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T. Jung, M. J. Miller, T. N. Palmer, P. Towers, N. Wedi, D. Achuthavarier, J. M. Adams, E. L. Altshuler, B. A. Cash, J. L. Kinter III, L. Marx, C. Stan, and K. I. Hodges

provided climate scientists from four centers, namely the Center for Ocean–Land–Atmosphere Studies (COLA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the University of Tokyo, and the Japan Agency for Marine–Earth Science and Technology (JAMSTEC), dedicated access for a 6-month period starting on 1 October 2009 to Athena, a 166-teraflop Cray XT4 system located at the National Institute for Computational Sciences (NICS) in Tennessee, hence, the name Project Athena (Kinter et al. 2011

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David Rodrigues, M. Carmen Alvarez-Castro, Gabriele Messori, Pascal Yiou, Yoann Robin, and Davide Faranda

meaning of these quantities and the way they are computed below. a. Local dimension The Freitas et al. (2010) theorem and its modification in Lucarini et al. (2012) states that the probability p of entering a ball centered on ζ with a radius ε for chaotic attractors obeys a generalized Pareto distribution ( Pickands 1975 ). To compute such probability, we first calculate the series of distances δ [ x ( t ), ζ ] between the point on the attractor ζ and all other points x ( t ) on the

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Jian Li, Haoming Chen, Xinyao Rong, Jingzhi Su, Yufei Xin, Kalli Furtado, Sean Milton, and Nina Li

1. Introduction Extreme precipitation events are of considerable interest because they can have major impacts on the environment, society, and economy ( Sugiyama et al. 2010 ). According to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes are significantly influenced by climate change ( Jiang et al. 2015 ). Severe and extreme precipitation events are more

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Jesús Vergara-Temprado, Nikolina Ban, Davide Panosetti, Linda Schlemmer, and Christoph Schär

information for the results over different seasons. 2. Methods a. Model description For our simulations, we use the Consortium for Small-Scale Modeling Weather and Climate Model (COSMO; Steppeler et al. 2003 ) in its graphics processing unit (GPU) enabled version ( Fuhrer et al. 2014 ; Leutwyler et al. 2016 ). The model is a nonhydrostatic limited-area model that solves the fully compressible governing equations of fluid dynamics on a structured grid using finite difference methods ( Förstner and Doms

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Hugues Goosse and Marika M. Holland

Venegas (1998) have proposed that modifications of ice extent could have a significant influence on the atmospheric circulation. This hypothesis is part of a feedback loop in which large positive ice anomalies are created in the Beaufort Sea during the positive phase of the NAO index. These anomalies are then transported out of the Arctic resulting in a higher-than-normal ice concentration in the Greenland Sea after 3–4 yr. This results in a reduced winter heat flux to the atmosphere in this region

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Christophe Maes, Joël Picaut, and Sophie Belamari

developed by Morcrette (1990) ; deep and shallow convection are based on the mass-flux scheme of Bougeault (1985) and on a simple modification of the Richardson number ( Geleyn 1987 ). The different coefficients employed in the convection and cloudiness treatment are derived from the sensibility analyses detailed in Terray (1998) . The OGCM is based on the Océan Parallélisé (OPA) code ( Madec et al. 1998 ), and the present version is a tropical Pacific basin version adapted by Maes et al. (1997

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Xiaoqing Wu and Stephen Guimond

). Cloud systems over the tropical oceans are known to be essential in assessing the surface energy balance through the modification of surface fluxes from mesoscale and cloud-scale motions, yet they remain subgrid scale in most GCMs. Observational studies using long-term surface meteorological data suggested that the enhancement of surface heat fluxes by atmospheric mesoscale systems can reach as high as 30% of the fluxes (e.g., Esbensen and Reynolds 1981 ; Liu 1988 ; Zhang 1995 ; Esbensen and

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Jin-Yi Yu and Carlos R. Mechoso

extends the work of Ma et al. (1996) in a way that allows for an estimate of the effects of annual variations of Peruvian stratocumulus. Our approach is also based on sensitivity experiments with a CGCM. One of the experiments is a repeat of Ma et al. (1996) , which is required in view of recent modifications in the model’s code. In the others, Peruvian stratocumulus are enhanced either in the first or second-half of the calendar year. Hence, the annual variations of enhanced clouds in these two

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Larry W. O’Neill, Dudley B. Chelton, and Steven K. Esbensen

-scale perturbations in the crosswind and downwind components of the SST gradient, respectively ( Chelton et al. 2001 , 2004 , 2007 ; Chelton 2005 ; O’Neill et al. 2003 , 2005 ). The surface wind stress curl and divergence fields are related linearly to the crosswind and downwind SST gradients, respectively. These curl and divergence dependencies are simulated to varying degrees in numerical weather predication and climate models ( Maloney and Chelton 2006 ; Haack et al. 2008 ) and in regional mesoscale

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James Doss-Gollin, Ángel G. Muñoz, Simon J. Mason, and Max Pastén

-to-seasonal predictions. Fig . 1. Topographical map of the study area. Colors indicate log 10 of elevation (m) from the Global Land 1-km Base Elevation Project (available online at http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NGDC/.GLOBE/.topo/ ). (a) All of South America, with the domains of the LPRB and the domain used for weather typing indicated in red and blue boxes, respectively. (b) As in (a), the LPRB is marked with a red box. (Streamflow time series shown in Fig. 3 were taken from the four stations

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