Estimation and Impact of Nonuniform Horizontal Correlation Length Scales for Global Ocean Physical Analyses

Optimally modeling background-error horizontal correlations is crucial in ocean data assimilation. This paper investigates the impact of releasing the assumption of uniform background-error correlations in a global ocean variational analysis system. Spatially varying horizontal correlations are introduced in the recursive filter operator, which is used for modeling horizontal covariances in the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) analysis system. The horizontal correlation length scales (HCLSs) were defined on the full three-dimensional model space and computed from both a dataset of monthly anomalies with respect to the monthly climatology and through the so-called National Meteorological Center (NMC) method. Different formulas for estimating the correlation length scale are also discussed and applied to the two forecast error datasets. The new formulation is tested within a 12-yr period (2000–11) in the ½° resolution system. The comparison with the data assimilation system using uniform background-error horizontal correlations indicates the superiority of the former, especially in eddy-dominated areas. Verification skill scores report a significant reduction of RMSE, and the use of nonuniform length scales improves the representation of the eddy kinetic energy at midlatitudes, suggesting that uniform, latitude, or Rossby radius-dependent formulations are insufficient to represent the geographical variations of the background-error correlations. Furthermore, a small tuning of the globally uniform value of the length scale was found to have a small impact on the analysis system. The use of either anomalies or NMC-derived correlation length scales also has a marginal effect with respect to the use of nonuniform HCLSs. On the other hand, the application of overestimated length scales has proved to be detrimental to the analysis system in all areas and for all parameters

Steric sea level changes from ocean reanalyses at global and regional scales

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

Global ocean re-analyses for climate applications

One of the main objectives of the global ocean modelling activities at Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC) is the production of global ocean re-analyses over multidecadal periods to reconstruct the state of the ocean and the large scale cir- culation over the recent past. The re-analyses are used for climate applications and for the assessment of the benefits of assimilating ocean observations on seasonal and longer predictions. Here we present the main characteristics of an optimal interpola- tion based assimilation system used to produce a set of global ocean re-analyses validated against a set of high quality in situ observa- tions and independent data. Differences among the experiments of the set are analyzed in terms of improvements in the method used to assimilate the data and the quality of observations them- selves. For example, the integrated ocean heat content, which can be taken as an indicator of climate changes, is examined to detect possible sources of uncertainty of its long-term changes. Global and basin scale upper ocean heat content exhibits warming trends over the last few decades that still depend in a significant way on the assimilated observations and the formulation of the background covariances. However, all the re-analyses show a global warming trend of the oceanic uppermost 700 m over the last five decades that falls within the range of the most recent observation-based estimates. The largest discrepancies between our estimates and observational based ones are confined in the upwelling regions of the PacificandAtlanticOceans.Finally,theresultsshow that the climatological heat and salt transports as a function of latitude also fall within the range of the estimates based on observations and atmospheric re-analyses

Assessing the Impact of Different Ocean Analysis Schemes on Oceanic and Underwater Acoustic Predictions

Assimilating oceanic observations into prediction systems is an advantageous approach for real-time ocean environment characterization. However, its benefits to underwater acoustic predictions are not trivial due to the nonlinearity and sensitivity of underwater acoustic propagation to small-scale oceanic features. In order to assess the potential of oceanic data assimilation, integrated ocean-acoustic Observing System Simulation Experiments are conducted. Synthetic altimetry and in situ data were assimilated through a variational oceanographic data assimilation system. The predicted sound speed fields are then ingested in a range-dependent acoustic model for transmission loss (TL) predictions. The predicted TLs are analyzed for the purpose of (i) evaluating the contributions of different sources to the uncertainties of oceanic and acoustic forecasts and (ii) comparing the impact of different oceanic analysis schemes on the TL prediction accuracy. Using ensemble member clustering techniques, the contributions of boundary conditions, ocean parameterizations, and geoacoustic characterization to acoustic prediction uncertainties are addressed. Subsequently, the impact of three-dimensional variational (3DVAR), 4DVAR, and hybrid ensemble-3DVAR data assimilation on acoustic TL prediction at two signal frequencies (75 and 2,500 Hz) and different ranges (30 and 60 km) are compared. 3DVAR significantly improves the predicted TL accuracy compared to the control run. Promisingly, 4DVAR and hybrid data assimilation further improve the TL forecasts, the hybrid scheme achieving the highest skill scores for all cases, while being the most computationally intensive scheme. The optimal scheme choice thus depends on requirements on the accuracy and computational constraints. These findings foster developments of coupled data assimilation for operational underwater acoustic propagation

A Neural network based observation operator for coupled ocean acoustic variational data assimilation

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 theNNreconstruction of TL is significantly better than CCA. BothCCAandNNare 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

Internal tides in the central Mediterranean Sea: observational evidence and numerical studies

Internal tides are studied in the central Mediterranean Sea using observational data and numerical experiments. Both numerical results and observations indicate that the baroclinic variability in this area is dominated by the K1 diurnal tide. In agreement with previous studies, the diurnal internal tides have the characteristics of Kelvin-like bottom trapped waves. They are mainly generated by the interaction of the induced barotropic tidal flow with the steep bathymetric gradient connecting the Ionian Sea with the shallow Sicily Channel. The bathymetric gradient appears to be the major forcing shaping the propagation paths of the internal tides. The most energetic internal tides follow the steep bathymetric gradient, propagating southward and tending to dissipate rapidly. Other waves cross the continental shelf south of Malta and then split with one branch moving toward the southern coast of Sicily and the other moving toward the west. Internal tides propagate with a variable phase velocity of about 1 ms(-1) and a wavelength of the order of 100 km. During their journey, the internal waves appear to be subject to local processes that can modify their characteristics. The induced vertical shear strongly dominates the vertical turbulence and generates vertical mixing that alters the properties of the water masses traversing the area. Barotropic and internal tides remove heat from the ocean surface, increasing atmospheric heating, and redistributing energy through increased lateral heat fluxes. Lateral heat fluxes are significantly greater in the presence of internal tides due to the simultaneous increase in volume fluxes and water temperatures

A Revised Scheme to Compute Horizontal Covariances in an Oceanographic 3D-VAR Assimilation System.

We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of an RFwith first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An advantage of the proposed scheme is that the CPU's time can be substantially reduced with benefits on the large scale applications. Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications. The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation