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Guillaume Penide, Vickal V. Kumar, Alain Protat, and Peter T. May

atmosphere associated with higher humidity profiles than the other easterly regimes. The ME regime corresponds to break monsoon conditions and occurs on about half of the days during the north Australian wet season. To illustrate the variability and length of each regime, the temporal evolution of the regime classification is shown in Fig. 3 . One can see that the active monsoon regime (DW) and the break periods (ME) are the only two regimes that, once established, tend to last several days. Except

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Damien Specq, Gilles Bellon, Alexandre Peltier, Jérôme Lefèvre, and Christophe Menkes

-state classification performed on daily New Caledonia rainfall, Moron et al. (2016) have even managed to explain the relationships between the local precipitation and the large-scale atmospheric dynamics at various time scales. However, the link between the subseasonal variability and the diurnal cycle of precipitation in New Caledonia has not been investigated so far and the present study intends to fill this gap. The following sections document the influence of the MJO and wind regimes on the diurnal cycle of

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James E. Favors and John T. Abatzoglou

seasonal window of the NAM in the southwestern United States has been referred to a variety of ways in the literature, we constrain our focus to the core monsoonal season defined as 1 July to 15 September (e.g., Adams and Comrie 1997 ) for the 28-yr period (1980–2007). We employ three classes of data in our analysis: (i) hourly surface observations for Yuma from the Western Regional Climate Center ( ), (ii) synoptic and mesoscale reanalysis from the National Centers for

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Gabriele Villarini, Gabriel A. Vecchi, and James A. Smith

frequency and large-scale climate conditions is an essential step in order to improve our predictive and explanatory understanding of TS variations. Multiple studies have associated tropical storm activity with different climate indices, such as Atlantic (e.g., Shapiro and Goldenberg 1998 ; Landsea et al. 1999 ; Vitart and Anderson 2001 ; Emanuel 2005 ; Jagger and Elsner 2006 ; Bell and Chelliah 2006 ; Hoyos et al. 2006 ; Saunders and Lea 2008 ) and tropical (e.g., Latif et al. 2007 ; Vecchi

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Simon Caine, Christian Jakob, Steven Siems, and Peter May

could be performed over a much larger time scale to determine how or if the precipitation regimes change with time and determine any possible links with climate change. Finally it would be very interesting to investigate if models, in particular those that resolve deep convection, are able to reproduce the observed regimes when forced with realistic large-scale conditions. Recent field data will enable such studies in the near future. Acknowledgments We thank Dr. Courtney Schumacher for many

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L. Gustavo Pereira and Steven A. Rutledge

), and the East Pacific Investigation of Climate Processes in the Coupled Ocean–Atmosphere System (EPIC), have identified alternating regional synoptic wind regimes that possess distinct thermodynamic and convective characteristics. It remains unknown how these alternating environmental conditions differentially impact the diurnal cycle of shallow and deep convective clouds on tropical ocean and tropical land locations. Elucidating these impacts may help future studies to shed light on the

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Vickal V. Kumar, Alain Protat, Peter T. May, Christian Jakob, Guillaume Penide, Sushil Kumar, and Laura Davies

. Reeder , 2009a : Regimes of the north Australian wet season . J. Climate , 22 , 6699 – 6715 . Pope , M. , C. Jakob , and M. Reeder , 2009b : Objective classification of tropical mesoscale convective systems . J. Climate , 22 , 5797 – 5808 . Protat , A. , J. Delanoë , P. T. May , J. Haynes , C. Jakob , E. O’Connor , M. Pope , and M. C. Wheeler , 2011 : The variability of tropical ice cloud properties as a function of the large-scale context from ground

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

, all of the presented statistics will be estimated with this validation dataset. The results presented are estimated on the validation dataset. c. Uniformization of the learning dataset A classical difficulty in statistical techniques appears also in the classification problem. If regimes or classes are underrepresented in the dataset used to train the classifier, they have a limited incidence on the classifier design and performance. If the goal of the statistical retrieval is to perform uniformly

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N. Vigaud, A.W. Robertson, and M. K. Tippett

between a week and a season) since the theoretical limit of atmospheric deterministic predictability is also about 10–15 days ( Ghil and Robertson 2002 ). For the Europe–North Atlantic sector, the four-regime wintertime classification of Vautard (1990) is still used as a reference and has recently been used to explain some ECMWF model biases related to blocking transitions and persistence ( Ferranti et al. 2015 ). The use of weather regimes to express forecasts is, however, less common in North

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Heather M. Archambault, Lance F. Bosart, Daniel Keyser, and Anantha R. Aiyyer

. This additional normalization ensures that the NAO and PNA index time series both have unit standard deviation ( Wilks 2006 , p. 49), thereby facilitating the objective identification of regimes in the respective time series. Although there is no universally accepted method for constructing daily NAO and PNA index time series, the NOAA/ESRL/PSD and the NCEP/Climate Prediction Center (CPC) both provide daily NAO and PNA index time series that are used operationally on a widespread basis. As in the

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