Spatiotemporal variability of the ocean since 1900: testing a new analysis approach using global sea level reconstruction

A new approach for analysis of spatiotemporal variability across ocean basins was tested using global 1 degrees x 1 degrees monthly sea level reconstruction (RecSL) for 1900-2015. Each sea level cross section eta(x,t) was converted into a single time series that contains both spatial and temporal variabilities by connecting back and forth repeated monthly sections. For example, a single zonal section between 50 degrees W and 50 degrees E would create a time series of 116 years x 12 months x 100 degrees = 139,200 data points. The long record allowed great statistical significance and direct comparison between energy in spatial variability and energy in temporal variability. Time and length scales found in 116 years of RecSL data are compared with 23 years of altimeter data. Empirical Mode Decomposition (EMD) was then used to break the record into high-frequency modes representing spatial variability across the section and lower frequency modes representing temporal variability (capturing time scales of a few months to multidecadal). Examples of the spatiotemporal analysis in the Pacific Ocean showed how the method detected an increase in El Nino amplitude in equatorial regions and characterized spatiotemporal changes in the Kuroshio Current in mid-latitudes. In the Atlantic Ocean, the analysis showed the latitudinal dependency of spatiotemporal variability: for example, sections near the Gulf Stream (GS) and the Antarctic Circumpolar Current (ACC) showed energy dominated by small-scale spatial variability while sections across equatorial and subpolar North Atlantic regions showed energy dominated by long-term temporal variability and diminishing energy in spatial variability. The impact of the North Atlantic Oscillation (NAO) index on spatiotemporal variability showed that interannual variations in NAO are highly correlated with subpolar sea level variability, while decadal and longer variations in NAO are linked with sea level variations at the equatorial South Atlantic and the Antarctic zone. This analysis can be useful for other observations and various climate data.