It’s Been Snowing All Day

I’m a bit tied up with some things here, and probably won’t be putting up a post with an meaningful analysis for at least a couple days more. Last week I went up north to close up our camper, to find that the awning was torn off and flopped on to the roof. My fault for not rolling it up, but it’s never happened to us before, so that must have been one heckuva wind. Had some damage that needed repairing immediately to prevent water damage. Ugh.

I felt compelled to share that it’s freakin’ 25 degrees below normal temperatures today and has been snowing all day long. The temp is just over freezing, so despite what would normally have been 1-2 inches of snow accumulation, it’s just wet, mostly. But still…

Doc, I still stand by my prediction of a milder winter – at least a milder start to winter. But if we get many more days like this, then I shall accept my ridicule.

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.

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.

The Real Cost of Cap and Trade

There is a lot of stuff flying around for and against the Cap-n-Trade Scheme that dares to lurk as a possibility for enactment. So, what do the internal goverment memos say about it? I’ve heard it won’t really cost anything. And it’s green. If it’s green, it must be good, right? Yeah. We like green. Go into a store now and you’ll find green everything. It’s green because batteries last 10% longer, or because it used 5% less energy. It’s green because it’s made with 10% recycled plastic. We just moved into a new corproate office not long ago, and I’m pleased to let everyone know that it’s green. Ironically, they damn A/C is always on too high, and I’m one of the few who seems to think it’s a waste of energy, even though I’m not really a “greenie” because I don’t buy into global warming.

But I digress.

In a Washington Times article we get the scoop, thanks to the Freedom of Information Act:

Officials at the Treasury Department think cap-and-trade legislation would cost taxpayers hundreds of billion in taxes, according to internal documents circulated within the agency and provided to The Washington Times.

Huh. Go figure. Who’d have thought that this might actually cost us money?

Arctic Temperatures Since the mid-90s – Climate Sanity

I ran across a recent post on Climate Sanity that I thought was an interesting follow-up to my Arctic update using the RSS anomaly data.

To summarize my post: one can plainly see that since 2001, the RSS anomalies have declined on an overall basis, with the rate of decline being steeper the shorter the time frame. Beyond that, the linear best-fit on all longer periods is increasing. My analysis was done on the RSS data from latitude 70-85, which should cover things pretty well (though there is that pesky 5 degrees that isn’t covered). It is based on a best-fit of the anomalies on a month-by-month basis through July 2009. The satellite data goes back to 1979.

Our friends at Climate Sanity have looked at the DMI data by going through a process of extracting data from graphs.

The overall conclusions seem pretty similar if you read the post (linked above in the first line – or click on the link under my blogroll. I have not asked permission to duplicate the post here, so I’ll provide reference links instead.) Fitting linear trends yields overall warming on longer periods, but the highest slope occurs from the mid-90s to current.