Search

Sea level rise is one of the greatest socio-economic impacts of climate change in the 21st Century. Whilst global mean sea level is an essential climate variable (ECV) for assessing the integrated response of the Earth system to climate change, regional sea level variability is of primary concern for policy-making decisions and the development of adaptation strategies in coastal localities. Redistribution of terrestrial mass, in the form of hydrological and land ice mass fluxes, partly drives this regional sea level variability due to its impact on the Earth’s gravity, rotation and deformation (GRD), termed ‘Sea Level Fingerprints’ or Barystatic-GRD fingerprints. With increasing mass losses projected from ice sheets and glaciers over the coming centuries, the magnitude and relative contribution of these Barystatic-GRD fingerprints to regional sea level change are expected to increase. As a result, accurately quantifying this phenomenon and its uncertainty is critical when assessing contemporary and future regional sea level variability.Current contemporary Barystatic-GRD fingerprints are typically either calculated using a single mass loading observation source or provide discontinuous coverage since 1992 (the satellite altimetry era). Here, we present a continuous monthly Barystatic-GRD fingerprint product from 1992-2017, computed from an ensemble of mass loadings derived from differing observation techniques. To achieve this, we use the Ice Sheet and Sea Level Model (ISSM) sea level equation solver, which uses a finite element approach to solving the sea level equation at high spatial-temporal resolution, whilst maintaining computational efficiency. This enables us to use an ensemble modelling framework, ensuring the computed Barystatic-GRD fingerprint encompasses the variability between differing observation techniques. Additionally, it allows us to propagate the observation uncertainties into the fingerprint uncertainty in a robust manner. As well as the total Barystatic-GRD fingerprint, we assess the contribution of individual terrestrial components (Antarctica, Greenland, Glaciers, and hydrological mass change). This work is part of the Fingerprinting Approach to Close Regional Sea Level Budgets using ESA-CCI (FACTORS), a European Space Agency Climate Change Initiative Research Fellowship.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of energy in the climate system. While necessary to better understand the Earth’s warming climate, measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5-1 W.m−2) compared to the amount of energy entering and leaving the climate system (~ 340 W.m-2). Accuracies better than 0.1 W.m−2 are needed to evaluate the temporal variations of the EEI at decadal and longer time-scales. The CERES experiment provides EEI time variations with a typical uncertainty of ± 0.1 W.m−2 and shows a trend in EEI of 0.50 +/- 0.47 W.m−2 per decade over the period 2005-2019. The combination of space altimetry and space gravimetry measurements provides an estimate of the ocean heat content (OHC) change which is an accurate proxy of EEI (because >90% of the excess of energy stored by the planet in response to the EEI is accumulated in the ocean in the form of heat). In Marti et al. (2021), the global OHC was estimated at global scales based on the combination of space altimetry and space gravimetry measurements over 2002-2016. Changes in the EEI were then derived with realistic estimates of its uncertainty. Here we present the improvements brought to the global OGC and EEI over an extended period (2002-2021), such as the calculation of the expansion efficiency of heat over the total water column, the improvement of ocean mass solution, the empirical correction of the wet tropospheric correction of Jason-3 altimeter measurements (Barnoud et al., 2022). The space geodetic GOHC-EEI product based on space altimetry and space gravimetry is available on the AVSIO website at https://doi.org/10.24400/527896/a01-2020.003. References: Barnoud A., Picard B., Meyssignac B., Marti F., Ablain M., Roca R. Reducing the uncertainty in the satellite altimetry estimates of global mean sea level trends using highly stable water vapour climate data records. Submitted to JGR: Oceans. Marti, F., Blazquez, A., Meyssignac, B., Ablain, M., Barnoud, A., Fraudeau, R., Jugier, R., Chenal, J., Larnicol, G., Pfeffer, J., Restano, M., and Benveniste, J.: Monitoring the ocean heat content change and the Earth energy imbalance from space altimetry and space gravimetry, Earth Syst. Sci. Data, 14, 229–249, https://doi.org/10.5194/essd-14-229-2022, 2022.

This paper reviews the recent progress in our estimation of ocean dynamic topography and the derived surface geostrophic currents, mainly based on multiple nadir radar altimeter missions. These altimetric observations provide the cornerstone of our ocean circulation observing system from space. The largest signal in sea surface topography is from the mean surface dominated by the marine geoid, and we will discuss recent progress in observing the mean ocean circulation from altimetry, once the geoid and other corrections have been estimated and removed. We then address the recent advances in our observations of the large-scale and mesoscale ocean circulation from space, and the particular challenges and opportunities for new observations in the polar regions. The active research in the ocean barotropic tides and internal tidal circulation is also presented. The paper also addresses how our networks of global multi-satellite and in situ observations are being combined and assimilated to characterize the four-dimensional ocean circulation, for climate research and ocean forecasting systems. For the future of ocean circulation from space, the need for continuity of our current observing system is crucial, and we discuss the exciting enhancement to come with global wide-swath altimetry, the extension into the coastal and high-latitude regions, and proposals for direct total surface current satellites in the 2030 period.

This presentation provides an update on the ESA radar altimetry processing services portfolio, known as SARvatore, for the exploitation of CryoSat-2 (CS-2) and Sentinel-3 (S-3) data from L1A (FBR) data products up to SAR/SARin L2 geophysical data products. The following on-line & on-demand services compose the portfolio, now hosted in the ESA Altimetry Virtual Lab at the EarthConsole® (https://earthconsole.eu): The ESA-ESRIN SARvatore (SAR Versatile Altimetric Toolkit for Research & Exploitation) for CS-2 and S-3 services. These processor prototypes allow the users to customize the processing at L1b & L2 by setting a list of configurable options, including those not available in the operational processing chains (e.g., SAMOSA+ and ALES+ SAR retrackers). The TUDaBo SAR-RDSAR (TU Darmstadt – U Bonn SAR-Reduced SAR) for CS-2 and S-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS, SINCS OV) are available. The TU München ALES+ SAR for CS-2 and S-3 service. It allows users to process L1b data applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution. The Aresys FF-SAR (Fully-Focused SAR) for CS-2 & S-3 service. It provides the capability to produce L1b products with several configurable options and with the possibility of appending the ALES+ FFSAR output to the L1b products. In the future, the service will be extended to process Sentinel-6 data. The following new services will be made available: the CLS SMAP S-3 FF-SAR processor (s-3-smap, http://doi.org/10.5270/esa-cnes.sentinel-3.smap) and the ESA-ESTEC/isardSAT L1 Sentinel-6 Ground Prototype Processor. All output data products are generated in standard netCDF format and are therefore also compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info). The Altimetry Virtual Lab is a community space for simplified processing services and knowledge-sharing, hosted on the EarthConsole®, a powerful EO data processing platform now on the ESA Network of Resources. This enables SARvatore Services to remain open for worldwide scientific applications, including for R&D studies on the retrieval of radar altimetry measured variables contributing to Inland Water monitoring (write to altimetry.info@esa.int for further information).

Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modeling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges but also greatly enhance the study of storm surges and other coastal phenomena.

Just as CryoSat-2, Sentinel-3 embarks on board a radar altimeter (SRAL) with the novel Synthetic Aperture Radar (SAR) mode that enables higher resolution and more accurate altimeter-derived parameters in the coastal zone, thanks to the reduced along-track footprint. Exploiting the SAR data in the recent years, many researchers have already proven that the performance of SAR altimetry with specific coastal retrackers is superior to collocated Pseudo-Low Resolution Mode (PLRM) coastal altimetry algorithms but they also pointed out that residual errors due to land contamination are still present in the very proximity of the land (0–3 km). The objective of this work is to further improve these results by exploiting extra information provided by SAR altimeters, namely the so-called Range Integrated Power (RIP), the new waveform built by a simple integration of the Doppler beams in the range direction. The RIP characterizes the backscattering state of the ground cell, towards which all the Doppler beams have been steered. These developments lead to a new retracker, here coined SAMOSA++, in which the RIP, as computed from the L1B-S data, is converted into a surface backscattering profile and directly integrated in the SAMOSA retracker as part of the model formulation itself. In this way, the modified SAMOSA model is automatically and autonomously able to cope with the different return waveform shapes from different surface types: either diffusive or specular. The mean square slope computed from the RIP is also estimated, representing a new output of the retracker. The performance of this new retracker is here cross-compared against its previous version, SAMOSA+, and against the standard Sentinel-3 marine PDGS (Payload Data Ground Segment) SAR retracker (SAMOSA2) in both coastal zone and open ocean in order to ensure a seamless transition between these zones. The new retracker SAMOSA++ is validated in the North East Atlantic region, where appropriate in situ validation data are available. The retrievals from the new retracker are cross-compared against the network of tide gauges and buoys in the German Bight and versus the output of the GCOAST Helmholtz-Zentrum Geesthacht (HZG) regional circulation and wave model. In addition, sea level estimates derived with different ocean tide and wet path delay geophysical correction models are compared. Results indicate that in this region the best geophysical correction models are the FES2014b tide model and the GPD+ wet tropospheric correction that incorporates data from the Sentinel-3 on-board radiometer. Analyses show that both SAMOSA+ and SAMOSA++ ensure the continuity of the PDGS SAR Marine retracker in the open ocean, leading to clear improvements in the coastal zone, larger for SAMOSA++ than for SAMOSA+. In summary, the new SAMOSA++ retracker retrieves more accurate altimetric parameters in the coastal zone, with a better consistency with respect to regional ocean models and in situ data.

As the first satellite altimetry mission operating in sar (delay-Doppler) mode, CryoSat-2 is an interesting mission to analyze its performance for water level monitoring over inland water bodies. It offers the opportunity to make comparison of sar and conventional altimeters by a multi-mode altimeter mounted on the same platform with a long repeat orbit. This comparison gives us more knowledge about the performance of the sar altimeter. Even tough it is not possible to perform it over same objects. In this paper we analyze the CryoSat-2 performance for water level monitoring via full- and sub-waveform retracking against in-situ gauge and L2 products of other satellite altimetry missions, e.g. Envisat and Jason-2. To this end, we retrack the full-waveforms and sub-waveforms with different empirical and physical retracking algorithms such as ocog , threshold, β -parameters and samosa 3. We evaluate its capability in all measurement modes, i.e. lrm , sar and sari n, over inland water bodies located in different climatic zones. We selected study areas with different shapes and sizes. Relative to in situ measurements we find a precision of the CryoSat-2 lrm mode of 15 cm rms over Qinghai lake (China) and 13 cm over Erie lake ( usa ). This is an improvement over Envisat, yielding precision of 17 cm in both cases. For the sar mode over Neagh lake (Northern Ireland) and Derg lake (Ireland) we obtain 15 cm and 13 cm where Envisat yields 28 cm and 100 cm , respectively. The sari n mode’s precision is assessed over Nasser lake (Egypt) with 25 cm rms and Athabasca lake (Canada) with 16 cm . Over these lakes Jason-2 achieved 54 cm and Envisat 19 cm , respectively. The most precise results of CryoSat-2 are obtained with our retracking and sub-waveform selection scenarios. Comparing CryoSat-2 results from our retracking scenarios using L1b data with those results obtained from L2 products (data) of this mission shows an improvement of 4–17 cm. The minimum and maximum improvements belong to Erie and Derg lakes respectively, the largest and smallest lakes. From the waveform analysis over lakes with different shapes and sizes, we found that the first and the mean-all sub-waveforms (mean correction from all sub-waveforms) retracked with the threshold and samosa 3 (only for sar mode) retrackers are appropriate to retrieve water level variation of small lakes and complex shaped lakes in this study. Over large lakes the full-waveform retracking leads to better results. In the case of icy-lake objects, sub-waveform retracking scenarios (the first and mean-all sub-waveforms) are more precise than the other scenarios. These are our findings over few samples, though more samples need to be analyzed to support them strongly.

We present results of contemporary coastal sea level changes along the coasts of Western Africa, obtained from a dedicated reprocessing of satellite altimetry data done in the context of the ESA ‘Climate Change Initiative’ sea level project. High sampling rate (20 Hz) sea level data from the Jason-1 and Jason-2 missions over a 14-year-long time span (July 2002 to June 2016) are considered. The data were first retracked using the ALES adaptative leading edge subwaveform retracker. The X-TRACK processing system developed to optimize the completeness and accuracy of the corrected sea level time series in coastal ocean areas was then applied. From the 14-year long sea level time series finally obtained, we estimate sea level trends along the Jason-1 & 2 tracks covering the study region. We analyze regional variations in sea level trends, with a focus on the changes observed between the open ocean to the coastal zone. Compared to the conventional 1 Hz sea level products dedicated to open ocean applications, the retracked 20 Hz measurements used in this study allow us to retrieve valid sea level information much closer to the coast (less than 3–4 km to the coast, depending on the satellite track location). The main objective of this study is twofold: (1) provide sea level products in the coastal areas from reprocessed altimetry data and (2) check whether sea level changes at the coast differ from that reported in the open ocean with conventional altimetry products. In the selected region, results show that over the study period, sea level trends observed near the coast of Western Africa are significantly different than offshore trends. In order to assess the robustness of the results, detailed analyses are performed at several locations to discriminate between possible drifts in the geophysical corrections and physical processes potentially able to explain the sea level changes observed close to the coast.

