Results on deciding trends

No, the delay was not because the results came out differently than I'd expected. Just the more mundane business that I've been doing a number of different things and getting the figures done up reasonably is taking some time as I change my mind about what constitutes reasonable.

In brief (in a journal paper, this would be the 'abstract'):

• You need 20-30 years of data to define a climate trend in global mean temperature
• Forward and backward trends are markedly different
• Therefore, to discuss climate trends in global mean temperature, you need to use 20-30 years of data centered on the date of interest.
  

As with any abstract, it's too brief to show you why any of these are true, just some simple declarations. Now, if you trust me absolutely (which I don't recommend -- and if I'm talking science, you don't need to), you can stop and move on to some other reading. But let's take a look at the whys. As before, I'm putting the data and programs on my personal web site and you can run the analysis yourself, and modify the programs to work on different assumptions, methods, data sets.

Let's consider the first point -- how long it takes to determine a climate trend in global mean temperature. We could define a trend with 2 minutes of data -- temperature at one minute, temperature at the next minute, and draw a straight line through the two numbers. We'd wind up with wildly varying trends, though, from minute to minute through the year. This is weather and turbulence. Make it daily or monthly averages, and we still have the wildly varying trends, and the magnitude of those trends will depend on what time period we chose. Rather than declare that 'this is the right period', we'll determine it by looking at the data itself.

If it is meaningful to talk about climate as opposed to weather, there has to be a time span over which our result for describing climate does not depend much on how long a time span we choose. For average climate temperature, we found 20-30 years as the appropriate time span. I didn't show the figures then, but it's in the program and output you can pick up from my web site that this is also the appropriate time span for deciding a climate temperature variance (how much scatter there is about the average; even if the average didn't change, we would probably consider it a climate change to have winter lows vary from -30 to +15 instead of -10 to -5).

Figure 1 here shows the trends for all years (remember I'm lopping off the first 31 and last 31 from the NCDC record) that I computed trends for, by all 3 methods, in terms of the length of data record used. So at 36 (months) we see a range in the computed trends between +15 C/century and -15 C/century. These are enormous values cmpared to what we think of for climate change. If I wanted to give you a wrong impression about climate, then, I could use such short records. The range declines as we take longer periods. And then flattens out for trend periods of 252-372 months (21-31 years -- remember I took only odd averaging periods). In this part of the display, the range is about +1.5 C/century to -1.5 C/century -- and it is independant of how long an average I took. This, then, supports that a) there is such a thing as a climate temperature trend and b) that you need 21-31 years to find it (we can round to 20-30, given how 19 years is close to 21 also, we expect 20 even to be so as well).



Statistical aside: To compute variance, we find the deviation of each observation from the mean, square it, and then add this value up for all observations and divide by the number of observations. (Or use the appropriate function in your spreadsheet.) This is a fundamentally meaningful quantity. If we then take the square root of this number, and the numbers have a normal distribution, then we have a standard deviation. We can always take the square root of the variance, but it will not always be a standard deviation.

In figure 2, I plot instead the maximum and minumum trends, and the square root of variance -- again in terms of how long a period is used to compute the trends. This shows fundamentally the same information, but perhaps a little more clearly. Aside: It's a good idea to look at your data from several different vantages. Sometimes the display method you use in one step can hide something that's blindingly obvious in another method. Again, we see that the figures (maximum trend, minimum trend, average trend, square root of variance in trend) all stabilize once the data length used is 20-30 years. And, conversely, that for periods of 3-13 years, the figures all depend sensitively on how long an averaging period you choose.



Choose is a key word in doing science. We try to avoid having choices. Choices can be made differently by different people, for different reasons, and not all those reasons will turn out to be good ones. Finding a scientific principle and then looking for how to satisfy that principle is far better. Here, the principle is that the length of data used should not affect your conclusion about what the climate trend is. This is a strong principle. So when you see someone violating it (say by using a 7 year span without doing some real work to justify it -- work like I'm doing here), they're probably not doing good science.

Now, in figure 3, let's look at what the trends are like if we use 7 years of data, versus using 25 years. I'm computing all these by using centered information -- data evenly on either side of the time of interest. We'll get to why this is best in a minute. The main thing I think this shows is that if you use the short period, you present a false impression that climate is highly variable, trends changing from some very high positive value to a high negative value in a span not terribly longer than the 7 years' data you used in either case. This makes no sense for climate, but does for weather, or for misleading people. Weather, we know, does change rapidly. We can be warmer than usual for a few days (or months, or years) and then cool a few days/months/years later. Nobody (scientific) has ever said weather was going to end. Go out to the 25 year data period trends and we see, instead, that the trends have more stable behavior. They do change, which is reasonable since we do expect that climate changes. But it's no longer large magnitude flip-flops. That, too, makes sense as climate is a big beast and turning on a dime has to be a rare if ever occurrence.



To look a little differently at it ... if someone shows you a trend over 3 years, about 90% of what they're showing you is weather (real trends of up to 1.5 C/century, 3 year trends of up to 15 C/century -- 90% of that 15 C/century is weather). For a 7 year trend, it's about 70% weather. Weather is interesting, but if you're interested in climate, and they're claiming to be talking about climate, then they're misleading you by those 70-90% of weather they've thrown in by using such short spans.


On to which data to use for computing trends (or, for that matter, averages, but I'm focusing on trends today). Figure 4 shows trends in degrees per century as computed with data forward from the date given, backwards from the date given, and centered on the date shown. It also shows, and this is why I went to degrees per decade -- the magnitudes come out comparable, the NCDC monthly anomalies. I computed the trends using 25 years of data (300 months).



We immediately see that, indeed, the forward and backward trends are quite different, as expected in the planning note 'deciding climate trends'. The curves themselves are actually the same -- but shifted by 25 years (the period used to compute the trend -- when I use 31 years, it's shifted by 31 years, and so on). The centered trends show, again, the same behavior, but now 12.5 years (half my data period) off from the forward, or backward trends. So, look at the data for anomalies versus the different trends as computed. If we look at, say, 1945 (month 780) -- a generally warm year, we see a modestly negative trend from the centered trends, an extremely negative trend if we look forward, and extremely positive if we look backward. What's happening? And which makes most sense for thinking about the 1945 climate trend?

