Overview:

Based on extensive evaluation of AMWG, UV-CDAT, and NCO codes for generating climatology files (see here), we have determined that NCO provides the most correct answers, has the best metadata, and is fastest. Until UV-CDAT bests NCO in these measures we advocate using NCO for creating climatologies.

The NCO command, ncclimo (formerly climo_nco.sh) will generate and regrid all climatology files (NB: from model or measurement monthly input files).  The primary documentation is here. This presentation, given at the Albuquerque workshop on 20151104, conveys much of the information presented below, and some newer information, in a more graphical format. Currently (20160422) the major difference between the commands is that ncclimo fully implements the CF climatology-bounds and climatological statistics (for cell_methods) conventions, whereas climo_nco.sh does not.

Prerequisites:

Use ncclimo if possible. It requires and comes with NCO version 4.6.0 and later.  Its predecessor climo_nco.sh (which is deprecated) requires NCO version 4.5.2 or later. The newest versions of NCO are installed on rhea/titan.ccs.ornl.gov at ORNL, pileus.ornl.gov (CADES at ORNL), cooley/mira.alcf.anl.gov at ANL, cori/edison.nersc.gov (NERSC), aims4.llnl.gov (LLNL), roger.ncsa.illinois.edu (NCSA), and yellowstone.ucar.edu (NCAR). The ncclimo and ncremap scripts are hard-coded to find the latest versions automatically, and do not require any module or path changes. To use other (besides the ncclimo and ncremap scripts) NCO executables from the command-line or from your own scripts may require loading modules. This is site-specific and not under my (CZ's) control. At OLCF, for example, "module load gcc" helps to run NCO from the command-line or scripts. For other machines check that the default NCO is recent enough (try "module load nco", then "ncks --version") or use developers' executables/libraries (in ~zender/[bin,lib] on all machines). Follow these directions (on the NCO homepage) to install on your own machines/directories.

The older version of ncclimo is climo_nco.sh which is distributed separately from NCO, and therefore cannot be as tightly coupled to new NCO features. The two are still nearly interchangeable so all documented features (except where indicated otherwise) apply to both.  You can obtain the deprecated script at https://github.com/ACME-Climate/PreAndPostProcessingScripts/blob/master/generate_climatologies/climo_nco.sh or check-out the entire PreAndPostProcessingScripts Git repo (a better option for keeping up-to-date with changes to this script) with "git clone https://github.com/ACME-Climate/PreAndPostProcessingScripts.git". Sometimes this works better: "git clone git@github.com:ACME-Climate/PreAndPostProcessingScripts.git". Here's the ACME Git Tutorial. If you have permissions problems, try this: https://help.github.com/articles/generating-ssh-keys/.

Using ncclimo:

Basic:

The basic way to use ncclimo is to bring up a terminal window and simply type:

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MPAS O/I considerations:

MPAS ocean and ice models currently have their own (non-CESM'ish) naming convention for monthly output files. ncclimo recognizes input files as being MPAS-style when invoked with "-c hist" and "-m ocn" or "-m ice". Use the optional "-f fml_nm" switch to replace "hist" with a more descriptive simulation name for the output. Invocation looks like this:

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If/when MPAS O/I generates the _FillValue attributes itself, this step can and should be skipped. All other ncclimo features like regridding (below) are invoked identically for MPAS as for CAM/CLM users although under-the-hood ncclimo does do some special pre-processing (dimension permutation, metadata annotation) for MPAS. A five-year oEC60to30 MPAS-O climo with regridding to T62 takes < 10 minutes on rhea.

Regridding (climos and other files):

ncclimo will (optionally) regrid during climatology generation and produce climatology files on both the native and desired analysis grids. This regridding is virtually free, because it is performed on idle nodes/cores after the monthly climatologies have been computed and while the seasonal climatologies are being computed. This load-balancing can save half an hour on ne120 datasets. To regrid, simply pass the desired mapfile name with "-r map.nc", e.g., "-r ${DATA}/maps/map_ne120np4_to_fv257x512_aave.20150901.nc". Although this should not be necessary for normal use, you may pass any options specific to regridding with "-R opt1 opt2". 

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The atmosphere and ocean model output is significantly larger than the land and ice model output. These commands recognize that by using different parallelization strategies that may (rhea standard queue) or may not (cooley or rhea bigmem queue) be required, depending on the fatness of the analysis nodes, as explained below.

Memory Considerations:

It is important to employ the optimal ncclimo  parallelization strategy for your computer hardware resources. Select from the three available choices with the '-p par_typ' switch. The options are serial mode ('-p nil' or '-p serial'), background mode parallelism ('-p bck'), and MPI parallelism ('-p mpi'). The default is background mode parallelism, which is appropriate for lower resolution (e.g., ne30L30) simulations on most nodes at high-performance computer centers. Use (or at least start with) serial mode on personal laptops/workstations. Serial mode requires twelve times less RAM than the parallel modes, and is much less likely to deadlock or cause OOM (out-of-memory) conditions on your personal computer. If the available RAM (+swap) is < 12*4*sizeof(monthly input file), then try serial mode first (12 is the optimal number of parallel processes for monthly climos, the computational overhead is a factor of four). CAM-SE ne30L30 output is about ~1 GB per month so each month requires about 4 GB of RAM. CAM-SE ne30L72 output (with LINOZ) is about ~10 GB/month so each month requires ~40 GB RAM. CAM-SE ne120 output is about ~12 GB/month so each month requires ~48 GB RAM. The computer does not actually use all this memory at one time, and many kernels compress RAM usage to below what top reports, so the actual physical usage is hard to pin-down, but may be a factor of 2.5-3.0 (rather than a factor of four) times the size of the input file. For instance, my 16 GB MacBookPro will successfully run an ne30L30 climatology (that requests 48 GB RAM) in background mode, but the laptop will be slow and unresponsive for other uses until it finishes (in 6-8 minutes) the climos. Experiment a bit and choose the parallelization option that works best for you. 

