Tony Heller in this video exposes the climate fraud in the US Climate Assessment.
SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, United States
The reliability of general circulation climate model (GCM) global air temperature projections is evaluated for the first time, by way of propagation of model calibration error. An extensive series of demonstrations show that GCM air temperature projections are just linear extrapolations of fractional greenhouse gas (GHG) forcing. Linear projections are subject to linear propagation of error. A directly relevant GCM calibration metric is the annual average ±12.1% error in global annual average cloud fraction produced within CMIP5 climate models. This error is strongly pair-wise correlated across models, implying a source in deficient theory. The resulting long-wave cloud forcing (LWCF) error introduces an annual average ±4 Wm–2 uncertainty into the simulated tropospheric thermal energy flux. This annual ±4 Wm–2 simulation uncertainty is ±114 × larger than the annual average ∼0.035 Wm–2 change in tropospheric thermal energy flux produced by increasing GHG forcing since 1979. Tropospheric thermal energy flux is the determinant of global air temperature. Uncertainty in simulated tropospheric thermal energy flux imposes uncertainty on projected air temperature. Propagation of LWCF thermal energy flux error through the historically relevant 1988 projections of GISS Model II scenarios A, B, and C, the IPCC SRES scenarios CCC, B1, A1B, and A2, and the RCP scenarios of the 2013 IPCC Fifth Assessment Report, uncovers a ±15 C uncertainty in air temperature at the end of a centennial-scale projection. Analogously large but previously unrecognized uncertainties must therefore exist in all the past and present air temperature projections and hindcasts of even advanced climate models. The unavoidable conclusion is that an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables.
Full Paper is HERE.
Pat Frank makes the scientific argument that “climate models cannot predict future global air temperatures; not for one year and not for 100 years. Climate model air temperature projections are physically meaningless. They say nothing at all about the impact of CO₂ emissions, if any, on global air temperatures.”
H/T to Watts Up With That
Something for our global warmer to think about:
This paper by John Christy, director of the Earth System Science Center and Distinguished Professor of Atmospheric Science at the University of Alabama in Huntsville, and the Alabama State Climatologist, is highly interesting. Christy describes work that he and Dick McNider have done to check the accuracy of the climate models that are the source of frenzy over global warming. His technical analysis is clear and comprehensible, but for now I will merely quote the conclusion of that part of the paper. Note that warming that is caused by additional CO2 (or other greenhouse gases) occurs in the atmosphere, not at the Earth’s surface:
The warming trend we found suggests we are having a relatively minor impact on global temperatures. From the IPCC, we know what the forcing was over that 37.5 years – how many extra greenhouse gas molecules there were and what forcing they would represent. We also know about the effect of aerosols. Taking all this data together, we can calculate what I call – and we were the first to use this term – the ‘tropospheric transient climate response’. In other words: how much temperature actually changes due to extra greenhouse gas forcing. The calculation includes a major assumption, namely that there are no natural variations left in the temperature data, and in particular that there are no long-term natural variations. It’s a huge assumption, but it allows us to move on.
Our result is that the transient climate response2 – the short-term warming – in the troposphere is 1.1◦C at the point in time when carbon dioxide levels double. This is not a very alarming number. If we perform the same calculation on the climate models, you get a figure of 2.31◦C, which is significantly different. The models’ response to carbon dioxide is twice what we see in the real world. So the evidence indicates the consensus range for climate sensitivity is incorrect.
Another approach involves a method that Christy and Ross McKitrick used. The climate models predict a “hot spot” at about 30,000 to 40,000 feet in the tropics. This “hot spot” has been called the “signature” of man-made global warming, and early alarmists said that we watch for it over time. That is what Christy and McKitrick have done:
Figure 7 shows the model projections in pink and different observational datasets in shades of blue. You can also easily see the difference in warming rates: the models are warming too fast. The exception is the Russian model, which has much lower sensitivity to carbon dioxide, and therefore gives projections for the end of the century that are far from alarming. The rest of them are already falsified, and their predictions for 2100 can’t be trusted. If an engineer built an aeroplane and said it could fly 600 miles and the thing ran out of fuel at 200 and crashed, he wouldn’t say ‘Hey, I was only off by a factor of three’. We don’t do that in engineering and real science. A factor of three is huge in the energy balance system. Yet that’s what we see in the climate models.
Click to enlarge:
A model is a theory, a hypothesis. A model that is falsified by observation is a wrong hypothesis. That really is all there is to it.
We just wrapped up summer without any long term periods of temperatures over 100 degrees. Yes, we had some 3-4 day periods. Now it looks like September is going to be cooler than average. So, the question is how much cooling have we had?
One method is to calculate the Growing Degree Days. What are the growing degree days?
Growing degree days (GDD) are a measure of heat accumulation used by horticulturists, gardeners, and farmers to predict plant and animal development rates such as the date that a flower will bloom, an insect will emerge from dormancy, or a crop will reach maturity.
In the absence of extreme conditions such as unseasonal drought or disease, plants grow in a cumulative stepwise manner which is strongly influenced by the ambient temperature. In other words, GDD values provide a best case outlook as to plants’ pace to maturity. (wikipedia)
The Ice Age Farmer has developed an interactive method for calculating the GDD for every zip code in the US, comparing the difference from last year (2018). The link is HERE.
GDD has decreased in Nevada City, CA to 78.44% of previous value (-21.56% drop) in 95959.
GDD has decreased in Grass Valley, CA to 81.16% of previous value (-18.84% drop) in 95945.
The greatest impact is in the grain, soybean, and corn growing belt in the Northern Hemisphere. Here is a quick look at some Iowa Counties:
Two California grape-growing counties:
A long term reduction in GDD could result in crop failures and the expansion of global hunger. In this video, the Ice Age Farmer discusses the impact of early fall frosts.
We must understand how Google does it, why it is wrong and how it hurts America
Several months ago, Google quietly released a 32-page white paper, “How Google Fights Disinformation.” That sound good. The problem is that Google not only controls a whopping 92.2% of all online searches. It is a decidedly left-wing outfit, which views things like skepticism of climate alarmism, and conservative views generally, as “disinformation.” The white paper explains how Google’s search and news algorithms operate, to suppress what Google considers disinformation and wants to keep out of educational and public discussions.
The algorithms clearly favor liberal content when displaying search results. Generally speaking, they rank and present search results based on the use of so-called “authoritative sources.” The problem is, these sources are mostly “mainstream” media, which are almost entirely liberal.
Google’s algorithmic definition of “authoritative” makes liberals the voice of authority. Bigger is better, and the liberals have the most and biggest news outlets. The algorithms are very complex, but the basic idea is that the more other websites link to you, the greater your authority.
It is like saying a newspaper with more subscribers is more trustworthy than one with fewer subscribers. This actually makes no sense, but that is how it works with the news and in other domains. Popularity is not authority, but the algorithm is designed to see it that way.
This explains why the first page of search results for breaking news almost always consists of links to liberal outlets. There is absolutely no balance with conservative news sources. Given that roughly half of Americans are conservatives, Google’s liberal news bias is truly reprehensible.
In the realm of public policies affecting our energy, economy, jobs, national security, living standards and other critical issues, the suppression of alternative or skeptical voices, evidence and perspectives becomes positively dangerous for our nation and world
The full article is HERE