El Nino Project (Part 7)

18 August, 2014

So, we’ve seen that Ludescher et al have a way to predict El Niños. But there’s something a bit funny: their definition of El Niño is not the standard one!

Precisely defining a complicated climate phenomenon like El Niño is a tricky business. Lots of different things tend to happen when an El Niño occurs. In 1997-1998, we saw these:

But what if just some of these things happen? Do we still have an El Niño or not? Is there a right answer to this question, or is it partially a matter of taste?

A related puzzle: is El Niño a single phenomenon, or several? Could there be several kinds of El Niño? Some people say there are.

Sometime I’ll have to talk about this. But today let’s start with the basics: the standard definition of El Niño. Let’s see how this differs from Ludescher et al’s definition.

The standard definition

The most standard definitions use the Oceanic Niño Index or ONI, which is the running 3-month mean of the Niño 3.4 index:

• An El Niño occurs when the ONI is over 0.5 °C for at least 5 months in a row.

• A La Niña occurs when the ONI is below -0.5 °C for at least 5 months in a row.

Of course I should also say exactly what the ‘Niño 3.4 index’ is, and what the ‘running 3-month mean’ is.

The Niño 3.4 index is the area-averaged, time-averaged sea surface temperature anomaly for a given month in the region 5°S-5°N and 170°-120°W:

Here anomaly means that we take the area-averaged, time-averaged sea surface temperature for a given month — say February — and subtract off the historical average of this quantity — that is, for Februaries of other years on record.

If you’re clever you can already see room for subtleties and disagreements. For example, you can get sea surface temperatures in the Niño 3.4 region here:

Niño 3.4 data since 1870 calculated from the HadISST1, NOAA. Discussed in N. A. Rayner et al, Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res. 108 (2003), 4407.

However, they don’t actually provide the Niño 3.4 index.

You can get the Niño 3.4 index here:

TNI (Trans-Niño Index) and N3.4 (Niño 3.4 Index), NCAR.

You can also get it from here:

Monthly Niño 3.4 index, Climate Prediction Center, National Weather Service.

The actual temperatures in Celsius on the two websites are quite close — but the anomalies are rather different, because the second one ‘subtracts off the historical average’ in a way that takes global warming into account. For example, to compute the Niño 3.4 index in June 1952, instead of taking the average temperature that month and subtracting off the average temperature for all Junes on record, they subtract off the average for Junes in the period 1936-1965. Averages for different periods are shown here:

You can see how these curves move up over time: that’s global warming! It’s interesting that they go up fastest during the cold part of the year. It’s also interesting to see how gentle the seasons are in this part of the world. In the old days, the average monthly temperatures ranged from 26.2 °C in the winter to 27.5 °C in the summer — a mere 1.3 °C fluctuation.

Finally, to compute the ONI in a given month, we take the average of the Niño 3.4 index in that month, the month before, and the month after. This definition of running 3-month mean has a funny feature: we can’t know the ONI for this month until next month!

You can get a table of the ONI here:

Cold and warm episodes by season, Climate Prediction Center, National Weather Service.

It’s not particularly computer-readable.

Ludescher et al

Now let’s compare Ludescher et al. They say there’s an El Niño when the Niño 3.4 index is over 0.5°C for at least 5 months in a row. By not using the ONI — by using the Niño 3.4 index instead of its 3-month running mean — they could be counting some short ‘spikes’ in the Niño 3.4 index as El Niños, that wouldn’t count as El Niños by the usual definition.

I haven’t carefully checked to see how much changing the definition would affect the success rate of their predictions. To be fair, we should also let them change the value of their parameter θ, which is tuned to be good for predicting El Niños in their setup. But we can see that there could be some ‘spike El Niños’ in this graph of theirs, that might go away with a different definition. These are places where the red line goes over the horizontal line for more than 5 months, but no more:

Let’s see look at the spike around 1975. See that green arrow at the beginning of 1975? That means Ludescher et al are claiming to successfully predict an El Niño sometime the next calendar year. We can zoom in for a better look:

The tiny blue bumps are where the Niño 3.4 index exceeds 0.5.

Let’s compare the ONI as computed by the National Weather Service, month by month, with El Niños in red and La Niñas in blue:

1975: 0.5, -0.5, -0.6, -0.7, -0.8, -1.0, -1.1, -1.2, -1.4, -1.5, -1.6, -1.7

1976: -1.5, -1.1, -0.7, -0.5, -0.3, -0.1, 0.2, 0.4, 0.6, 0.7, 0.8, 0.8

1977: 0.6, 0.6, 0.3, 0.3, 0.3, 0.4, 0.4, 0.4, 0.5, 0.7, 0.8, 0.8

1978: 0.7, 0.5, 0.1, -0.2, -0.3, -0.3, -0.3, -0.4, -0.4, -0.3, -0.1, -0.1

So indeed an El Niño started in September 1976. The ONI only stayed above 0.5 for 6 months, but that’s enough. Ludescher and company luck out!

Just for fun, let’s look at the National Weather service Niño 3.4 index to see what that’s like:

1975: -0.33, -0.48, -0.72, -0.54, -0.68, -1.17, -1.07, -1.19, -1.36, -1.69 -1.45, -1.76

1976: -1.78, -1.10, -0.55, -0.53, -0.33, -0.10, 0.20, 0.39, 0.49, 0.88, 0.85, 0.63

So, this exceeded 0.5 in October 1976. That’s when Ludescher et al would say the El Niño starts, if they used the National Weather Service data.

Let’s also compare the NCAR Niño 3.4 index:

1975: -0.698, -0.592, -0.579, -0.801, -1.025, -1.205, -1.435, -1.620, -1.699 -1.855, -2.041, -1.960

1976: -1.708, -1.407, -1.026, -0.477, -0.095, 0.167, 0.465, 0.805, 1.039, 1.137, 1.290, 1.253

It’s pretty different! But it also gives an El Niño in 1976 according to Ludescher et al’ definition: the Niño 3.4 index exceeds 0.5 starting in August 1976.

For further study

This time we didn’t get into the interesting question of why one definition of El Niño is better than another. For that, try:

• Kevin E. Trenberth, The definition of El Niño, Bulletin of the American Meteorological Society 78 (1997), 2771–2777.

There could also be fundamentally different kinds of El Niño. For example, besides the usual sort where high sea surface temperatures are centered in the Niño 3.4 region, there could be another kind centered farther west near the International Date Line. This is called the dateline El Niño or El Niño Modoki. For more, try this:

• Nathaniel C. Johnson, How many ENSO flavors can we distinguish?, Journal of Climate 26 (2013), 4816-4827.

which has lots of references to earlier work. Here, to whet your appetite, is his picture showing the 9 most common patterns of sea surface temperature anomalies in the Pacific:

At the bottom of each is a percentage showing how frequently that pattern has occurred from 1950 to 2011. To get these pictures Johnson used something called a ‘self-organizing map analysis’ – a fairly new sort of cluster analysis done using neural networks. This is the sort of thing I hope we get into as our project progresses!

The series so far

Just in case you want to get to old articles, here’s the story so far:

El Niño project (part 1): basic introduction to El Niño and our project here.

El Niño project (part 2): introduction to the physics of El Niño.

El Niño project (part 3): summary of the work of Ludescher et al.

El Niño project (part 4): how Graham Jones replicated the work by Ludescher et al, using software written in R.

El Niño project (part 5): how to download R and use it to get files of climate data.

El Niño project (part 6): Steve Wenner’s statistical analysis of the work of Ludescher et al.

El Niño project (part 7): the definition of El Niño.

Exploring Climate Data (Part 1)

1 August, 2014

joint with Dara O Shayda

Emboldened by our experiments in El Niño analysis and prediction, people in the Azimuth Code Project have been starting to analyze weather and climate data. A lot of this work is exploratory, with no big conclusions. But it’s still interesting! So, let’s try some blog articles where we present this work.

This one will be about the air pressure on the island of Tahiti and in a city called Darwin in Australia: how they’re correlated, and how each one varies. This article will also be a quick introduction to some basic statistics, as well as ‘continuous wavelet transforms’.

Darwin, Tahiti and El Niños

The El Niño Southern Oscillation is often studied using the air pressure in Darwin, Australia versus the air pressure in Tahiti. When there’s an El Niño, it gets stormy in the eastern Pacific so the air temperatures tend to be lower in Tahiti and higher in Darwin. When there’s a La Niña, it’s the other way around:

The Southern Oscillation Index or SOI is a normalized version of the monthly mean air pressure anomaly in Tahiti minus that in Darwin. Here anomaly means we subtract off the mean, and normalized means that we divide by the standard deviation.

So, the SOI tends to be negative when there’s an El Niño. On the other hand, when there’s an El Niño the Niño 3.4 index tends to be positive—this says it’s hotter than usual in a certain patch of the Pacific.

Here you can see how this works:

When the Niño 3.4 index is positive, the SOI tends to be negative, and vice versa!

It might be fun to explore precisely how well correlated they are. You can get the data to do that by clicking on the links above.

But here’s another question: how similar are the air pressure anomalies in Darwin and in Tahiti? Do we really need to take their difference, or are they so strongly anticorrelated that either one would be enough to detect an El Niño?

You can get the data to answer such questions here:

Southern Oscillation Index based upon annual standardization, Climate Analysis Section, NCAR/UCAR. This includes links to monthly sea level pressure anomalies in Darwin and Tahiti, in either ASCII format (click the second two links) or netCDF format (click the first one and read the explanation).

