Dias, D. F., A. Subramanian, L. Zanna and A. J. Miller, 2019:
Remote and local influences in forecasting Pacific SST: A Linear Inverse
Model and a multimodel ensemble study
Climate Dynamics, 52, 3183-3201.
Abstract.
A suite of statistical linear inverse models (LIMs) are used to understand
the remote and local SST variability that
influences SST predictions
over the North Pacific region. Observed monthly SST anomalies in the Pacific
are used to construct different regional LIMs for seasonal to decadal predictions.
The seasonal forecast skills of the LIMs are compared to that from three
operational forecast systems in the North American Multi-Model Ensemble
(NMME), revealing that the LIM has better skill in the Northeastern Pacific
than NMME models. The LIM is also found to have comparable forecast skill
for SST in the Tropical Pacific with NMME models. This skill, however, is
highly dependent on the initialization month, with forecasts initialized during
the summer having better skill than those initialized during the winter. The
data are also bandpass ltered into seasonal, interannual and decadal time
scales to identify the relationships between time scales using the structure of
the propagator matrix. Moreover, we investigate the in
uence of the tropics
and extra-tropics in the predictability of the SST over the region. The Extratropical
North Pacific seems to be a source of predictability for the tropics on
seasonal to interannual time scales, while the tropics enhance the forecast skill
for the decadal component. These results indicate the importance of temporal
scale interactions in improving the predictions on decadal timescales. Hence,
we show that LIMs are not only useful as benchmarks for estimates of statistical
skill, but also to isolate contributions to the forecast skills from different
timescales, spatial scales or even model components.
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