Double O zero, Licensed to Speak

Dr. James Hansen, hysteriologist and climatemongerer wrote an article slamming the Obama Cap and Steal ponzi scheme. Calling it exactly what it is:

For all its “green” aura, Waxman-Markey locks in fossil fuel business-as-usual and garlands it with a Ponzi-like “cap-and-trade” scheme. Here are a few of the bill’s egregious flaws:

• It guts the Clean Air Act, removing EPA’s ability to regulate CO2 emissions from power plants.
• It sets meager targets — 2020 emissions are to be a paltry 13% less than this year’s level — and sabotages even these by permitting fictitious “offsets,” by which other nations are paid to preserve forests – while logging and food production will simply move elsewhere to meet market demand.
• Its cap-and-trade system, reports former U.S. Undersecretary of Commerce for Economic Affairs Robert Shapiro, “has no provisions to prevent insider trading by utilities and energy companies or a financial meltdown from speculators trading frantically in the permits and their derivatives.”
• It fails to set predictable prices for carbon, without which, Shapiro notes, “businesses and households won’t be able to calculate whether developing and using less carbon-intensive energy and technologies makes economic sense,” thus ensuring that millions of carbon-critical decisions fall short.

Hansen Arrested for Science

The biggest scam about this bill may be the free pass for insider trading. Unfortunately Hansen failed to mention the obvious ability for manipulation by cooperation with powerful politicians. What happens to carbon credit prices when the repeatedly self labled ‘Most Powerful Woman in the World’ Nancy Pelosi makes a strong statement about tighter regulation or if she surprises everyone with a conciliatory NO intent to increase caps or a potential reduction. Just a few words and her campaign contributors have a big payday. This is Chicago/Moscow/Venezuela politics, brought to you by the Obaminator. Don’t be fooled by your friendly government people, taxation with representation ain’t much better.

Of course Hansen needs global warming to maintain his manly demeanor, expensive travel budget and powerful presence on the world stage. Apparently licensed by Michael Mann’s Real Climate ’scientists who are allowed to speak’ program, Hansen’s article pontificates about the glorious advantages of economic stimulus by wealth redistribution.  Stalin would be proud.

Fantastic High Resolution Video of Sea Ice

This link is provided by Bremen Universtity and utilizes the ASMR-E sensors to compute the sea ice.  I recommend right clicking and downloading to your harddrive for viewing.  The files are only 20mb in size so the download time isn’t that long.   The advantage of these videos over my own is a much higher resolution which really reveals the flow patterns of the sea ice.   You can see the currents in the Arctic push in through the Bearing strait melting away the ice in the summer.    Thanks to DeWitt Payne for emailing the link.

Arctic Sea Ice Video

Antarctic is below.

Off the Deep End

My god Mike Mann is full of himself.  His rubbish math and media appearances have taken him right off the deep end.  Now he’s demanding a new class of scientist – a super scientist who apparently can work in public policy complete with a reward system (public funded I assume).

Given that we (scientists) are part of the problem, it must stand to reason that we are also part of the solution. And indeed, this is a primary thesis advanced by Mooney and Kirshenbaum. The authors argue that we must fundamentally reinvent the way that scientists are trained, so as to encourage and reward those who choose to serve as much-needed science liasons and science communicators. Indeed, the reward system must be reworked in such a way as to facilitate the establishment of a whole new class of scientists, so-called ’science ambassadors’ who are rigorously trained in science, but have the proclivity and ability to engage in the broader discourse and to help bridge the growing rift between the ‘two cultures’. We can no longer rely on pure serendipity that figures such as Sagan will just come along. We must be proactive in establishing a pipeline of scientists who can fill this key nich

Science Ambassadors:

Area Weighted TPCA Check

I put these images together from Ryan’s latest post and my own closest station post to show how truncated PCA is doing a good job with locating station information at the correct grid points. TPCA has no method for knowing exactly where each station is located. Ryan added eigenvector weights to each surface station during imputation which improves the odds that during convergence the station information is located at the correct grid area. We think of this like a sanity check to demonstrate that the method is working. My own opinion is that it’s doing an excellent job now and the Antarctic reconstruction by expectation maximization is repaired thanks to Ryan’s huge efforts.

Figure 1 Ryan Recon 28 pc

tAV to REALCLIMATE: YOU CAN’T GET THERE FROM HERE

A few notes from Jeff,

First, this is exciting work Ryan has done,  The Air Vent has been lucky to have great guest posts lately.   The work by Ryan and Nic has been excellent in improving my own understanding of the original Steig et al. reconstruction and methods for improvement.

