The absolute sea level trend from May 1995 to May 2019 in the Baltic Sea is analyzed by means of a regional monthly gridded dataset based on a dedicated processing of satellite altimetry data. In addition, we evaluate the role of the North Atlantic Oscillation and the wind patterns in shaping differences in sea level trend and variability at a sub-basin scale. To compile the altimetry dataset, we use information collected in coastal areas and from leads within sea-ice. The dataset is validated by comparison with tide gauges and the available global gridded altimetry products. The agreement between trends computed from satellite altimetry and tide gauges improves by 9%. The rise in sea level is statistically significant in the entire region of study and higher in winter than in summer. A gradient of over 3 mm/yr in sea level rise is observed, with the north and east of the basin rising more than the south-west. Part of this gradient (about 1 mm/yr) is directly explained by a regression analysis of the wind contribution on the sea level time series. A sub-basin analysis comparing the northernmost part (Bay of Bothnia) with the south-west reveals that the differences in winter sea level anomalies are related to different phases of the North-Atlantic Oscillation (0.71 correlation coefficient). Sea level anomalies are higher in the Bay of Bothnia when winter wind forcing pushes waters through Ekman transport from the south-west toward east and north. The study also demonstrates the maturity of enhanced satellite altimetry products to support local sea level studies in areas characterized by complex coastlines or sea-ice coverage. The processing chain used in this study can be exported to other regions, in particular to test the applicability in regions affected by larger ocean tides.

Abstract. Vertical land motion (VLM) at the coast is a substantial contributor to relative sea level change. In this work, we present a refined method for its determination, which is based on the combination of absolute satellite altimetry (SAT) sea level measurements and relative sea level changes recorded by tide gauges (TGs). These measurements complement VLM estimates from the GNSS (Global Navigation Satellite System) by increasing their spatial coverage. Trend estimates from the SAT and TG combination are particularly sensitive to the quality and resolution of applied altimetry data as well as to the coupling procedure of altimetry and TGs. Hence, a multi-mission, dedicated coastal along-track altimetry dataset is coupled with high-frequency TG measurements at 58 stations. To improve the coupling procedure, a so-called “zone of influence” (ZOI) is defined, which confines coherent zones of sea level variability on the basis of relative levels of comparability between TG and altimetry observations. Selecting 20 % of the most representative absolute sea level observations in a 300 km radius around the TGs results in the best VLM estimates in terms of accuracy and uncertainty. At this threshold, VLMSAT-TG estimates have median formal uncertainties of 0.58 mm yr−1. Validation against GNSS VLM estimates yields a root mean square (rmsΔVLM) of VLMSAT-TG and VLMGNSS differences of 1.28 mm yr−1, demonstrating the level of accuracy of our approach. Compared to a reference 250 km radius selection, the 300 km zone of influence improves trend accuracies by 15 % and uncertainties by 35 %. With increasing record lengths, the spatial scales of the coherency in coastal sea level trends increase. Therefore, the relevance of the ZOI for improving VLMSAT-TG accuracy decreases. Further individual zone of influence adaptations offer the prospect of bringing the accuracy of the estimates below 1 mm yr−1.

For nearly 30 years, space-based radar altimetry has been routinely measuring changes in sea level at global and regional scales. But this technique designed for the open ocean does not provide reliable sea level data within 20 km to the coast, mostly due to land contamination within the radar echo in the vicinity of the coast. This problem can now be overcome through dedicated reprocessing, allowing the retrieval of valid sea level data in the 0-20 km band from the coast, and then the access to novel information on sea level change in the world coastal zones. Here we present sea level anomalies and associated coastal sea level trends at 756 altimetry-based virtual coastal stations located along the coasts of North and South America, Northeast Atlantic, Mediterranean Sea, Africa, North Indian Ocean, Asia and Australia. This new dataset, derived from the reprocessing of high-resolution (300 m) along-track altimetry data from the Jason-1, 2 and 3 missions from January 2002 to December 2019, allows the analysis of the decadal evolution of coastal sea level and fills the coastal gap where sparse sea level information is currently available.

