Climate

by Jill and David Archer

Briffa’s new paper on Yamal is online today here, together with Supplementary Information here. Yamal has been a longstanding issue at Climate Audit. The new article appears to be their long awaited response to criticism from Climate Audit (though this criticism is not referred to anywhere in the aticle.) In resisting FOI requests for their withheld 2006 Yamal-Urals regional chronology, CRU said that it was incomplete, as they were continuing to work on its development. However, they did undertake to disclose the 2006 regional chronology as part of the present publication. On my first reading, instead of living up to their undertaking to develop a regional chronology, CRU has instead provided reasons against using a regional chronology and do not present one in the paper – instead focussing on a variation of the original Yamal chronology. In resisting the FOI, CRU said that production of the 2006 regional chronology would damage the reputation of CRU scientists. The 2006 version appears to be the “Urals raw” chronology illustrated in SM9 as Greater Urals (shown below), though it is not identified as such in my first reading. Readers can judge for themselves whether their foreboding was justified. Figure 1. Compare to GU2 top panel. Readers who are convinced by Briffa’s arguments against a regional chronology may well wonder whether, for example, the Avam-Taimyr regional chronology of Briffa et al 2008 would pass corresponding tests, since no similar analysis has ever appeared in Briffa articles in which he presented earlier regional chronologies. Or whether these tests only became of interest when the regional chronology went the wrong way. CA readers will recall the original controversy in September 2009 over the Schweingruber Khadyta River series in Yamal, a controversy on which I’ll review in the present context on a subsequent occasion. Leaving nothing on the table, Briffa excluded the Khadyta River from the present reconstruction, pointing out that recent trees in this area had been growing poorly (thereby lowering the late 20th century uptick.) They state: the site report (and statistical evidence) demonstrating the anomalous “signal” in the Khadytla data lead us to omit them from the new Yamal chronology constructed here (see SM5 for details)

From the Washington Times, idiocy only bureaucrats could muster: “Since man first rubbed a pair of sticks together to make a fire, we’ve gathered around a campfire to cook food, enjoy good company and bask in the warmth of the … Continue reading →

Roman Murieka has a great statistical analysis of the Cook ‘consensus’ paper over at Climate Audit. There’s a surprise result: The number of papers endorsing AGW is falling, while the number of papers with no position is increasing. Looks like … Continue reading →

Yet another propaganda essay masquerading as a scientific paper has been published (SI here) in the journal, Environmental Research Letters.   The latest entry, Quantifying the Consensus on Anthropogenic Global Warming in the Scientific Literature, written by a team of activist bloggers led by John Cook of the antithetically named Skeptical Science blog,  attempts to further the meme of a 97% consensus of scientific support for a faltering Global Warming movement. There have been a number of posts, for example,  here, here and here at Lucia’s Blackboard or this one and that at WUWT which discuss the weak data gathering / data interpretation methodology and the truly incredible spin-one’s-head- around algorithm for generating a value of “97” which conveniently ignores a large proportion of the data.  My focus in this post will be to examine some of the other “quantifying” material. Given the virtual absolute absence  of available data and statistics in this paper, this will not be that easy a task.  The authors have apparently bought into the Climate Science tradition of why should I make the data available if you will only use it to prove me wrong.  I should point out that I have spent much of my academic career doing just that and become reasonably adept at recognizing situations where work might  be shoddy.  This is due to my experiences with consulting on academic research projects, Ph.D. and Master’s theses work as well as outside the university.  When  interviewing the researchers about their project, I would tell them that I would attempt to everything that was wrong with their planned research.  When it got to the point that this was no longer possible to do, I would be satisfied that the statistical aspects would be adequate for answering the questions that they wished to answer. One of the items that caught my eye was Figure 2b:  Percentage of self-rated endorsement, rejection and no position papers. This figure was discussed in the text of the paper: Figure 2(a) shows the level of self-rated endorsement in terms of number of abstracts (the corollary to figure 1(a)) and figure 2(b) shows the percentage of abstracts (the corollary to figure 1(b)). The percentage of self-rated rejection papers decreased (simple linear regression trend  -0.25% ±0.18% yr-1,  95% CI, R2 = 0.28; p =0.01, figure 2(b)). The time series of self-rated no position and consensus endorsement papers both show no clear trend over time. Figure 2(a) showed that the number of abstracts each year was increasing at a very high rate.  This implied that the variance of the percentages calculated for figure2(b) would change substantially from year to year.  Since a “simple linear regression” assumes homoscedasticity (i.e. equal variability) of the data from year to year, this would mean that the early years with very few abstracts would have an inordinately strong influence in the calculation of the parameter estimates as well as possibly distorting the interpretation of the significance of the results. As well, the model for this regression (which for convenience we will do for a probability rather than a percentage) looks like:  Pk = ? + ?Tk where Pk is the probability that a paper in year Tk will belong in the category for which the regression is being done (e.g. “rejection papers”).  An observation for that year will look like pk = nk / Nk where nk is the count of papers in years k and Nk is the total number of abstracts in that year.  One should note that pk is assumed to have a binomial distribution with parameters Nk and Pk.  The full model  becomes  pk = ? + ?Tk + ?k where ?k is a random variable with mean 0 and variance equal to Pk (1 – Pk ) / Nk . The data needed to create Figure 2(b) was, you guessed it, unavailable, so I digitized the data in the graph using a simple R programme.  The large symbols used in the graph were not helpful to the process, but I managed to get what appeared to be a very reasonable replica of the various plots. 