The scope of this presentation is to provide an update on the ESA radar altimetry services portfolio for the exploitation of CryoSat-2 (CS-2) and Sentinel-3 (S-3) data from L1A (FBR) data products up to SAR/SARin L2 geophysical data products. At present, the following on-line & on-demand services compose the portfolio:The ESA-ESRIN SARvatore (SAR Versatile Altimetric TOolkit for Research & Exploitation) for CS-2 and S-3 services. These processor prototypes allow the users to customize the processing at L1b & L2 by setting a list of configurable options, including those not available in the operational processing chains (e.g. SAMOSA+ and ALES+ SAR retrackers). The TUDaBo SAR-RDSAR (TU Darmstadt – U Bonn SAR-Reduced SAR) for CS-2 and S-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS, SINCS OV) are available. The TU München ALES+ SAR for CS-2 and S-3 service. It allows users to process official L1b data and produces L2 products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution. The Aresys FF-SAR (Fully-Focused SAR) for CS-2 service. Currently under development, it will provide the capability to produce L1b products with several configurable options and with the possibility of appending the ALES+ FFSAR output to the L1b products. In the future, these services will be extended and the following new services will be made available: the Aresys FF-SAR services for S-3 & Sentinel-6, the CLS SMAP S-3 FF-SAR processor (s-3--smap) and the ESA-ESTEC/isardSAT L1 Sentinel-6 Ground Prototype Processor. All output data products are generated in standard netCDF format, and are therefore also compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info).The SARvatore Services are being migrated from the ESA G-POD (https://gpod.eo.esa.int/) to the Altimetry Virtual Lab, a community space for simplified services access and knowledge-sharing. It will be hosted on EarthConsole (https://earthconsole.eu), a powerful EO data processing platform now also on the ESA Network of Resources. This enables SARvatore Services to remain open for worldwide scientific applications (info at altimetry.info@esa.int).

HYDROCOASTAL is a two-year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland water, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided.The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small-scale processes that govern these interactions. Also, the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes.The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also, an improved Wet Troposphere Correction will be developed and evaluated.The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

The sea level retrievals from the latest generation of radar altimeters (the SAR altimeters) are still challenging in the coastal zone and areas covered by sea ice and require a dedicated fitting (retracking) strategy for the waveforms. In the framework of the European Space Agency’s Baltic + Sea Level (ESA Baltic SEAL) project, an empirical retracking strategy (ALES + SAR), including a dedicated sea state bias correction, has been designed to improve the sea level observations in the Baltic Sea, characterised by a jagged coastline and seasonal sea ice coverage, without compromising the quality of open ocean data. In this work, the performances of ALES + SAR are validated against in-situ data in the Baltic Sea. Moreover, variance, crossover differences and power spectral density of the open ocean data are evaluated on a global scale. The results show that ALES + SAR performances are of comparable quality to the ones obtained using physical-based retrackers, with relevant advantages in coastal and sea ice areas in terms of quality and quantity of the sea level data.

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.

Climate-related sea level changes in the world coastal zones result from the superposition of the global mean rise due to ocean warming and land ice melt, regional changes caused by non-uniform ocean thermal expansion and salinity changes, and by the solid Earth response to current water mass redistribution and associated gravity change, plus small-scale coastal processes (e.g., shelf currents, wind & waves changes, fresh water input from rivers, etc.). So far, satellite altimetry has provided global gridded sea level time series up to 10–15 km to the coast only, preventing estimation of sea level changes very close to the coast. Here we present a 16-year-long (June 2002 to May 2018), high-resolution (20-Hz), along-track sea level dataset at monthly interval, together with associated sea level trends, at 429 coastal sites in six regions (Northeast Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, Southeast Asia and Australia). This new coastal sea level product is based on complete reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 missions., Measurement(s) coastal sea level changes Technology Type(s) satellite imaging of a planet • computational modeling technique Factor Type(s) year of data collection Sample Characteristic - Environment coastal sea water • sea coast • ocean Sample Characteristic - Location Northeast Atlantic Ocean • Mediterranean Sea • West Africa • North Indian Ocean • Southeast Asia • Australia Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12999596

This article aims to provide a tour of satellite missions for Coastal Hazards Monitoring, of relevant applications, as well as the downstream International Services such as the Copernicus Ocean and Land Monitoring Services. Earth observation (EO) satellite remote sensing provides global, repetitive and long-term observations with increasing resolution with every new generation of sensors. They permit the monitoring of small-scale signals like the ones impacting the coastal zone. EO missions are showcased in this article. Transforming the data products based on the satellite mission ground segment (usually called geophysical products, geophysical data records or so-called Level 2 products) into information useable by managers and decision-makers is done by downstream international services. This is an essential step to increase the uptake of satellite data for the benefit of society. Here, the type of services provided by, e.g., the European Copernicus Programme, is described along with examples of applications, such as monitoring storm surges.

Given the major role of the Atlantic Ocean in the climate system, it is essential to characterize the temporal and spatial variations of its heat content. The 4DATLANTIC-OHC Project (https://eo4society.esa.int/projects/4datlantic-ohc/) aims at developing and testing space geodetic methods to estimate the local ocean heat content (OHC) changes over the Atlantic Ocean from satellite altimetry and gravimetry. The strategy developed in the frame of the ESA MOHeaCAN Project (https://eo4society.esa.int/projects/moheacan/) is pursued and refined at local scales both for the data generation and the uncertainty estimate. At two test sites, OHC derived from in situ data (RAPID and OVIDE-AR7W) are used to evaluate the accuracy and reliability of the new space geodetic based OHC change. The Atlantic OHC product will be used to better understand the complexity of the Earth’s climate system. In particular, the project aims at better understanding the role played by the Atlantic Meridional Overturning Circulation (AMOC) in regional and global climate change, and the variability of the Meridional Heat transport in the North Atlantic. In addition, improving our knowledge on the Atlantic OHC change will help to better assess the global ocean heat uptake and thus estimate the Earth’s energy imbalance more accurately as the oceans absorb about 90% of the excess energy stored by the Earth system.The objectives of the 4DATLANTIC-OHC Project will be presented. The scientific requirements and data used to generate the OHC change products over the Atlantic Ocean and the first results in terms of development will be detailed. At a later stage, early adopters are expected to assess the OHC products strengths and limitations for the implementation of new solutions for Society. The project started in June 2021 for a 2-year duration.Visit https://www.4datlantic-ohc.org to follow the main steps of the project.

