February 2009 Update on Global Temperature – GISS

I didn’t look at GISS last month, so here’s a quick update for the January month-end numbers:

The overall trend since January 1880 has a slope of 0.047, which corresponds to warming of 0.564 degrees Celsius per Century.

The furthest back we can go to see a flat or cooling trend is January 2001, 8 years and a month.

The stats:

1. The anomaly for January was 52, which was actually not as large a jump as the satellite data showed. 
2. The historical rank of that anomaly, though, still shows it to be the 5th warmest January on record and the 62nd highest anomaly out of 1,549.
3. This is the fifth consecutive year-over year warmer anomaly

 
The rest of the charts show the various slope change charts, which shows how the various slopes have changed since the last peak.

The 60-month slope values have trended down since early 2004, with some cyclical fluctuations on the way. The current slope value of -0.1147 represents 1.37 degrees cooling per century. After a small bounce, this latest value reversed a bit.

The 120-month slope values have trended down overall since early 2002. The current slope value of +0.1535 represents 1.84 degrees warming per century. This is a slight decrease from previous, but in the range where it's been holding for a few months. It is anticipated that this will decline later this year as some low front-end anomalies fall off.

The 180-month slope values have trended down since 2007. The current slope value of +0.1489 represents 1.79 degrees warming per century. This has declined continuosly in that time and is at its lowest value since the period ending December 2002.

The 240-month slope values have generally trended down overall since early 2004. The current slope value of +0.1554 represents 1.86 degrees warming per century. The most recent slope vlaues have dropped quickly after a period of level slopes for over a year.

The 300-month slope values have trended down overall since early 2007, though the trend over the last year or so is actually positive. The current slope value of +0.1579 represents 1.89 degrees warming per century. Other than a dip in mid-2008, the slope level has hoverred around the 0.158 mark since October 2007.

The 360-month slope values have trended down overall since 2005. The current slope value of +0.1339 represents 1.61 degrees warming per century. This has declined steadily for over a year and is now at the lowest value since the period ending September 2002.

What’s Going on?

Yes, loyal climate change and temperature analysis addicts, I am still here.

Let me explain why there has been a lull. I am hoping (really!) to get to some things this week that will update a lot of the stuff I look at before the month ends. So don’t lose hope and keep checking.

Over the last couple weeks, I have been very busy with work, which unfortunately, pays infinitely more bills than this blog does. So, when work obligations increase, blogging must decrease for the sake of food, clothing, and shelter. In addition, it just so happened that there was a confluence of meetings and obligations among the few outside organizations/programs I am involved with, so even my personal time has been quite affected.

And, admittedly, I have filled the remaining time with another endeavor that has involved a huge learning curve, and has successfully driven my wife bonkers.

I am just like the rest of you – seeing my 401(k) drop to a 201(k). And while, under the theory that it will rebound by the time I need to retire in 20-25 years or so, I continue to faithfully contribute to what seems at the moment to be a losing cause, I have decided to not just sit around and do nothing with my money. I have really been taking my time to study some of the other markets, and have opened up a demo account on the foreign exchange markets. Yes, the risks are great. Depending on what you read, anywhere from 75% to 95% of all traders in the market end up losing money. Based on what I have read, the successful traders seem to have the ability to develop and stick with a system, accept bad trades, never get into a gambler’s mentality, and obviously just practice and learn the markets. So, my plan is long term and to use demo accounts to learn the actual trading. In the meantime, I have been learning the trading platform, trying to understand the lingo, getting a full understanding of what the optimal amount to risk at any one time is and just what to look for, etc. This has taken up all my time recently as I try to get a foothold in this market.

I only share this to let you all know (1) why I dropped the stock experiment. That money will go to this. And, quite honestly, right now as risky as the Forex market may be, I don’t really see it as any more risky than the stock market, and (2) just to let you know that, while I’m trying to juggle my priorities, I have not forgotten about you and will be working on some things very soon.

Thanks for continuing to check in.

Any Willing Guest Posters?

This post is a sticky… scroll down for new posts.

