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M. R. P. Sapiano, J. E. Janowiak, P. A. Arkin, H. Lee, T. M. Smith, and P. Xie

equator crossing times (ECTs) for each of the satellites (note that NOAA-10 is excluded but had an ECT around 0730 LT). Not only do the ECTs vary among the satellites, but each satellite exhibits orbital drift that causes the ECT to vary with time within the record of a single satellite. These lead to artificial variability in the OLR and ultimately in OPI precipitation estimates. The sudden switches between satellites and the effect of the drift can be seen in the time series of tropical OLR

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Gottfried Hänel and Karin Kastner

from the last four decades The total heating of the atmosphere during the daylight period due to absorption of solar radiation by atmospheric particles is of importance regarding the atmospheric radiation budget and thereby long-term weather and climate prediction. For a discussion of this, in Table 3 , we have compiled the variability of the heating in the lowest kilometer of the atmosphere [because of the regression equation (18) ] as well as the variability of the maximum heating rate. In

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Sheldon Bacon, Helen M. Snaith, and Margaret J. Yelland

clearly. The instrument was very stable, with AS varying slowly and by less than 0.003 overall. We cannot tell whether the short-term variability of amplitude, approximately ±0.0005 about the long-term drift, is due to the salinometer or to within-batch SSW variability, or to a combination of both. We use the first comparison set. We denote the label salinity by the batch number and the subscript L, and denote the measured salinity difference by the batch numbers and the subscript M. The inferred

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Craig L. Stevens and Murray J. Smith

. Underestimation of χ T means that all estimates of ε will be a lower bound for larger values ( Gregg 1999 ). The variability of the dissipation-rate estimates for the 30-profile sequences (a single sequence is shown in Fig. 11 ) ranges over nearly two decades at any given depth. The concentration in data points near the surface (and the apparent gap at around 0.1-m depth) is due to the surface being used as the starting point for the depth binning, and there is less cumulative variability at the top of

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Lee-Lueng Fu and Clement Ubelmann

than 20 years ( Fu et al. 2010 ; Willis et al. 2010 ; Hamlington et al. 2012 ). This data record is the driver of the new enterprise of ocean state estimation that produces estimates of the physical state of the ocean from ocean models constrained by a variety of observations ( Wunsch and Heimbach 2013 ). The resulting knowledge of the state of the global ocean and its evolution over decadal scales has provided a framework for assessing the role of the ocean in climate change and the feasibility

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G. Reverdin, F. Marin, B. Bourlès, and P. Lherminier

1. Introduction Data from expendable bathythermographs (XBTs) launched from ships form the core of the subsurface ocean temperature data available between the 1970s and the early 2000s ( Levitus et al. 2005 ). There have been recent indications from studies on ocean heat content variability that the errors in temperature profiles derived from XBTs in the archived datasets are still large, and that they might have evolved in time, inducing, if not corrected, spurious decadal variability ( Willis

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Thomas M. Smith and Richard W. Reynolds

that pass the QC, for two representative decades in the Southern Hemisphere extratropics ( Fig. 1 ). Here individual observations are considered. For both the 1890s and the 1970s, anomalies of ±5 mb are minimally affected, and the frequencies of larger anomalies are only slightly reduced. Note that in the 1970s there are some extreme negative outlier observations that are off the scale. Those outliers are all screened out by the QC. In other times and regions results are similar. The global and

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M. Hamon, G. Reverdin, and P.-Y. Le Traon

anomalous low-frequency variability [e.g., the artificial “global” heat content increase of the 1970s or the recent problems identified in Willis et al. (2009)] . Gouretski and Koltermann (2007) used an ocean climatology based on high-quality data [conductivity–temperature–depth (CTD) and Nansen casts] to identify biases in XBT observations. They found a positive bias by 0.2°–0.4°C on average with some variations from year to year. Based on this study and further comparisons between data types

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R. F. Milliff, P. P. Niiler, J. Morzel, A. E. Sybrandy, D. Nychka, and W. G. Large

from one to tens of hours (e.g., Pierson 1983 ; Austin and Pierson 1999 ), and on spatial scales from tens to hundreds of kilometers. The mesoscale variability often occurs as intermittent and organized fluctuations within identifiable regimes of the synoptic-scale patterns. For example, in Fig. 1 , the surface pressure minimum occurs at about 58°N, 55°W at the center of a characteristic spiral cloud formation. In a mesoscale regime to the southeast, cloud streaks align in the direction of a

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Claire Gourcuff, Pascale Lherminier, Herlé Mercier, and Pierre Yves Le Traon

1. Introduction With more than 15 yr of continuous measurements, sea surface height (SSH) data from satellite altimetry have become a key source of ocean observations, providing complementary information to in situ measurements. The simultaneous satellite missions available since 1992 (up to four) afford an accurate description of the variability of ocean surface currents on weekly to decadal time scales from the mesoscale to basin scale ( Ducet et al. 2000 ). Thanks to the progress made in

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