Search

Long-term changes in ocean heat content (OHC) represent a fundamental global warming indicator and are mostly caused by anthropogenic climate-altering gas emissions. OHC increases heavily threaten the marine environment, therefore, reconstructing OHC before the well-instrumented period (i.e., before the Argo floats deployment in the mid-2000s) is crucial to understanding the multi-decadal climate change in the ocean. Here, we shed light on ocean warming and its uncertainty for the 1961-2022 period through a large ensemble reanalysis system that spans the major sources of uncertainties. Results indicate a 62-year warming of 0.43 ± 0.08 W m−2, and a statistically significant acceleration rate equal to 0.15 ± 0.04 W m−2 dec−1, locally peaking at high latitudes. The 11.6% of the global ocean area reaches the maximum yearly OHC in 2022, almost doubling any previous year. At the regional scale, major OHC uncertainty is found in the Tropics; at the global scale, the uncertainty represents about 40% and 15% of the OHC variability, respectively before and after the mid-2000s. The uncertainty of regional trends is mostly affected by observation calibration (especially at high latitudes), and sea surface temperature data uncertainty (especially at low latitudes). The authors used a state-of-the-science ensemble ocean reconstruction to analyze ocean heat content evolution over the last 62 years, focusing on the analysis of warming acceleration and the main sources of its uncertainty. [ABSTRACT FROM AUTHOR]

Recent estimates of the global warming rates suggest that approximately 9% of Earth's excess heat has been cumulated in the deep and abyssal oceans (below 2000-m depth) during the last two decades. Such estimates assume stationary trends deducted as long-term rates. To reassess the deep ocean warming and potentially shed light on its interannual variability, we formulate the balance between Earth's energy imbalance (EEI), the steric sea level, and the ocean heat content (OHC), at yearly time scales during the 2003–18 period, as a variational problem. The solution is achieved through variational minimization, merging observational data from top-of-atmosphere EEI, inferred from Clouds and the Earth's Radiant Energy System (CERES), steric sea level estimates from altimetry minus gravimetry, and upper-ocean heat content estimates from in situ platforms (mostly Argo floats). Global ocean reanalyses provide background-error covariances for the OHC analysis. The analysis indicates a 2000-m–bottom warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to 13% of the total ocean warming (0.62 ± 0.08 W m−2), slightly larger than previous estimates but consistent within the error bars. The analysis provides a fully consistent optimized solution also for the steric sea level and EEI. Moreover, the simultaneous use of the different heat budget observing networks is able to decrease the analysis uncertainty with respect to the observational one, for all observation types and especially for the 0–700-m OHC and steric sea level (more than 12% reduction). The sensitivity of the analysis to the choice of the background time series proved insignificant. Significance Statement: Several observing networks provide complementary information about the temporal evolution of the global energy budget. Here, satellite observations of Earth's energy imbalance (EEI) and steric sea level and in situ–derived estimates of ocean heat content anomalies are combined in a variational analysis framework, with the goal of assessing the deep ocean warming. The optimized solution accounts for the uncertainty of the different observing networks. Furthermore, it provides fully consistent analyses of global ocean heat content, steric sea level, and EEI, which show smaller uncertainty than the original observed time series. The deep ocean (below 2000-m depth) exhibits a significant warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to the 13% of the total ocean warming. [ABSTRACT FROM AUTHOR]

