, the IPCC models give a 2019 temperature increase of 2.0°C (1.3°C + 0.7°C). This 54 percent error is due to the positive water feedback applied in climate models, which doubles the impact of other climate forcings and which, according to this natural experiment by climate, does not exist.

The amount of carbon dioxide in the atmosphere has increased by 32% since 1750. According to the AR6, this is only due to man-made anthropogenic emissions staying there (remain, accumulate) by an average of 44% per year and the rest has been absorbed by oceans and vegetation.

Approximately 25% of the atmospheric carbon dioxide changes annually from the oceans and vegetation. As a result, less than 6% of the initial amount of carbon dioxide in the atmosphere remains after 10 years, and therefore the increased amount of carbon dioxide in the atmosphere cannot be entirely anthropogenic origin with a permille value of -28%. The IPCC remains silent on permille values, as in the AR6 there is no word “permille”, which is a measure of the ratio of carbon isotopes and it has been used to analyze the origin of carbon dioxide, suitable for validating carbon cycle models.

Cover-up

The cover-up of this issue continues with the anthropogenic carbon dioxide lifetime in the atmosphere, which is now vaguely from hundreds of years to thousands of years. The removal rate of radioactive carbon from the atmosphere (a perfect tracer test for anthropogenic carbon dioxide) after 1964 is only 64 years. The recovery time of the total atmospheric amount of carbon dioxide to the level of 1750 can be estimated to be similar to that of its accumulation period, i.e. just under 300 years.

The AR6 report no longer shows the IPCC’s very own definition of the greenhouse effect, except in the glossary. The definition no longer contains the description for how greenhouse gas absorption of 158 Wm-2, which causes the greenhouse effect, creates downward infrared radiation downwards on the ground of 342 Wm-2. This is against fundamental physical laws because energy comes from nothing. The radiation to the surface consists of four energy fluxes, which according to the IPCC’s energy balance are: greenhouse gas absorption of 158 Wm-2, latent water heat 82 Wm-2, sensible heat (warm air) 21 Wm-2, and solar radiation absorption in the atmosphere 80 Wm-2. The three firstly mentioned energy fluxes totaling 261 Wm-2 maintain the greenhouse effect.

Fudging the forcings

The IPCC’s attribution methodology is fundamentally flawed

by R. McKitrick, Aug 18, 2021 in ClimateEtc.


One day after the IPCC released the AR6 I published a paper in Climate Dynamics showing that their “Optimal Fingerprinting” methodology on which they have long relied for attributing climate change to greenhouse gases is seriously flawed and its results are unreliable and largely meaningless. Some of the errors would be obvious to anyone trained in regression analysis, and the fact that they went unnoticed for 20 years despite the method being so heavily used does not reflect well on climatology as an empirical discipline.

My paper is a critique of “Checking for model consistency in optimal fingerprinting” by Myles Allen and Simon Tett, which was published in Climate Dynamics in 1999 and to which I refer as AT99. Their attribution methodology was instantly embraced and promoted by the IPCC in the 2001 Third Assessment Report (coincident with their embrace and promotion of the Mann hockey stick). The IPCC promotion continues today: see AR6 Section 3.2.1. It has been used in dozens and possibly hundreds of studies over the years. Wherever you begin in the Optimal Fingerprinting literature (example), all paths lead back to AT99, often via Allen and Stott (2003). So its errors and deficiencies matter acutely.

The abstract of my paper reads as follows:

New Confirmation that Climate Models Overstate Atmospheric Warming

by R. McKitrick, Aug 17, 2021 in ClimateEtc


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.

How Climate Scenarios Lost Touch With Reality

by R. Pielke Jr & J. Ritchie, Aug, 4, 2021 in CO2Coalition


A failure of self-correction in science has compromised climate science’s ability to provide plausible views of our collective future.

The integrity of science depends on its capacity to provide an ever more reliable picture of how the world works. Over the past decade or so, serious threats to this integrity have come to light. The expectation that science is inherently self-correcting, and that it moves cumulatively and progressively away from false beliefs and toward truth, has been challenged in numerous fields—including cancer research, neuroscience, hydrology, cosmology, and economics—as observers discover that many published findings are of poor quality, subject to systemic biases, or irreproducible.

In a particularly troubling example from the biomedical sciences, a 2015 literature review found that almost 900 peer-reviewed publications reporting studies of a supposed breast cancer cell line were in fact based on a misidentified skin cancer line. Worse still, nearly 250 of these studies were published even after the mistaken cell line was conclusively identified in 2007. Our cursory search of Google Scholar indicates that researchers are still using the skin cancer cell line in breast cancer studies published in 2021. All of these erroneous studies remain in the literature and will continue to be a source of misinformation for scientists working on breast cancer.