Ryan has been working on a concept he had to improve two items of the original reconstruction.   The first one I want to mention is the imputation of Steig et al. is actually backwards.  The Steig reconstruction is  a reconstruction of satellite surface skin temperature (AVHRR)  rather than a surface temperature reconstruction as it’s billed.  It needs to be recalibrated to match surface station trend to be considered a valid surface station reconstruction.  As we know from previous work here, the trends of the Steig reconstruction are substantially different from surface station trends.  The second point has to do with RegEM converging to a local or global minima.

Ryan employs a weighting method in a TSVD reconstruction which involves two separate steps.

The method Ryan used to correct for both of these problems involves pre-weighting surface stations in relation to satellite information .  By applying a large equal multiplier to the surface stations in relation to sat PC’s, Ryan gives a strong weighting to the surface stations vs sat and negates the need for a post-reconstruction calibration.

The second weighting is more clever and important to climate science.  While Ryan explains it pretty well below, sometimes two explanations can help communicate it and this mathematical step should be very important in EM processing using similar data fields.   In  EM where large qty of data is missing,  spurious correlations can occur and a non-global minimum can be reached.  In this case large portions of the data are missing.   Imputing 1 PC at a time, Ryan weighted the individual surface stations by the pca eigenvector weighting of the original AVHRR data.  This means areas with low information content for the pc are less likely to accidentally become heavily weighted as each iteration progresses.  – Very important.  Think of it as an improved start point for the iteration, or an improved station location in the imputation rather than RegEM figuring out where stations belong.

Ryan, please feel free to correct any details you see wrong with this description.

Of course Ryan couldn’t resist a little discussion with RC .  Read on, I think you’ll like it.

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Guest post by Ryan O

Most of you are aware that Dr. Steig posted a response to our reconstructions over at RealClimate. The link is here: http://www.realclimate.org/index.php/archives/2009/06/on-overfitting/

There were two salient points in his post that we should look at. One point was that “someone called Ryan O” had obtained better verification statistics by first calibrating the satellite data to ground data. This point is easily addressed. To all RealClimate readers: everything that follows was done using the cloud masked AVHRR data provided by Dr. Steig as-is. No calibration. In fact, given the way our reconstructions are done, any such calibration would not affect the results. This, too, will be shown later.

The second point is that Dr. Steig claimed that the verification statistics were degraded as additional AVHRR PCs were included. This is certainly true, if you use the original tools (RegEM TTLS) and the original methodology (impute the whole mess at once). I think this point was lost at RealClimate. There are problems with the method and the math behind the method – so we changed the method to address these issues.

Here is a short summary of the issues:

1. TTLS assumes errors in both the predictors and predictands. However, an error in a PC (which is an abstract quantity) does not mean the same thing as an error in a temperature measurement at a specific location. Additionally, if the pre-1982 portion is calculated based on assuming errors in the ground stations and the PCs, then it is inappropriate to simply add the original, unmodified PCs onto the end. The post-1982 solution needs to be calculated the same way as the pre-1982 solution: assuming errors in both.

2. While Steig refers to the ground stations as the predictors and the satellite PCs as the predictands, in their method, this is not strictly true. Any existing values are the predictors and the missing values are the predictands. This means the ground stations and satellite PCs are both predictors and predictands. Not only that, but the satellite PCs affect each other’s imputation by interacting with each other and with the ground stations.

3. Because the solution is based on the truncated SVD of the correlation matrix, the pre-1982 portion of the AVHRR PCs is not truly an extrapolation of the PCs. It is a rotation of the PCs to the ground station data. This means that the original AVHRR PCs should not simply be tacked on to the end. The rotated PCs should be used from 1957 to 2006. The standard RegEM TTLS algorithm does not return the rotated (unspliced) solution (though Nic L’s modification does return that solution). This problem can be done as an extrapolation, but Steig’s method does not accomplish that.

4. The ground stations are used to predict PC values without regard to whether the PC explains any variance at the station location. This is not necessarily a problem – unless you subsequently recover gridded temperatures using the eigenvector. Because Steig uses the eigenvectors to recover gridded temperatures, then the eigenvector must be used to constrain the imputation. RegEM TTLS has no means of doing this.

5. An insufficient number of PCs are used. The claim that 3 PCs can represent land the size of Antarctica when the ERSST reconstructions required 15+ PCs to represent open ocean areas of equivalent size defies belief.

