Miranda Devine: Perth electrical engineer’s discovery will change climate change debate
October 4, 2015 3:00am
MIRANDA DEVINE
Dr David Evans has unpacked the architecture of the basic climate model which underpins all climate science. Picture: Thinkstock
A MATHEMATICAL discovery by Perth-based electrical engineer Dr David Evans may change everything about the climate debate, on the eve of the UN climate change conference in Paris next month.
A former climate modeller for the Government’s Australian Greenhouse Office, with six degrees in applied mathematics, Dr Evans has unpacked the architecture of the basic climate model which underpins all climate science.
He has found that, while the underlying physics of the model is correct, it had been applied incorrectly.
He has fixed two errors and the new corrected model finds the climate’s sensitivity to carbon dioxide (CO2) is much lower than was thought.
Miranda Devine. Picture: Peter Brew-Bevan
It turns out the UN’s Intergovernmental Panel on Climate Change has over-estimated future global warming by as much as 10 times, he says.
“Yes, CO2 has an effect, but it’s about a fifth or tenth of what the IPCC says it is. CO2 is not driving the climate; it caused less than 20 per cent of the global warming in the last few decades”.
Dr Evans says his discovery “ought to change the world”.
“But the political obstacles are massive,” he said.
His discovery explains why none of the climate models used by the IPCC reflect the evidence of recorded temperatures. The models have failed to predict the pause in global warming which has been going on for 18 years and counting.
“The model architecture was wrong,” he says. “Carbon dioxide causes only minor warming. The climate is largely driven by factors outside our control.”
There is another problem with the original climate model, which has been around since 1896.
While climate scientists have been predicting since the 1990s that changes in temperature would follow changes in carbon dioxide, the records over the past half million years show that not to be the case.
So, the new improved climate model shows CO2 is not the culprit in recent global warming. But what is?
Dr Evans has a theory: solar activity. What he calls “albedo modulation”, the waxing and waning of reflected radiation from the Sun, is the likely cause of global warming.
He predicts global temperatures, which have plateaued, will begin to cool significantly, beginning between 2017 and 2021. The cooling will be about 0.3C in the 2020s. Some scientists have even forecast a mini ice age in the 2030s.
If Dr Evans is correct, then he has proven the theory on carbon dioxide wrong and blown a hole in climate alarmism. He will have explained why the doomsday predictions of climate scientists aren’t reflected in the actual temperatures.
Dr David Evans, who says climate model architecture is wrong, with wife Jo Nova, Picture: australianclimatemadness.com
“It took me years to figure this out, but finally there is a potential resolution between the insistence of the climate scientists that CO2 is a big problem, and the empirical evidence that it doesn’t have nearly as much effect as they say.”
Dr Evans is an expert in Fourier analysis and digital signal processing, with a PhD, and two Masters degrees from Stanford University in electrical engineering, a Bachelor of Engineering (for which he won the University medal), Bachelor of Science, and Masters in Applied Maths from the University of Sydney.
He has been summarising his results in a series of blog posts on his wife Jo Nova’s blog for climate sceptics.
He is about half way through his series, with blog post 8, “Applying the Stefan-Boltzmann Law to Earth”, published on Friday.
When it is completed his work will be published as two scientific papers. Both papers are undergoing peer review.
“It’s a new paradigm,” he says. “It has several new ideas for people to get used to.”
You heard it here first!
http://www.adelaidenow.com.au/news/...27555674611?sv=c408b33cef164795b9a30e809c6cb8
Now for the other angle of the dangle;
In The Land of El Nino
Willis Eschenbach / 1 week ago
Guest Post by Willis Eschenbach
[UPDATE: When reading the comments, you’ll notice a number of nasty untrue personal attacks made on me by three commenters with the screen names “Lady Gaiagaia”, “Gloria Swansong”, and “Sturgishooper”. One of them makes an attack, another jumps in to agree, the third one says the first two are right … that kind of thing. Here’s the funny part … alert work by a moderator has revealed that all three are nothing but sock-puppets for some unknown scumball with an axe to grind. I see this as a testament to the desperation of the person involved, that they are willing to try these despicable ploys in a vain effort to discredit real science. Anyhow, keep that in mind when going through the comments.]
While I was involved in an interesting interchange with David Douglass here, I stumbled across an interesting discovery. Before I get to that, though, I have to give high marks to David and his co-author, Robert Knox, for showing up on WUWT to defend their paper. Most authors don’t have the albondigas to do that, so I definitely tip my hat to them, much appreciated.