In fact this website has some nice graphs already made, which I might as well show you! Here’s the SOI and also the sum of the air pressure anomalies in Darwin and Tahiti, normalized in some way:

(Click to enlarge.)

If the sum were zero, the air pressure anomalies in Darwin and Tahiti would contain the same information and life would be simple. But it’s not!

How similar in character are the air pressure anomalies in Darwin and Tahiti? There are many ways to study this question. Dara tackled it by taking the air pressure anomaly data from 1866 to 2012 and computing some ‘continuous wavelet transforms’ of these air pressure anomalies. This is a good excuse for explaining how a continuous wavelet transform works.

Very basic statistics

It helps to start with some very basic statistics. Suppose you have a list of numbers

x = (x_1, \dots, x_n)

You probably know how to take their mean, or average. People often write this with angle brackets:

\displaystyle{ \langle x \rangle = \frac{1}{n} \sum_{i = 1}^n x_i }

You can also calculate the mean of their squares:

\displaystyle{  \langle x^2 \rangle = \frac{1}{n} \sum_{i = 1}^n x_i^2 }

If you were naive you might think \langle x^2 \rangle = \langle x \rangle^2, but in fact we have:

\langle x^2 \rangle \ge \langle x \rangle^2

and they’re equal only if all the x_i are the same. The point is that if the numbers x_i are spread out, the squares of the big ones (positive or negative) contribute more to the average of the squares than if we had averaged them out before squaring. The difference

\langle x^2 \rangle - \langle x \rangle^2

is called the variance; it says how spread out our numbers are. The square root of the variance is the standard deviation:

\sigma_x = \sqrt{\langle x^2 \rangle - \langle x \rangle^2 }

and this has the slight advantage that if you multiply all the numbers x_i by some constant c, the standard deviation gets multiplied by |c|. (The variance gets multiplied by c^2.)

We can generalize the variance to a situation where we have two lists of numbers:

x = (x_1, \dots, x_n)

y = (y_1, \dots, y_n)

Namely, we can form the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle

This reduces to the variance when x = y. It measures how much x and y vary together — ‘hand in hand’, as it were. A bit more precisely: if x_i is greater than its mean value mainly for i such that y_i is greater than its mean value, the covariance is positive. On the other hand, if x_i tends to be greater than average when y_i is smaller than average — like with the air pressures at Darwin and Tahiti — the covariance will be negative.

For example, if

x = (1,-1), \quad y = (1,-1)

then they ‘vary hand in hand’, and the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle = 1 - 0 = 1

is positive. But if

x = (1,-1), \quad y = (-1,1)

then one is positive when the other is negative, so the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle = -1 - 0 = -1

is negative.

Of course the covariance will get bigger if we multiply both x and y by some big number. If we don’t want this effect, we can normalize the covariance and get the correlation:

\displaystyle{ \frac{ \langle x y \rangle - \langle x \rangle \langle y \rangle }{\sigma_x \sigma_y} }

which will always be between -1 and 1.

For example, if we compute the correlation between the air pressure anomalies at Darwin and Tahiti, measured monthly from 1866 to 2012, we get
-0.253727. This indicates that when one goes up, the other tends to go down. But since we’re not getting -1, it means they’re not completely locked into a linear relationship where one is some negative number times the other.

Okay, we’re almost ready for continuous wavelet transforms! Here is the main thing we need to know. If the mean of either x or y is zero, the formula for covariance simplifies a lot, to

\displaystyle{  \langle x y \rangle = \frac{1}{n} \sum_{i = 1}^n x_i y_i }

So, this quantity says how much the numbers x_i ‘vary hand in hand’ with the numbers y_i, in the special case when one (or both) has mean zero.

We can do something similar if x, y : \mathbb{R} \to \mathbb{R} are functions of time defined for all real numbers t. The sum becomes an integral, and we have to give up on dividing by n. We get:

\displaystyle{  \int_{-\infty}^\infty x(t) y(t)\; d t }

This is called the inner product of the functions x and y, and often it’s written \langle x, y \rangle, but it’s a lot like the covariance.

Continuous wavelet transforms

What are continuous wavelet transforms, and why should we care?

People have lots of tricks for studying ‘signals’, like series of numbers x_i or functions x : \mathbb{R} \to \mathbb{R}. One method is to ‘transform’ the signal in a way that reveals useful information. The Fourier transform decomposes a signal into sines and cosines of different frequencies. This lets us see how much power the signal has at different frequencies, but it doesn’t reveal how the power at different frequencies changes with time. For that we should use something else, like the Gabor transform explained by Blake Pollard in a previous post.

Sines and cosines are great, but we might want to look for other patterns in a signal. A ‘continuous wavelet transform’ lets us scan a signal for appearances of a given pattern at different times and also at different time scales: a pattern could go by quickly, or in a stretched out slow way.

To implement the continuous wavelet transform, we need a signal and a pattern to look for. The signal could be a function x : \mathbb{R} \to \mathbb{R}. The pattern would then be another function y: \mathbb{R} \to \mathbb{R}, usually called a wavelet.

Here’s an example of a wavelet:

If we’re in a relaxed mood, we could call any function that looks like a bump with wiggles in it a wavelet. There are lots of famous wavelets, but this particular one is the fourth derivative of a certain Gaussian. Mathematica calls this particular wavelet DGaussianWavelet[4], and you can look up the formula under ‘Details’ on their webpage.

However, the exact formula doesn’t matter at all now! If we call this wavelet y, all that matters is that it’s a bump with wiggles on it, and that its mean value is 0, or more precisely:

\displaystyle{ \int_{-\infty}^\infty y(t) \; d t = 0 }

As we saw in the last section, this fact lets us take our function x and the wavelet y and see how much they ‘vary hand it hand’ simply by computing their inner product:

\displaystyle{ \langle x , y \rangle = \int_{-\infty}^\infty x(t) y(t)\; d t }

Loosely speaking, this measures the ‘amount of y-shaped wiggle in the function x’. It’s amazing how hard it is to say something in plain English that perfectly captures the meaning of a simple formula like the above one—so take the quoted phrase with a huge grain of salt. But it gives a rough intuition.

Our wavelet y happens to be centered at t  = 0. However, we might be interested in y-shaped wiggles that are centered not at zero but at some other number s. We could detect these by shifting the function y before taking its inner product with x:

\displaystyle{ \int_{-\infty}^\infty x(t) y(t-s)\; d t }

We could also be interested in measuring the amount of some stretched-out or squashed version of a y-shaped wiggle in the function x. Again we could do this by changing y before taking its inner product with x:

\displaystyle{ \int_{-\infty}^\infty x(t) \; y\left(\frac{t}{P}\right) \; d t }

When P is big, we get a stretched-out version of y. People sometimes call P the period, since the period of the wiggles in y will be proportional to this (though usually not equal to it).

Finally, we can combine these ideas, and compute

\displaystyle{ \int_{-\infty}^\infty x(t) \; y\left(\frac{t- s}{P}\right)\; dt }

This is a function of the shift s and period P which says how much of the s-shifted, P-stretched wavelet y is lurking in the function x. It’s a version of the continuous wavelet transform!

Mathematica implements this idea for time series, meaning lists of numbers x = (x_1,\dots,x_n) instead of functions x : \mathbb{R} \to \mathbb{R}. The idea is that we think of the numbers as samples of a function x:

x_1 = x(\Delta t)

x_2 = x(2 \Delta t)

and so on, where \Delta t is some time step, and replace the integral above by a suitable sum. Mathematica has a function ContinuousWaveletTransform that does this, giving

\displaystyle{  w(s,P) = \frac{1}{\sqrt{P}} \sum_{i = 1}^n x_i \; y\left(\frac{i \Delta t - s}{P}\right) }

The factor of 1/\sqrt{P} in front is a useful extra trick: it’s the right way to compensate for the fact that when you stretch out out your wavelet y by a factor of P, it gets bigger. So, when we’re doing integrals, we should define the continuous wavelet transform of y by:

\displaystyle{ w(s,P) = \frac{1}{\sqrt{P}} \int_{-\infty}^\infty x(t) y(\frac{t- s}{P})\; dt }

The results

Dara Shayda started with the air pressure anomaly at Darwin and Tahiti, measured monthly from 1866 to 2012. Taking DGaussianWavelet[4] as his wavelet, he computed the continuous wavelet transform w(s,P) as above. To show us the answer, he created a scalogram:

This is a 2-dimensional color plot showing roughly how big the continuous wavelet transform w(s,P) is for different shifts s and periods P. Blue means it’s very small, green means it’s bigger, yellow means even bigger and red means very large.

Tahiti gave this:

You’ll notice that the patterns at Darwin and Tahiti are similar in character, but notably different in detail. For example, the red spots, where our chosen wavelet shows up strongly with period of order ~100 months, occur at different times.

Puzzle 1. What is the meaning of the ‘spikes’ in these scalograms? What sort of signal would give a spike of this sort?

Puzzle 2. Do a Gabor transform, also known as a ‘windowed Fourier transform’, of the same data. Blake Pollard explained the Gabor transform in his article Milankovitch vs the Ice Ages. This is a way to see how much a signal wiggles at a given frequency at a given time: we multiply the signal by a shifted Gaussian and then takes its Fourier transform.