We have developed a global ocean and sea‐ice ensemble forecasting system based on the operational forecasting ocean assimilation model (FOAM) system run at the Met Office. The ocean model Nucleus for European Modelling of the Ocean (NEMO) and the community ice code (CICE) sea‐ice model are run at 1/4∘$$ {}^{\circ } $$ resolution and the system assimilates data using a three‐dimensional variational assimilation (3DVar) version of NEMOVAR. This data assimilation (DA) system can perform hybrid ensemble/variational assimilation. A 36‐member ensemble of hybrid ensemble variational assimilation systems with perturbed observations (values and locations) has been set up, with each member forced at the surface by a separate member of the Met Office Global–Regional Ensemble Prediction System (MOGREPS‐G). The unperturbed member is forced by atmospheric fields from the Met Office operational numerical weather prediction (NWP) deterministic system. The system includes stochastic model perturbations and a relaxation to prior spread (RTPS) inflation scheme. A control run of the system using an ensemble of 3DVars is shown to be generally reliable for Sea‐Level Anomaly (SLA), temperature, and salinity (the ensemble spread being a good representation of the uncertainty in the ensemble mean), although the ensemble is underspread in eddying regions. The ensemble mean gives a 4% reduction in error in SLA compared with the deterministic 3DVar system currently used operationally. The system was tested with different weights for the ensemble component of the hybrid background‐error covariance matrix and different inflation factors. The best results, in terms of short‐range forecast error and ensemble reliability statistics, were obtained with hybrid three‐dimensional ensemble variational DA (3DEnVar). The RTPS inflation scheme is shown to be beneficial in producing an appropriate ensemble spread in response to hybrid DA. 3DEnVar with an ensemble hybrid weight of 0.8 leads to a reduction of 20% (5%) in the ensemble mean error for SLA (profile temperature and salinity) compared with an ensemble of standard 3DVars. [ABSTRACT FROM AUTHOR]

Variational data assimilation requires implementing the tangent-linear and adjoint (TA/AD) version of any operator. This intrinsically hampers the use of complicated observations. Here, we assess a new data-driven approach to assimilate acoustic underwater propagation measurements [transmission loss (TL)] into a regional ocean forecasting system. TL measurements depend on the underlying sound speed fields, mostly temperature, and their inversion would require heavy coding of the TA/AD of an acoustic underwater propagation model. In this study, the nonlinear version of the acoustic model is applied to an ensemble of perturbed oceanic conditions. TL outputs are used to formulate both a statistical linear operator based on canonical correlation analysis (CCA), and a neural network–based (NN) operator. For the latter, two linearization strategies are compared, the best-performing one relying on reverse-mode automatic differentiation. The new observation operator is applied in data assimilation experiments over the Ligurian Sea (Mediterranean Sea), using the observing system simulation experiments (OSSE) methodology to assess the impact of TL observations onto oceanic fields. TL observations are extracted from a nature run with perturbed surface boundary conditions and stochastic ocean physics. Sensitivity analyses indicate that the NN reconstruction of TL is significantly better than CCA. Both CCA and NN are able to improve the upper-ocean skill scores in forecast experiments, with NN outperforming CCA on the average. The use of the NN observation operator is computationally affordable, and its general formulation appears promising for the adjoint-free assimilation of any remote sensing observing network. Significance Statement: Deep learning algorithms are now widely spread in a diverse range of fields to help with solving automatic classification and regression problems. Here, we present and assess a strategy aimed at introducing an observation operator based on neural networks in data assimilation. Linearization of such an operator, required by variational schemes, is also discussed and implemented. The methodology is applied to the coupled oceanic–acoustic data assimilation problem, and provides promising results. Our approach may be extended in the future to assimilate any remotely sensed type of observations. [ABSTRACT FROM AUTHOR]

