Previously, I have talked about isolating the long term signal in climate data using a technique I thought up. Today, I’ve used it to create an index of ENSO with no long term variation and a sable level of variance throughout the series, which should have not the sort of thing that doesn’t raise questions about “contamination” of the index with the “forced signal”. Using the HADISST1 data for the NINO 3.4 index at KNMI Climate Explorer, (because the newer HADSST3 data are sadly incomplete, but HADISST1 is temporally complete) I first removed the long term component, then took absolute values of the deviations from it. I then computed the long term component of that, for the variations in the variance. *That* was divided by it’s long term average, and then the NINO 3.4 series that had the long term component removed is divided by *that*. The result is an ENSO data set which has a stable mean and variance *by construction*. Why would I do this? Heck if I know, mostly because I just like playing with data. I also wanted to have an ENSO index I could make reference to without people saying it was “contaminated” by AGW. This series *cannot* contain any AGW component, again, *by construction*, although I have probably also removed natural inter-decadal variability. So this is what the index looks like, with the original data in red along with it:

## Archive for July, 2011

### A Normalized Short term ENSO Index

July 29, 2011### Relating Lower Tropospheric Temperature Variations to The Surface Temperature Variations: An observation based approach

July 16, 2011When discussing climate data, it is often the case that people attempt to compare, directly, the surface and lower tropospheric temperature anomalies. This neglects the fact that the two measures are actually of different things. The satellite temperatures are measuring large parts of the atmosphere, whereas the surface data is attempting to measure temperature variations in a much shallower layer. So what, then, to make of the relationship between the two? While models generally predict that surface temperature variations will translate into basically proportional variations aloft, it is not necessarily the case that this would be so in reality. So how to see how the atmosphere actually behaves? Well, we have observational data, and I will argue that apart from potential long term biases, we can be pretty confident that both the surface and tropospheric data reflect, on an inter-annual basis the same natural climate fluctuations, from ENSO and volcanoes, which represent real signals. By comparing HadCRUT from November 1978 to May 2011, the currently available overlap with UAH, I will illustrate how these fluctuations are related between the two. To start with I will remove the OLS trends from both datasets (I will also try some other methods of removing the long term variations, like detrending using my smoothing technique) Which looks like this:

UAH residuals are in blue, HadCRUT in red. Now my first test was to see what the ratio their standard deviations is, and it turns out to be about 1.46 (0.179/0.123) of UAH residual standard deviation and HadCRUT residual standard deviation. I expect that this overestimates the ratio of their actual co-varying behavior, due to the presence of un-related noise at from month to month. At any rate, a best fit of a regression of HadCRUT residuals on the UAH residuals yields a slope of about 1.07 and an r squared of about .54. So clearly there is a lot of noise still, but the satellite data clearly varies slightly more than the surface data in the same changes. Next, I do the same thing with twelve months smooths:

Now the ratio of standard deviations is about 1.49 (0.135/0.091) the slope is about 1.32 and the r squared about .78. At this point I think it is pretty clear that variations, at least in the short term, of surface temperatures are amplified in the troposphere. So now we come to the interesting bit (to me anyway): while this behavior is seen in the short term variations of surface and tropospheric temperatures, it is distinctly absent from the long term trends, which suggests something different is going on with the data’s long term trends than in the fluctuations: our ratios for the short term range from 1.07 to 1.49, while the trend ratio is about 0.898. Clearly there is something different going on with the long term trend.

Now personally, I think that the satellite data are pretty well supported by careful analyses done by John Christy, so I think there are essentially three possible explanations (not necessarily mutually exclusive) of what is going on here:

1. The surface data contain a spurious warm bias due to contamination from nonclimatic effects and data quality (see for example this)

2. This a real feature of climate behavior due to different physical processes controlling the long term relationship between surface and tropospheric temperatures.

3. The surface contains additional real, climatic long term warming due to an effect which does not extend into the troposphere as a whole. This process must be pretty different from greenhouse gases, which should create warming throughout the troposphere, not isolated to the surface. Landuse seems a probable candidate.