Guest posts always welcome!

If interested, just shoot something to my geezep@yahoo.com address.

If you’d like to do so, I’d prefer the following to be done for ease in posting:
1) Type up the post in a word document. Place any charts or pics in the word document where you want them.
2) Separately attach the charts to the e-mail for simple saving/uploading.
3) Let me know how you’d prefer a guest post be handled from an accreditation standpoint.

I’m also fine with posting a guest post from someone else’s blog with a link to that blog, if desired, with the same format as above.

Applauding a Positive Anomaly

I have lamented the bitter cold in Wisconsin this winter (and cooler temps overall for well over a year now) while I’ve looked upon data that has told me that the rest of the globe (and even other parts of the U.S.) has experienced warmer temperatures.

I am pleased to present the following chart, from NOAA, for the first week of February:

 

 

If one looks closely at my particular area, a strange and beautiful thing presents itself…   pink!   Yes, it was actually slightly above average late last week and into the weekend.   It carried over into this week, as well, and we have enjoyed some warmer temperatures – above average even – in the Dairy State. 

It has been nice.  But, as I type this, temps are heading back to about average, so we’ll be back in the 20s for daytime highs over the next few days.

In any event, for one short, glorious, time period my wish was granted.

I’m happy now.

Deconstructing the HadCrut Data

In this post I took a look at the PDO, AMO, and ENSO data and went through the exercise of fitting a sine wave to see how the fit looked.  On all three, the fit seemed to be a good and reasonable way of estimating the general level of the curves at a given time.  Obviously, there are fluctuations about those curves, but all in all it seemed to be fairly adequate.

So, the other day I was thinking about the implications of hypothesizing the contribution of these
periodic elements to the temperature data, and I figured that it may be an interesting exercise to deconstruct the HadCrut data in the same way.  Understanding that there may be multiple oscillations going on, I set out to fit multiple waves to the data to see what I could make of it.

I used HadCrut because their records data back the furthest.  And also because the older data has not undergone the continual adjustments that the GISS data has.  Since HadCrut protects their process in determining the anomaly, it is not transparent to the user of the data what kind of spreading and adjustments they make to it.  However, I am inclined to place a little more trust in it than I do GISS because the data is more in line over the last 30 years with the satellite data than GISS is.  (Admittedly, based on observation.  I haven’t done a rigorous analysis on that).

Let me start by introducing the most recent chart that shows the overall linear trend on HadCrut over time. I didn’t do a January update, but the additional data point won’t have much of an effect.

The overall trend since January 1850 has a slope of 0.0003649, which corresponds to warming of 0.4378 degrees Celsius per Century. Light blue lines are raw anomalies, and the black line is a 12-month smoothed number.

Here are the required parameters and procedures to get the best fit:

1. A parameter that assigns the optimal wave length. This is done by determining a degree increment per month that gets added to the previous degree amount. That value is then converted to radians, and the sine value is calculated.
2. A wave scale factor is used to determine amplitude of the wave.
3. The starting wave position in January 1850
4. Linear trend that the wave is centered on
5. Vertical wave shift – since the anomalies over time aren’t necessarily centered about zero, and even if they were there may still be a shift needed depending on wave cycle weights, the wave needs to be shifted up or down to achieve optimal fit.
6. Parameters 1-3 are wave specific when optimizing multiple waves, while parameters 4 and 5 are applied after the cooperative effect of all the waves tested are determined.
7. To determine the best fit, I set up my calculations using the parameters above to compare against the historical HadCrut anomalies, determine the difference, and square the result. I then minimized the sum of the squares.

The first run I did was against only one wave. Even this fit provides substantial improvement over a simple linear fit, and starts to demonstrate the fact that there are clear cycles in the data.

The best-fit single sine wave along a linear trend.