Some of these issues cannot be resolved when using RegEM TTLS. To that end, we started using a different imputation tool based on a truncated SVD approach (originally written for R by Steve McIntyre). The benefits of the truncated SVD approach are that it is faster, allows direct access to the unspliced solution, and is simpler to understand. That last benefit is important because we will discover that we need to modify the truncated SVD approach to address some of the methodological problems.

Declaration of Independence

It’s good to remember where we’ve been so we have perspective on where we’re going.

The complete text.

Original spelling and capitalization have been retained.

Unprecedented Again

From Science Daily where I read fairly often, I found this headline.

Sea Ice At Lowest Level In 800 Years Near Greenland

The title makes you instantly skeptical because sea ice isn’t at it’s lowest even in the last two years. Of course we then have to wonder how they determined sea ice levels back 800 years.

ScienceDaily (July 2, 2009) — New research, which reconstructs the extent of ice in the sea between Greenland and Svalbard from the 13th century to the present indicates that there has never been so little sea ice as there is now. The research results from the Niels Bohr Institute, among others, are published in the scientific journal, Climate Dynamics.

What do you know it’s another tree ring study in noise blending.

There are of course neither satellite images nor instrumental records of the climate all the way back to the 13th century, but nature has its own ‘archive’ of the climate in both ice cores and the annual growth rings of trees and we humans have made records of a great many things over the years – such as observations in the log books of ships and in harbour records. Piece all of the information together and you get a picture of how much sea ice there has been throughout time.

If someone has a copy of the PDF for this paper, I would appreciate it. In the meantime, I’ve copied the abstract below. It sounds straight from the team bendahockeystick playbook. The abstract can be found HERE

Antarctic Sea Ice Complete Video

Antarctic temperatures and sea ice are becoming quite a hobby. It should make for some interesting discussion around the campfire this summer – not really. It takes my computer about 15 hours to calculate this movie and it took all day to figure out how to make the movie work. Actually it takes a minute then wait, then a minute and wait again. I finally got a reasonable quality video at 15 frames per second, one frame per day from 1978 – 2009. Before you watch the video Figure 1 is a map of the Wilkins ice shelf which apparently is about to melt every hot January summer at the south pole.

Figure 1 - Wilkins Ice Shelf - A map of the Antarctic Peninsula with the location of the the Wilkins Ice Sheet, which is on the southern portion of the peninsula. Credit: British Antarctic Survey

The melting of the Wilkins ice shelf has happened over and over prompting numerous articles like the following.

RegEM weight calculation – a different approach

This is a guest post by NicL  who has been working on Antarctic station weighting in the background.  His method uses a brute force method for calculating trend sensitivity on the Antarctic reconstruciton.  After discovering that the B matrix in TTLS created a different weighting for each set of years that had different active surface station data, creating a solution which gives weighting becomes more complex.

Nic found less weighting for the peninsula and more for the ross ice shelf which is more in line with my original suspicion.  However, my own impression is that by area the peninsula is still heavily weighted and several stations are negatively weighted.

I haven’t checked the calculations myself but the method is straightforward although time consuming.  I hope you like it.

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Jeff Id and Ryan O have done a lot of good work on calculating the effective weights of the different surface stations in Steig’s main satellite AVHRR data based reconstruction.  I would like to present the results of a slightly different approach to this issue.

As Jeff Id has pointed out (Improved Weight Calculation, Air Vent, 15 June 2009), the B matrices used in TTLS RegEM vary across time.  That is because the set of input series having actual values varies with time: the B matrices are identical for different times that have the same set of missing values.  Obviously, whenever an input series has no actual value it has a zero weight in the reconstruction values for that month.  However, I am not sure that fluctuations over time in the weight of individual surface stations is of particular relevance.

To my mind, the key question is what weight the temperature trends in each surface station have in the overall temperature trend of the reconstruction – both Steig’s reported (spliced) trend based on the actual satellite data post 1981, and the trend implicit in the “unspliced solution”, being that using the three principal components (PCs) implicit in the missing values imputed by the reconstruction.  I suspect that time-averaging the weights derived from the B matrices would in particular not give accurate weights for the unspliced solution, since the B matrices only give weights for the missing data points that are imputed.  Moreover, actual data from one station may, through its effect on the covariance matrix, affect the weights given in the B matrices to data from other stations in months where the first station does not have actual reading.  Therefore, I don’t think that the weights in the B matrices can be relied on to give an accurate measure of the effect of the data from a station on the overall reconstruction.

Hockey Stick CPS Revisited – Part 1

Updated to include Dr. Mann’s words.

