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Automatic ocean eddy identification algorithms are crucial for global eddy research. In this study, a scale-selective eddy identification algorithm (SEIA) that features improvements in the detection and tracking processes is presented for the global ocean based on sea level anomalies. First, the previous strategy of using thresholds to define eddy boundaries is replaced with a scale-selective scheme, which restricts the numbers of upper and lower grid points based on the data resolution and eddy spatial scale. Under such conditions, overestimated eddy boundaries will be flexibly removed. Furthermore, an effective overlap scheme is used to track eddies by calculating the intersection ratio of time-step-successive eddies. The SEIA generates approximately 1.6 million anticyclonic eddies and 1.5 million cyclonic eddies by the satellite altimetry product from the French Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) over a 29-year period (1993–2021; https://doi.org/10.11922/sciencedb.o00035.00004 ). Assessments of the global distribution of eddies, eddy propagation speed, eddy path and evolution characteristics, and observation-based eddy hydrological conditions verify the validity of the SEIA. This study provides solid support for ocean eddy-related research in a warming climate.

The aim of this paper is to present the skills of two statistical models in anticipating the development of El Niño based on sea level anomaly (SLA) forecasts with lead time up to 12 weeks. The models are: (1) the polynomial-harmonic model (PH) combined with the threshold autoregressive model (TAR), known as the PH+TAR, and (2) PH integrated with the multivariate autoregressive model (MAR), referred to as PH+MAR. Five powerful El Niño events are considered: 1997/1998, 2002/2003, 2006/2007, 2009/2010, 2015/2016. The performance of the prediction models is calculated in specific locations in the equatorial Pacific, i.e. centres of Niño 1+2, Niño 3, Niño 3.4 and Niño 4 regions. It is found that the SLA predictions hitting the El Niño peaks reveal different accuracy for dissimilar El Niño events, with the most skillful prognoses for El Niño 1997/1998. Two specific regions are identified in which the model performance fulfils the assumed accuracy limit of 5 cm, namely the Niño 1+2 and Niño 4 regions. In addition, the PH+MAR model performed better than the PH+TAR solution.

Surface currents in oceanic environment are of vital importance from economical, biological and environmental aspects. Modelling ocean currents has generally been performed using numerical ocean circulation models as a solution to initial-boundary value problems in oceanic domain. Due to lack of knowledge about model parameters as well as initial and boundary values, they need to be externally calibrated for accurate local and regional applications. In this study, an alternative approach is proposed to incorporate spaceborne geodetic observations as well as hydrographic data to estimate the total surface current in the Persian Gulf and the Oman Sea. Being the data-driven approach, the method is comparable to numerical ocean models and regionally it is more accurate and simpler in application. The proposed method focuses on the computation of dynamic topography (DT) by least squares variance component estimation combining two different schemes. They are (1) DT estimation via direct observations of sea surface height from satellite altimetry and (2) steric and non-steric modeling of sea level anomaly using temperature and salinity data for the steric component; and Gravity Recovery and Climate Experiment observations for the non-steric component. Ultimately, the total surface current is obtained by computing the horizontal gradient of DT using geostrophic equation and adding the components of the Ekman current. Moreover, the estimated total surface current is further improved by assimilating with in situ current meter data using 3D-Variational data assimilation method and it is validated against two control stations. This assimilation leads to improvement of about 3 to 15 cm/s in total surface current computed using geostrophic equation and Ekman current. Besides, to illustrate the significance of the proposed approach, the estimated total surface current is externally validated and compared with the output of Copernicus Marine Environment Monitoring Service (CMEMS), as a numerical ocean model developed for oceanographic applications. Our comparison reveals that the proposed method is more accurate and reliable than CMEMS products. As for the circulation and current pattern, the estimated surface velocities reveal the existence of eddies in the region of the Persian Gulf and the Oman Sea, indicating the occurrence of cyclonic and anti-cyclonic circulations. Moreover, they elucidate that the velocities are lower in spring and summer and higher in autumn and winter.

