So now the fun starts. We have established the integration order of the variables in the RadF file, we impose the rule that only variables of the same order can be combined, and in particular that they cannot be cointegrated with temperature which is I(1). In this case all the anthropogenic variables in RadF are I(2) — W-M_GHGs, O3, StratH2O, LandUse, SnowAlb, BC, ReflAer, AIE — while Solar and StratAer are I(1) or I(0).
CommentsThe test of integration order from the previous post is applied to the major atmospheric forcings used in the GISS global climate models in recent years. These are available for 1880 to 2003 in a file called RadF.txt The codes for the forcings are self explanatory: W-M_GHGs, O3, StratH2O, Solar, LandUse, SnowAlb, StratAer, BC, ReflAer, AIE.
CommentsI try not to pen editorials. OK here goes. I respect the attention given to this blog, as there are plenty of other great blogs on climate change, politics, finance, etc to read. I try to stay an ‘on message’ advocate for numeracy. Everyone has something to offer from their experiences though. Right at this moment, there is something to say that is important about numeracy, but takes a bit to explain.
CommentsPlease discuss the new paper by Michael Beenstock and Yaniv Reingewertz here.
Way back in early 2006 I posted on an exchange with R. Kaufmann, whose cointegration modelling is referenced in the paper, entitled Peer censorship and fraud. He was complaining at RealClimate about the supression of these lines of inquiry by the general circulation modellers. The post gives a number of examples that were topical at the time. ClimateGate bears it out.
Steve McIntyre wrote a long post on the affair here.
Comments[R]ealclimate’s commitment to their stated policy that “serious rebuttals and discussions are welcomed” in the context that they devoted a post to criticize Ross and me and then refused to post serious responses. In this case, they couldn’t get away with censoring Kaufmann, but it’s pretty clear that they didn’t want to have a “serious” discussion online.
Environment minister Peter Garratt claimed recent figures on Australian temperature prove Opposition leader Tony Abbott was wrong to claim that the world had stopped warming.
Substitute Australia for the World, and the last 100 years for 10 years, and you might get close to the actual claim, similar to that made by respected climate physicist Roy Spencer that “there has been no net warming in the last 11 years or so”.
It’s easy — but confused — to find a limited region with a different climate, over a different time period, then use it for rebuttal.
David Jones of the BoM claims, referring to their Annual Report that the upward trend of temperature since 1920(?) should silence the critics once and for all.
CommentsOur approach so far has been to model natural climate variation of global temperature with sinusoidal curves, and potential AGW as increasing trends. A new algorithm called EMD (Empirical Mode Decomposition) promises to more robustly identify cyclical natural variation (NV), showing the contribution of NV and AGW to global temperature, and testing the IPCC claim that most of the recent warming is due to AGW.
Underestimation of natural variation (NV) is a crucial flaw in the IPCC’s logic, according to Dr Roy Spencer:
They ignore the effect of natural cloud variations when trying to diagnose feedback, which then leads to overestimates of climate sensitivity. … By ignoring natural variability, they can end up claiming that natural variability does not exist. Admittedly, their position is internally consistent. But then, so is all circular reasoning.
The relative contribution of AGW to temperature increase in the late 20th century underpins the IPCC global warming claims, according to the Wiki page on Scientific Opinion on Climate Change:
National and international science academies and scientific societies have assessed the current scientific opinion, in particular on recent global warming. These assessments have largely followed or endorsed the Intergovernmental Panel on Climate Change (IPCC) position of January 2001 that states:
An increasing body of observations gives a collective picture of a warming world and other changes in the climate system… There is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities.[1]
Since 2007, no scientific body of national or international standing has maintained a dissenting opinion.
So estimating the relative proportion of natural variation vs. trend is very important. While widely used in other fields, EMD is relatively little used in climate science.
As an example, Lin and Wang (2004) used EMD for analysis of solar insolation. They claim that the solar eccentricity signal is much larger than previously estimated, more than 1% of solar irradiance, and adequate for controlling the formation and maintenance of quaternary ice sheets. This is a potential resolution of the 100,000 year problem, that has also been used to justify the necessity of CO2 feedback in producing ice ages.