The subject of the interchange was the area in the Pacific Ocean called the “Nino3.4 Region”, which goes from 5°N to 5°S and from 170°W to 120°W. It started with a look at the sea surface temperature (SST) in the area. When the Nino3.4 region is running hot, it means that there is an El Nino in progress. Here is that graph:
Figure 1. Sea surface temperature (SST) in the Nino 3.4 region of the Pacific Ocean, decomposed into seasonal and residual components. Top panel shows the observations. Middle panel shows the seasonal component of the observations, that is to say, the average monthly changes in the data. Bottom panel shows the “residuals”, which is what’s left after we subtract the seasonal component from the observations. DATA SOURCE
In the process of the discussion I looked at something I’d never examined, which is how much solar radiation the surface actually receives in the Nino3.4 region. This is measured as what is left of the downwelling solar radiation after cloud reflections and atmospheric absorption, minus the amount that is reflected from the surface of the ocean. So we’re measuring how much solar energy is actually absorbed by the ocean surface. The data is from the CERES radiation-measuring satellite.
Figure 2. Absorbed solar energy in the Nino 3.4 region of the Pacific Ocean, decomposed into seasonal and residual components. Top panel shows the observations. Middle panel shows the seasonal component of the observations, that is to say, the average monthly changes in the data. Bottom panel shows the “residuals”, which is what’s left after we subtract the seasonal component from the observations. DATA SOURCE
Warmer Ocean ==> Earlier-forming and More Daily Clouds ==> More Solar Reflection and Absorption ==> Less Available Solar Energy
and
Cooler Ocean ==> Later-Forming and Fewer Daily Clouds ==> Less Solar Reflection and Absorption ==> More Available Solar Energy
Obviously, this is a self-regulating system. When it is running cool it lets in more energy, and when it is running hot it lets in less energy. This is the heart of the system of emergent climate phenomena that has kept the planet from either frying or freezing into a snowball for millions of years.
In order to compare the two datasets, SST and absorbed solar, I used what is called a “cross-correlation” analysis. This calculates the correlation (a measure of similarity) between the two at a variety of lags. Let me first say what I hoped to find.
First, I hoped to find that there was a strong negative correlation between absorbed energy and sea surface temperature (SST). This would mean that as SST rises, absorbed solar energy goes down, and vice versa. Note that this is the opposite of what we’d expect—normally, as the absorbed solar energy increases the temperature increases.
Next, I hoped to find that there was a very short lag between the temperature and the downwelling solar. Normally, when the sun heats the ocean there’s about a 2-month plus lag between peak insolation and peak temperature, because of the thermal mass of the ocean. But if the temperature is controlling the clouds as my hypothesis states, the lag should be much shorter, one month or less.
Finally, I hoped to find that the cross-correlation analysis would be convincingly shaped, which means a clear peak at zero or short lags, and falling away quickly on both sides of the peak.
With that said, here are the results of the cross-correlation analysis:
Figure 3. Cross-correlation analysis, absorbed solar energy and sea surface temperature in the Nino3.4 region. The climatology (monthly averages Jan-Dec) has been removed from both datasets.Positive lag indicates absorbed solar lagging the change in temperature.
Of course, I couldn’t leave it at that, I had to look to see how widespread this phenomenon might be. One can use the CERES satellite data for this, but there is a challenge. CERES has no surface temperature dataset … but it does have a surface upwelling radiation dataset, which can be converted using the Stefan-Boltzmann to temperature. How accurate is this CERES estimate of the SST? Very accurate everywhere I’ve tested it … but this gave me another chance to test it. Here is the NOAA sea surface temperature in the Nino3.4 region compared to the CERES estimate of the SST for the same region …
Figure 4. The CERES satellite dataset estimated sea surface temperature in the Nino3.4 region (red) compared to the NOAA SST for the same region.
Dang … well done, CERES scientists.
Greatly encouraged by that, I took a look at the relationship between temperature and absorbed solar radiation worldwide. Figure 5 shows that result:
Figure 5. The correlation of surface temperature and the solar radiation absorbed by the surface. The mid-Pacific red rectangle shows the Nino3.4 region. DATA SOURCE
But in the area of the inter-tropical convergence zone north and south of the equator, what’s sometimes called the deep wet topics, the reverse is true. There, the emergent climate phenomena of cumulus clouds, thunderstorms, and squall lines act to regulate the incoming sunlight. And as it turns out, the Nino3.4 zone is not even the area of the strongest negative correlation. The strongest is centered on the equator and the international date line at 180° West (or 180° East).
Finally, let me call attention to the size of the restorative force. During the 2010 El Nino, the absorbed solar in the region dropped by about 40W/m2. This gives us an idea of the strength of this part of the temperature regulation system.
Not much else I can say except that this is very strong support for my hypothesis that the climate is not a simple function of the forcing, but instead is regulated such that it varies only a very small amount (e.g. ± 0.3°C over the entire 20th century).
http://wattsupwiththat.com/2015/09/26/in-the-land-of-el-nino/
