Search

Abstract The Brazil Current Overshoot regions feature the strongest sea surface temperature variations over the global ocean but forecast skill for these extreme variations is limited. Here we investigate the drivers of marine heatwaves and cold spells in this region and find that ocean flows and sea surface heat fluxes are the primary drivers. We also find that sea surface temperature extremes driven by air-sea heat fluxes produce consistent changes in sea surface temperature and related atmospheric variables over a wide horizontal area, whereas ocean flow-driven sea surface temperature extremes exhibit low consistency in air variables and limited consistency in the spatial extent of sea variables across most sea surface temperature extreme events. These differing air-sea characteristics associated with extremes driven by different drivers offer potential avenues for improving prediction skills and advancing the understanding of three-dimensional air-sea impacts associated with extreme events.

The interannual-decadal variability in the upper-ocean salinity of the southeast Indian Ocean (SEIO) was found to be highly correlated with the El Niño-Southern Oscillation (ENSO). Based on multisource data, this study revealed that this ENSO-like salinity variability mainly resides in the domain between 13°S-30°S and 100°E-120°E, and at depths above 150 m. This variability is principally driven by meridional geostrophic velocity (MGV), which changes with the zonal pattern of the sea surface height (SSH). Previous studies have reported that the variability in the SSH in the south Indian Ocean is principally driven by local-wind forcing and eastern-boundary forcing. Here the eastern-boundary forcing denotes the influence of SSH anomaly radiated from the western coast of Australia. A recent study emphasized the contribution of local-wind forcing in salinity variability in the SEIO, for its significant role in generation of the zonal dipole pattern of SSH anomaly in the south Indian Ocean, which was considered to be responsible for the anomalous MGV in the SEIO. While our results revealed a latitudinal difference between the domain where the SSH dipole pattern exists (north of 20°S) and the region in which the ENSO-like salinity variability is strongest (20°S-30°S), suggesting that this salinity variability cannot be attributed entirely to the SSH dipole pattern. Our further investigation shows that, the MGV in the SEIO changes with local zonal SSH gradient that principally driven by eastern-boundary forcing. In combination with the strong meridional salinity gradient, the boundary-driven MGV anomalies cause significant meridional salinity advection and eventually give rise to the observed ENSO-like salinity variability. This study revealed the leading role of eastern-boundary forcing in interannual variability of the upper-ocean salinity in the SEIO.

The Karimata Strait (KS) throughflow between the South China Sea (SCS) and Java Sea plays an essential role in heat and freshwater budget in the SCS and dual roles in strengthening/reducing the primary Indonesian throughflow (ITF) in the Makassar Strait. A sustained long-term monitoring of the ITF is logistically challenging and expensive; therefore, proxies are needed. Here, we use a combination of in situ measurement of the KS throughflow and satellite-derived sea surface height (SSH) and sea surface wind (SSW) to determine the interannual and decadal modulations in seasonal amplitude of the KS throughflow associated with El Niño-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Pacific Decadal Oscillation (PDO). Linear regression, correlation, harmonic and power spectrum analyses are used. The results manifest that there are significant interannual to decadal modulations in the seasonal amplitude of the KS throughflow. The modulations of the seasonal amplitude in the volume and heat transports range 1.36-1.92 Sv (1 Sv = 106 m3 s-1) and 126.41-173.36 TW (1 TW = 1012 W), respectively, with a significant cycle of ~9 years. From 1994 to 2020, the seasonal amplitude of volume transport through the KS shows an increasing trend of 37.75 ± 15.69 mSv decade-1 (1 mSv = 103 m3 s-1). The seasonal amplitude of the heat transport also increases, at a rate of 4.78 ± 1.52 TW decade-1. The KS volume transport is positively correlated with PDO and ENSO indices (r2 = 0.69 and r2 = 0.58), with a lag of 12 and 10 months, respectively. The results of composite analysis suggest that the interannual variability of the KS transport is related to the interannual anomalies of the SSH gradient and the local SSW fields in boreal winter.