River discharge monitoring is crucial for many activities ranging from the management of water resources to flood risk mitigation. Due to the limitations of the in situ stations (e.g., low station density, incomplete temporal coverage as well as delays in data access), the river discharge is not always continuously monitored in time and in space. This prompted researchers and space agencies, among others, in developing new methods based on satellite observations for the river discharge estimation.In the last decade, ESA has funded the SaTellite based Runoff Evaluation And Mapping and River Discharge Estimation (STREAMRIDE) project, which proposes the combination of two innovative and complementary approaches, STREAM and RIDESAT, for estimating river discharge. The innovative aspect of the two approaches is an almost exclusive use of satellite data. In particular, precipitation, soil moisture and terrestrial water storage observations are used within a simple and conceptual parsimonious approach (STREAM) to estimate runoff, whereas altimeter and Near InfraRed (NIR) sensors are jointly exploited to derive river discharge within RIDESAT. By modelling different processes that act at the basin or at local scale, the combination of STREAM and RIDESAT is able to provide less than 3-day temporal resolution river discharge estimates in many large rivers of the world (e.g., Mississippi, Amazon, Danube, Po), where the single approaches fail. Indeed, even if both the approaches demonstrated high capability to estimate accurate river discharge at multiple cross sections, they are not optimal under certain conditions such as in presence of densely vegetated and mountainous areas or in non-natural basins with high anthropogenic impact (i.e., in basin where the flow is regulated by the presence of dams, reservoirs or floodplains along the river; or in highly irrigated areas).Here, we present some new advancements of both STREAM and RIDESAT approaches which help to overcome the limitations encountered. In particular, specific modules (e.g., reservoir or irrigation modules for STREAM approach) as well as algorithm retrieval improvements (e.g., to take into account the sediment and the vegetation for RIDESAT algorithm) were implemented. Furthermore, in order to exploit the complementarity of the two approaches, the two river discharge estimates were also integrated within a simple data integration framework and evaluated over sites located on the Amazon and Mississippi river basins. Results demonstrated the added-value of a complementary river discharge estimate with respect to a stand-alone estimate.

Understanding and sustainably managing complex environments such as marine ecosystems benefits from an integrated approach to ensure that information about all relevant components and their interactions at multiple and nested spatiotemporal scales are considered. This information is based on a wide range of ocean observations using different systems and approaches. An integrated approach thus requires effective collaboration between areas of expertise in order to improve coordination at each step of the ocean observing value chain, from the design and deployment of multi-platform observations to their analysis and the delivery of products, sometimes through data assimilation in numerical models. Despite significant advances over the last two decades in more cooperation across the ocean observing activities, this integrated approach has not yet been fully realized. The ocean observing system still suffers from organizational silos due to independent and often disconnected initiatives, the strong and sometimes destructive competition across disciplines and among scientists, and the absence of a well-established overall governance framework. Here, we address the need for enhanced organizational integration among all the actors of ocean observing, focusing on the occidental systems. We advocate for a major evolution in the way we collaborate, calling for transformative scientific, cultural, behavioral, and management changes. This is timely because we now have the scientific and technical capabilities as well as urgent societal and political drivers. The ambition of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030) and the various efforts to grow a sustainable ocean economy and effective ocean protection efforts all require a more integrated approach to ocean observing. After analyzing the barriers that currently prevent this full integration within the occidental systems, we suggest nine approaches for breaking down the silos and promoting better coordination and sharing. These recommendations are related to the organizational framework, the ocean science culture, the system of recognition and rewards, the data management system, the ocean governance structure, and the ocean observing drivers and funding. These reflections are intended to provide food for thought for further dialogue between all parties involved and trigger concrete actions to foster a real transformational change in ocean observing., This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 862626, project EuroSea (Improving and Integrating European Ocean Observing and Forecasting Systems for Sustainable Use of the Oceans).

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of energy in the climate system. While necessary to better understand the Earth’s warming climate, measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5-1 W.m−2) compared to the amount of energy entering and leaving the climate system (~ 340 W.m-2). Accuracies better than 0.1 W.m−2 are needed to evaluate the temporal variations of the EEI at decadal and longer time-scales, characteristic of the response to anthropogenic and natural forcing. Since the ocean absorbs about 90% of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) provides an accurate proxy of the EEI. Here, the OHC is estimated at global scale based on the combination of space altimetry and space gravimetry measurements. Changes in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74±0.22 W m-2 (90% confidence level) between August 2002 and August 2016 and this value is increasing at a rate of 0.02 ± 0.05 W.m-2 (90% confidence level). Comparisons against independent estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. On the other hand, discrepancies are also detected at inter-annual scales indicating that the current accuracy of EEI needs further improvement at these time scales. Estimates of the regional OHC change are also provided preliminarily and will be improved in the following months with a focus on the Atlantic Ocean. In particular, the role of the halosteric effects will be further investigated and the resulting product will be assessed against hydrographic data. The space geodetic OHC-EEI product is freely available at https://doi.org/10.24400/527896/a01-2020.003.

Africa stands to gain from Earth Observation (EO) science, products and applications. However, its use and application remain below potential on the continent. This article examines how EO can better serve the needs of African users. First, we argue that a successful uptake of EO services is conditional on understanding the African context and matching EO development and deployment to it. Using reference cases, we find that actors outside Africa drive most EO initiatives, whereas country-level expenditures on EO remain low. Recent developments, such as the African space policy and strategy, and initiatives in partnerships with Africa-based organisations to develop a community of practice on EO hold the potential to fill the identified gaps. The analysis indicates that most EO users are either government organisations or researchers, with very few cases involving other types of users. It is generally assumed that users at the local levels are educated and digitally literate, or that the transmission of EO-based knowledge is achieved by government officers and researchers. Although still very few, potentials are emerging for the private sector to deploy EO products and services such as crop or index-based insurance directly to farmers. These private initiatives have prospects for further developing indigenous EO capacity as envisioned in the African space policy and strategy. We then formulate recommendations for a transdisciplinary approach that integrates user contexts, attributes and needs to enhance the uptake of EO products and services in Africa. We conclude by proposing actions to close some of the identified gaps and seize emerging opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s10712-022-09724-1.

