# Predictability of low-frequency planetary waves in a simple low-resolution model

## Abstract

A low-resolution global spectral model truncated at zonal wavenumber 5 and meridional mode 15 is developed to simulate the low-frequency variability of planetary-scale atmospheric motions. The effects of unresolved time and space scales on the slow evolution of the flow are deduced by analyzing their contribution to the tendencies of low-pass-filtered planetary-scale modes in a higher-resolution (R15 truncation) version of the model. This unresolved forcing is parameterized by a quasi-stochastic method formulated on the transform grid of the low-resolution model. The deterministic component of the parameterization consists of a linear regression of the unresolved forcing on the resolved forcing, which represents the autonomous dynamics of the low-resolution model. The regression parameters vary spatially and with the dependent variable. The stochastic component of the parameterization consists of kth-order univariate autoregressive statistical models, AR(k), which simulate the residuals from the linear regression. Measured against the spatially and temporally filtered flow from the R15 model, the skill exhibited by the low-resolution model without the parameterization, with the deterministic component only, and with the full parameterization using Ar(2) and Ar(4) model is determined from a set of 30-day simulations. The full parameterization with the AR(4) model demonstrates the greatest skill. For all dependent variables,more »

- Authors:

- (Environmental Dynamics Research, Inc., Miami, FL (United States))

- Publication Date:

- OSTI Identifier:
- 7162862

- Resource Type:
- Journal Article

- Journal Name:
- Monthly Weather Review; (United States)

- Additional Journal Information:
- Journal Volume: 122:3; Journal ID: ISSN 0027-0644

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 54 ENVIRONMENTAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ATMOSPHERIC CIRCULATION; MATHEMATICAL MODELS; EARTH PLANET; REGRESSION ANALYSIS; MATHEMATICS; PLANETS; STATISTICS; 540110*; 990200 - Mathematics & Computers

### Citation Formats

```
Stewart, D.A..
```*Predictability of low-frequency planetary waves in a simple low-resolution model*. United States: N. p., 1994.
Web. doi:10.1175/1520-0493(1994)122<0405:POLFPW>2.0.CO;2.

```
Stewart, D.A..
```*Predictability of low-frequency planetary waves in a simple low-resolution model*. United States. doi:10.1175/1520-0493(1994)122<0405:POLFPW>2.0.CO;2.

```
Stewart, D.A.. Tue .
"Predictability of low-frequency planetary waves in a simple low-resolution model". United States. doi:10.1175/1520-0493(1994)122<0405:POLFPW>2.0.CO;2.
```

```
@article{osti_7162862,
```

title = {Predictability of low-frequency planetary waves in a simple low-resolution model},

author = {Stewart, D.A.},

abstractNote = {A low-resolution global spectral model truncated at zonal wavenumber 5 and meridional mode 15 is developed to simulate the low-frequency variability of planetary-scale atmospheric motions. The effects of unresolved time and space scales on the slow evolution of the flow are deduced by analyzing their contribution to the tendencies of low-pass-filtered planetary-scale modes in a higher-resolution (R15 truncation) version of the model. This unresolved forcing is parameterized by a quasi-stochastic method formulated on the transform grid of the low-resolution model. The deterministic component of the parameterization consists of a linear regression of the unresolved forcing on the resolved forcing, which represents the autonomous dynamics of the low-resolution model. The regression parameters vary spatially and with the dependent variable. The stochastic component of the parameterization consists of kth-order univariate autoregressive statistical models, AR(k), which simulate the residuals from the linear regression. Measured against the spatially and temporally filtered flow from the R15 model, the skill exhibited by the low-resolution model without the parameterization, with the deterministic component only, and with the full parameterization using Ar(2) and Ar(4) model is determined from a set of 30-day simulations. The full parameterization with the AR(4) model demonstrates the greatest skill. For all dependent variables, rms errors are less than their respective saturation values out to 7-11 days. Northern Hemisphere anomaly correlations greater than 0.6 are produced out to 6-8 days, comparable to the low-frequency skill of operational models. 26 refs., 12 figs., 2 tabs.},

doi = {10.1175/1520-0493(1994)122<0405:POLFPW>2.0.CO;2},

journal = {Monthly Weather Review; (United States)},

issn = {0027-0644},

number = ,

volume = 122:3,

place = {United States},

year = {1994},

month = {3}

}