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  • '''DEFINITION''' The Black Sea Rim Current index (BSRCI) reflects the intensity of the Rim current, which is a main feature of the Black Sea circulation, a basin scale cyclonic current. The index was computed using sea surface current speed averaged over two areas of intense currents based on reanalysis data. The areas are confined between the 200 and 1800 m isobaths in the northern section 33-39E (from the Caucasus coast to the Crimea Peninsula), and in the southern section 31.5-35E (from Sakarya region to near Sinop Peninsula). Thus, three indices were defined: one for the northern section (BSRCIn), for the southern section (BSRCIs) and an average for the entire basin (BSRCI). BSRCI=(V ̅_ann-V ̅_cl)/V ̅_cl where V ̅ denotes the representative area average, the “ann” denotes the annual mean for each individual year in the analysis, and “cl” indicates the long-term mean over the whole period 1993-2020. In general, BSRCI is defined as the relative annual anomaly from the long-term mean speed. An index close to zero means close to the average conditions a positive index indicates that the Rim current is more intense than average, or negative - if it is less intense than average. In other words, positive BSRCI would mean higher circumpolar speed, enhanced baroclinicity, enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, intensification of the heat redistribution, etc. The BSRCI is introduced in the fifth issue of the Ocean State Report (von Schuckmann et al., 2021). The Black Sea Physics Reanalysis (BLKSEA_REANALYSIS_PHYS_007_004) has been used as a data base to build the index. Details on the products are delivered in the PUM and QUID of this OMI. '''CONTEXT''' The Black Sea circulation is driven by the regional winds and large freshwater river inflow in the north-western part (including the main European rivers Danube, Dnepr and Dnestr). The major cyclonic gyre encompasses the sea, referred to as Rim current. It is quasi-geostrophic and the Sverdrup balance approximately applies to it. The Rim current position and speed experiences significant interannual variability (Stanev and Peneva, 2002), intensifying in winter due to the dominating severe northeastern winds in the region (Stanev et al., 2000). Consequently, this impacts the vertical stratification, Cold Intermediate Water formation, the biological activity distribution and the coastal mesoscale eddies’ propagation along the current and their evolution. The higher circumpolar speed leads to enhanced dispersion of pollutants, less degree of exchange between open sea and coastal areas, enhanced baroclinicity, intensification of the heat redistribution which is important for the winter freezing in the northern zones (Simonov and Altman, 1991). Fach (2015) finds that the anchovy larval dispersal in the Black Sea is strongly controlled at the basin scale by the Rim Current and locally - by mesoscale eddies. Several recent studies of the Black Sea pollution claim that the understanding of the Rim Current behavior and how the mesoscale eddies evolve would help to predict the transport of various pollution such as oil spills (Korotenko, 2018) and floating marine litter (Stanev and Ricker, 2019) including microplastic debris (Miladinova et al., 2020) raising a serious environmental concern today. To summarize, the intensity of the Black Sea Rim Current could give valuable integral measure for a great deal of physical and biogeochemical processes manifestation. Thus our objective is to develop a comprehensive index reflecting the annual mean state of the Black Sea general circulation to be used by policy makers and various end users. '''CMEMS KEY FINDINGS''' The Black Sea Rim Current Index is defined as the relative annual anomaly of the long-term mean speed. The BSRCI value characterizes the annual circulation state: a value close to zero would mean close to average conditions, positive value indicates enhanced circulation, and negative value – weaker circulation than usual. The time-series of the BSRCI suggest that the Black Sea Rim current speed varies within ~30% in the period 1993-2020 with a positive trend of ~0.1 m/s/decade. In the years 2005 and 2014 there is evidently higher mean velocity, and on the opposite end are the years –2004, 2013 and 2016. The time series of the BSRCI gives possibility to check the relationship with the wind vorticity and validate the Sverdrup balance hypothesis. '''Figure caption''' Time series of the Black Sea Rim Current Index (BSRCI) at the north section (BSRCIn), south section (BSRCIs), the average (BSRCI) and its tendency for the period 1993-2020. '''DOI (product):''' https://doi.org/10.48670/mds-00326

  • '''DEFINITION''' The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and are also available in the Copernicus Marine Service catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the global ocean (hereafter GMSL) is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The time series is corrected for the effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound. During 1993-1998, the GMSL has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record. Accounting for this correction changes the shape of the time series, which is no more linear but quadratic, indicating mean sea level acceleration during the altimetry era. The trend uncertainty of 0.3 mm/yr is provided at 90% confidence interval (Guérou et al., 2022). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. '''CONTEXT''' Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2022/08/04] period, global mean sea level rises at a rate of 3.3  0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA correction to consider the ongoing movement of land (Peltier, 2004). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Niño Southern Oscillation (Cazenave and Cozannet, 2014). '''Figure caption''' Daily global mean sea level evolution (in cm) from satellite altimetry from January 1993 to August 2022. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) se a level gridded products distributed by the Copernicus Climate Change Service (C3S), also available in the CMEMS catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The timeseries corresponds to the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed and the timeseries is low-pass filtered (175 days cut-off). The timeseries is corrected for Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land. During 1993-1998, the dashed line shows an estimate of the global mean sea level corrected for the TOPEX-A instrumental drift, based on comparisons between altimeter and tide gauges measurements (Ablain et al., 2017; Legeais et al., 2020). The GMSL trend given in the figure is deduced from the dashed curve, including the TOPEX-A drift correction, up to 1998 and the solid line up to August 2022. '''DOI (product):''' https://doi.org/10.48670/moi-00237

