# The Azimuth Project Blog - El Niño project (part 4) (Rev #8, changes)

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This is a blog article in progress, written by John Baez. To see discussions of the article as it is being written, visit the Azimuth Forum.

As the first big step in our El Niño prediction project, Graham Jones replicated the paper by Ludescheret al that I explained in Part last 3 time. Let’s see how this works!

Graham did this using R , a programming language that’s good for statistics. So, But I’ll if tell you how everything works with R. 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 7 × 23 grid in the Pacific Ocean:

Each square here is 7.5° × 7.5°. To get this data, you have to first download area-averaged temperatures on a grid with smaller squares that are 1.5° × 1.5° in size:

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

You can get the website to deliver you temperatures in a given rectangle in a given time interval. It gives you this data in a format called NetCDF, meaning Network Common Data Form. We’ll take a different approach. We’ll download all the Earth’s temperatures from 1948 to 2013, and then extract the data we need using R scripts. That way, when we play other games with temperature data later, you’ll we’ll already have it.

So, go ahead and download all files from air.sig995.1948.nc to air.sig995.2013.nc . Or It if will you take just a want while… to but do you’ll this own one the project, world. just up toair.sig.995.1980.nc. It will take a while…

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) {
"ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.dailyavgs/surface/air.sig995.",
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 1.5° by 1.5° grid, grid from 1948 to 2013. Make sure all these files are in your working directory for R, and download this R script from GitHub:

Graham wrote it; I just modified it a bit. You can use this to get the temperatures in any time interval and any rectangle of grid points you want. However, The details are explained in the script. But the defaults are set to precisely what you need now!

When So, you just run this, this. you You should get a file calledPacific-1948-1980.txt . This has daily average temperatures in the region we care about, from 1948 to 1980. It starts with a really long line listing locations in a 9 × 69 grid. Then come hundreds of lines listing temperatures in kelvin at those locations on successive days. The first of these lines starts with Y1948P001, meaning the first day of 1948.

And I know what you’re dying to ask: yes, leap days are omitted!

### 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 data. index. Remember from last time, this says how much hotter than average the surface water is in this patch of the ocean Pacific happens Ocean: to be at any time:

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

Put this file in your working directory.

### Predicting El Niños

Now 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 a about bit 45 more minutes than on half my an laptop. hour. It computes the average link strength$S$ that I explained last time, time. with one mathematical nuance I’ll mention later. It plots$S$ in red, and plots 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 declare say that there’s an El Niño! 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. year!

This Our graph almost matches the corresponding graph in Ludescheret al:

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 a different source. sources. But the red curves are more interesting, since that’s where all the work is involved, and we we’re are starting with the same data. Beside 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:

So, This you has don’t the actually average have link to strength run all these programs to get our final results. Scientists should never make data hard to get. However, these programs will help you tackle some programming challenges which I’ll list now!$S$ at 10-day intervals, starting from day 730 (where the first of January 1948 is day 1) and going until day 12040.

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 I’m easy really ones interested in. The Azimuth gang hasn’t had time to try get these yet, so if you started. If you do them any we’d of be these, interested! or I’ll anything start else, with let a me really know! easy one, and work on up.

Challenge 1. Repeat the calculation with temperature data from 1980 to 2013. You’ll have to get the relevant temperature data and 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 some names numbers of years inludescher-replication.R. Search the file for the string 19 and make the necessary changes. Ask me if you get stuck.

Challenge 1. 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 squares: it starts with temperatures on a 27 × 69 grid and averages them out to get obtain temperatures on the temperature 9 data × Ludescher 23 grid shown here:et al want. It starts with 27 × 69 temperatures per day and averages them out to obtain 9 × 23 temperatures. Here’s how:

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)])
}
}
}
ssvals
}


So, you’d 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 important: importantly: can you get better at predicting El El Niños this way way?

Running The 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, So if this is too tough, you don’t have a powerful computer, maybe you can go the way other and way: use a coarser grid and see how much (if any) thatdegrades your ability to predict El Niños.

### Mathematical nuances

Challenge 3. Right now the average link strength for 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:

I mentioned last time that Ludescher et al claim to normalize their time-delayed cross-covariances in a somewhat peculiar way which involves running averages of (functions of) running averages. For reasons I explained, I don’t think they could have actually used this method.

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))
list(lats=lats,lons=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 Niño basin.

There’s a lot more you can do… the sky’s the limit!

### Annoying nuances

Here are two reasons our result for the average link strength 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

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

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

Another reason might be the ‘subsampling’ procedure: 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 for the El Niño basin:

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

Maybe Ludescher et al used something slightly different!

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

category: blog, climate