Climate is about normal, our expectations. For 1945, then, would we describe the typical change as one of rapid warming? rapid cooling? a modest cooling? If we look around then, the best description of the tendency is that there's a modest cooling going on. 1930s were warm, 1945 was a particularly warm year, but going in to the 1950s, temperatures were cooler. The large trends, in opposite directions, the forward and backward trend computations give us, even though for an appropriate period, mislead us. The centered trend computation gives us the right idea of what is going on around 1945. Repeat this inspection for other years, and think you'll rapidly come to the conclusion that the best description of what is going on around any given time is the one from a centered computation. If a trend is downwards (negative numbers), then we expect that times before our year of interest are generally warmer than later times. If it's upwards (positive trend), the we expect later years to be warmer. Only the centered computation consistently gives us this result.

We now have 2 conclusions: trends should be computed with 20-30 years of data, and they should be centered on the date of interest. Let's see how it works, applied to as much data as possible. To cover the greatest time span possible, I'll take the shortest data length reasonable for computing trends -- 20 years. Since I'm doing centered computations, this lets me get to within 10 years of the start and end of the record (remember I had been skipping the first and last 31 years so that all three methods could be used, and because I didn't know if some number shorter than 31 years would be long enough). Figure 5 gives the result, in degrees per decade, and with the NCDC monthly anomalies as well.



The most recent year we can compute a good quality trend for is 1998. The trend then is a warming of 0.19 degrees per decade, 1.9 per century. We see that the trend is higher towards the end of the record (i.e., towards the present) than at other times, though we'd have to do additional work to decide whether the difference was physically meaningful. The thing which surprised me about the curve is that most of the time -- from 1890 to 1998 that we can compute a quality trend for -- the climate trend has been a warming. Not a matter of sometimes up, sometimes down, but rather a basically up except for a bit of down in mid-20th century. 70% of the time, we've been seeing warming.

By way of summary, or postscript, or some such ...

• Whether it is to compute the average climate temperature, the variance of climate temperature, or the trends in climate temperature, we need 20-30 years of data.
• On the other hand, it is possible to compute a climate with 20-30 years of data. This didn't have to be the case. I'll show in a later post a curve with no climate in the sense I've been talking about.
  
• Most of the time for the period of data, we've been experiencing a climate warming.
  

Conclusions are, of course, limited by how data were analyzed. In this analysis, I looked only at the global mean temperature data themselves. If I'd done something more sophisticated, like carefully removing the effects of some 'weather' (short term or from outside the climate system) processes such as El Nino-La Nina oscillations, volcanoes, and solar variability, and then looking at how long was needed to get a stable estimate of the mean, or trend, I might wind up with a shorter period. On the other hand, that shorter period would only apply when people did indeed do a careful analysis of the effects of those things on global mean temperatures. This can be done, but is more sophisticated than I wanted to start with. It's also vastly more sophisticated than the many blogs and such out there which are misleading people by doing sloppy short term analysis and pretending that it's climate they're talking about.

Update  13 February 2010: The data file I worked on and the program I used are in a tar file.  You'll need a fortran compiler for this, or translate it to a language of your choice.  Nothing very fortran-ish is being done and the program is short.

What cooling trend?

Nonsense about the 'current cooling trend' is rife across the blogosphere, and the science minded folks usually point to the fact that you need 20-30 years to define a climate trend. The lies as such don't interest me, or make for a good topic for this blog.

What's useful or interesting is that the statement itself, often linked to 'last 10 years', is not true even after allowing for substantial cherry picking. This brings us back to the interesting matter of trying to define climate. And a further reminder that if you're reading bad sources, you can't trust even the simplest of statements.

To find current temperature trends, I used the NCDC monthly temperature anomalies. The most recent month is May, 2009. To look in to current trends, then, I computed the trends from every month of the last 30 years, through to May 2009. The trend shown for January 1979 is the trend from then to May 2009. The trend for April 2009 is to May 2009. Figure 1 gives the results (actually back to 1977).



Wow, current warming trend of 120 C per century! Surely we're all going to be boiling soon? Of course not. That trend was computed from a 1 month span -- April to May of 2009. It is yet another reminder that short term variations, namely weather, can be large. It isn't climate. Climate shouldn't depend sensitively on when exactly you start your trend computation. Unfortunately that figure shows us nothing new, beyond confirming yet again (not a bad process itself, and part of the scientific approach) that weather happens, and weather variability is much larger than climate variability. So in figure 2, I zoom in a little and ignore positive trends greater than 20 C/century.



So now we can see that if someone chooses very carefully (namely, cherry picks) the starting date, they can find a cooling trend between then and May 2009 ('current'). But notice how carefully they have to choose that starting date. If it's 10 years (or any number greater than that, back to the record's start date), the trend is a warming. In fact, you can only get cooling trends occur if you choose a start date between January 2001 and January 2007 (including those months), or October, 2008. Anything farther back, or more recent, shows warming.

Both for deciding climate, and for doing science, we want our conclusions not to depend sensitively on arbitrary choices. Ending with the most recent data is not arbitrary, so we're ok there. But choosing a starting date? Science-minded folks take a figure in the range of 20-30 years, in particular 30, because over a century of experience says that 30 years is a good time period to be able to look at climate trends as opposed to weather fluctuations. i.e., not arbitrary. Choosing 2.4-8.4 years (and not 9.4, or 12, ...)? Why would we do that? Well, if we wanted to support some particular conclusion, we might do so. But that is not science.

Let's zoom our attention to the period in late 2006 through early 2007. The largest 'cooling trend' you can contrive is to start with September or October 2006, giving 3.3 C per century cooling. Of course you're flagrantly violating sensible climate practice by using 30 months instead of 30 years. But now look to April 2007, where the trend is already a warming of 3.3 C/century, and remains higher than 3.3 to the present, except for that 1 month, October 2008. If 30 months are ok for cherry pickers, why are 24 months not? They're not very different time periods; if either one is acceptable, both must be.

On the science side, as my results post illustrated, if you take 20-30 years to determine your trends, then changing the length doesn't change your answer much. We see this again in figure 2, where any trend computed with from 15-30 years of data gives nearly the same answer as to the current trend -- about 1.8 C per century (1.49 for 15 years, 1.79 for 20, 1.92 for 25, and 1.62 for 30 years). The figures do fluctuate some, which is to be expected. But changing from 30 to 24 years doesn't take us from a large cooling to an equally large warming, the way it can for months.

