MESoscale Ocean eddies and CLImate Predictability (MESO-CLIP)

We developed a method to determine the ‘memory’ of the surface ocean circulation. By ‘memory’ we mean the time it takes for the information contained in anomalies of the ocean circulation at a given time (e.g., position of meanders in large currents such as the Gulf Stream or the Kuroshio or of ocean eddies) to be lost. The ‘memory’ in the ocean is equivalent to the typical length of the time between passing weather systems in the atmosphere (e.g., a succession of high and low pressure centres) – a time often referred to as the synoptic timescale. We found the ocean ‘memory’ of the surface currents to typically be between 10 and 30 days with the longest times of more than 70 days found in the North Pacific. We could also show that the times are similar in observations and in a state-of-the-art ocean model.

We have found a new mechanism linking the Loop Current in the Gulf of Mexico to the variability of the Gulf Stream along the U.S. east coast and into the North Atlantic. About one a year the length of the Loop Current expands until and eddy is being shed. During expansion (contraction) phases of the Loop Current length we find negative (positive) velocity anomalies in the Gulf Stream downstream of the Gulf of Mexico. Both in observations and a high resolution ocean model these anomalies are seen as pulses that within a few weeks propagate from the southern tip of Florida to Cape Hatteras along the U.S. coast.

The key goal of MESO-CLIP was to move towards a better understanding of how small scale ocean features (order of a few tens to a few hundred kilometres) can affect the predictability of ocean and climate. In models which are used in forecasting these small scale ocean features (ocean mesoscale eddies) are very sensitive to small changes in temperature and salinity.

In MESO-CLIP we developed an approach how to optimally perturb the ocean mesoscale in high resolution ocean models which is an important step toward better exploiting these high resolution models for forecasts.