Below are my replications of Figure 4 and Figure 5 of the controversial paper “Influence of the Southern Oscillation on tropospheric temperature” by J. D. McLean, C. R. de Freitas, and R. M. Carter.
Above is the comparison of global atmospheric temperature (RATPAC-A) with the SOI, smoothed with a flat 12 month filter.
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.
CommentsA new page in the “abouts” section lists my non-self-published contributions to discrediting AGW since my transmogrification in 2004 to a skeptic remarkably similar to the one described by Luboš Motl in his review of Ralph Alexander’s new book. Read the rest of this entry…
CommentsThe Hon Wilson “Ironbar” Tuckey MP on the ETS. Worth a read. ETS doesn’t have the numbers in the Senate — via Crikey.
CommentsThe revision of the Copenhagen Synthesis Report was advertised at the ANU Climate Change Institute, directed by Prof. Will Steffen. But they just can’t seem to get it right. The ANU web site refers to Stefan Rahmstorf as Stefan Rahmonstorf.
Ian Castles on the July 5th, 2009 compiled the list of amendments of errors. Below is an update of the current situation.
Swanson’s projection for future temperature using presumed regime shifts can be compared with our projection.
The above figure was posted at RealClimate (note this figure is not contained in their paper) with flat temperature intersecting with an extrapolated underlying rate of around 1C/Century seen between 1950 and 1998 at around 2020.
Our projection for temperature, from figure 3 of our paper archived at arXiv and submitted to IJF, Structural break models of climatic regime-shifts: claims and forecasts is based on the current flat rate intersecting with an extrapolated underlying rate of around 0.5C/Century between 1910 and 1976, at around 2050.
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.
RealClimate was so concerned with our paper Structural break models of climatic regime-shifts that they felt compelled to post a paper by Swanson and Tsonis in response 
, see Warming, interrupted: Much ado about natural variability.
There are a number of similarities and differences. Firstly, ST09 uses a very different method, fitting periodic fluctuations in ocean temperature to spatially distributed data. Regime-shifts are recognized with a measure of coherence between areas, and their theory is that shifts are more likely when synchronized. With this approach, they arrive at major shifts in 1976 as we do, and in 2002, after the big 1998 El Nino.
Using a simple Chow test for structural breaks, we get a break in 1978 and also in 1997, just before the big El Nino. These dates are within bounds of error, and differences in the method, so one might as well say they are consistent.
CommentsRenewable energy is a nice idea, but Peter Lang crunches the numbers and finds solar and wind power are crushingly expensive, do little for greenhouse gas reduction, and are ecologically dangerous. Cap and trade is actually a giant scheme to tax and redistribute, for the benefit of political insiders.
A letter submitted by Peter Lang argues that the numbers prove nuclear power is the only way.
Solar realities: Solar power is uneconomic. The capital cost of solar power would be 25 times more than nuclear power to provide for demand. The minimum power output, not peak or average, is the main factor governing solar power’s economic viability. The least cost solar option would emit 20 times more CO2 (over the full life cycle) and use at least 400 times more land area compared with nuclear Government mandates and subsidies hide the true cost of renewable energy.
Wind realities: Wind power does not avoid significant amounts of greenhouse gas emissions. Wind power is very high cost way to avoid greenhouse gas emissions. Wind power, even with high capacity penetration, can not make a significant contribution to reducing greenhouse gas emissions.
Nuclear power is the least-cost, low-emission electricity generation technology that can provide the large amounts of electricity needed to power modern economies.
CommentsPublished 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.
Just for clarity, the influential Rahmstorf 2007 paper that is contradicted by published evidence here, was irrevocably discredited by his own admission here, that apparent increased climate sensitivity was only due to ‘weather’.
In hindsight, the averaging period of 11 years that we used in the 2007 Science paper was too short to determine a robust climate trend. The 2-sigma error of an 11-year trend is about +/- 0.2 ºC, i.e. as large as the trend itself. Therefore, an 11-year trend is still strongly affected by interannual variability (i.e. weather).
Its not discredited by the smoothing method necessarily, that involves padding the data series, although padding data series is problematic in a number of ways.
Questions about the validity of the smoothing in Rahmstorf et al 2007 (see CA here for a belly-laugh) invariably turn into questions about how to smooth in general. With so many ways to do it, what is the right or best way?
One aspect of the question is illustrated in the figure below, which is a replication of Rahmstorf 2007 done a few different ways. The blue lines, are the trends using singular spectrum analysis (SSA) with embedding periods of m=11 (dashed) and m=14 (solid) respectively, and the minimum roughness criterion (MRC). Embedding period makes some difference. The red lines, however, are the result of applying ssa without MRC. This makes a bigger difference. (The smooths are translated for readability).
