Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.

Fission track analyses of ODP Hole 129-800A samples

Four models of fission track annealing in apatite are compared with measured fission track lengths in samples from Site 800 in the East Mariana Basin, Ocean Drilling Program Leg 129, given an independently determined temperature history. The temperature history of Site 800 was calculated using a one-dimensional, compactive, conductive heat flow model assuming two end-member thermal cases: one for cooling of Jurassic ocean crust that has experienced no subsequent heating, and one for cooling of Cretaceous ocean crust. Because the samples analyzed were only shallowly buried and because the tectonic history of the area since sample deposition is simple, resolution of the temperature history is high. The maximum temperature experienced by the sampled bed is between 16°-21°C and occurs at 96 Ma; temperatures since the Cretaceous have dropped in spite of continued pelagic sediment deposition because heat flow has continued to decay exponentially and bottom-water temperatures have dropped. Fission tracks observed within apatite grains from the sampled bed are 14.6 +/- 0.1 µm (1 sigma) long. Given the proposed temperature history of the samples, one unpublished and three published models of fission track annealing predict mean track lengths from 14.8 to 15.9 µm. These models require temperatures as much as 40°C higher than the calculated paleotemperature maximum of the sampled bed to produce the same degree of track annealing. Measured and predicted values are different because annealing models are based on extrapolation of high temperature laboratory data to geologic times. The model that makes the closest prediction is based on the greatest number of experiments performed at low temperature and on an apatite having composition closest to that of the core samples.