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

Response to Comment: A Refresher on Cycles, a Quick Packers Comment, and something about Acorns

Bob H left me the following comment on a recent post, and by the time I was done responding, I decided that I’d make it a post. May as well reward my long-windedness with another blog entry:

EDITED COMMENT: Upon a re-read of this, I realized that Bob refers to the Solar Retrograde cycle in the comment, and for some reason my brain read that as precession of the earth’s axis. Why I saw that, I have no idea, since it’s not even close. I blame Brett Favre. In any case, I’ve revisited some of the cycles and my understanding of them below. it really is kind of a random post.

Also, Bob… one of these days I still intend to get to your guest post submissions.

“Good Morning Joe,
I remember Robert Felix referring to the solar retrograde cycle. It’s length has been measured at (correct me if I’m wrong) about 180 years, and he referred to another cycle at 360 years. Your analysis matches well with this time scale, especially since it is based on 60 years of data. It’s a little short, but by the same token it excludes some obvious other periods, such as a 10 or 20 year cycle. Longer cycles can’t of course be excluded, but are not as probable as a cycle that nearly matches a known solar system cycle.

By the way, how are the Packers and the weather doing in Wisconsin?”

My response:
Bob, thanks for the comment. It’s my understanding that the axis has a number of cycles within it. There is the longer term cycle of 23,000 years where the tilt of the earth traces a complete circle about a vertical line through the earth as one would observe it against the solar plane (or something like that). That means that, every 11,500 years or so, our seasons would be completely flipped on a calendar year basis, which is kind of an interesting thought. I never really considered that before, but I think that means that our seasons will shift by a day every 35 years or so. Depending on the direction of the precession, our seasons have shifted by a week earlier or later from when our country was founded.

Sorry for the random ADD moment.

Anyway, within that cycle, the axis doesn’t trace a perfect circle, but “wobbles” with an axial tilt that ranges from 21.5 degrees to 24.5 degrees. We are currently at 23.5 degrees. A complete cycle here is estimated at 41,000 years.

A third Milankovitch cycle is the elliptical nature of the earth’s orbit, which ranges a smaller degree of elliptical (more circular) to more elliptical in cyucles of 100,000 years.

Then, there is Chandler’s wobble, which is a slight wobble of the axis due to the fact that earth is not a sphere and there are slight changes in the center of gravity. This is 433 days, but it combines with other factors so that a full “wobble” period is about 7 years. It’s myt understanding that this is very slight (around 15 meters).

I’m not familiar with a 180 or 360 year period as it relates to the earth’s axis or precession. Those numbers sound more in line with the cycle of the Sun’s position in the solar system relative to the Center of Mass, which Landscheidt believes is key in understanding the sunspot cycles and potential ramifications to our climate. And of course, we are all familiar with the average cycle of sunspots of 11 years. Most of these cycles aren’t perfectly defined, and it’s likely that they vary directly in relation to the interaction caused by the other cycles.

And then, we have all these different Ocean Cycles to consider, as well.

It’s very intriguing, all these cycles, and I am personally a firm believer that all these things are far and away the primary explanatory vehicle in discussing long-term as well as short-term climate changes on earth. I don’t dismiss some contributory aspect of short-term effects from things such as human activity, but when looked at in relation to everything else, I think it’s really a stretch to give us much credit or blame for much of anything. One may even call it a kind of egoism.

As for the Packers, it’s been a mixed review. There is a lot of expectation here given the talent of the offensive skill position and the early cohesiveness of the 3-4 defense (which we all expected to have more growing pains than there has been so far). But the offensive line is really struggling, which is messing up a lot of what they want to do on offense. I’m not sure if it will get to where it needs to be to compete as we’d like. But they looked better yesterday – admittedly against a subpar Rams team (on the road, though).

Gotta admit, I never expected the Broncos to be 3-0 at this point. Must be a nice surprise.

As for the weather, the entire last month has been our summer. The entire summer looked like a lost cause, but since the end of August, we have had very sweet weather. It finally looks to be coming to an end this week, and the normal fall weather is going to be setting in, but it was nice getting a reprieve from the cold and rainy weather that plagued us through mid-August.

