Forecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures

Journal article

Abstract:: An empirical statistical model is constructed to assess the forecast skill and the linear predictability of Atlantic Ocean sea surface temperature (SST) variability. Linear inverse modeling (LIM) is used to build a dynamically based statistical model using observed Atlantic SST anomalies between latitudes 20 degrees S and 66 degrees N from 1870 to 2009. LIM allows one to fit a multivariate red-noise model to the observed annually averaged SST anomalies and to test it. Forecast skill is assessed and is shown to be O(3-5 yr). After a few years, the skill is greatly reduced, especially in the subpolar region. In the stable dynamical system determined by LIM, skill of annual average SST anomalies arises fromfour damped eigenmodes. The four eigenmodes are shown to be relevant in particular for the optimal growth events of SST variance, with a pattern reminiscent of the low-frequency mode of variability, and in general for the predictability and variability of Atlantic SSTs on interannual time scales. LIM might serve as a useful benchmark for interannual and decadal forecasts of SST anomalies that are based on numerical models. Copyright 2012 American Meteorological Society.

Files:: jcli-d-11-00539%2E1.pdf

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Language:: English
Keywords:: Climate variability

Climate prediction

Singular vectors

North Atlantic Ocean

Statistical forecasting
Pubs id:: pubs:199395
UUID:: uuid:0d66f862-0dd3-4e63-b82e-975541514de8
Local pid:: pubs:199395
Source identifiers:: 199395
Deposit date:: 2012-12-19

Copyright holder:: American Meteorological Society
Notes:: Copyright 2012 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be "fair use" under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC section 108, as revised by P.L. 94-553) does not require the AMS's permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected].

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