It is known that one-way lateral boundary conditions (LBCs) artificially constrain error growth in limited-area model (LAM) forecasts. While previous studies have considered LBC constraints on error growth for individual LAM forecasts, this work emphasizes the impact of coarsely resolved and temporally interpolated LBCs on the dispersion of LAM ensemble forecasts. An expression is developed that links error variance spectra to ensemble spread while accounting for spatial and ensemble biases. The balances required by this expression are used to show that LBC constraints on small scale error variance growth are sufficient to explain underdispersive LAM ensemble simulations.

The hypothesis is tested in a controlled and efficient manner using a modified barotropic channel model. Ten-member ensemble simulations are produced over many cases on the periodic channel domain and each of four smaller nested domains. Lateral boundary effects are specifically isolated since the simulations are perfect except for initial condition perturbations and the use of coarsely resolved and/or temporally interpolated one-way LBCs. Statistical results accumulated over 100 independent cases demonstrate that LAM ensembles remain underdispersive even when using a set of LBCs from an external ensemble forecast. The effect is present in any modeling system using one-way LBC forcing and suggests the need to apply statistically consistent finescale LBC perturbations at every time step throughout the LAM simulations. A procedure for implementing such perturbations is proposed and shown to capably restore much of the LAM ensemble dispersion lost through the LBC constraints described above.