I'll probably take this up in a separate note, as it illustrates a different way of misleading yourself with graphs. For now, I'll just observe that if you compute the 10 year trends, rather than telling people to 'just look', then the most recent time there was a 10 year cooling trend was the 10 years ending with January, 1987 (with 0.03 C/century). The last time you had several months in a row where the 10 year trend to that month was a cooling was in the late 1970s -- 30 years and more back. At no time that the '10 year cooling trend' claim has been getting made, has it been true.

AMS feeds for scientific articles and a sampling

Very few scientific articles get any splash in the media, even those parts which pay some attention to science.  Readers then can build a mistaken impression that there are only a few scientists, a few dozen, tops, working in any given area. 

The American Meteorological Society is one of very many professional societies which publish scientific journals.  They also have RSS feeds for their journals, and a relatively open access policy to their older (than 2 years) articles.  Below is a biased sampling (the bias being that I'm interested in these articles and will be pulling them down at work) of recent articles.  There's quite a lot more going on than you're liable to hear of in media, by quite a few more people than you'll ever hear of. 

Some of the papers will probably inspire a need to make use of my guide to science jabberwocky.  That's not a knock on the papers, just a reminder that fields do develop vocabularies to ensure that the professionals all know what each other means.


If you are fairly good with your technique, you can set up your own double-diffusive staircases.  (The first paper discusses observations in nature of the effect.)

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Sea level's climate time scale

My 'reality-based decision making' post prompted a comment asking for my thoughts about sea level rise, which is more than sufficient excuse to turn to that.  An additional excuse is that it provides a chance to look at how to decide climate time scales for something other than temperatures.  For global mean temperature trends, I found that you need 20-30 years to determine a climate trend.  We'll see that it is 40-60 years, 60 for preference, for sea level.

My starting point for data was the University of Colorado sea level group.  They provide satellite data back to late 1992.  High quality data, but only for a short period of time.  If global sea level's time scales are like global mean temperature's, then it's only just gotten long enough to provide a climate number.  Fortunately they list links to other sea level groups, including the Permanent Service for Mean Sea Level.  They have three global reconstructions available.  I'll take this one -- published in the scientific literature as: Recent global sea level acceleration started over 200 years ago?", Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth (2008), Geophys. Res. Lett., 35, L08715, doi:10.1029/2008GL033611 -- on the grounds that it covers the longest time period and has the most recent literature publication date.  It will be a good project for a reader to see if the conclusions here change, and how, if you use one of the others instead.

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How not to compute trends

Did you know that scientists are lying about the trend in sea ice extent?  That's the conclusion if you apply the popular trend analysis technique those who claim that the earth has cooled, or 'not warmed', since 1998, or 2005.  The probable reason you don't hear about this is that the 'lie' would be that scientists are grossly underestimating how drastic the trend to less ice is if you believe that method.

The method used to claim that there's a cooling trend, or no warming trend, is to cherry-pick a too-recent start year that is exceptionally high and compute the difference between that and a particular recent year (any one, repeat the 'no cooling trend for the last decade' for years afterwards, even if more recent years are warmer).

So I'll take a recent year that had large sea ice extent -- 1996, and compute the trend between there and a recent year that had a low extent -- 2007.  Here's the straight line computed that way, plotted against the observations between 1996 and present.  These data are September ice extents from the NSIDC:

And from eye-inspection of it, it even looks like the average error is about 0.  Sometimes high, sometimes low.  This trend is for ice pack extent to lose about 330,000 km^2 per year, against the about 78,000 km^2 that is computed for a linear trend by climate scientists.  Clearly climate scientists are trying to hide the decline!

I've already done some things more honest than what the 'no warming since 1998' folks do, not least is, I showed you the trend line and the data.  But this is far from sufficient to have a reasonable trend analysis.

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Harry Bulkeley: A few questions about global warming -- Answered

An opinion writer (a retired judge) asked a few questions in his Galesburg, IL local paper, and I'll provide some answers here.  As always, I encourage you to read the original.

The good judge, like the usually informative Mr. Krauthammer, starts off on a very wrong foot, with bad philosophy of science.  There are many facts in science -- the earth is round, the sun is hot, there is a greenhouse effect, and CO2 is a greenhouse gas.  All can be questioned -- but not in the trivial way that Bulkeley and Krauthammer seem to think.  'I question it' is trivial, and pointless.  If you have a _scientific_ question about these things, or any other, it is because, and only because, you have scientific evidence that the 'fact' is false.

Climate change, as even commenters in agreement with Bulkeley note, is indeed a fact.  Climate changes, that's a fact.  One of the tasks of science is to try to understand the hows and whys of that fact.

Let's see about the questions:
1) Average temperature has indeed gone up the past 15 years.  This is a question, apparently, because the author didn't bother to look at the data. One can experiment with time periods and trends at NOAA/NCDC.
http://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/1/2/1880-2014  
It's worth paying attention to the fact that climate trends are defined on 30 year periods, not 15.  Some discussion of why this is the case is at
http://moregrumbinescience.blogspot.com/2009/01/results-on-deciding-trends.html

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Technological progress

A couple of videos that caught my eyes. First is one on an upside of technological progress -- cars today are enormously safer in a crash than cars 50 years ago. This video shows the collision, and the driver crash test dummies, between a 1959 and 2009 car. The 2009 car undoubtedly weighed far less than the 1959. Superior engineering is the key -- a point that Consumer Reports routinely winds up making in their vehicle reviews.
Crash test video.

Digressing a second: It occurs to me that Consumer Reports is probably the popularly available magazine that does the most consistent job of displaying a scientific approach. The typical review article shows what they were testing, how they tested it, adds information about how significant the test differences are like, and so on.

The second video is one on a topic that weather and climate folks are probably more than a little tired of. Namely, the accusation that we didn't realize that there's such a thing as an urban heat island effect. I've never taken up the search seriously, but a few years ago, an urban heat island reference was in the Bulletin of the American Meteorological Society's '50 years ago' column. So, well-known (the referenced article was clearly not the discovery of the effect, just another illustration) by the early 1950s.

The video is from Peter Sinclair's Climate Crock of the week. In it, he carries out a good practice for science -- suppose an argument is correct, then look for observations that will confirm or reject that argument. The argument is that the urban heat island is producing the observed warming trend. Ok, says Peter, if that's the case, we should see that the trend is the strongest (most positive) in urban areas. Now, it isn't hard to figure out where the urban areas are. Nor is it hard to map out what the trends are for different areas of the globe. Compare the two.

