El Nino is back with the Fury of a Woman Scorned!

OK, not really. But the headline is kind of catchy, no?

El Nino is, in fact, back. And to hear some of the early prognostications about it, we would all melt like the Wicked Witch of the West mighty soon. And this was going to prove once and for all that global warming was real, because – we heard – the recent cooler temperatures were a byproduct of recent La Ninas. (Please forgive my laziness in not including the squiggly lines over my n).

I admit to not quite understanding that argument. The skeptics among us have pointed out that the increase in global temperatures that took place a decade ago were driven by a Super El Nino. And at the time, we heard that global warming was causing more severe El Ninos. But then the severity decreased and we had La Nina, and we were told that such statements were never really made. Or, at least, not by serious scientists. Which, if true, would mean that they should have agreed that the increase in warming at that time was exacerbated by the big and mean El Ninos. (Which, as an aside, brought very enjoyable winters in the Midwest. Why do people want to send us really cold weather all the time?) But other than some footnoted statements on page 23 of the reference section in a boring document, few people have been told the story about how El Nino affects should be viewed independently from overall warming.

That is, they didn’t know this until La Nina affects brought us some cooler temperatures. Then, suddenly, we heard about some “unusually cold” La Ninas, and how this affected global temperatures, and skeptics were being disingenuous by not properly considering that. And to the extent that such a criticism is true, they are right. But there is a strange thing that happens when ideology is part of the equation: you fail to heed your own criticism when the reverse occurs.

And so we have now seen three consecutive measures above 0.5 in the ENSO index. This is hardly unusual, but it does qualify – to my understanding – as a true El Nino. And before that, the La Nina waned, so we had a relatively neutral index for a couple months leading up to El Nino. So it’s been 5 consecutive measurements now since the La Nina has ceased. I remember when it became evident that an El Nino was on the way. This was going to prove skeptics wrong! Why? I have no idea. If El Nino had an anomaly of 1.00, 2.00, or 5,432.00 it would not prove anything other than when there is a natural warming of the Ocean, it warms our global temps. Wow… there’s a revelation. The fact that this has nothing to do with Carbon emissions is beside the point when it fits the argument.

Even stranger, skeptics tend to accept the cyclic variations as the legitimate explanation for warming. We don’t dispute warming periods. So, the skeptic will nod and agree that an elevated ENSO index will probably lead to warmer global temperatures. But then, we kindly point out, don’t blame carbon. Or people. And don’t get all in a tizzy when a La Nina comes around and we see cooler temperatures. What the hell do you expect? Sorry it doesn’t fit the model.

Having said all that, I certainly don’t expect any records to be broken in this recent El Nino. Sorry, experts. I base this simply on data analysis, admittedly knowing very little about all the climatolological influences that could prove me wrong. But what does the data indicate? Looks like it’s time for a chart:

ENSO Data as of 200908

The first observation from the data is that we’ve had four consecutive positive anomalies, and three consecutive positive anomalies greater than 0.5. Note here that a single data point is actually a two-month running average, which helps smooth out month-to-month fluctuations. The latest reading is 0.978, which is the largest of the four positive anomalies. Prior to this period, there were 9 consecutive negative anomalies, with a stretch of 7 months less than -0.50. This was on the heels of only a two month set of barely positive anomalies after a stretch of 12 consecutive negative anomalies that included an eith-month stretch less than -0.5.

So, it is pretty clear that after some real solid La Nina-esque reality, we’ve now flipped to El Nino. What is not clear is the ultimate magnitude and persistence of our new friend, Mr. Nino. But we can talk likelihoods. And for that, we observe the path of the best-fit sine wave.

The red curve below has been fitted in accordance with the other Ocean Oscillations I have observed. Take a sine wave and manipulate it in a few ways in order to ascertain the minimum least-squares deviation from the curve. You see, while El Nino exhibits noticeable short-term variation, it seems to do so about a longer-term cyclical pattern. Thus, a large deviation in one direction at point A on the curve will not produce the same magnitude El Nino at point B on the curve.

The specifics of the best-fit curve are as follows: The 1950 starting point in the data looks to be at 268 degrees in the full 360 degree cycle. The length of the best-fit curve appears to be 102 years for a full cycle. This is an imperfect estimate, since we don’t even have 102 years of data. It is also a longer fit than what was made last year when I did a similar exercise. But the calculation is what it is.

