Gold, silver, and their miners’ stocks suffer their weakest seasonals of the year in early summers. With traders’ attention normally diverted to vacations and summer fun, interest in and demand for precious metals usually wane. Without outsized investment demand, gold tends to drift sideways dragging silver and miners’ stocks with it. Feared as the summer doldrums, sometimes unusual catalysts short-circuit them.
This doldrums term is very apt for gold’s summer predicament. It describes a zone in the world’s oceans surrounding the equator. There hot air is constantly rising, creating long-lived low-pressure areas. They are often calm, with little or no prevailing winds. History is full of accounts of sailing ships getting trapped in this zone for days or weeks, unable to make headway. The doldrums were murder on ships’ morale.
The seasonally-adjusted SGS Alternate Unemployment Rate reflects current unemployment reporting methodology adjusted for SGS-estimated long-term discouraged workers, who were defined out of official existence in 1994. That estimate is added to the BLS estimate of U-6 unemployment, which includes short-term discouraged workers.
The U-3 unemployment rate is the monthly headline number. The U-6 unemployment rate is the Bureau of Labor Statistics’ (BLS) broadest unemployment measure, including short-term discouraged and other marginally-attached workers as well as those forced to work part-time because they cannot find full-time employment.
The stock market promoting mainstream media this morning reported “U.S. Retail Sales Rose Record 18% in May” (e.g. the Wall St Journal). The S&P futures jumped from up 45 points to up 90 points.
But, as usual, the details are in the fine print of the report itself, and it’s apparent that nobody in the financial media bothered to look beyond the headlines.
In fact, the 18% rise is measured from April’s report, which was heavily depressed due to the shelter-in-place restrictions and the closure of many retail businesses. Funny thing about using the percentage change as the metric of measurement. If April had one dollar of retail sales and May had two dollars, the percentage gain would have been 100%.
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.
The Arctic and Antarctic regions are different and yet similar in many ways. The Arctic has ocean surrounded by land and the Antarctic is a continent surrounded by water. Both are cold, glaciated and located at Earth’s poles some 11,000 miles apart. While sea ice has been retreating in the Arctic, it has been relatively stable in the Antarctic. This post examines surface temperature trends, solar insolation, and CO2 at the polar Arctic and Antarctic regions during the Holocene interglacial period.
Holocene Polar Temperature Trends are Out of Phase
Disruptive Wind: The electrical grid operators provide reliable electricity with narrow tolerances. Generally, grid operators plan that power sources can be shut down for maintenance, usually in the spring and the fall. To keep costs down, grid operators desire to have maximum operating capacity in the summer (cooling) and in the winter (heating). According to the EIA’s description of electricity generating capacity:
To ensure a steady supply of electricity to consumers, operators of the electric power system, or grid, call on electric power plants to produce and place the right amount of electricity on the grid at every moment to instantaneously meet and balance electricity demand.
THE health establishment was looking away when the coronavirus struck; it had other priorities. If you look at the World Health Organisation’s list of health threats, number one is climate change. Pandemics were down in third place, behind ‘non-communicable diseases’ such as diabetes and obesity.
Wherever you look, you will find some of the biggest names in the public health establishment declaiming on the risks of climate change to world health. On the eve of the outbreak, the Royal Society of Tropical Medicine and Hygiene declared that we would be seeing ‘mass migration, emerging infectious diseases such as dengue and a shortage of food’. As the first people fell ill in Wuhan, the WHO announced that in ten years we would be seeing 250,000 additional deaths per year from malnutrition, malaria, diarrhoea and heat stress as a result of global warming. Epidemiologist Professor Andy Haines told readers of the Telegraph that ‘climate change is a threat to global and national security that is costing lives and livelihoods right now’.
Now that the media is fixated on fanning the flames of racial tensions, watch for them to completely drop all news about the virus. But the evidence against lockdowns continues to mount, whether the media discusses it or not.
