DECR: The message starts to slide

In a recent interview, Kevin Hennessy backpedals on a key claim in the Drought Exceptional Circumstances report (DECR) explaining:

(1:20m) … there has not been a clear indication of changes in exceptional low rainfall years.
(1:40m) … but in terms of a long term trend its not very clear in terms of exceptional low rainfall years.

This totally contradicts the the confident expectations of more years of exceptionally low rainfall as stated clearly in the summary (emphasis added):

Continue reading DECR: The message starts to slide

Comparison of Models and Observations in CSIRO/BoM DECR

There must have been some way that the models of exceptionally low rainfall (drought) were validated in the CSIRO/BoM Drought Exceptional Circumstances Report. Usually, models are checked against observations to make sure they have ‘skill’ at the purpose for which they are intended. In this case, the global climate models used in the Drought Exceptional Circumstances Report must have been compared with observations, before they were used to show increasing frequency and severity of droughts in the next 30 years.

In Figure 10 of the report (above right), the exceptionally low rainfall observations are plotted against model projections. However, the figure is more of a cartoon and hard to read, so I replotted it using data from the report provided by Kevin Hennessy (above left). There are a number of other difficulties with Figure 10 that make it hard to see miss-match between the models and observations. The y axis is a large 30% exceptional low rainfall area, the drought observations are averaged over 10 years instead of the usual climatological average of 30 years, and the lines of the model are different again, with a jagged appearance. The confidence intervals are not standard deviations, but yet another novel metric. In replication of their figure 10, using R for the statistics, the last panel shows data for all regions.

On the figure in red are the 30 year moving average of observed percent area with exceptionally low rainfall. In black with dashed confidence intervals (1 s.d.) are the averages of the 13 models used in the study. This is the same data as figure 10.

It looks to me that in the last half-century of observations (1950-2007) in almost all regions droughts are decreasing (red), while the models show drought increasing (black). SW-WA and VicTas are a little different as the observations are constant, while models are increasing.

These graphical observation are consistent with the numerical results in Table 1 of the report Tests of Regional Climate Model Validity in the Drought Exceptional Circumstances Report that the regions SW-WA and VicTas are the only regions where the models of drought are not of the opposite sign to the observations.

So we conclude that while the observations of drought are decreasing in the last 50 years (in terms of a climatological average of frequency and areal extend of exceptional low rainfall), the projections of drought are increasing over the same period. This is supported by the last panel in the figure where all regions are averaged, the models predict increasing droughts, but the observations show decreasing droughts. The models go in the opposite direction to observations.

I have been in contact with Kevin Hennessy a number of times about the report, but unfortunately cannot report much progress. He maintains that the authors were satisfied with the validation of the models in the report, but has not provided details of the validation procedures or results that they used. Unfortunately the validation of the models was not reported in the DECR either. The only reference I can find is in Section 4.3 where it states:

The observations are generally within the range of individual model results.

It seems like the ‘skill’ they are thinking of is in the range of model results enclosing the observations (generally). But projections of linear regressions are the more usual way of projecting model results, and more statistically well defined. Moreover, their statement above is not quantitative, and is done by what is known as ‘eyeballing’. It is not a validation test, per se.

As the data provided by Kevin Hennessy as used in the report were obtained after considerable pressure from a number of blogs across the web, originally withheld due to ‘Intellectual Property’ reasons, I was hoping he would be more forthcoming this time.

Looking at the forward projections of the models, droughtedness increases in SW-WA, SWAust and VicTas, but decreases in the other models. Given that the trend of models don’t resemble the observations at all in the 1950-2007 period, I think little confidence can be put in these forward projections. It is very strange to find models performing so differently to observations, and yet the projections of the consequences of warming to be stated with such certainty, as in the quote from the summary (below). Note also that the regions where model projects decreasing droughts are interpreted as ‘little detectable change’, suggesting strong interpretive bias in order to create alarm over increasing droughts.

If rainfall were the sole trigger for EC declarations, then the mean projections for 2010-2040 indicate that more declarations would be likely, and over larger areas, in the SW, SWWA and Vic&Tas regions, with little detectable change in the other regions.

I am very curious to see the methods of validation they used and the actual results they obtained. I would also like to know why the results of the model validation were not reported.

Rybski Model Proof

This post is the first cut at R statistics for the Rybski approach to detecting change in global temperature. It follows Global Warming Statics giving the literature context to the analysis, and the introductory post July 2008 Global Temperatures that caused all the fuss at ClimateAudit.

