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Abstract Along with the mean sea level rise due to climate change, the sea level exhibits natural variations at a large number of different time scales. One of the most important is the one linked with the seasonal cycle. In the Northern Hemisphere winter, the sea level is as much as 20 cm below its summer values in some locations. It is customary to associate these variations with the seasonal cycle of the sea surface net heat flux which drives an upper-ocean thermal expansion creating a positive steric sea level anomaly. Here, using a novel framework based on steric sea level variance budget applied to observations and to the Estimating the Circulation and Climate of the Ocean state estimate, we demonstrate that the steric sea level seasonal cycle amplitude results from a balance between the seasonal sea surface net heat flux and the oceanic advective processes. Moreover, for up to 50% of the ocean surface, surface heat fluxes act to damp the seasonal steric sea level cycle amplitude, which is instead forced by oceanic advection processes. We also show that eddies play an important role in damping the steric sea level seasonal cycle. Our study contributes to a better understanding of the steric sea level mechanisms which is crucial to ensure accurate and reliable climate projections.

Abstract Satellite observations of significant wave height (Hs) have recently reached 30 years of continuous record. Is this length sufficient to detect the effect of anthropogenically forced climate change on wave height trends? Wave height decadal variability is influenced by a combination of internal variability and forced variability caused by both anthropogenic and natural forcing. Using a statistical model to derive Hs from sea level pressure field and exploiting ERA‐5 reanalysis data as well as 80 members of the Community Earth System Model v2 large ensemble, we show that, over the North Atlantic (NA), altimetry‐based Hs trends are mostly caused by internal variability. This suggests that Hs changes computed over the satellite era are not yet controlled by anthropogenic climate change. Starting from 1993, the date of emergence, defined as the date when the forced signal becomes dominant over the internal variability, is later than 2050 for Hs in the NA.

Accurate near-term predictions of global temperatures are required to determine some of the key impacts of climate change. Here the authors develop a novel probabilistic forecast system that shows anomalously warm temperatures for the next years with increased risk of extreme warming.