While we can see visually that it is not entirely perfect, we can definitely see the general wave it’s tracing. The parameters on this wave are as follows:

• Wave length parameter = 0.45658. This corresponds to a 788.5 month complete cycle, or 65.7 years. This fit lines up right along with the cycle lengths determined in the PDO/AMO/ENSO study.
• Wave scale parameter = 0.13222. Basically, this means that the overall wave doesn’t deviate from the trend line by more than this value.
• Start Wave position = -31.71316. (0 would be right on the trend line, and +/-90 would be max deviation from the trend line in either direction.)
• Linear trend = 0.00037. This is spot on with the overall linear trend observed without the sine curve applied.
• Vertical Wave Shift = -0.53644 means that the start of the sin wave had to be shifted downward by this amount.

The least squares result was 56.18. This compares to the least squares fit on the linear trend line only of 72.58. This is nearly a 25% improvement.

The conclusions of this:

• there is still a definite linear trend, but most of the fluctuation about that trend can be explained by adding a single sin wave.
• the most recent decade or two is not satisfactorily explained by the sine wave, and the latest anomalies are above the wave. This could be consistent with the idea that the something has changed (e.g. increased Carbon Dioxide has accelerated what had been a linear trend).  Alternatively, it may simply be that a single sine wave is insufficient and there are other periodic influences that need to be examined.

An interesting exercise is to extrapolate the linear trend with the single sine curve forward. Taking this to 2050 shows us the following:

The best-fit single sine wave along a linear trend, extrapolated to 2050.

Observations:

• The graph indicates that we are at or near the peak of the single sine curve fit, and that the next 23 years will cool. 
• There is still the linear trend line observed at just over 0.4 degrees Celsius per Century, so the anticipated trough won’t be as severe, but we will still be cooler than today for the next 30 years or so.
• The trough of the curve occurs October 2032, where single-digit anomalies would be the norm.

While the single sine wave fit provides interesting information, from observing the AMO/PDO information, it seemed clear that there would likely be at least one additional periodic wave in the data. So I added an additional wave and the following chart ensued:

The best-fit double sine wave along a linear trend.

The interesting thing about this fit is that the cycles of both waves are shorter than the singularly combined wave (59.1 years and 58.5 years), and the amplitude of both waves is around 1.5. The combined wave is only 0.13222 because the phases of these two waves at the start of the period are almost perfectly offset (182 degrees apart). The linear trend is still apparent, though just a shade less (0.42 degrees per century). The vertical wave shift is nearly the same as the single wave. Overall least squares fit isn’t remarkably better, at 54.118.

All in all, the waves do seem to do a pretty good job of fitting the curve. In the early 1900s, the wave seemed to ride above the curve a bit and in the last decade or two the wave rides below a bit. So, it’s not perfect. It is possible that a linear curve is not the best approximation to have the sine curve fluctuate around. I may do further tests with other alternatives, such as geometric or exponential approaches.

The extrapolated chart, though, shows a little more severity in projected cooling (the title is wrong – it should read “Double Sin”  I would have corrected it except that I need to re-create it and don’t have time at the moment.  My apologies for the oversight:

The best-fit double sine wave along a linear trend, extrapolated to 2050.

Observations:

• The shifting of the wave phases over time lead to more fluctuation in the peaks and troughs, which explains why the right hand side of the chart fluctuates more than the left.  Since the amplitudes of these waves are 1.5, one can imagine a time centuries from now where there would be astonishing swings in the temperature trends over 50-year periods of time.
• The projected trough in temperature is expected to be June 2030 according to this chart.   Average anomalies at that time would be slightly negative.   The last half of next century would warm considerably.
• All this fluctuation occurs over a linear trend that is less than a haf-degree Celsius per Century.

I didn’t stop there. I tested three waves. However, the results that provide the best fit don’t make a lot of sense. The fit of existing data is certainly impressive, so let’s look at that first:

The best-fit triple sine wave along a linear trend.

Adding a third sine wave certainly appears to put our temperature observations spot on with the generated curve. One may feel inclined to get all puffy and declare that the case has been solved.

But looks can be deceiving. The best fit with three waves – a reduction to 45.47 – uses parameters that are not sensible. And this becomes a case study in trying to be “too accurate” with model forecasting.