Puzzle 3. Read about continuous wavelet transforms. If we want to reconstruct our signal x from its continuous wavelet transform, why should we use a wavelet y with

\displaystyle{\int_{-\infty}^\infty y(t) \; d t = 0 ? }

In fact we want a somewhat stronger condition, which is implied by the above equation when the Fourier transform of y is smooth and integrable:

Continuous wavelet transform, Wikipedia.

Another way to understand correlations

David Tweed mentioned another approach from signal processing to understanding the quantity

\displaystyle{  \langle x y \rangle = \frac{1}{n} \sum_{i = 1}^n x_i y_i }

If we’ve got two lists of data x and y that we want to compare to see if they behave similarly, the first thing we ought to do is multiplicatively scale each one so they’re of comparable magnitude. There are various possibilities for assigning a scale, but a reasonable one is to ensure they have equal ‘energy’

\displaystyle{  \sum_{i=1}^n x_i^2 = \sum_{i=1}^n y_i^2 }

(This can be achieved by dividing each list by its standard deviation, which is equivalent to what was done in the main derivation above.) Once we’ve done that then it’s clear that looking at

\displaystyle{  \sum_{i=1}^n (x_i-y_i)^2 }

gives small values when they have a very good match and progressively bigger values as they become less similar. Observe that

\begin{array}{ccl}  \displaystyle{\sum_{i=1}^n (x_i-y_i)^2 }  &=& \displaystyle{ \sum_{i=1}^n (x_i^2 - 2 x_i y_i + y_i^2) }\\  &=& \displaystyle{ \sum_{i=1}^n x_i^2 - 2 \sum_{i=1}^n x_i y_i + \sum_{i=1}^n y_i^2 }  \end{array}

Since we’ve scaled things so that \sum_{i=1}^n x_i^2 and \sum_{i=1}^n y_i^2 are constants, we can see that when \sum_{i=1}^n x_i y_i becomes bigger,

\displaystyle{ \sum_{i=1}^n (x_i-y_i)^2 }

becomes smaller. So,

\displaystyle{\sum_{i=1}^n x_i y_i}

serves as a measure of how close the lists are, under these assumptions.

El Niño Project (Part 6)

23 July, 2014

guest post by Steven Wenner

Hi, I’m Steve Wenner.

I’m an industrial statistician with over 40 years of experience in a wide range applications (quality, reliability, product development, consumer research, biostatistics); but, somehow, time series only rarely crossed my path. Currently I’m working for a large consumer products company.

My undergraduate degree is in physics, and I also have a master’s in pure math. I never could reconcile how physicists used math (explain that Dirac delta function to me again in math terms? Heaviside calculus? On the other hand, I thought category theory was abstract nonsense until John showed me otherwise!). Anyway, I had to admit that I lacked the talent to pursue pure math or theoretical physics, so I became a statistician. I never regretted it—statistics has provided a very interesting and intellectually challenging career.

I got interested in Ludescher et al’s paper on El Niño prediction by reading Part 3 of this series. I have no expertise in climate science, except for an intense interest in the subject as a concerned citizen. So, I won’t talk about things like how Ludescher et al use a nonstandard definition of ‘El Niño’—that’s a topic for another time. Instead, I’ll look at some statistical aspects of their paper:

• Josef Ludescher, Avi Gozolchiani, Mikhail I. Bogachev, Armin Bunde, Shlomo Havlin, and Hans Joachim Schellnhuber, Very early warning of next El Niño, Proceedings of the National Academy of Sciences, February 2014. (Click title for free version, journal name for official version.)


I downloaded the NOAA adjusted monthly temperature anomaly data and compared the El Niño periods with the charts in this paper. I found what appear to be two errors (“phantom” El Niños) and noted some interesting situations. Some of these are annotated on the images below. Click to enlarge them:


I also listed for each year whether an El Niño initiation was predicted, or not, and whether one actually happened. I did the predictions five ways: first, I listed the author’s “arrows” as they appeared on their charts, and then I tried to match their predictions by following in turn four sets of rules. Nevertheless, I could not come up with any detailed rules that exactly reproduced the author’s results.

These were the rules I used:

An El Niño initiation is predicted for a calendar year if during the preceding year the average link strength crossed above the 2.82 threshold. However, we could also invoke additional requirements. Two possibilities are:

1. Preemption rule: the prediction of a new El Niño is canceled if the preceding year ends in an El Niño period.

2. End-of-year rule: the link strength must be above 2.82 at year’s end.

I counted the predictions using all four combinations of these two rules and compared the results to the arrows on the charts.

I defined an “El Niño initiation month” to be a month where the monthly average adjusted temperature anomaly rises up to at least 0.5 C and remains above or equal to 0.5 °C for at least five months. Note that the NOAA El Niño monthly temperature estimates are rounded to hundredths; and, on occasion, the anomaly is reported as exactly 0.5 °C. I found slightly better agreement with the authors’ El Niño periods if I counted an anomaly of exactly 0.5 °C as satisfying the threshold criterion, instead of using the strictly “greater than” condition.

Anyway, I did some formal hypothesis testing and estimation under all five scenarios. The good news is that under most scenarios the prediction method gave better results than merely guessing. (But, I wonder how many things the authors tried before they settled on their final method? Also, did they do all their work on the learning series, and then only at the end check the validation series—or were they checking both as they went about their investigations?)

The bad news is that the predictions varied with the method, and the methods were rather weak. For instance, in the training series there were 9 El Niño periods in 30 years; the authors’ rules (whatever they were, exactly) found five of the nine. At the same time, they had three false alarms in the 21 years that did not have an El Niño initiated.

I used Fisher’s exact test to compute some p-values. Suppose (as our ‘null hypothesis’) that Ludescher et al’s method does not improve the odds of a successful prediction of an El Nino initiation. What’s the probability of that method getting at least as many predictions right just by chance? Answer: 0.032 – this is marginally more significant than the conventional 1 in 20 chance that is the usual threshold for rejecting a null hypothesis, but still not terribly convincing. This was, by the way, the most significant of the five p-values for the alternative rule sets applied to the learning series.

I also computed the “relative risk” statistics for all scenarios; for instance, we are more than three times as likely to see an El Niño initiation if Ludescher et al predict one, than if they predict otherwise (the 90% confidence interval for that ratio is 1.2 to 9.7, with the point estimate 3.4). Here is a screen shot of some statistics for that case:

Here is a screen shot of part of the spreadsheet list I made. In the margin on the right I made comments about special situations of interest.

Again, click to enlarge—but my whole working spreadsheet is available with more details for anyone who wishes to see it. I did the statistical analysis with a program called JMP, a product of the SAS corporation.

My overall impression from all this is that Ludescher et al are suggesting a somewhat arbitrary (and not particularly well-defined) method for revealing the relationship between link strength and El Niño initiation, if, indeed, a relationship exists. Slight variations in the interpretation of their criteria and slight variations in the data result in appreciably different predictions. I wonder if there are better ways to analyze these two correlated time series.

El Niño Project (Part 5)

12 July, 2014


And now for some comic relief.

Last time I explained how to download some weather data and start analyzing it, using programs written by Graham Jones. When you read that, did you think “Wow, that’s easy!” Or did you think “Huh? Run programs in R? How am I supposed to do that?”

If you’re in the latter group, you’re like me. But I managed to do it. And this is the tale of how. It’s a blow-by-blow account of my first steps, my blunders, my fears.

I hope that if you’re intimidated by programming, my tale will prove that you too can do this stuff… provided you have smart friends, or read this article.

More precisely, this article explains how to:

• download and run software that runs the programming language R;

• download temperature data from the National Center for Atmospheric Research;

• use R to create a file of temperature data for a given latitude/longitude rectangle for a given time interval.

I will not attempt to explain how to program in R.

If you want to copy what I’m doing, please remember that a few details depend on the operating system. Since I don’t care about operating systems, I use a Windows PC. If you use something better, some details will differ for you.

Also: at the end of this article there are some very basic programming puzzles.

A sad history

First, let me explain a bit about my relation to computers.

I first saw a computer at the Lawrence Hall of Science in Berkeley, back when I was visiting my uncle in the summer of 1978. It was really cool! They had some terminals where you could type programs in BASIC and run them.

I got especially excited when he gave me the book Computer Lib/Dream Machines by Ted Nelson. It espoused the visionary idea that people could write texts on computers all around the world—“hypertexts” where you could click on a link in one and hop to another!

I did more programming the next year in high school, sitting in a concrete block room with a teletype terminal that was connected to a mainframe somewhere far away. I stored my programs on paper tape. But my excitement gradually dwindled, because I was having more fun doing math and physics using just pencil and paper. My own brain was more easy to program than the machine. I did not start a computer company. I did not get rich. I learned quantum mechanics, and relativity, and Gödel’s theorem.

Later I did some programming in APL in college, and still later I did a bit in Mathematica in the early 1990s… but nothing much, and nothing sophisticated. Indeed, none of these languages would be the ones you’d choose to explore sophisticated ideas in computation!

I’ve just never been very interested… until now. I now want to do a lot of data analysis. It will be embarrassing to keep asking other people to do all of it for me. I need to learn how to do it myself.

Maybe you’d like to do this stuff too—or at least watch me make a fool of myself. So here’s my tale, from the start.

Downloading and running R

To use the programs written by Graham, I need to use R, a language currently popular among statisticians. It is not the language my programmer friends would want me to learn—they’d want me to use something like Python. But tough! I can learn that later.