The R script is here. You should uncomment the lines that grab the data after running it one time, or else it will be slow: e.g.
#tlt readRSS(file=TLT)
#crut readCRU(file=,temps=14)

The script contains a function implementing the Rybski statistic, based on Rybski et al. [2006].

D(k)(i,l) := X(k)(i) – X(k)(i–l)

This tests whether or not a climate variable, defined on a time scale k, has changed in a statistically significant sense, over l periods (starting from period i). Time scale i is the average of i periods. The periods could be in months or years.

The expression lag l/k refers to the period at a particular time scale k. That is lag 2/30 is the period of 60 years.

In order to verify the coding, I replicated figure 2 in Koutsoyiannis 2007. His figure is below, where he plots D(30)(i,1:4)

Original Caption: Figure 2 Graphical depiction of the pseudo-test based on StD[D] with known H. The continuous solid curve represents the CRU time series averaged over climatic scale k =30. The series of points represent values of D for the indicated lags l/k. Horizontal lines represent the critical values of the pseudo-test, which are the estimates of StD[D] times a factor 2.58 corresponding to a double-sided test with significance level 1% and assuming normality (only the positive critical values are plotted).

Below is my replication of his figure. The critical values are not right yet, a bit low compared with the original figure. But the values for D(30)(i,1:3) over the range are about right. You can get this graph by running kou(crut,k=30,DS=F,lags=3).

Below is the same result for D(30)(i,1) with the addition of D(30)(2007,j) in red. that is, instead of looking at the differences between each 30 years (l/k=1/30) it plots the difference in temperature from 2007 for every year from 1979 back to the start of the series in 1850. The values are the dotted lines, and the confidence limit is the dashed line. This plot can be obtained by running kou(crut,k=30,DS=T,lags=1).

Finally, here is the same result on the monthly satellite data from 1979 to the present (July 2008). The variables are D(30)(i,1) and D(30)(2007,j) above. This can be achieved by running kou(tlt,k=12,DS=T,lags=1).

The last value on the fine red dotted line, is essentially the value reported in July 2008 Global Temperatures. The difference in temperature between july 1979 and july 2008 is about 0.22. The critical value at that time is about 0.51.

What have we proved? The approach using calculations in the post July 2008 Global Temperatures finding that temperatures had not risen significantly since july 1979 is:

a) based on methods published in the peer reviewed literature by Rybski 2006 and Koutsoyiannis 2007.
b) the results were approximately correct, even when modified slightly to be consistent with the published methods.

I will be rewriting this script to express D as a function. Then I should be able to post an analyses that directly parallels Lucia’s analysis at her Blackboard (Where the Climate Talk Gets Hot!), only based on the Rybski method of detecting temperature rise instead of the trend based approach. I may not get back to this until later this week though as I have to go away.

Refrences:

Koutsoyiannis, D., and A. Montanari, Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resources Research, 43 (5), W05429.1–9, 2007.

Rybski, D., A. Bunde, S. Havlin, and H. von Storch (2006), Long-term persistence in climate and the detection problem, Geophys. Res. Lett., 33, L06718, doi:10.1029/2005GL025591.

Global Warming Statics

It is often stated that global temperature has increased over some specific time frame. Few realize there are different ways to answer this question, and the increase may not actually be significant, particularly in view of persistent correlation between temperature over long time scales (LTP).

In Statistical analysis of hydroclimatic time series: Uncertainty and insights Koutsoyiannis evaluates two publications using two different approaches to this issue: the evaluation of trends as done in Cohn, T. A., and H. F. Lins (2005), or as the simple change in temperature between two points as in Rybski et al. (2006).

Continue reading Global Warming Statics

July 2008 Global Temperatures

Much ado has been made about global warming stopping since 2001, since 1998 or not increasing in the last decade. Here’s more grist to the skeptic mill. The analysis below shows the global temperature has not increased significantly since July 1979!

Data are from the TLT Satellite measurements of the Earth’s lower troposphere at RSS MSU. When you calculate the global surface temperatures from July to July 1979-2008, the earth has warmed the grand amount of 0.295 degrees C. The standard deviation of the temperature changes for each July to July is 0.2522C, putting the change over 30 years at just over a non-significant one standard deviation (actually p=0.13, significant if p<0.05) of the expected change in just one year. Stated another way, every one out of eight years, global temperature changes by a similar amount to the total increase in the last 30 years.