  • '''DEFINITION''' The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047, including “my” (multi-year) & “myint” (multi-year interim) datasets) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). '''CONTEXT''' The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014). '''CMEMS KEY FINDINGS''' The different states of the Kuroshio extension phase have been presented and validated by (Bessières et al., 2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. '''Figure caption''' Standardized Eddy Kinetic Energy over the Kuroshio region (following Bessières et al., 2013) Blue shaded areas correspond to well established strong elongated states periods, while orange shaded areas fit weak contracted states periods. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) completed by DUACS near Real Time (“nrt”) sea level multi-mission gridded products. The vertical red line shows the date of the transition between “myint” and “nrt” products used. '''DOI (product):''' https://doi.org/10.48670/moi-00222

  • '''DEFINITION''' The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the North-West Shelf region is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound. During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. '''CONTEXT''' Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018).. At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). In this region, the RMSL trend is modulated decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming (IPCC WGII, 2021). Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). Hermans et al., 2020 also reported the dominant influence of wind on interannual sea level variability in a large part of this area. They also underscored the influence of the inverse barometer forcing in some coastal regions. '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2022/08/04] period, the basin-wide RMSL in the NWS area rises at a rate of 3.1  0.83 mm/year. '''Figure caption''' Regional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the North-West Shelf region, derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off), and the curve is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004). '''DOI (product):''' https://doi.org/10.48670/moi-00271

  • '''DEFINITION''' Ocean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth: ¯S=1/V ∫V S dV Time series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3. '''CONTEXT''' The freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link 
to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity. The OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the “multi-product” approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year. Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: The Mediterranean Sea Reanalysis at 1/24°horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020) Four global reanalyses at 1/4°horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, GLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022) Two observation-based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI: https://doi.org/10.17882/46219, Szekely et al., 2022) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). Details on the products are delivered in the PUM and QUID of this OMI. '''CMEMS KEY FINDINGS''' The Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2019 at rate of 5.6*10-3 ±3.5*10-4 psu yr-1. The overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993–2019 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s. '''Figure caption''' Time series of annual mean volume ocean salt content in the Mediterranean Sea (basin wide), integrated over the 0-300m depth layer during 1993-2019 (or longer according to data availability) including ensemble mean and ensemble spread (shaded area). The ensemble mean and associated ensemble spread are based on different data products, i.e., Mediterranean Sea Reanalysis (MED-REA), global ocean reanalysis (GLORYS, C-GLORS, ORAS5, and FOAM) and global observational based products (CORA and ARMOR3D). Details on the products are given in the corresponding PUM and QUID for this OMI. '''DOI (product):''' https://doi.org/10.48670/mds-00325

  • '''DEFINITION''' The omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish Seas over the period 1993-2022, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v2.1.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' Over the period 1993-2022, the Iberia-Biscay-Irish Seas mean Sea Surface Temperature (SST) increased at a rate of 0.013 ± 0.001 °C/Year. '''Figure caption''' Sea surface temperature trend over the period 1993-2022 in the Iberia-Biscay-Irish Seas. The trend is the rate of change (°C/year).The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005) and Sen’s method (Sen 1968). '''DOI (product):''' https://doi.org/10.48670/moi-00257

  • '''DEFINITION''' The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry base d on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Baltic Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004) to consider the ongoing movement of land due to post-glacial rebound. During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimates for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g., in situ) - necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. '''CONTEXT''' Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). The Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gräwe et al., 2019). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2022/08/04] period, the basin-wide RMSL in the Baltic Sea rises at a rate of 4.8  0.84 mm/year. '''Figure caption''' Regional mean sea level daily evolution (in cm) over the [1993/01/01, 2022/08/04] period, from the satellite altimeter observations estimated in the Baltic Sea, derived from the basin-wide average of the gridded sea level maps weighted by the cosine of the latitude. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) altimeter sea level gridded product distributed by the Copernicus Climate Change Service (C3S), and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The annual and semi-annual periodic signals are removed, the timeseries is low-pass filtered (175 days cut-off) and the time series is corrected for the GIA using the ICE5G-VM2 GIA model (Peltier, 2004). '''DOI (product):''' https://doi.org/10.48670/moi-00202