To download R to my Windows PC, I cleverly type download R into Google, and go to the top website it recommends:


I click the big fat button on top saying

Download R 3.1.0 for Windows

and get asked to save a file R-3.1.0-win.exe. I save it in my Downloads folder; it takes a while to download since it was 57 megabytes. When I get it, I click on it and follow the easy default installation instructions. My Desktop window now has a little icon on it that says R.

Clicking this, I get an interface where I can type commands after a red


symbol. Following Graham’s advice, I start by trying

> 2^(1:8)

which generates a list of powers of 2 from 21 to 28, like this:

[1] 2 4 8 16 32 64 128 256

Then I try

> mean(2^(1:8))

which gives the arithmetic mean of this list. Somewhat more fun is

> plot(rnorm(20))

which plots a bunch of points, apparently 20 standard normal deviates.

When I hear “20 standard normal deviates” I think of the members of a typical math department… but no, those are deviants. Standard normal deviates are random numbers chosen from a Gaussian distribution of mean zero and variance 1.

Downloading climate data

To do something more interesting, I need to input data.

The papers by Ludescher et al use surface air temperatures in a certain patch of the Pacific, so I want to get ahold of those. They’re here:

NCEP/NCAR Reanalysis 1: Surface.

NCEP is the National Centers for Environmental Prediction, and NCAR is the National Center for Atmospheric Research. They have a bunch of files here containing worldwide daily average temperatures on a 2.5 degree latitude × 2.5 degree longitude grid (that’s 144 × 73 grid points), from 1948 to 2010. And if you go here, the website will help you get data from within a chosen rectangle in a grid, for a chosen time interval.

These are NetCDF files. NetCDF stands for Network Common Data Form:

NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data.

According to my student Blake Pollard:

… the method of downloading a bunch of raw data via ftp (file transfer protocol) is a great one to become familiar with. If you poke around on ftp://ftp.cdc.noaa.gov/Datasets or some other ftp servers maintained by government agencies you will find all the data you could ever want. Examples of things you can download for free: raw multispectral satellite images, processed data products, ‘re-analysis’ data (which is some way of combining analysis/simulation to assimilate data), sea surface temperature anomalies at resolutions much higher than 2.5 degrees (although you pay for that in file size). Also, believe it or not, people actually use NetCDF files quite widely, so once you know how to play around with those you’ll find the world quite literally at your fingertips!

I know about ftp: I’m so old that I know this was around before the web existed. Back then it meant “faster than ponies”. But I need to get R to accept data from these NetCDF files: that’s what scares me!

Graham said that R has a “package” called RNetCDF for using NetCDF files. So, I need to get ahold of this package, download some files in the NetCDF format, and somehow get R to eat those files with the help of this package.

At first I was utterly clueless! However, after a bit of messing around, I notice that right on top of the R interface there’s a menu item called Packages. I boldly click on this and choose Install Package(s).

I am rewarded with an enormous alphabetically ordered list of packages… obviously statisticians have lots of stuff they like to do over and over! I find RNetCDF, click on that and click something like “OK”.

I’m asked if I want to use a “personal library”. I click “no”, and get an error message. So I click “yes”. The computer barfs out some promising text:

trying URL 'http://cran.stat.nus.edu.sg/bin/windows/contrib/3.1/RNetCDF_1.6.2-3.zip'
Content type 'application/zip' length 548584 bytes (535 Kb)
opened URL
downloaded 535 Kb

package ‘RNetCDF’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in


But now I need to figure out how to download a file and get R to eat it and digest it with the help of RNetCDF.

At this point my deus ex machina, Graham, descends from the clouds and says:

You can download the files from your browser. It is probably easiest to do that for starters. Put
into the browser, then right-click a file and Save link as…

This code will download a bunch of them:

for (year in 1950:1979) {
download.file(url=paste0("ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.dailyavgs/surface/air.sig995.", year, ".nc"), destfile=paste0("air.sig995.", year, ".nc"), mode="wb")

It will put them into the “working directory”, probably C:\Users\JOHN\Documents. You can find the working directory using getwd(), and change it with setwd(). But you must use / not \ in the filepath.

Compared to UNIX, the Windows operating system has the peculiarity of using \ instead of / in path names, but R uses the UNIX conventions even on Windows.

So, after some mistakes, in the R interface I type

> setwd("C:/Users/JOHN/Documents/My Backups/azimuth/el nino")

and then type

> getwd()

to see if I’ve succeeded. I’m rewarded with

[1] "C:/Users/JOHN/Documents/My Backups/azimuth/el nino"


Then, following Graham’s advice, I cut-and-paste this into the R interface:

for (year in 1950:1979) {
download.file(url=paste0("ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.dailyavgs/surface/air.sig995.", year, ".nc"), destfile=paste0("air.sig995.", year, ".nc"), mode="wb")

It seems to be working! A little bar appears showing how each year’s data is getting downloaded. It chugs away, taking a couple minutes for each year’s worth of data.

Using R to process NetCDF files

Okay, now I’ve got all the worldwide daily average temperatures on a 2.5 degree latitude × 2.5 degree longitude grid from 1950 to 1970.

The world is MINE!

But what do I do with it? Graham’s advice is again essential, along with a little R program, or script, that he wrote:

The R script netcdf-convertor.R from


will eat the file, digest it, and spit it out again. It contains instructions.

I go to this URL, which is on GitHub, a popular free web-based service for software development. You can store programs here, edit them, and GitHub will help you keep track of the different versions. I know almost nothing about this stuff, but I’ve seen it before, so I’m not intimidated.

I click on the blue thing that says netcdf-convertor.R and see something that looks like the right script. Unfortunately I can’t see how to download it! I eventually see a button I’d overlooked, cryptically labelled “Raw”. I realize that since I don’t want a roasted or oven-broiled piece of software, I should click on this. I indeed succeed in downloading netcdf-convertor.R this way. Graham later says I could have done something better, but oh well. I’m just happy nothing has actually exploded yet.

Once I’ve downloaded this script, I open it using an text processor and look at it. At top are a bunch of comments written by Graham:


# You should be able to use this by editing this
# section only.


lat.range <- 13:14
lon.range <- 142:143

firstyear <- 1957
lastyear <- 1958

outputfilename <- paste0("Scotland-", firstyear, "-", lastyear, ".txt")


# Explanation

# 1. Use setwd() to set the working directory
# to the one containing the .nc files such as
# air.sig995.1951.nc.
# Example:
# setwd("C:/Users/Work/AAA/Programming/ProgramOutput/Nino")

# 2. Supply the latitude and longitude range. The
# NOAA data is every 2.5 degrees. The ranges are
# supplied as the number of steps of this size.
# For latitude, 1 means North Pole, 73 means South
# Pole. For longitude, 1 means 0 degrees East, 37
# is 90E, 73 is 180, 109 is 90W or 270E, 144 is
# 2.5W.

# These roughly cover Scotland.
# lat.range <- 13:14
# lon.range <- 142:143

# These are the area used by Ludescher et al,
# 2013. It is 27x69 points which are then
# subsampled to 9 by 23.
# lat.range <- 24:50
# lon.range <- 48:116

# 3. Supply the years
# firstyear <- 1950
# lastyear <- 1952

# 4. Supply the output name as a text string.
# paste0() concatenates strings which you may find
# handy:
# outputfilename <- paste0("Pacific-", firstyear, "-", lastyear, ".txt")


# Example of output

# S013E142 S013E143 S014E142 S014E143
# Y1950P001 281.60000272654 281.570002727211 281.60000272654 280.970002740622
# Y1950P002 280.740002745762 280.270002756268 281.070002738386 280.49000275135
# Y1950P003 280.100002760068 278.820002788678 281.120002737269 280.070002760738
# Y1950P004 281.070002738386 279.420002775267 281.620002726093 280.640002747998
# ...
# Y1950P193 285.450002640486 285.290002644062 285.720002634451 285.75000263378
# Y1950P194 285.570002637804 285.640002636239 286.070002626628 286.570002615452
# Y1950P195 285.92000262998 286.220002623275 286.200002623722 286.620002614334
# ...
# Y1950P364 276.100002849475 275.350002866238 276.37000284344 275.200002869591
# Y1950P365 276.990002829581 275.820002855733 276.020002851263 274.72000288032
# Y1951P001 278.220002802089 277.470002818853 276.700002836064 275.870002854615
# Y1951P002 277.750002812594 276.890002831817 276.650002837181 275.520002862439
# ...
# Y1952P365 280.35000275448 280.120002759621 280.370002754033 279.390002775937

# There is one row for each day, and 365 days in
# each year (leap days are omitted). In each row,
# you have temperatures in Kelvin for each grid
# point in a rectangle.

# S13E142 means 13 steps South from the North Pole
# and 142 steps East from Greenwich. The points
# are in reading order, starting at the top-left
# (Northmost, Westmost) and going along the top
# row first.

# Y1950P001 means year 1950, day 1. (P because
# longer periods might be used later.)


The instructions are admirably detailed concerning what I should do, but they don't say where the output will appear when I do it. This makes me nervous. I guess I should just try it. After all, the program is not called DestroyTheWorld.

Unfortunately, at this point a lot of things start acting weird.

It's too complicated and boring to explain in detail, but basically, I keep getting a file missing error message. I don't understand why this happens under some conditions and not others. I try lots of experiments.