You can see from the chart that the magnitude of ENSO events can have quite a range: -2 to +3 in the data provided. The scale factor applied to the wave is +1.24 in order to achieve the best fit. However, it looks as if the anomalies in the index may be significantly overstated, at least near the beginning of the curve. The best fit line requires an upward shift of all values of the curve of +0.98. This means that the early part of the curve should have appeared “colder” than it did. The interesting thing to me is that, despite the apparent rise in the average ENSO index levels, the best-fit curve actually has a negative linear slope element to it that is pretty significant: -0.00316, or -3.792 degrees Celsius per Century. This actually means that those high El Nino anomalies are centered around a curve that, without that negative trend line, would have been significantly higher – possibly as much as a degree and a half.

So, where are we now? We are 122 degrees into the cycle, which means we have a ways to go into the negative yet, if this best-fit curve is correct. While it appears to the eye that we’re past the 180-degree point, this is not so because of the negative linear slope the curve lies along. No, if this is right, we will not reach the minimum depth of the ENSO curve until around 2050. The curve itself has a staggering implication of coldness – what was a depth of around -0.4 degrees in the 1950s would be -4.0 degrees in 2050. Should we proceed along these lines, we can continue to expect positive and negative significant deviations from the curve, as we see today. But the positive deviations will produce fewer, shorter and less severe El Ninos while the negative deviations produce more, greater and more persistent La Ninas.

OK, here’s the good news: unlike climate modelers, I don’t proclaim this analysis to be infallible. First of all, we’re fitting the best curve to data that is quite variable in its short-term fluctuations. Second of all, the best-fit curve tells us that the cycle period is a longer period than the data period for which we are evaluating. I already know that this supposed cycle period has fluctuated quite a bit from analysis a year ago.

If I had to rank my certainty on the subject, I would bet confidently that (1) there is a long-term ENSO cycle of somewhat indeterminate period, probably somewhere between 60 and 100 years, (2) that we are entering the negative phase of the cycle and we can expect less severe El Ninos and more severe La Ninas.

I am far less certain about the linear trend of the cycle, and the extent of any such trend, as I am about the shift of the curve. These elements are probably much better measured as more data arises over time.

However, in any case, I think it looks very unlikely that we will see any record-breaking El Ninos for quite some time, in either persistence or in magnitude. We may, however, see some major La Ninas surface over the next few decades.

And that won’t be our fault, either.

A Gander at the Caribbean Oscillation Index

We hear a lot about the ENSO index, the PDO Index, and the AMO Index.

I’ve also taken a look at the Arctic and Antarctic Oscillation Indices.

But there are more. Quite a few more, actually. While I have not done a comprehensive study on these, myself, one would assume that the reason we hear about the PDO, AMO, and ENSO is because these have quite evidently shown correlation to weather patterms that affect a lot of people. And it may well be the case that these are the main drivers that matter, and all the other ones have only negligible contributory effects.

Nevertheless, some time ago I decided I wanted to take a closer look at these. Readers here will notice that I’ve been focusing on Ocean Index posts recently. Based on the wide discussion that has broken out on these posts (yes, that’s sarcasm) it doesn’t appear that this is the main point of interest to a lot of people. That’s OK. Hopefully it’s interesting at some level. But it’s an important aspect of a full study on global temperature. And I still have the goal of doing a full analysis at some point combining the impacts of all these measures, along with solar cycles and CO2 levels.

As the next step in this journey, I have compiled the data from the Caribbean index. It is found as part of this data set (under the CAR column). The permanent link to this is on the right of this page.

I wasn’t sure what to expect on this index. As you recall, the polar regions didn’t demonstrate any shorter-term cyclicality to speak of, while the majors that everyone speaks of did show clear cyclicality.

The results of the raw data plot with the best-fit sine curve are shown here:

Caribbean Oscillation Data as of 200908

It certainly looks as if there’s some cyclical thing occurring here, based on a best-fit analysis. It’s not quite as predominant as the AMO and PDO, but it’s there. There do seem to be short-term spikes with some months of persistence, similar to ENSO.

The best-fit line has the following parameters: A phase reduction of 0.61 degrees per month implies a full cycle of 49.15 years. There is a vertical shift downward of -0.053 needed, with a linear trend of 0.000187 – which is a rate of 0.224 degrees Celsius per Century.

