Magellium (France)

Abstract. This study evaluates the impact of freshwater fluxes on the Atlantic Ocean. The river discharge has been estimated at the outlet of 18 major rivers flowing into the Atlantic by solving the water mass balance equation at the river basin scale. In this approach, water storage changes are evaluated with satellite gravimetry measurements. In contrast, atmospheric fluxes (i.e., precipitation, evapotranspiration) are assessed with atmospheric reanalyses, in situ measurements from a global network of rain gauges, or a global hydrological model. The river discharge estimated with the water mass balance is consistent with independent river gauge measurements across all South American rivers, in particular the Amazon, where the annual and monthly climatology can be estimated with an error of less than 5 % when compared with in situ measurements. Larger discrepancies are observed for other basins, likely due to uncertainties in the precipitation and evapotranspiration fluxes. When climatological estimates of the river discharge are replaced by the water mass balance approach in ocean model simulations, a decrease in the sea surface salinity bias is observed at the outlet of the Amazon and across the whole Atlantic. The water mass balance approach can bring new observational constraints on freshwater fluxes flowing from the continent to the ocean, but is not universally reliable due to potential biases in atmospheric flux estimates. When combined with in situ measurements or hydrological model predictions, the water mass balance approach can enhance freshwater flux quantification, leading to improved ocean model simulations near major river outlets. In particular, the enhanced river runoff dataset significantly reduces salinity biases, with associated changes in reduced vertical stratification, enhanced upper ocean circulation, and meridional transports.

The closure of the Sea Level Budget (SLB) is a key challenge for modern physical oceanography. First, it is essential that we ensure the proper identification and quantification of each significant contributor to sea level change through this closure. Second, it provides an efficient means to closely monitor and cross-validate the performance of intricate global observation systems, such as the satellite altimetry constellation, satellite gravimetry missions (GRACE/GRACE-FO), and the Argo in-situ network. Third, this closure reveals to be a beneficial approach for assessing how well the observed climate variables, such as sea level, barystatic sea level, temperature and salinity, land ice melt, and changes in land water storage, comply with conservation laws, in particular those related to mass and energy.In this presentation, we will discuss the state of knowledge of global mean and regional sea level budget with up-to-date observations, encompassing 1) an up-to-date assessment of the budget components and residuals, along with their corresponding uncertainties, spanning from 1993 to 2023 in global mean and throughout the GRACE and Argo era for spatial variations; 2) the identification of the periods and areas where the budget is not closed, i.e. where the residuals are significant; 3) advancements in the analysis and understanding of the spatial patterns of the budget residuals. To investigate the sea level budget (SLB) misclosure, we developed an objective solution that closes the SLB globally. This approach is based on an inverse method that optimally combines the contributions to sea level, weighted by their estimated instrumental uncertainties, and draws from publications such as those by Rodell et al. (2015) and L’Ecuyer et al. (2015).This objective method allows us to precisely identify the dates when the SLB misclosure falls outside the uncertainty estimates, as well as the contributor most likely responsible for the discrepancy. The results of this analysis will be detailed during the presentation.A focus will be made on the North Atlantic Ocean where the residuals are significantly high. We investigate the potential errors causing non-closure in each of the components (e.g., in situ data sampling for the thermosteric component, geocenter correction in the gravimetric data processing) as well as potential inconsistencies in their processing that may impact large-scale patterns (e.g., centre of reference and atmosphere corrections). This work is performed within the framework of the Sea Level Budget Closure Climate Change Initiative (SLBC_cci+) programme of the European Space Agency (https://climate.esa.int/en/projects/sea-level-budget-closure/). This project was initiated by the International Space Science Institute Workshop on Integrative Study of Sea Level Budget (https://www.issibern.ch/workshops/sealevelbudget/).