Eventually I discover that one year of temperature data failed to download—the year 1949, right after the first year available! So, I'm getting the error message whenever I try to do anything involving that year of data.

To fix the problem, I simply download the 1949 data by hand from here:


(You can open ftp addresses in a web browser just like http addresses.) I put it in my working directory for R, and everything is fine again. Whew!

By the time things I get this file, I sort of know what to do—after all, I've spent about an hour trying lots of different things.

I decide to create a file listing temperatures near where I live in Riverside from 1948 to 1979. To do this, I open Graham's script netcdf-convertor.R in a word processor and change this section:

lat.range <- 13:14
lon.range <- 142:143
firstyear <- 1957
lastyear <- 1958
outputfilename <- paste0("Scotland-", firstyear, "-", lastyear, ".txt")

to this:

setwd("C:/Users/JOHN/Documents/My Backups/azimuth/el nino")
lat.range <- 23:23
lon.range <- 98:98
firstyear <- 1948
lastyear <- 1979
outputfilename <- paste0("Riverside-", firstyear, "-", lastyear, ".txt")

Why? Well, I want it to put the file in my working directory. I want the years from 1948 to 1979. And I want temperature data from where I live!

Googling the info, I see Riverside, California is at 33.9481° N, 117.3961° W. 34° N is about 56 degrees south of the North Pole, which is 22 steps of size 2.5°. And because some idiot decided everyone should count starting at 1 instead of 0 even in contexts like this, the North Pole itself is step 1, not step 0… so Riverside is latitude step 23. That's why I write:

lat.range <- 23:23

Similarly, 117.5° W is 242.5° E, which is 97 steps of size 2.5°… which counts as step 98 according to this braindead system. That's why I write:

lon.range <- 98:98

Having done this, I save the file netcdf-convertor.R under another name, Riverside.R.

And then I do some stuff that it took some fiddling around to discover.

First, in my R interface I go to the menu item File, at far left, and click on Open script. It lets me browse around, so I go to my working directory for R and choose Riverside.R. A little window called R editor opens up in my R interface, containing this script.

I'm probably not doing this optimally, but I can now right-click on the R editor and see a menu with a choice called Select all. If I click this, everything in the window turns blue. Then I can right-click again and choose Run line or selection. And the script runs!


It huffs and puffs, and then stops. I peek in my working directory, and see that a file called


has been created. When I open it, it has lots of lines, starting with these:

Y1948P001 279.95
Y1948P002 280.14
Y1948P003 282.27
Y1948P004 283.97
Y1948P005 284.27
Y1948P006 286.97

As Graham promised, each line has a year and day label, followed by a vector… which in my case is just a single number, since I only wanted the temperature in one location. I’m hoping this is the temperature near Riverside, in kelvin.

A small experiment

To see if this is working, I’d like to plot these temperatures and see if they make sense. Unfortunately I have no idea how to get R to take a file containing data of the sort I have and plot it! I need to learn how, but right now I’m exhausted, so I use another method to get the job done— a method that’s too suboptimal and embarrassing to describe here. (Hint: it involves the word “Excel”.)

I do a few things, but here’s the most interesting one—namely, not very interesting. I plot the temperatures for 1963:

I compare it to some publicly available data, not from Riverside, but from nearby Los Angeles:

As you can see, there was a cold day on January 13th, when the temperature dropped to 33°F. That seems to be visible on the graph I made, and looking at the data from which I made the graph, I see the temperature dropped to 251.4 kelvin on the 13th: that’s -7°F, very cold for here. It does get colder around Riverside than in Los Angeles in the winter, since it’s a desert, with temperatures not buffered by the ocean. So, this does seem compatible with the public records. That’s mildly reassuring.

But other features of the graph don’t match, and I’m not quite sure if they should or not. So, all this very tentative and unimpressive. However, I’ve managed to get over some of my worst fears, download some temperature data, and graph it! Now I need to learn how to use R to do statistics with this data, and graph it in a better way.


You can help me out by answering these puzzles. Later I might pose puzzles where you can help us write really interesting programs. But for now it’s just about learning R.

Puzzle 1. Given a text file with lots of lines of this form:

Y1948P001 279.95
Y1948P002 280.14
Y1948P003 282.27
Y1948P004 283.97

write an R program that creates a huge vector, or list of numbers, like this:

279.95, 280.14, 282.27, 283.97, ...

Puzzle 2: Extend the above program so that it plots this list of numbers, or outputs it to a new file.

If you want to test your programs, here’s the actual file:


More puzzles

If those puzzles are too easy, here are two more. I gave these last time, but everyone was too wimpy to tackle them.

Puzzle 3. Modify the software so that it uses the same method to predict El Niños from 1980 to 2013. You’ll have to adjust two lines in netcdf-convertor-ludescher.R:

firstyear <- 1948
lastyear <- 1980

should become

firstyear <- 1980
lastyear <- 2013

or whatever range of years you want. You’ll also have to adjust names of years in ludescher-replication.R. Search the file for the string 19 and make the necessary changes. Ask me if you get stuck.

Puzzle 4. Right now we average the link strength over all pairs (i,j) where i is a node in the El Niño basin defined by Ludescher et al and j is a node outside this basin. The basin consists of the red dots here:

What happens if you change the definition of the El Niño basin? For example, can you drop those annoying two red dots that are south of the rest, without messing things up? Can you get better results if you change the shape of the basin?

To study these questions you need to rewrite ludescher-replication.R a bit. Here’s where Graham defines the El Niño basin:

ludescher.basin <- function() {
  lats <- c( 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6)
  lons <- c(11,12,13,14,15,16,17,18,19,20,21,22,16,22)
  stopifnot(length(lats) == length(lons))

These are lists of latitude and longitude coordinates: (5,11), (5,12), (5,13), etc. A coordinate like (5,11) means the little circle that’s 5 down and 11 across in the grid on the above map. So, that’s the leftmost point in Ludescher’s El Niño basin. By changing these lists, you can change the definition of the El Niño basin.

Next time I’ll discuss some criticisms of Ludescher et al’s paper, but later we will return to analyzing temperature data, looking for interesting patterns.

El Niño Project (Part 4)

8 July, 2014

As the first big step in our El Niño prediction project, Graham Jones replicated the paper by Ludescher et al that I explained last time. Let’s see how this works!

Graham did it using R, a programming language that’s good for statistics. If you prefer another language, go ahead and write software for that… and let us know! We can add it to our repository.

Today I’ll explain this stuff to people who know their way around computers. But I’m not one of those people! So, next time I’ll explain the nitty-gritty details in a way that may be helpful to people more like me.

Getting temperature data

Say you want to predict El Niños from 1950 to 1980 using Ludescher et al’s method. To do this, you need daily average surface air temperatures in this grid in the Pacific Ocean:

Each square here is 7.5° × 7.5°. To compute these temperatures, you have to start with temperatures on a grid with smaller squares that are 2.5° × 2.5° in size:

• Earth System Research Laboratory, NCEP Reanalysis Daily Averages Surface Level, or ftp site.

This website will give you daily average surface air temperatures in whatever rectangle and time interval you want. It delivers this data in a format called NetCDF, meaning Network Common Data Form.

We’ll take a different approach. We’ll download all the temperatures in this database, and then extract the data we need using R scripts. That way, when we play other games with temperature data later, we’ll already have it.

So, go ahead and download all the files from air.sig995. 1948.nc to air.sig995.2013.nc. It will take a while… but you’ll own the world.

There are different ways to do this. If you have R fired up, just cut-and-paste this into the console:

for (year in 1950:1979) { 
  year, ".nc"), 
  destfile=paste0("air.sig995.", year, ".nc"), mode="wb") 

Getting the temperatures you need

Now you have files of daily average temperatures on a 2.5° by 2.5° grid from 1948 to 2013. Make sure all these files are in your working directory for R, and download this R script from GitHub:


You can use this to get the temperatures in any time interval and any rectangle of grid points you want. The details are explained in the script. But the defaults are set to precisely what you need now!

So, just run this script. You should get a file called Pacific-1948-1980.txt. This has daily average temperatures in the region we care about, from 1948 to 1980. It should start with a really long line listing locations in a 27 × 69 grid, starting with S024E48 and ending with S248E116. I’ll explain this coordinate scheme at the end of this post. Then come hundreds of lines listing temperatures in kelvin at those locations on successive days. The first of these lines should start with Y1948P001, meaning the first day of 1948.

And I know what you’re dying to ask: yes, leap days are omitted! This annoys the perfectionist in me… but leap years make data analysis more complicated, so Ludescher et al ignore leap days, and we will too.

Getting the El Niño data

You’ll use this data to predict El Niños, so you also want a file of the Niño 3.4 index. Remember from last time, this says how much hotter the surface of this patch of seawater is than usual for this time of year:

You can download the file from here:


This is a copy of the monthly Niño 3.4 index from the US National Weather Service, which I discussed last time. It has monthly Niño 3.4 data in the column called ANOM.

Put this file in your working directory.

Predicting El Niños

Now for the cool part. Last time I explained the `average link strength’, which Ludescher et al use to predict El Niños. Now you’ll compute it.

You’ve got Pacific-1948-1980.txt and nino3.4-anoms.txt in your working directory. Download this R script written by Graham Jones, and run it:


It takes about 45 minutes on my laptop. It computes the average link strength S at ten-day intervals. Then it plots S in red and the Niño 3.4 index in blue, like this:

(Click to enlarge.) The shaded region is where the Niño 3.4 index is below 0.5°C. When the blue curve escapes this region and then stays above 0.5°C for at least 5 months, Ludescher et al say that there’s an El Niño.

