The SIROCCO ocean circulation climate simulations cover the western Mediterranean Sea ( 33°N - 44.5°N / 1.2°W - 16.3°E ) and the periods from 2000 to 2020, from 2030 to 2049, and form 2080 to 2099. It is based on the BlueLion configuration of the Symphonie hydrodynamic model (Marsaleix et al., 2008, 2019). The horizontal resolution of the model bipolar grid (1230 x 1308) varies from 340 m in the northwestern Mediterranean to 4.5 km in the Ionian Sea. The vertical grid has vanishing quasi-sigma 50 levels. This dataset includes daily files of sea surface height, temperature, salinity and current velocities. This simulations were developed in the framework of the ANR POPNCO and the PPR RIOMAR. The initial and open boundary conditions, the atmospheric forcing and the rivers discharge were provided by the CNRM-RCSM6B, regional climate system model (atmosphere, ocean, land surface hydrology) developed at CNRM (Météo-France) over the Mediterranean CORDEX domain (Med-CORDEX). This coupled simulation is based on the NEMO model at 1/12° for the ocean, the Aladin-Climat model at 12 km for the atmosphere and the ISBA-CTRIP land surface hydrology model.

This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices. The latest version of Copernicus delayed-mode Sea level product is also distributed from Copernicus Marine catalogue.

The SIROCCO ocean circulation simulation covers the western Mediterranean Sea ( 33°N - 44.5°N / 1.2°W - 16.3°E ) and the period from 1990 to 2023. It is based on the BlueLion configuration of the Symphonie hydrodynamic model (Marsaleix et al., 2008, 2019). The horizontal resolution of the model bipolar grid (1230 x 1308) varies from 340 m in the northwestern Mediterranean to 4.5 km in the Ionian Sea. The vertical grid has vanishing quasi-sigma 50 levels. This dataset includes daily files of sea surface height, temperature, salinity and current velocities. This simulation was developed in the framework of the PPR RIOMAR. The initial and open boundary conditions, the atmospheric forcing and the rivers discharge (except from the Gulf of Lion) were provided by the CNRM-RCSM6B, regional climate system model (atmosphere, ocean, land surface hydrology) developed at CNRM (Météo-France) over the Mediterranean CORDEX domain (Med-CORDEX). This coupled simulation is based on the NEMO model at 1/12° for the ocean, the Aladin-Climat model at 12 km for the atmosphere and the ISBA-CTRIP land surface hydrology model. The Gulf of Lion rivers were considered using the hydrology database HydroPortail (http ://www.hydro.eaufrance.fr).

The CORE-II simulations provide a framework to evaluate ocean model performance, to study mechanisms of ocean phenomena and their variability from seasonal to decadal timescales, to identify forced variability changes, and to develop mechanistic descriptions of observed climate variability and change.

This dataset contains the dynamical outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

Satellite altimeters routinely supply sea surface height (SSH) measurements which are key observations to monitor ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in the signal-to-noise ratio, making it very challenging to fully exploit available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinctive methodology emerged to be systematically applied in operational products. To best cope with this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination in the along-track SSH signals and more innovative and adapted noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. Here demonstrated, a fully data-driven approach is developed and applied to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is now found to best resolve the distribution of the sea surface height variability in the mesoscale 30-120 km wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal to noise ratio, but also for uncertainties in the denoising process, which assumes that SLA variability results in part from a stochastic process. Here, measurements from the Jason-3, Sentinel-3 A and SARAL/AltiKa altimeters are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. Anticipating data from the upcoming Surface Water and Ocean Topography (SWOT) mission, these denoised SLA measurements for three reference altimeter missions already yield valuable opportunities to assess global small mesoscale kinetic energy distributions. This dataset was developed within the Ocean Surface Topography Science Team (OSTST) activities. A grant was awarded to the SASSA (Satellite Altimeter Short-scale Signals Analysis) project by the TOSCA board in the framework of the CNES/EUMETSAT call CNES-DSP/OT 12-2118. Altimeter data were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and by the Sea State Climate Change Initiative (CCI) project.

Times series of water level at four stations of the Gironde Estuary (Pointe de Grave, Le Marquis, Bordeaux and Cadillac) for the the years 1953, 1971, 1982, 1994, 2005 and 2014. The dataset includes all of the time seris used in the publication: To what extent multidecadal changes in morphology and fluvial discharge impact tide in a convergent (turbid) tidal river (2018), Journal of Geophysical Research: Oceans.

Rapid changes in ocean circulation and climate have been observed in marine-sediment and ice cores over the last glacial period and deglaciation, highlighting the non-linear character of the climate system and underlining the possibility of rapid climate shifts in response to anthropogenic greenhouse gas forcing. To date, these rapid changes in climate and ocean circulation are still not fully explained. One obstacle hindering progress in our understanding of the interactions between past ocean circulation and climate changes is the difficulty of accurately dating marine cores. Here, we present a set of 92 marine sediment cores from the Atlantic Ocean for which we have established age-depth models that are consistent with the Greenland GICC05 ice core chronology, and computed the associated dating uncertainties, using a new deposition modeling technique. This is the first set of consistently dated marine sediment cores enabling paleoclimate scientists to evaluate leads/lags between circulation and climate changes over vast regions of the Atlantic Ocean. Moreover, this data set is of direct use in paleoclimate modeling studies.

he Global ARMOR3D L4 Reprocessed dataset is obtained by combining satellite (Sea Level Anomalies, Geostrophic Surface Currents, Sea Surface Temperature) and in-situ (Temperature and Salinity profiles) observations through statistical methods. References : - ARMOR3D: Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845–857. - ARMOR3D: Guinehut S., P.-Y. Le Traon, G. Larnicol and S. Philipps, 2004: Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields - A first approach based on simulated observations. J. Mar. Sys., 46 (1-4), 85-98. - ARMOR3D: Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep Sea Research Part II : Topical Studies in Oceanography, 77–80(0):70–81.

Satellite altimetry missions provide a quasi-global synoptic view of sea level over more than 25 years. The satellite altimetry constellation is used to build sea level maps and regional sea level indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address some current climate science questions such as climate change detection and attribution or regional sea level budget closure for example. Previous studies have estimated the uncertainty for the global mean sea level (GMSL), but no uncertainty information is available at regional scales. In this study we estimate a regional satellite altimetry error budget and use it to derive maps of confidence intervals for local sea rise rates and accelerations. We analyze 27 years of satellite altimetry maps and derive the satellite altimetry error variance-covariance matrix at each grid point, prior to the estimation of confidence intervals on local trends and accelerations at the 90% confidence level using extended least squares estimators. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr-1 with local values ranging from 0.78 to 1.22 mm.yr-1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr-2, with a mean value of 0.063 mm.yr-2.   Change history: - 2020/07/08: initial dataset submission over 1993-2018 - 2020/10/21: 1993-2019 update and addition of error levels