Conventional spectral methods are strictly periodic — the period is constant in both frequency and amplitude. EMD relaxes these assumptions, allowing quasi-periodicity, which might explain why more variation is potentially explained. The EMD algorithm proceeds by first extracting out the highest frequency, called an intrinsic mode function (IMF) and leaving a residual. It does this to the next highest frequency, and so on, until only a trend is left.
While it is possible the residual is also part of a cycle — it is always possible to model a trend with a sinusoidal of long enough period — we treat this as AGW trend in order to estimate the maximum possible contribution of AGW to global warming.
Here are the results of applying EMD to the CRU global temperature series. Figure 1 below shows each of the 5 IMF’s and the residual, the remainder after subtracting out the periodics.
Each of the IMF’s is shown, with mean periods of 4.0, 6.6, 11.9, 23.4, and 55.1 years respectively. Most readers would be well aware of the similarity of these periods to major solar and oceanic cycles.
CommentsThis numeracy exercise for schools can be adapted to grades pre-up. You need:
1. A number of strong magnets, at least two per group
2. Iron filings
3. Selection of nuts of various metals: iron, copper, brass, some lead sinkers.
Here is the abstract for our comment submitted to Geophysical Research Letters today. Bob Tisdale is acknowledged as the source of the idea in the first paragraph. Lets see how it goes. If you would like a copy, contact me via the form above.
Update: Now available from arXiv
Comment on “Influence of the Southern Oscillation on tropospheric temperature” by J. D. McLean, C. R. de Freitas, and R. M. Carter
David R.B. Stockwell and Anthony Cox
Abstract
We demonstrate an alternative correlation between the El Nino Southern Oscillation (ENSO) and global temperature variation to that shown by McLean et al. [2009]. We show 52% of the variation in RATPAC-A tropospheric temperature (and 59% of HadCRUT3) is explained by a novel cumulative Southern Oscillation Index (cSOI) term in a simple linear regression model and 65% of RATPAC-A variation (67% of HadCRUT3) when volcanic and solar effect terms are included. We review evidence from physical and statistical research in support of the hypothesis that accumulation of the effects of ENSO can produce natural multi-decadal warming trends. Although it is not possible to reliably determine the relative contribution of anthropogenic forcing and SOI accumulation from multiple regression models due to collinearity, these results suggest a residual accumulation of around 5 ± 1% and up to 9 ± 2% of ENSO-events has contributed to the global temperature trend.
CommentsA potential AGW buster, attributing decadal temperature variation largely to internal oceanic effects, ENSO and over the longer term the 1976 Great Pacific Climate Shift, as we did here, is a new paper by Australian John McLean, with New Zealander Chris de Freitas, and Australian ex-pat Kiwi Bob Carter.
That mean global tropospheric temperature has for the last 50 years fallen and risen in close accord with the SOI of 5–7 months earlier shows the potential of natural forcing mechanisms to account for most of the temperature variation.
While the bottom line of this paper is that the change in SOI accounts for 72% of the variance in global temperature for the 29-year-long MSU record and 68% of the variance in global temperature for the longer 50-year RATPAC record, I think the claim of a longer term temperature effect could have been better supported. They stated:
Lean and Rind [2008] stated that anthropogenic warming is more pronounced between 45°S and 50°N and that no natural process can account for the overall warming trend in global surface temperature. We have shown here that ENSO and the 1976 Great Pacific Climate Shift can account for a large part of the overall warming and the temperature variation in tropical regions.
However, the assertion comes down to Figure 4 where they identify that the mean of the SOI (and temperature) seems to change at 1976. This model is not identified rigorously with any analysis, but is stated as an observation in the text.
CommentsThe replication of the highly influential Rahmstorf 2007 A Semi-Empirical Approach to Sea Level Rise, one of the main sources of projected sea level rise, was reported in the previous post.
In a now discredited (and disowned) Rahmstorf et al 2007 publication, Steve McIntyre showed that Rahmstorf had pulled an elaborate stunt on the community by dressing up a simple triangular filter with “singular spectrum analysis” with “embedding dimensions”, I can now report another, perhaps even more spectacular stunt.
His Figure 2 is crucial, as it is where the correlation between the rate of sea level increase, deltaSL, and the global temperature, Temp, is established. If these were not correlated, then there would be no basis for his claims of a significant “acceleration” in the increase in sea level when temperature increases, and his estimates of sea level rise by 2100 would not be nearly so high.
