Tag Archives: Experiment

News

Computer models utterly fail to predict climate changes

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From the Financial Post, an editorial by Ross McKitrick of the University of Guelph. He is an expert reviewer for the UN’s Intergovernmental Panel on Climate Change. (H/T ECM)

Excerpt:

[I]n 2008 and 2010, a team of hydrologists at the National Technical University of Athens published a pair of studies comparing long-term (100-year) temperature and precipitation trends in a total of 55 locations around the world to model projections. The models performed quite poorly at the annual level, which was not surprising. What was more surprising was that they also did poorly even when averaged up to the 30-year scale, which is typically assumed to be the level they work best at. They also did no better over larger and larger regional scales. The authors concluded that there is no basis for the claim that climate models are well-suited for long-term predictions over large regions.

A 2011 study in the Journal of Forecasting took the same data set and compared model predictions against a “random walk” alternative, consisting simply of using the last period’s value in each location as the forecast for the next period’s value in that location. The test measures the sum of errors relative to the random walk. A perfect model gets a score of zero, meaning it made no errors. A model that does no better than a random walk gets a score of 1. A model receiving a score above 1 did worse than uninformed guesses. Simple statistical forecast models that have no climatology or physics in them typically got scores between 0.8 and 1, indicating slight improvements on the random walk, though in some cases their scores went as high as 1.8.

The climate models, by contrast, got scores ranging from 2.4 to 3.7, indicating a total failure to provide valid forecast information at the regional level, even on long time scales. The authors commented: “This implies that the current [climate] models are ill-suited to localized decadal predictions, even though they are used as inputs for policymaking.”

Indeed. Nor is the problem confined just to a few models. In a 2010 paper, a co-author and I looked at how well an average formed from all 23 climate models used for the 2007 IPCC report did at explaining the spatial pattern of temperature trends on land after 1979, compared with a rival model that all the experts keep telling me should have no explanatory power at all: the regional pattern of socioeconomic growth. Any effects from those factors, I have been told many times, are removed from the climate data before it is published. And yet I keep finding the socioeconomic patterns do a very good job of explaining the patterns of temperature trends over land. In our 2010 paper we showed that the climate models, averaged together, do very poorly, while the socioeconomic data does quite well.

The computer models have to be able to predict changes in specific regions, otherwise we have no reason to trust that they are accurate. We have to be able to evaluate whether the models work by testing them. When we can test them to predict climate change in specific regions, they fail.

News

New study: Gorilla genome calls common ancestry thesis into question

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ECM sent me this must-read article from Evolution News.

Excerpt:

A whopping 30% of the gorilla genome — amounting to hundreds of millions of base pairs of gorilla DNA — contradicts the standard supposed evolutionary phylogeny of great apes and humans. That’s the big news revealed last week with the publication of the sequence of the full gorilla genome.

[…]The standard evolutionary phylogeny of primates holds that humans and chimps are more closely related to one-another than to other great apes like gorillas. In practice, all that really means is that when we sequence human, chimp, and gorilla genes, human and chimp genes have a DNA sequence that is more similar to one-another’s genes than to the gorilla’s genes. But huge portions of the gorilla genome contradict that nice, neat tidy phylogeny. That’s because these gorilla genes are more similar to the human or chimp version than the human or chimp versions are to one-another. In fact, it seems that some 30% of the gorilla genome contradicts the standard primate phylogeny in this manner.

[…]The bottom line is that the gorilla genome has confirmed that there is not a consistent story of common ancestry coming from the genomes of the great apes and humans. Hundreds of millions of base pairs in the gorilla genome conflict with the supposed phylogeny of great apes and humans. They might think their explanation salvages common ancestry, but clearly the gorilla genome data badly messes up the supposedly nice, neat, tidy arguments which they use to claim humans are related to the great-apes.

Read the whole thing to see how the Darwinists wiggle and squirm to avoid the implications of the data.