Influenced by local mixing and coastal runoff, water masses in the South China Sea degenerate significantly. The K-means algorithm is used to classify the water masses based on WOD13 temperature and salinity observations from 1966 to 2013 because its principle is consistent with the definition of a shallow water mass. The numbers and initial centers of the water masses are determined using functions of in-cluster distance and density values. The result shows that there are ten water masses in the South China Sea. In combination with the T-S scatter diagram, the properties of the South China Sea water masses were analyzed, including their distribution, the seasonal variability, and the degeneration processes. The temperatures of water masses were higher in summer and lower in winter, with the amplitudes of variation gradually reduced from the surface to the bottom. The seasonal variation in salinity of the surface water masses was high in winter and low in summer, which mainly depends on the amount of river discharge and precipitation. The subsurface water masses were strongly affected by water from the Pacific Ocean; thus, the seasonal variability of these water masses is weak, especially for the intermediate water mass that characterized by prominent low salinity. The water mass formed by the Kuroshio water invading the South China Sea has insignificant seasonal variations in temperature and salinity. The properties and seasonal variabilities of the water masses derived from the K-means algorithm are in agreement with the existing conclusions, suggesting that the improved K-means algorithm is efficient and accurate in the shallow water mass division.

Sea surface salinity (SSS) extremes, characterized as events surpassing a certain threshold percentile, pose a threat to stenohaline organisms worldwide. This study presents the first global mapping of SSS extreme metrics and investigates their underlying drivers using daily reanalysis data. Our key findings have revealed that mesoscale eddies drive SSS extremes over most of the global ocean with peaks in duration ranging from 5 to 10 d and peaks in intensity ranging from 0.2 to 0.3 g kg ^−1 . Freshwater fluxes (FWFs) and mean currents are generally associated with the occurrence of prolonged and intense SSS extremes in tropical and extratropical oceans, respectively. FWFs related to interannual climate modes contribute to the asymmetric distribution of high and low SSS extremes in the central Pacific Ocean and the western Indian Ocean. These results highlight the distinct impacts of different local drivers on the mean states of SSS extremes.

Marine heatwaves (MHWs) and low sea surface salinity (SSS) events can significantly impact marine ecosystems and dynamic systems, respectively. Compound marine extreme events can cause more significant damage than individual extreme events. However, the spatiotemporal patterns of compound MHW-low SSS extremes are not well understood. Daily reanalysis data were used to identify the basic patterns of compound extreme events and their drivers. These events mainly occur over the central tropical Pacific Ocean during record-breaking El Niño events. This analysis revealed that extreme sea surface warming associated with El Niño drives increased convection, which subsequently leads to increased rainfall. It ultimately causes extreme sea surface freshening. This analysis highlights the significance of air-sea interactions and low-frequency climate variability in shaping compound extreme events.

The North Brazil Undercurrent (NBUC) is a narrow (16 Sv) and plays an important role in the Atlantic Meridional Overturning Circulation and the South Atlantic Subtropical Cell. Strong salinity and temperature fronts occur over the NBUC region. The role of temperature and salinity gradients on the genesis of NBUC variability has never been explored. This study uses three high-resolution (≤0.1°) and one low-resolution (=0.25°) model outputs to explore the linear trend of NBUC transport and its variability on annual and interannual time scales. We find that the linear trend and interannual variability of the geostrophic NBUC transport show large discrepancies among the datasets. Thus, the linear trend and variability of the geostrophic NBUC are associated with model configuration. We also find that the relative contributions of salinity and temperature gradients to the geostrophic shear of the NBUC are not model dependent. Salinity-based and temperature-based geostrophic NBUC transports tend to be opposite-signed on all time scales. Despite the limited salinity and temperature profiles, the model results are consistent with the in-situ observations on the annual cycle and interannual time scales. This study shows the relationship of salinity-based and temperature-based geostrophic NBUC variations in the annual and interannual variability and trend among different models and highlights the equal important roles of temperature and salinity in driving the variability of NBUC transport.

Exploring the relationship between evaporation (E)-minus-precipitation (P) and sea surface salinity (SSS) is vital for understanding global hydrological cycle changes and investigating the salinity budget. This study quantifies the uncertainty in the relationship between E−P and SSS based on satellite data over the 50°S–50°N ocean from 2012 to 2017 in 140 sets of combinations of E, P and SSS. We find that the uncertainty (10%) in the variability of freshwater flux (FWF) over 2012–2017 is smaller than that in SSS (15%). The difference in the combination of sets of “E-P-SSS” products can lead to the 10% difference in RMSD and 25% difference in area-weighted mean correlation coefficients between SSS tendency and FWF. There is a 24.1~58% area over the global ocean with a significant (p value < 0.05) positive correlation between the FWF and SSS tendency derived from satellite products. The seasonal EMP and SSS tendencies show larger correlation coefficients and lower RMSDs over most sets compared with those on nonseasonal time scales. Large uncertainty in the FWF-SSS tendency relation associated with spread among products prevents the use of one combination of E, P and SSS from satellite-based products for salinity budget analysis.