Sentinel-3 is a multi-instrument mission designed to measure sea-surface topography, sea- and land-surface temperature, ocean color and land color with high resolution and accuracy. The S3 mission is based on a constellation of two (3A and 3B) polar-orbiting satellites and it is designed and operated in the framework of the Copernicus programme, with planned 3C and 3D to ensure continuity. The mission builds up the legacy of ERS-1, ERS-2, ENVISAT and particularly CryoSat for the altimeter. Seninel-3A was launched in February 2016 and Seninel-3B in April 2018. They are equipped with a dual-frequency (Ku- and C-band) altimeter and can work both in low resolution (LRM) and SAR mode, the latter being designed to achieve high along-track discrimination. The low-resolution mode exploits conventional pulse-limited altimeter operation at C band. To approximate LRM operation at Ku band, a pseudo low-resolution mode is achieved by properly processing SAR acquisitions. Recently, a new research project funded by the European Space Agency, i.e., ALtimetry for BIOMass (ALBIOM), has been initiated to study the possibility of deriving forest biomass using Sentinel-3 altimetry data. ALBIOM aims at improving biomass global dataset, which is defined and classified as an Essential Climate Variable. In the last two decades, the exploitation of radar altimetry for studying land parameters has received renewed interest, including processing for the characterization of vegetation features and soil moisture. The vegetation cover has two main effects on the nadir backscatter measured by the altimeter. It attenuates the coherent reflection of the soil and add an incoherent volume scattering contribution. The relative weight of the two contributions depends of course form the frequency. To assess in what extent radar altimetry data are sensitive to the presence of vegetation forest, a study of the dynamic of the Sentinel-3 power waveforms with respect to the above ground biomass is needed. More importantly, the way radar waveforms are affected by disturbing land parameters, such as soil moisture, topography and surface roughness, has to be understood. In this work, an analysis considering both high- and low-resolution data made available by the Copernicus hub service is carried out. The sensitivity study of Sentinel-3 altimetry data to forest biomass over Africa is based on calibrated Sentinel-3 waveforms combined in space and time with forest biomass maps and ancillary information on the soil topography derived from a Digital Elevation Model. Comparison among Ku- and C-band waveforms are discussed, highlighting the critical aspect of the correct positioning of the time-tracking window over land, which often appears partly or completely misplaced, determining waveforms either truncated or containing noise only. The detrimental effect of the waveform truncation for the estimation of biomass and the possible mitigation approach has been considered. The study revealed that both waveforms and NRCSs can be sensitive to the presence of biomass in the order of 100-400 tons/ha, even if they can be strongly influenced by the presence of irregular topography within the system footprint. Different sensitivities with respect to the three channels (i.e., bandwidths and resolution modes) have been observed. A study about the use of differential NRCSs, defined as the difference between two different bandwidths, proposed by previous studies, is under investigation. Further research activities also connected to a modelling approach are in progress and will be discussed at the conference.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of heat in the climate system. Monitoring the EEI is therefore necessary to better understand the Earth's warming climate. Measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5–1 W m−2) compared to the amount of energy entering and leaving the climate system (∼340 W m−2). Since the ocean absorbs more than 90 % of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) change provides an accurate proxy of the EEI. This study provides a space geodetic estimation of the OHC changes at global and regional scales based on the combination of space altimetry and space gravimetry measurements. From this estimate, the global variations in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74±0.22 W m−2 (90 % confidence level) between August 2002 and August 2016. Comparisons against estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. Further improvements are needed to reduce uncertainties and to improve the time series, especially at interannual timescales. The space geodetic OHC-EEI product (version 2.1) is freely available at https://doi.org/10.24400/527896/a01-2020.003 (Magellium/LEGOS, 2020).

ALtimetry for BIOMass (ALBIOM) is a research project funded by the European Space Agency to study the possibility of estimating above ground biomass by means of low- and high-resolution Sentinel-3 altimetry data. We present preliminary results of a sensitivity analysis, developed to assess in what extent waveforms and altimetry observables are affected by the presence of forests. Ku- and C-band altimetry data have been collected and processed to properly select well-tracked waveforms over land, which are then related to collocated biomass data. A statistical analysis has been performed, highlighting a sensitivity of the estimated normalized radar cross section with respect to forest biomass, even though it is strongly disturbed by the soil topography within the radar footprint. Additionally, the inaccurate positioning of the time-tracking window limits the number of useful data.

The launch of Sentinel-3A in February 2016 represented the beginning of a new long-term series of operational satellite radar altimeters, which will provide Delay-Doppler altimetry measurements over ice sheets for decades to come. Given the potential benefits that these satellites can offer to a range of glaciological applications, it is important to establish their capacity to monitor ice sheet elevation and elevation change. Here, we present the first analysis of Sentinel-3 Delay-Doppler altimetry over the Antarctic ice sheet, and assess the accuracy and precision of retrievals of ice sheet elevation across a range of topographic regimes. Over the low-slope regions of the ice sheet interior, we find that the instrument achieves both an accuracy and a precision of the order of 10 cm, with ∼98 % of the data validated being within 50 cm of co-located airborne measurements. Across the steeper and more complex topography of the ice sheet margin, the accuracy decreases, although analysis at two coastal sites with densely surveyed airborne campaigns shows that ∼60 %–85 % of validated data are still within 1 m of co-located airborne elevation measurements. We then explore the utility of the Sentinel-3A Delay-Doppler altimeter for mapping ice sheet elevation change. We show that with only 2 years of available data, it is possible to resolve known signals of ice dynamic imbalance and to detect evidence of subglacial lake drainage activity. Our analysis demonstrates a new, long-term source of measurements of ice sheet elevation and elevation change, and the early potential of this operational system for monitoring ice sheet imbalance for decades to come.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal and economic issues. This article reports about a new sea level dataset for the North Sea (named NorthSEAL) of monthly sea level anomalies (SLA), absolute sea level trends and sea level mean annual amplitudes over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data, preprocessed 5 with coastal dedicated approaches and gridded with innovative methods to a 6–8 km wide triangular mesh. The comparison of SLA and tide gauge time series shows a good consistency with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best 10 consistency with a median difference of 0.04 ± 1.15 mm/year is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections and studies on coastal adaptation measures. The NorthSEAL data is available at https://doi.org/10.17882/79673 (Müller et al., 2021).

The scope of this presentation is to feature and provide an update on the ESA G-POD/SARvatore family of altimetry services portfolio for the exploitation of CryoSat-2 and Sentinel-3 data from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. At present, the following on-line & on-demand services compose the portfolio:- The SARvatore (SAR Versatile Altimetric TOolkit for Research & Exploitation) for CryoSat-2 and Sentinel-3 services developed by the Altimetry Team in the R&D division at ESA-ESRIN. These processor prototypes are versatile and allow the users to customize and adapt the processing at L1b & L2 according to their specific requirements by setting a list of configurable options. The scope is to provide users with specific processing options not available in the operational processing chains (e.g. range walk correction, stack sub-setting, extended receiving window, zero padding, high-posting rate and burst weighting at L1b & SAMOSA+, SAMOSA++ and ALES+ SAR retrackers at L2). AJoin & Share Forum (https://wiki.services.eoportal.org/tiki-custom_home.php) allows users to post questions and report issues. A data repository is also available to the Community to avoid the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository).- The TUDaBo SAR-RDSAR (Technical University Darmstadt – University Bonn SAR-Reduced SAR) for CryoSat-2 and Sentinel-3 service. It allows users to generate reduced SAR, unfocused SAR & LRMC data. Several configurable L1b & L2 processing options and retrackers (BMLE3, SINC2, TALES, SINCS) are available. The processor will be extended during an additional activity related to the ESA HYDROCOASTAL Project (https://www.satoc.eu/projects/hydrocoastal/) to account in the open ocean for the vertical motion of the wave particles (VMWP) in unfocused SAR and in a simplified form of the fully focused SAR called here Low Resolution Range Cell Migration Correction-Focused (LRMC-F). - The ALES+ SAR for CryoSat-2 and Sentinel-3 service. It allows users to process official L1b data and produces L2 NetCDF products by applying the empirical ALES+ SAR subwaveform retracker, including a dedicated SSB solution, developed by the Technische Universität München in the frame of the ESA Sea Level CCI (http://www.esa-sealevel-cci.org/) & BALTIC+ SEAL Projects (http://balticseal.eu/).- The Aresys Fully Focused SAR for CryoSat-2 service. Currently under development, it will provide the capability to produce CS-2 FF-SAR L1b products thanks to the Aresys 2D transformed frequency domain AREALT-FF1 processor prototype. Output products will also include geophysical corrections and threshold peak & ALES-like subwaveform retracker estimates.The G-POD graphical interface allows users to select, in all the services, a geographical area of interest within the time-frame related to the L1A (FBR) & L1b data products availability in the service catalogue. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.Services are open, free of charge (supported by ESA) for worldwide scientific applications and available, after registration and activation (to be requested for each chosen service to eo-gpod@esa.int), at https://gpod.eo.esa.int.