Thus, the index, on average, has been understated enough to be noticeable and has exhibited an upward trend over time. This longer-term upward trend is more noticeable in the charts below as we collapse the data into longer-term rolling averages. Caution is needed to make sure we understand the autocorrelation, but even considering that there is an upward trend in the Caribbean surface temps exhibited.

Similar to ENSO, however, there look to be shorter-term spikes that may play more immediately into the local temperature/weather patterns. Since October 1989, we’ve seen the following stretches of positive/negative anomalies (keep in mind that the best-fit implies that there should be a shift from these figures):

198910 – 199108: 23 consecutive positive anomalies
199109 – 199410: 31 of the 38 months – negative anomalies
199411 – 199601: 15 consecutive positive anomalies
199602 – 199703: 5 consecutive negative / 4 consecutive positive / 3 negative / 1 positive / 1 negative
199404 – 199910: 31 consecutive positive anomalies
199911 – 200007: 9 consecutive negative anomalies
200008 – 2007121: 89 consecutive positive anomalies

Since then, it’s been back and forth.

Caribbean Oscillation Data as of 200908 - 12 month Smoothing

Caribbean Oscillation Data as of 200908 - 5 Year Smoothing

Caribbean Oscillation Data as of 200908 - 10 Year Smoothing

September 2009 Update on the AMO

I’ve looked at the AMO data set to see what it’s been up to lately, and after a brief plunge into negative territory, it’s come back to a level in accordance with the current sine wave path of the cycle. If you recall, there was a period of 77 consecutive months of positive AMO anomalies, with January 2009 finally breaking that string of positives. We then had 5 consecutive negative anomalies. Historically, such a deviation is not necessarily unexpected, as can be easily enough seen from chart observation. However, it was not an expectation for a persistent state, and we’ve now seen the last three anomalies come in at 0.175, 0.282, and most recently 0.205.

While these are positive anomalies, viewing them relative to the red wave line on the chart shows us that they are right around what would normally be expected.

When looking at certain Oceanic oscillations, the cycle is very clear, and the strength of a given anomaly should be placed in the context of the overall wave path. Deviations from the wave are a stronger indication of the unusualness of an anomaly than the actual value of it.

As I’ve noted in my previous write-ups, we’re nearing the peak of the wave. We should expect many more positive AMO anomalies. In fact, we should expect more positive AMO anomalies for the next 20 years (well, sort of – I’ll explain why this is probably shortened artificially in a bit). Since the wave is at its peak, we will soon see continually cooler anomalies for the next 34 years or so. However, the next few years will still be positive, but dropping.

Here’s the chart:

Atlantic Multidecadal Oscillation Data as of 200908

Maybe it’s just me, but this is one of my favorite charts. Admittedly, the change in it is nearly imperceptible on a month-by-month or quarter-by-quarter basis. But one reason I present it occasionally is because I think it really tells a good story.

The story begins with the cyclicality, which is very evident. This, combined with ENSO and PDO (and I’ll keep looking at other indices) show a clear cyclicality in Ocean Temperatures. In my humble opinion, this should raise a red flag with regard to temperature correlations to anyone who even remotely wishes to get to the truth of what drives global temperatures. Making this cyclicality more clear is the fact that the Arctic and Antarctic Oscillations do not show this behavior at all. Click on the link related to the Arctic to read more about the PDO and ENSO cycles as well.

The next story is, where are we in the cycle? As I’ve alluded to, we are at the peak of the cycle. That means that the anomaly has, on average, risen continually for over the last 30 years. The most rapid point of acceleration of the wave occurs in the early-mid 1990s. Since then, it’s been increasing at a decreasing rate. In a couple years, it will start decreasing at an increasing rate for the next decade and a half.

The AMO is about a quarter-phase out of sync with the PDO (Again, see link above – click on the word “Arctic”). Thus, the PDO has been decreasing over the last number of years. As we’ve seen in the Temperature Charts we’ve been flat for about 12 years now, and we’ve actually seen decreasing temperatures over the last handful of years. This seems to correspond will with an AMO that is only slightly increasing and a PDO that is cooling off more rapidly.

Antarctic Oscillation

I thought I’d start taking a look at the different Oceanic Oscillations to see if there’s anything at all I can glean from the data.