When the third wave is introduced, I was able to generate a number of “best fit” scenarios all around the same value of 45. One such scenario shows that we will go into dramatic cooling by 2051, and the anomaly will be -2 at that time. The next scenario had a best fit of 46.43, with dramatically different parameters. That model shows no downturn whatever in temperature after the current period, and suggests unabated warming through 2050, where the anomaly will be around 1.

This is common when adding multiple parameters to models. In the quest to get more accurate, you actually introduce so much additional uncertainty that the range of reasonable projections becomes meaningless.

My conclusion is that the best representation using a sine wave analysis is a simpler 2-wave representation.

The conclusions are basically in line with the PDO/AMO analysis, as well. These drive the longer-term cycles about a trend. Whether it is a linear trend or something else is worth looking into. Also, is the trend related to the sun or carbon dioxide, or other things? The cycles still do not explain the overall trend, but they do help explain why the recent linear trends cannot simply be extrapolated into the future.

Follow Up to the RSS Temperature Data

I had some confusion about the results of the RSS anomaly data as it regarded the Continental U.S. The anomaly was a significant increase from December, and was an overall positive 0.358. This was on the heels of some very cold weather throughout much of the U.S. In some places it was a record cold January. I was surprised to see a positive anomaly.

I sent a missive to RSS in hopes of some explanation, as follows:

Dear sirs,

I have a layman’s interest in reviewing the data you release each month, particularly on the TLT data, since that most corresponds with land and surface temperature measures.

I have my own opinions about global warming, but primarily I just enjoy seeing the data and presenting the information for lay readers like myself, on a blog entitled http://digitaldiatribes.wordpress.com.

The information I last used was from this source:
ftp://ftp.ssmi.com/msu/monthly_time_series/rss_monthly_msu_amsu_channel_tlt_anomalies_land_and_ocean_v03_2.txt

Thank you for your service in this regard. Occasionally, someone not fully versed in the ins and outs of this technology gets a little confused by the numbers. I do keep an eye on the NOAA climate maps, which I understand are unofficial, throughout the month. That, along with simple knowledge of weather throughout the month in the United States, has me a bit confused by the anomaly for the Continental U.S. for the month of January.

I live in the Midwest, and the entire region was below normal for nearly the entire month of January. I know that the East and Southeast also had its share of below normal temps. Tracking the NOAA maps showed a lot of purple and blue on a weekly basis over wide swaths of the U.S.

NOAA is based on surface station data. So, can you please explain to me in simple terms what drove the significantly higher anomaly in January over December for the Continental U.S.? Is it a difference between the surface and the lower tropospheric measure? Did heat in the West offset the cold in the East?

Any information you can provide would be useful, and if I may, I would like to pass the information on to my readers.

Any help you can offer is greatly appreciated.

Thanks,
Joe Tritz

I received a timely response from Marty Brewer, pointing me to a very cool image file. It shows that the Eastern U.S. was indeed cold, but the Western U.S. was quite warm. Interestingly, the area over the Pacific is warm, even though there are negative anomalies for both the PDO and ENSO indices. Anyway, Marty’s response was:

Hi Joe,

Take a look at the monthly images:

http://www.remss.com/msu/msu_data_monthly.html?channel=tlt

Click Anomaly.

In Jan 09, it looks like the U.S. west was as hot as the east was cold, maybe hotter.

Marty

You can follow the link above, but I have also placed the image here:

Sunspot Update – February 2009

The picture at the left is the now familiar sight of a blank sun, as seen on February 9, 2009. This continues to be a remarkably interesting time.

UPDATE:I would like to point out a comment (#4) by Tad Cook below. I am indebted to his helpful explanation on the nature of the determination of sunspot counts by month in the NOAA data set. With his explanation, take my presentation below in the spirit of a presentation of the data from a table where the last six months are preliminary estimates. In the future, when referencing that data, I will try and be sure to acknowledge that. Mr. Cook has, apparently, been digging into the daily sunspot numbers and believes that the actual January number will be around 3.2 when the final numbers are determined.