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The last time I did this, R was a brand new language to me. After 6 months messing around in my free time I speak rudimentary R with a C accent. R is a totally free language that anyone can download and learn. This post is a demonstration of the methods behind the Mann08 CPS hockey stick reconstruction. The difference between this and the numerous posts I did before is better R programming and a lot more comments in the code. If you’re serious about understanding vs advocating, you can figure this out. There is not one person I can think of who has ever commented here, incapable of figuring this out.

CPS is composite plus scale, which is an invented method for calibrating proxies to measured temperatures in paleoclimatology reconstructions. In paleoclimatology methods are too often invented to find the signal in the noise – this is not a new problem and it stems from the large signal to noise ration of paleo-data. If you happen to be a paleoclimatologist who does temperature reconstructions, please try your methods on ARIMA data with a known signal before employing it on whatever your proxy is.

I have hundreds of new readers, who didn’t get the day by day experience of my discovery of climatology math. Well some of my early work was a little rough, however it was correct and the specifics still stand uncriticized.

This post was prompted by some people in blogland (despite the complete lack of rational criticism) claiming that my demonstrations of the CPS hockey stick math is faulty rather than the actual hockey stick itself. An oddly reversed situation which could only exist in the new progressive anti-world. Actually, I don’t recall any real criticism of the method or the result other than statements around the AGW crowd that – ‘it’s been proven wrong’.

I’ve cleaned up the code and seriously over-commented it in the hopes that honest people will be able to understand what is going on here. I’ll do a short explanation for it but the primary explanation is in the programming code. To understand it fully you should to read it step by step and run it.

Michael Mann knows full well that this result exists, his explanation is as follows from an RC thread.

Actually, this line of attack is even more disingenuous and/or ill-informed than that. Obviously, if one generates enough red noise surrogate time series (especially when the “redness” is inappropriately inflated, as is often done by the charlatans who make this argument), one can eventually match any target arbitrarily closely.

You can note that this post uses his Actual data rather than red noise data. Dr. Mann’s continued explanation is here.

What this specious line of attack neglects (intentionally, one can safely conclude) is that a screening regression requires independent cross-validation to guard against the selection of false predictors. If a close statistical relationship when training a statistical model arises by chance, as would be the case in such a scenario, then the resulting statistical model will fail when used to make out-of-sample predictions over an independent test period not used in the training of the model. That’s precisely what any serious researchers in this field test for when evaluating the skillfulness of a statistical reconstruction based on any sort of screening regression approach.

So a potentially overstated statistical check is what determines if CPS works. This is in fact false, CPS is incapable of recovering an accurate signal from data. A fact which I will demonstrate in these next few posts. This post however, does not address the statistical validation issue, it does however demonstrate that Dr. Mann is correct that any signal at all can be made using Mann08 math and data. A truth for many of his hockey stick creation methods.

An explanation of Composite Plus Scale (CPS).

Historic Hockey Stick – Pt 2 Shock and Recovery

This post is about testing how well Mann 08 CPS (composite plus scale) can recover a signal from artificial ARMA proxy data. ARMA is just a fancy method to create artificial signals which match the noise and autocorrelation of a measured one. If you’re not familiar with this – don’t worry, it doesn’t matter.

In my last post we saw that Mann CPS hockey stick maker can make any shape you want using the same method and data used to make a hockey stick temperature curve. It happens because any data which doesn’t correlate to a pre-determined curve is discarded. This leads reasonable folks to say incorrectly – If it is temperature it should correlated so the method is reasonable. What’s missing from this seemingly reasonable understanding is that the noise level is large in comparison to the temp signal. In high or medium noise cases, correlation becomes just a cherry pick of your favorite noise. If you haven’t seen this in my hockey stick posts before, there is an interesting result of sorting large numbers of high noise level proxies which is not immediately obvious. To that end, this post takes the next step and looks at how well CPS does at retrieving a signal from both zero average random data and random data with a signal.

Hubris

On RC the entire team shows this statement in defense of the endless ‘faster and worse than expected’ we the unwashed are continually bombarded with. LINK HERE

Some aspects of climate change are progressing faster than was expected a few years ago – such as rising sea levels, the increase of heat stored in the ocean and the shrinking Arctic sea ice.

Since I’ve only looked at sea ice myself and have gained some knowledge from several months of study, let’s look at the scientifically moot point of Arctic sea ice trend.

3. Arctic Sea Ice. The Synthesis Report states:

One of the most dramatic developments since the last IPCC Report is the rapid reduction in the area of Arctic sea ice in summer. In 2007, the minimum area covered decreased by about 2 million square kilometres as compared to previous years. In 2008, the decrease was almost as dramatic.