On May 22, I publicized groundbreaking data from the CDC’s website suggesting that the bottom-line infection fatality rate of COVID-19 is just 0.26%, even assuming a likely low estimate that 35% of all infections are asymptomatic. What was viewed by the political and scientific “experts” as hearsay from right-wingers was confirmed by their own gold standard agency. Now we have corroborating evidence from a very large sample size in the state of Colorado.
Even worse than we thought ™ – global warming estimates have been raised, just in time for next year’s COP26 conference. But one of high end CMIP6 models, CESM2 (highlighted above), has already been invalidated by a paleo study.
Just how hot will it get this century? Latest climate models suggest it could be worse than we thought
Climate scientists use mathematical models to project the Earth’s future under a warming world, but a group of the latest modelshave included unexpectedly high values for a measure called “climate sensitivity”.
Red Team Vs Blue Team: Various organizations, such as the military, cybersecurity, etc. use a red team vs blue team conflict where the blue team uses the conventional thinking and tactics of the organization and the red team tries to break and / or exploit weaknesses in the conventional approach. Over the past several years there has been an effort to establish such a mental conflict to demonstrate the strengths and weaknesses of the approach used by the UN Intergovernmental Panel on Climate Change (IPCC) and its followers. Thus far the effort has failed, and Washington is geared to the election cycle, making it unlikely such an approach will be used until after the elections, if ever.
Between 1990 and 2017, the cumulative age-standardized death rate (ASDRs) from climate-sensitive diseases and events (CSDEs) dropped from 8.1% of the all-cause ASDR to 5.5%, while the age-standardized burden of disease, measured by disability-adjusted life years lost (DALYs) declined from 12.0% to 8.0% of all-cause age-standardized DALYs. Thus, the burdens of death and disease from CSDEs are small, and getting smaller.
Figure 1: Climate-related deaths are a small proportion of all-cause fatalities (1990–2017). Based on data per IHME (2019).
But readers of the 2019 report of the Lancet Countdown (hereafter ‘the Countdown’), a partnership of 35 academic institutions and UN agencies, established by the prestigious Lancet group of medical journals and supported by the equally-esteemed Wellcome Trust to track progress on the health impacts of climate change, may well be left with the opposite impression, particularly if they do not delve beyond the Executive Summary, the section most likely to be read by busy policymakers or their advisors.
Not once does it mention that cumulative annual rates of death and disease from CSDEs are declining, and declining faster than the corresponding all-cause rates. The Countdown also fails to provide adequate context for the reader to judge the burdens of mortality or disease posed by CSDEs, individually or cumulatively, relative to other public-health threats. In fact, it even suggests that the health effects of climate change are ‘worsening’. But the data do not support that claim. Moreover, an analysis of the text makes it clear that the Countdown conflates estimates of increasing exposure, ‘demographic vulnerability’, and increased ‘suitability’ of disease transmission with actual health effects. These estimates are used as proxies, but trends in these estimates have not been verified to reflect, and do not track, long-term trends in deaths or death rates.
Figure 2: Burden of mortality from CSDEs, 1990–2017. The Forces of Nature group excludes deaths from geophysical causes per EMDAT (2019). Data per IHME (2019).
Summary:Atmospheric levels of carbon dioxide (CO2) continue to increase with no sign of the global economic slowdown in response to the spread of COVID-19. This is because the estimated reductions in CO2 emissions (around -11% globally during 2020) is too small a reduction to be noticed against a background of large natural variability. The reduction in economic activity would have to be 4 times larger than 11% to halt the rise in atmospheric CO2.
Changes in the atmospheric reservoir of CO2 occur when there is an imbalance between surface sources and sinks of CO2. While the global land and ocean areas emit approximately 30 times as much CO2 into the atmosphere as humans produce from burning of fossil fuels, they also absorb about an equal amount of CO2. This is the global carbon cycle, driven mostly by biological activity.
