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Christopher L. Brest, William B. Rossow, and Miriam D. Roiter

, since we only remove a single linear calibration trend over the lifetime of each radiometer, we have not arbitrarily removed interannual variability (although the NOAA-11 case remains in doubt). Finally, because we use only the surface radiances, we have not removed any long-term changes in the cloud properties that might appear in the ISCCP analysis results. By adjusting the AVHRR calibrations to produce global annual mean VIS and IR radiances that are constant over more than a decade, the

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K. G. Hubbard, S. Goddard, W. D. Sorensen, N. Wells, and T. T. Osugi

variance allows Eq. (1) to again be used, and an analysis of the data (1971–2000) determines the relationship between f and the potential Type I errors for the SC test. The persistence test checks the variability of the measurements. When a sensor fails it will often report a constant value; thus the standard deviation ( σ ) will become smaller, and if the sensor is out for an entire reporting period, σ will be zero. In other cases the instrument may work intermittently and produce reasonable

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James O’Donnell, Arthur A. Allen, and Donald L. Murphy

sensitive to the functional form selected for F d ( u ), the uncertainty in the estimated upper bound for the slip velocity was at least 0.04 m s −1 . Further, because of the variability in the observed speeds of drift in the North Atlantic, Poulain and Niiler (1990) argued that neither the linear or quadratic drogue drag model could legitimately be rejected statistically. Since the results of the combination of simple theories and experiments to estimate the performance of spar buoys with window

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Gregory C. Johnson, John M. Lyman, and Sarah G. Purkey

estimated from repeated measurements in the deep Greenland Sea ( Somavilla et al. 2013 ). This latter trend is similar in magnitude to the global average trend in sea surface temperature warming from 1970 to 2014 (~115 m °C decade −1 ) using the NOAA ERSST analysis ( Smith et al. 2008 ). Deep variability in temperature and salinity can reflect variations in deep convection that connect the substantial heat capacity of the deep ocean directly to the ocean surface and also reflect changes in circulation

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John E. Frederick, Xufeng Niu, and Ernest Hilsenrath

-year datasetcorrected for instrument drifts by comparison with aseries of SSBUV flights. The first task involves simulating a ten-year dataset of true backscatter ratios,for use in (4) and ( 6 ). For this we used measurementsmade near the equator made by the Nimbus 7 SBUVinstrument. We first detrended the dataset for the fiveyear period 1979 through 1983 inclusive, and then repeated the time series to simulate a full decade. Thesebackscatter ratios include all annual, interannual, andrandom variability, but contain

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Nadya T. Vinogradova and Rui M. Ponte

been dedicated to estimating and reducing the error budgets for Aquarius (e.g., Le Vine and Abraham 2004 ; Le Vine et al. 2005 ). One potential error that remains poorly understood is related to the Aquarius sampling rate. The orbit repeat cycle of the Aquarius satellite is 7 days, with an irregular sampling interval that can vary with location from daily to once in 7 days, implying that variability at periods <14 days can alias into longer periods. Thus, the question arises as to whether

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Christophe Maes, David Behringer, Richard W. Reynolds, and Ming Ji

observations depending on whether T/P sea level data are assimilated ( Ji et al. 2000 ). The origin of these differences is believed to be related to the uncorrected salinity field in the ocean model. The use of sea level in an assimilation system that corrects only the temperature field neglects the fact that sea level is determined by salinity as well as temperature. In the western tropical Pacific Ocean, the influence of salinity on sea level variability is strong enough to be detectable by an altimeter

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Johanna Baehr, David McInerney, Klaus Keller, and Jochem Marotzke

), suggesting that this monitoring strategy would be able to capture the main features of the time mean and the variability of the MOC at 26°N, but not at 53°N. Figure 1 shows the resulting MOC reconstructions compared to the original (model) MOC at 1000 m for both latitudes, based on a simulated measurement at every grid cell, that is, the maximum number of profiles ( n = n max ; n max ≈ 200 for 26°N, and n max ≈ 140 for 53°N). Each profile simulates a full-depth mooring, measuring temperature

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Florent Gasparin, Dean Roemmich, John Gilson, and Bruce Cornuelle

1. Introduction Observing and modeling the tropical Pacific are crucial for describing and predicting the evolution of the El Niño–Southern Oscillation (ENSO) phenomena in addition to tropical, regional-to-global scale, decadal variability, and multidecadal change. The interannual variability of the ocean–atmosphere system is associated with a combination of processes having different spatial and temporal scales ( Kessler and Kleeman 2000 ; Roundy and Frank 2004 ; Eisenman et al. 2005 ), and

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Norbert Didden and Detlef Stammer

midlatitude North Atlantic are analyzed with andwithout water vapor (WV) corrections to study the WV influence on along-track SSH anomaly profiles, mesoscaleSSH variability, wavenumber spectra, and objectively mapped fields of SSH anomaly. Three different WVdatasets were used, one from the Fleet Numerical Oceanographic Center (FNOC) model and two from theSpecial Sensor Microwave/lmager (SSM/I) based on different WV retrieval algorithms. These WV datasetsshow significant differences, in particular in the

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