Climate-related sea level changes in the world coastal zones result from the superposition of the global mean rise due to ocean warming and land ice melt, regional changes mostly caused by non-uniform ocean thermal expansion and salinity changes, and small-scale coastal processes (e.g., shelf currents, wind & waves changes, fresh water input from rivers, etc.). So far, satellite altimetry has provided global gridded sea level time series up to 10-15 km to the coast only, preventing estimation of sea level changes very close to the coast. In the context of the ESA Climate Change Initiative coastal sea level project, we have developed a complete reprocessing of high-resolution (20 Hz) Jason-1, 2 and 3 altimetry data along the world coastal zones using the ALES (Adaptative Leading Edge Subwaveform) retracker combined with the XTRACK system dedicated to improve geophysical corrections at the coast. Here we present coastal sea level trends over the period 2002-2020 along the whole African continent. Different coastal sea level trend behaviors are observed over the study period. We compare the computed coastal trends in Africa with results we previously obtained in other regions (Mediterranean Sea, Northeastern Europe, north Indian Sea, southeast Asia and Australia).

STREAM -SaTellite based Runoff Evaluation And Mapping- is a conceptual hydrological model able to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. The model is very simple and versatile: It requires a limited number of parameters (only eight) to simulate river discharge.The model simulates river discharge and gridded runoff at daily time scale with a 25 km spatial resolution. Forced by TRMM 3B42 rainfall data and ESA CCI soil moisture data and GRACE over five pilot large basins (Mississippi, Amazon, Niger, Danube and Murray Darling) the model already provided good runoff estimates especially over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the basin outlet and over several inner stations in the basin. Good results have been also obtained for Mississippi, Niger and Danube with KGE index greater than 0.75 for all the gauging stations.By considering the good performances of the STREAM model and by the continuous availability (in space and time) of satellite observations, this work presents an attempt to regionalize the STREAM model parameters. The Mississippi river basin has been taken as case study and specific relationships between model parameters and different predictors (climate variables such as precipitation and evaporation, soil vegetation and topography characteristics) have been developed. By using these relationships, STREAM parameter values have been directly obtained from readily available climatic and physiographic basin characteristics and model performances are still satisfactory (median KGE over the basin equal to 0.60). The capability to use these relationships in other hydrologically similar catchments will be investigated for the Danube and Amazon river basins. The final target is to obtain global relationships as to provide to provide daily, 25 km, global runoff maps from the STREAM approach.

The ALtimetry for BIOMass (ALBIOM) project is an ESA-funded Permanent Open Call Project that aims to retrieve forest biomass using Copernicus Sentinel-3 (S3) altimeter data. The overall goal of ALBIOM is to estimate biomass with sufficient accuracy to be able to increase existing satellite data for biomass retrieval, as well as to improve the global mapping and monitoring of this fundamental variable. The project core tasks consist of 1) an analysis of the sensitivity of altimetry backscatter data on land parameters; 2) the development and validation of a Sentinel-3 altimeter backscatter simulator, including the effect of both topography and vegetation and 3) the development and validation of a machine-learning biomass estimation algorithm.Here we present a summary of the results obtained from the project. The sensitivity analysis reveals that both the altimetric waveforms and the corresponding Normalised Radar Cross Sections (NRCSs) can be sensitive to the presence of biomass in the order of 100-400 tons/ha, but they are also influenced by topography and water bodies. Different sensitivities with respect to the different frequencies and resolution modes are observed, highlighting non-linear behaviours of the NRCSs. The use of differential NRCSs, defined as the difference among those calculated over two different bandwidths, was demonstrated to be not necessarily more sensitive to vegetation, as it was instead highlighted by previous studies like [Papa et al., 2003].The tracking window often appears partly or completely misplaced, when the tracking mode is in open-loop mode prescribing a predetermined range, and its size is often not long enough when collecting data over land, especially over regions with complex topography. The length and correct positioning of the tracking window over land represent therefore critical aspects for a study like ALBIOM.The modelling work has been focused on the development of a merged model approach to simulate altimeter waveforms over vegetated areas. The merging is obtained via the simultaneous use of the modifiedTor Vergata Scattering Model (TOVSM) [Ferrazzoli and Guerriero, 1995, 1996] to simulate the waveform of a flat surface covered by forest vegetation, and the use of the Soil And Vegetation Reflection Simulator (SAVERS) [Pierdicca et al., 2014], originally conceived for GNSS-Reflectometry, and here adapted to the Altimetry system. The simulator developed within ALBIOM shows promising ability to reproduce the general characteristics of the S3 waveforms. The simulations related to forested surfaces present at least two peaks, due to the top of canopy and to the ground, but the presence of topography may introduce other peaks in the waveforms, making the identification of vegetation and topographic effects challenging.Initial results on the algorithm development using Artificial Neural Networks (ANN) highlight some promising biomass estimates over specific areas (e.g. Central Africa) but also differences in algorithm performances among different regions. The corrected “ice” backscatter coefficient showed the highest sensitivity to biomass, but its values are often invalid over land, which limits the number of meaningful retrievals. The different altimeter tracking mode of Sentinel-3 over different areas of the globe (i.e., open loop and closed loop) could also be responsible for the differences in results.

Satellite altimetry missions provide a quasi-global synoptic view of sea level variations over more than 25 years and provide regional sea level (SL) indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address current climate science questions. While uncertainty estimates are available for the global mean sea level (GMSL), information is not available at local scales so far. We estimate a local satellite altimetry error budget and use it to derive local error variance-covariance matrices, and estimate confidence intervals on trends and accelerations at the 90% confidence level. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr−1 with 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 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets.A dataset consisiting of a single NetCDF file containing local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties is provided (https://doi.org/10.17882/74862). Code to reproduce the study is also distributed (https://github.com/pierre-prandi/rsl). With this information, users should be able to reuse these error levels to derive uncertainties on any metric (e.g. inter annual variability) or time period.