The CDC just came out with a report that should be earth-shattering to the narrative of the political class, yet it will go into the thick pile of vital data and information about the virus that is not getting out to the public. For the first time, the CDC has attempted to offer a real estimate of the overall death rate for COVID-19, and under its most likely scenario, the number is 0.26%. Officials estimate a 0.4% fatality rate among those who are symptomatic and project a 35% rate of asymptomatic cases among those infected, which drops the overall infection fatality rate (IFR) to just 0.26% — almost exactly where Stanford researchers pegged it a month ago.
One of the most important principles of epidemiology is weighing benefits and harms. A failure to do this can make virtually any medical treatment seem helpful or destructive. In the words of Ronald C. Kessler of the Harvard Medical School and healthcare economist Paul E. Greenberg, “medical interventions are appropriate only if their expected benefits clearly exceed the sum of their direct costs and their expected risks.”
Likewise, a 2020 paper about quarantines published in The Lancet states: “Separation from loved ones, the loss of freedom, uncertainty over disease status, and boredom can, on occasion, create dramatic effects. Suicide has been reported, substantial anger generated, and lawsuits brought following the imposition of quarantine in previous outbreaks. The potential benefits of mandatory mass quarantine need to be weighed carefully against the possible psychological costs.”
Around the world, both state and local governments looked at wildly exaggerated computer model projections of millions of virus deaths, declared a “State Of Emergency”, and foolishly pulled the wheels off of their own economies. This has caused pain, suffering, and loss that far exceeds anything that the virus might do.
The virus hardly affects anyone—it has killed a maximum of 0.1% of the population in the very worst-hit locations. One-tenth of one measly percent.
Ah, I hear you saying, but that’s just deaths. What about hospitalizations? Glad you asked. Hospitalizations in the worst-hit areas have been about three times that, about a third of one percent of the population. Still not even one percent.
By Christopher Monckton of Brenchley – Re-Blogged From WUWT
This column does not constitute medical advice. Check with your doctor. Many nostrums are being recommended, with varying justification, to reduce the harm from the Chinese virus. Some, like hydroxychloroquine, have side-effects and should only be taken if prescribed; others, like remdesivir, work (if at all) only during the early stages of disease; others, like the BCG virus against TB, have not yet been subjected to clinical trials.
However, early studies showed an interesting result, which the Marxstream media lazily attributed to capitalism’s imagined failures: the darker your skin, the more your risk from the infection. In Britain, all of the first ten doctors to die from Covid-19 were dark-skinned.
A great deal of the recommendation that the world should modify its energy infrastructure to combat climate change, costing tens to hundreds of trillions of dollars, is based on computer simulations. While this author is not what is called a ‘climate scientist’, a great deal of science is interdenominational, and experience from one field often can fertilize another. That is the spirit in which this opinion is offered. The author has spent a good part of his more than 50-year scientific career developing and using computer simulations to model complex physical processes. Accordingly, based on this experience, he now gives his own brief explanation of his opinion, on what computer simulations can and cannot do, along with some examples. He sees 3 categories of difficulty in computer simulations, where the simulations go from mostly accurate to mostly speculative. He makes the case that the climate simulations are the most speculative.
By Christopher Monckton of Brenchley – Re-Blogged From WUWT
One of the most frequently-asked questions about the Chinese virus is how many of those who die after becoming infected die of the virus, and how many merely die with it? The Office for National Statistics in the UK has now studied that question. Of the deaths occurring in March 2020 in England Wales in patients known to be infected with the virus, five-sixths were deaths of the virus and the other one-sixth were deaths with it. Of those who died of the virus, 91% had pre-existing comorbidities.
It is not particularly surprising that the overwhelming majority of virus-related deaths were caused by the virus, for it has a drastic effect on the respiratory systems of those whom it puts into intensive care, leaving little room for doubt as to the proximate cause of death.
In response to the coronavirus pandemic, the federal government has been heavily influenced by the Institute of Health Metrics and Evaluation’s computer model, which has projected from 60,000 to 240,000 COVID-19 deaths in the U.S.