In truth, as he illustrates, the warmings are highest in areas that have very few people -- Siberia, the Arctic, and Hudson Bay being leading zones. His figure is for the 2008 anomalies -- after the 'decade of cooling' (what cooling?), rather than the 30 year trends ending that date. If anything, the trend map is worse for the urban heat island fans, as it shows large trends across northern Canada as well.

Nothing obvious connecting the two videos. But the thing is, I have a lot of confidence in engineers to solve engineering problems. One such problem is car safety. Others would be things like more efficient cars, new and better ways of producing energy, and so on. In the 1950s and 60s, it was an article of faith in the car industry that customers did not care about safety. And that if they were forced to engineer safety, they'd go out of business (it would be too expensive). Instead, we have vastly safer cars today, and tens of thousands of people are still alive because of it. The engineers were more than up to the challenge. On the other hand, if the engineers aren't allowed to work on a solution, they won't find it.

I'm not taking up geoengineering in this; that's a topic for a lengthier post of its own. I'm just minded that there are quite a few climate-related technology issues that exist regarding efficiency of old technologies, or new technologies to develop, that we're being told would drive companies out of business, cost jobs, and other alarmist statements -- as there were in the 50s and 60s regarding automobile safety. Yet the engineers found ways of improving safety even as we drove more, drove lighter cars, and so on. And the companies didn't go out of business, indeed make quite a lot of money.

How to decide climate trends

Back to trying to figure out what climate might be, conceptually, and then trying to figure out what numbers might represent it. A while ago, I looked at trying to find an average value (in that case, for the global mean surface temperature) and found that you need at least 15 years for your average to stabilize, 20-30 being a reasonable range. Stabilize means for the value of the average for a number of years to be close to the average to a somewhat longer or shorter span of years. While weather can and does vary wildly, climate, if there is such a thing, has to be something with slower variation.

But most tempests in blog teapots are about trends. I'm going to swipe an idea from calculus/analysis and have a look at deciding about trends. One of the reasons to take a variety of courses, including ones that may not seem relevant at the time, is to have a good store of ideas to swipe. Er, a strong research background.

As before, I'm going to require that the trend -- to be specific, that the slope of the best fit (in terms of the sum of the squares of the errors being as small as possible) line should become stable in the above sense. This is sometimes referred to as ordinary least squares, and even more breezily as OLS. I don't like that acronyming since I keep reading it as optical line scanner, curtesy of a remote sensing instrument.

There's a little more, however, that we can do. When I looked at averages, I took them centered on a given month or year. So estimating the climate temperature for 1978, say, involved using data from 1968 to 1988. The reason, which I didn't explain at the time, is that if climate is a slowly changing thing, then the temperature a year later tells you as much about this year as the temperature a year earlier. And, as a rule, tells you more about this year than the observations 2 years earlier.

My preference for a centered data span conflicts with what people would generally like to do -- to know what the climate of 2008 (for instance) was during, or at least only shortly after, 2008. On the other hand, you can't always get what you want. The priority for science is to represent something accurately. If you can't do that, then you have to keep working. A bad measure (method, observation, ...) is worse than no measure.

So we have two methods to look at already: 1) compute the trend using some years of data centered on our time of interest and 2) compute the trend using the same number of years of data but ending with our time of interest . I'll add a third: 3) compute the trend using the same number of years of data but starting with the year of interest. (This is the addition prompted by Analysis.)

In numerical analysis, we refer to these as the forward, centered, and backwards computations (we move forward towards the point/time of interest, we center ourselves at the point/time of interest, or we look backwards to the point of interest). For a wide variety of reasons, we generally prefer in numerical analysis to use centered computations. In real analysis (a different field), where one deals with infinitesimal quantities, it is required that the forward and backward methods give the same result -- or else the quantity (I'm thinking about defining a derivative) is considered not to exist at that point. We're not dealing with infinitesimals here, so can't require that they be exactly equal. On the other hand, if the forward and backward methods give very different answers from each other, it greatly undermine our confidence in those methods. If the difference is large enough, we'll have to throw them out.

So what I will be doing -- note that I haven't done the computations yet, so I don't know how it will turn out -- is to
1) take a data set of a climate sort of variable (I'll pick on mean surface air temperature again since everybody does; specifically, the NCDC monthly global figures)
2) for every year from 31 years after the first year of data to 31 years before the last year of data
(I'm taking 31 be able to compute forward slopes for the first year I show over periods as long as that, likewise the 31 years at the end for backwards)
I)
a) Compute forward slope using 3-31 years (for 3, 5, 7, 9, .. 31)
b) Compute centered slope using 3-31 years (meaning the center year plus or minus 1, 2, 3, 4 ... to 15)
c) Compute backward slope using 3-31 years (again 3, 5, 7, 9, .. 31)
II)
a-c) For each, look to see how long a period is needed for the result of the slope computation to settle down (as we did for the average). I expect that it will be the same 20-30 years, maybe longer, that the average took. If it's a lot faster, no problem. If it's longer, then I have to restart with, say, the data more than 51 years from either end.

3) Start intercomparisons:
a) compute differences between forward and backward slopes (matching up the record length -- only look at 3 years forward vs. 3 years backward, not vs. 23 years backward), look for whether the differences tend toward zero with length of record used. If not, likely rejection of forward/backward method. If so, then the span where it is close to zero is probably the required interval for slope determination.
b) ditto between the forward and centered slope computations. The differences will be smaller than 3a since half the data the centered computation uses is what the forward computation also used. Still, I'll look for whether the two slopes converge towards each other. If they don't, then the forward computation is toast.

4) Write it up and show you the results. I'm planning this for next Monday. Those of you with the math skills are welcome (and encouraged) to take your own shot at it, especially if you use more sophisticated methods than ordinary least squares, or other data sets than NCDC. But I'll ask you to hold on putting them to your blogs until after this one appears.

I'll also be providing links to sources (tamino, real climate, stoat, ... and others to be found) which have already done similar if not quite the same things.