Generating optimal perturbations is a key requirement of several data assimilation schemes. Here, we present a newly developed stochastic physics package for ocean models, implemented in the NEMO ocean general circulation model. The package includes three schemes applied simultaneously: stochastically perturbed parametrization tendencies (SPPT), stochastically perturbed parameters (SPP) and stochastic kinetic energy backscatter (SKEB) schemes. The three schemes allow for different temporal and spatial perturbation scales. Within a limited‐area ocean model configuration, ensemble free‐running simulations were performed to assess the impact and reliability of the schemes. They prove complementary in increasing the ensemble spread at different scales and for different diagnostics. The ensemble spread appears reliable; for instance, it proves consistent with the root‐mean‐square differences with respect to higher‐resolution (sub‐mesoscale) simulations that here represent the "truth" (in the sense that it includes "unresolved physics"). Interestingly, both the SPPT and the SKEB schemes lead to an increase of eddy kinetic energy at small spatial scales (2–10 km), and contribute to modify the ensemble mean state, mitigating warm biases near the thermocline due to the enhancement of the upper ocean vertical mixing. As an application of the stochastic packages, the ensemble anomaly covariances coming from the ensemble free‐running simulations are used to feed large‐scale anisotropic covariances that complement smaller‐scale ones in a hybrid‐covariance regional analysis and forecast system in the Mediterranean Sea. Ensemble‐derived covariances are formulated as slowly varying three‐dimensional low‐resolution empirical orthogonal functions (EOFs). The improvements due to the addition of such covariances to the stationary ones are found significant in real‐data experiments, within verification skill scores against glider profile data, remotely sensed observations and current speed measurements from drifters, radar and moorings. [ABSTRACT FROM AUTHOR]

Recent advances in global ocean prediction systems are fostered by the needs of accurate representation of mesoscale processes. The day-by-day realistic representation of its variability is hampered by the scarcity of observations as well as the capability of assimilation systems to correct the ocean states at the same scale. This work extends a 3DVAR system designed for oceanic applications to cope with global eddy-resolving grid and dense observational datasets in a hybridly parallelized environment. The efficiency of the parallelization is assessed in terms of both scalability and accuracy. The scalability is favored by a weak-constrained formulation of the continuity requirement among the artificial boundaries implied by the domain decomposition. The formulation forces possible boundary discontinuities to be less than a prescribed error and minimizes the parallel communication relative to standard methods. In theory, the exact solution is recovered by decreasing the boundary error toward zero. In practice, it is shown that the accuracy increases until a lower bound arises, because of the presence of the mesh and the finite accuracy of the minimizer. Atwin experiment has been set up to estimate the benefit of employing an eddy-resolving grid within the assimilation step, as compared with an eddy-permitting one, while keeping the eddy-resolving grid within the forecast step. It is shown that the use of a coarser grid for data assimilation does not allow an optimal exploitation of the present remote sensing observation network. Aglobal decrease of about 15% in the error statistics is found when assimilating dense surface observations, and no significant improvement is seen for sparser observations (in situ profilers). [ABSTRACT FROM AUTHOR]

Since 2016, the Copernicus Marine Environment Monitoring Service (CMEMS) has produced and disseminated an ensemble of four global ocean reanalyses produced at eddy-permitting resolution for the period from 1993 to present, called GREP (Global ocean Reanalysis Ensemble Product). This dataset offers the possibility to investigate the potential benefits of a multi-system approach for ocean reanalyses, since the four reanalyses span by construction the same spatial and temporal scales. In particular, our investigations focus on the added value of the information on the ensemble spread, implicitly contained in the GREP ensemble, for temperature, salinity, and steric sea level studies. It is shown that in spite of the small ensemble size, the spread is capable of estimating the flow-dependent uncertainty in the ensemble mean, although proper re-scaling is needed to achieve reliability. The GREP members also exhibit larger consistency (smaller spread) than their predecessors, suggesting advancement with time of the reanalysis vintage. The uncertainty information is crucial for monitoring the climate of the ocean, even at regional level, as GREP shows consistency with CMEMS high-resolution regional products and complement the regional estimates with uncertainty estimates. Further applications of the spread include the monitoring of the impact of changes in ocean observing networks; the use of multi-model ensemble anomalies in hybrid ensemble-variational retrospective analysis systems, which outperform static covariances and represent a promising application of GREP. Overall, the spread information of the GREP product is found to significantly contribute to the crucial requirement of uncertainty estimates for climatic datasets. [ABSTRACT FROM AUTHOR]