In 2021, climate research finds itself in a situation similar to breast cancer research in 2007. Our research (and that of several colleagues) indicates that the scenarios of greenhouse gas (GHG) emissions through the end of the twenty-first century are grounded in outdated portrayals of the recent past. Because climate models depend on these scenarios to project the future behavior of the climate, the outdated scenarios provide a misleading basis both for developing a scientific evidence base and for informing climate policy discussions. The continuing misuse of scenarios in climate research has become pervasive and consequential—so much so that we view it as one of the most significant failures of scientific integrity in the twenty-first century thus far. We need a course correction.

In calling for this change, we emphasize explicitly and unequivocally that human-caused climate change is real, that it poses significant risks to society and the environment, and that various policy responses in the form of mitigation and adaptation are necessary and make good sense. However, the reality and importance of climate change does not provide a rationale or excuse for avoiding questions of research integrity any more than does the reality and importance of breast cancer. To the contrary, urgency makes attention to integrity that much more important.

Scenarios and baselines

Are Climate Feedbacks Strongly Non-Linear?

by Bob Irvine, Aug 3, 2021 in WUWT


Is it possible that the Earth’s system is strongly buffered with strong positive ice and dust feedbacks prevailing at colder temperatures, and strong negative convection/evaporation feedbacks prevailing in warmer times?

Feedback Factor (FF) is defined as the total temperature change at equilibrium for a given forcing divided by the calculated “no feedback” temperature from that forcing.

The term CO2 will be used here to represent all the non-compressing GHGs. (CO2, MH4, N2O, CFCs, HCFs etc.)

Claim: Machine Learning can Detect Anthropogenic Climate Change

E. Worrall, July 8, 2021 in WUWT


According to the big computer we are doomed to suffer ever more damaging weather extremes. But researchers can’t tell us exactly why, because their black box neural net won’t explain its prediction.

As an IT expert who has built commercial AI systems, I find it incredible that the researchers seem so naive as to think their AI machine output has value, without corroborating evidence. They admit they are going to try to understand how their AI works – but in my opinion they have jumped the gun, making big claims on the basis of a black box result.

Consider the following;

….

Gavin’s Falsifiable Science

by W. Eschenbach, Apr 2020 in WUWT


Gavin Schmidt is a computer programmer with the Goddard Institute of Space Sciences (GISS) and a noted climate alarmist. He has a Ph.D. in applied mathematics. He’s put together a twitter threadcontaining what he sees as some important points of the “testable, falsifiable science that supports a human cause of recent trends in global mean temperature”. He says that the slight ongoing rise in temperature is due to the increase in carbon dioxide (CO2) and other so-called “greenhouse gases”. For simplicity, I’ll call this the “CO2 Roolz Temperature” theory of climate. We’ve discussed Dr. Schmidt’s ideas before here on WUWT.

Now, Gavin and I have a bit of history. We first started corresponding by way of a climate mailing list moderated by Timo Hameraanta back around the turn of the century, before Facebook and Twitter.

The interesting part of our interaction was what convinced me that he was a lousy programmer. I asked him about his program, the GISS Global Climate Model. I was interested in how his model made sure that energy was conserved. I asked what happened at the end of each model timestep to verify that energy was neither created nor destroyed.

He said what I knew from my own experience in writing iterative models, that there is always some slight imbalance in energy from the beginning to the end of the timestep. If nothing else, the discrete digital nature of each calculation assures that there with be slight roundoff errors. If these are left uncorrected they can easily accumulate and bring the model down.

He said the way that the GISS model handled that imbalance was to take the excess or the shortage of energy and sprinkle it evenly over the entire planet.

Now, that seemed reasonable for trivial amounts of imbalance coming from digitization. But what if it were larger, and it arose from some problem with their calculations? What then?

So I asked him how large that energy imbalance typically was … and to my astonishment, he said he didn’t know.

Amazed, I asked if he had some computer version of a “Murphy Gauge” on the excess energy. A “Murphy Gauge” (below) is a gauge that allows for Murphy’s Law by letting you set an alarm if the variable goes outside of the expected range … which of course it will, Murphy says so. On the computer, the equivalent would be something in his model that would warn him if the excess or shortage of energy exceeded some set amount.