Still looking at the ENSO index and seeing positive anomalies, so I’m hoping all these things I’m hearing of a nasty winter are wrong. My own guess would be average to mild based on the ENSO index. But I’m also being wishful when I focus on that and ignore other predictions/evidence.

Here’s an interesting question on the upcoming Winter weather: Do Oak Trees know something we don’t? I’m talking about Acorns.

A couple years ago, we got hit with our most severe winter in memory, both in terms of bitter cold and snow cover. Our wildlife suffers during these years because they run out of food. A friend of mine – a hunter – commented to me at that time that he could see a harsh winter coming because there were so many acorns on the ground. Puzzled at that, I asked him what that had to do with anything. His point was that nature has a way of taking care of its own, and this was the most acorns he could remember seeing. Plenty of food for the small scavengers to tuck away for a long, cold winter. I was skeptical of this, but I could not argue the fact that his prediction bore itself out. And then again last year there were a lot of acorns and we had the most snowfall on record during the first half of the winter.

Well, this year’s crop of acorns is probably the most I’ve ever seen!

To be fair, I’ve searched around a bit to see if there have been correlation analyses done on this, and the resutls – at best – are mixed. It seems more likely that different Oaks produce in cycles, and it is unclear whether or not those cycles correspond with weather cycles. It is possible that the crops do correlate with the weather in the months leading up to winter, which may in itself be a forecaster for the winter weather.

But it’s fun to speculate, even if there’s nothing to it. Kind of makes me feel like one of those farmers who can tell that it’s about to rain because the dog’s eating grass.

Arctic Ocean Oscillation Data Update – September 2009

It’s been nearly a year since I looked at the Arctic Oscillation data. One reason I haven’t paid closer attention to this is because it doesn’t show the cyclical patterns that AMO, PDO, and ENSO do. I haven’t run a correlation analysis on the data (yet) to determine whether or not it appears to depend more on regional temperature, or whether it seems to drive the regional temperature, but it doesn’t appear – at least in the short term – that there is a clear cycle that we can hang our hat on and say with any certainty that certain conditions can or cannot be expected over the next few years.

The same kind of analysis is done here as presented in my previous posts. I do have a correction to make on the long-term sine wave, however. In my previous post I made an observation that the long-term sine wave suggested a pattern for the Arctic on a 9500 year cycle. That calculation pulled the wrong values. The new fitting and corrected calculation indicates a sine wave with a full cycle completed in 368 years.

Even that number is nothing I’d hang my proverbial hat on. Trying to speak to the length of a cycle that is hundreds or thousands of years old on the basis of 60 years of data is a suspect exercise. I only point it out because I mentioned it as a point of interest in my previous post. I now see that the comparison is not apt and that particular point of interest is meaningless. I apologize for the confusion.

Arctic Oscillation Data as of 200908

Since the last update, we saw a stretch of positive anomalies in 7 of the next eight months. The last three anomalies have been negative. The anomalies for June and July were both less than -1.3000.

The best-fit curve itself is scaled by a factor of 2.929. Whereas the AMO, for example, ranged between +/-0.20, the Arctic ranges between +/-4.00, but mostly between +/-3.00. Thus, the higher scale factor. As mentioned, the curve itself is quite flat, fitted to reveal a 367 year cycle.

There is little vertical shift required, so the zero line is right about where it should be based on the dispersion of the data. The shift is a mere -0.0031, which is close enough to zero to call it that.

One interesting thing I noted in looking through the data was the average squared distance from the curve in different time periods – a variance of sorts, not from the overall mean, but from the best-fit curve. Here are the time-periods and the average variance value:

1950-1954: 0.6603
1955-1959: 1.0570
1960-1964: 0.9414
1965-1969: 1.2891
1970-1974: 0.5337
1975-1979: 1.1788
1980-1984: 0.7463
1985-1989: 1.2549
1990-1994: 1.1941
1995-1999: 0.7523
2000-2004: 0.7847
2005-current: 0.7929

I wish I could tell you if that has any deep meaning. But what I can tell you for sure is that the period-to-period deviations around the curve over the last 15 years shows the most consistent limited fluctuation values in the data. A couple periods were lower, but they are bookended by much higher values. I have no idea if this is an indicator of anything in particular, but I thought it to be an interesting observation.

