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Peter C. Chu

1. Introduction Complexity in climate systems makes prediction difficult. One way to simplify the climate systems is to represent low-frequency variability of atmospheric circulations by teleconnection patterns, such as the Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), Pacific–North American pattern (PNA), and Southern Oscillation (SO). Temporally varying indices, s ( t ), were calculated for these patterns, where t denotes time. Among them, the SO

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Anthony Arguez, Peng Yu, and James J. O’Brien

expected variance. (Subroutines of these techniques are available via in the IDL programming language.) The techniques are tested on daughter time series (created using a Monte Carlo technique) of three well-known climate modes: the El Niño–Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the MJO. For the ENSO project, empirically determined filter weights are also presented, representing a minimum bound for estimation error. In addition to

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Wen-Yu Huang, Bin Wang, Li-Juan Li, and Yong-Qiang Yu

LICOM2 uses an artificial island in the Arctic Ocean from 88° to 90°N. Currently, grid transformation techniques are widely used in global ocean models for treating the polar singularity or for including the whole Arctic Ocean: 1) a combination of two coordinate systems ( Coward et al. 1994 ; Eby and Holloway 1994 )—that is, two grid systems are patched together with a suitable boundary; 2) a dipole or tripole grid system ( Murray 1996 ; Murray and Reason 2001 )—that is, a grid with two or three

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P. Wadhams, J. P. Wilkinson, and A. Kaletzky

atmospheric changes associated with the Arctic Oscillation (AO) ( Thompson and Wallace 1998 ), and that these will reverse when the AO itself changes phase. To resolve this question, it is essential to monitor the Arctic ice cover on a seasonal and interannual basis. This is easy from the point of view of extent, because of the availability of satellites, but difficult from the point of view of thickness. Current methods of ice thickness monitoring have been reviewed by Wadhams (2000) . Essentially, the

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Mark D. Orzech, Fengyan Shi, Jayaram Veeramony, Samuel Bateman, Joseph Calantoni, and James T. Kirby

oscillation was computed utilizing the mean period and amplitude decay rate of the block and is included in the figure for comparison. As the figure illustrates, the block’s oscillation period was somewhat lengthened during the early cycles ( T = 2.4 s) then gradually stabilized to T ≈ 2.2 s as the amplitude decreased. This is consistent with the experimental results (e.g., Chung 2008 ), which indicate that the period of an oscillating object at the water surface is greater for larger displacements

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H. Vömel, V. Yushkov, S. Khaykin, L. Korshunov, E. Kyrö, and R. Kivi

measurements in the lower troposphere, leading to extreme oscillations of the mirror temperature, since the feedback controller was unable to maintain a constant frost layer. Measurements made while the controller was unstable or during the period of gain change are flagged and ignored in the data analysis. The controller stability is the largest source of uncertainty in frost-point measurements. The combined uncertainty of controller stability, mirror-temperature measurement, and electronic uncertainties

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Alice C. Bradley, Scott Palo, Gabriel LoDolce, Doug Weibel, and Dale Lawrence

ice velocity. National Snow and Ice Data Center, Boulder, CO, accessed 1 September 2014 , doi: 10.7265/N53X84K7 . Rigor, I. G. , Wallace J. M. , and Colony R. L. , 2002 : Response of sea ice to the Arctic Oscillation . J. Climate , 15 , 2648 – 2663 , doi: 10.1175/1520-0442(2002)015<2648:ROSITT>2.0.CO;2 . Insitu Inc. , 2011 : ScanEagle backgrounder. Doc. PR092211, accessed 23 February 2013, 7 pp . Squire, V. , 2007 : Of ocean waves and sea-ice revisited . Cold Reg. Sci. Technol

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Mario Brito, Gwyn Griffiths, James Ferguson, David Hopkin, Richard Mills, Richard Pederson, and Erin MacNeil

1. Introduction An important category of missions for deep-diving long-range autonomous underwater vehicles (AUVs) is to carry out observations that cannot be conducted by ships or any other instruments. Unlike smaller AUVs, these large AUVs typically operate in uncertain environments, well beyond acoustic range, for example, under sea ice ( Ferguson et al. 1999 ) or in complex seabed terrain ( McPhail 2009 ). In the Arctic, gathering information from beneath sea ice has long had scientific

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Alexander P. Trishchenko, Louis Garand, and Larisa D. Trichtchenko

1. Introduction It has been previously shown that the two-satellite system on a Molniya-type highly elliptical orbit (HEO) with a 12-h period can provide continuous observations of the Arctic region above approximately 58° and 38°N for the maximum viewing zenith angles of 70° and 90°, respectively ( Trishchenko and Garand 2011 ). In other words, one or two satellites are seen above the horizon (at 0° elevation) or at 20° elevation at any particular point in time within latitude circles of 38

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Anna P. M. Michel, David J. Miller, Kang Sun, Lei Tao, Levi Stanton, and Mark A. Zondlo

, permafrost peatlands, and termites ( Anderson et al. 2010 ). Global climate change is accelerating natural CH 4 emissions in arctic regions with organic carbon-rich permafrost ( Cole et al. 2007 ; IPCC 2013 ). These arctic CH 4 emissions are highly uncertain due to large spatial and temporal variability and a lack of field measurements ( Walter et al. 2007 ; Walter-Anthony and Anthony 2013 ). Ground-based CH 4 measurement techniques will ideally be capable of high precision and stability with high

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