This paper presents an innovative approach, STREAM – SaTellite based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite soil moisture, precipitation and terrestrial water storage anomalies observations. Within a very simple model structure, the first two variables (precipitation and soil moisture) are used to estimate the quick-flow river discharge component while the terrestrial water storage anomalies are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then summed up to obtain river discharge and runoff estimates. The method is tested over the Mississippi river basin for the period 2003–2016 by using Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall data, European Space Agency Climate Change Initiative (ESA CCI) soil moisture data and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data. Despite the model simplicity, relatively high-performance scores are obtained in river discharge simulations, with a Kling-Gupta efficiency index greater than 0.65 both at the outlet and over several inner stations used for model calibration highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications (from flood warning systems to the understanding of water cycle), the added-value of the STREAM approach is twofold: 1) a simple modelling framework, potentially suitable for global runoff monitoring, at daily time scale when forced with satellite observations only, 2) increased knowledge on the natural processes, human activities and on their interactions on the land.

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. 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.

Satellite altimetry missions provide a quasi-global synoptic view of sea level variations over more than 25 years and provide regional sea level (SL) indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address current climate science questions. While uncertainty estimates are available for the global mean sea level (GMSL), information is not available at local scales so far. We estimate a local satellite altimetry error budget and use it to derive local error variance-covariance matrices, and estimate confidence intervals on trends and accelerations at the 90% confidence level. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr−1 with values ranging from 0.78 to 1.22 mm.yr−1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr−1, with a mean value of 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets. Local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties are provided., Measurement(s) sea surface height Technology Type(s) satellite radar altimetry Factor Type(s) year of data collection Sample Characteristic - Environment sea • ocean Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13297757

International audience; In the context of the ESA Climate Change Initiative project, a new coastal sea level altimetry product has been developed in order to support advances in coastal sea level variability studies. Measurements from Jason-1,2&3 missions have been retracked with the Adaptive Leading Edge Subwaveform (ALES) Retracker and then ingested in the X-TRACK software with the best possible set of altimetry corrections. These two coastal altimetry processing approaches, previously successfully validated and applied to coastal sea level research, are combined here for the first time in order to derive a 16-year-long (June 2002 to May 2018), high-resolution (20-Hz), along-track sea level dataset in six regions: Northeast Atlantic, Mediterranean Sea, West Africa, North Indian Ocean, Southeast Asia and Australia. The study demonstrates that this new coastal sea level product called X-TRACK/ALES is able to extend the spatial coverage of sea level altimetry data ~3.5 km in the land direction, when compared to the X-TRACK 1-Hz dataset. We also observe a large improvement in coastal sea level data availability from Jason-1 to Jason-3, with data at 3.6 km, 1.9 km and 0.9 km to the coast on average, for Jason-1, Jason-2 and Jason-3, respectively. When combining measurements from Jason-1 to Jason-3, we reach a distance of 1.2-4 km to the coast. When compared to tide gauge data, the accuracy of the new altimetry near-shore sea level estimations also improves. In terms of correlations with a large set of independent tide gauge observations selected in the six regions, we obtain an average value of 0.77. We also show that it is now possible to derive from the X-TRACK/ALES product an estimation of the ocean current variability up to 5 km to the coast. This new altimetry dataset, freely available, will provide a valuable contribution of altimetry in coastal marine research community.

International audience; Since 2010, the CryoSat-2 satellite mission has enabled to largely improve sea ice freeboard estimations. But due to the complexity of radar echoes over sea ice, freeboard retrieval from altimetry still presents some errors and biases that further limit the potential of these observations for climate studies or for assimilation into models. Various methods have been explored, producing a large range of freeboard estimations. In this study, we analyze the main steps of the radar freeboard computation developed as part of the Cryo-SeaNice Project. The objective is to quantify the impacts of each processing method and to identify optimal strategies to improve freeboard estimations from SAR altimetry measurements. We consider two SAR processing options: the Hamming Window (HW) and with the Zero-Padding (ZP), and 2 retrackers: the Threshold First Maximum Retracker Algorithm (TFMRA) based on heuristic measurements and SAMOSA+ a retracker declined from model based analysis of the surface back-scatter. Four freeboard solutions are generated from combinations of the 2 processing options (HW and ZP or ZP only) and the 2 types of retrackers. In addition, an alternative to the Hamming Window method to filter out side-lobes errors is presented. The impacts of the different approaches to estimate freeboard are quantified from comparisons with Operation Ice Bridge (OIB) and the Beaufort Gyre Exploration project (BGEP) in situ data. Our results show that SAMOSA+ provides more precise freeboard estimations. This new time-series is available on CTOH website. We also identified some impacts of the Hamming Window for both retrackers. Finally, we present the potential of using the simpler threshold retracker but with a correction to account for the surface roughness that is calibrated against SAMOSA+.

The monitoring of rivers is not the primary objective of the Sentinel-3 mission. The first satellite of the constellation was launched in February 2016 and so far no study has investigated the joint use of altimeter, near-infrared and thermal sensors for discharge estimation. Nevertheless, similar sensors onboard other platforms have showed their ability to estimate river discharge also in scarcely gauged areas. The advantage of altimetry lies in the observation of water surface elevation, which can be proficiently used in approaches based on rating curve, empirical formulae or hydraulic modeling. Even though their use is limited, near-infrared sensors are successfully used to detect the variability of river discharge thanks to their high capacity to discriminate water from land. Thermal sensors are nearly completely unused, but the unique study that uses the difference in temperature of the river water between day and night for the estimation of water level, encourages its use for river discharge assessment as well. To improve the estimation of river discharge and foster studies that are aimed at monitoring ungauged rivers, the combination of the sensors is considered a viable path. The aim of this manuscript is to review these studies to show the limitations and the potentials of each sensor onboard the Sentinel-3 satellite and to investigate the added value of using these three sensors co-located on the same platform for river discharge monitoring.

Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal, and economic issues. This article reports about a new sea level dataset for the North Sea (named North SEAL) of monthly sea level anomalies (SLAs), absolute sea level trends, and amplitudes of the mean annual sea level cycle over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data preprocessed with coastal dedicated approaches and gridded with an innovative least-squares procedure including an advanced outlier detection to a 6–8 km wide triangular mesh. The comparison of SLAs and tide gauge time series shows good consistency, with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8 %–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best consistency with a median difference of 0.04±1.15 mm yr−1 is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections, and studies on coastal adaptation measures. The North SEAL data are available at https://doi.org/10.17882/79673 (Müller et al., 2021).

