GFDL - Geophysical Fluid Dynamics Laboratory

title>Quickstart Guide: Idealized Global Atmospheric Models with Spectral Dynamics

Quickstart Guide: Idealized Global Atmospheric Models with Spectral Dynamics

Table of Contents

1. Overview
2. Description of models
2.1. Barotropic model
2.1.1 Basic barotropic model
2.1.2 Barotropic model with random stirring
2.2. Shallow water model
2.2.1 Basic shallow water model
2.3. Model with full spectral dynamics
2.3.1 HSt42
2.3.2 Polvani_2004
2.3.3 Jablonoski_2006
2.3.4 Polvani_2007_LC1
3. Acquire the Source Code and Runscripts
4. Run the Model
4.1. The Sample Runscripts
4.2. The compilation templates
4.3. Grid decomposition
4.4. Restarting and cold-starting
4.4.1. restarting
4.4.2. cold-starting
4.5. Initial condition vs forcing experiments
4.6. Control of model time and length of integration
4.7. diag_table
4.8. field_table
5. Examine the Output

1. Overview

A global spectral atmospheric model decomposes the flow into spherical harmonic components. It provides an elegant algorithm for atmospheric modeling on global scales. It is not the algorithm currently favored for comprehensive climate modeling at GFDL, due to the difficulty of exactly conserving total mass of tracers and of dry air, and due to problems associated with Gibbs’ ripples created by trying to represent the Earth’s topography with a finite set of spherical harmonics. However, the spectral model continues to be useful for research with idealized models and for education.

2. Description of models

2.1. Barotropic model

The barotropic model solves the vorticity equation for the evolution of a two-dimensional non-divergent flow on the surface of a sphere. For documentation of the Fortran code and tunable namelist parameters, see http://www.gfdl.noaa.gov/barotropic-dynamics For a full description of the model and algorithms, see: The barotropic vorticity equation

2.1.1 Basic barotropic model

Default setting generates the free evolution of an eddy of a given zonal wavenumber on a stable mid-latitude zonal jet, as in Held and Phillipps, 1986: Linear and Nonlinear barotropic decay on the Sphere. Journal of the Atmospheric Sciences, 44(1), 200-207. Optionally, two passive tracers may be included, one advected with the spectral algorithm and another advected with a piecewise linear finite-volume scheme.

2.1.2 Barotropic model with random stirring

An alternative setting is available that illustrates how random stirring can create zonal jets., following Vallis,G.K., E.P.Gerber, P.J.Kushner, and B.A.Cash, 2004:

A Mechanism and Simple Dynamical Model of the North Atlantic Oscillation and Annular Modes.
Journal of the Atmospheric Sciences, 61(3), 264-280.

2.2. Shallow water model

The shallow water model solves for the evolution of a uniform density, incompressible flow on a sphere in the hydrostatic approximation (valid when the horizontal scale of the motion is large compared to the depth of the fluid). For documentation of the Fortran code and tunable namelist parameters, see: documentation-of-dynamics-code

For a full description of the model and algorithms, see: The shallow water equations

2.2.1 Basic shallow water model

The response to heating/cooling in the atmosphere in such a model is mimicked by specifying mass sources/sinks. Default mass sources/sinks provide relaxation of height to a profile which has a ridge at the equator and an isolated hump in mid-latitudes, representing heating in the intertropical Convergence Zone and monsoonal heating, respectively, with background radiatie cooling.

2.3. Model with full spectral dynamics

For documentation of the Fortran code and tunable namelist parameters, see http://www.gfdl.noaa.gov/documentation-of-spectral-dynamics-code For a full description of the model and algorithms, see: Equations and numerics of the spectral dynamics code

2.3.1 HSt42

The model setup follows the standard described in A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models Bulletin of the American Meteorological Society, 75(10), 1825-1830. Several default settings are provided. for running in climate mode (force, dissipative flow in which one is interesting in the long term behavior, independent of initial conditions) and in initial value model (idealized initial conditions, illustrating the development of midlaittude cyclones)

2.3.2 Polvani_2004

Sets up the initial value problem describe in Polvani, L. M., R. K. Scott, and S. J. Thomas, 2004: Numerically Converged Solutions of the Global Primitive Equations for Testing the Dynamical Core of Atmospheric GCMs Mon. Weather Rev., 132, 2539-2552.

2.3.3 Jablonoski_2006

Is an alternative initial value problem of the same style: Jablonowski, C. and D. L. Williamson, 2006: A baroclinic instability test case for atmospheric model dynamical cores Q.J.R. Meteorol. Soc., 2006, 132, 2943-297

2.3.4 Polvani_2007_LC1

Adds a detailed specification of idealized tracers in the environment of a developing baroclinic wave: Polvani, L. M. and J. G. Esler, 2007: Transport and mixing of chemical air masses in idealized baroclinic life cycles J. Geophys. Res., 112, D23102, doi:10.1029/2007JD008555.

3. Acquire the Source Code and Runscripts

You may download the release package here. This package contains source code and scripts to compile and run the models.

4. Run the Model

4.1. The Sample Runscripts

This release includes compile and run scripts for the idealized models in the exp directory. It is organized into three different models: full_dynamics, barotropic and shallow. Each of these involves a different set of source code, although there is much code in common.

For each of these models, one or more experiments may be run. The experiment is chosen at the top of the run scripts. The experiment is determined only by the input data, using the same model executable.

In addition, for each model there are three pairs of compile and run scripts. Each of these runs on a different platform. The platforms are:

  • The gaea machine at ORNL.
  • The workstation environment at GFDL, using the mpi software.
  • The workstation environment at GFDL, without the mpi software, running on a single processor.