Historical ocean reanalyses combine ocean general circulation models with data assimilation schemes that ingest rescued observations. They can be used as a tool to investigate long-term changes in the ocean climate. However, large uncertainties, due to the poorly developed atmospheric and oceanic observing networks in early periods, still remain. Thus, detailed studies to assess the uncertainty and its time dependency are required to quantify the feasibility of historical ocean reanalyses for climate change assessment. In this work, we estimate the ocean heat content variability from a set of ocean reanalyses that cover the period 1900-2010. The ocean reanalyses include realizations forced by two different atmospheric reanalyses (20CRv2 and ERA-20C), combined with different data assimilation strategies, in the attempt to evaluate the relative weight of the atmospheric forcing and observation uncertainties on the resulting ocean heat content estimates. Results suggest that even when observing networks are poor, the observations are able to shape the upper ocean heat content variability, in terms of long-term trends and reproduction of individual warming/cooling events related to volcanic eruptions. The assimilation of in-situ profiles has an effect even on the sea surface temperature variability and is able to constrain the top 700 m heat content since the 1950s with respect to the uncertainty borne by the atmospheric forcing. The vertical propagation of the upper ocean observational information is however slow (with typically decadal time scale). Consequently, the total column heat content is constrained by observations only in the latest two decades. We conclude that upper ocean heat content diagnostics from historical ocean reanalyses bear the climate change signature and may be considered for long-term studies when complemented by proper uncertainty estimation. [ABSTRACT FROM AUTHOR]

A hybrid variational-ensemble data assimilation scheme to estimate the vertical and horizontal parts of the background-error covariance matrix for an ocean variational data assimilation system is presented and tested in a limited area ocean model implemented in the western Mediterranean Sea. An extensive dataset collected during the Recognized Environmental Picture Experiments conducted in June 2014 by the Centre for Maritime Research and Experimentation has been used for assimilation and validation. The hybrid scheme is used to both correct the systematic error introduced in the system from the external forcing (initial, lateral and surface open boundary conditions) and model parameterization and improve the representation of small scale errors in the Background Error Covariance matrix. An ensemble system is run off-line for further use in the hybrid scheme, generated through perturbation of assimilated observations. Results of four different experiments have been compared. The reference experiment uses the classical static formulation of the background error covariance matrix and has no systematic error correction. The other three experiments account, or not, for systematic error correction and hybrid daily estimates of the background error covariance matrix combining the static and the ensemble derived errors statistics. Results show that the hybrid scheme when used in conjunction with the systematic error correction reduce the mean absolute error of temperature and salinity misfit by 55% and 42% respectively versus statistics arising from standard climatological covariances without systematic error correction. [ABSTRACT FROM AUTHOR]

Exploiting the potential of space-borne oceanic measurements to characterize the sub-surface structure of the ocean becomes critical in areas where deployment of in situ sensors might be difficult or expensive. Sea Surface Temperature (SST) observations potentially provide enormous amounts of information about the upper ocean variability. However, the assimilation of daytime SST retrievals, e.g., from infrared sensors into ocean prediction systems, requires a specific treatment of the diurnal cycle of skin SST, which is generally under-estimated in current ocean models due to poor vertical resolution at the air–sea interface and lack of proper parameterizations. To this end, a simple off-line bias correction scheme is proposed, where the bias predictors include, among others, the warm layer and cool skin warming/cooling deduced from a prognostic model. Furthermore, a localization procedure that limits the vertical penetration of the SST information in a hybrid variational-ensemble data assimilation system is formulated. These two novelties are implemented and assessed within a regional ocean prediction system in the Ligurian Sea for the assimilation of daytime SST data retrieved with hourly frequency from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary satellite Meteosat-10. Experiments are validated against independent measurements collected by gliders, moorings, and drifters during the Long-term Glider Missions for Environmental Characterization (LOGCMEC17) sea trial. Results suggest that the simple bias correction scheme is effective in improving both the sea surface and mixed layer accuracy, correctly thinning the mixed layer compared to the control experiment, outperforming experiments with night-only data assimilation, and improving the forecast skill scores. Localization further improves the prediction of the mixed layer depth. It is therefore recommended that sophisticated bias correction and localization procedures are adopted for fruitfully assimilating daytime SST data in operational oceanographic analysis systems. [ABSTRACT FROM AUTHOR]