Part of the idea here is to illustrate to my proverbial jr. high readers what a science project looks like, start to finish. Some aspects are:

• lay out a method before you start, and consider what it means both if the results are as you expect them to be, and if they're the other way around.
• consider what you'll do if they're different
• look at what other people have already done
• write it all up so that others can learn from what you did

One item, unwritten but required: share the result even if it clobbers what you expected, or wanted, to be the case. I mentioned before that 7-8 years was too short to determine a climate trend. But I only shows you results for looking at averages. Well, maybe when I reach the end of this work, it will turn out that trends are much better-behaved than averages, and that you can indeed use the forward slope computation with only 7-8 years of data. Given that others have looked at this before, in different ways from each other and me and found this not to be the case, I doubt it. But an essential part of doing science is letting the result of the work provide your conclusions rather than what you think the result will or should be.

Who can do science

Everyone can do science. Most people, especially younger children, do so on a routine basis. Science is just finding out more about the universe around you. Infants playing peekaboo are conducting a profound experiment. They cover their eyes and everything vanishes. When they uncover their eyes, everything is back. Wow! Things have persistent existence! Even if you can't see them, they're still there. Then you cover your eyes, and the child can still see you. Existence continues. Whoa. Elaborate the game. One of you hides behind something. Then pops out. Whee! Things continue to exist even with your eyes open, even if they pass from view. No wonder children giggle at the game. This is a profound discovery about the nature of the universe.

In similar vein, we can all speak prose in our native languages, or run. The thing which becomes a question later along is whether you are doing it at professional level. I run, for instance. Most of us can. And, with appropriate training, almost all of us can, say, run a marathon. Very few us of can run a marathon, however, at the pace that elite runners do. Even fewer could do so without undertaking very serious, elite-level, training to make the attempt. Similarly, while we can all talk, and most of us write, very, very few are realistic candidates for the best seller's lists, or Nobel prizes.

So it goes with science. Doing it at a professional level is a lot harder than doing it at all. One thing you often encounter in coming up with ideas is to discover that your wonderful, creative, idea was already thought of. I give myself points (when looking outside my field) for how recently it was thought of. More than 300 years ago, only 1 point. Less than 200 is more, and less than 100 even more. Every so often I manage to go out of field and come up with a new idea (to me) that professionals thought of only 30-50 years ago. On the rare occasion, I come up with one that they didn't come up with until within the last 30 years. I give myself a lot of points for those. They're pretty rare.

That's one part of doing science at a professional level -- your idea or discovery has to be not only new to you, but new to the world. Consequently, a lot of the training for becoming a professional involves learning what is already known. The answer is, unfortunately for we who'd like to make a grand splash of some kind, a lot. Worse, there are now centuries of very creative, knowledgeable people who have been working at it. Coming up with something novel is, therefore, hard.

A high school friend illustrated this neatly, if accidentally, for me. We met up over a holiday early in our college careers and he was complaining about the lack of creativity in computer science. For instance, thinking that something new and good was to be had by looking at 3 value logic systems rather than 2 value as was expressed in binary computers. He was confident such an idea would never be looked at. The week before, I was at a presentation about 3 value logic circuits and why they'd be useful. And the novel part was not the idea, which was much older, but how the speaker planned to implement it in hardware.

Conversely, if you'd like to do something novel, you're much better off looking at some area that is new, using new equipment, etc., so hasn't had a long history for people to work out a large number of ideas. In that vein, it's much easier to pull off on the satellite remote sensing of tropospheric temperatures for climate (a topic less than 20 years old) than for the surface thermometer record (well over 100 years old). A fellow I know published in the prestigious Geophysical Research Letters, largely on the strength of this point. Paper came out in 2003. He looked at how the Spencer and Christy satellite algorithm worked, and realized that it assumed something which in high latitudes was not a good assumption. He then worked out what the implications were (i.e., the trends in sea ice cover would be falsely reported as trends in temperatures), and documented it well enough to be published in the professional literature:
Swanson R. E., Evidence of possible sea-ice influence on Microwave Sounding Unit tropospheric temperature trends in polar regions, Geophys. Res. Lett., 30 (20), 2040, doi:10.1029/2003GL017938, 2003. (You can follow this up at http://www.agu.org/)

Now, the thing is, Richard did not have a doctorate. He had a master's. And his master's was not in science, it was engineering. What mattered is that he saw something that hadn't been noticed, documented it well, and submitted it to the professional publication. And he got published in this high profile journal even though he had no PhD, nor even previously worked in the field. I keep him in mind when people talk about the 'conspiracy to keep out' ... whoever.

On a different hand, as an undergraduate I did do work worth a coauthorship on a significant journal paper. (Significant journal, that is, whether the paper was significant, I leave to its readers.) But that was working for a faculty member, and while my contribution was indeed (I realized later, I assumed that Ed was simply a nice guy -- which he was, but that turned out to be a different matter) worthy of a coauthorship, I couldn't have gotten the project started on my own. Once started in a fruitful area, I could have finished it, but for a professional, you want to see the person be able to find out what the fruitful area is.

So how young can you go; how much experience is needed? Well, if you choose right, and are creative enough, jr. high. My niece managed a science fair project last year that I still encourage her to write up for serious publication. She hit on an idea in an area that hasn't been studied a lot already (it's new and people there have been assuming an answer, she documented it -- good science) and a way of testing it (ditto) and collected the data and evaluated it scientifically. Yay! She might need a hand on the professional writing and statistics description, but the science part, she nailed solo.

Where does that leave us as readers of blogs and such? Alas, it means we have to think. The presence of a PhD is not a guarantee of correctness. Nor is the absence of one a guarantee of error. And this remains true even if we consider what area the PhD was in and the like. What is more reliable is that the older an area (surface temperature record interpretation, for instance) the less likely it is that someone can make a contribution or correction without doing quite a lot of work. The field is long past the point where it's likely that they've not noticed the urban heat island, for instance. (I haven't searched seriously, but have already run across reference to it from the early 1950s.) Blog commentators who plop this one down as if it were an ace of trump: "Ha, they didn't consider the urban heat island. Therefore, I can conclude whatever I want." or the like, can speedily be added to your list of unreliable sources. Urban heat island has been considered, quite often, for longer than they've been alive. It's an old field. Tackling ARGO buoys, less overwhelming an obstacle. (But be sure your math is up to the work! )

As younger people (those of you who are, which doesn't seem to be many, alas; but you parents remember it for your kids' sake) it means that the time to start working on doing science is today. Do your own science (meaning, learn things about the world), and try to do some professional level science too (try to learn things that nobody else has figured out yet). The heart of science is in finding things out. This doesn't have to have anything to do with what you're doing in school. But things that interest you, whatever that may be.

