9 research outputs found
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Influence of systematic error correction on the temporal behavior of an ocean model
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Use of bias-aware data assimilation to improve the GOCINA mean dynamic topography
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Salinity assimilation using S(T) relationships. Part 1: Theory
Assimilation of salinity into ocean and climate general circulation models is a very important problem. ARGO data now provide far more salinity observations than ever before. In addition a good analysis of salinity over time in ocean reanalyses can give important results for understanding climate change. Here we show from the historical ocean database that over large regions of the globe (mainly mid and lower latitudes) variance of salinity on an isotherm S(T) is often less than variance measured at a particular depth S(z). We also show that the dominant temporal variability of S(T) is slower than S(z) based on power spectra from the Bermuda timeseries, and from ocean models we show that the horizontal spatial covariance of S(T) often
has larger scales than S(z).
These observations suggest an assimilation method based on analysing S(T). We present an algorithm for applying S(T) assimilation and show how it can be made orthogonal to the multivariate assimilation of temperature data which produces its own salinity correction. We argue that the larger space and timescales should allow larger gain matrices to be used for the S(T) assimilation leading to better use of scarce salinity observations.
Finally we show results of applying the salinity assimilation algorithm to a single analysis time within the
ECMWF seasonal forecasting ocean model. The separate salinity increments coming from temperature and salinity data are identified and the independence of these increments is demonstrated. Results of an ocean reanalysis with this method will appear in a companion paper
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Combining altimetric/gravimetric and ocean model mean dynamic topography models in the GOCINA region
Initially, existing mean dynamic topography (MDT) models were collected and reviewed. The models were corrected for the differences in averaging period using the annual anomalies computed from satellite altimetry. Then a composite MDT was derived as the mean value in each grid node together with a standard deviation to represent its error. A new synthetic MDT was obtained from the new mean sea surface (MSS) KMS04 combined with a regional geoid updated using GRACE gravity and gravimetric data from a recent airborne survey. Compared with the composite MDT the synthetic MDT showed very similar results.
Then combination methods were tested for the computation of MDT models from gravity data and MSS data. Both a rigorous and an iterative combination method have been tested in the GOCINA region. At this stage, the iterative combination method with its efficient handling of large data sets covering the whole region appears to give the best solution. Naturally, the errors associated with the MDT can be obtained using the rigorous method onl