Reemergence mechanisms for North Pacific sea ice revealed through nonlinear Laplacian spectral analysis

Research output: Contribution to journalArticle

Abstract

This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies.

Original languageEnglish (US)
Pages (from-to)6265-6287
Number of pages23
JournalJournal of Climate
Volume27
Issue number16
DOIs
StatePublished - 2014

Fingerprint

spectral analysis
sea ice
sea surface temperature
anomaly
gyre
temperature anomaly
oscillation
Pacific Decadal Oscillation
spring (season)
winter
summer
conditioning
climate modeling
melt
family

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Reemergence mechanisms for North Pacific sea ice revealed through nonlinear Laplacian spectral analysis. / Bushuk, Mitchell; Giannakis, Dimitrios; Majda, Andrew J.

In: Journal of Climate, Vol. 27, No. 16, 2014, p. 6265-6287.

Research output: Contribution to journalArticle

@article{93fe6721daf6473d87f2d15913321661,
title = "Reemergence mechanisms for North Pacific sea ice revealed through nonlinear Laplacian spectral analysis",
abstract = "This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies.",
author = "Mitchell Bushuk and Dimitrios Giannakis and Majda, {Andrew J.}",
year = "2014",
doi = "10.1175/JCLI-D-13-00256.1",
language = "English (US)",
volume = "27",
pages = "6265--6287",
journal = "Journal of Climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "16",

}

TY - JOUR

T1 - Reemergence mechanisms for North Pacific sea ice revealed through nonlinear Laplacian spectral analysis

AU - Bushuk, Mitchell

AU - Giannakis, Dimitrios

AU - Majda, Andrew J.

PY - 2014

Y1 - 2014

N2 - This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies.

AB - This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies.

UR - http://www.scopus.com/inward/record.url?scp=84905917185&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905917185&partnerID=8YFLogxK

U2 - 10.1175/JCLI-D-13-00256.1

DO - 10.1175/JCLI-D-13-00256.1

M3 - Article

AN - SCOPUS:84905917185

VL - 27

SP - 6265

EP - 6287

JO - Journal of Climate

JF - Journal of Climate

SN - 0894-8755

IS - 16

ER -