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Jérôme Sirven, Christophe Herbaut, Julie Deshayes, and Claude Frankignoul

1. Introduction There is increasing evidence that the decadal variability of the midlatitude ocean (e.g., Kushnir 1994 ; Deser et al. 1999 ) primarily reflects the variability of the atmosphere via stochastic wind stress forcing. Considering a stratified model with an open western boundary, Frankignoul et al. (1997) found that the oceanic response was largest at decadal frequency, but no spectral peak appeared (see also Sirven et al. 2002 ). LaCasce (2000) and Cessi and Louazel (2001

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Weiqing Han, Jérôme Vialard, Michael J. McPhaden, Tong Lee, Yukio Masumoto, Ming Feng, and Will P.M. de Ruijter

Improved definition and understanding of decadal timescale variability in the Indian Ocean region will support climate prediction efforts and have the potential to benefit a large percentage of the world's population living in Indian Ocean rim countries and elsewhere around the globe. Existing records of upper-ocean temperature exhibit clear fluctuations at time scales ranging from one to a few decades, which for simplicity we refer collectively to as “decadal variability” in this paper 1 (see

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Greg Kociuba and Scott B. Power

( Bellenger et al. 2014 ; Stoner et al. 2009 ; Newman et al. 2003 ; Smith and Sardeshmukh 2000 ; Trenberth and Hoar 1996 , 1997 ). Here we consider the level of interannual variability and the degree of persistence that exists in the interannual variability. The larger the interannual variability and the larger the persistence, then the larger the trends can be. Here we will examine the level of interannual variability. We will focus here on the standard deviation (σ) of both interannual and decadal

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Hui Shi, Bin Wang, Jian Liu, and Fei Liu

). Since the decadal to centennial variations and responses to external forcing often occur beyond regional scales, it is more proper to look at larger spatial scale rainfall reconstructions for detecting coherent changes of the EASM and ISM, as well as adjacent regions in Asia. This has not been done. Among the major challenges to understand decadal to multidecadal climate variability are to distinguish whether such changes arise from internal coupled dynamic modes or are driven by forcings external

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Changgui Lin, Kun Yang, Jun Qin, and Rong Fu

1. Introduction Widespread surface wind speed declines have been observed from ground measurements over the past few decades in many tropical and midlatitude regions including China ( McVicar et al. 2012 , and references therein). These declines of surface wind speed may contribute to declines of atmospheric potential evaporation, as measured by pan evaporation ( Chen et al. 2006 ; McVicar et al. 2012 ; Roderick et al. 2007 ), and the weakening trend in atmospheric sensible heat over some

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Ben P. Kirtman and Paul S. Schopf

. 1995 ), the Zebiak and Cane (1987) model has lower skill during the 1990s. Similar decadal variability in forecast skill has also been detected in the Kirtman et al. (1997) prediction system and by Balmaseda et al. (1995) in their hybrid coupled model. The contrast in prediction skill between the 1980s and the early 1990s is quite clear. What remains unclear is whether this contrast was a statistical fluke or whether there was some fundamental change in the coupled ENSO system. The results

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Rym Msadek, T. L. Delworth, A. Rosati, W. Anderson, G. Vecchi, Y.-S. Chang, K. Dixon, R. G. Gudgel, W. Stern, A. Wittenberg, X. Yang, F. Zeng, R. Zhang, and S. Zhang

al. 2012 ). This is consistent with a number of studies that point to the North Atlantic SPG as a region where internal variability is the main driver of decadal fluctuations ( Lozier et al. 2008 ; Ting et al. 2009 ; Terray 2012 ). A strong warming of the North Atlantic SPG was observed in the mid-1990s, with surface temperatures increasing by more than 1°C in less than five years ( Robson et al. 2012a ). Observations also reveal a weakening and a westward contraction of the SPG as the North

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Wenchang Yang, Richard Seager, Mark A. Cane, and Bradfield Lyon

.05 level. In this paper, we will investigate the decadal variability of the East African long rains and its relationship with SSTs in observations and compare them with a series of model simulations, including the International Research Institute for Climate and Society (IRI) forecast models, SST-forced models from the CMIP5 Atmospheric Model Intercomparison Project (AMIP) experiments, and the CMIP5 historical simulations (coupled models). The key questions include the following: What is the character

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Franco Biondi, Alexander Gershunov, and Daniel R. Cayan

1. Introduction Ice-free waters of the Pacific Ocean cover about one-third of earth’s surface, an area larger than all land masses combined. The global impact of Pacific-wide interannual anomalies has been clearly demonstrated by the far-reaching effects of El Niño–Southern Oscillation (ENSO; Glantz 1996 ). It is now clear that Pacific climate also undergoes decadal-scale shifts ( Trenberth and Hurrell 1994 ) as part of a coherent interdecadal mode of variability ( Latif and Barnett 1994

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Álvaro González-Reyes, James McPhee, Duncan A. Christie, Carlos Le Quesne, Paul Szejner, Mariano H. Masiokas, Ricardo Villalba, Ariel A. Muñoz, and Sebastián Crespo

–50 and 1985–2000 periods, using 16-yr running blocks. However, a greater correlation between IPO and the southern subregion is present beginning in the 1970s ( Fig. 5c ). Despite this, the decadal and multidecadal variability related to the PDO and IPO shows general nonsignificant relationships over the analyzed period. Fig . 5. (a) Correlation coefficients between the northern and southern subregional hydroclimatic series and the winter (May–August) Niño-3.4 SST using a 16-yr running block. (b

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