Simple multi-layer greenhouse

Here are the results of my simple multi-layer greenhouse experiment, conducted in December when the weather was hot and stable, not mild and rainy as it is now. The experimental setup is shown below, with two laboratory thermometers, and a mercury one to check. One sensor was attached to a 6in black tile sitting on the EPS box, the other on the glass surface. On top were up to 5 alternating layers of EPS and picture glass, as shown below.

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The temperatures are in C, and were measured by recording the maximum temperature over the period. As far as possible, I tried to obtain measurements on a clear calm day. The location is on the tropic of Capricorn in December, so the sun was virtually overhead.

Time	Layers	Tile	Glass

12:00	5	116	50
12:30	5	110	49
12:45	3	110	49
12:50	2	106.9	52.2
1:00	1	110.1	60.1
1:10	4	104.6	46.6
1:25	5	102.9	46.5

Below is a graph of the data above. Its fairly clear that the number of layers has very little effect on the temperature of the black tile. The temperature of the external glass layer does decrease however, with more layers. This I would think is due to the increased heat losses from the sides of the stack of alternating glass and EPS blocks.

image001

Once again, conducting this with precision outside is not possible without better equipment. As I reported with an earlier post, the temperature of the tile was beginning to melt the EPS foam.

The same result, of little change in the temperature of the tile with additional layers, was reported in a post by JQ Public:

My son and I repeated the experiment as mentioned and we the same results. We then used two glass jars, one as a control and one with water vapor and got the same results. We tried the two jar experiment again, but his time we stayed indoors and used a heat lamp and got the same results. In our fourth experiment we use one jar as a control and added vinegar and baking soda to the second jar to produce CO2. After and hour into the experiment we added even more vinegar and backing soda to create even more CO2 and yet again the temperature did not increase. The mean control jar temperature was 34.87 while the experimental jar was 35.43. The mean humidity for the control was <20% (we could not measure below 20%) and the mean humidity of the experimental jar was 42.73%.

Theon eviscerates climate warming community

This is not the sort of news I usually pick up on, but I quote below the retired senior NASA atmospheric scientist Dr. John S. Theon, and former supervisor of James Hansen, both because of the relevance to modelling practise, and because he captures so exactly what has driven me out of science over the last 5 years, and onto the blogosphere.

Theon declared “climate models are useless.” “My own belief concerning anthropogenic climate change is that the models do not realistically simulate the climate system because there are many very important sub-grid scale processes that the models either replicate poorly or completely omit,” Theon explained. “Furthermore, some scientists have manipulated the observed data to justify their model results. In doing so, they neither explain what they have modified in the observations, nor explain how they did it. They have resisted making their work transparent so that it can be replicated independently by other scientists. This is clearly contrary to how science should be done. Thus there is no rational justification for using climate model forecasts to determine public policy,” he added.

If climate models are useless, what does this make the order of magnitude larger community of scientists in the climate change effects community who use these models to prophesy impending doom: dupes, enabelers, or marketers of the latest trendy idea? Sorry guys.

New tests and enhancements to WikiChecks

I have tweaked the interface of WikiChecks and added some new analysis. It will take a range of analysis before I get a good enough sample, but already there is an amazing degree of insight coming out of this technique. Below is a list of some of the new additions, and whether the last digit deviates from randomness.

PDO Monthly values, significant, excess 6’s, possibly massaged.
Fidelity Mutual Fund daily adjusted price, not significant
UBS AG hedge fund monthly returns, not significant
MSCI Barra Hedge Funds, significant, excess 0’s and 5’s, possibly rounding.
CAL Global Hedge Fund, not significant

Somebody added the PDO Monthly values, which turned up a significant excess of sixes. In other tests I have done, and in the literature, excess of 6 is a sign that human preferences are influencing the numbers.

Whoever recently added PDO Monthly values had a problem, and I have changed the interface to overcome it. Its set up so that the data sets people enter are recorded as posts, so everyone benefits from the results. So if you have a data set you want to try, be it science or finance or something else, please feel free to give it a go. I will be watching to make sure tests goes through, and improve the system with use.

Simple Greenhouse Proofs

A reported increase in the longwave downward radiation in the Swiss Alps, proves the ‘‘theory’’ of greenhouse warming with direct radiation observations according to this paper, “Radiative forcing – measured at Earth’s surface – corroborate the increasing greenhouse effect”, by Rolf Philipona, Bruno Durr, Christoph Marty, Atsumu Ohmura and Martin Wild.

Supposed direct observational proofs of the enhanced greenhouse effect have been reviewed here in the past.

  1. Rahmstorf, who claimed climate responding faster than expected on the basis of a dubious graph with no statistical test;
  2. Harries who claimed to detect the greenhouse effect from CO2 spectral brightening but whose later (underreported) publications were much more equivocal;
  3. Soden, whose claims to have detected increase in specific water vapor from
    spectral brightening were reported as proof in the IPCC AR4, despite conflicting evidence.

Any comments on this radiative proof from the radiation experts here?

Hansen's Regression to Zero

While reading Hansen’s latest mailout I came upon an intriguing reference that I followed up. I suspect this paper is as important as Douglass et al. in describing an important way the models do not agree with the observations. It may be more important, in redefining the role of the Sun in recent warming.