Merged satellite altimeter products are widely used in ocean-related fields. Currently, the altimeter merged products of archiving validation and interpretation of satellite oceanographic (AVISO) data are widely used internationally. Chinese National Satellite Ocean Application Service also released merged altimeter products (ALT MUL) in 2023. However, there are few studies on the quality assessment of ALT MUL. Based on the data of AVISO merged products, Jason3 satellite, tide gauge and drifter buoy, the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index, interpolation along rails, velocity inversion and power spectrum. The result shows that the average sea level anomaly (SLA) of ALT MUL is about 2 cm smaller than that of AVISO. And they are consistent with the large-scale characteristics and spatial distribution. These two SLA products are both in accordance with normal distribution. Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO. This difference may be attributed to the fact that AVISO products use Jason3 satellite as cross-calibrated reference satellite during the merged process. Comparing the matching effect of the two merged products with the tide gauge and drifter buoy, ALT MUL merged products are superior to AVISO in general. The energy spectral density was calculated by using Jason3 satellite data along the orbit, and the two merged products were interpolated to the data points along the orbit. The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation, indicating that AVISO merged products have higher effective resolution.

The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This paper gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors, ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a priori bias correction scheme, observation quality control and assimilation method for sea-level anomalies. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes five ensemble members and covers the period from 1979 onwards. Updated versions of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 through sensitivity experiments suggests that all system components contribute to an improved fit to observation in reanalyses, with the most prominent contribution from direct assimilation of ocean in situ observations. Results of observing system experiments further suggest that the Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out for several key ocean state variables and verified against reference climate data sets from the ESA CCI (European Space Agency Climate Change Initiative) project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level and continues exhibiting large SST biases in the Gulf Stream and its extension regions which are possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble spread is under-dispersive for SST.

The ocean takes up about 93% of the global warming heat entering Earth’s climate system. In addition, the associated thermal expansion contributes substantially to sea-level rise. Hence, quantifying the oceanic heat uptake rate and its statistical significance has been a research focus. Here we use gridded ocean heat content maps to examine regional trends in ocean warming for 0–700 m depth from 1993–2019 and 1968–2019, periods based on sampling distributions. The maps are from four research groups, three based on ocean temperature alone and one combining ocean temperature with satellite altimeter sea-level anomalies. We show that use of longer periods results in larger percentages of ocean area with statistically significant warming trends and less ocean area covered by statistically significant cooling trends. We discuss relations of these patterns to climate phenomena, including the Pacific Decadal Oscillation, the Atlantic Meridional Overturning Circulation and global warming.A large proportion of anthropogenic heat energy is being taken up by ocean warming. Analysis of yearly 0–700 m ocean heat content maps from four different estimates shows that the longer the period over which regional trends are estimated, the larger the area of statistically significant warming.

Sea ice variability in the opposite polar regions is examined holistically by applying the self-organizing map (SOM) method to global monthly sea ice concentration data over two periods. The results show that the variability modes of sea ice decrease in the Arctic correspond to an overall sea ice increase in the Antarctic, and vice versa. In particular, the monthly sea ice anomaly patterns are dominated by in-phase variability across the Arctic that is stronger in the marginal seas particularly the Barents Sea than the central Arctic Ocean. The corresponding Antarctic sea ice variability is characterized by a zonal wavenumber-3 structure or a dipole pattern of out-of-phase variability between the Bellingshausen/Amundsen Seas and the rest of the Southern Ocean. The frequency of occurrence of these dominant patterns exhibits pronounced seasonal as well as decadal variability and the latter is closely related to the Pacific decadal oscillation and Atlantic multidecadal oscillation. Other less frequent patterns seem to be associated with the central Pacific ElNi˜no and spatially heterogeneous interannual variability of sea surface temperature (SST) in the Indian and the Atlantic Oceans. The dominant modes explain 57% of the four-decade domain-averaged trends in the annual polar sea ice concentration, with more explained in the eastern than western Arctic Ocean and in the Weddell Sea and the Amundsen Sea in the Antarctic. The spatial patterns of the leading modes can be largely explained by the dynamic (sea ice drift) and thermodynamic (sea ice melt) effects of the anomalous atmospheric circulations associated with SST and sea level pressure anomalies.