Sea level has risen significantly in the recent decades and is expected to rise further based on recent climate projections. Ocean reanalyses that synthetize information from observing networks, dynamical ocean general circulation models, and atmospheric forcing data offer an attractive way to evaluate sea level trend and variability and partition the causes of such sea level changes at both global and regional scales. Here, we review recent utilization of reanalyses for steric sea level trend investigations. State-of-the-science ocean reanalysis products are then used to further infer steric sea level changes. In particular, we used an ensemble of centennial reanalyses at moderate spatial resolution (between 0.5 × 0.5 and 1 × 1 degree) and an ensemble of eddy-permitting reanalyses to quantify the trends and their uncertainty over the last century and the last two decades, respectively. All the datasets showed good performance in reproducing sea level changes. Centennial reanalyses reveal a 1900–2010 trend of steric sea level equal to 0.47 ± 0.04 mm year−1, in agreement with previous studies, with unprecedented rise since the mid-1990s. During the altimetry era, the latest vintage of reanalyses is shown to outperform the previous ones in terms of skill scores against the independent satellite data. They consistently reproduce global and regional upper ocean steric expansion and the association with climate variability, such as ENSO. However, the mass contribution to the global mean sea level rise is varying with products and its representability needs to be improved, as well as the contribution of deep and abyssal waters to the steric sea level rise. Similarly, high-resolution regional reanalyses for the European seas provide valuable information on sea level trends, their patterns, and their causes. [ABSTRACT FROM AUTHOR]

Three-dimensional ocean dynamics (e.g. coastal upwelling, Ekman Pumping, meridional currents) in the Eastern Boundary Upwelling Systems (EBUS) is largely dictated by the strength of equatorward wind and its cross-shore gradients. Ocean dynamics, in turn, affects the coastal sea surface temperature (SST). Mis-representing the wind fields may therefore lead to inaccurate current structures and, thus, to significant SST biases. Analysis of two atmospheric reanalysis products, ERAInterim (ERAINT) and JRA55-do version 1.1 (JRA55), shows differences in the structure of the wind stress and wind stress curl (WSC) over the EBUS regions, when compared to the satellite records from QuikSCAT. Our study shows that ERAINT product is affected by too strong and wide wind-drop off in the cross-shore direction, whereas the wind field is more accurate in the JRA55 product, characterized by weak and narrow wind drop-off and strong coastal winds. To better understand the impact of different fine structures of the near-coast wind on upwelling dynamics, we performed long-term ocean hindcast simulations over the period 1985–2015 with a global eddy-permitting configuration of the NEMO ocean general circulation model. We find that (1) weak ERAINT coastal wind stress and strong WSC promote offshore Ekman pumping, weak coastal vertical velocity and meridional currents and transport dominated by upwind flows; (2) stronger JRA55 coastal wind stress associated with weak WSC generates weaker Ekman Pumping, localized intense coastal upwelling and strong downwind currents and transport; (3) SST discrepancies between experiments are mostly related to the different near-shore wind stress rather than incoming heat fluxes. • Eastern Boundary Upwelling Systems dynamics driven by different atmospheric forcing • Wind from state-of-the-art atmospheric reanalyses compared to scatterometers • Near-shore wind impact on the EBUS currents and sea surface temperature • Assessment of the coherency of EBUS response to wind forcing • Mitigation of the EBUS warm biases by the bias-corrected wind forcing [ABSTRACT FROM AUTHOR]