The horizontal red line shows the threshold \theta = 2.82. When S exceeds this, and the Niño 3.4 index is not already over 0.5°C, Ludescher et al predict that there will be an El Niño in the next calendar year!

Our graph almost agrees with theirs:

Here the green arrows show their successful predictions, dashed arrows show false alarms, and a little letter n appears next to each El Niño they failed to predict.

The graphs don’t match perfectly. For the blue curves, we could be using Niño 3.4 from different sources. Differences in the red curves are more interesting, since that’s where all the work is involved, and we’re starting with the same data. Besides actual bugs, which are always possible, I can think of various explanations. None of them are extremely interesting, so I’ll stick them in the last section!

If you want to get ahold of our output, you can do so here:


This has the average link strength S at 10-day intervals, starting from day 730 and going until day 12040, where day 1 is the first of January 1948.

So, you don’t actually have to run all these programs to get our final result. However, these programs will help you tackle some programming challenges which I’ll list now!

Programming challenges

There are lots of variations on the Ludescher et al paper which we could explore. Here are a few easy ones to get you started. If you do any of these, or anything else, let me know!

I’ll start with a really easy one, and work on up.

Challenge 1. Repeat the calculation with temperature data from 1980 to 2013. You’ll have to adjust two lines in netcdf-convertor-ludescher.R:

firstyear <- 1948
lastyear <- 1980

should become

firstyear <- 1980
lastyear <- 2013

or whatever range of years you want. You’ll also have to adjust names of years in ludescher-replication.R. Search the file for the string 19 and make the necessary changes. Ask me if you get stuck.

Challenge 2. Repeat the calculation with temperature data on a 2.5° × 2.5° grid instead of the coarser 7.5° × 7.5° grid Ludescher et al use. You’ve got the data you need. Right now, the program ludescher-replication.R averages out the temperatures over little 3 × 3 squares. It starts with temperatures on a 27 × 69 grid and averages them out to obtain temperatures on the 9 × 23 grid shown here:

Here’s where that happens:

# the data per day is reduced from e.g. 27x69 to 9x23. 

subsample.3x3 <- function(vals) {
  stopifnot(dim(vals)[2] %% 3 == 0)
  stopifnot(dim(vals)[3] %% 3 == 0)
  n.sslats <- dim(vals)[2]/3
  n.sslons <- dim(vals)[3]/3
  ssvals <- array(0, dim=c(dim(vals)[1], n.sslats, n.sslons))  
  for (d in 1:dim(vals)[1]) {
    for (slat in 1:n.sslats) {
      for (slon in 1:n.sslons) {
        ssvals[d, slat, slon] <- mean(vals[d, (3*slat-2):(3*slat), (3*slon-2):(3*slon)])

So, you need to eliminate this and change whatever else needs to be changed. What new value of the threshold \theta looks good for predicting El Niños now? Most importantly: can you get better at predicting El El Niños this way?

The calculation may take a lot longer, since you’ve got 9 times as many grid points and you’re calculating correlations between pairs. So if this is too tough, you can go the other way: use a coarser grid and see how much that degrades your ability to predict El Niños.

Challenge 3. Right now we average the link strength over all pairs (i,j) where i is a node in the El Niño basin defined by Ludescher et al and j is a node outside this basin. The basin consists of the red dots here:

What happens if you change the definition of the El Niño basin? For example, can you drop those annoying two red dots that are south of the rest, without messing things up? Can you get better results if you change the shape of the basin?

To study these questions you need to rewrite ludescher-replication.R a bit. Here’s where Graham defines the El Niño basin:

ludescher.basin <- function() {
  lats <- c( 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6)
  lons <- c(11,12,13,14,15,16,17,18,19,20,21,22,16,22)
  stopifnot(length(lats) == length(lons))

These are lists of latitude and longitude coordinates: (5,11), (5,12), (5,13), etc. A coordinate like (5,11) means the little circle that’s 5 down and 11 across in the grid on the above map. So, that’s the leftmost point in Ludescher’s El Niño basin. By changing these lists, you can change the definition of the El Niño basin. You’ll also have to change these lists if you tackle Challenge 2.

There’s a lot more you can do… the sky’s the limit! In the weeks to come, I’ll show you lots of things we’ve actually done.

Annoying nuances

Here are two reasons our average link strengths could differ from Ludescher’s.

Last time I mentioned that Ludescher et al claim to normalize their time-delayed cross-covariances in a sort of complicated way. I explained why I don’t think they could have actually used this method. In ludescher-replication.R, Graham used the simpler normalization described last time: namely, dividing by

\sqrt{\langle T_i(t)^2 \rangle - \langle T_i(t) \rangle^2} \; \sqrt{\langle T_j(t-\tau)^2 \rangle - \langle T_j(t-\tau) \rangle^2}

instead of

\sqrt{ \langle (T_i(t) - \langle T_i(t)\rangle)^2 \rangle} \; \sqrt{ \langle (T_j(t-\tau) - \langle T_j(t-\tau)\rangle)^2 \rangle}

Since we don’t really know what Ludescher et al did, they might have done something else.

We might also have used a different ‘subsampling’ procedure. That’s a name for how we get from the temperature data on a 9 × 69 grid to temperatures on a 3 × 23 grid. While the original data files give temperatures named after grid points, each is really an area-averaged temperature for a 2.5° × 2.5° square. Is this square centered at the grid point, or is the square having that grid point as its north-west corner, or what? I don’t know.

This data is on a grid where the coordinates are the number of steps of 2.5 degrees, counting from 1. So, for latitude, 1 means the North Pole, 73 means the South Pole. For longitude, 1 means the prime meridian, 37 means 90° east, 73 means 180° east, 109 means 270°E or 90°W, and 144 means 2.5° west. It’s an annoying system, as far as I’m concerned.

In ludescher-replication.R we use this range of coordinates:

lat.range <- 24:50
lon.range <- 48:116 

That’s why your file Pacific-1948-1980.txt has locations starting with S024E48 and ending with S248E116. Maybe Ludescher et al used a slightly different range or subsampling procedure!

There are probably lots of other nuances I haven’t noticed. Can you think of some?

El Niño Project (Part 3)

1 July, 2014

In February, this paper claimed there’s a 75% chance the next El Niño will arrive by the end of 2014:

• Josef Ludescher, Avi Gozolchiani, Mikhail I. Bogachev, Armin Bunde, Shlomo Havlin, and Hans Joachim Schellnhuber, Very early warning of next El Niño, Proceedings of the National Academy of Sciences, February 2014. (Click title for free version, journal name for official version.)

Since it was published in a reputable journal, it created a big stir! Being able to predict an El Niño more than 6 months in advance would be a big deal. El Niños can cause billions of dollars of damage.

But that’s not the only reason we at the Azimuth Project want to analyze, criticize and improve this paper. Another reason is that it uses a climate network—and we like network theory.

Very roughly, the idea is this. Draw a big network of dots representing different places in the Pacific Ocean. For each pair of dots, compute a number saying how strongly correlated the temperatures are at those two places. The paper claims that when a El Niño is getting ready to happen, the average of these numbers is big. In other words, temperatures in the Pacific tend to go up and down in synch!

Whether this idea is right or wrong, it’s interesting—and it’s not very hard for programmers to dive in and study it.

Two Azimuth members have done just that: David Tanzer, a software developer who works for financial firms in New York, and Graham Jones, a self-employed programmer who also works on genomics and Bayesian statistics. These guys have really brought new life to the Azimuth Code Project in the last few weeks, and it’s exciting! It’s even gotten me to do some programming myself.

Soon I’ll start talking about the programs they’ve written, and how you can help. But today I’ll summarize the paper by Ludescher et al. Their methodology is also explained here:

• Josef Ludescher, Avi Gozolchiani, Mikhail I. Bogachev, Armin Bunde, Shlomo Havlin, and Hans Joachim Schellnhuber, Improved El Niño forecasting by cooperativity detection, Proceedings of the National Academy of Sciences, 30 May 2013.

The basic idea

The basic idea is to use a climate network. There are lots of variants on this idea, but here’s a simple one. Start with a bunch of dots representing different places on the Earth. For any pair of dots i and j, compute the cross-correlation of temperature histories at those two places. Call some function of this the ‘link strength’ for that pair of dots. Compute the average link strength… and get excited when this gets bigger than a certain value.

The papers by Ludescher et al use this strategy to predict El Niños. They build their climate network using correlations between daily temperature data for 14 grid points in the El Niño basin and 193 grid points outside this region, as shown here:

The red dots are the points in the El Niño basin.

Starting from this temperature data, they compute an ‘average link strength’ in a way I’ll describe later. When this number is bigger than a certain fixed value, they claim an El Niño is coming.

How do they decide if they’re right? How do we tell when an El Niño actually arrives? One way is to use the ‘Niño 3.4 index’. This the area-averaged sea surface temperature anomaly in the yellow region here:

Anomaly means the temperature minus its average over time: how much hotter than usual it is. When the Niño 3.4 index is over 0.5°C for at least 5 months, Ludescher et al say there’s an El Niño. (By the way, this is not the standard definition. But we will discuss that some other day.)

