Paz pointed out that the normalization used previously might not remove geographic biases introduced by fugitive weather stations, so here is another approach. I have differenced each of the records, averaged the differences and then cumulative summed the result.
If you have seen the articles on the NZ temperature adjustments and Nordic temperature adjustments you might be interested in the Australian data.
The issue with NZ and Nordic data that the raw temperature data for weather stations do not show the temperature increases indicated by the IPCC, raising the question of how the data have been adjusted.
As Prof. Karlen states in the ClimateGate email #1221683947, temperature at many stations has not exceeded early 20th century temperatures:
.. data sets show an increase after the 1970s to the same level as in the late 1930s or lower. None demonstrates the distinct increase IPCC indicates.
Here is the plot of means of Australian raw data for 103 temperature stations, based on the file Aus.tab downloaded from the Australian BoM web site and collated by Steve McIntyre.
Below are the results of applying the EMD algorithm (Empirical Mode Decomposition) to Australian Rainfall, and predicting the future rainfall with a VAR model (Vector Autoregression).
First, EMD splits the rainfall into IMF’s (Intrinsic Mode Functions) that are cyclical but variable in amplitude and frequency.
CommentsThe one message I’d like to convey is we do not need to rush on this. This will be around to examine and feed our discussions for a long time to come. If we start right, it will go better for us. There seems to be some indications of possible unethical behaviour, if these are true representations of email communications. It isn’t right to tell people to delete emails that may be the subject of FOI requests, at the very least. But we don’t need to pile onto this right now.
Have you noticed a distinct change in the rhetoric around global warming? Seems like revisionism going on in the mainstream media in the form of shift in focus to the most likely values, or expectations of global warming, rather than emphasizing the low probability, worse possible scenario.
For example, this one on sea level from nature.com.
CommentsSea level rise – not so fast.
In the latest salvo of the scientific debate over future sea level rise, a new report counters claims that rapidly swelling seas will soak estimates published by the UN climate planel in 2007.
A major “it’s worse than we thought” story out of March’s Copenhagen Climate Congress, for example, was that sea level could climb more than a metre by 2100 – seemingly far worse than the rise of up to 59 centimetres indicated in the 2007 report from the Intergivernmental Panel on Climate Change (IPCC). This was in fact something of a straw-man comparison, since the IPCC total explicitly excluded the impacts of accelerated glacier melt, and the new studies were attempting to add these impacts in.
How to predict with EMD? Because the EMD algorithm decomposes time series into a number of periodics of different frequency (IMFs), and a residue trend, prediction in EMD is by extrapolating each of the IMFs separately (a VAR model is recommended) and fitting a cubic polynomial to the residue (example code at end of here). The predictions are then added together.
Below are a couple of examples of EMD predictions on familiar data sets, the HadCRU global surface temperature, and the TLT series from the satellite MSU RSS data. In both I have also applied the recommended prediction techniques to extend the result into the future.
The first is the satellite TLT, and I have used up to IMF 5 so that variation up to annual quasi-periodicity is represented. The residue in this case, the red arch, has peaked and is projected to decline, along with the overall temperature, in the next few years. The amplitude of variability is also declining.
Our 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.
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