Continue reading July 2008 Global Temperatures

William M. Briggs, Blogger

William M. Briggs, Statistician, is one of the outstanding technical blogs on the internet today. As indicated by the sub-title, “All manner of statistical analyses cheerfully undertaken”, it occupies a similar niche to Niche Modeling, recognizing and filling a felt need for basic statistical analysis of everyday events. The posts are often illustrated by programming in R code, providing a wonderful introduction to programming in R for statistics. The subjects range from global warming to clinical trials. As writing, the posts are literary, fluid and print-ready. In particular, W.M. Briggs is master of the arresting opening sentence, essential in a surfing medium. Here are some notable examples.

Says Paul Krugman, a writer for a local New York paper, “The only way we’re going to get action, I’d suggest, is if those who stand in the way of action come to be perceived as not just wrong but immoral.” He means “action” on man-made global warming. From Wrong -> Immoral -> Illegal?

The other day, as a favor, I posted a scientific article from a friend of mine, Dr H. Harrister, PhD, who conclusively showed that fitter people have larger carbon footprints than do fatter people. From Stop making babies to reduce global warming

Here’s the problem. You are a scientist, working on measuring the levels of aragonite in ocean water. It’s not very sexy and nobody beyond a small cadre seems to care. But it’s grant time and you and your team are “figuring out how to make the issue more potent” so that you can bring in the bucks. From At least they’re admitting it.

It is an understatement to say that there has been a lot of attention to the relationship of temperature and CO2. Two broad hypotheses are advanced: (Hypothesis 1) As more CO2 is added to the air, through radiative effects, the temperature later rises; and (Hypothesis 2) As temperature increases, through ocean-chemical and biological effects, CO2 is later added to the atmosphere. From CO2 and Temperature: which predicts which?

Much is made of the fact that these various GCMs show rough agreement with each other. People have the sense that, since so many “different” GCMs agree, we should have more confidence that what they say is true. Today I will discuss why this view is false. From Why multiple climate model agreement is not that exciting.

My friends, I need your help. From Quantifying uncertainty in AGW.

I often say—it is even the main theme of this blog—that people are too certain. You cannot measure a mean.

I am one of the scientists that attended the recent Heartland Climate Conference in Manhattan, where I live. It is my belief that the strident and frequent claims of catastrophes caused by man-made global warming are stated with a degree of confidence not warranted by the data. From Heartland Climate Conference Summary.

Kevin Rudd on Scientists

The Prime Minister of Australia, Kevin Rudd, made more indecorous remarks about scientists on 60 minutes last night. He obviously felt the need to retell his joke about scientists, first made on the 18th July, as nobody laughed the first time.

There’s a group of scientists called the International (sic) Panel on Climate Change – 4000 of them. Guys in white coats who run around and don’t have a sense of humour. They just measure things. 17 August 2008 — 60 minutes

Climate change is real, an international panel of climate change scientists, 4,000 essentially humourless guys in white shirts and white coats with their heads stuck down test tubes … 18 July 2008 — Interview with Rhys Muldoon, 666 AM Radio, Canberra

He might not realize that it is National Science Week this week. It must be like a blow to the midsection for teachers struggling to interest schoolchildren in science when the PM ad homs scientists at the start of their National Science Week events. Well I thought this joke was funny.

Continue reading Kevin Rudd on Scientists

Scientific Fraud and HIV/AIDS

Author and scientist Dr Henry Bauer invites reviews of his book “The Origin, Persistence and Failings of HIV/AIDS Theory” (McFarland 2007) at WikiChecks.

I was led through a by-the-way remark in a “dissident” HIV/AIDS book, and subsequent astonishment as I looked at the original source, to collect essentially all the published data on HIV tests in the USA. The demographics are stunningly regular, during more than two decades. The rates of positive tests, in any given group, vary uniformly with age, sex, and race, and the geographic distribution has remained unchanged; moreover it’s the same for different “risk” groups: blood donors, gay men, injecting drug abusers, military personnel. As to race, the differences are close to quantitative, roughly Asian 0.3–0.6 with White arbitrarily 1; Native American ~1.5, black more than 5. As to age, “HIV+” rates increase from adolescence into middle age and then decrease again; males test HIV+ more often than females except in the early teens.