The first set of data is the AAO Index data which goes back to 1979 here. This could be a mistake, but I pulled in data from this source for the pre-1979 numbers. The reason I say it could be a mistake is because the latter source’s numbers do not match all that well to the former’s. This puts the pre-1979 data in question as it relates to the newer data. And, as the chart results came into view, I think we see that splicing this data probably does more harm than good. With that said, I have kept it in for this view of the data, but may choose to eliminate it in future reviews.

We’ll start here with the raw data, and a fitted line that I will explain:

Antarctic Oscillation Data as of 200908

The current anomaly value is -0.686, which is the fourth consecutive negative anomaly. Previously, there were 11 consecutive positive anomalies.

The line on the chart isn’t actually a linear fit, though it is very close. It is actually a best-fit sine wave. However, there are no short-term waves evident in the data, so the best-fit wave is a long-term wave that completes a 360-degree phase over 12,000 years. Obviously, trying to make a strong case for a 12,000 year cycle is a bit silly based on 60 years of data. Nevertheless, it’s interesting to consider that the earth’s precession of the poles moves along this approximate time period. It’s also worth noting that this same approach on the Arctic Oscillation yielded a sine wave of 9500 years. I make no assertions as to the accuracy of this, I just find it a point of interesting congruence.

So, with a 12,000 year fitted wave, the 60 years essentially has a linear fit associated with it. In the chart, it is clearly an upward trend. However, observe the differene between the pre-1979 and post-1979 areas of the chart.

Two things are apparent: (1) the volatility in the anomaly values is much higher prior to 1979, and (2) the upward trend occurs in the pre-1979 data.

Putting numbers to these observations:
1) The Standard Deviation of observations for the data prior to 1979 is 1.749. The standard deviation for 1979 – current is 0.988. The average positive anomaly prior to 1979 is 0.960. The average positive anomaly 1979-current = 0.746. The average negative anomaly pre-1979 is -1.778. The average negative anomaly for 1979-current is -0.830.

2) The slope from 1948 – 1978 represents warming of 5.26 degrees Celsius per Century. The slope from 1979 – current represents 0.68 degrees warming per Century.

It seems pretty clear that the data prior to 1979 is a different animal than post-1979.

The NOAA and Claims of the Highest August Ocean Temperature

I was going to write a post on this whole thing, but before I did, I decided to scan some of the otehr blogs. It would appear that I have little to add to this, so I am gooing to link to them instead.

Here’s the basic summary: NOAA announced that we’ve just seen the highest Ocean Surface temperatures in, like, ever! Here’s the link at Watts giving us that story.

Jeff at The Air Vent also provided us with a skeptical take on the announcement. His post has since been updated, to now refer to ICECAP’s Joe D’Aleo, who summarizes the now-discovered issue:

Icecap Note: to enable them to make the case the oceans are warming, NOAA chose to remove satellite input into their global ocean estimation and not make any attempt to operationally use Argo data in the process. This resulted in a jump of 0.2C or more and “a new ocean warmth record” in July. ARGO tells us this is another example of NOAA’s inexplicable decision to corrupt data to support political agendas.

Bob Tisdale now has a guest post on Watts Up With That, showing the incongruence in NOAA’s assessment versus others.

This is so blatant, it’s almost amazing.

Whatever one’s thoughts on climate change, nobody should stand for such shoddy science, changes in methodology, and exaggerated statements of “fact.”

It does nobody any good.

AMO Update – June 2009

The Antlantic Multidecadal Oscillation data has been released and the May index value is -0.014. That is the fifth consecutive negative value, and it’s the first time we’ve seen 5 consecutive negative months since the period ending October 1994. Previous to this negative stretch, there had been 77 consecutive positive months.

Similar to my look at the ENSO index, I have updated my best-fit sine wave against the AMO data. The chart is presented here:

Best-fit wave pattern against AMO data.

One nice thing about the AMO data as compared to the ENSO data is that it goes back to the mid 1800s. I cannot speak to the robustness of the index value, particularly for the older periods. I can only assume that it’s as good a measure as we have to work with.

A couple things can be noticed about this graph, especially in relation to the ENSO chart in the post referenced above. First of all, we are right now at the peak of the AMO wave. It last reached a trough in 1979, and has been increasing for the last 30 years. Since we are now at the peak, we will be descending down that wave now for the next 30 years, if the cycle is as presented. It should, however, remain in the warmer mode for the next 15 years of so, even though overall it is in a decline. Compare this to ENSO, where we just crossed the zero line.