Guest essay by Nic Lewis The Otto et al. paper has received a great deal of attention in recent days. While the paper’s estimate of transient climate response was low, the equilibrium/effective climate sensitivity figure was actually slightly higher than … Continue reading →

Junkscience.com reports this is what the print copy looks like today for this article by Eugene Robinson. Note what looks like black unfiltered pollutants spewing skyward: But when you look at the original photo, you notice something different: The caption … Continue reading →

This is a guest post by Caroline Selle Much of the debate around the Keystone XL pipeline has focused on the dangers of extracting and transporting the tar sands. Left out, however, are those in the United States who are guaranteed to feel the impacts of increased tar sands usage. Spill or no spill, anyone living near a tar sands refinery will bear the burden of the refining process. Tar sands oil is produced from a mixture of sand, clay, water, and the sticky, peanut-butter like form of petroleum known as bitumen. Unlike conventional crude, it’s essentially solid at room temperature, has a higher heavy metal content, and has to be diluted for transport. The diluents are trade secrets, and the content mixture - which often contains benzene, a human carcinogen - isn’t something companies are required to report. DeSmogBlog has covered the impacts of tar sands extraction on indigenous communities, and the dangers of moving tar sands through a network of pipelines is aptly covered here. And while major nonprofits have completed studies on the dangers of transporting tar sands, there is significantly less information available on how refining tar sands differs from processing conventional crude. Additional heavy metals and benzene might sound like a recipe for disaster anywhere, but the location of several major tar sands refineries is already overburdened with pollutants. In Harris County, Texas – home to the city of Houston – people are already surrounded by refineries and factories spewing toxins into the air. And as the southern leg of the Keystone XL project slowly fills in its missing pieces, the spectre of toxic bitumen looms. Harris County, Texas, is home to the Lyondell Houston Refinery. The Lyondell facility is expanding its ability to refine tar sands bitumen, attempting to more than double its capacity to process Canadian crude. Nevermind that the refinery already has numerous Clean Air and Clean Water Act violations. If the facility expands as planned, its capacity will increase from 60,000 to 175,000 barrels a day. The air quality in Harris County is already dangerous without the added burden of tar sands pollution. Although levels of many chemicals have decreased in recent years, residents are reaping the rewards of living so close to massive amounts of carcinogenic pollution. A 2006 study by the Texas government found increased rates of certain cancers in the Harris County population. Children living in the areas with the highest level of 1,3 butadiene had a 153% higher chance of developing acute myeloid leukemia than those living in areas with the lowest concentrations of the same chemical. A 2003 study found a positive correlation between ozone density and asthma in Harris County residents, and the cancer risk of Houston residents living in the most contaminated areas is increased by a factor of 1000. Though the Lyondell website advertises winning “13 national safety awards in the past five years,” the Lyondell Chemical Co. has the second highest rate of disposing of toxics of any industrial facility in Harris County. The more you learn, the worse it gets. In 2011, Harris County accounts for 14.74 percent of total Toxic Release Inventory (TRI) measured chemical releases and transfers in Texas. Texas is already ranks 3rd highest (worst) in terms of pounds of toxics released an or transferred  - out of 56 states and territories. Lyondell Chemical Co. owns Houston Refining, a facility in Manchester, Texas which has been the subject of four formal EPA enforcement acts in the last five years. The company was fined $549,055 for violating the Clean Air Act, and yet, as of April, 2013, the facility has had 12 consecutive quarters of non-compliance. The area around Houston refining is 80% minority, with about one out of every four people below the federal poverty line. Despite these grim statistics, the refinery was granted a pollution permit from the Texas Council on Environmental Quality without the requested public he

Felt at my location in Northern CA, details when I have them. Nothing on USGS website yet.   5.7 11km WNW of Greenville, California 2013-05-23 20:47:07-07:00 About 40 miles away from me. Map: http://earthquake.usgs.gov/earthquakes/eventpage/nc71996911#summary Whole cluster of quakes near Lake … Continue reading →

Image Credit: Walter Dnes By Walter Dnes – Edited by WUWT Regular Just The Facts I have developed a methodology and spreadsheet to capture and chart HadCRUT3, HadCRUT4, GISS, UAH, RSS, and NOAA monthly global temperature anomaly data. Calculations are … Continue reading →