Continue reading Scientific Fraud and HIV/AIDS

Avoid Bias in Climate Research

The author Paul Spite invites reviews at WikiChecks of his book “A Climate Crisis a la Gore.” It is organized as follows:

• Introduction – What motivated the assembly of this research for
public use.

• Part 1 – excerpted ideas from Mr. Gore’s book, The Assault on
Reason, regarding what he claims to be the proper and reasonable way to
enter an argument or evidence in the marketplace of ideas, the forum of
reason, the real power behind democracy.

Continue reading Avoid Bias in Climate Research

Temperature Index Drought

Following up on the post from yesterday, I test the assumption underpinning the regional climate change work in Australia.

The most common approach has been to assess how well each of the available models simulates the present climate of the region (e.g. Dessai et al. 2005), on the assumption that the more accurately a model is able to reproduce key aspects of the regional climate, the more likely it is to provide reliable guidance for future changes in the region.

As far as I can see this is an untested assumption, and may be a case of ‘accident chasing’.

Continue reading Temperature Index Drought

Western Australia Future Rainfall

I have started looking into the way GCMs are used to produce regional climate predictions. The method seems to consist of weighting GCMs according to their regional concordance. I wonder if they are aware of the pitfalls of ‘chasing higher correlations’.

The most common approach has been to assess how well each of the available models simulates the present climate of the region (e.g. Dessai et al. 2005), on the assumption that the more accurately a model is able to reproduce key aspects of the regional climate, the more likely it is to provide reliable guidance for future changes in the region. The method of weighting models is presented shortly. From Climate Change in Australia 2007, Technical Report – Chapter 5: Regional climate change projections (temperature and precipitation)

Continue reading Western Australia Future Rainfall

Feedback on Review of CSIRO Drought EC Report

Its time to address comments on my review of the CSIRO Drought Exceptional Circumstances Report. Thanks to Lazar for taking the time to provide the following feedback at Open Mind, placed at WikiChecks here. I have not yet received any comments from the authors, or Kevin Hennessy of CSIRO.

I think its clear that the core issue of the lack of skill of climate models at simulating frequency of extremely low rainfall is unaffected by Lazar’s points.

Why the Climate Audit / David Stockwell attack on CSIRO “Drought Exceptional Circumstances Report” is wrong.

Continue reading Feedback on Review of CSIRO Drought EC Report

Linear Regression Example

Here is an example of doing statistics in R, illustrating a pitfall of simple linear regression, using the global temperatures by satellite from 1979 to 2008. I have never seen the problem of ‘spurious compensation’ mentioned, but it is a common problem when trying to develop a model from a set of additive factors.

The linear regression below ‘invents’ large positive and negative factors that when added together, create a small difference that is a very good fit. The factors themselves are pronounced ‘highly significant’. Could ‘spurious compensation’ explain high positive forcing from CO2 and high negative forcing from aerosols found in some attribution studies?

Continue reading Linear Regression Example

Cherry-picking in Australia

One of the very first questions that a person who is promoting a model encounters from scientists and engineers is “has your model been validated?”. By validation we mean, has it been shown to adequately perform its intended use.

According to Charles M. Macal, Argonne National Laboratory, if the answer to this critical question is No, then
1. Experience has shown that the model is unlikely to be adopted or even tried out in a real-world setting
2. Often the model is “sent back to the drawing board”
3. The challenge then becomes one of being able to say “yes” to this critical question

I asked the validation question recently of the climate code red report from CSIRO, the Drought Exceptional Circumstances report, (DEC). The answer was No.

Continue reading Cherry-picking in Australia

Five Steps to Statistical Prediction

These are the generally accepted steps to prediction using statistics. When you obey these rules, you have taken out insurance by demonstrating good practice. The chances of reliable prediction are maximized. When steps are missed out or done badly, poor predictions result. Either people don’t know them, or they just forget a step, like validation in the Drought Exceptional Circumstances report. If you apply them to the latest climate change analysis and research, it is easier to see where the problems are. They are as follows:

1. Formulation
2. Calibration
3. Validation
4. Extrapolation
5. Replication

Continue reading Five Steps to Statistical Prediction

Selling Carbon Credits

For those interested in how to make money from carbon credits, here is an interesting example of a successful scheme from the Northern Territory of Australia. The WALFA project (West Arhnem Land Fire Abatement) certifies it will create a minimum annual carbon offsetting of 100,000 tonnes of cabon dioxide equivalent by controlling late-season wildfires. In return, Darwin LNG (Liquid Natural Gas) pays approximately $1 million per year to create a carbon abatement of 100,000 tonnes. Cheap at $10 per tonne. In 2007 the Northern Territory Government paid $130,000 to the Tropical Savannas Cooperative Research Center, $380,000 for indigenous employment, and $500,000 on vehicles and operations. The project is proudly proclaimed as producing ‘quadruple bottom line outcomes': economic, environmental, social and cultural.


