{"id":261684,"date":"2023-06-11T20:56:49","date_gmt":"2023-06-11T18:56:49","guid":{"rendered":"https:\/\/climatescience.press\/?p=261684"},"modified":"2023-06-11T20:56:52","modified_gmt":"2023-06-11T18:56:52","slug":"dig-deeper-learn-more","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=261684","title":{"rendered":"Dig Deeper, Learn More"},"content":{"rendered":"\n
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From Watts Up With That?<\/a><\/p>\n\n\n\n

Guest Post by Willis Eschenbach<\/em><\/strong><\/p>\n\n\n\n

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Over at Phys.Org, there\u2019s a\u00a0new article<\/a>\u00a0claiming the following:<\/p>\n\n\n\n

Order in chaos: Atmosphere\u2019s Antarctic oscillation has natural cycle, discover researchers<\/mark><\/em><\/strong><\/p>\n\n\n\n

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Climate scientists at Rice University have discovered an \u201cinternally generated periodicity\u201d\u2014a natural cycle that repeats every 150 days\u2014in the north-south oscillation of atmospheric pressure patterns that drive the movement of the Southern Hemisphere\u2019s prevailing westerly winds and the Antarctic jet stream.<\/p>\n<\/blockquote>\n\n\n\n

Here, from the\u00a0underlying study<\/a>, are their Fourier analyses supporting their claim.<\/p>\n\n\n\n

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\u201cHmmm\u201d<\/em>, sez I, \u201cseems kinda unlikely\u201d<\/em> \u2026 so I took a look at the study. For unknown reasons, the Antarctic Oscillation (AAO) is also called the Southern Annular Mode (SAM). The abstract says:<\/p>\n\n\n\n

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However, here we show using observational data, model data, and theory that SAM has an intrinsic 150-day periodicity arising from the internal dynamics of the extratropical atmosphere. This 150-day oscillation clearly influences the variability of the hemispheric-scale precipitation and ocean surface wind stress, suggesting broader impacts of this periodicity on the SH weather and climate.<\/p>\n\n\n\n

We also found that many state-of-the-art climate models cannot faithfully reproduce this periodicity, providing an explanation for some of the previously reported shortcomings of these models in simulating SAM\u2019s variability. Based on these findings, we propose new metrics and ideas for evaluating these models and understanding their shortcomings, and potentially, improving them.<\/p>\n<\/blockquote>\n\n\n\n

\u201cHmmm\u201d<\/em>, sez I \u2026<\/p>\n\n\n\n

So what is the Antarctic Oscillation (AAO) when it\u2019s at home? Well, it\u2019s the difference in average sea level \u201czonal\u201d pressure in the ring around the planet at 40\u00b0 South latitude, and the corresponding zonal pressure at 60\u00b0S. Here\u2019s a map of the area in question. 60\u00b0S is the dotted circle that goes between the tip of South America and Antarctica. 45\u00b0S is the next dotted circle outside of that one.<\/p>\n\n\n\n

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Original Caption, from the link below: \u201cSpatial pattern of the AAO.\u201d<\/em><\/p>\n\n\n\n

A description of how the AAO is calculated, the above graphic, and daily data for the AAO, are available from NOAA here<\/a> for the period 1871 to 2012.<\/p>\n\n\n\n

And how many observations do we have of the daily zonal pressure around the planet at 60\u00b0 South latitude from 1871?<\/p>\n\n\n\n

Well \u2026 approximately none. No land there. And for 40\u00b0S, maybe a few 1871 observations from Tierra Del Fuego in South America, or Tasmania or New Zealand \u2026 or not. Seriously. Almost none.<\/p>\n\n\n\n

But never fear, that\u2019s why we have \u201creanalysis\u201d climate models. These are climate models that are regularly kept from running too far off the rails by including whatever observations we do have.<\/p>\n\n\n\n

So, we start with a disadvantage. We have almost no observational data, so we\u2019re analyzing the output of a reanalysis climate model. Not auspicious.<\/p>\n\n\n\n

Setting that aside for the sake of discussion, I did a CEEMD analysis of the entire NOAA AAO computer model results from the site linked above. And what, you might reasonably ask, is \u201cCEEMD\u201d?<\/p>\n\n\n\n

CEEMD is \u201cComplete Ensemble Empirical Mode Decomposition\u201d<\/em>. Similar to Fourier analysis, CEEMD is a way to \u201cdecompose\u201d a complex signal into underlying \u201cempirical modes\u201d containing signals of various frequencies which, when added together, reconstitute the original signal. I describe the CEEMD analysis method in my post \u201cNoise-Assisted Data Analysis<\/a>\u201c. Here\u2019s one view of the CEEMD analysis of the full NOAA AAO results.<\/p>\n\n\n\n

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Figure 1. CEEMD analysis, full NOAA AAO dataset. This shows the empirical modes C1 to C14. The colored lines show the strength of the underlying signals at various periods that combine to make up the original signal.<\/em><\/p>\n\n\n\n

This shows that the AAO is comprised of a variety of signals ranging in length from about 40 to 1,000 days. However, the strongest signal is not at 150 days. Here\u2019s a closeup of the range of the above graphic from 100 to 200 days.<\/p>\n\n\n\n

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Figure 2. As in Fig.1, but showing the range from 100 to 200 days. The peak showing the maximum value is at 183 days. There\u2019s nothing of note at 150 days.<\/em><\/p>\n\n\n\n

Now, having looked at dozens and dozens of CEEMD analyses, I know that there are often what I call \u201cpseudocycles\u201d in natural datasets. These are cycles that appear at some given time, persist for some length of time, and then disappear. So before I declare that a real enduring cycle exists, I run the same analysis on subsets of the data. If there is a true persistent cycle in the data, it will show up in each of the subsets.<\/p>\n\n\n\n