It appears, then, that the ENSO (and PDO, as well) is offset almost exactly 90 degrees from the AMO. If true, this helps explain the contribution to warming over the last 30 years, and the stagnation of the last few years. 30 years ago, the AMO wave was at its trough, and the ENSO wave had crossed the zero line, moving upward. The next 15 years showed a situation in which all three waves were increasing. AMO from its trough up to the zero line, and ENSO/PDO from zero to its peak. Then, AMO increased while ENSO/PDO decreased, but both were above the zero line, so temperatures remained elevated, but the increase in temperature slowed and/or stagnated. Is it just coincidence? I suppose it could be. I consider that doubtful, however, because of the identification of waves in the HadCrut data that I presented.

Well, today, we are in the opposite siutuation as we were 30 years ago. The AMO is at a peak, and the ENSO/PDO index is at zero. It would seem, then, that we will experience significant cooling, along the same order as the warming of the 1980-2000 period over the next 20 years or so. Then, the period of time after that will likely be a stagnation of colder weather for 10-15 years, before we see the next warming cycle occur.

I’m sort of repeating what I did in this post. But it bears repeating. It seems fairly obvious to me, and it’s almost implausible to believe that such a relatively straighforward analysis that shows the Ocean cycles and how it affects the temperature cycles is not considered noteworthy when projecting forward temperature changes.

The other thing to note regarding the AMO chart above is that we seem to prematurely be getting some negative index values. I wouldn’t read much into that. Looking back at the chart, there are numerous times where negative readings – even consecutive ones – occur during the warmer side of the wave. In fact, the negative readings we see probably do indicate that we are in the warm wave yet, because the magnitude isn’t great, despite some persistence.

I may just be a layman and a data guy, but between this kind of analysis, a quiet sun and the analysis I’ve done as temperature relates to that, and the simple observation that we haven’t warmed in 12+ years, I can’t help but feel somewhat perturbed at the continuing warnings of global warming, and outright amazed at the audacity of predictions of multi-degree temperature increases by the end of the Century.

At this point, I would give odds on cooling until 2030 or so, regardless of what the fancy climate models say. It’s the only reasonable conclusion by looking at strict data. I suppose maybe the models say something that can’t be foound in the data. We’ll see.

ENSO Update – A Bounce Upward

The ENSO Index has been updated for May month-end, and we see a two-month average index value of 0.344.

This is the first reading that is positive since last July, but last year the positive index values barely reached above 0 for a short, two-month stay before dipping back into La Nina territory.

The new value, strangely enough, seems to have certain pro-AGW members of the blogosphere salivating about how it looks like there is a new El Nino on the way. Interestingly, the same people who wring their hands about what warming is going to do to us can’t seem to wait for higher temperatures driven by a new El Nino. They are as much as telegraphing the fact that they will use El Nino-driven elevated temperatures, should they occur, to assist in their case for the idea that CO2 is increasing temperatures. It is unfortunate that they will likely have a voice in this claim – reasonable or not – since there is no correlation between the ENSO cycles and Carbon Dioxide levels (at least that I am aware of).

In the meantime, we can look at the data to see what’s going on with the index. El Nino and La Nina have somewhat sketchy official designations, but I think it’s a fairly common rule of thumb to say that when the index reads above 0.5 for three consecutive months, we’ve got ourselves an El Nino. When the index goes below -0.5 for three consecutive months, it’s a La Nina. I have an interesting observation regarding that a little later on. Based on that rule of thumb, it looks like we’ve come out of a recent La Nina in March. The last one wasn’t particularly strong or lengthy, though it did come on the heels of a stronger one the year before.

In April, the index increased quite a bit, though the 2-month average was still negative. The May value increased again from that level. However, talk about a new El Nino, while entirely possible, is a bit premature. We’re not even at one data point that qualifies yet.

We know that both the upside and downside happens whether we’re in a cooling or warming cycle, so regardless of what side of the argument you are on, you can’t really make any wild claims about what the latest cycle means. It’s probably more accurate to assess the ENSO index over time.

I have updated the best-fit wave pattern against the available ENSO data and shown it below:

Best-fit wave pattern against ENSO data.