Serial mode, as its name implies, uses one core at a time for climos, and proceeds sequentially from months to seasons to annual climatologies. Serial mode means that climos are performed serially, but regridding will employ OMP threading on platforms that support it, and use up to 16 cores. By design each month and each season is independent of the others, so all months can be computed in parallel, then each season can be computed in parallel (using monthly climatologies), then the annual average can be computed. Background parallelization mode exploits this parallelism and executes the climos in parallel as background processes on a single node, so that twelve cores are simultaneously employed for monthly climatologies, four for seasonal, and one for annual. The optional regridding will employ up to two cores per process. MPI parallelism executes the climatologies on different nodes so that up to (optimally) twelve nodes are employed performing monthly climos. The full memory of each node is available for each individual climo. The optional regridding will employ up to eight cores per node. MPI mode or Background mode on a big memory queue must be used to process ne30L72 and ne120L30 climos on some, but not all, DOE computers. For example, attempting an ne120L30 climo on in background mode on rhea (i.e., on one 128 GB compute node) will fail due to OOM. (OOM errors do not produce useful return codes so if your climo processes die without printing useful information, the cause may be OOM). However the same climo will succeed if executed on a single big-memory (1 TB) node on rhea (use -lpartition=gpu, as shown below). Or MPI mode can be used for any climatology. The same ne120L30 climo will also finish blazingly fast in background mode on cooley (i.e., on one 384 GB compute node), so MPI mode is unnecessary on cooley. In general, the fatter the memory, the better the performance. 

For a Single, Dedicated Node on LCFs:

The basic approach above (running the script from a standard terminal window) works well for small cases can be unpleasantly slow on login nodes of LCFs and for longer or higher resolution (e.g., ne120) climatologies. As a baseline, generating a climatology of 5 years of ne30 (~1x1 degree) CAM-SE output with ncclimo takes 1-2 minutes on rhea (at a time with little contention), and 6-8 minutes on a 2014 MacBook Pro. To make things a bit faster at LCFs, you can ask for your own dedicated node (note this approach doesn't make sense except on supercomputers which have a job-control queue). On rhea do this via:

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-A: the name of the account to charge for time used. This page may be useful for figuring that out if the above defaults don't work: Computational Resources

For a 12 node, MPI Job:

The above parallel approaches will fail when a single node lacks enough RAM (plus swap) to store all twelve monthly input files, plus extra RAM for computations. One should employ MPI multinode parallelism (-p mpi) on nodes with less RAM than 12*3*sizeof(monthly input).  The longest an ne120 climo will take is less than half an hour (~25 minutes on Edison or Rhea), so the simplest method to run MPI jobs is to request 12-interactive nodes using the above commands (though remember to add -p mpi), then execute the script at the command line. It is also possible, and sometimes preferable, to request non-interactive compute nodes in a batch queue. Executing an MPI-mode climo (on machines with job scheduling and, optimally, 12 available nodes) in a batch queue can be done in 2 commands. First, write an executable file which calls the ncclimo script with appropriate arguments. We do this below by echoing to a file ~/ncclimo.pbs, but you could also open an editor and copy the stuff in quotes below into a file and save it:

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The basic idea of this script is very simple. For monthly climatologies (e.g. JAN), ncclimo passes the list of all relevant January monthly files to NCO's ncra command, which averages each variable in these monthly files over their time dimension (if it exists) or copies the value from the first month unchanged (if no time axis exists). Seasonal climos are then created by taking the average of the monthly climo files using ncra. In order to account for differing numbers of days per month, the "-w" flag in ncra is used, followed by the number of days in the relevant months. For example, the MAM climo is computed from: "ncra -w 31,30,31 MAR_climo.nc APR_climo.nc MAY_climo.nc MAM_climo.nc" (details about file names and other optimization flags have been stripped here to make the concept easier to follow). The ANN climo is then computed by doing a weighted average of the seasonal climos.

Assumptions, Approximations, and Algorithms (AAA) Employed:

A climatology embodies many algorithmic choices, and regridding from the native to the analysis grid involves still more choices. A separate method should reproduce the ncclimo and NCO answers to round-off precision if it implements the same algorithmic choices. For example, ncclimo agrees to round-off with AMWG diagnostics when making the same (sometimes questionable) choices. The most important choices have to do with converting single- to double-precision (SP and DP, respectively), treatment of missing values, generation/application of regridding weights. For concreteness and clarity we describe the algorithmic choices made in processing a CAM-SE monthly output into a climatological annual mean (ANN) and then regridding that. Other climatologies (e.g., daily to monthly, or annual-to-climatological) involve similar choices.

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