How to pick cherries

The not-so fine art of contriving to support the conclusion you predetermined is cherry picking.  Really not a good thing for a scientist to do or condone, but pretty common in politics.  The latest example comes from politician (now presidential candidate) Ted Cruz, being condoned/defended (even praised) by scientist Judy Curry.

Suppose you're interested in global warming, just in understanding what's going on -- not in 'proving' that there is warming, or cooling, or that temperatures are unchanged.  You're an actual skeptic -- looking for evidence and where the evidence leads.  One thing you learn pretty quickly in your skeptical explorations is that you need 20-30 years of data to define a global climate temperature.  Shorter than that, and your answer depends sensitively on your averaging period.  As a skeptic, you don't want such unreliable methods.  Apply the 30 years to a number of data records (below), and you get the answer that climate has been warming, 1.3-1.7 K/century (2.3-3.1 F).

As a cherry-picker, committed to finding a particular answer, however, you go straight for the option of using short spans -- look for a record length that will give you the answer you want, then ignore the fact that your answer changes if a couple years are added or subtracted.

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Science on the way

No, not articles I'm going to be writing, but a sampling of sessions that are coming at the fall meeting of the American Geophysical Union:

Session names here, descriptions below.

Satellite Geodesy of the Polar Oceans
Progress in Quaternary Geochronology in Polar Regions
Cryospheric Climate Data Records
Rapid Arctic Environment Change
Environmental Impacts of a Shrinking Arctic Sea Ice Cover
Long-Term Trends in the General Circulation of the Atmosphere: Observations, Simulations, Mechanisms, and Impacts
Large Scale Cryosphere – Climate Connectivity
Ice Sheet Hydrology and Dynamics

This is just a small sampling of the sessions that will be going on. To see more, check out the AGU for the Fall meeting's program. The sessions turn on those good science questions: what are the data, how good are they, how can we get and retain better, what does it mean, how well do we understand what's already going on, what might happen in the future, and what would that mean, etc.

Now for the details, happy reading:

Satellite Geodesy of the Polar Oceans
New satellite geodetic techniques and observations are providingunprecedented views of the polar oceans, their ice cover as well as the topography and tectonic fabric of the seafloor below. Satellite altimeter measurements of ice and snow elevation from radar and laser
systems, such as Envisat and ICESat are providing wide area, continuous, information on ice thickness changes. Satellite measurements of sea surface topography and gravity for example from GRACE offer the potential for significant advances in our understanding of the Polar
Oceans, particularly when combined with in-situ observations. Marine gravity fields from satellite altimetry as well as satellite and surface gravimetry are also providing new maps of the tectonic structure and bathymetry of the poorly mapped polar oceans. The current satellite
capabilities will be significantly enhanced in the near future with the launch of new missions such as GOCE, CryoSat-2 and IceSat-2. We welcome contributions on all aspects of satellite geodesy of the polar oceans with emphasis on applications of these data to science problems in the
areas of oceanography, the marine cryosphere and tectonics.


Progress in Quaternary Geochronology in Polar Regions
The main purpose of this session is to provide an opportunity for discussion of recent progress and challenges in developing chronologies in Arctic and Antarctic regions. Session organizers hope to include, but are not limited to: research in the fields of ice-core chronology,
tephrochronology, dendrochronology and DNA analysis, as well as cosmogenic nuclide, radiocarbon and luminescence dating. Session conveners encourage a discussion of new developments in chronological methods with regard to different paleoclimate archives and the advantages and weaknesses of various methods.

Cryospheric Climate Data Records
The generation of cryospheric climate data records (CDRs) is a critical step in providing the necessary information for scientists, decision-makers, and stakeholders to make adaptive choices that could improve the nation's resiliency to environmental change and variability, maintain our economic vitality, and improve the safety and comfort of U.S. citizens. This session will bring together recent efforts in the development of CDRs over Arctic and Antarctic ocean, ice, and
terrestrial surfaces, including snow cover, sea and land ice, melt onset, surface temperature, sea surface temperature, etc. Contributions are encouraged covering the full spectrum of CDR development and use, including initial formulation of algorithms, validation of CDRs, and use
of CDRs for scientific inquiry.

Rapid Arctic Environment Change
The extent of Arctic perennial sea ice was reduced by another million square kilometers between the winters of 2007 and 2008 with seasonal ice occupying the North Pole region in mid-winter for the first time in the observational record. There were major warm temperature anomalies in the
central Arctic in fall 2007 and accelerated ice drift along the Transpolar Drift Stream in spring and summer 2007. Many impellent science issues remain: Is the summer ice extent more influenced by
initial conditions or by summer forcing? What is the relative role of dynamics, thermodynamics, and feedbacks in Arctic ice reduction? Will the current change continue, accelerate, or decelerate? Has Arctic change crossed into a new state or can it be reversed? What are the
regional and global impacts? Fortunately, the International Polar Year program has been ongoing with international research efforts providing new and crucial results on Arctic change and its impacts. In this regard, this session calls for presentations on 2007-2008 Arctic change, the historical context of recent change, predictions of change, and impacts of Arctic change on ocean, land, and atmosphere.

Environmental Impacts of a Shrinking Arctic Sea Ice Cover
Arctic sea ice extent at the end of the summer melt season has declined sharply over the period of satellite observations and is projected to disappear entirely as concentrations of atmospheric greenhouse gases continue to rise. The record low ice extent of September 2007 served as an exclamation point on the downward trend and further raised concern that the Arctic may be on the verge of rapid transition to a seasonal ice cover. While the factors forcing this trend have and will continue to be widely studied, less attention has been paid to the environmental impacts of current and future sea ice loss. Continued loss of the ice cover may result in strong rises in atmospheric temperature and water vapor content, not just at and near the surface, but extending through a considerable depth of the troposphere. Changes in humidity and the
boundary layer structure are likely to alter cloud conditions, a key determinant of the surface energy balance. Through atmospheric transports, warming will likely extend well beyond areas of ice loss, potentially influencing arctic land areas, glaciers, ice caps, and the Greenland ice sheet. Extensive open water areas will promote increased wave action and coastal erosion. Ice loss may in turn have impacts on patterns of atmospheric circulation and precipitation not just within
the Arctic, but potentially extending into middle latitudes. While evidence is growing that some of these effects are already occurring, they are likely grow in coming decades. This session will address emerging and projected environmental impacts of arctic sea ice loss through both observational and modeling studies.

