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T. F. Pinheiro, M. I. S. Escada, D. M. Valeriano, P. Hostert, F. Gollnow, and H. Müller

, with the fourth highest deforestation rate according to the last estimate ( INPE 2013 ). Approximately 14.25% (5441 km 2 ) of the forests in this county have been converted to other land-cover types ( INPE 2013 ), primarily cattle production ( IBGE 2015 ; INPE 2010 ). Figure 1. Study site location (Landsat path 227 and row 65) within the Amazon state of Pará (PA), Brazil. 3. Methodology 3.1. Landsat imagery We used Landsat Thematic Mapper (TM) images (path 227, row 65) from 1984 to 2011 to

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R. Garreaud, M. Falvey, and A. Montecinos

passage of frontal systems embedded in midlatitude cyclones. To this end, we use an enhanced network of surface observations, a full-physics simulation using the Weather Research and Forecasting (WRF) Model, and results from a linear theory (LT) model developed by Smith and Barstad (2004) . Details on the observations and models are provided in section 2 . A geographical background and meteorological context is provided in section 3 and appendix A . A description of the orographic precipitation

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Stephanie N. Stevenson, Kristen L. Corbosiero, and Sergio F. Abarca

lightning (cloud-to-ground and intracloud) activity over the open oceans. The OTD instrument observed cloud-top lightning illumination with a 46%–69% detection efficiency and 20–40-km location accuracy during 1995–2000 ( Boccippio et al. 2000 ). The LIS instrument, a follow-on to the OTD sensor, collected data during 1998–2015 with a detection efficiency of 62%–97% depending on the time of day ( Boccippio et al. 2002 ). Cecil and Zipser (1999) analyzed the relationship between satellite

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Cristian Martinez-Villalobos and Daniel J. Vimont

shown to influence meridional mode variation. The mean state can influence meridional mode variations through the ITCZ structure, the mean trade wind strength and location, and the variation in stochastic forcing that is likely required for meridional mode variations in nature ( Xie 1999 ; Vimont 2010 ). The mean ITCZ structure can affect how the atmosphere responds to tropical SST variations via either deep heating ( Gill 1980 ; Zebiak 1986 ; Battisti et al. 1999 ) or boundary layer convergence

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Joseph T. Jurisa, Jonathan D. Nash, James N. Moum, and Levi F. Kilcher

location in (c). Profile 1 is an initial profile, and profile 2 is located further offshore and is the result of mixing profile 1. In (a) and (b), h 1 and h 2 mark the plume depth before and after a mixing event. The purpose of this study is to examine the relationship between ε , S 2 , and N 2 to quantify the effectiveness of various parameterized forms for the dissipation rate and to assess how these act to set the structure and composition of an energetic tidal river plume. Previous attempts

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Clara Orbe, Paul A. Newman, Darryn W. Waugh, Mark Holzer, Luke D. Oman, Feng Li, and Lorenzo M. Polvani

quantify the fraction of air at location r that last contacted the PBL over the origin region Ω i . (Note that the term “origin” is used in reference to the region where air last contacted the PBL.) In practice f ( r | Ω i ) is calculated as a simple equilibrated tracer mixing ratio that shows where in the Arctic, and with what dilution, the air from an origin region can be found. Air-mass origin climatologies for NH winter [December–February (DJF)] and NH summer [June–August (JJA)] were presented

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Wilken-Jon von Appen, Ursula Schauer, Tore Hattermann, and Agnieszka Beszczynska-Möller

( Beszczynska-Möller et al. 2015 ; Bauerfeind et al. 2015 ). Table 1. Deployment details of the 19 moorings considered in this study. Over the years, the mooring and instrument locations slightly varied as indicated by the ranges. The ranges of the instrument depths for the upper level and the middepth level are given in the table and in the text these levels are called 75-m level and 250-m level for ease of notation. The duration of the temperature and velocity measurements is given as is the duration of

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Roop Saini, Guiling Wang, and Jeremy S. Pal

with RegCM. The locations of the strongest soil moisture anomalies generally agree well between RegCM and GLDAS, with exceptions in the early spring months of 1988 and 2012 over Mexico and part of the southern Great Plains, where RegCM produces a strong wet signal while GLDAS data indicate a drought signal. The large magnitude of spring–summer soil moisture anomalies over the central United States makes it possible for soil moisture–precipitation feedback to contribute to the development of summer

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Irena Vaňková and David M. Holland

2014. The first mooring was placed at the northern end of the fjord and the remaining four were spaced in intervals of 10.5, 12.1, 17.8, and 13.8 km, respectively, to cover the full length of the fjord. The moorings were placed along the deeper western side of the fjord at depths of 595, 638, 683, 880, and 908 m, respectively. Positions of the moorings are shown in Fig. 1 . The moorings were recovered within 100 m of the location where they were deployed the year before. Each mooring was designed

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J. H. LaCasce, R. Ferrari, J. Marshall, R. Tulloch, D. Balwada, and K. Speer

in the Southern Ocean is also strongly anisotropic because of the influence of the Antarctic Circumpolar Current (ACC). Diffusivity estimates in the direction parallel to the current typically exceed those in the perpendicular direction ( section 4a ). Determining along-stream diffusivities requires removing the mean contribution, usually by averaging drifter velocities in geographical bins; the diffusivities are then calculated from the residuals ( Davis 1991 ). However, a perfect mapping of the

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