<strong class="journal-contentHeaderColor">Abstract.</strong> Closure of the regional sea level budget is investigated over the 2004&ndash;2022 time span by comparing trend patterns from the satellite altimetry-based sea level with the sum of contributions, i.e. the thermosteric, halosteric, manometric and GRD (Gravitational, Rotational, and Deformational fingerprints due to past and ongoing land ice melt) components. The thermosteric and halosteric components are based on Argo data. For the manometric component, two approaches are considered: one using GRACE/GRACE-Follow On satellite gravimetry data, and the other using ocean reanalyses-based sterodynamic sea level data corrected for local steric effects. For the latter, six different ocean reanalyses are considered, including two reanalyses that do not assimilate satellite altimetry data. The results show significantly high residuals in the North Atlantic for both approaches. In other regions, small-scale residuals of smaller amplitude are observed and attributed to the finer resolution of altimetry data compared to the coarser resolution of data sets used for the components. Focus on the strong residual signal seen in the North Atlantic suggests Argo-based salinity errors in this region. However, it is not excluded that other factors also contribute to the non-closure of the budget in this region.

<strong class="journal-contentHeaderColor">Abstract.</strong> Large scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages 20 years of high-resolution discharge data (2000&ndash;2020) from the ESA Climate Change Initiative (CCI) to enhance CTRIP and MGB models via ensemble Kalman Filter frameworks (HyDAS and HYFAA). Applied to the Niger and Congo basins, the models assimilate discharge data derived from altimetry and multispectral imagery, alongside water surface elevation (WSE) anomaly data, to evaluate their impact on model performance. Discharge assimilation was more effective than WSE anomaly assimilation, as it provided a more direct input for improving model accuracy. Temporal data density was the key factor in reducing bias and enhancing the simulation of seasonal flow patterns, with spatial coverage and data quality also playing important roles. In the Niger Basin, the assimilation of denser discharge data resulted in a significant bias reduction, which should improve the representation of long-term climate trends. Furthermore, the higher temporal resolution allowed for better capture of flow variability, which is crucial for both seasonal climate studies and short-term predictions, such as extreme hydrological events. The study also emphasizes the trade-offs between data resolution and quality, particularly in the Congo Basin. Future advancements include merging altimetry and multispectral discharge data, improving the discharge retrieval algorithms using SWOT data, and refining data assimilation techniques to improve climate studies and river system modeling in complex, climate-impacted basins.

Abstract. The spatial distribution of mountain snow water equivalent (SWE) is key information for water management. We implement a tool to simulate snowpack properties at high resolution (100 m) by using only global datasets of meteorology, land cover and elevation. The meteorological data are obtained from ERA5, which makes the method applicable in near real time (5 d latency). We evaluate the output using 49 SWE maps derived from airborne lidar surveys in the Sierra Nevada. We find very good agreement at the catchment scale using uncalibrated lapse rates. Larger biases at the model grid scale are especially evident at high elevation but do not alter the catchment-scale snow mass accuracy. We additionally compare the simulated snow depth to Sentinel-1 retrievals and find a similar accuracy with respect to synchronous airborne lidar surveys. However, Sentinel-1 snow depth products are sparse and often masked during the melt season, whereas ERA5–SnowModel provides a spatially and temporally continuous SWE.

"Gathering users and developers to shape together the next-generation ocean reanalyses: Ocean reanalyses workshop of the European Copernicus Marine Service" published on 01 Apr 2024 by American Meteorological Society.

Abstract. Closure of the regional sea level budget is investigated over the 2004–2022 time span by comparing trend patterns from the satellite altimetry-based sea level with the sum of contributions, i.e. the thermosteric, halosteric, manometric and GRD (Gravitational, Rotational, and Deformational fingerprints due to past and ongoing land ice melt) components. The thermosteric and halosteric components are based on Argo data. For the manometric component, two approaches are considered: one using GRACE/GRACE-Follow On satellite gravimetry data, and the other using ocean reanalyses-based sterodynamic sea level data corrected for local steric effects. For the latter, six different ocean reanalyses are considered, including two reanalyses that do not assimilate satellite altimetry data. The results show significantly high residuals in the North Atlantic for both approaches. In other regions, small-scale residuals of smaller amplitude are observed and attributed to the finer resolution of altimetry data compared to the coarser resolution of data sets used for the components. Focus on the strong residual signal seen in the North Atlantic suggests Argo-based salinity errors in this region. However, it is not excluded that other factors also contribute to the non-closure of the budget in this region.