The monitoring of rivers by satellite is an up-to-date subject in hydrological studies as confirmed by the interest of space agencies to finance specific missions that respond to the quantification of surface water flows. We address the problem by using multi-spectral sensors, in the near-infrared (NIR) band, correlating the reflectance ratio between a dry and a wet pixel extracted from a time series of images, the C/M ratio, with five river flow-related variables: water level, river discharge, flow area, mean flow velocity and surface width. The innovative aspect of this study is the use of the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3 satellites, compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) used in previous studies. Our results show that the C/M ratio from OLCI and MODIS is more correlated with the mean flow velocity than with other variables. To improve the number of observations, OLCI and MODIS products are combined into multi-mission time series. The integration provides good quality data at around daily resolution, appropriate for the analysis of the Po River investigated in this study. Finally, the combination of only MODIS products outperforms the other configurations with a frequency slightly lower (~1.8 days).

Coastal zones have large social, economic and environmental values. They are more densely populated than the hinterland and concentrate large economic assets, critical infrastructures and human activities such as tourism, fisheries, navigation. Furthermore, coastal oceans are home to a wealth of living marine resources and very productive ecosystems. Yet, coastal zones are exposed to various natural and anthropogenic hazards. To reduce the risks associated with marine hazards, sustained coastal zone monitoring programs, forecasting and early warning systems are increasingly needed. Earth observations (EO), and in particular satellite remote sensing, provide invaluable information: satellite-borne sensors allow an effective monitoring of the quasi-global ocean, with synoptic views of large areas, good spatial and temporal resolution, and sustained time-series covering several years to decades. However, satellite observations do not always meet the precision required by users, in particular in dynamic coastal zones, characterized by shorter-scale variability. A variety of sensors are used to directly monitor the coastal zone and their observations can also be integrated into numerical models to provide a full 4D monitoring of the ocean and forecasts. Here, we review how EO, and more particularly satellite observations, can monitor coastal hazards and their drivers. These include coastal flooding, shoreline changes, maritime security, marine pollution, water quality, and marine ecology shifts on the one hand, and several physical characteristics (bathymetry, topography, vertical land motion) of coastal zones, meteorological and oceanic (metocean) variables that can act as forcing factors for coastal hazards on the other hand.

The Earth Energy Imbalance (EEI) is a key indicator to understand the Earth’s changing. However, measuring this indicator is challenging since it is a globally integrated variable whose variations are small, of the order of several tenth of W.m-2, compared to the amount of energy entering and leaving the climate system of ~340 W.m-2. Recent studies suggest that the EEI response to anthropogenic GHG and aerosols emissions is 0.5-1 W.m-2. It implies that an accuracy of the direct measurement of in situ temperature based on temperature/Salinity profiles (e.g. ARGO floats), the measurement of the net ocean surface heat fluxes from space (CERES), the estimate from ocean reanalyses that assimilate observations from both satellite and in situ instruments, the measurement of the thermal expansion of the ocean from space based on differences between the total sea-level content derived from altimetry measurements and the mass content derived from GRACE data (noted “Altimetry-GRACE”). To date, the best results are given by the first method based on ARGO network. However ARGO measurements do no sample deep ocean below 2000 m depth and marginal seas as well as the ocean below sea ice. Re-analysis provides a more complete estimation but large biases in the polar oceans and spurious drifts in the deep ocean mask a significant part of the OHC signal related to EEI. The method based on estimation of ocean net heat fluxes (CERES) is not appropriate for OHC calculation due to a too strong uncertainty (±15 W.m-2). In the MOHeaCAN project supported by ESA, we are being developed the “Altimetry-GRACE” approach which is promising since it provides consistent spatial and temporal sampling of the ocean, it samples the entire global ocean, except for polar regions, and it provides estimates of the OHC over the ocean’s entire depth. Consequently, it complements the OHC estimation from ARGO. However, to date the uncertainty in OHC from this method is close to 0.5 W.m-2, and thus greater than the requirement of 0.3 W.m-2 needed to a good EEI estimation. Therefore the scientific objective of the MOHeaCan project is to improve these estimates :by developing novel algorithms in order to reach the challenging target for the uncertainty quantification of 0.3 W. m−2; by estimating realistic OHC uncertainties thanks to an error budget of measurements applying a rigorous mathematical formalism; by developing a software prototype systems that allow to perform sensitivities studies and OHC product and its uncertainty generation; by assessing our estimation by performing comparison against independent estimates based on ARGO network, and based on the Clouds and the Earth’s Radiant energy System (CERES) measurements at the top of the atmosphere.

Water is at the centre of economic and social development; it is vital to maintain health, grow food, manage the environment, produce renewable energy, support industrial processes and create jobs. Despite the importance of water, to date over one third of the world's population still lacks access to drinking water resources and this number is expected to increase due to climate change and outdated water management. As over half of the world’s potable water supply is extracted from rivers, either directly or from reservoirs, understanding the variability of the stored water on and below landmasses, i.e., runoff, is of primary importance. Apart from river discharge observation networks that suffer from many known limitations (e.g., low station density and often incomplete temporal coverage, substantial delay in data access and large decline in monitoring capacity), runoff can be estimated through model-based or observation-based approaches whose outputs can be highly model or data dependent and characterised by large uncertainties. On this basis, developing innovative methods able to maximize the recovery of information on runoff contained in current satellite observations of climatic and environmental variables (i.e., precipitation, soil moisture, terrestrial water storage anomalies and land cover) becomes mandatory and urgent. In this respect, within the European Space Agency (ESA) STREAM Project (SaTellite based Runoff Evaluation And Mapping), a solid “observational” approach, exploiting space-only observations of Precipitation (P), Soil Moisture (SM) and Terrestrial Water Storage Anomalies (TWSA) to derive total runoff has been developed and validated. Different P and SM products have been considered. For P, both in situ and satellite-based (e.g., Tropical Rainfall Measuring Mission, TRMM 3B42) datasets have been collected; for SM, Advanced SCATterometer, ASCAT, and ESA Climate Change Initiative, ESA CCI, soil moisture products have been extracted. TWSA time series are obtained from the latest Goddard Space Flight Center’s global mascon model, which provides storage anomalies and their uncertainties in the form of monthly surface mass densities per approximately 1°x1° blocks. Total runoff estimates have been simulated for the period 2003-2017 at 5 pilot basins across the world (Mississippi, Amazon, Niger, Danube and Murray Darling) characterised by different physiographic/climatic features. Results proved the potentiality of satellite observations to estimate runoff at daily time scale and at spatial resolution better than GRACE spatial sampling. In particular, by using satellite TRMM 3B42 rainfall data and ESA CCI soil moisture data, very good runoff estimates have been obtained over Amazon basin, with a Kling-Gupta efficiency (KGE) index greater than 0.92 both at the closure and over several inner stations in the basin. Good results found for Mississippi and Danube are also encouraging with KGE index greater than 0.75 for both the basins.