Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics
Abstract The operational dynamic subseasonal to seasonal (S2S) models for Madden‐Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators u...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-03-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2020GL091930 |
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| Summary: | Abstract The operational dynamic subseasonal to seasonal (S2S) models for Madden‐Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators using the linear inverse modeling (LIM) approach, we propose a method to partly correct the errors in MJO linear dynamic operators to improve the MJO predictions of three operational dynamic S2S models. Correcting the deficiencies of the too‐fast decay rates and the unrealistic propagating phase speeds lead to MJO prediction skills being extended by approximately 2–4 days. The improvements are more significant for the models with larger biases in MJO amplitude and propagation. This approach in principle may be extendable to predictions of other types of climate variability such as ENSO on one hand, and possible inclusions of nonlinear dynamics effects on the other hand. |
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| ISSN: | 0094-8276 1944-8007 |