Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.

The Arctic Ocean cold halocline separates the cold surface mixed layer from the underlying warm Atlantic Water, and thus provides a precondition for sea ice formation. Here, we introduce a new method for detecting the halocline base and compare it to two existing methods. We show that the largest differences between the methods are found in the regions that are most prone to a halocline retreat in a warming climate, and we discuss the advantages and disadvantages of the three methods.