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):

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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).

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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.

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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.