We have to form our beliefs about origins based on scientific evidence, not based on philosophical assumptions. It does not good at all to assume materialism and then determine in advance that some sort of physical process must be responsible for the diversity of life. We have to eschew philosophical assumptions and led the scientific experiments determine our conclusions. Experiments are good, naturalistic religious dogma is bad.

News

List of peer-reviewed papers supporting intelligent design now up to 50

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Ann Gauger working away in her lab
Molecular biologist Ann Gauger working in her lab

From Evolution News.

Excerpt: (links removed)

While intelligent design research is a new scientific field, recent years have been a period of encouraging growth, producing a strong record of peer-reviewed scientific publications. New publications continue to appear, now listed at our updated page.

The current boom goes back to 2004, when Discovery Institute senior fellow Stephen Meyer published a groundbreaking paper advocating ID in the journal Proceedings of the Biological Society of Washington. There are multiple hubs of ID-related research.

Biologic Institute, led by molecular biologists Doug Axe and Ann Gauger, is “developing and testing the scientific case for intelligent design in biology.” Biologic conducts laboratory and theoretical research on the origin and role of information in biology, the fine-tuning of the universe for life, and methods of detecting design in nature. That’s Dr. Gauger at the Biologic lab pictured above.

Another ID research group is the Evolutionary Informatics Lab, founded by senior Discovery Institute fellow William Dembski along with Robert Marks, Distinguished Professor of Electrical and Computer Engineering at Baylor University. Their lab has attracted graduate-student researchers and published multiple peer-reviewed articles in technical science and engineering journals showing that computer programming “points to the need for an ultimate information source qua intelligent designer.”

Other pro-ID scientists around the world are publishing peer-reviewed pro-ID scientific papers. These include biologist Ralph Seelke at the University of Wisconsin Superior, Wolf-Ekkehard Lönnig who recently retired from the Max Planck Institute for Plant Breeding Research in Germany, and Lehigh University biochemist Michael Behe.

Researchers have published their work in a variety of relevant technical venues, including peer-reviewed scientific journals, peer-reviewed scientific books from mainstream university presses, trade-press books, peer-edited scientific anthologies, peer-edited scientific conference proceedings and peer-reviewed philosophy of science journals and books.

These papers have appeared in scientific journals such as Protein Science, Journal of Molecular Biology, Theoretical Biology and Medical Modelling, Journal of Advanced Computational Intelligence and Intelligent Informatics, Quarterly Review of Biology, Cell Biology International, Rivista di Biologia/Biology Forum, Physics of Life Reviews, Annual Review of Genetics, and many others. At the same time, pro-ID scientists have presented their research at conferences worldwide in fields such as genetics, biochemistry, engineering, and computer science.

This body of research is converging on a consensus: complex biological features cannot arise by unguided Darwinian mechanisms, but require an intelligent cause.

My favorite area of ID research is the area of protein formation. I like to read about the research done by Doug Axe and Ann Gauger in that area. Research performed by Doug Axe at Cambridge University, and published in the peer-reviewed Journal of Molecular Biology, has shown that the number of functional amino acid sequences (ones that can form functioning proteins) is tiny:

Doug Axe’s research likewise studies genes that it turns out show great evidence of design. Axe studied the sensitivities of protein function to mutations. In these “mutational sensitivity” tests, Dr. Axe mutated certain amino acids in various proteins, or studied the differences between similar proteins, to see how mutations or changes affected their ability to function properly. He found that protein function was highly sensitive to mutation, and that proteins are not very tolerant to changes in their amino acid sequences. In other words, when you mutate, tweak, or change these proteins slightly, they stopped working. In one of his papers, he thus concludes that “functional folds require highly extraordinary sequences,” and that functional protein folds “may be as low as 1 in 10^77.”

The problem of forming DNA by sequencing nucleotides faces similar difficulties. And remember, mutation and selection cannot explain the origin of the first sequence, because mutation and selection require replication, which does not exist until that first living cell is already in place. I think that this very valuable research, indeed.

You can read more about the problem of protein synthesis in this previous post.