Original Caption: Plate 1: Long grass in West Arnhem Land.
Source: Lendrum (2007).

However, the project does not reduce emissions of CO2, but methane and nitrous oxide. As it states:

The WALFA project abates carbon dioxide equivalent in the form of methane and nitrous oxide only; the carbon dioxide released by fire is assumed to be reabsorbed by the landscape in the next growing season.

The technique is to apply cooler early dry-season prescriptive burns, so there are fewer hotter late-dry season burns. One would assume there is good scientific evidence to back up the claims that cool burns produce less methane and nitrous oxide than hot burns. I have sent off a few requests for data to support this, and will update the post when I hear from them.

IPCC Predictions

I don’t recommend blogs or blog posts often; that’s for lazy bloggers. But here is an exception. William M. Briggs, Statistician is a man who beats his own path through the underbrush of uncertainty. His article, “Don’t be too sure” is worth reading. His polygenous blog is worth adding to your feed.

“I think the lesson for conservationists today is that, yes, the world is full of surprises. There’s a lot of uncharted territory.” I wonder if she’ll still feel the same way during the next round of fund raising.

This week’s Science magazine has an article (subscription required) on how Purdue is castigating Taleyarkhan. They suspected he fudged his data, but couldn’t prove it, so like the feds with Al Capone, they got him on a technicality

Doubt, therefore, is the proper emotion.

Now check out this letter from an APS member, Roger W. Cohen, in support of Lord Monckton’s paper published in the July APS newsletter:
Continue reading IPCC Predictions

Effects of Global Warming

My evaluation of the validity of the modelling in the CSIRO Drought Exceptional Circumstances Report, and the R code used to produce it, is available below. A pdf and invitation for review is posted on WikiChecks.

Tests of Regional Climate Model Validity in the Drought Exceptional Circumstances Report

Abstract

In a statistical re-analysis of the data from the Drought Exceptional Circumstances Report, all climate models failed standard internal validation tests for regional droughted area in Australia over the last century. The most worrying failure was that simulations showed increases in droughted area over the last century in all regions, while the observed trends in drought decreased in five of the seven regions identified in the CSIRO/Bureau of Meteorology report. Therefore there is no credible basis for the claims of increasing frequency of Exceptional Circumstances declarations made in the report. These results are consistent with other studies finding lack of adequate validation in global warming effects modeling, and lack of skill of climate models at the regional scale.

Directions for Doing Analysis Offline

Continue reading Effects of Global Warming

What is Niche Modeling Up To?

WikiChecks started out so I didn’t have to search for my reviews, then grew when I realized it might be of use to the whole community. I am still writing the software, but it is usable. I need help populating it with reviews, documents and web design. If you are slick with a digital camera, email any images. I thought of calling it McIntyre’s Jack Russell Terrier but few would have got the reference. It will be hosted on the landshape server for the moment, but I have registered the domain names wikichecks.org and wikichecks.com also. Below is from the FAQ:

All documents can benefit from independent checks. Whether you have a scientific article or document you would like checked, or would like one of your own torn apart (rationally) prior to publication, the goal of WikiChecks is to bring you together with someone who can add this value.

To explore the way the site works, follow the tabs at the top of this page.

If you have a document you want checked, you can add it to the site.

Registered users submit a document for checking (→ Documents). This includes a link to the document, and information on the type of checks required or desirable to perform. You can also contact me with the link or document and I will add it for you.

A some point, another user will enter a review of that document (→ Reviews). The reviews are also available for user comment, the same as a blog. The review includes some scores that are listed next to the review. Checkers might then determine if the data agree with the conclusions, the sources were accurately quoted, whether the correct statistical tests were performed, and whether they were statistically significant (see How to Check). After sufficient checks with satisfactory ratings, the review will be closed.

After a checker has completed a number of reviews they can become an expert checker. For checkers who have gained a reputation, potential clients can evaluate your work, specialization and experience, and in the near future, you can expect to earn money for your services.