Atmospheric, Non-Tidal Oceanic and Hydrological Loading Effects Observed with GPS Measurements

Monday, 15 December 2014: 8:00 AM
Jean-Paul Boy, EOST École et Observatoire des Sciences de la Terre, Strasbourg Cedex, France, Anthony MEMIN, University of Tasmania, Hobart, TAS, Australia, Christopher Watson, University of Tasmania, Hobart, Australia and Paul Tregoning, Australian National University, Canberra, Australia
Surface displacements due to loading processes can be modeled using atmospheric, non-tidal oceanic and hydrological general circulation model (GCM) outputs convolved with the appropriate Green’s functions, describing the Earth’s elastic response to surface loads. We compute atmospheric loading effects using surface pressure provided by the latest reanalysis (ERA interim) and the operational models of the European Centre for Medium-range Weather Forecasts (ECMWF). A model describing the ocean response to the pressure forcing is required:

- The inverted barometer (IB) hypothesis is usually chosen, but this approximation is valid for periods exceeding typically a week.

- However at higher frequencies, the dynamics of the ocean response cannot be neglected. We use a global barotropic ocean model forced by air pressure and winds, namely the Toulouse Unstructured Grid Ocean model (TUGO-m).

The latest (ECMWF+TUGO-m) computation is incompatible with non-tidal ocean loading estimates with bottom pressure computed with classical baroclinic ocean GCMs (such as ECCO) forced by atmospheric winds, heat and fresh-water fluxes.

We combine the atmospheric loading with the induced oceanic loading estimated from the two models filtering out short and long periods of the ECMWF+IB and EMCWF+TUGO-m loading time series, respectively, and summing the resulting time series; then non-tidal oceanic loading effects can be included in the modeling.

As hydrological loading effects also cause a large part of the seasonal displacements, we add to our loading estimates the modeled displacements using different global hydrology models.

We compare our loading models to a global set of reprocessed GPS observations, and investigate how they can be reduced with the different hypotheses, for example IB vs TUGO-m, looking at different frequency bands (from seasonal to a few days).