  • '''DEFINITION''' The method for calculating the ocean heat content anomaly is based on the daily mean sea water potential temperature fields (Tp) derived from the Baltic Sea reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011. The total heat content is determined using the following formula: HC = ρ * cp * ( Tp +273.15). Here, ρ and cp represent spatially varying sea water density and specific heat, respectively, which are computed based on potential temperature, salinity and pressure using the UNESCO 1983 polynomial developed by Fofonoff and Millard (1983). The vertical integral is computed using the static cell vertical thicknesses sourced from the reanalysis product BALTICSEA_MULTIYEAR_PHY_003_011 dataset cmems_mod_bal_phy_my_static, spanning from the sea surface to the 300 m depth. Spatial averaging is performed over the Baltic Sea spatial domain, defined as the region between 13° - 31° E and 53° - 66° N. To obtain the OHC annual anomaly time series in (J/m2), the mean heat content over the reference period of 1993-2014 was subtracted from the annual mean total heat content. We evaluate the uncertainty from the mean annual error of the potential temperature compared to the observations from the Baltic Sea (Giorgetti et al., 2020). The shade corresponds to the RMSD of the annual mean heat content biases (± 35.3 MJ/m²) evaluated from the observed temperatures and corresponding model values. Linear trend (W/m2) has been estimated from the annual anomalies with the uncertainty of 1.96-times standard error. '''CONTEXT''' Ocean heat content is a key ocean climate change indicator. It accounts for the energy absorbed and stored by oceans. Ocean Heat Content in the upper 2,000 m of the World Ocean has increased with the rate of 0.35 ± 0.08 W/m2 in the period 1955–2019, while during the last decade of 2010–2019 the warming rate was 0.70 ± 0.07 W/m2 (Garcia-Soto et al., 2021). The high variability in the local climate of the Baltic Sea region is attributed to the interplay between a temperate marine zone and a subarctic continental zone. Therefore, the Ocean Heat Content of the Baltic Sea could exhibit strong interannual variability and the trend could be less pronounced than in the ocean. '''CMEMS KEY FINDINGS''' Ocean heat content of the Baltic Sea has an increasing trend of 0.3±0.1 W/m2 superimposed with multi-year oscillations. The OHC increase in the Baltic Sea is smaller than the global OHC trend (Holland et al. 2019; von Schuckmann et al. 2019) and in some other marginal seas (von Schuckmann et al. 2018; Lima et al. 2020). Trend values are low due to the shallowness of the Baltic Sea, which limits the accumulation of heat in the water. The highest ocean heat content anomaly was observed in 2020. During the last two years, the heat content anomaly has decreased from its peak value. '''Figure caption''' The time series of horizontally averaged ocean heat content anomaly integrated over 0-300 m depth, for the period of 1993-2022. The temperature from Copernicus Marine Service regional reanalysis product (BALTICSEA_MULTIYEAR_PHY_003_011) have been averaged over the Baltic Sea domain (13 °E - 31 °E; 53 °N - 66 °N; excluding the Skagerrak strait). The shaded area shows the estimated RMSD interval of annual heat content biases. '''DOI (product):''' https://doi.org/10.48670/mds-00322

  • '''DEFINITION''' The omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1993-2022, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-NORTHWESTSHELF_v2.1.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' Over the period 1993-2022, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.016 ± 0.001 °C/Year. '''Figure caption''' Sea surface temperature trend over the period 1993-2022 in the European North West Shelf Seas. The trend is the rate of change (°C/year). The trend map in sea surface temperature is derived from the CMEMS SST_ATL_SST_L4_REP_OBSERVATIONS_010_026product (see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf). The trend is estimated by using the X-11 seasonal adjustment procedure (e.g. Pezzulli et al., 2005;) and Sen’s method (Sen 1968). '''DOI (product):''' https://doi.org/10.48670/moi-00276

  • '''DEFINITION''' The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The regional sea level trends are derived from a linear fit of the altimeter sea level maps. The altimeter data have not been corrected for the effect of the Glacial Isostatic Adjustment nor the TOPEX-A instrumental drift during the period 1993-1998. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. '''CONTEXT''' Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). '''CMEMS KEY FINDINGS''' The altimeter mean sea level trends over the [1993/01/01, 2022/08/04] period exhibit large-scale variations at rates reaching up to more than +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016). Prandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. '''Figure caption''' Spatial distribution of the trends of the satellite altimeter sea level observations (in mm/yr) over the [1993/01/01, 2022/08/04] period, in the global ocean. The ocean monitoring indicator is derived from the DUACS delayed-time (reprocessed version DT-2021, “my” (multi-year) dataset used when available, “myint” (multi-year interim) used after) altimeter sea level gridded products distributed by the Copernicus Climate Change Service (C3S), and the Coperncius Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). No Glacial Isostatic Adjustment correction is applied on the altimeter data. '''DOI (product):''' https://doi.org/10.48670/moi-00238