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Pankajakshan Thadathil, C. C. Bajish, Swadhin Behera, and V. V. Gopalakrishna

1999, scaled up to global deployments by 2004, and achieved its target of 3000 active instruments in 2007 ( Roemmich et al. 2009 ). Observations from this large number of floats have been contributing significantly toward our continuous effort in understanding climate variability. During the last decade, Argo has generated a large volume of data, and more will be generated in the future. Data, in general, are susceptible to errors. In the case of Argo, the susceptibility is higher. Because 90% of

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Brian D. Dushaw

frequency caused by tidal variations in sea surface height was also measured. 4) Tomography was central to the detection of coherent, mode-1, internal-tide radiation far into the interior of the North Pacific ( Dushaw et al. 1995 ; Ray and Mitchum 1996 ) and 5) the determination that, much like the barotropic tides, such variability is predictable over most of the world’s oceans ( Dushaw et al. 2011 ; Dushaw 2015 ). 6) An O (1000)-km-scale tomographic array deployed during 2000 in the equatorial

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T. R. Anoop, V. Sanil Kumar, P. R. Shanas, and Glejin Johnson

. , Daida S. , Schiber D. , Fukada E. , and Sampson C. R. , 2012 : JTWC northern Indian Ocean best track data. Naval Research Laboratory and JTWC, accessed 6 January 2013. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/ioindex.php .] Clark, C. O. , Cole J. E. , and Webster P. J. , 2000 : Indian Ocean SST and Indian summer rainfall: Predictive relationships and their decadal variability . J. Climate , 13 , 2503 – 2519 , doi: 10

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Jennifer C. Wei, Laura L. Pan, Eric Maddy, Jasna V. Pittman, Murty Divarkarla, Xiaozhen Xiong, and Chris Barnet

profiles and other trace gases such as O 3 , CO, CH 4 , CO 2 , HNO 3 , and N 2 O. Trace gas retrievals for nadir-viewing IR instruments are essential for the retrieval accuracy of the primary products: the temperature and moisture profiles ( Duda and Barth 2005 ). Given that these satellite instruments are scheduled to provide soundings for multiple decades, the potential of long-term, high-density ozone and other trace gas information should also be of significant value to the atmospheric chemistry

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G. Reverdin, S. Morisset, J. Boutin, N. Martin, M. Sena-Martins, F. Gaillard, P. Blouch, J. Rolland, J. Font, J. Salvador, P. Fernández, and D. Stammer

1. Introduction Near-surface salinity is largely determined by the global hydrological cycle as well as by the oceanographic circulation and vertical mixing processes ( Schmitt 2008 ). Sparse near-surface salinity observations have been used to detect signatures associated with known modes of climate variability [ Cravatte et al. (2009) ; Singh and Delacroix (2011) in the tropical Pacific; Gordon and Giulivi (2008) for the tropical North Atlantic; and Reverdin (2010) in the North Atlantic

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J. Zeng, Y. Nojiri, P. Landschützer, M. Telszewski, and S. Nakaoka

subtropical and subarctic North Atlantic based on measurements from a volunteer observing ship . J. Geophys. Res. , 111, C06024 , doi: 10.1029/2005JC003101 . Midorikawa, T. , and Coauthors , 2006 : Interannual variability of winter oceanic CO 2 and air-sea CO 2 flux in the western North Pacific for 2 decades . J. Geophys. Res. , 111, C07S02 , doi: 10.1029/2005JC003095 . Monterey, G. , and Levitus S. , 1997 : Seasonal Variability of Mixed Layer Depth for the World Ocean. NOAA Atlas

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Jochen Grandell and Rolf Stuhlmann

horizontal variability provided by the AIRS data is fractal such that it can be downscaled to be representative for the MTG IRS scales of about 4 km. It is important to note that the pseudonoise has a strong biaslike spectrally correlated component, and only a smaller contribution will be similar to a spectrally uncorrelated random noise, which is comparable to the instrument intrinsic noise. It is anticipated that a specifically “diffraction”-designed retrieval algorithm would be capable to correct for

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Kenneth E. Kunkel, Michael A. Palecki, Kenneth G. Hubbard, David A. Robinson, Kelly T. Redmond, and David R. Easterling

1. Introduction Snow plays a critical role in the climate system through its effect on surface albedo and emissivity. It is likely to be a sensitive indicator of climate change in the cold season. Temporal variability in snow properties reflects fluctuations in both precipitation and temperature, sometimes with great sensitivity, and thus can add interpretive information about those elements. Snow on the ground and snowfall have a variety of significant socioeconomic positive and negative

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Matthew D. Lebsock and Kentaroh Suzuki

the dual-frequency (W band/Ka band) scanning Doppler radar for an Aerosol–Cloud Ecosystems (ACE) mission recommended by the 2007 decadal survey ( National Research Council 2007 ). 2. Models a. Cloud model A large-eddy simulation (LES) model ( Matheou and Chung 2014 ) is coupled to a bin microphysical model ( Suzuki et al. 2010 ) and used to produce a simulation of shallow precipitating cumulus. The simulated scenario is from the Rain in Cumulus over the Ocean (RICO) experiment ( Rauber et al. 2007

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S. R. Laney, R. M. Letelier, R. A. Desiderio, M. R. Abbott, D. A. Kiefer, and C. R. Booth

substantial mesoscale variability occurs in the chlorophyll fluorescence efficiency ( Abbott et al. 2000) . In contrast to the comparatively sizeable amount of sun-stimulated fluorescence observations, to the best of our knowledge no detailed laboratory studies of natural fluorescence have been published. Controlled experiments using phytoplankton cultures are essential for understanding how ocean physics and chemistry influence the quantum yield of natural fluorescence and thus how variability in

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