Long-Term Trends in the General Circulation of the Atmosphere: Observations, Simulations, Mechanisms, and Impacts
An increasing body of evidence indicates that key-elements of the large-scale atmospheric general circulation have undergone significant change over the past several decades, which may be an important indicator of climate change. Examples include widening of the Hadley cell, changes in the position of the subtropical jets and extratropical storm tracks, lifting of the tropopause, and trends in the annular modes. However, there is still much to be learned about the observational evidence of these changes, their causes, and their societal and environmental impacts. Several causal mechanisms have been suggested, such as changes in tropospheric stability, extratropical eddy activity, and stratospheric dynamics. But it is still unclear which may
be most appropriate and whether there exists a single unifying theory. This session will bring renewed focus to this issue by examining further evidence for these processes, exploring their cause(s), and improving our theoretical understanding of their linkages. We invite studies on all aspects of long-term change in the general circulation, particularly papers that combine observational, modeling, and theoretical approaches.

Large Scale Cryosphere – Climate Connectivity
High-latitude regions are recognized as being critically sensitive to anthropogenic climate change, and are also subject to large scale climate phenomena such as the northern and southern annular modes. The cryosphere is a dominant but highly variable feature in these regions, and exists in numerous forms including snow cover, ice sheets, permafrost and sea ice. Therefore components of the cryosphere can be expected to interact with climate variability and change, either as a passive responder to climate, as an instigator of climate perturbations, or through feedback mechanisms that affect both facets of the polar environment. Moreover, this cryosphere – climate connectivity is not constrained to high latitudes, but can also occur in snow-covered mid-latitudes and hence potentially affect the global climate system. This session brings cryospheric, atmospheric, hydrologic and climate scientists together to share recent advances in our current understanding of this connectivity. Investigations are solicited involving all components of the cryosphere, and both climate variability and change. Studies that target large, regional – continental scales are particularly encouraged.

Ice Sheet Hydrology and Dynamics
The extent to which fluctuations in basal and surface hydrology affect ice sheets is a topic of widespread and recent concern; changes in the flow of the Greenland Ice Sheet correlate with changes in its surface hydrology. Melting at the Antarctic Peninsula has factored in the disintegration of ice shelf sections, and exchanges of water between lakes at the base of the Antarctic Ice Sheet could modulate mass losses through episodic drainage and lubrication. Although relationships between the hydrology and flow of mountain glaciers have been studied for much of the past century, attention has only recently turned to ice sheets, so a detailed understanding is lacking. This session will explore the effects hydrology has on ice sheet dynamics by collating the results of field experiments, satellite observations, and numerical modeling. Contributions from each of these areas are encouraged.

Heartland on Ice

What's old is new once again.  I'd actually written most of this in February, when Heartland was in the news.  But between one thing and another, hadn't posted it.  Now that  they're in the news again, it seems once again to be apt.  As usual, my interest is on the science.  Since a number of Heartland supporters are saying things in the vein of Heartland made a misstep in acting as they did, because it takes away from their message on the science.  The supporters think Heartland is doing well on the science.

Now that I've looked in to what Heartland Institute has had to say on sea ice, I can say with confidence that they are not doing well on the science.  They don't know (or lie about) the difference between ice area and ice extent, don't know how much area of ice there is, don't know where it forms, make up numbers even if you ignore the difference between area and extent, lie about what authors say in their scientific papers, treat 2 years as plenty for establishing a climate trend if it is in one direction, but ignore the 30 year trends when it isn't, don't know the difference between sea ice and ice shelves, don't understand how sea level changes, and others I'll let you classify yourself.

The gory details, examples being from their web site, are below the fold.  A different point is, I don't expect everyone to be expert on sea ice, or even pay attention to it.  If Heartland had ignored sea ice, that's fine (at least it is if they're not saying things which require understanding sea ice).  But they chose to write about it.  And the people whose articles I'm quoting are Jay Lehr, their science director, and James M. Taylor, a senior fellow for the Heartland Institute focusing on environmental issues.  In other words, major players in deciding what Heartland says about science, not someone who might once have said something stupid about sea ice while passing through the office.

Having done my homework, I'm comfortable in saying that Jay Lehr and James M. Taylor are unreliable sources on the science, and Heartland Institute is as well.  See below for my homework example.

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Data are ugly

Current news about whether there really is an increase in Antarctic sea ice cover is reinforcing my belief, shared by most people who deal with data, that data are ugly.  This work argues that the trend that some have seen in some trend analyses has more to do with the data processing than with nature.  I encourage you to read the article in full itself.  It is freely available.

From the abstract:

Although our analysis does not definitively identify whether this change introduced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid- 2000s, and numerous studies that have relied on these observations should be reexamined to determine the sensitivity of their results to this change in the data set.

One of the obnoxious things about data sources is that they don't remain the same forever.  This is not so much a problem for my concerns about weather prediction, since the atmosphere forgets what you said you observed in a few days.  But for a climate trend, the entire record is important.  For the data set being discussed, the Bootstrap Algorithm (Comiso) applied to passive microwave, we immediately run in to data obnoxing.  Since 1978, there have been several passive microwave instruments -- SMMR, SSMI F-8, SSMI-F11, SSMI-F13, 14, 15, AMSR-E, SSMI-S F16, 17, 18, and AMSR-2.  They didn't all fly at the same time, and they don't have exactly the same methods of observation.  And none of them exactly observe 'sea ice', which leads to a universal problem which we (people who want to use these instruments to say something about sea ice) all have to deal with.

So a few considerations of what all is behind the scenes of this paper and the earlier Screen, 2011.  The latter paper involved some of my work (read deep in to the acknowledgements).  This one doesn't, but the fundamental issues are the same ...

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Making your own sea ice estimates

The Sea Ice Outlook does accept estimates from outside the professional community.  Maybe not everybody involved is thrilled by this, but I do think it's a good idea from my distant vantage.  And Jim Overland, one of the people behind the SIO, is strongly in favor of it.  (I had a chance to talk with him about the outlook and other ice matters a few weeks ago.)

In the most recent report, there are 3 submissions from 'outsiders' -- Chris Randles (you've seen him comment here as crandles) and Larry Hamilton, both at Neven's Arctic Sea Ice Blog, and one from Wattsupwiththat.

