Archive for June, 2011

A Step Change In Northern Hemisphere Snowcover?

June 28, 2011

While I was in the middle of looking at various climate datasets to analyze for various things, I stumbled upon something curious in the Northern Hemisphere Snowcover data: what appears to be a sudden shift  towards less snow in the late eighties, preceded and followed by fairly stable conditions before and after. What’s curious about this is that I haven’t seen this identified by anyone previously. As we shall see, the strangeness does not stop there. But first, a little bit on how I came across this interesting feature of the data:

The NH snowcover data is unfortunately missing several months in the earlier part of the data set. My solution for dealing with this was to identify the missing months in separate time series, and estimate the missing values based on the same months in surrounding years. This doesn’t really have an influence on the identification of the step shift, as the missing months are mainly in the late sixties, and the apparent shift is in the late eighties.

The next step was to remove the seasonal effects by taking a twelve month moving average of the complete time series. This is the result, with the average line for 1967-2010, here:

I have split the series at the point at which values go from predominantly above average to predominantly below average.

Curiously, this step shift, while quite apparent when the data are averaged in this way. But except for the summer seasonal graph, which is not available for some reason, it is not clear to me that such a step shift can be seen in the seasonal plots. The winter actually shows an increasing trend, and the spring trend might possibly look step-ish but appears more continuous after.

Radiation Redux

June 25, 2011

Previously we discussed how climate sensitivity relates to radiation flux. While I am still looking into Steve F.’s idea examining the radiation/heat balance, I have done some cursory analysis of Roy Spencer’s global CERES and Aqua data of the radiation flux and middle tropospheric temperatures (note that these probably vary, in the short term, about 1.2 times as much as the surface temps, so relating them to radiation will over estimate sensitivity) available here. I have analyzed it in a simplistic manner, not doing smoothing/lead-lag analysis, just simple 31 day slopes, and 365 day averages of those slopes. The average of those averages is 5.28 W m^-2 K^-1 which based on my earlier equations corresponds to an f of -.6 and a climate sensitivity of .75 K for a doubling of CO2. Results like these suggest to me that the satellite flux data strongly support a low climate sensitivity and make higher sensitivities highly implausible. Anyway, here is a plot of the 365 averages of 31 day slopes:

Personally I think there are a lot of reasons why this result may overestimate sensitivity. For one thing, I probably significantly include some bias towards positive feedback by not explicitly accounting for convolution of forcing with feedback. Additionally, if I take into account the amplified variability of tropospheric temperatures compared to the surface, the sensitivity is more like .63 K for a doubling of CO2. This is so strikingly different from the commonly claimed sensitivities that, again, I just can’t see how one can justify them, as they fly in the face of real world measurements.

Low Frequency Variability in the ENSO Phenomena

June 20, 2011

I’ve recently been exploring a new analysis technique for examining the the slow, long period variations in climate datasets, as distinguished from inter-annual variability. The approach I am working on involves “smoothing” the data in such a way that the length and time resolution of the data are preserved, while the short term fluctuations in the data are ironed out, revealing, I think, what is mainly a decadal and longer term signal, all using only actual data, no “padding” or extrapolation involved. I would simply like to offer an example of this approach here, without much further comment, except to explain the methodology.

First, this smoothing technique, which I carry out in Excel, involves successive three point averages, which taper off at the point at which only one or two such averages are possible. This is done mainly to determine how many such averages one can take. The next step is to take the first smooth, and at the end points, take the average of the last and first two points, double weighting the very endpoints. Now it is possible to carry out such averages indefinitely, however I have chosen to stop at the point at which the number of successive three point averages stop. At this point, with a hundred year or more monthly dataset, I have so many timeseries Excel is starting to strain and sometimes seizes up, but I’m not done yet. I have one more step I will take just to see if I can see what happens when I average at each point all the series I have just created, and the original data set. Doing so (and I have tried this on a few different time series) restores some of the amplitude of variability and a little of the more short term variation, but still emphasizes the “slow” variations. Today I am going to show you the results I have gotten from the Southern Oscillation Index [EDIT: for the purposes of this analysis, to examine the ENSO phenomena correlated, rather than anti-correlated with ENSO sea surface temperatures, the SOI values were multiplied by negative one.] with first the data with the greatest smoothing (Before averaging all generated timeseries and the original) then the version with averaging of all timeseries done to restore some of the variance.

I would like to hear people’s thoughts about this technique. I am prepared to be told how stupid or useless you think this is, and any other thoughts. Thanks!