What I find interesting is that we hear all sorts of different opinions as to what, if anything, this all means to us. I find the sunspot cycles fascinating from a cycles/data perspective. I have done some correlation analysis that seems to imply that the length of the cycle matters. However, there are limitations to that analysis because it does not remove the effects of other major variables, so until I do a more thorough job on that it is simply a curiosity. There is this paper that implies the same thing, though, in stating that length of the cycle is inversely related to temperature. I have shown the collapsed charts that consider both magnitude and length in recent decades. I have summarized two papers by John A. Eddy here and here that seems to make the case for solar minimums driving temperature downward. Overall, the Landscheidt papers certainly focus on correlations of solar and planetary activity in conjunction with climate. Some feel that focusing on cyclical movements of planets and the sun becomes too astrological. I disagree with that assessment, but the criticism is out there.

But those aren’t the only opinions. If you frequent Watts Up With That? you will be familiar with a frequent commenter/contributor by the name of Leif Svalgaard. Leif, as far as I can tell, is a reasonable fellow who is interested in the science of the sun, and not particularly concerned with advancing one agenda over another. I have seen him equally berate the way NOAA uses the information as well as those who he feels makes equally inastute predictions/observations of the meaning of what the sun is currently doing. He has extensive work here which I admit to not spending as much time on as I’d like. I have, however, kept an eye on many interesting charts he has put together in this source. I mention Leif as an example of someone who I think has the right attitude and motivations regarding the science, but who does not embrace the correlation between sunspot cycles and climate like many others do. Agree or disagree with him, I don’t see any agenda with his opinion. He has scientific bases for having reached that conclusion. He is also not a fan of many of Landscheidt’s methods.

So, basically, we don’t know a whole lot. We have correlations of past events, and different and improving instruments to assess all aspects of the sun. But we don’t necessarily know a lot.

What we do have, nonetheless, is sunspot data. And whether you think the sun tells us little aboout climate or whether you think we’re heading into another ice age, the data is fascinating.

The January sunspot number was another extremely low value (1.5). Once again, this drove down the averages we see. It also lengthened the current cycle (as measured from 12-month average minimum value to 12-month average minimum) to 148 months. It was already longer than the previous 13 cycles, and this status continues. If this cycle lengthens by 2 more months (which is possible – February 2008 was pretty low [2.1] but March 2008 had a count of 9.3), it will become longer than the previous 17 cycles, and we will only know about 2 other cycles that were longer.

As for observed averages, the 12-month, 24-month, and 36-month average sunspot numbers are all the lowest values since 1914, 1914, and 1915 respectively. The 48, 60, 72, and 84 month averages are lowest since 1935, 1936, 1936, and 1937 respectively. The 96, 108, and 120 month averages are the lowest since 1979, and the 132 and 144 month averages are the lowest since 1946.

For those who prefer charts, here are a few:

A Strange Thing Happened on the Way to Global Cooling… (February 2009 Update on Global Temperature – RSS)

One of the things that got me interested in looking at the trends and doing analysis on the actual temperature numbers was because the story I saw painted by looking at the data was not at all the story I heard painted on the evening news. One thing that has upset me about this whole ordeal is the willingness of AGW proponents to be openly hypocritical. For example, when there is a drought or wildfire in some region, they have no problem pointing to Carbon Dioxide as the culprit, as if these things only first started happening in the 20th Century. But, if it snows in Dubai, it’s brushed aside as an interesting anecdote. Or, worse yet, we are told that it is perfectly consistent with some theory or another on global warming and/or climate change. We are told that only long-term trends mean anything, and short-term trends are just noise.

I am, admittedly, a skeptic. There are many reasons why I am skeptical about the anthropogenic contribution to the warming climate. There are many reasons why I am skeptical about the veracity of the data itself. I am not, however, skeptical about the fact that there has been a warming trend over the last century+. The data is clear. But it is not a catastrophic warming, and I see no particular reason to believe it’s due in its entirety to man-made causes. That is my position. I understand that we do not all agree. I also happen to believe that we are headed for a cooling period over the next two, 3, or 4 decades. This is based on historical observation of different things that I’ve presented here.