Again, I present the smoothed charts. The longer-term averages have a lot of autocorrelation, and the spike in average is driven and sustained by 3 pretty high anomalies in the early-mid 1990s. The overall trend of the average is upward because of the combination of those anomalies and the dropping out of some lower anomalies in the 1970s. It’s kind of interesting to see that show up in the longer-period averages since the raw data chart doesn’t seem to show that as much. However, part of the reason for this is the scale. The scale on the longer-term average charts is much lower (+/-0.5 vs. +/-4.0) so the trend looks steeper than it probably is. That said, the 10-year average is what it is, and it is definitely higher now than it was 40 years ago, though it is quite a bit lower than the peak averages of a decade ago.

Arctic Oscillation Data as of 200908 - 12-month smoothing

Arctic Oscillation Data as of 200908 - 60 month smoothing

Arctic Oscillation Data as of 200908 - 120 month 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.

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.

And There it Was… Gone.

There’s an old Wisconsinism that people have some fun with. It’s often used in the context, say, of deer hunting. Somebody tells the story about how they were in their stand, and heard the big buck behind them. They glance around and catch a glimpse of the trophy rack. Slowly, they maneuver themselves into position, secure their rifle, and as they relay the story about when they turned around and looked past the trunk of the tree they were in, they say “And there it was… gone!”

A few days ago I mentioned a sunspot cluster that you could actually see pretty well. While you can still see some of the remnants of it, it didn’t last long.

Bob Heiderstadt, a guest poster who has occasionally commented here as well, sent me some data on the timing of when the sunspots have appeared.

The number of points observed doesn’t make for anything scientific, so all I can really say about it is that it’s an interesting exercise. The data showed the dates of sunspot appearances and whether or not the spots were Cycle 23 or Cycle 24 spots.

He noticed that in a previous stretch of consecutive Cycle 24 spots, there was an expansion of time between appearances, while recently there has been a contraction.

I took his numbers and further looked at that by simply ignoring Cycle 23 spots altogether.

The last 12 Cycle 24 spots have appeared with the following 11 gaps in between them (in # of days): 3, 10, 11, 14, 29, 31, 46, 55, 22, 10, 9, ?

I will be interested in seeing how this continues. One can’t draw any conclusions from one oscillation (or, almost like a contraction of labor) but as we move forward it may be interesting to see if there is some kind of a “breathing” or oscillation pattern to how the sunspots ramp up.

On the other hand, it could be entirely coincidental and mean absolutely nothing. Time will tell.

No Warming Trend for Over 12 Years – May 2009 Update on current temps – RSS

Yes, it’s true. We’re now at 12+ years and running with a flat trend line. We’ve discussed the viability of statistics like these before. Let me reiterate, because two things will happen with this: (1) pro-AGW theorists will immediately discredit the trend line as “too short” to be statistically viable, and some will actually bizarrely claim that the trend line isn’t factual and they can “prove it” through, for example, an ARMA analysis. (2) Those skeptical of AGW will proclaim warming to be dead.

Addressing the first point, the trend line is what it is. It is certainly within the bounds of good statistics to talk about how there can certainly be flat periods like this within an overall warming trend. Where the argument usually breaks down, though, is that those who point this out consider this an argument for continued warming. It is not. It is an argument for some probability of continued warming, and some probability of a flat line, and some probability of a reversal. These probabilities change over time for both the near term and the long term. The AGW proponents rightfully look at month-to-month changes as not dramatically changing the probability of the long term trends, but they often do so in ways that make you scratch your head. The trend line is a best-fit line. That’s all it is. The r-squared is low. Yep. That means that you could probably tilt the line up or down, without too much difference in the error term. It’s a valid point. What is implied is that the way you’d tip the line is up. But it could go either way. In this debate, I find a lot of valid points being made that are then applied dishonestly to come to a conclusion that is not in line with all the facts. Quite simply, the longer the trend goes where there is no warming, the higher the probability that we are wrong about runaway global warming. But it doesn’t completely eliminate the possibility.