This epidemiological model is now being criticized as flawed and misleading as a source of public information and for government decision-making. Besides the institute’s model, all other COVID-19 models are grounded in important assumptions about which there is currently little knowledge.
I’ve been saying for some time now that the number of confirmed cases is a very poor way to measure the spread of the coronavirus infection. This, I’ve said, is because the number of new cases you’ll find depends on how much testing is being done. I’ve claimed that if you double your tests, you’ll get twice the confirmed cases.
However, that position was based on logic alone. I did not have one scrap of data to support or confirm it.
40. There has been essentially no global warming during the 21st Century. This reality has been called ‘The Pause’ by some, who claim that the real rise in temperature is actually going on, but that for some unexplained reason, has paused for a while.
There is debate about the ‘Pause,’ with some saying that there were gaps in data; the variations are too small to be statistically significant; etc. If this is so, how come climate change enthusiasts have been so utterly certain of their position and their figures for the past 20 years plus.
Because it is a destructive weapon that came from Communist China. This doesn’t mean it was an engineered bio-weapon or that it was intentionally used to attack almost every nation on Earth. Continue reading →
This morning I awoke to a mid-April morning temperature of -11F. The 1981 to 2010 climate normals indicate our average daily minimum temperature per this date as about 23F, and the standard deviation as 8F. Thus, our morning low temperature is a 4-sigma event. Surely something to evoke comment. Yet, it did not so far as I know.
This caused me to ponder something I observed two months ago. Ten minutes from my home, in the mountains to my east, is great nordic skiing. It was at one time home to what we call, the Norwegian olympics. There was unusually good snow this winter, and people came from near and far to enjoy it. What I heard often in conversation in the parking lot in February was that we were having an “unusually warm” winter. I thought not. I have lived in this area, off and on, for 40 years, and I thought this winter was pretty typical, even possibly slightly cool.
The recent economic reports show that the current coronavirus crisis will be bigger than the Great Recession. What does it imply for the gold market?
US Economic Data Paints a Gloomy Picture
This week was full of new reports about the US economy. And guess what, I don’t have good news… First of all, let’s start with the update about the weekly initial unemployment benefits. In normal times, the initial claims are not too keenly watched by investors. But in times of a pandemic, they are very informative. The spike in the initial claims may even become the symbol of this crisis. Anyway, the number of new claims for the unemployment benefits declined from 6.6 million in the previous week to 5.2 million in the week from April 4 to April 11, as the chart below shows.
Chart 1: Initial jobless claims from April 2019 to April 2020
The topic of global warming and climate change is far more scientifically complex than the public is led to believe.
Myriads of newspaper, magazine and TV items over decades have tended to simplify the science to the point at which the general public believes that it is all so simple that any fool can see what is happening. Public groups often accuse world leaders and scientists of being fools, if they do not instantly act on simple messages projected by individuals or public groups.
One often hears phrases like: ‘The science is settled.’ It is not. Even more worrying is that the reality of the correct science is actually very different to much of the simple public perception.
No planning is likely possible without calculations of what the future may hold, but such calculations are fraught with uncertainty when they also involve exponential processes. Indeed, as the author of one chapter in a recent book  states:
“One characteristic of an exponential growth process that humans find it really difficult to comprehend is how fast such a process actually is. Our daily experiences do not prepare us to judge such a process accurately, or to make sensible predictions.” [emphasis is mine.]
Quests to reveal a future governed by exponential processes, or what people guess to be exponential processes, run through many themes here at WUWT — future climate, energy demand, economics, epidemics. This guest contribution takes a selected look at exponential growth. Two examples are historical, and perhaps obscure, but pertinent. The third one, which comprises the bulk of this essay, is an examination of R0, which dominates the present imagination.
OMG! The world is going to end, and we caused it. This story, in one form or another, goes back to biblical times. According to Genesis (6:9 to 9:17) God decided that humans had sinned too much and must be punished, so he called up a great flood to destroy the world. A similar story also appears in the earlier Epic of Gilgamesh. End of the world predictions are very popular and recur regularly in human history.
