Trend patterns in global sea surface temperature

Barbosa, Susana; Andersen, Ole

doi:10.1002/joc.1855

Cited by 32 publications

(22 citation statements)

References 20 publications

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“…The striking feature of the method is its ability to systematically extract the trend patterns, albeit small. Application to the global sea surface temperature (SST) data showed that trend EOF analysis has advantages at capturing trend rather than maximum variance patterns over the usual EOF analysis (Barbosa and Andersen 2009). …”

Section: Introductionmentioning

confidence: 99%

“…In particular, when considering low-frequency variability, traditional EOF analysis may encounter technical difficulties: although a trend pattern may be physically relevant, it may not explain a large fraction of variance. As a consequence, the trend signal is often split into different modes (Hannachi 2007; Barbosa and Andersen 2009). To eliminate the aforementioned limitation of conventional EOF analysis, a novel method named trend EOF analysis was suggested by Hannachi (2007) by means of looking for a nonlinear, rank-based modification to the usual EOF analysis.…”

Section: Introductionmentioning

confidence: 99%

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Trend Singular Value Decomposition Analysis and Its Application to the Global Ocean Surface Latent Heat Flux and SST Anomalies

Ren

Zheng

et al. 2011

Journal of Climate

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Given the complexity of trends in the actual climate system, distinguishing between different trends and different trend modes is important for climate research. This study introduces a new method called ''trend singular value decomposition (TSVD) analysis,'' which is designed for systematically extracting coupled trend modes, albeit small, by performing an eigenanalysis of the inverse-rank covariance matrix between two fields. Applications to simple time series models and annual mean surface latent heat flux (LHF) and SST data for 1958-2006 are presented and discussed. Results show that the TSVD analysis can capture different coherent trends into different leading modes. The first TSVD mode between the global LHF and SST anomalies, similar to the first conventional SVD mode, generally represents a large-scale increasing LHF trend induced by a warming SST trend; whereas, interestingly, unlike the second SVD mode that is mainly associated with the familiar ENSO, the second TSVD mode is mainly associated with the Pacific decadal oscillation (PDO). TSVD analysis casts the (global) long-term and (Pacific) decadal trends into the leading two modes, respectively. Compared to SVD analysis, the advantages of the TSVD analysis in detecting coupled low-frequency modes are even more evident in the tropical Pacific (TP), where the coherent trend signals (i.e., the long-term trends and the decadal trends) are smaller than the ENSO-related signals. Thus, TSVD analysis performs better than SVD analysis when focusing on trends rather than on maximum covariance patterns, particularly on relatively small coherent trend patterns, such as the coupled long-term trends and decadal trends in the TP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trend Singular Value Decomposition Analysis and Its Application to the Global Ocean Surface Latent Heat Flux and SST Anomalies

Ren

Zheng

et al. 2011

Journal of Climate

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“…A multivariate trend analysis based on the empirical orthogonal function (hereinafter TEOFA) of ranked anomalies was recently proposed by Hannachi (2007) to perform such a decomposition. Compared to the conventional covariance-based EOF analysis, the superiority of TEOFA in isolating the low-frequency ENSO signal from the long-term trend of global sea surface temperature was recently demonstrated by Barbosa and Andersen (2009). This current study aims at applying this new methodology to the DTR data in Taiwan to detect multiple trends and at examining their teleconnectivity to large-scale circulations in order to answer the aforementioned questions.…”

Section: Introductionmentioning

confidence: 99%

Changes of Diurnal Temperature Range in Taiwan and Their Large-Scale Associations: Univariate and Multivariate Trend Analyses

Weng

2010

Journal of the Meteorological Society of Japan

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By analyzing the long-term variability of diurnal temperature range [DTR; daytime maximum ðT max Þ minus nighttime minimum ðT min Þ] at 21 stations in Taiwan, this study applies univariate analysis, a trend-free prewhitening procedure combined with a modified Mann-Kendall test, and EOF-based multivariate trend analysis (TEOFA) to the ranked station DTR, T max , and T min . To reveal the large-scale associations with the local TEOFA results, this study also uses global gridded T max , T min , nighttime marine air temperature, and sea level pressure (SLP) datasets archived at the U.K. Meteorological O‰ce Hadley Centre. On the basis of the signs and relative magnitudes between the annually-mean T max and T min trends, the stations can be classified into three types through univariate analysis. With a common increasing T min , type-A (type-B) stations have an increasing T min ðT max Þ faster than T max ðT min Þ whereas type-C stations have a decreasing T max . Both type-A and type-C (type-B) thus show(s) a decreasing (an increasing) DTR. For most stations, the increasing T min is the largest in December-February. In contrast, all type-B stations have an increasing T max peaking in June-August. Noticeably, six of nine type-B stations, either in the remote islands or in the seaports, are particularly influenced by the ocean. Three DTR trend modes stand out of TEOFA. The first mode, TEOF 1 , captures increasing DTR trends at four type-B stations and decreasing DTR trends elsewhere. The associated increasing T min ðT max Þ trend is consistent with the increasing (decreasing) clouds during nighttime (daytime) and is well-correlated with the largescale patterns, suggesting that it is part of the global warming scenario. Nevertheless, TEOF 1 also captures a decreasing T max trend in Taiwan's highly developed western plains (i.e., type C) and eastern China where an increasing SLP pattern is observed, implying the anthropogenic forcings on DTR. TEOF 2 (TEOF 3 ) depicts the decadal-to-interdecadal DTR variability in central (northern) Taiwan. Evolution of TEOF 2 shows smaller (larger) amplitude before 1970s (after mid-1980s). The associated large-scale patterns suggest that TEOF 2 (TEOF 3 ) captures the relationship between an intensified (weakened) East Asian winter monsoon and La Niña (El Niñ o)-like condition in eastern (central) equatorial Pacific. Embedded in a Pacific-Japan-like teleconnection pattern, the southwestward intrusion of Pacific subtropical anticyclone in June-August that signifies the weakened southwesterly monsoon is also depicted by TEOF 2 .

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“…Trend extraction is an important topic in climate research (see Hannachi 2007;Barbosa and Andersen 2009;Li et al 2011). Climate scientists perform RTA to study how the trend component M(t) changes over time.…”

Section: Resultsmentioning

confidence: 99%

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

et al. 2015

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Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the ''running trends series,'' which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that RTA might be a useful descriptive tool, but, despite its general use in applied research, precisely what it reveals about the underlying time series is unclear and, as a result, its interpretation is unclear too. This paper contributes to such interpretation in two ways: 1) an explicit formula is obtained for the set of time series with a given series of running trends, making it possible to show that running trends, alone, perform very poorly as summary statistics for univariate time series and time series association; and 2) an equivalence is established between RTA and the estimation of a (possibly nonlinear) trend component of the underlying time series using a weighted moving average filter. Such equivalence provides a solid ground for RTA implementation and interpretation/validation. In this respect, the authors propose as diagnostic tools for RTA 1) the plot of the original series, with RTA trend estimation superposed, 2) the average R 2 value and the percentage of statistically significant running trends across windows, and 3) the plot of the running trends series with the corresponding confidence intervals.

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Trend patterns in global sea surface temperature

Cited by 32 publications

References 20 publications

Trend Singular Value Decomposition Analysis and Its Application to the Global Ocean Surface Latent Heat Flux and SST Anomalies

Trend Singular Value Decomposition Analysis and Its Application to the Global Ocean Surface Latent Heat Flux and SST Anomalies

Changes of Diurnal Temperature Range in Taiwan and Their Large-Scale Associations: Univariate and Multivariate Trend Analyses

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

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