I Know Calculus You Know…

June 10, 2011

I’ve, uh, never been accused of believing in Zeno’s paradox before. Perhaps most ridiculously, I’ve never been accused of believing in Zeno’s paradox after taking the sum of an infinite series finding it’s value finite. That, uh, that’s stupid. Rock stupid. Impossibly improbably stupid. Well…

This is elementary stuff. And if I believed in Zeno’s paradox I wouldn’t get A’s in calculus, heck I’d flunk if I were a Zeno-ite. Needless to say, I’m not. You know, I’d have a lot more to say if I wasn’t just totally floored by this kind of complete absurdity.

An Update on Category 4 & 5 Tropical Cyclones: Global and Northern Hemisphere through 2010

June 1, 2011

With the Atlantic Hurricane season officially starting today I think it’s past time I updated my previous post on Intense Tropical cyclone trends. Southern Hemisphere data can also be included through the end of the 2009-2010 season which I have decided to comment on because, even though I have doubts about the quality of that data, it appears that it now matters less than previously. First, let’s look at the Northern Hemisphere data updated to 2010:

Not only does it remain the case that no positive trend in these intense cyclones has occurred in the Northern Hemisphere, the negative trend has actually become larger than previously. Before it was obvious that the trend’s difference from zero was probably insignificant, I won’t just eyeball it this time since it is now more noticeable, but I have not verified either that it is or is not different from zero statistically.

Looking globally, we have the problem that while in the Northern Hemisphere a cyclone season sits nicely within a year, the Southern Hemisphere basins straddle the boundary between years because the seasons are inverted there. So there are two possible ways to add the time series together to get global data: Either add all the cyclones from a season to the Northern Hemisphere cyclones from the start year of the season, or the end year. Interestingly, which of these options you chose actually determines whether there is a slight increase or no trend at all. That any supposed “trend” is sensitive to such a subjective (as far as I can tell) decision does not give one confidence that such a trend is “significant” in any meaningful sense. Nevertheless the data say what they say. To me it’s clear that there is no dramatic increase in intense cyclone activity going on.

As can be seen above, the global trend in category 4 and 5 tropical cyclones is only positive (and even then rather small) if one decides to add their totals to the Northern Hemisphere’s in a particular manner. This suggests that claims of globally increasing intense tropical cyclones are overblown at best.

Please refer to this post for information on the methodology and references relevant to the analysis in this post. Data originate from their official reporting centers.

What exactly is “acceleration”?

June 1, 2011

Hey, I’m still alive people. Well, today’s topic is pretty dull, “technical stuff” but I can’t think of much else to do at the moment. I am over do for examining the satellite trends, I guess I’ll do that later.

We have talked before about the idea that there have been “accelerations” in  various climate parameters that are also ongoing at least according to some. But how does one define “acceleration” exactly? Well first let’s deal with the question of instantaneous acceleration. A time series at any given time may be increasing or decreasing. By analogy to motion of a projectile mass, this motion has “velocity” represented by it’s first derivative. If the time series is decreasing, the “velocity” has a negative value, if it is increasing the “velocity” has a positive value. Acceleration is occurring at a time when the time series is decreasing or increasingly faster with time, or the second derivative is positive for increasing values or negative for decreasing values. Many people miss define “acceleration” by simply say it is when the second derivative is positive, which is completely wrong. Acceleration is when the first and second derivatives have the same sign. This however is not generally the problem with claims of “acceleration” in climate data. That is a problem of average acceleration. You see, time series are not continuous functions, and true differentiation requires continuous data. But more than that, statistical “fitting” of data allows one to determine “trends” in the data and derivatives there of, but when done over an entire data set can tell you if there was an overall increase in the rate of change (which is, again, not the same thing as acceleration) not whether that change is ongoing. For instance, the following illustrative diagram shows an “accelerating” time series:

The graph isn’t of anything in particular, just some numbers. The rate of change is clearly not constant. Someone could conceivably “differentiate” this data and then fit a line to the derivative and conclude that, since the average rate of change is positive and the slope of the fitted line is positive, the data is “accelerating” the only problem being that they would be flat wrong. True, acceleration did take place in this data, but not continuously and ongoing, and the data is not “currently”, that is, at the end of the series, “accelerating”.  Previously we have shown that datasets are not currently undergoing acceleration by pointing out that similar length periods with the same rates of change have occurred in the earlier part of the data set, but really all that would actually be necessary is to show that rate may have accelerated at some points but is not presently doing so. In the case of GMST it most certainly isn’t, as the analysis involving comparisons we have done more than adequately demonstrates. But what about claims of acceleration in Ocean Heat Content, for example? Well, looking back at the data from that post (undoubtedly an updated analysis would look different) you can see where claims of acceleration can get misleading. The rates of change in that data set went from negative early on to positive, and not withstanding some ups and downs, stabilized to fairly constant plateaus from which it dropped and rose back to a couple times. The most recent period had the rate completely flat, which suggests that the data were not showing current and ongoing acceleration. This distinction can hardly be emphasized enough. Hopefully this post will help people more carefully look at this question in the future.