Lorenz 96 2-scale

Overview

The Lorenz 96 2-scale model was first described by Edward Lorenz during a seminar at the European Centre for Medium-Range Weather Forecasts in the Autumn of 1995, the proceedings of which were published as Lorenz (1996) [1] the following year, hence the model is commonly referred to as Lorenz 96.

The model state varies on two separate time scales, one for the X dimension and another in the Y dimension. It is constructed by coupling together two implementations of the Lorenz 96 single-scale model. The constant F term in Lorenz 96 single-scale model is replaced by a term that couples the two scales together.

Lorenz 96 2-scale is a widely studied model because the differing timescales can be viewed as an analog of processes that occur on different time and spatial scales in the atmosphere such as large-scale flow and localized convection. The references contain some of the earlier studies including Palmer (2001), [2] Smith (2001), [3] Orrell (2002), [4] Orrel (2003), [5] Vannitsem and Toth (2002), [6] Roulston and Smith (2003), [7] and Wilks (2005). [8]

The Lorenz 96 2-scale model has a work/workshop_setup.csh script that compiles and runs an example. This example may be explored in the DART tutorial and is intended to provide insight into model/assimilation behavior. The example may or may not result in good (or even decent!) results!

Development History

This DART model interface was developed by Josh Hacker as an adaptation of the Lorenz 96 implementation. The 2-scale model is the second model described in Lorenz (1996).

Quick Start

To run Lorenz 96 2-scale with its default settings:

1. Ensure you have the correct settings in mkmf.template in <DARTROOT>/build_templates/mkmf.template

2. Build the DART executables using the quickbuild.sh script in the ./work directory.

3. Once the executables have been built, the two Perl scripts provided in the ./shell_scripts directory, spinup_model.pl and run_expt.pl, can be used to spin up the model and run an experiment.

Namelist

The model also implements the variant of Smith (2001), which can be invoked by setting local_y = .true. in the &model_nml namelist in the input.nml file.

The &model_nml namelist is read from the input.nml file. Namelists start with an ampersand & and terminate with a slash /. Character strings that contain a / must be enclosed in quotes to prevent them from prematurely terminating the namelist.

&model_nml
   model_size_x        = 36,
   y_per_x             = 10,
   forcing             = 15.00,
   delta_t             = 0.005,
   coupling_b          = 10.0,
   coupling_c          = 10.0,
   coupling_h          = 1.0,
   local_y             = .false.,
   time_step_days      = 0,
   time_step_seconds   = 3600
   template_file       = 'filter_input.nc'
/

Description of each namelist entry

Item	Type	Description
model_size_x	integer	Number of variables in x-dimension.
y_per_x	integer	Scaling factor for number of variables in y-dimension compared to x-dimension.
forcing	real(r8)	Forcing, F, for model.
delta_t	real(r8)	Non-dimensional timestep. This is mapped to the dimensional timestep specified by time_step_days and time_step_seconds.
coupling_b	real(r8)
coupling_c	real(r8)
coupling_h	real(r8)
local_y	boolean
time_step_days	integer	Number of days for dimensional timestep, mapped to delta_t.
time_step_seconds	integer	Number of seconds for dimensional timestep, mapped to delta_t.
template_file	character(len=256)	this in script

References

[1]

Lorenz, Edward N., 1996: Predictability: A Problem Partly Solved. Seminar on Predictability. 1, ECMWF, Reading, Berkshire, UK, 1-18.

[2]

Palmer, Timothy N., 2001: A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models. Quarterly Journal of the Royal Meteorological Society, 127, 279–304. https://doi.org/10.1002/qj.49712757202

[3]

Smith, Leonard A., 2001: Disentangling uncertainty and error: On the predictability of nonlinear systems. Nonlinear dynamics and statistics, Alistair I. Mees, Editor, Birkhauser, Boston, USA, 31–64.

[4]

Orrell, David, 2002: Role of the metric in forecast error growth: How chaotic is the weather? Tellus, 54A, 350–362.

[5]

Orrell, David, 2003: Model error and predictability over different timescales in the Lorenz ‘96 Systems. Journal of the Atmospheric Sciences, 60, 2219–2228.

[6]

Vannitsem, Stéphane and Zoltan Toth, 2002: Short-term dynamics of model errors. Journal of the Atmospheric Sciences, 59, 2594–2604.

[7]

Roulston, Mark S. and Leonard A. Smith, 2003: Combining dynamical and statistical ensembles. Tellus, 55A, 16–30.

[8]

Wilks, Daniel S., 2005: Effects of stochastic parametrizations in the Lorenz ’96 system. Quarterly Journal of the Royal Meteorological Society. 131. 389-407. https://doi.org/10.1256/qj.04.03