More recently, prognosticators have predicted climate change disasters that are due to human actions (sins?). During the Little Ice Age (see Figure 3 in the link), the European public blamed the cold weather on witches and Jews, over 50,000 “witches” and tens of thousands of Jews were killed because they supposedly caused the cold weather and glacial advances. Thus, the idea that humans somehow control climate change is very old. We have no more proof that this is the case today than people had in 800AD, which is about when Archbishop Agobard of Lyons, France said:
“I’d expect that some probing by independent experts into the economic calculations, and the assumptions on which they are built, might bear fruit.” But where are these calculations, and who are the unbiased experts who have quality controlled them? I couldn’t find any such calculations, or the names of any such experts. Perhaps, I thought, I’d better take a look at this myself.
So, I set out to learn as much as I could about the economic calculations which – so we’re supposed to believe – justify the extreme measures proposed, all the way up to total de-carbonization of the UK economy, to avoid alleged catastrophic damage from global warming. This essay is the result of that exercise. If it reads like a cross between a layman’s guide to the economics of global warming and a political rant, that’s because it’s both!
OK, here are my questions. We had a perfect petri-dish coronavirus disease (COVID-19) experiment with the cruise ship “Diamond Princess”. That’s the cruise ship that ended up in quarantine for a number of weeks after a number of people tested positive for the coronavirus. I got to wondering what the outcome of the experiment was.
As you might imagine, before they knew it was a problem, the epidemic raged on the ship, with infected crew members cooking and cleaning for the guests, people all eating together, close living quarters, lots of social interaction, and a generally older population. Seems like a perfect situation for an overwhelming majority of the passengers to become infected.
How do we measure success in helping people understand the climate issue? I don’t think we can measure it by unique visits to WUWT or various videos that many of us know well. But they simply attract the same audience over and over. I think the only way to measure success is by somehow measuring minds changed. This is a quick announcement of a new video I recently released, my philosophy on how to change minds, and a request from the community to help me with some data science.
My name is David Siegel. In 1991, I wrote a book explaining how the greenhouse effect worked and how we have to cut back on CO2 emissions or suffer dire consequences. Then, in about 2014, a partner at a green fund told me “the science is settled.” That prompted me to revisit the subject, and I was surprised to find that the data didn’t support the “common wisdom” that I had believed for so long. So I started reading papers, blogs, and web sites like WUWT.
After considering the tide gauge records around Fairbourne in my last post, I wanted to look at a larger picture. Remember that we’ve been repeatedly told that acceleration in sea level rise is not just forecast, it’s actually occurring. I wrote about some of these claims in my post entitled “Accelerating The Acceleration“. Plus we’ve been deluged, if you’ll excuse the word, with endless cartoons and memes and movies and earnest predictions about the Statue of Liberty going underwater, cities being drowned, islands being overtopped by the sea, and the like. And not only that, but we’re assured that we can see and measure the acceleration in both the tide gauge and the satellite sea-level records.
So I went to get the satellite sea-level records from the University of Colorado. But when I plotted them up, I realized that they stopped in 2018. I couldn’t find anything on their website that explained why. Here’s their data.
Figure 1. University of Colorado sea-level record. Note that it is a splice of four satellite datasets that all seem to be in quite good agreement.
Google says many people ask this question so here is the correct answer: polar bears are not going extinct. If you have been told that, you have misunderstood or have been misinformed. Polar bears are well-distributed across their available habitat and population numbers are high (officially 22,000-31,000 at 2015 but likely closer to 26,000-58,000 at 2018): these are features of a healthy, thriving species. ‘Why are polar bears going extinct?’ contains a false premise – there is no need to ask ‘why’ when the ‘polar bears [are] going extinct’ part is not true.1
Recipe for Australia’s climate ‘truth bomb’: dubious manipulations of the historical temperature record, ignorance of the climate dynamics of the Southern Hemisphere, and ignorance of Australia’s ecological and social history.
