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A mathematical connection is established between the ocean near-surface geostrophic kinetic energy and the small-scale variance of its surface height. The latter is defined as the spatial variance of sea surface height inside a given grid box and represents a basic statistical characteristic of the field, necessary for estimating its vulnerability to sampling error. The former is also computed from sea surface height fields and, being an important dynamical attribute of the ocean, is often used to describe its mesoscale variability, or eddy energy. Under the condition of isotropic distribution of mesoscale energy, simple formulas connecting the two are obtained for the long- and short-wave (compared to the grid scale) portions of the ocean power spectrum. Without these simplifying assumptions, a factor depending on the actual location-dependent two-dimensional wavenumber power spectrum enters the equation. Approximations based on the Stammer (1997) one-dimensional power spectrum estimates are developed. They are verified by application to the Ducet et al. (2000) gridded satellite altimetry fields.

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Observations of Sea Surface Temperature (SST) made by merchant ships have been analysed to identify biases which depend on how the measurement was made. SST and other meteorological data from the Comprehensive Ocean-Atmosphere Dataset (COADS) were combined with metadata from the World Meteorological Organisation 'List of Selected, Supplementary and Auxiliary Ships' to give additional information on the methods of measurement. The analysis is complicated by the circular nature of the problem: biases in SST depend on the heat fluxes, which are calculated from the SST. The analysis method uses pairs of co-located SST observations obtained by different measurement techniques: one ship using an insulated bucket to measure the SST and the other reporting the temperature of the engine intake water. A simple physically based model is used to parameterise the expected difference between the two observations based on environmental conditions. The simplest model parameterises the night-time heat loss from bucket-measured SST by the air-sea temperature difference and allows for a constant offset between the two reports. In order to estimate the empirical coefficients in the model it is necessary to account for the error structure of the dataset. Random errors for each variable in the model are calculated from the data, along with correlations between these errors. The effect of the errors and correlations are then removed, and the empirical coefficients derived. The results suggest that night-time bucket SST may be biased cold. The magnitude of the bias varies with the air-sea temperature difference, on average being 0.3°C. The mean offset between the bucket SST and engine intake SST is close to zero, once the cold bias in the bucket SST is accounted for. This contradicts previous studies which concluded that engine intake SST is, on average, biased warm due to heating of the water by the ships engines. There was no evidence for that conclusion. This bias in the bucket-derived SST observations of order a few tenths °C is climatologically significant; the magnitude of the effect will vary with time due to trends in the proportion of reports made by different observing methods.

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The sea ice response of the Arctic Ocean to the Northern Annular Mode (NAM) is studied both in observations and in a numerical ocean general circulation model. The analysis of the observed sea ice concentrations shows the well known seesaw in response between the Labrador Sea and the Greenland and Barents Seas. After band pass filtering the data, it reveals a variation in response in the Greenland Sea between interannual and multidecadal NAM periodicity. In the numerical model experiments idealized NAM-like wind and windstress forcing anomalies of varying periodicity are applied to the model. This setup allows us to investigate variations in the response to the NAM in a controlled environment. The analysis of the numerical experiments reveals a similar change in response in the Greenland Sea as we found in the observational data. The changes in response appear to be caused by a slow oceanic response component which on interannual timescales does not get strong enough to modify the quicker windstress driven response of the sea ice.

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