But what I do not want to do is start making lame excuses for my position. I prefer to let the data play out. I’m either right or I’m wrong.

And so, the last thing I do is run away from new data that doesn’t fit in conveniently with my position.

The fact is, the latest RSS temperature anomaly is a somewhat surprisingly warm one. I say “somewhat surprisingly” in the context of the data trends and in the context of things like La Nina, cooler PDO, and a non-increasing AMO, on top of a quiet sun for an extended time period. However, for those of us who kept an eye on the different temperature maps, the result was not necessarily surprising. There were a number of warm anomalies around the globe during the month, so the result was not a surprising one in that respect. But had you asked me before the month began if I could foresee that jump, I would have said “no.”

But it is what it is. I could talk about how one month doesn’t make a trend and isn’t in and of itself indicative of anything. And I’d be correct. But this is the same argument AGW proponents use whenever there’s a cold month. Instead, I just prefer to add the data point and see what the trends say.

There are a couple points I’d like to add, though: The first is a point that I saw made on the Watts thread regarding the RSS anomaly. It’s right on the mark, in my opinion. (I’m paraphrasing. I’d link to it, but I’m getting an error at the moment trying to head over to Watts). Basically, the point is that we skeptics are in a somewhat unusual psyche. Most of us actually want warmer weather because we believe the warming is a natural cycle, and is overall very beneficial to us – less disease, longer growing seasons, etc. And yet, we somewhat perversely find ourselves rooting for cooler weather simply to try and quiet the AGW crowd. Our concern is that they are going to do stupid things like implement horrible and costly policy in the name of combating global warming, and that this risks higher taxes, limitations on freedoms, more regulation, and deficit spending all for a non-existent problem. So, it’s a conundrum to us that we at once want warmer weather and colder weather. We can’t win.

On a personal note, I can’t tell you how much this latest anomaly sucks. I happen to be in a region where we have experienced one of the coldest Januaries on record. I am not exaggerating the brutality of the cold. We had a number of -10 to -20 F days, with dangerous wind chill. If we’ve had any above-normal days, they were sparse and short-lived. My heating bill is through the roof. I hate cold weather. So, this anomaly is the worst of both worlds for me. Not only do I not get the skepticist benefit of a cold anomaly, but I did not get the personal benefit of the warm anomaly. My region is purple on the map, and has been all January. This, while I flip around the rest of the globe to see mostly pink or red. Frankly, I’m a little P.O.’d by the unfairness of it all! Wisconsin has been below normal now for a year and a half. I freakin’ want some of these warm anomalies to come my way.

Well, anyway, let’s get to the data…

Data
The RSS data can be found here.

Data Point
The January anomaly was 0.322. It is the highest anomaly since August 2007.

The current anomaly is 0.388 degrees Celsius warmer than January 2008, and is 0.150 higher than December 2008.

Streak
This is the third consecutive year-over-year increase. It is the seventh consecutive anomaly over a value of 0.14, following eight consecutive anomalies under that value.

Average
The 12-month average is 12.4, which has increased back up to the level for the period ending June 2008. The average increased from 9.2 a month ago.

Trends
Overall:
The overall trend is now a little over 30 years long for the RSS satellite data. The current slope in terms of 0.01 degrees Celsius per month is 0.13045. This equates to a rate of 0.1565 degrees Celsius per decade.

Current flat/cooling trend
The furthest back we can go to see an essentially flat line with a slightly negative slope (non-warming trend line) is April 1997. The period length for this measure stayed the same this month. Due to the higher anomaly, the front month moved forward one month, while we added January 2009 at the end. So, we’re still at a length of time 2 months shy of 12 years.

Fastest Arctic Ice Growth…

Jeff at The Air Vent has done some work and noticed that something interesting has happened in the Arctic. You know how we heard all about that “Fastest ice melt ever” that sent the AGW proponents into a panic a while back? Well, interestingly (but not surprisingly), they seem to have neglected to inform us that the reverse has occurred in recent months.

Check out his post, Arctic Sea Ice Increases at Record Rate.

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).