Thermal coupling of the Indo-Pacific warm pool and Southern Ocean over the past 30,000 years

The role of the tropical Pacific Ocean and its linkages to the southern hemisphere during the last deglacial warming remain highly controversial. Here we explore the evolution of Pacific horizontal and vertical thermal gradients over the past 30 kyr by compiling 340 sea surface and 7 subsurface temperature records, as well as one new ocean heat content record. Our records reveal that La Niña-like conditions dominated during the deglaciation as a result of the more intense warming in the western Pacific warm pool. Both the subsurface temperature and ocean heat content in the warm pool rose earlier than the sea surface temperature, and in phase with South Pacific subsurface temperature and orbital precession, implying that heat exchange between the tropical upper water column and the extratropical Southern Ocean facilitated faster warming in the western Pacific. Our study underscores the key role of the thermal coupling between the warm pool and the Southern Ocean and its relevance for future global warming.

The mechanism of the last deglacial global warming is key for future climate. Here, the authors shed light on the pivotal role of the thermal coupling between the western Pacific warm pool and the Southern Ocean.

Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method

Accurately estimating the ocean’s interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.

Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

Effects of different freshwater flux representations in an ocean general circulation model of the tropical Pacific

Freshwater flux (FWF) is a major forcing that affects the ocean through several processes. The effects of FWF may be represented in ocean modeling as real freshwater flux (RFF) formulations and virtual salt flux (VSF) methods. RFF formulations have been implemented in the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 5 (MOM5) as a replacement for the non-physical VSF method, which is primarily used in state-of-the-art ocean models. Here, we systematically evaluated the effects of RFF-related processes on the GFDL MOM5-based simulations in the tropical Pacific. When the FWF was treated as the natural boundary condition (NBC), it directly decreased the local temperature and the salinity by changing the volume of the top model layer, and it increased the temperature in the eastern Pacific by triggering an eastward Goldsbrough-Stommel circulation in the subsurface. Moreover, the heat content induced by the FWF tended to counteract the decreasing effects of the NBC on sea surface temperatures (SSTs) in the western-central tropical Pacific. The relationships between SST perturbations and the FWF representation in ocean modeling are also discussed.

Salinity anomaly as a trigger for ENSO events

According to the classical theories of ENSO, subsurface anomalies in ocean thermal structure are precursors for ENSO events and their initial specification is essential for skillful ENSO forecast. Although ocean salinity in the tropical Pacific (particularly in the western Pacific warm pool) can vary in response to El Niño events, its effect on ENSO evolution and forecasts of ENSO has been less explored. Here we present evidence that, in addition to the passive response, salinity variability may also play an active role in ENSO evolution, and thus important in forecasting El Niño events. By comparing two forecast experiments in which the interannually variability of salinity in the ocean initial states is either included or excluded, the salinity variability is shown to be essential to correctly forecast the 2007/08 La Niña starting from April 2007. With realistic salinity initial states, the tendency to decay of the subsurface cold condition during the spring and early summer 2007 was interrupted by positive salinity anomalies in the upper central Pacific, which working together with the Bjerknes positive feedback, contributed to the development of the La Niña event. Our study suggests that ENSO forecasts will benefit from more accurate salinity observations with large-scale spatial coverage.