We investigate how ocean-driven multidecadal sea surface temperature (SST) variations force the atmosphere to jointly set the pace of Atlantic multidecadal variability (AMV). We generate periodic low-frequency Atlantic Meridional Overturning Circulation oscillations by implementing time-dependent deep-ocean-density restoring in MPI-ESM1.2 to explicitly identify variations driven by Atlantic Meridional Overturning Circulation without any perturbation at the ocean-atmosphere interface. We show in a coupled experiment that ocean heat convergence variations generate positive SST anomalies, turbulent heat release, and low sea level pressure in the subpolar North Atlantic (NA) and vice versa. The SST signal is communicated to the tropical NA by wind-evaporative-SST feedbacks and to the North-East Atlantic by enhanced northward atmospheric heat transport. Such atmospheric feedbacks and the characteristic AMV-SST pattern are synchronized to the multidecadal time scale of ocean circulation changes by air-sea heat exchange. This coupled ocean-atmosphere mechanism is consistent with observed features of AMV and thus supports a key role of ocean dynamics in driving the AMV. Plain Language SummaryThe Atlantic multidecadal variability is an observed fluctuation of North Atlantic ocean surface temperatures on multidecadal time scales. It strongly influences climatic conditions over the surrounding continents in the North Atlantic region as well as in remote areas. Therefore, it is essential to understand the underlying mechanisms, particularly in regard to predict the Atlantic multidecadal variability itself and its impacts. However, the respective contributions from fast atmospheric forcing and slow ocean variations to such long-term climate variations have been controversially discussed. Here, by artificially increasing the variability of ocean dynamics in a climate model, we improve the mechanistic understanding of the role of ocean dynamics in driving the Atlantic Multidecadal Variability. We believe our results suggest a major role for ocean dynamics. A climate model reacts to an increase in ocean circulation by accumulating heat in the subpolar North Atlantic. Associated atmospheric responses to ocean forcing contribute to heat redistribution to form the basin-wide sea surface temperature pattern of Atlantic Multidecadal Variability. None of these fundamental imprints of ocean dynamics can be reproduced by a model experiment that excludes ocean dynamics (slab-ocean model). Our results therefore substantiate Atlantic multidecadal variability as a coupled mode of ocean-atmospheric variability, which strongly relies on the slow circulation variations of the ocean.

One of the major sources of uncertainty affecting vertical land motion (VLM) estimations are discontinuities and trend changes. Trend changes are most commonly caused by seismic deformation, but can also stem from long-term (decadal to multidecadal) surface loading changes or from local origins. Although these issues have been extensively addressed for Global Navigation Satellite System (GNSS) data, there is limited knowledge of how such events can be directly detected and mitigated in VLM, derived from altimetry and tide-gauge differences (SATTG). In this study, we present a novel Bayesian approach to automatically and simultaneously detect such events, together with the statistics commonly estimated to characterize motion signatures. Next to GNSS time series, for the first time, we directly estimate discontinuities and trend changes in VLM data inferred from SATTG. We show that, compared to estimating a single linear trend, accounting for such variable velocities significantly increases the agreement of SATTG with GNSS values (on average by 0.36 mm/year) at 339 globally distributed station pairs. The Bayesian change point detection is applied to 606 SATTG and 381 GNSS time series. Observed VLM, which is identified as linear (i.e. where no significant trend changes are detected), has a substantially higher consistency with large-scale VLM effects of glacial isostatic adjustment (GIA) and contemporary mass redistribution (CMR). The standard deviation of SATTG (and GNSS) trend differences with respect to GIA+CMR trends is by 38% (and 48%) lower for time series with constant velocity compared to variable velocities. Given that in more than a third of the SATTG time series variable velocities are detected, the results underpin the importance to account for such features, in particular to avoid extrapolation biases of coastal VLM and its influence on relative sea-level-change determination. The Bayesian approach uncovers the potential for a better characterization of SATTG VLM changes on much longer periods and is widely applicable to other geophysical time series.