This decline is clearly faster than expected by models, as the following graph indicates.

This is one graph they present to show how wrong an accomplished scientist like Dr. Pielke is.

Figure 1 Arctic Ice vs Models

So the model sits above the projection by 1-2M km from 1977ish until 2009 and they just now realize that perhaps the “model” may have a problem. I’m still a rookie to climate science (for about 1 more month) but you can’t sell me this nonsense and expect me to pay. Ain’t happenin’ Dr’s.

WUWT Cloud Creation

Everyone who hasn’t seen the slide show on cloud aerosol should take a look at this post at WUWT.

http://wattsupwiththat.com/2009/07/01/message-in-the-cloud-for-warmists-the-end-is-near/

Inhofe Got the Message – EPA Busted

Sen. Inhofe Calls for Inquiry Into ‘Suppressed’ Climate Change Report

A top Republican senator has ordered an investigation into the Environmental Protection Agency’s alleged suppression of a report that questioned the science behind global warming.

The 98-page report, co-authored by EPA analyst Alan Carlin, pushed back on the prospect of regulating gases like carbon dioxide as a way to reduce global warming. Carlin’s report argued that the information the EPA was using was out of date, and that even as atmospheric carbon dioxide levels have increased, global temperatures have declined.

“He came out with the truth. They don’t want the truth at the EPA,” Sen. James Inhofe, R-Okla, a global warming skeptic, told FOX News, saying he’s ordered an investigation. “We’re going to expose it.”

It’s Simple

Climate Bill Helps Utilities More Than Oil Companies

The bill, which creates a market for carbon dioxide permits potentially worth more than $100 billion a year by 2020, regulates the way the allowances could be traded to guard against speculation with derivatives that lawmakers say might drive up the prices of electricity and gasoline.

“This bill tries to help utilities and manufacturers move to a low-carbon economy without harming consumers, draw farmers into the carbon market and keep that market transparent to prevent improper profit-taking,” Tim Profeta, director of the Durham, North Carolina-based Nicholas Institute of Environmental Policy Solutions at Duke University, said in a telephone interview. “The oil industry got fewer free permits because lawmakers believe these firms can pass the relatively low cost to their consumers without affecting their bottom line.”

Well the bill has nothing to do with not harming consumers, but you can say anything these days.

More than 70 percent of the allowances would initially be given away.

CO2 Currency of Corruption

Associated Press-Henry Waxman

The ugly face of Global Warming Greed

America has got to wake up. We’ve fed our children too much cool-aid and they’ve grown the critical thinking ability of fish. Their minds rot in our school systems taught by unionized government officials espousing an ever more watered down curriculum which deliberately discourages competition in favor of feelings. Everything is reasonable – always. The rule of law, the right to vote, free speech, how can they learn what made America great?

The government under the not so long in the tooth Department of Education, has succeeded in breeding a culture where everything is reasonable, nothing is ever unreasonable just vote your mind, help everyone. Recently Acorn worked to steal an election, lied about it’s intent and received large contracts from government after the party they worked so hard to put in office got voted in – yet we hear almost nothing. I’m not saying Obama would have lost, but do we really know? Consider that more people registered to vote in Detroit Michigan than actually live there. This did not happen in twenty years ago this was not and is not acceptable, nor is the complete radio silence of the media on this issue. Acorn still managed to ruin their false image so badly they are changing their name, but they still got the money. You rarely hear about it because, everything is reasonable in America and our behind the scenes state controlled media is happy with the result.

When you listen to the news and media both left and right leaning, the message is freedom is what made America great, free speech and the right to vote. Of course this is important and it’s a nice feel-good message but the truth is somewhat different from the sales pitch. In reality, the freedom to compete is what made America great. It’s a much colder and more difficult reality but it’s the truth. Freedom to compete gave us car companies which were unmatched. Freedom to compete gave us a powerful retail industry, plentiful energy and companies which were flexible enough to grow and shrink as needed to supply the world with food and goods, anyone with the will and understanding could start a company and succeed against world competition. Freedom to compete gave us the best medical system in the world.

Corrections to Surface Temperatures

There is a very interesting post by Michael Hammer at Jennifer Marohasy which looks into the magnitude of the corrections to temperature data.

How the US Temperature Record is Adjusted

She looks at the corrections applied by the NOAA, apparently the UHI correctoin has been removed. The only reducing factor in the correction group.

Figure 1 Corrections applied