Time–Frequency Variability of ENSO and Stochastic Simulations

Kestin, Tahl; Karoly, David J.; Yano, Jun–Ichi; Rayner, N. A.

doi:10.1175/1520-0442(1998)011<2258:tfvoea>2.0.co;2

Cited by 147 publications

(118 citation statements)

References 16 publications

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“…In addition, long integrations of this stochastic model show that there is a decadal variation of its spectrum qualitatively similar to the observations seen in figure 2. Such agreement is not surprising as it may also be observed in very simple stochastically forced oscillators as demonstrated in figure 9 of Kestin et al (1998) for the case of an AR(3) process. The variance growth in time predicted by this theory of ENSO indices has the form of rather rapid growth for the first six to nine months with considerably slower growth subsequently.…”

Section: Stochastically Forced Model Resultssupporting

confidence: 75%

Stochastic theories for the irregularity of ENSO

Kleeman

2008

Phil. Trans. R. Soc. A.

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The El Niño/Southern Oscillation (ENSO) phenomenon is the dominant climatic fluctuation on interannual time scales. It is an irregular oscillation with a distinctive broadband spectrum. In this article, we discuss recent theories that seek to explain this irregularity. Particular attention is paid to explanations that involve the stochastic forcing of the slow ocean modes by fast atmospheric transients. We present a theoretical framework for analysing this picture of the irregularity and also discuss the results from a number of coupled ocean-atmosphere models. Finally, we briefly review the implications of the various explanations of ENSO irregularity to attempts to predict this economically significant phenomenon.

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Section: Stochastically Forced Model Resultssupporting

confidence: 75%

Stochastic theories for the irregularity of ENSO

Kleeman

2008

Phil. Trans. R. Soc. A.

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“…Zhou and Lau 2001). The first mode of variability shows spatial patterns consistent with Weare et al (1976) and Rasmusson and Carpenter (1982) and time variability similar to Kestin et al (1998) and Sperber (2002, 2006) (not shown).…”

Section: Relationship Between Sad and Samssupporting

confidence: 70%

“…AchutaRao and Sperber (2006) verified that in the NIÑ O3 region, 75% of the WCRP-CMIP3 models succeed in reproducing the spectral maximum of the observed SST, which occurs between 2 and 7 years (e.g. Kestin et al 1998;AchutaRao and Sperber 2002). In addition, Vera and Silvestre (2009) showed that most WCRP-CMIP3 models are not able to reproduce the PSA pattern, which suggests a low skill of these models in reproducing large scale patterns of precipitation variability on interannual time scales in South America.…”

Section: Introductionmentioning

confidence: 77%

The South Atlantic dipole and variations in the characteristics of the South American Monsoon in the WCRP-CMIP3 multi-model simulations

Bombardi

Carvalho

2010

Clim Dyn

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This study investigates relationships between Atlantic sea surface temperature (SST) and the variability of the characteristics of the South American Monsoon System (SAMS), such as the onset dates and total precipitation over central eastern Brazil. The observed onset and total summer monsoon precipitation are estimated for the period 1979-2007. SST patterns are obtained from the Empirical Orthogonal Function. It is shown that variations in SST on interannual timescales over the South Atlantic Ocean play an important role in the total summer monsoon precipitation. Negative (positive) SST anomalies over the topical South Atlantic along with positive (negative) SST anomalies over the extratropical South Atlantic are associated with early (late) onsets and wet (dry) summers over southeastern Brazil and late (early) onset and dry (wet) summers over northeastern Brazil. Simulations from Phase 3 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP-3) are assessed for the 20th century climate scenario . Most CMIP3 coupled models reproduce the main modes of variability of the South Atlantic Ocean. GFDL2.0 and MIROC-M are the models that best represent the SST variability over the South Atlantic. On the other hand, these models do not succeed in representing the relationship between SST and SAMS variability.

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“…The time series for this region is shown for 1875 onward, because there are very few data in this region prior to this. The new data confirm the relatively large ENSO fluctuations before 1920, followed by the reduction in variance between 1920 and about 1980 and subsequent increase during the last 30 yr seen, for example, in Kestin et al (1998). In this particular area of the central and east tropical Pacific, the pre-1920 fluctuations are as large as those of recent decades, allowing for the modest warming tendency, so do not provide evidence of a long-term increase in ENSO variance.…”

Section: B Global and Hemispheric Averagessupporting

confidence: 60%

Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset

et al. 2006

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'Improved analyses of changes and uncertainties in sea surface temperature measured in situ sice the midnineteenth century: The HadSST2 dataset' Journal of Climate, vol. 19, no. 3, General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. (HadSST2)] is based on data contained within the recently created International Comprehensive OceanAtmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-correctionrelated uncertainties with those arising from measurement errors and undersampling of intragrid box variability. These are calculated by partitioning the variance in grid box averages between real and spurious variability. From month to month in individual grid boxes, sampling uncertainties tend to be most important (except in certain regions), but on large-scale averages bias-correction uncertainties are more dominant owing to their correlation between grid boxes. Changes in large-scale SST through time are assessed by two methods. The linear warming between 1850 and 2004 was 0.52°Ϯ 0.19°C (95% confidence interval) for the globe, 0.59°Ϯ 0.20°C for the Northern Hemisphere, and 0.46°Ϯ 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67°Ϯ 0.04°C, 0.71°Ϯ 0.06°C, and 0.64°Ϯ 0.07°C.

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Time–Frequency Variability of ENSO and Stochastic Simulations

Cited by 147 publications

References 16 publications

Stochastic theories for the irregularity of ENSO

Stochastic theories for the irregularity of ENSO

The South Atlantic dipole and variations in the characteristics of the South American Monsoon in the WCRP-CMIP3 multi-model simulations

Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset

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