In this case, I divided the data into four quarters and analyzed each one separately. The results are shown below, starting with the earliest quarter of the data.<\/p>\n\n\n\n

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Figure 3. As in Fig.2, but showing the first quarter of the data. As in the full dataset, the peak showing the maximum value is at 183 days. There\u2019s nothing of note at 150 days.<\/em><\/p>\n\n\n\n

Now, the results are scaled so that the strongest cycle in the entire dataset has a value of 1.0. In the full dataset and the earliest quarter shown above, that\u2019s been the 183-day cycle. But here\u2019s the second quarter of the data.<\/p>\n\n\n\n

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Figure 4. As in Fig.2, but showing the second quarter of the data. As in the full dataset, the peak showing the maximum value is at 183 days. There\u2019s a smaller peak at 147 days.<\/em><\/p>\n\n\n\n

There are a couple of differences in the second quarter of the data. The 183-day cycle is not the strongest cycle in the dataset. That cycle is at 365 days, an annual cycle. The 183 and 147 day cycles are only about half the strength of the annual cycle. Here\u2019s an expanded graphic with data out to 400 days showing the strongest cycle, the annual cycle.<\/p>\n\n\n\n

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Figure 5. As in Fig.4, but showing the cycles out to 400 days. The largest peak is now at 365 days<\/em>.<\/p>\n\n\n\n

\u201cHmmm\u201d<\/em>, sez I \u2026 moving on to the third quarter we find the strongest peak is again at 365 days, but \u2026<\/p>\n\n\n\n

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Figure 6. As in Fig.2, but showing the third quarter of the data. The peak showing the maximum value is again at 365 days (not shown). The largest peak in the 100-200 day range is at 153 days, the closest we\u2019ve come to their value. However, there is another peak of nearly the same size, at 172 days.<\/em><\/p>\n\n\n\n

Curiouser and curiouser. Bear in mind that we\u2019re moving from the computer reanalysis model output of the oldest quarter, the one with the least actual observational data, towards modern times when we actually at least have a few, however sparse, observations in the area of interest. Here\u2019s the final quarter of the data.<\/p>\n\n\n\n

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Figure 7. As in Fig.2, but showing the fourth and most recent quarter of the data. In this part of the data, the peak showing the maximum value is not at 365 days (not shown). Instead, it is at 144 days.<\/em><\/p>\n\n\n\n

\u201cHmmm\u201d<\/em>, sez I \u2026 \u201cnot seeing it.<\/em><\/p>\n\n\n\n

Next, I thought I\u2019d look at how big these underlying cycles are. One of the strongest ones is the 183-day cycle in the first quarter of the data \u2026 so here is the best fit of a 183-day and a 150-day sine wave to the first quarter of the AAO data.<\/p>\n\n\n\n

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Figure 8. Best fits of 183-day (red) and 150-day (yellow) sine waves to the AAO first quarter data.<\/em><\/p>\n\n\n\n

Note that the largest regular cycle in the data, the 183-day cycle, is quite small \u2026 and the 150-day cycle is basically nonexistent.<\/p>\n\n\n\n

There\u2019s another way that we can verify all of this. Here\u2019s a Fourier periodogram of the first quarter of the AAO.<\/p>\n\n\n\n

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Figure 9. Fourier periodogram of the first quarter of the AAO data.<\/em><\/p>\n\n\n\n

Note that as in the CEEMD data, there are a whole host of cycles from around 40 days to 1,000 days. The 183-day cycle is the largest, and the cycles with periods around 150 days are far smaller.<\/p>\n\n\n\n

And note also, Figure 9 shows the same result as Figure 8\u2014the 183-day cycle is quite small, only 8% of the total range of the data.<\/p>\n\n\n\n

[UPDATE]<\/strong>\u00a0Bob Weber points out in the comments that they used a\u00a0different dataset<\/a>\u00a0for their AAO data, available by FTP. I got that. It\u2019s much shorter, only since 1979. Here are the relevant graphics of the FTP 1979-on dataset:<\/p>\n\n\n\n

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Figure 10. CEEMD analysis, full NOAA AAO FTP 1979-on dataset. This shows the empirical modes C1 to C14. The colored lines show the strength of the underlying signals at various periods that combine to make up the original signal.<\/em><\/p>\n\n\n\n

Curiously, in this dataset the largest cycle is around 650-750 days.<\/p>\n\n\n\n

Here\u2019s the range from 100 to 200 days of the FTP 1979-on dataset.<\/p>\n\n\n\n

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Figure 11. As in Fig.10, but showing the range from 100 to 200 days. The peak showing the maximum value is at 145 days. However, it\u2019s quite small.<\/em><\/p>\n\n\n\n

Here\u2019s the best fit of the 145-day sine wave to the FTP 1979 on dataset:<\/p>\n\n\n\n

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Figure 12. Best fit of 145-day (red) sine waves to the AAO FTP 1979-on full data.<\/em><\/p>\n\n\n\n

And here\u2019s the Fourier periodogram of the AAO FTP 1979-on data.<\/p>\n\n\n\n

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Figure 13. Fourier periodogram of the first quarter of the AAO FTP 1979-on full data.<\/em><\/p>\n\n\n\n

The 145-day cycle is there, but it is tiny, only a bit more than 4% of the range of the data. As you can see, as far as finding a significant 150-day cycle, this FTP 1979-on data is even worse than the data I originally used.<\/p>\n\n\n\n

What can we conclude from all of this? Well, I\u2019d draw these conclusions.<\/p>\n\n\n\n