A correspondent of The Guardian newspaper writes that her personal ‘climate truth bomb’ hit her while she was picking ash from her glass at a wine tasting event – the Sydney Harbour bridge being dimly seen through the murk of bushfires. The truth came to her, she wrote, in the eloquent rage of Greta Thunberg and also in heat, smoke and fire.
Other data shows the USA wasn’t even close to a record.
In a report generating substantial media attention this month, the National Oceanic and Atmospheric Administration (NOAA) claimed January 2020 was the hottest January on record. In reality, the claim relies on substantial speculation, dubious reporting methods, and a large, very suspicious, extremely warm reported heat patch covering most of Russia.
The January 2020 Climate Assessment Report, released by NOAA’s National Center for Environmental Information (NCEI), was accompanied by a map showing a giant red menace of extraordinary asserted warmth extending from the Russian border with Poland well into Siberia. Yet, the asserted hot spot appears nowhere else.
Figure 1: Map of temperature departure provided by NOAA/NCEII. Note the huge red spot over Russia.
Who says there is no recession anywhere in sight? It depends on where you are looking. In short, manufacturing remains in recession; corporate profits remain in recession; freight remains deep in recession; Carmageddon remains in recession; and the Retail Apocalypse remains a recession for brick-and-mortar stores, while employment — the last holdout — is now also turning downward.
The manufacturing recession that everyone acknowledges as having begun last summer continues:
Employment has been the one stickler in my recession prediction for 2019, and finding a trustworthy measurement from the government’s statistics is like finding a virgin in a brothel. Depending on which official figures you look at, employment has refused to fall and new jobs are strong … or they stink.
We recently got two extremely conflicting reports from the same government agency that reveal, AGAIN, how dubious the official numbers from the government are. Because one report was stellar, the stock market blasted off like a rocket when it heard the news. Because the other report of the same item was abysmal, the stock market ignored it. The Fed, however, did not ignore it, and carries it in its official charts.
A surprising comment published January 29th in the leading scientific journal Nature said; “Emissions – the ‘business as usual’ story is misleading – Stop using the worst-case scenario for climate warming as the most likely outcome — more-realistic baselines make for better policy.” This has thrown a monkey wrench in hundreds of studies and media stories that previously predicted dire climate consequences in the future due to increased carbon dioxide (CO2) in our atmosphere.
The consequences were predicted by a computer model called Representative Carbon Pathways (RCP) and the worst case scenario model, RCP8.5 had been cited over 2500 times in scientific journals and in hundreds of media stories as the primary need for “urgent action” on climate. Predictions from RCP8.5 model suggested maximum global temperature increases of nearly 6°C (10.8°F) by the year 2100, shown in Figure 1.
Figure 1 – Image Credit: Neil Craik, University of Waterloo
Johns Hopkins has a Corona Virus map that appears to be updated daily showing where confirmed cases have been reported, along with stats on deaths and recoveries. It gives a breakdown by country – I was surprised that the US showed 15 confirmed cases.
It is not disputed that Blair Trewin under the supervision of David Jones (both working at the Australian Bureau of Meteorology) remodel all the historical temperature data generating trends and statistics that look quite different from the actual measurements.
The remodelled series are then passed on to university and CSIRO climate scientists who base much of their climate research on these ‘second-hand’ statistics.
So, when Michael Mann and David Karoly tell you it’s getting hotter and hotter, this is their interpretation of Blair Trewin’s statistics, not their interpretation of the actual data.
Note: What I present below is scarcely believable to me. I have looked for an error in my analysis, but cannot find one. Nevertheless, extraordinary claims require extraordinary evidence, so let the following be an introduction to a potential issue with current carbon cycle models that might well be easily resolved by others with more experience and insight than I possess.