It is well known that smoothing introduces spurious autocorrelations into data that can artificially inflate correlations, and one of the comments on his paper (attached to the first link above) picked up on this. Rahmstorf’s procedure introduces no less than 5 different types of smoothing to produce his Figure 2:
1. singular spectrum analysis – the first EOF
2. he then pads the end of the series with a linear extrapolation of 15 points
3. convolution, (or 15 point filtering)
4. calculates the linear trend from 15 points (on the sea level data only)
5. binning of size 5
I replicated his procedure in the previous post in the series. Here, the entire procedure is substituted with a single binning (averaging each successive M data points). The figure below compares the Rahmstorf procedure at parameters m=13:16 (red line), and the result of binning the same data into bins of size m=13:16 (black line). The sea level data is differenced after binning to get a delta SL.
Published in Science, this Rahmstorf 2007 article provides a high-end estimate of sea level rise of over a meter by the end of the century (rate of 10mm/yr). Linear extrapolation puts the rate of increase at only 1.4mm and 1.7mm per year depending on start date (1860 or 1950).
The paper was followed by two critical comments, both bashing the statistics, and these are attached to the link above. Rahmstorf replied to those comments. The issues raised are familiar to readers of this, CA, Lucia, and other statistical blogs: significance, autocorrelation, etc. and worth a read.
Worthwhile as the comments are, they do not look into the problem of the end-treatment used by Rahmstorf, and I look at that here.
All of the papers projecting these high end rates, and they all depend on the assumption of recent ‘acceleration’ in sea levels. That is, seem to depend on the rate of increase getting faster and faster.
Rahmstorf 2007 paper uses the smoothing method most recently savaged at CA here, where it was shown despite all the high-falutin’ language to be equivalent to a simple triangular filter of length 2M, padded with M points of slope equal to the last M points. My main concern is that at this crucial end-section, the data has been duplicated by the padding, effectively increasing the number of data points of very high slope.
The figure below shows a replication of the Rahmstorf smoothing with and without padding (moved down for clarity) (code below). Two sea level data sets are shown, one by Church “A 20th century acceleration in global sea level rise” (used in Rahmstorf, data available from CSIRO here) another by Jevrejeva “Recent global sea level acceleration started over 200 years ago?” (data here)
It should be noted this data ends in 2001-2, a truncation bound to maximize recent temperature increases.
Download: Structural break models of climatic regime-shifts: claims and forecasts
Anthony asked if it would be difficult to statistically justify the breaks in temperature between 1976 and 1979 proposed by Quirk (2009) for Australian temperature. He has an interest in oceanographic regime-shifts and climate change. Sure, I said, a simple Chow test.
We ended up rebutting the Easterling & Wehner (2009) claim that describing temperatures since 1998 as declining is ‘cherry picking’, finding a major regime shift occurred in 1997, statistically justifying the use of 1997 as a starting point for temperature trends.
A regime-shift based temperature forecast follows logically from identification of significant breaks. Our paper, “Structural break models of climatic regime-shifts: claims and forecasts“, has been submitted to the International Journal of Forecasting, and is downloadable from arXiv.
The code for plotting the non-linear temperature trend, using SSA (singular spectrum analysis) in the figure below is here – ssa-demo. I have made it as turnkey as I can. The steps are:
1. Get and Install package ssa (http://r-forge.r-project.org/projects/ssa/). I had to hand-compile and move the C shared library around so it would find it, not sure why.
2. Run the script below with source(”filename”). Uncomment line indicated after the first time to speed it up. You should get the following replication of the Rahmstorf figure with 11 and 14 embedding period.
Still on my way home, after the lecture at Newcastle University by Miklós Zágoni and myself, this will be short. The lecture was well attended, with around 50 people — surprising considering the campus is on a break and parking at a premium. The lectures were well received with a very engaged and relevant question time. There were some suggestions of disruption by anti-skeptics, but they did not eventuate.