His mailing contains a massive revision of his estimate of the rate of warming down from 0.2C per decade to 0.15C per decade. Near the end of his mailing he notes:

Solar irradiance has a non-negligible effect on global temperature [see, e.g., Reference 7, which empirically estimates a somewhat larger solar cycle effect than that estimated by others who have teased a solar effect out of data with different methods].

Reference 7, a paper by Tung, K.K., J. Zhou, C.D. Camp, Constraining model transient climate response using independent observations of solar-cycle forcing and response, recently published in Geophys. Res. Lett. in 2008 is freely available. It uses the geographical distribution of global temperature in response to the 11 yr solar cycle to isolate the transient climate response (TCR). Noting previously that the 19 IPCC models have a very large range in TCR, it compares the empirically derived TCR with the TCR of all the major IPCC climate models. The result is damning:

[14] The TCRs of 19 coupled atmosphere-ocean GCMs in IPCC AR4 listed in Table 1 fall within the rather low range of 1.2–2.2 K with the exception of one, and thus fail the lower constraint of 2.5 K determined by ERA-40, GISS and HadCRUT3. The only exception is the Japanese MIROC (hi-res), with a TCR of 2.6 K. All models fail the higher constraint of 3.6 K determined by the NCEP data.

The emphasis was added by the authors. As I understand it, they are saying that GCM’s have grossly underestimated solar forcing. Like the tropospheric ‘hotspot’ due to GHG’s in Douglass et al., they are not even in the right ballpark. The problem, it seems, is with the rate of transfer of heat into the ocean.

[16] It is seen that most of the current generation of general circulation models assessed by IPCC AR4 have too low a transient climate response as compared with the observed range. This is consistent with the independent finding by Forest et al. [2006] that these models simulate too large an ocean heat uptake as compared to observations of ocean temperature changes during the period 1961–2003. See Raper et al. [2002] and Meehl et al. [2004] for different views on how ocean heat uptake affects TCR. This excessive heat into the oceans tends to reduce the transient climate response for the atmosphere, but does not affect the modeled equilibrium climate sensitivity, which was calculated with a slab ocean in thermal equilibrium with the atmosphere.

The last sentence notwithstanding, there is an argument that underestimating TCR must lead to overestimating GHG forcing in the recent past. This would be a confirmation of the AGW skeptical view that recent response to solar forcing has been grossly underestimated, and GHG forcing exaggerated.

For the paper to be acknowledged by James Hansen himself is intriguing. Perhaps backpedaling is his way of avoiding jousting with jesters (climate skeptics).

Global Temperature Graphs

The next step in the statistical forensics process is to breakdown the data in ways that reveal where the anomolous divergences are coming from. Here I am indulging in classical scientific reduction methodology by examining overall phenomena in terms of the sum of its parts.

The previous post in the series identified significant divergence in the distribution of the last digits of two global temperatrue data sets, from GISS (Pr<0.05) and CRU (Pr<0.01). Two other data sets based on satellite data were cleared of non-randomness, from RSS and UAH.

LuboÅ¡ Motl confirmed my results on GISS and CRU. Steve McIntyre initally disagreed, but then found an order of magnitude mistake in his calculations which he reported in a comment here. So there can be no doubt that the anomaly in distribution of digits in these datasets is real. This can be caused by many factors, only one of which is ‘manipulation’ of the data. How do we find the cause?

The graphs below show changes in chi-sq values (red) over the time scale of the GISS and CRU temperature series (blue) from 1880 to the present. I show them now to indicate where I am going. Sorry they are very basic but I am developing the code in php from scratch, so it can be used on the WikiChecks website. I used a 100 data point window, and plotted the significance of the dirvergence from a uniform over time (red).

The regions where the distribution of digits diverges is shown clearly, and will be the basis for more detailed examination.

GISS temperature and digit divergence.

giss

CRU temperature and digit divergence.

cru

UBS returns show significant management

Cross posted at WikiChecks.

I pasted in monthly data from the Swiss bank UBS and found significant management. The file used was this. The digit frequency shows an excess of zeros and ones and a deficiency of 7s and 8s. One possible explanation is that figures slightly below a whole number have been boosted to slightly above a whole number (eg. 3.9% to 4.1%).

For comparison, I looked at overall returns from a number of funds here. These fund returns showed no signs of management.

Interestingly, a google search on ‘UBS fraud’ gives plenty of hits. UBS was charged with fraud in July 2008 by the SEC. The linked article states of the charges:

Of course, the Commission’s complaint are only allegations, thus far unproven, and UBS has not yet responded to the compliant. However, if true, the allegations are serious, and provide significant insight into a corporate mindset at UBS which put its profits ahead of the well being of its customers, and its own employees.

Announce: New fraud detection website

Detecting ‘massaging’ of data by human hands is an area of statistical analysis I have been working on for some time, and devoted one chapter of my book, Niche Modeling, to its application to environmental data sets.

The WikiChecks web site now incorporates a script for doing a Benford’s analysis of digit frequency, sometimes used in numerical analysis of tax and other financial data.

I have posted some initial tests on the site: random numbers and the like. I also ran each of the major monthly global temperature indices through the site: GISS, RSS, UAH and CRU. The results, listed from lowest deviation to highest are listed below.

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