Despite its significance, the snow water equivalent (SWE) is poorly characterized in many mountain regions due to i) a lack of in situ measurements and ii) the difficulty to measure the SWE directly from satellite observations.&amp;#160;We developed a tool to simulate the spatial distribution of SWE at high resolution (typically 100 m) in any region of interest using SnowModel (Liston and Elder, 2006a, 2006b), global meteorological data (ERA5) and satellite observations of the snow cover fraction (SCF). Satellite observations are used to mitigate errors in the model parameterization and the meteorological forcings using the particle batch smoother&amp;#160; (Margulis et al., 2015). This method consists in computing N perturbed simulations on the whole hydrological season and transforming the simulated SWE in a simulated SCF. In this study, we used the formula linking the SWE and the SCF of the Noah Land Surface Model as the measurement operator. After that, the simulations are compared to remotely sensed SCF data and weighted according to their agreements with the observations.&amp;#160;We implemented this data assimilation method with both MODIS and Sentinel-2 SCF data. We are testing it on the Bassies catchment in the French Pyrenees, and on the Tuolumne River Catchment in the Sierra Nevada, USA. In the Bassies catchment, we compare the results with snow depth maps from Pl&amp;#233;iade satellites. We perturbed the precipitations with a log-normal law and found a very good agreement between the posterior simulated SCF and the observed one in Bassies as expected. However, the simulated snow depths in this catchment do not match the Pl&amp;#233;iades snow depths observations. We added the perturbation of the air temperature with a normal law and found similar results. We also found a very small sensitivity of the posterior snow depth on the empirical parameters of the measurement operator. These results suggest that the SWE-SCF relationship may not be sufficiently informative in the study site of Bassi&amp;#232;s.&amp;#160; We will present the extension of this work to the Tuolumne river basin where the Airborne Observatory provides SWE maps over a larger region.

This study uses an oceanic energy budget to estimate the ocean heat transport convergence in the North Atlantic during 2005–2018. The horizontal convergence of the ocean heat transport is estimated using ocean heat content tendency primarily derived from satellite altimetry combined with space gravimetry. The net surface energy fluxes are inferred from mass-corrected divergence of atmospheric energy transport and tendency of the ECMWF ERA5 reanalysis combined with top-of-the-atmosphere radiative fluxes from the clouds and the Earth’s radiant energy system project. The indirectly estimated horizontal convergence of the ocean heat transport is integrated between the rapid climate change-meridional overturning circulation and heatflux array (RAPID) section at 26.5°N (operating since 2004) and the overturning in the subpolar north atlantic program (OSNAP) section, situated at 53°–60°N (operating since 2014). This is to validate the ocean heat transport convergence estimate against an independent estimate derived from RAPID and OSNAP in-situ measurements. The mean ocean energy budget of the North Atlantic is closed to within ± 0.25 PW between RAPID and OSNAP sections. The mean oceanic heat transport convergence between these sections is 0.58 ± 0.25 PW, which agrees well with observed section transports. Interannual variability of the inferred oceanic heat transport convergence is also in reasonable agreement with the interannual variability observed at RAPID and OSNAP, with a correlation of 0.54 between annual time series. The correlation increases to 0.67 for biannual time series. Other estimates of the ocean energy budget based on ocean heat content tendency derived from various methods give similar results. Despite a large spread, the correlation is always significant meaning the results are robust against the method to estimate the ocean heat content tendency.

Abstract. This study presents an improved space geodetic approach to estimate the global ocean heat content (GOHC) change and the Earth energy imbalance (EEI) over 1993–2022. The EEI exhibits a positive trend of 0.29 W m−2 per decade, significant at the 90 % confidence level, indicating accelerated ocean warming in line with independent Clouds and the Earth's Radiant Energy System (CERES) data. The study highlights the importance of comparing various estimates (e.g. in-situ-based GOHC) and their uncertainties to reliably assess EEI changes.