Improving ocean bottom pressure fields using space gravity data in state estimation

Improved Global Ocean Heat Content Estimation by Modeling Vertical Spatio-Temporal Dependence

Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.

Improving simulation of Earth system variability through weakly coupled ocean data assimilation in E3SM

Abstract. Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal-to-decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly coupled ocean data assimilation (WCODA) system within the DOE Energy Exascale Earth System Model version 2 (E3SMv2) on global and regional climate variability. By assimilating monthly ocean temperature and salinity from the EN4.2.1 reanalysis into the fully coupled model, we demonstrate substantial improvements in simulating both interannual and decadal climate variability. Compared with the free-running coupled simulation, the assimilation experiment exhibits markedly enhanced interannual correlations with observations for global mean surface air temperature and precipitation anomalies. The temporal variability of key climate modes, including ENSO, the Indian Ocean Dipole, and multidecadal variability in the Pacific and Atlantic Oceans, also shows markedly improved phase agreement with observations. Regional evaluation over the contiguous United States further shows enhanced skill in simulating winter surface air temperature and precipitation, particularly in the northern and southern regions, respectively, with these improvements linked to improved ENSO simulation. Additional hindcast experiments initialized from the WCODA system exhibit no appreciable initialization shock in the early years and reproduce physically coherent ENSO teleconnection patterns, suggesting the dynamical consistency of the coupled initialization framework. These findings underscore the critical role of coupled forecasts in the data assimilation cycle for propagating observational information across Earth system components. By assimilating ocean reanalysis within the fully coupled framework, the WCODA system enables cross-component information exchange among the ocean, atmosphere, and land, thereby generating dynamically consistent initial conditions that support more accurate simulations of Earth system variability and lay the foundation for seasonal-to-decadal prediction applications.

Assessing Ocean Forcing on Sea Surface Temperature Variability from Surface Heat Budget

This paper compares three methods for quantifying stochastic ocean forcing to low frequency sea surface temperature (SST) variability from the surface heat budget in the framework of a simple stochastic climate model, especially in the case of red noise ocean forcing. The three methods are: PT21 (Patrizio and Thompson, 2021), PT22 (Patrizio and Thompson, 2022) and LGD23 (Liu et al., 2023). PT21 estimates the ratio of ocean over atmosphere forcing as the ratio of the covariance of SST tendency with ocean heat transport over the covariance with surface heat flux, while PT22 and LGD23 first derive the time series of oceanic and atmospheric forcing before estimating their ratio. The three methods are first applied to synthetic data of the stochastic climate model and then to the mid-latitude North Atlantic in observations. It is found that the LGD23 method provides an unbiased estimation of oceanic forcing with a modest sampling error at low frequency, if the persistence time of the sea surface salinity can be treated as a good approximation of that of SST associated with ocean heat transport. The PT22 method has the smallest sampling error, but tends to underestimate the ocean forcing modestly when the ocean forcing is a red noise process. The PT21 method gives the correct ratio of oceanic over atmospheric forcing in spectral density in theory, but suffers from a very large sampling error for practical application to a data set of a finite length of decades. We recommend the use of both LGD23 and PT22 as two complimentary methods for the estimation of ocean forcing, with the PT22 providing likely a lower bound for red noise ocean forcing.

Data Driven Reconstruction of Upper Ocean Profiles for Improved State Estimation in the Philippine Sea

Despite significant advances in observational systems such as the global Argo array of autonomous profiling floats, the spatiotemporal coverage of subsurface ocean observations remains limited compared to the dense data provided by satellite platforms. This study develops a data‐driven framework to reconstruct synthetic profiles of upper ocean temperature and salinity by training a self‐attention‐based neural network with satellite‐derived sea surface height (SSH) and sea surface temperature anomalies, using 17 years of collocated Argo float measurements. Daily synthetic profiles for the upper 650 m of the Philippine Sea were generated for the entirety of 2010 and assimilated into a regional ocean model via 4D‐Var data assimilation. Results show overall improved effectiveness of state estimation when synthetic profiles are utilized. Diagnostic variables like temperature, salinity, SSH, horizontal velocity all show improvement. Synthetic profiles of subsurface temperature had an overall positive impact on SSH analysis and forecast, especially in regions east of the Luzon Strait and the southern domain influenced by the North Equatorial Current. In the vertical range of 150–600 m, the impact of synthetic profiles on various observations was promising, leading to substantial reductions in the analysis and forecast error.

Wide-swath satellite altimetry and novel subsurface temperature observations improve predictions in a dynamic western boundary current: System optimization and performance

A New Approach to Estimate Ocean Surface Velocity from High-Resolution Surface Water and Ocean Topography (SWOT) Data

This study presents a novel cyclostrophic balance correction method for estimating submesoscale ocean surface currents in the Northern Arabian Sea using surface water and ocean topography (SWOT) mission altimetry. High-resolution Ocean Color Monitor (OCM-3) data from the EOS-06 satellite reveal fine-scale eddies and filaments with high chlorophyll-a concentrations (>0.4 mg m−3), spatially coherent with geostrophic current patterns from SWOT. At these scales, the geostrophic assumption is invalid; therefore, we introduce a curvature-based cyclostrophic correction that accounts for enhanced centripetal accelerations. Validation against high-resolution model simulations shows that our approach is in better agreement with model outputs than uncorrected and previously published corrected fields, particularly in regions with strong vorticity and strain. When applied to SWOT data, the corrected velocities demonstrate spatial correspondence with chlorophyll patterns and suppress spurious gradients. Probability density functions of normalized vorticity and strain also match theoretical expectations, emphasizing the potential of SWOT for advancing submesoscale ocean dynamics.