The best-fit cycle shows around a 60-year wave pattern. We are now entering the downside of that wave. One thing I noticed is that the best-fit requires a vertical shift upward of the wave. This means that the zero-point of the index should probably be higher than it is. The latest maximum, for example, reached a level of 0.4687 on the crest of the wave (meaning that El Nino will be elevated by nearly half a degree during that time) and the latest trough of the wave was -0.3966 (El Nino will be lowered by about 04 tenths of a degree). The index should really be calibrated down by about 0.05 of a degree. Otherwise, the significance of El Nino will be overstated while the corresponding La Nina will be understated.

The best-fit ENSO wave pattern actually has a negative linear trend that makes a longer-term extrapolation questionable. I didn’t particularly believe that element of it. It fits the current data well, but the gut-check test tells me it would be best to simply leave this parameter at zero. When I run that, the least-squares fit is only marginally worse, but the long-term, extrapolated values make a world more sense. The graph as shown is not much affected, and the same vertical effect is still shown. Just another lesson in modeling, where simpler is often superior. The chart above excludes a linear trend assumption.

According to the chart above, the cold phase of ENSO is just beginning. Yes, we will have El Ninos, but the next 25 years or so will probably look closer to the left half of the chart than the right half of the chart in terms of magnitude of the peaks in relation to a zero index value. In relation to the wave, the peaks seem to ride the wave nicely.

April 2009 Update on the ENSO Index

It’s been a few months since I’ve taken a good look at the ENSO index, so I thought I’d check that out and provide some context for that data.  First, let’s start with a couple nifty little charts (click on them for larger charts):

ENSO Chart One - Raw Anomalies with best-fit sine wave.

ENSO Chart Two - 5-year moving average of ENSO index readings.

I’ll discuss those in a moment. First, a little housekeeping on the data and the latest readings, and recent historical context.

First, keep in mind that the ENSO index is based on a two-month average. The data is released in terms of JanFeb, FebMar, MarApr, … So, by default, the “monthly” readings are really a two-month moving average. I don’t think that matters all that much to the analysis, but it’s worth noting. For simplicity in conversation, I’ll refer to the anomalies as “monthly” anomalies, but we all know what that means. (I’m lazy)

The current anomaly is -0.737, which is the fourth consecutive month where the reading is lower than the previous month. It is the seventh consecutive month where the reading is below -0.500, which is that point where they consider the period a “La Nina” period. It is the eighth consecutive negative anomaly. This stretch followed two readings that barely peeked above zero (0.050 and 0.028) after a previous period of 12 consecutive readings below zero. The prior stretch was colder (Spetember 2007 – December 2007 were all below -1.100).

The last time there was a stretch where 21 of the last 23 readings were below zero occurred during the period ending March 2002. However, the average anomaly during this stretch is a bit cooler than that one. The average of the last 23 readings is -0.703, and the last time we had 23 readings with at least that low of an average was the period ending September 2000.

Back to the charts…

Chart one shows the raw anomalies. We often hear of the ENSO index cycling in a somewhat irregular, short-term manner. It is evident from the chart that this is the case from a more short-term basis. However, I think we also need to recognize that there is also a longer-term cycle underlying the data. Admittedly, due to the paucity of the data period, this is based on what appears to be roughly one full cycle, and after another 200-300 years, we’ll know more. Since I won’t be around then, I can only work with what I have to work with.

The explanation makes some sense, though. Simple observation of the chart seems to indicate a general low phase and a high phase. If you look a the peaks and valleys from the zero anomaly only, recent spikes look like an aberration. If you look at them relative to the sine wave, it’s less of an aberration.

The sine curve was determined by utilizing the “Solver” add-in in Excel. It simultaneously solves for the parameter values that minimize the least square differences from the raw anomalies to the sine curve.

The sine curve was determined by solving for (1) point on the curve at January 1950, that optimally fits the rest of the data, (2) the scale of the wave, in magnitude (in terms of degrees Celisus), (3) the monthly phase reduction of the wave (basically this establishes the length of the optimal wave), and (4) vertical shift in the curve.

The results show an optimized sine curve fit that started 17.3% into its downward cycle as of January 1950, with a full (360 degree) cycle length of just over 61 years. This differs slightly from a previous analysis, and the main difference is that I did not consider a vertical shift in that previous analysis. But that needs to be considered because just because we’re told that there’s a zero anomaly for purposes of measurement doesn’t mean that it works that way in reality.

The scale of the long term curve is 0.4337. This may not seem overly large, but it is significant. From peak to trough, the difference is nearly 0.9 degrees. Consider an ENSO event that deviates in a given month positively by 2 degrees Celsius. At the trough of the longer-term cycle, this will be a 1.5-1.6 anomaly. At the peak of the cycle, it’s a 2.4-2.5 anomaly. The short-term event is no different, but it’s happening at a different point in the cycle, and the conclusions that may be drawn from it as a startlingly high event could be erroneous.

In addition to that, there is, in fact, a small bias towards higher anomalies in the data. One would expect all anomalies to balnce out to zero on a best-fit basis if there were no bias. I found that a vertical shift of 0.0325 degrees was needed in an upward direction to get the best fit of the sine wave. This is not a large amount, but in conjunction with the scale factor, it helps put the recent spikes in perspective.

Take, for example, the peak value in the index from 1997 (2.872). The sine curve vlaue at that point was 0.360, for a difference of 2.512. This is a significant deviation, to be sure. But if we review the data, is it completely out of the norm? I guess it depends on what one decides is out of the norm, but here are otehr months where the deviation was at least as large:

• April 1983 – 2.644
• March 1983 – 2.759
• February 1983 – 2.615

That’s it.  So, that deviation was still pretty significant, although it was less that the deviation in each of those readings in 1983.   However, it is still not quite as significant as it first appeared.  Compare the raw anomaly of 2.872 (deviation from curve of 2.512) to a reading, for example, for the good old days of the cold and freezing 1970s.   July, 1972 showed a raw anomaly of 1.816.  That’s a decent anomaly, but it is more than a full degree less than the 1997 reading we just looked at.   However, the sine curve in 1972 had a negative value of -0.091, creating a difference of 1.907.  the gap in the difference is now only 0.6 degrees Celsius.    This in now disregards the 1997 peak value as a significant deviation, but it mitigates the degree to what the actual deviation was, and helps put it in context.

There are very few of these data points to draw any kind of conclusion, but the peak positive deviations (+2.0 or more) do outweight the “peak” negative deviations (-2.0 or less).  1983 and 1997 experienced those peak positive deviations, while  1988 experienced the sole negative deviation of at least two degrees.  Note that, while 1988 was in fact the largest negative deviation from the wave in the data, it is only the 5th lowest trough point on a raw basis.  

As for Chart 2, it is simply a 5-year smoothed presentation of the ENSO data.  Purely for observation, it basically corresponds to the idea that there is a longer-term cyclical nature to the ENSO index.   Pre-1990, anomalies, on average, were negative.   Post-1980, anomalies, on average, have been positive.  It is much more akin to a step function, or what could be expected with a cycle, than any sort of linear trend.   What is happening on the right hand side of the chart indicates a potential transition point, though one should be a little careful about declaring positive anomalies dead.  On the left side of the chart, I don’t know how far back the actual anomalies fell below the zero line.   Looking at the double peak in the periods ending 1960 and 1970 may have looked like a transition point at that time.  But La Nina wasn’t quite done yet, giving us one last good blast in the mid 1970s.   After that, there was a clear transition into a stronger El Nino phase.   The 2003 and current dip looks similar in nature to me.   However, the double maximum peak in 1983 – 1997 looks similar to the double negative trough in 1957 and 1976, as well.  So, this is where I could go either way on whether or not the transition is now or in another 5-10 years.   But if I consult Chart 1,  1976 was just entering the positive phase of the cycle, and we’re just now entering the negative phase.  So if I had to wager my 3rd child, I’d go with “the transition is now.”

Of course, entering the cold phase doesn’t mean there will be no more El Ninos (or at the very least, positive deviations from the wave).  They still occur, perhaps as frequently, but the deviations from a negative phase wave translates into a lower raw anomaly.

At least, that’s how I see it.

A Closer Look At Oceanic Oscillation Cycles

In the past, I’ve presented some charts on the different Oceanic Oscillations for PDO, AMO, and ENSO. I’ve started to take a look at these again with an eye towards running a correlation analysis. The initial work I’ve done today is something I considered somewhat interesting, so I thought I’d share it.

The first thing I’ll present is the chart for Arctic Ocean Oscillation Indices since 1950, smoothed at one year, 5 years, and 10 years. These are presented below:

The overall Arctic Oscillation index data since 1950 - 1 year smoothing.