Here is what they get:

The blue peaks are El Niños: episodes where the Niño 3.4 index is over 0.5°C for at least 5 months.

The red line is their ‘average link strength’. Whenever this exceeds a certain threshold \Theta = 2.82, and the Niño 3.4 index is not already over 0.5°C, they predict an El Niño will start in the following calendar year.

The green arrows show their successful predictions. The dashed arrows show their false alarms. A little letter n appears next to each El Niño that they failed to predict.

You’re probably wondering where the number 2.82 came from. They get it from a learning algorithm that finds this threshold by optimizing the predictive power of their model. Chart A here shows the ‘learning phase’ of their calculation. In this phase, they adjusted the threshold \Theta so their procedure would do a good job. Chart B shows the ‘testing phase’. Here they used the value of \Theta chosen in the learning phase, and checked to see how good a job it did. I’ll let you read their paper for more details on how they chose \Theta.

But what about their prediction now? That’s the green arrow at far right here:

On 17 September 2013, the red line went above the threshold! So, their scheme predicts an El Niño sometime in 2014. The chart at right is a zoomed-in version that shows the red line in August, September, October and November of 2013.

The details

Now I mainly need to explain how they compute their ‘average link strength’.

Let i stand for any point in this 9 × 23 grid:

For each day t between June 1948 and November 2013, let \tilde{T}_i(t) be the average surface air temperature at the point i on day t.

Let T_i(t) be \tilde{T}_i(t) minus its climatological average. For example, if t is June 1st 1970, we average the temperature at location i over all June 1sts from 1948 to 2013, and subtract that from \tilde{T}_i(t) to get T_i(t).

They call T_i(t) the temperature anomaly.

(A subtlety here: when we are doing prediction we can’t know the future temperatures, so the climatological average is only the average over past days meeting the above criteria.)

For any function of time, denote its moving average over the last 365 days by:

\displaystyle{ \langle f(t) \rangle = \frac{1}{365} \sum_{d = 0}^{364} f(t - d) }

Let i be a point in the El Niño basin, and j be a point outside it. For any time lag \tau between 0 and 200 days, define the time-delayed cross-covariance by:

\langle T_i(t) T_j(t - \tau) \rangle - \langle T_i(t) \rangle \langle T_j(t - \tau) \rangle

Note that this is a way of studying the linear correlation between the temperature anomaly at node i and the temperature anomaly a time \tau earlier at some node j. So, it’s about how temperature anomalies inside the El Niño basin are correlated to temperature anomalies outside this basin at earlier times.

Ludescher et al then normalize this, defining the time-delayed cross-correlation C_{i,j}^{t}(-\tau) to be the time-delayed cross-covariance divided by

\sqrt{\Big{\langle} (T_i(t)      - \langle T_i(t)\rangle)^2      \Big{\rangle}} \;     \sqrt{\Big{\langle} (T_j(t-\tau) - \langle T_j(t-\tau)\rangle)^2 \Big{\rangle}}

This is something like the standard deviation of T_i(t) times the standard deviation of T_j(t - \tau). Dividing by standard deviations is what people usually do to turn covariances into correlations. But there are some potential problems here, which I’ll discuss later.

They define C_{i,j}^{t}(\tau) in a similar way, by taking

\langle T_i(t - \tau) T_j(t) \rangle - \langle T_i(t - \tau) \rangle \langle T_j(t) \rangle

and normalizing it. So, this is about how temperature anomalies outside the El Niño basin are correlated to temperature anomalies inside this basin at earlier times.

Next, for nodes i and j, and for each time point t, they determine the maximum, the mean and the standard deviation of |C_{i,j}^t(\tau)|, as \tau ranges from -200 to 200 days.

They define the link strength S_{i j}(t) as the difference between the maximum and the mean value, divided by the standard deviation.

Finally, they let S(t) be the average link strength, calculated by averaging S_{i j}(t) over all pairs (i,j) where i is a node in the El Niño basin and j is a node outside.

They compute S(t) for every 10th day between January 1950 and November 2013. When S(t) goes over 2.82, and the Niño 3.4 index is not already over 0.5°C, they predict an El Niño in the next calendar year.

There’s more to say about their methods. We’d like you to help us check their work and improve it. Soon I want to show you Graham Jones’ software for replicating their calculations! But right now I just want to conclude by:

• mentioning a potential problem in the math, and

• telling you where to get the data used by Ludescher et al.

Mathematical nuances

Ludescher et al normalize the time-delayed cross-covariance in a somewhat odd way. They claim to divide it by

\sqrt{\Big{\langle} (T_i(t)      - \langle T_i(t)\rangle)^2      \Big{\rangle}} \;     \sqrt{\Big{\langle} (T_j(t-\tau) - \langle T_j(t-\tau)\rangle)^2 \Big{\rangle}}

This is a strange thing, since it has nested angle brackets. The angle brackets are defined as a running average over the 365 days, so this quantity involves data going back twice as long: 730 days. Furthermore, the ‘link strength’ involves the above expression where \tau goes up to 200 days.

So, taking their definitions at face value, Ludescher et al could not actually compute their ‘link strength’ until 930 days after the surface temperature data first starts at the beginning of 1948. That would be late 1950. But their graph of the link strength starts at the beginning of 1950!

Perhaps they actually normalized the time-delayed cross-covariance by dividing it by this:

\sqrt{\big{\langle} T_i(t)^2 \big{\rangle} - \big{\langle} T_i(t)\big{\rangle}^2} \; \sqrt{\big{\langle} T_j(t-\tau)^2 \big{\rangle} - \big{\langle} T_j(t-\tau)\big{\rangle}^2}

This simpler expression avoids nested angle brackets, and it makes more sense conceptually. It is the standard deviation of T_i(t) over the last 365 days, times of the standard deviation of T_i(t-\tau) over the last 365 days.

As Nadja Kutz noted, the expression written by Ludescher et al does not equal this simpler expression, since:

\Big{\langle} T_i(t) \; \langle T_i(t) \rangle \Big{\rangle} \neq \big{\langle} T_i(t) \big{\rangle} \; \big{\langle} T_i(t) \big{\rangle}

The reason is that

\begin{array}{ccl} \Big{\langle} T_i(t) \; \langle T_i(t) \rangle \Big{\rangle} &=& \displaystyle{ \frac{1}{365} \sum_{d = 0}^{364} T_i(t-d) \langle T_i(t-d) \rangle} \\  \\  &=& \displaystyle{ \frac{1}{365} \sum_{d = 0}^{364} \frac{1}{365} \sum_{D = 0}^{364} T_i(t-d) T_i(t-d-D)} \end{array}

which is generically different from

\Big{\langle} \langle T_i(t) \rangle \;\langle T_i(t) \rangle \Big{\rangle} =

\displaystyle{ \frac{1}{365} \sum_{D = 0}^{364} (\frac{1}{365} \sum_{d = 0}^{364} T_i(t-d-D))(\frac{1}{365} \sum_{d = 0}^{364} T_i(t-d-D) ) }

since the terms in the latter expression contain products T_i(t-364-364)T_i(t-364-364) that can’t appear in the former.


\begin{array}{ccl} \Big{\langle} (T_i(t) - \langle T_i(t) \rangle)^2 \Big{\rangle} &=& \Big{\langle} T_i(t)^2 - 2 T_i(t) \langle T_i(t) \rangle + \langle T_i(t) \rangle^2 \Big{\rangle} \\  \\  &=& \langle T_i(t)^2 \rangle - 2 \big{\langle} T_i(t) \langle T_i(t) \rangle \big{\rangle} +  \big{\langle} \langle T_i(t) \rangle^2 \big{\rangle} \end{array}

But since \big{\langle} T_i(t) \langle T_i(t) \rangle \big{\rangle} \neq \big{\langle} \langle T_i(t) \rangle \; \langle T_i(t) \rangle \big{\rangle}, as was just shown, those terms do not cancel out in the above expression. In particular, this means that

-2 \big{\langle} T_i(t) \langle T_i(t) \rangle \big{\rangle} + \big{\langle} \langle T_i(t) \rangle \langle T_i(t) \rangle \big{\rangle}

contains terms T_i(t-364-364) which do not appear in \langle T_i(t)\rangle^2, hence

\Big{\langle} (T_i(t) - \langle T_i(t) \rangle)^2 \Big{\rangle}  \neq \langle T_i(t)^2\rangle - \langle T_i(t)\rangle^2

So at least for the case of the standard deviation it is clear that those two definitions are not the same for a running mean. For the covariances this would still need to be shown.

Surface air temperatures

Remember that \tilde{T}_i(t) is the average surface air temperature at the grid point i on day t. You can get these temperatures from here:

• Earth System Research Laboratory, NCEP Reanalysis Daily Averages Surface Level, or ftp site.

These sites will give you worldwide daily average temperatures on a 2.5° latitude × 2.5° longitude grid (144 × 73 grid points), from 1948 to now. Ihe website will help you get data from within a chosen rectangle in a grid, for a chosen time interval. Alternatively, you can use the ftp site to download temperatures worldwide one year at a time. Either way, you’ll get ‘NetCDF files’—a format we will discuss later, when we get into more details about programming!

Niño 3.4

Niño 3.4 is the area-averaged sea surface temperature anomaly in the region 5°S-5°N and 170°-120°W. You can get Niño 3.4 data here:

You can get Niño 3.4 data here:

Niño 3.4 data since 1870 calculated from the HadISST1, NOAA. Discussed in N. A. Rayner et al, Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res. 108 (2003), 4407.

