Warming due to the greenhouse effect can cause an increased melting of glaciers and reduced margins of major ice sheets. In addition, it may cause a potential collapse of inherently unstable ice sheets. For example, the collapse of the Western Antarctic Ice Sheet would result in a several meter rise of the global sea level, which would have enormous societal and economic impact. Our investigation objective of providing a definitive measurement of the global mean sea level requires an accurate knowledge of volume change of the global ice sheets. The mass balance of the ice sheets will be measured by the EOS sensor GLAS, and the currently operating radar altimeters (i.e., ERS-1 and ERS-2) can potentially contribute to this measurement, despite of the much larger footprints of the pulse-limited radar altimeters.
Our goal is to help establish a long-term time series of ice sheet change measurements from radar altimeters (Seasat, Geosat, ERS-1, and ERS-2) to complement the GLAS-measured ice sheet elevations. The following section describes our progress to date in this area of research.188.8.131.52. Radar Altimeter Observed Ice Sheet Elevation Changes
Specific tasks in this area of research during our investigation period have primarily been focused on the computation and verification of improved orbits and media corrections for radar altimeters (i.e., Geosat and ERS-1) which provided and are currently providing measurements of ice sheet elevations.
Improved ephemerides have been computed for ESA's ERS-1 spacecraft and analysis has indicated that the orbit can be computed with a radial accuracy of 6-7 cm rms, with no significant degradation over the Arctic and Antarctic regions [Shum et al., 1993a; Kozel et al., 1994; Kozel, 1995]. Algorithm development was conducted using pressure and temperature model fields from the NMC general circulation model to compute tropospheric delays (dry and wet) for the altimeter instrument over ice. Analysis has shown that the resulting correction is an improvement over the original techniques. The original altimeter release had significant error as a result of using sea level meteorology instead of surface meteorology for the corrections. The improved algorithm was shown to reduce the variances in the ERS-1 altimeter crossover measurement time series over the Greenland and Antarctic ice sheets, supplied to us by J. Zwally at Goddard Space Flight Center and D. Wingham at Mullard Space Science Laboratory at University College London.3.1.8. Data Assimilation for General Ocean Circulation Models
To achieve the investigation goals of improved computation of oceanic angular momentum (see also Sections 3.1.2 and 184.108.40.206) and interpretation of global sea level measurements (Section 3.1.6), one method is to assimilate satellite measurements (heat fluxes, wind stress, sea surface topography, etc.) available from pre-EOS and EOS sensors to improve the current oceanic general circulation models (OGCM).220.127.116.11. Comparison of Ocean Model and Observations
The current computer simulation of eddy-resolving OGCM driven by ECMWF winds and heat fluxes shows good agreement between the predicted model sea surface, and the sea surface observed by TOPEX/Poseidon altimeter measurements, over a broad range of temporal and spatial scales [Fu and Smith, 1995]. In our study, OGCM output for 10 years or longer have been provided to us by R. Smith at Los Alamos and B. Semtner at the Naval Postgraduate School.
Figure 10 shows a comparison of TOPEX/Poseidon observed sea level rms variability (top panel) during 1992-1994, and the predicted sea level variability from the Los Alamos National Laboratory model (bottom panel). The model and the observations compare well in regions of strong currents (Gulf Stream, Kuroshio and the Antarctic Circumpolar Current), however, the model has deficiencies in predicting the exact paths for the Kuroshio Extension and the Gulf Stream [Fu and Smith, 1995], and is missing the East Australian current and the current in the southeastern coast of Africa (Figure 10). Future improvement in OGCM can be achieved by assimilating pre-EOS and EOS measurements into the model to obtain an optimal estimate of the general ocean circulation.