While R is a vector language, it is mostly known as a language for statistics. The R language statistics capability and the packages developed by users to extend it is the main reason people come to use R. Read the rest of this entry…
CommentsUpdate: See “Recent Climate Observations: Disagreement With Projections” 2009, Energy & Environment.
CommentsProgramming in R is fast, enjoyable, powerful, and free. Below is an introduction to programming in the R language. R can be used for everything from data storage, analysis, modeling, graphics, and together with latex, writing articles. The R language program examples below can be run by pasting the code into the R console.
CommentsHere are notes about the package WhyWhere 2.2. This is a first version of a work in progress, for the convenience of collaborators.
CommentsThe following is a simple php application that dynamically generates a graph of past measured and simulated random future temperatures using a fractional differencing algorithm. In addition, the R2 and RE statistics are calculated for a short calibration period and displayed on the graph. This illustrates the pitfalls of the RE statistic for validation. A post on this application can be found here.
CommentsGets context sensitive random images for Wordpress as shown at left by querying altavista for thumbnails, and saving to named upload directories. Subsequent calls use random images from named directory.
CommentsThe Portal of the new WhyWhere 2.0 server available here. The algorithm has been rearranged to provide a streaming experience that works better for data mining large numbers of variables, interacting with web browsers, and embedding in other applications. This is the first predictive algorithm to be implemented in this way.
CommentsSince this controversial peer-reviewed paper was mentioned by Lubos Motl I have noticed increasing side-talk about it. It warrants more attention and effort to understand in depth.
While this is a mathematical paper, simple equations of radiative flux balance would present no challenge to people familiar with the field, although some of the concepts are new, such as the ‘virial theorem’. His previous paper, “The greenhouse effect and the spectral decomposition of the clear-sky terrestrial radiation (Miskolczi & Mlynczak 2004)“, provides a gentler introduction to the field of greenhouse spectral analysis, and is a better first read. As in this paper, he drops some real zingers. This paper redefines global warming. But not in a weird way. Of additional equations related to Kirchhoff’s law, constraining the atmosphere to be in thermal equilibrium with the surface, Miskolczi writes:
The physical interpretation of these two equations may fundamentally change the general concept of greenhouse theories.
The model suggests negligible sensitivity of 0.24C surface temperature increase to doubling CO2 increase.
CommentsHere is a simple statistical analysis using linear regression showing global warming of 0.2C this decade (as projected in the IPCC Fourth Assessment Report 2007) is “unlikely”.
Below are graphs for the last ten years and the trend line for global temperatures for four sources from Anthony Watts over the period January 1998 to February 2008. The simple linear regression line through the points shows the 10 year trend.
One of the main claims of the theory of global warming is that greenhouse gases in the atmosphere cause increasing temperatures. If temperatures stop increasing for long enough, while greenhouse gases such as CO2 continue to rise, then we could be justified in not believing the theory.
The basic numeracy skill from statistics is the hypothesis test. To set up the test we assume no difference between the datum being tested (called a null hypothesis or H0) and estimate the probability of assuming incorrectly, based on the data. The hypothesis test on these data would be as follows:
CommentsHow cold does it need to be to prove the theory of global warming wrong? What are the exact conditions under which global warming statements can be falsified? Over at the blackboard, Lucia has been giving this controversial topic well deserved attention. After all, it is pretty much agreed that scientific theories should be falsifiable. One would think that understanding of climate is flawed if three conditions occur together: a constant temperature trend, IPCC predicts a rising temperature trend, and CO2 continues to rise. She states:
If, the average trend for the years 2008-2017 is negative, then any trend of 2.0 C/year or higher, will be found to be “inconsistent with the measured trend” to the 95% confidence level. That is: current projections of that warming may be 2.0 C/year or higher will be falsified.
By my calculations using more robust statistics, it would take 20 years or more than twice as many years to produce a 95% confidence interval of 0.2C per decade or 2C per century.
The problem is, she makes some assumptions — which she correctly acknowledges — and one of the main ones is independence of the temperature data. On the contrary, temperature measurements are serially correlated to a degree that affects statistics such as measures of variability, particularly the standard deviation (SD). In a serial correlation the new temperature is determined in part by the previous one. Stated another way, there appears to be more information in the series than there really is. One of consequences is that the calculated SD is lower than it should be, making the confidence limits smaller than they should be.
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February global temperatures for 2008 have started to come out with UAH lower troposphere temperatures the first off the blocks. As reported by Anthony Watts global temperatures were stable with a slight increase of 0.05C from the january 2008 global temperature.
However, the big change in the UAH this month was in the southern hemisphere, continuing to decline to -0.21C from 0.02C the previous month.
Update: RSS reports a global increase of +0.06C and southern hemisphere change of -0.12C.
It will be interesting to see what happens to global temperatures in March 2008. In past years, downswings in temperatures have rapidly rebounded to higher levels. There is some evidence that this downswing corresponds with a discrete change in ocean circulation, in which case we may not see a rebound this time. Here are the links to the data sets.
CommentsHere is an expert strategy straight out of the natural world for rapidly increasing traffic to your website. We know that a great deal of traffic these days comes from search engines, based on keywords. This traffic is free, relevant, and never dies. It is also well known that posts with quality content are the key to attracting free traffic from web searches. It is less well known that posting on events well ahead of time is a very effective strategy for generating new traffic.
This expert SEO strategy is straight out of the natural world. Many organisms live by exploiting ephemeral niches. Because most food resources appear seasonally, where they can predict ahead, organisms prepare themselves to exploit it. A herd of caribou starts to migrate to pastures before spring arrives. Ants build nests before the summer rains.
The same pre-emptive strategy is very effective on the web. Of course, retailers have always used the strategy of stocking up on in-demand article ahead of time. Who is interested in buying Christmas decorations the day after Christmas? Similarly, a post after the event is less effective than a post before the event when interest is high.
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