You can also get Niño 3.4 data here:

Monthly Niño 3.4 index, Climate Prediction Center, National Weather Service.

The actual temperatures in Celsius are close to those at the other website, but the anomalies are rather different, because they’re computed in a way that takes global warming into account. See the website for details.

Niño 3.4 is just one of several official regions in the Pacific:

• Niño 1: 80°W-90°W and 5°S-10°S.

• Niño 2: 80°W-90°W and 0°S-5°S

• Niño 3: 90°W-150°W and 5°S-5°N.

• Niño 3.4: 120°W-170°W and 5°S-5°N.

• Niño 4: 160°E-150°W and 5°S-5°N.

For more details, read this:

• Kevin E. Trenberth, The definition of El Niño, Bulletin of the American Meteorological Society 78 (1997), 2771–2777.

El Niño Project (Part 2)

24 June, 2014

Before we dive into the exciting world of El Niño prediction, and ways that you can help, let’s have a very very basic crash course on the physics of El Niño.

El Niños are still rather mysterious. But that doesn’t mean we should ignore what the experts know, or suspect.

The basics

Winds called trade winds blow west across the tropical Pacific, from the Americas to Asia. During La Niña years, water at the ocean’s surface moves along with the wind, warming up in the sunlight as it travels. So, warm water collects at the ocean’s surface off the coast of Asia. This creates more clouds and rainstorms there.

Meanwhile, since surface water is being dragged west by the wind, cold water from below gets pulled up to take its place in the eastern Pacific, off the coast of South America.

So, the temperature at the ocean’s surface looks like this:

This situation is actually reinforced by a feedback loop. Since the ocean’s surface is warmer near Asia, it heats the air and makes it rise. This helps the trade winds blow toward Asia: they go there to fill the ‘gap’ left by rising air.

Of course, you should be wondering: why do the trade winds blow west in the first place?

Without an answer to this, the story so far would work just as well if we switched the words ‘west’ and ‘east’. That wouldn’t mean the story is wrong. It might just mean that there were two stable states of the Earth’s climate: a La Niña state where the trade winds blow west, and another state—say, the El Niño—where they blow east. One could imagine a world permanently stuck in one of these phases. Or perhaps it could flip between these two phases for some reason.

Something roughly like the last choice is actually true. But it’s not so simple: there’s not a complete symmetry between west and east!

Why not? Mainly because the Earth is turning to the east. Air near the equator warms up and rises, so new air from more northern or southern regions moves in to take its place. But because the Earth is fatter at the equator, the equator is moving faster to the east. So, this new air from other places is moving less quickly by comparison… so as seen by someone standing on the equator, it blows west. This is called the Coriolis effect, and it produces winds like this:

Beware: a wind that blows to the west is called an easterly. So the westward-blowing trade winds I’m talking about are called "northeasterly trades" and "southeasterly trades" on this picture.

It’s also good to remember that the west Pacific touches the part of Asia also called the ‘Far East’, while the east Pacific touches the part of America also called the ‘West Coast’. So, it’s easy to get confused! If you find yourself getting confused, just repeat this sentence:

The easterlies blow west from West Coast to Far East.

Everything will instantly become much clearer.

Terminology aside, the story so far should be clear. The trade winds have a good intrinsic reason to blow west, but in the La Niña phase they’re also part of a feedback loop where they make the western Pacific warmer… which in turn helps the trade winds blow west.

But now comes an El Niño! Now for some reason the westward winds weaken. This lets the built-up warm water in the western Pacific slosh back east. And with weaker westward winds, less cold water is pulled up to the surface in the eastern Pacific. So, the eastern Pacific warms up. This makes for more clouds and rain in the eastern Pacific—that’s when we get floods in Southern California. And with the ocean warmer in the eastern Pacific, hot air rises there, which tends to counteract the westward winds even more.

In other words, all the feedbacks reverse themselves! Here’s how it looked in the big El Niño of 1997:

But note: the trade winds never mainly blow east. Even during an El Niño they still blow west, just a bit less. So, the climate is not flip-flopping between two symmetrical alternatives. It’s flip-flopping between two asymmetrical alternatives.

Here’s how it goes! The vertical height of the ocean is exaggerated here to show how water piles up:

Here we see the change in trade winds and ocean currents:

By the way, you can click on any of the pictures to get more information.

But why?

One huge remaining question is: why do the trade winds weaken? We could also ask the same question about the start of the La Niña phase: why do the trade winds get stronger then?

The short answer is: nobody knows! At least there’s no one story that everyone agrees on. There are actually several stories… and perhaps more than one of them is true. So, at this point it is worthwhile revisiting some actual data:

The top graph shows variations in the water temperature of the tropical Eastern Pacific ocean. When it’s hot we have El Niños: those are the red hills in the top graph. The blue valleys are La Niñas. Note that it’s possible to have two El Niños in a row without an intervening La Niña, or vice versa!

The bottom graph shows the Southern Oscillation Index or SOI. This is basically the air pressure in Tahiti minus the air pressure in Darwin, Australia, divided by its standard deviation.

So, when the SOI is high, the air pressure is higher in the east Pacific than in the west Pacific. This is what we expect in an La Niña: that’s why the westward trade winds are strong then! Conversely, the SOI is low in the El Niño phase. This variation in the SOI is called the Southern Oscillation.

If you look at the graphs above, you’ll see how one looks almost like an upside-down version of the other. So, El Niño/La Niña cycle is tightly linked to the Southern Oscillation.

Another thing you’ll see from is that the ENSO is far from perfectly periodic! Here’s a graph of the Southern Oscillation Index going back a lot further:

So, there’s something inherently irregular about this oscillation. It could be chaotic—meaning that tiny changes amplify as time goes by, making long-term prediction impossible. It could be noisy—meaning that the randomness is mainly due to outside influences. It could be somewhere in between! But nobody is sure.

The graph above was made by William Kessler, an expert on El Nño. His FAQs are worth a look:

• William Kessler, What is El Niño and how does it relate to the usual situation in the tropical Pacific?

• William Kessler, El Niño: How it works, how we observe it.

He describes some theories about why an El Niño starts, and why it ends. These theories involve three additional concepts:

• The thermocline is the border between the warmer surface water in the ocean and the cold deep water, 100 to 200 meters below the surface. During the La Niña phase, warm water is blown to the western Pacific, and cold water is pulled up to the surface of the eastern Pacific. So, the thermocline becomes deeper in the west than the east:

When an El Niño occurs, the thermocline flattens out:

Oceanic Rossby waves are very low-frequency waves in the ocean’s surface and thermocline. At the ocean’s surface they are only 5 centimeters high, but hundreds of kilometers across. The surface waves are mirrored by waves in the thermocline, which are much taller, 10-50 meters in height. When the surface goes up, the thermocline goes down!

• The Madden-Julian oscillation or MJO is the largest form of variability in the tropical atmosphere on time scales of 30-90 days. It’s a pulse that moves east across the Indian Ocean and Pacific ocean at 4-8 meters/second. It manifests itself as patches of anomalously high rainfall and also anomalously low rainfall. Strong Madden-Julian Oscillations are often seen 6-12 months before an El Niño starts!

With this bit of background, I hope you’re ready for what Kessler wrote in his El Niño FAQ:

There are two main theories at present. The first is that the event is initiated by the reflection from the western boundary of the Pacific of an oceanic Rossby wave (type of low-frequency planetary wave that moves only west). The reflected wave is supposed to lower the thermocline in the west-central Pacific and thereby warm the sea surface temperature by reducing the efficiency of upwelling to cool the surface. Then that makes winds blow towards the (slightly) warmer water and really start the event. The nice part about this theory is that the oceanic Rossby waves can be observed for months before the reflection, which implies that El Niño is predictable.

The other idea is that the trigger is essentially random. The tropical convection (organized large-scale thunderstorm activity) in the rising air tends to occur in bursts that last for about a month, and these bursts propagate out of the Indian Ocean (known as the Madden-Julian Oscillation). Since the storms are geostrophic (rotating according to the turning of the earth, which means they rotate clockwise in the southern hemisphere and counter-clockwise in the north), storm winds on the equator always blow towards the east. If the storms are strong enough, or last long enough, then those eastward winds may be enough to start the sloshing. But specific Madden-Julian Oscillation events are not predictable much in advance (just as specific weather events are not predictable in advance), and so to the extent that this is the main element, then El Niño will not be predictable.

In my opinion both these two processes can be important in different El Niños. Some models that did not have the MJO storms were successful in predicting the events of 1986-87 and 1991-92. That suggests that the Rossby wave part was a main influence at that time. But those same models have failed to predict the events since then, and the westerlies have appeared to come from nowhere. It is also quite possible that these two general sets of ideas are incomplete, and that there are other causes entirely. The fact that we have very intermittent skill at predicting the major turns of the ENSO cycle (as opposed to the very good forecasts that can be made once an event has begun) suggests that there remain important elements that are await explanation.

So it’s complicated!

Next time I’ll talk about a new paper that tries to cut through these complications and predict El Niños more than 6 months in advance, using a simple idea. It’s a great opportunity for programmers to dive in and try to do better. But I think we need to keep the subtleties in mind… at least somewhere in the back of our mind.


Get every new post delivered to your Inbox.

Join 2,824 other followers