As for the argument that warming has stopped, that’s in the eye of the beholder. Again, there can, in all probability be periods of a lull within a larger trend. Also, a best fit trend line only shows the trend over a particular period of time. In fact, while the 12-year trend is negative, the 10-year trend is positive. That’s because there was a little dip in anomalies between the 10-12 year mark in history that drives the front of the line down. The 10-year line is sure to decrease and go negative by the end of the year (unless anomalies shift upward considerably), but things like that can happen. So, we have a situation where we can say that the trend is negative over the last 12 years, but someone can accurately counter that the trend line is positive over the last 10 years.

There is always an argument about cherry-picking, and which trend is best. AGW proponents used to say that you needed at least 10-15 years of data. That used to be safe, but they never say that anymore. To be fair, though, I usually hear the 30-year period tossed out as the magic number. As best I can tell, there’s no good reason for that other than it has the feel of a long enough time period to matter, it currently gives an answer that supports their conclusion, and people have a tendency to think that only things that happen within the course of their own memory are relevant. However, my own analysis and research has led me to understand that the major Ocean cycles last anywhere from 60 to 100 years, and that these cycles surely help drive global temps. So it seems to me that anything short of 60 years is insufficient to discern the actual temperature trend, that 100 years is better, and more is even better. There will always be some issue with how out of whack the starting point of your trend is with the end point with regard to where we are in the various ocena cycles. Nobody seems to concern themselves with that, but I’ve shown the potential difference on that with the application of sine waves to the temperature anomalies. As far as I’m concerned, going back further is better.

Unfortunately, surface temps are the only measures we have that go back over 100 years. These data are rightly questioned for accuracy. Others have taken up that issue, so I have chosen simply to use the data as it is, simply with the caveat of “let the consumer beware.” The long term trend certainly appears to be up anywhere from 0.4 to 0.5 degrees Celsisus per Century over the full term. But when one accounts for the waves in the data, it has not accelerated as we have been told.

The satellite data have questions as well, but not nearly as many as the surface data. The issue here is that we only have 30 years to work with. So, that all said, I now get to showing you the updated RSS data.

Edit: It was noted that I failed to provide the link to the RSS data used in this post. My apologies. The link can be found to the right. Click on “RSS Data.” I often assume followers of this blog know that my sources are listed on the side of the page, but I often get linked to and those who are new to the site may not take the time to look at all the different links, or understand which links relate to a given post. I’ll try to remember to either put links in my posts, or direct people more clearly to the proper link. As an aside, I am often a bit confused about statements along the lines of “what makes Joe some kind of authority?” What confuses me is that my posts are not really anything more than data crunching that is presented for all to see. As an actuary, I do have some authority as it relates to helping people understand the statistical significance of what’s being presented, but I claim no particular authority on climatology. But the data isn’t an argument about climatology. It is a presentation of actual trends. My sources can be checked, and anyone else can duplicate exactly what I’m doing, check my accuracy, and correct as necessary. Questions of authority when presenting trend lines, in my opinion, simply show an unwillingness to absorb the information that’s being presented. Conclusions about the data can certainly differ, drawing upon other knowledge. But to just dismiss the trend because it’s “The Diatribe Guy” is pretty weak.

Overall slope RSS. Over 30 years with a trend at about 1.5 degrees C per Century.

Here is that 12 year + 2 month flat/cooling trend I mentioned.

Current: The current anomaly for April is 0.2020 (in degrees Celsius).

Rank: The current anomaly ranks 11th out of 31 April anomalies (64th percentile); it ranks 102nd of 364 total anomalies (71st percentile).

Averages: The twelve month average = 16.8, which is the highest such average since the period ending April 2008. The most recent minimum average of 7.9 occurred last October.

Consecutive streak:This is the 6th consecutive anomaly higher than the previous year.