It is not likely that any of these will run out-of-the-box on some other platform, but comparison between the three pairs of scripts shows what sort of things need modification when platforms change. See section 5.2 below.

The run script:

  • Creates a working directory where the model will be run.
  • Creates or copies the required input data for the particular experiment chosen into the working directory.
  • Runs the model.
  • Combines distributed output and renames the output files using the timestamp.

Note that the directory paths and file paths are variables. They are initially set to correspond to the directory structure as it exists after extraction from the tar file, but are made variables to accommodate changes to this directory structure.

The directory path most likely to need changing is workdir. workdir is a temporary directory where the model will run. A large amount of data will be copied into the work directory, and output from the model is also written to the work directory. workdir must be large enough to accommodate all of this. The input data is approximately 20GB and model output is potentially even larger.

4.2. The compilation templates

If you encounter a compile error when executing the compile script, first check whether you have correctly customized your mkmf template. The scripts use the mkmf utility, which creates a Makefile to facilitate compilation. The mkmf utility uses a platform-specific template for setting up system and platform dependent parameters. Sample templates for various platforms are provided in the bin directory. You may need to consult your system administrator to set up a compilation template for your platform and ensure the locations for system libraries are defined correctly. For documentation on the use of mkmf, see the file named “mkmf.html”, which is supplied with this package

4.3. Grid decomposition

The spectral model decomposes the horizontal grid into latitude bands with each band assigned to a processor. When 2 processors are used it is decomposed into northern and southern hemispheres. The number of processors that may be used is restricted such that lat_max/npes must be a multiple of 2. For example, for t42 resolution there are 64 latitudes so that the choices for npes are 1,2,4,8,16,32 The diagnostic output is distributed, that is, each processor writes its own files. The processor number is appended to the file name. For example, a diagnostic file named “atmos_daily.nc” would appear as these four files when npes=4:

atmos_daily.nc.0000

atmos_daily.nc.0001

atmos_daily.nc.0002

atmos_daily.nc.0003

A tool is provided to combine these distributed files into files of data on a single grid. Combining the four files shown above would result in a single file named “atmos_daily.nc”

4.4 Restarting and cold-starting

4.4.1 Restarting the model to continue a previous integration

Restart files are written to a sub-directory, named RESTART, off the working directory. Information about the state of the model at the point of termination is contained in these files. Each component model and/or sub-component may have restart files. To continue a previous integration these files are put in the INPUT directory. They are read at initialization to restore the state of the model as it was at termination of the previous integration.

4.4.2 Cold-starting the model

If a component and/or sub-component does not find its restart files in the INPUT directory then it performs a default initialization, also referred to as a cold-start. The default initialization of each component is required to be compatible with other model components, but otherwise is entirely at the discretion of the developer(s).

The atmospheric models typically fill the model fields with constant values for a cold-start. The result is a model state that is very flat and far away from anything scientifically interesting. As a result, a cold-started model needs to be spun-up.

4.5 Initial condition vs forcing experiments

The experiments included with this package can be categorized as either initial condition or forcing experiments. The initial condition experiments are initialized with a perturbation on top of some stable basic flow. The forcing experiments are initialized with either a quiescent state or some stable basic flow, and evolve as the forcing exerts its influence. These are typically integrated longer than the initial condition experiments and are more likely to be candidates for stopping and restarting.

The names of the initial condition experiments included with this package are:

  • t85barotropic
  • t42_polvani_2004
  • t42_polvani_2007_LC1
  • t42_jablonowski_2006

The names of the forcing experiments included with this package are:

  • t85barotropic_stirring
  • t85shallow
  • HSt42

4.6. Control of model time and length of integration

The length of integration is set by setting days, hours, minutes and seconds in main_nml. The default integration period is zero, so it must be set.

The model time starts at zero by default. It can be changed by setting current_time in main_nml, but this is not recommended. current_time is an array of four elements representing days, hours, minutes and seconds. The model time is saved with the restart data (in atmos_model.res). Upon restarting, the model time will be read from atmos_model.res and any setting via namelist will be ignored.

The second line of diag_table is an initial date, by default a string of six zeros. This is the model initial time. The six zeros represent years, months, days, hours, minutes and seconds. years and months must be zero. The others should always be equal to what is set for current_time. The date in diag_table is intended to be used only for the time axis of the diagnostic output files, but unfortunately this standard has not been strictly adhered to. If it is not equal to that set for current_time the results are unpredictable.

4.7 diag_table

The diagnostic output is controlled via the diagnostics table, which is named “diag_table”. For documentation on the use of diag_table, see the file named “diag_table.html”, which is supplied with this package.

4.8 field_table

Aside from the model’s required prognostic variables; velocity, pressure, temperature and humidity, the model may or may not have any number of additional tracers. Tracers are advected by the dynamical code, but do not feed back on the model solution. Tracers are specified in field_table. For each tracer, the field_table specifies the method of advection, convection, and the tracers source and sinks.

For a more thorough description of field_table, see the file named “field_manager.html”, which is supplied with this package.

5. Examine the Output

You may download sample output data for comparison here. The size of the sample output, after unzipping, is 474 MB. This output was generated on a workstation at GFDL. The file output.tar.gz contains three directories: ascii, history and restart. The ascii directory contains text output of the model including stdout and log messages. The history directory contains netCDF diagnostic output, governed by your entries in the diag_table. History and ascii files are labeled with the timestamp corresponding to the model time at the beginning of execution. The restart directory contains files which describe the state of the model at termination. To restart the model running from this state, these restart files are moved to the INPUT directory to serve as the initial conditions for your next run.