http://bellm.org/blog/2011/05/27/why-astronomers-should-program-in-python/

If you want to have a go at a relatively simple climate model, you can try the Portable University Model of the Atmosphere (PUMA) http://www.mi.uni-hamburg.de/Downloads-an.245.0.html?&L=3

It’s targeted at students and (for your standard research software package) relatively easy to get up and running.
All written in Fortran90 with an emphasis on being easy to understand and modify.

To throw some more software into the mix: if you start to use Python, matplotlib/scipy/numpy is getting much traction in the scientific community.

Once you start running models you will have to deal with how to access their output data, sometimes in different formats. CDAT (the Climate Data Analysis Tools) might come in handy, at the very least check out their CDMS subsystem which gives you a unified interface to different file formats.

D.

To utilize GMT we need some programming of Unix (or GNU) shell scripts, which, in my experience, requires considerable trial and error rather than systematic computer science practice.

GMT is good for 2-dimensional geographical data. NCL seems to be better in handling 3- and 4- dimensional atmospheric data, and R seems to be better (more systematic) for non-geographic data, though I am entrenched in GMT in all cases myself.

The shell scripts to use GMT sometimes call the programming language Awk, which has evolved together with the Unix operating system. Mastering Awk is a helpful thing, not only with GMT but also in various needs of converting available data to match requirements of available software. (If you master Python or Ruby, you may think replacing Awk with either of them, though.)

The best way to learn languages is to set yourself little projects that are suited to each language. In Fortran, Matlab, or NumPy write a 0D energy balance model, a 1D diffusive ocean model, or a 1D radiative-convective model. (Or write the same model in several languages, for comparison.) Use NCL to download some climate model output and process it (compute zonal averages, plot meridional sections, etc.) Use R to compute trends or estimate model parameters. And so on.

vimtutor is your friend: http://vim.wikia.com/wiki/Tutorial#Vim_tutor

I recommend “The Practice of Programming” by Kernighan and Pike. Simplicity, Clarity, Generality.

And of course, yay Python!

Admittedly, I already knew C inside and out (and about 10 other languages too!).

Our climate model is written in Fortran 90, but most of its codes are still written with the syntax of Fortran 77. Only such parts that support parallel processing use newer features. This is just an example and I am not sure about the situation of other models.

But anyway, there are many textbooks of Fortran 77 (most of which are likely to out of print but perhaps still available in university libraries) but much fewer ones for the newer standards.

Thanks, Kooti. I will take a look at both versions. -Kate

If you want to learn Fortran, I recommend you to follow the 77 standard. But I think it is better to include one feature of the 90 standard.

That is the “DO … END DO” structure for making loops. This structure is supported by GNU’s g77 compiler. GNU now develops gfortran instead of g77, but it seems upward compatible. I have been using gfortran as if it is g77 without a trouble so far.

“DO … END DO” structure with the loop count specified:

DO k = 1, 10
…
END DO

Within the 77 standard, we had to write like this:

DO 100 k = 1, 10
…
100 CONTINUE

“DO … END DO” structure with the condition to loop specified (where “condition” is a logical formula, which may be a variable declared as LOGICAL):

DO WHILE( condition )
…
END DO

Within the 77 standard, we had to write like this:

900 IF(.NOT. (condition)) GO TO 990
…
GO TO 900
990 CONTINUE