Archives par mot-clé : Model(s)

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

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).