Transcript: errors-of-agw-science
Powerpoint: newcastle-presentation
Press Release: herald-article-14-4-09
Miklós Zágoni talk: long version
CommentsMiklós Zágoni and I will be speaking in a public lecture at 1pm on Wednesday the 15th of April at the Engineering faculty, Newcastle University, in lecture theater ES203. Miklós will speak on the theory of Ferenc Miskolczi and I will give a short introduction of the work from the blog in the last 3 years in the global warming arena.
A much longer version of my talk is incorporated into a new “Highlights” page.
CommentsSome time ago I had a brief discussion with Leif Svalgaard on ClimateAudit blog inspired by an exchange between Leif and David Archibald when the latter complained that Leif’s TSI reconstruction was “too flat”.
The sunspots exhibited cyclic variability in terms of the frequency of the cycles and that most thermostats work by pulse width modulation and some digital music with pulse frequency modulation. Both these work in a similar manner the thermal inertia of whatever the thermostat is controlling smooths the temperature variability and the pulse frequency modulation’s demodulator is a simple low pass filter often just a series resistor and shunt capacitor. In both these cases only the duty cycle or the frequency varies but not the amplitude. Below is a description of how this behaviour can be simulated with an electrical circuit emulator called ‘qucs’.
CommentsEdward Vul, Christine Harris, Piotr Winkielman, & Harold Pashler have published research that provides useful insights into the practice of ‘cherry picking’ or prior selection of desirable results leading to exaggerated significance. They also demonstrates the effect in a comprehensive survey of studies in the field of social neuroscience.
CommentsGlobal warming real’ say new studies according to the Financial Times, February 18, 2005. Tim Barnett of Scripps Institute of Oceanography crowed: Read the rest of this entry…
Should we believe the cosmic ray flux theory (CRF)? Here I attempt to answer this question quantitatively, by calculating the strength of evidence so-far presented for CRF as a major forcing factor in climate change. Specifically we need to ask, what is the probability of being wrong about CRF? This can be calculated by combining the significance values of independent lines of evidence.
CommentsEvery month we conduct a competition to guess the change in global temperatures for the previous month. The results from RSS normally are released on the 5th of the month. Voting is now open for February. You can place your vote below.
RSS global temperature in the lower atmosphere increased 0.148C from the previous month. The two early leaders in the ‘Guess the monthly global temperatures’ competition are still CoRev and Jan Pompe:
CommentsNir’s 2005 paper “On climate response to changes in the cosmic ray flux and radiative budget”, available as pdf here, provides a solid case linking cosmic ray flux (CRF) variations to global climate change. The effect is consistent over hugely different timescales, using completely different indicators — from cosmic sources of CRF at the Phanerozoic, to the shortest time scale of the 11-yr solar cycle. The fit is extraordinary. The statistics competent. The bottom line?
CommentsThe following is an approximate propagation of uncertainty through Dessler et als. equation for estimating the strength of water vapor feedback λ. We have been looking at the error-bars in his recent paper Water-vapor climate feedback inferred from climate fluctuations, 2003-2008, not calculated in the published paper. Assumptions made are noted. Refer to wiki for propagation of error equations.
CommentsA concerned reader sent me this recent paper Water-vapor climate feedback inferred from climate fluctuations, 2003-2008, writing:
CommentsThe following (ala Hansen) IMO should never have been accepted in a "peer reviewed" journal. "The existence of a strong and positive water-vapor feedback means that projected business-as-usual greenhouse gas emissions over the next century are virtually guaranteed to produce warming of several degrees Celsius. The only way that will not happen is if a strong, negative, and currently unknown feedback is discovered somewhere in our climate system."
Ian Castles organized a review of the Drought Exceptional Circumstances Report by two Accredited Statisticians, who also review my first report on the skill of the climate models.
The statisticians find inadequate validation of the models of drought, as well as suboptimal regionalization in the DECR. They also find my analysis lacked force, and so I have done additional analysis in line with their suggestions.
The last few posts in the series have consisted of reviews of an unsuccessful submission to the Australian Meteorological Magazine (AMM), showing how contradictions between models and observations were suppressed from the conclusions of the DECR. These reviews cover similar ground from a different angle: the skill of the climate models in the DECR, failing to identify any real skill in the predictions of drought, and ways of showing variation between the model (increasing drought) and their real world observations (decreasing drought) at the climatic time scale.