Sixty years of Mauna Loa CO2 data compared to yearly estimates of anthropogenic CO2 emissions shows that Mother Nature has been removing 2.3%/year of the “anthropogenic excess” of atmospheric CO2 above a baseline of 295 ppm. When similar calculations are done for the RCP (Representative Concentration Pathway) projections of anthropogenic emissons and CO2 concentrations it is found that the carbon cycle models those projections are based upon remove excess CO2 at only 1/4th the observed rate. If these results are anywhere near accurate, the future RCP projections of CO2, as well as the resulting climate model projection of resulting warming, are probably biased high.
I was recently reminded of one of the most common misconceptions about our changing climate that is often accepted as fact by climate skeptics and true believers alike. Last week a commentary written by a fellow geologist and colleague lamented the less snow and cold in recent winters compared to the winters of his youth in Kentucky in the 1950s and 60s. He also related a talk he had with an octogenarian in Europe over the holidays who told him that he also recalled common snow during Christmas in Germany but alas, no longer.
This nearly universally held belief that even the most skeptical of us tend to believe is “warming by recollection.” Virtually every person from snowy climes claims that winters today are nothing like they were when they were a child. This recollection reinforces the thought that we are experiencing global warming within our own lifetime. Never mind that the slight warming of ~0.6 oF (0.3 oC) that a typical 45-year-old may have experienced since that big snowfall when he was five years old is much too slight to be recognizable by anyone.
Challenging the claim that a large set of climate model runs published since 1970’s are consistent with observations for the right reasons.
Zeke Hausfather et al. (2019) (herein ZH19) examined a large set of climate model runs published since the 1970s and claimed they were consistent with observations, once errors in the emission projections are considered. It is an interesting and valuable paper and has received a lot of press attention. In this post, I will explain what the authors did and then discuss a couple of issues arising, beginning with IPCC over-estimation of CO2 emissions, a literature to which Hausfather et al. make a striking contribution. I will then present a critique of some aspects of their regression analyses. I find that they have not specified their main regression correctly, and this undermines some of their conclusions. Using a more valid regression model helps explain why their findings aren’t inconsistent with Lewis and Curry (2018) which did show models to be inconsistent with observations.
Outline of the ZH19 Analysis:
A climate model projection can be factored into two parts: the implied (transient) climate sensitivity (to increased forcing) over the projection period and the projected increase in forcing. The first derives from the model’s Equilibrium Climate Sensitivity (ECS) and the ocean heat uptake rate. It will be approximately equal to the model’s transient climate response (TCR), although the discussion in ZH19 is for a shorter period than the 70 years used for TCR computation. The second comes from a submodel that takes annual GHG emissions and other anthropogenic factors as inputs, generates implied CO2 and other GHG concentrations, then converts them into forcings, expressed in Watts per square meter. The emission forecasts are based on socioeconomic projections and are therefore external to the climate model.
Today, at the big 100 year anniversary shindig of the American Meteorological Society (AMS) there was a press release session that featured NOAA and NASA GISS talking about how their climate data says that the world in 2019 was the second warmest ever.
In my opinion, the NOAA/NASA press release (and slideshow) is inconsistently presented. For example, they can’t even agree on a common base period for comparisons. Some graphs use 1951-1980 while others compare to 1981-2010 averages to create anomaly plots. NOAA and NASA owe it to the public to present climate data with a consistent climate period for comparison, otherwise it’s just sloppy science. NASA GISS has consistently resisted updating the 1951-1980 NASA GISS baseline period to the one NOAA and other datasets use, which is 1981-2010. GISS stubbornly refuses to change even though they have been repeatedly excoriated for keeping it.
WUWT reader Max alerted us to a 1994 Naomi Oreskes et. al. paper published in the prestigious journal Science. Her paper was a critical analysis of Earth Science numerical models.
I asked Rud to take a look, since he had previously written on climate models both here and in the ebook Blowing Smoke. What follows is an edited version of what Rud sent us, approved for publication by him.
After a quick read of Oreskes’s paper, I felt a double whammy was in order:
1. Explain Oreskes ‘science’ per se.
2. And then explain her later duplicitous conversion to rabid climate alarmist.