You may be looking at the increasing averages and consecutive streaks and asking, “Hey, Joe. How can the flat/cooling period actually have gone further back to March 1997 when we see higher anomalies over the last few months from prior year?” Good question. Thanks for asking. The recent anomalies are higher than prior year, and while that certainly will affect the trend line as far as it goes, the entire period is affected by the relative level of all periods within the data set. So, there is some positive anomaly that would drive the overall slope of the line higher, or move the period forward. Theoretically, that could be a lower anomaly that the previous year’s if the prior year values were very high. Lower from prior year or not, if the current value is still much higher than most anomalies, and in particular the front-end anomalies, then you will see an impact on the potential length of the line. What we see here is that a year ago we had some much lower anomalies than in recent years. The current values are a little above average compared to history (see ranks) and the trend line continues to lengthen. I took a quick look at the 10-year line, for example, to see what the next anomaly would need to be just to keep the slope the same next month. The anomaly would need to double. So, we could see a higher anomaly next month and still see a decrease in slope. It’s all in the math.

Trends: I won’t say too much about these. I’ve blabbed enough already. Presented here are the raw slopes for 60 month, 240 month, and 300 month periods. I presented the cycle of slope values for 120 month slopes, and presented a peak recent historical slope for the 180 month period. here they are:

May 2009 Sunspot Update – Fun Facts / Cool Outlook

We continue to see a quiet sun. I know there is a lot of debate about whether it’s truly “unusual” activity, or whether it means anything at all to temperature and climate. I honestly don’t know the answer to the second. From what I understand, the theory is that a quiet sun means that solar flares and other wild activity are not there to hamper cosmic rays from pelting the earth. (As an aside, whenever I hear things like “Cosmic Rays” it takes me back to my youth, where different space ray exposure meant you would be turned int a Super Hero. So, maybe one byproduct of a quiet sun will be people who can fly, or are really flexible, or who can turn invisible. Let’s just pray they don’t turn into orange, rock-like monstrosities strong enough to lift tanker-trucks into the air).

What was I saying? Oh, yeah… Cosmic Rays. Anyway, unimpeded rays apparently stir up more evaporation, elevating the level of water vapor, elevating cloud cover, elevating rain… eventually, between clouds blocking the sun and the rains cooling the land, we get cooler temperatures.

There have been some correlation analysis done that linked the length of solar cycles to changes in temperature. These, as far as I can tell, have been fairly simple and, while compelling, have limited data points. So, it’s interesting as far as it goes.

I’ve done a similar exercise, but by looking at the full set of data as it relates to lags, using a minimization of least squares simultaneous approach. I have updated that study and will present it here. In addition, I’ll throw out the latest observations on how current data trends in sunspot counts stack up to history.

Before I begin with that, I need to throw out a caveat that was rightly pointed out to me in a previous post I made regarding the sunspot numbers: if you go to the NOAA Sunspot data, which is the source I am using (linked to the right), you will see a statement at the bottom of the page that tells you that the data after such and such a month (about 6 months ago, depending on when you’re checking) is preliminary. That is because the traditional way of counting sunspots incorporates formulas that, today, don’t seem to make a whole lot of sense. You might think that the count is the count, but it’s not quite that simple. The count itself is a formula, based on number of days of observation adjusted for a formula to account for the other side of the sun, clusters, etc. Then, after that, the monthly average count is adjusted for an annual average. It can all be pretty confusing, and at one time I could rattle off all the details, but it’s been a while since I refreshed my memory on it, and quite honestly I’m admitting to laziness by not digging into it at the moment. The important thing to realize is that the last few months could see adjustments to the numbers over the coming months, but I will treat them for presentation purposes as “the number.” The reasons for these adjustments, I believe, have more to do with maintaining consistency in the method so that we can compare a reading today to a reading 200 years ago. We can argue about differences in technology affecting that, but at least the determination of the count number is consistent.

OK, so on to some data tidbits:

In each of the last five months, the sunspot count is preliminarily showing values of 1.5 or less in each month. The last time we saw a consecutive 5-month stretch like that was the period ending September 1913. Yes, that’s over 95 years ago.

