Sea level variations prior to the launch of satellite altimeters are estimated by analyzing historic tide gauge records. Recently, a number of groups have reconstructed sea level by applying EOF techniques to fill missing observations. We complement this study with alternative methods. In a first step gaps in 178 records of sea level change are filled using the pattern recognition capabilities of artificial neural networks. Afterward satellite altimetry is used to extrapolate local sea level change to global fields. Patterns of sea level change are compared to prior studies. Global mean sea level change since 1900 is found to be inline image on average. Local trends are essentially positive with the highest values found in the western tropical Pacific and in the Indian Ocean east of Madagascar where it reaches about inline image. Regions with negative trends are spotty with a minimum value of about inline image south of the Aleutian Islands. Although the acceleration found for the global mean, inline image, is not significant, local values range from inline image in the central Indian Ocean to inline image in the western tropical Pacific and east of Japan. These extrema are associated with patterns of sea level change that differ significantly from the first half of the analyzed period (i.e., 1900–1950) to the second half (1950–2000). We take this as an indication of long period oceanic processes that are superimposed to the general sea level rise.
Helmholtz Research Programs > PACES II (2014-2018) > TOPIC 3: The earth system from a polar perspective > WP 3.3: From process understanding to enabling climate prediction