Editorial Type:
Article
Article Type:
Research Article

Analysis of the Atlantic Meridional Mode Using Linear Inverse Modeling: Seasonality and Regional Influences

Daniel J. Vimont Department of Atmospheric and Oceanic Sciences, and Center for Climatic Research, University of Wisconsin—Madison, Madison, Wisconsin

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Print Publication:
15 Feb 2012
DOI:
https://doi.org/10.1175/JCLI-D-11-00012.1
Page(s):
1194–1212
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Abstract

Predictability and variability of the tropical Atlantic Meridional Mode (AMM) is investigated using linear inverse modeling (LIM). Analysis of the LIM using an “energy” norm identifies two optimal structures that experience some transient growth, one related to El Niño–Southern Oscillation (ENSO) and the other to the Atlantic multidecadal oscillation (AMO)/AMM patterns. Analysis of the LIM using an AMM-norm identifies an “AMM optimal” with similar structure to the second energy optima (OPT2). Both the AMM-optimal and OPT2 exhibit two bands of SST anomalies in the mid- to high-latitude Atlantic. The AMM-optimal also contains some elements of the first energy optimal (ENSO), indicating that the LIM captures the well-known relationship between ENSO and the AMM.

Seasonal correlations of LIM predictions with the observed AMM show enhanced AMM predictability during boreal spring and for long-lead (around 11–15 months) forecasts initialized around September. Regional LIMs were constructed to determine the influence of tropical Pacific and mid- to high-latitude Atlantic SST on the AMM. Analysis of the regional LIMs indicates that the tropical Pacific is responsible for the AMM predictability during boreal spring. Mid- to high-latitude SST anomalies contribute to boreal summer and fall AMM predictability, and are responsible for the enhanced predictability from September initial conditions. Analysis of the empirical normal modes of the full LIM confirms these physical relationships. Results indicate a potentially important role for mid- to high-latitude Atlantic SST anomalies in generating AMM (and tropical Atlantic SST) variations, though it is not clear whether those anomalies provide any societally useful predictive skill.

Corresponding author address: Daniel J. Vimont, Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: dvimont@wisc.edu

Abstract

Predictability and variability of the tropical Atlantic Meridional Mode (AMM) is investigated using linear inverse modeling (LIM). Analysis of the LIM using an “energy” norm identifies two optimal structures that experience some transient growth, one related to El Niño–Southern Oscillation (ENSO) and the other to the Atlantic multidecadal oscillation (AMO)/AMM patterns. Analysis of the LIM using an AMM-norm identifies an “AMM optimal” with similar structure to the second energy optima (OPT2). Both the AMM-optimal and OPT2 exhibit two bands of SST anomalies in the mid- to high-latitude Atlantic. The AMM-optimal also contains some elements of the first energy optimal (ENSO), indicating that the LIM captures the well-known relationship between ENSO and the AMM.

Seasonal correlations of LIM predictions with the observed AMM show enhanced AMM predictability during boreal spring and for long-lead (around 11–15 months) forecasts initialized around September. Regional LIMs were constructed to determine the influence of tropical Pacific and mid- to high-latitude Atlantic SST on the AMM. Analysis of the regional LIMs indicates that the tropical Pacific is responsible for the AMM predictability during boreal spring. Mid- to high-latitude SST anomalies contribute to boreal summer and fall AMM predictability, and are responsible for the enhanced predictability from September initial conditions. Analysis of the empirical normal modes of the full LIM confirms these physical relationships. Results indicate a potentially important role for mid- to high-latitude Atlantic SST anomalies in generating AMM (and tropical Atlantic SST) variations, though it is not clear whether those anomalies provide any societally useful predictive skill.

Corresponding author address: Daniel J. Vimont, Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: dvimont@wisc.edu
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