The latest count = 1.2. The two-month average = 1.0, and the three month average = 1.1. While very low by historical levels, we did see similar 2-3 month averages in 2008.

Sunspot counts since 1850.

I took a look at the following averages, all of which are the lowest averages since similar periods ending in 1914-1915: 6-month (1.6), 12-month (1.8), 24-month (3.9), 36-month (7.0). So, any way you slice it, the sun in the last three years is unlike anything most of us have seen in our lifetimes. Does it mean anything? I don’t know for sure, but it’s still an interesting phenomenon.

Here are a couple of those charts, for your entertainment:

April 2009 Update on Global Temperature – GISS

VACATION ALERT: I will not be posting for the next couple of weeks. The family and I are driving cross-country in a big honkin’ white Cargo Van. We are going to Delaware. “Why Delaware”" you may ask. It’s a valid question. I won’t get into all the details, but let’s just say that we choose our vacation destinations by drawing them out of a hat, under the theory that every place has something to offer. That theory is about to be tested… Anyway, just wanted to let you know of the upcoming hiatus.

On to temperatures…

GISS is always an interesting data set to review, because you’re never quite sure what you’re going to get. In a world where people like consensus, it is somewhat striking to me that we have general agreement between the RSS and UAH, and even the HadCrut data sets, and whenever there is an outlier it’s likely to be the GISS data set. Yet, when people quote statistics on global temperature, the GISS is the preferred set. This, despite numerous documented algorithmic data adjustments that have, over time, a non-negligible impact. Also, there is the real question of whether or not the bias of James Hansen enters into the evaluation of the data.

Nonetheless, it’s the reality we deal with that this is data that is looked to for policy decisions. So, if for that reason alone, it is worth keeping an eye on it so we can speak intelligently to what it is telling us.

First, let’s review the most recent data point:

March anomaly = 47 (in terms of .01 degrees Celsius). This was the lowest March anomaly since the year 2000. It was 18 lower than the March 2008 anomaly, but 6 higher than the February 2009 anomaly. This is quite different than UAH, where the movements were reversed.

Streak: This month’s lower year-over-year anomaly broke a streak of 6 consecutive months where the anomaly was higher year-over-year.

Rank: March 2009 ranks as the 11th highest anomaly in the data (since 1880, or 130 anomalies. 8.5 percentile.) Overall it ranks as the 95th highest anomaly in the data set of 1,551 values (6.2 percentile).

Average: The 12 month average ticked down from a recent high last month, and stands at 46.8, which puts it right at the level it was in January.

SLOPES

Overall slope since 1880 is 0.04700 hundredths of a degree warming per month. For those of you who prefer simpler numbers, that translates to 0.564 degrees Celsius per century.

We can extend the current trend line back to December 2001 that indicates flat temperatures (slightly negative).

The period of time where a best-fit line can be drawn that indicates no change in global temperature is not 100 months long. The last time we had a stretch of 100 consecutive months where a negative trend line could be fit was the period beginning April 1988. In recent history, during the stretch of time for which we consider the warming to be measurable, the longest stretch where a horizontal/slightly negative trend line occurs is from January 1987 through April 1997. That spans 10 years and 4 months. If our current front anchor remains at December 2000 as a starting point, we will not reach that until March 2011 – 2 years from now. So we still have a ways to go to declare that this is beyond any previous hiatus in temperature rise during a potentially warming period. It could be less time than that if some cooler temperatures are forthcoming, driving our starting point back in time a bit. Supposing we would reach that point, the previous time a flat or negative trend line of that stretch occurred is the period beginning June 1969. At this point, it seems get pretty iffy whether or not the trend line can be explained away. But we’re not at that point quite yet.

Here are some notes on the different trend periods:
60-month: Current slope = -0.1123 (-1.35 degrees Celsius cooling per Century). Kind of hovering around the same level as the last four months (ranged from -0.1086 to -0.1151). Here is the comparison to its peak slope, period ending May 2007.