Note that if either 2 or 3 are correct, then there is an important factor which present climate models must be missing or getting quite wrong, since they do not decouple the surface and tropospheric rates this way, but rather amplify the long term trends, too. If 1 is correct, then current models are overestimating climate sensitivity by being fit to surface temperatures with a non-climatic warming. Whatever the explanation, current mainstream interpretations of surface warming need to be re-examined, since they do not account for this feature of the data.

Oh, and I am of course going to check on how a different method detrending (my smoothing, for example) effects the numbers, but that will take a little more time. So I’ll get back to you.

**UPDATE:** As promised, I have tried looking at amplification with a more nonlinear detrending method, using the smoothing method I described here, (the approach that does not average all the smooths). No plots this time since you can hardly tell the difference, I think. First, for the monthly detrended, the standard deviation ratio was about 1.49, the slope about .954 and r squared of .409, the twelve month averages standard deviation ratio of about 1.56, a slope of 1.38 r squared of .781, finally comparing the relationship between the non-linear trends, it’s a slope of .928 and r squared of .919; So the final result is a range of short term amplification factors from .954 to 1.56, and long term from .898 to .919, clearly while short term fluctuations at the surface tend to be amplified in the troposphere, this is *not* the case over the long term.

### New SST Record Effects on “AMO”

July 14, 2011So there is a new record of sea surface temperatures and a clever fellow on the Climate Audit thread says:

Someone should see what effect these changes to the sea surface temperature data have on the calculations of various so called “indices” of “variability”. There could be quite a lot of mis-interpretation of “PDO” and “AMO” etc “signatures” or “patterns” in the data if these things are taken into account. Seeing if it changes the “AMO” will be relatively easy, just using the data available on climate explorer. I’ll see what happens, and perhaps get back to everyone.

Well of course, that clever commenter 😉 was me, and I am happy to oblige myself by examining this issue. The data, along with many climate datasets, is conveniently available here. Which is good because I have a hard time using the Hadley website some times. The region of Atlantic Sea Surface Temperatures involve is from the equator to 70 degrees North latitude, and 80 to zero West longitude (-80 East to zero East). I got these SST for both the old HADSST2 and new HADSST3 data so I am comparing old apples to new apples. I calculated the differences (ie, the “corrections” that had been applied) I then calculated the AMO from the old data (ie detrended) and also another AMO which was the same data, but with the differences added in (zero after 2006 since no corrected data are yet available, and the differences towards the end are very small anyway. The three (HADSST2 AMO, HADSST3-HADSST2 data from the region, and HADSST2 AMO + “correction”) were then calculated as 12 month moving averages to eliminate the monthly noise that obscures the ability to discern what is going on. Here is what you get:

It looks to me like this may make the AMO data if HADSST is used more closely resemble the AMO calculated using Kaplan’s dataset, the differences previously looked like this, according to Bob Tisdale.

Anyway, make of it what you will, it looks to me like the “AMO” is indeed somewhat altered by this, but not hugely. The “PDO” will be another story, something for someone else to look at, I think.

### Step Change In NH Snow Revisted

July 14, 2011I have identified something in climate indices which may be related to the previously noticed sudden shift in Northern Hemisphere Snowcover: it appears to occur at about the same time as a sudden, large spike in the Arctic Oscillation. I have not yet verified if this change preceded or followed the change in snow cover (and I can indeed imagine snow/ice cover forcing changes in circulation, this probably played a major role in the glaciations) although the reverse is surely true as these circulation patterns are linked to weather systems that deposit snow in the first place. Anyway, as always I examine the data with a careful, curious eye for details others might miss, to examine the implications of these findings for various hypotheses. So far, I have no idea what to make of these findings, but readers feel free to speculate wildly!

The AO data from here:

http://www.esrl.noaa.gov/psd/data/correlation/ao.data

Are the average of the eleven and thirteen month centered averages. I realize this is slightly different from my method of averaging the Snow Cover data, which is part of why I have not yet determined the exact relationship between these two series. More later, maybe!

### Yet More Smoothing

July 3, 2011Using the method discussed earlier I have, for your consideration, HadCrut:

Make of it what you will.