CoAuthors

Ben Hamlington (Old Dominion University, USA)

Monitoring of the Earth's climate on an ongoing basis has taken on increasing importance as projections of continued, and possibly more rapid, climate change associated with global warming occurs in the coming decades. Sea level is an integral component of climate monitoring since, as a dynamic ocean variable, the changes in sea surface height (SSH) reflect the integrated density changes due to both mass (salinity) and temperature variations over the entire ocean water column. To differentiate between natural and anthropogenic variations in sea level associated with climate change, long-duration and accurate sea level mapping is needed before the advent of satellite altimetry. Accurately estimating climate signals from sea level maps reconstructed solely by fitting to sea level measured by tide gauges before 1950 is very difficult; however, multivariate reconstruction techniques for estimating sea level show significantly improved performance as far back as 1900. For example, the combination of in situ sea surface temperature observations and sea level from tide gauges improves estimation of the sea level signals associated with El Niño Southern Oscillation (ENSO) by accurately reconstructing both the canonical Eastern Pacific ENSO (EP-ENSO) and the more recently described Modoki or Central Pacific ENSO (CP-ENSO) variability over the entire 20th century. The multivariate reconstruction also captures Pacific multidecadal variability, including the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO). In this paper we describe SSH-based climate indices for the EP-ENSO, CP- ENSO and IPO/PDO, as well as indices for monitoring variability in the Atlantic and Indian Oceans using multivariate reconstructed sea level and the ongoing delayed-time and near real-time satellite altimeter records. Applications of both the SSH and climate index records for historical and near real time monitoring of ocean circulation and climate will be discussed with a focus on near real time climate applications.