Watts' entry was a poll of readers.  While perfectly legitimate as an entry, it's also perfectly useless scientifically.  One goal of science is to gain understanding of the system in order to spread the knowledge around.  Polling can't be spread.

Much more interesting are Chris's and Larry's methods.  Both are obviously methods of great brilliance, as they currently have the same estimate as I do from my statistical method -- 4.4 million km^2 for this September.  Beyond that, you can read their method descriptions in the Sea Ice Outlook report and start constructing your own method by not making the mistake they and I have made.  Whatever those turn out to be.  Larry Hamilton's write ups (one for ice extent, one for volume) are:
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-extent.html
http://neven1.typepad.com/blog/2011/04/trends-in-arctic-sea-ice-volume.html

And you can also examine model output from the PIPS replacement model ACNFS, PIOMAS, and CFS as a basis for making your own estimates.   (And please do cite others that you know of.)

All are welcome to post your methods here in addition to (or instead of) at the SIO.

Weather will still happen

It looks like this is a surprise to some folks, so I guess it bears repeating: Weather will still happen in the future. This is true regardless of whether the current scientific understanding about climate change is more or less correct or not. There's still going to be weather. By that, I mean that there will still be periods when your back yard will be markedly warmer or colder than usual, you get more (or less) snow than usual, and so on.

What's bringing it to mind is that with a recent cold spell around here (Washington DC area), I've heard many more comments about how "There can't be global warming because it's cold here." (Or that it was cold when a rally was held, etc.) Now, if the people saying such things were honest (even if not correct), when we get to about 20 F warmer than usual this weekend (which is the forecast, vs. having been 20 F cooler than usual), they'd turn around and say just as loudly that there is global warming after all.

But this is one of the easier flags on whether you're dealing with an honest source. They ignore data that goes against what they want to conclude. Conversely, it's a good self-check when you start reading material that annoys you -- is it annoying because it presents evidence just as good as, or better than, you have for the conclusion you prefer? In that vein, if you think I'm ignoring important evidence, do bring it up to me.

As you know from earlier notes, if you've seen them, it's a silly thing to draw conclusions about global climate from a few days of local temperatures. Or even a few years of even global temperatures. If you haven't been here before, take a look at

• Results for deciding climate trends


• How to decide climate trends


• What is climate - 2

Three feet of global warming

Now that that idiotic line is out of the way, let's do some thinking about what we should expect from climate change. I've been calling it climate change, rather than 'global warming', since at least 1993. A major reason being that there is more to climate than temperature, and changing temperatures affect more than just how hot it gets.

One of the things that temperature affects is how much water can be in the atmosphere. The hotter it is, the more water vapor you can have in the air before it starts to form a cloud. So one very simple expectation that we could have on climate is that warmer = more humid (absolute humidity that is). Since there's more than temperature to climate, we don't really expect it'll work out that simply everywhere, all the time. But it tells us one line of research to take -- look to see what has been happening to atmospheric moisture content.

Jeff Masters has written this up at his blog on the Weather Underground, and I'm relying partly on his notes in my write up.

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2011 Sea Ice Outlooks

For 2011, I added a third method of estimation.  Or, rather, I talked with someone who was using a third method and helped refine it some.  Our guesses are 4.4, 4.8, 5.0 million km^2 for September monthly average sea ice extent as computed by NSIDC.

Again, I'll put our estimates in context of some other estimation methods. 

• Climatology 1979-2000: 7.03 million km^2
• Climatology 1979-2008: 6.67 million km^2
• Linear Trend Climatology 1979-2008: 5.31 million km^2
• Wang, Wu, Grumbine model: 5.0 million km^2
• Wu, Grumbine, Wang model: 4.8 million km^2
• Grumbine, Wu, Wang statistical ensemble: 4.4 million km^2

We normally like 30 years for deciding a trend, but 20 years can be enough.  Since it's far from obvious how to decide what is 'climatology' when climate is changing, taking a few different approaches seems a good idea.  I include a climatology which has a (declining) linear trend on the grounds that there clearly is a declining trend to the sea ice extent, so we expect this year to be lower than last year to some degree (on average).

The two climatology means (22 and 30 years) are relatively close to each other, and are far away from anything we've seen in years.  Taking the 30 year trend, from the first 30 years of the satellite record, gives 5.31 million km^2, which is close to a figure seen in recent years (5.36 in 2009), but well above any of our estimates or the 4.9 seen last year.

Below the fold for a few more words about our 3 estimates:

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Last call for submissions

The deadline for submissions to the Openlab 2009 is midnight EST, 1 December.  This is aimed at being a collection of the best blogging from 1 December 2008 through 30 November 2009.  Use this submission form to submit your favorites (from here and elsewhere). The current summary of submitted articles is at Blog Around the Clock. Two of mine, Science Jabberwocky, and Results on Deciding Trends, are already submitted. If there's something else you like as well or better, time to submit them. If those are your favorites from here, no need to do anything.

Though it would probably help my odds if you didn't submit others' articles, I see there are several quite good articles from other blogs that haven't been submitted. I'll be doing some of that submission myself, and I encourage you to do so as well. I'd just like to make coturnix's (the editor) job as hard as reasonably possible :-) -- give him a lot of excellent articles to consider.

When was climate normal?

It's been a couple years since I took up the question of normal climate, so time for another go.  At that time, I used monthly data from Hadley, and arrived at the observation that if you're younger than 26, you've never seen a month where the global average as as cold as the 1850-2011 average, 317 consecutive months (at that point, now over 330) of warmer than 'normal' temperatures.  I'll cheat and give you some answers first, read on to see how they're established:

• Climate was 'normal' only between 1936-1977
• Every year 1987-present has been warmer than any year before that
• 1976 was warmer than any year before 1926
• 1978 (next coldest year of the recent run) was warmer than any year before 1940

Do read on to see what 'normal' winds up meaning; it's important!  One part of 'normal', as we intuitively think about it, is that you should some times above it, and sometimes below.  Having many consecutive years above 'normal' says that normal isn't really very good.  To help get quantitative about how to proceed, consider this plot of NCDC's data (warmer/colder than the 1880-2012 average -- the length of the entire record).

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Semi-open thread 1

This is the first semi-open thread for the blog, as opposed to the many question place threads. Please first read on commenting before putting up your comment. Given other comments I've been receiving lately, I'll also suggest reading:

Cherry Picking
Why you need 20-30 years to decide climate trends

Questions are, of course, welcome here as well!