For more than 20 years, the multi-satellite Data Unification and Altimeter Combination System (DUACS) has been providing near-real-time (NRT) and delayed-time (DT) altimetry products. DUACS datasets range from along-track measurements to multi-mission sea level anomaly (SLA) and absolute dynamic topography (ADT) maps. The DUACS DT2018 ensemble of products is the most recent and major release. For this, 25 years of altimeter data have been reprocessed and are available through the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S). Several changes were implemented in DT2018 processing in order to improve the product quality. New altimetry standards and geophysical corrections were used, data selection was refined and optimal interpolation (OI) parameters were reviewed for global and regional map generation. This paper describes the extensive assessment of DT2018 reprocessing. The error budget associated with DT2018 products at global and regional scales was defined and improvements on the previous version were quantified (DT2014; Pujol et al., 2016). DT2018 mesoscale errors were estimated using independent and in situ measurements. They have been reduced by nearly 3 % to 4 % for global and regional products compared to DT2014. This reduction is even greater in coastal areas (up to 10 %) where it is directly linked to the geophysical corrections applied to DT2018 processing. The conclusions are very similar concerning geostrophic currents, for which error was globally reduced by around 5 % and as much as 10 % in coastal areas.

Coastal locations around the United States, particularly along the Atlantic coast, are experiencing recurrent flooding at high tide. Continued sea-level rise (SLR) will exacerbate the issue where present, and many more locations will begin to experience recurrent high-tide flooding (HTF) in the coming decades. Here we use established SLR scenarios and flooding thresholds to demonstrate how the combined effects of SLR and nodal cycle modulations of tidal amplitude lead to acute inflections in projections of future HTF. The mid-2030s, in particular, may see the onset of rapid increases in the frequency of HTF in multiple US coastal regions. We also show how annual cycles and sea-level anomalies lead to extreme seasons or months during which many days of HTF cluster together. Clustering can lead to critical frequencies of HTF occurring during monthly or seasonal periods one to two decades prior to being expected on an annual basis.High-tide flooding (HTF) is more likely with sea-level rise. Projections along the United States coastline, considering likely sea-level rise and tidal amplitude cycles, suggest increased HTF event clustering in time and rapid increases in annual HTF frequency as early as the mid-2030s.

The new DUACS DT2014 reprocessed products have been available since April 2014. Numerous innovative changes have been introduced at each step of an extensively revised data processing protocol. The use of a new 20-year altimeter reference period in place of the previous 7-year reference significantly changes the sea level anomaly (SLA) patterns and thus has a strong user impact. The use of up-to-date altimeter standards and geophysical corrections, reduced smoothing of the along-track data, and refined mapping parameters, including spatial and temporal correlation-scale refinement and measurement errors, all contribute to an improved high-quality DT2014 SLA data set. Although all of the DUACS products have been upgraded, this paper focuses on the enhancements to the gridded SLA products over the global ocean. As part of this exercise, 21 years of data have been homogenized, allowing us to retrieve accurate large-scale climate signals such as global and regional MSL trends, interannual signals, and better refined mesoscale features.An extensive assessment exercise has been carried out on this data set, which allows us to establish a consolidated error budget. The errors at mesoscale are about 1.4 cm2 in low-variability areas, increase to an average of 8.9 cm2 in coastal regions, and reach nearly 32.5 cm2 in high mesoscale activity areas. The DT2014 products, compared to the previous DT2010 version, retain signals for wavelengths lower than  ∼  250 km, inducing SLA variance and mean EKE increases of, respectively, +5.1 and +15 %. Comparisons with independent measurements highlight the improved mesoscale representation within this new data set. The error reduction at the mesoscale reaches nearly 10 % of the error observed with DT2010. DT2014 also presents an improved coastal signal with a nearly 2 to 4 % mean error reduction. High-latitude areas are also more accurately represented in DT2014, with an improved consistency between spatial coverage and sea ice edge position. An error budget is used to highlight the limitations of the new gridded products, with notable errors in areas with strong internal tides.