The overall Arctic Oscillation index data since 1950 - 5 year smoothing.

The overall Arctic Oscillation index data since 1950 - 10 year smoothing.

Unlike the AMO, PDO, and ENSO charts, there is no apparent cyclicality showing up in the Arctic Oscillation chart. There does appear to be a trend upward overall, and there are certainly ups and downs within that. The 1-year chart looks much like an ENSO chart would be. Unlike ENSO, though, I’m not picking up a longer cycle.

Well, I wanted to show that chart to start, since the Arctic seems to be the focus of a lot of attention. I guess it bears musing whether or not the Oscillation has a root cause from the Ocean itself, or the sun, or melting ice, or freezing ice, or other factors that override any cyclical nature that would otherwise be apparent.

That’s all I really did on that piece. But I’d like to move on to some work I did with the AMO, PDO, and ENSO (as well as a look at those Arctic Oscillations).

The Unifying Theory of Earth’s Climate

Stephen Wilde contacted me regarding an article that he has presented exclusively on CO2Skeptics.com. It is definitely worth the read. I have posted a couple excerpts here, but I encourage you to CLICK HERE to read the entire paper. Many thanks to Stephen for giving me a heads up on this.

The Unifying Theory of Earth’s Climate
Guest post by Stephen Wilde – excerpts from his paper of the same name.

The claims of those who worry about human damage to the climate become ever more strident despite, or perhaps because of, the real world data rapidly diverging from that which they anticipated.

(Figure 1) The failure of alarmist predictions

It is now ten years since the 1998 culmination of a period of thirty years of unusual ocean warmth that resulted in the atmospheric temperature peak of that year. Additionally during that period the sun was more active than ever previously recorded. ( Figures 2 and 4)

Figure 2 The high solar activity from 1940 to 2000.

AGW proponents accept that the relative coolness of the past 10 years (Figure 3) is a result of cooler oceans but refuse to accept the corollary that the primary cause of the warmer period was warmer oceans. Warmer oceans also expand. ( Figure 5) and release natural CO2. The apparent levelling off in the sea level rise is coincident with recent cooler ocean surfaces.

See CO2Skeptics.com for the full presentation.

My own comments:
Mr. Wilde hits on a couple points I have made myself, which shows an understanding of cyclical changes in trends and the ability to apply simple logic. Most of us – even the skeptics – acknowledge that warming occurred from the 1970s to the end of the century. So, when there has been no warming for the last number of years, yet we hear the arguments that the latest year is still in the top X of our records, it’s kind of silly. I have used the analogy of climbing a mountain, getting to the top, and coming down the other side. Even though your are heading down once over the top, your first steps are still close to the top. In fact, if you trended elevation by time, you would continue to see a positive trend line for quite a while as you headed down, because those elevation points would be near the top of the mountain, even if descending. Ignoring the recent negative changes in elevation and only looking at the overall trend line would lead one to suggest that you’re still climbing up the mountain. While we cannot prove definitively that this is happening with temperature, to completely ignore the possibility is to put blinders on. If this truly is a cycle, then Mr. Wilde is absolutely correct in his assertion that points will cluster for a time at peaks and troughs.

In addition, Mr. Wilde criticizes climate models that are “built upwards from innumerable details rather than downwards from a verifiable overarching concept.” I have also addressed this issue here, and completely agree. I work in a profession that relies on modeling. I can’t tell you how often I scrap a more complex model that tries to capture all the details through numerous inputs in favor of a simple model that looks at things from a broad perspective. The issue is not that complex, comprehensive models are poor in concept. The issue is that if you are building a model in that fashion and you are missing anything, you end up with cross biases where things get inappropriately attributed to certain factors, and the results are nonsensical. More often than we’d like to admit, the better approach is to simply admit that we don’t fully understand all the impacts of all the components, and we need to accept that high-level, general, and simpler models are actually better. Mutliple times a year I read a story about something dealing with climate where there is some unanticipated effect of something-or-another. This tells me, then, that models built on a need for comprehensive analysis are erroneous.

Mr. Wilde’s summary conclusion is that it’s all about the oceans. This encourages me to get back to me more comprehensive correlation analysis on all the ocean indices. (Still waiting on that December PDO reading…)