Below are the abstracts:
Some comments on the Drought Exceptional Circumstances Report (DECR) and on Dr David Stockwell’s critique of it
K.R.W. Brewer1 and A.N. Other1
28 January, 20091. K.R.W. Brewer is an Accredited Statistician of the Statistical Society of Australia Inc. (SSAI) and a long term Visiting Fellow at the School of Finance and Applied Statistics within the College of Business and Economics at the Australian National University.
2. A.N. Other is a pseudonym for another Accredited Statistician of the SSAI who prefers to remain anonymous. Full responsibility for the content is taken by K.R.W. Brewer.Abstract
The Drought Exceptional Circumstances Report (DECR) was authored by a team drawn from the CSIRO and Australia’s Bureau of Meteorology, and was publicly released in July 2008. Almost immediately it became a source of controversy. This evaluation, both of the Report itself and of the critique of it written by Dr David Stockwell, finds good mixed with less than good in both. The DECR itself is criticized for its poor delineation of Regions within Australia, for the choices made of statistics to be constructed, for the manners of their construction, and for not getting the best out of the relevant available data. Dr Stockwell is criticized for his inappropriate choices of methodology and of time periods for analysis, and also for misunderstanding some parts of what the DECR’s authors had chosen to do. Nevertheless, both the Report itself and Dr Stockwell’s critique of it are welcome stimuli to further investigate a serious issue within the climate change debate.
Validation of Climate Effect Models: Response to Brewer and Other
David R.B. Stockwell
February 4, 2009Abstract
A review by independent Accredited Statisticians, Brewer and Other [KB09], suggested that some claims in the report “Tests of Regional Climate Model Validity in the Drought Exceptional Circumstances Report” [DS08] were premature. Additional tests suggested by KB09 support the claim made in the original report of “no credible basis for the claims of increasing frequency of Exceptional Circumstances declarations”. The contributions of KB09 and DS08 to the evaluation of skill of climate model simulations with, arguably, weakly validated idiosyncratic statistics are discussed. These include recommendations for greater rigor in evaluating the performance of climate effects simulations, such as those used in standardized forecasting practices [AG09].
One thing is clear, the climate models that all of these predictions rely on have not been validated to accepted standards. That is a major lapse on the part of the climatologists who nonetheless use the models to influence public opinion and action.
Contrast the quality and professionalism of the review by statisticians, with the error-ridden categorical reviews by climate scientists to the AMM article. The greater rigor of the statisticians is clearly evident.
Comments1. K.R.W. Brewer is an Accredited Statistician of the Statistical Society of Australia Inc. (SSAI) and a long term Visiting Fellow at the School of Finance and Applied Statistics within the College of Business and Economics at the Australian National University.
2. A.N. Other is a pseudonym for another Accredited Statistician of the SSAI who prefers to remain anonymous. Full responsibility for the content is taken by K.R.W. Brewer.
The Drought Exceptional Circumstances Report (DECR) was authored by a team drawn from the CSIRO and Australia’s Bureau of Meteorology, and was publicly released in July 2008. Almost immediately it became a source of controversy. This evaluation, both of the Report itself and of the critique of it written by Dr David Stockwell, finds good mixed with less than good in both. The DECR itself is criticized for its poor delineation of Regions within Australia, for the choices made of statistics to be constructed, for the manners of their construction, and for not getting the best out of the relevant available data. Dr Stockwell is criticized for his inappropriate choices of methodology and of time periods for analysis, and also for misunderstanding some parts of what the DECR’s authors had chosen to do. Nevertheless, both the Report itself and Dr Stockwell’s critique of it are welcome stimuli to further investigate a serious issue within the climate change debate.
CommentsThe Financial Times recently reported on the Australian bushfires, linking them to increases in greenhouse gases. We take another look at the data in the DECR and find Australia is getting wetter not drier:
CommentsPeter Gallagher reports that even while the coals are still warm, some are already blaming the Victorian fires on increases in greenhouse gases.
The following summarizes indications of decline in droughts in Australia from 1900 to the present, compiled from data provided with the Drought Exceptional Circumstances Report. Some of this information was provided in the submission the the Australian Meteorological Magazine (more about this tomorrow). Drought is defined as the percentage of area with rainfall lower than the 5th percentile. The areas are averaged over seven Australian regions.
Read the rest of this entry…