Yet Another Model-Based Claim Of Anthropogenic Climate Forcing Collapses

by K. Richard, Feb 25 2021 in NoTricksZone


High-resolution climate models have projected a “decline of the Atlantic Meridional Overturning Circulation (AMOC) under the influence of anthropogenic warming” for decades (Lobelle et al., 2020). New research that assesses changes in the deeper layers of the ocean (instead of “ignoring” these layers like past models have) shows instead that the AMOC hasn’t declined for over 30 years.

The North Atlantic has been rapidly cooling in recent decades (Bryden et al., 2020, Fröb et al., 2019). A cooling of “more than 2°C” in just 8 years (2008-2016) and a cooling rate of -0.78°C per decade between 2004 and 2017 has been reported for nearly the entire ocean region just south of Iceland. The cooling persists year-round and extends from the “surface down to 800 m depth”

CMIP6 and AR6, a preview

by Andy May, Feb 11, 2021 in WUWT


The new IPCC report, abbreviated “AR6,” is due to come out between April 2021 (the Physical Science Basis) and June of 2022 (the Synthesis Report). I’ve purchased some very strong hip waders to prepare for the events. For those who don’t already know, sturdy hip waders are required when wading into sewage. I’ve also taken a quick look at the CMIP6 model output that has been posted to the KNMI Climate Explorer to date. I thought I’d share some of what I found.

Meet The Team Shaking Up Climate Models

by C. Rotter, Jan 26, 2021 in WUWT


A new team tries a new approach to Climate Modeling using AI and machine learning. Time will tell if a positive effort or extremely complicated exercise in curve fitting. Their goal is regional scale predictive models useful for planning. Few admit publicly that these do not exist today despite thousands of “studies” using downscaled GCM’s.

“There are some things where there are very robust results and other things where those results are not so robust,” says Gavin Schmidt, who heads NASA’s respected climate modeling program at the Goddard Institute for Space Studies. But the variances push skeptics to dismiss the whole field.

“There’s enough stuff out there that people can sort of cherry-pick to support their preconceptions,” says Dr. Hausfather. “Climate skeptics … were arguing that climate models always predict too much warming.” After studying models done in the past 50 years, Dr. Hausfather says, “it turns out they did remarkably well.”

But climate modelers acknowledge accuracy must improve in order to plot a way through the climate crisis. Now, a team of climatologists, oceanographers, and computer scientists on the East and West U.S. coasts have launched a bold race to do just that.

They have gathered some of the brightest experts from around the world to start to build a new, modern climate model. They hope to corral the vast flow of data from sensors in space, on land, and in the ocean, and enlist “machine learning,” a kind of artificial intelligence, to bring their model alive and provide new insight into what many believe is the most pressing threat facing the planet.

Their goal is accurate climate predictions that can tell local policymakers, builders, and planners what changes to expect by when, with the kind of numerical likelihood that weather forecasters now use to describe, say, a 70% chance of rain.

Failing Computer Models

by P. Homewood ,Jan 21, 2021 in NotaLotofPeopleKnowThat


If anybody tries to tell you that the computer models are accurately predicting global warming, show them this:

http://www.remss.com/research/climate/#:~:text=The%20RSS%20merged%20lower%20stratospheric%20temperature%20data%20product,in%20well-mixed%20greenhouse%20gases%20causes%20by%20human%20activity.

It comes from RSS, who monitor atmospheric temperatures via satellite observation. They are ardent warmists, and here us what they have to say:

….

New Climate Models (CMIP6) Offer No Improvement, Model Discrepancies As Large As The Last Version (CMIP5)

by K. Richard, Dec 24, 2020 in NoTricksZone


The “unsatisfactorily large” magnitude of the discrepancies between models in estimating the various radiative contributions to Earth’s energy imbalance serves to undermine confidence that CO2’s small impact could even be detected amid all the uncertainty.

Scientists have engaged in offering their educated guesses, or estimates, of cloud radiative effects for decades.

In the latest models, CMIP6, the top of atmosphere (TOA) net cloud radiative effects (CRE) when considering clouds’ longwave and shortwave combined impact is somewhere between -17 W/m² and -31 W/m² (Wild, 2020). That’s a 14 W/m²spread in CRE modeling.

The discrepancy range between modeled estimates for downward longwave clear-sky radiation is 22.5 W/m². This is the component where CO2’s underwhelming 0.2 W/m² per decade impact (Feldman et al., 2015) is manifested. Modeling discrepancies are thus more than 100 times larger than CO2’s forcing contribution over a 10-year period.

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