Variability of the South Pacific Western Subtropical Mode Water and Its Relationship With ENSO During the Argo Period

Qi, Jia; Qu, Tangdong; Yin, Baoshu; Chi, Jinling

doi:10.1029/2020jc016134

Cited by 7 publications

(3 citation statements)

References 53 publications

(131 reference statements)

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“…A recent study (59) analyzing 30-year ocean reanalysis data showed that the North Pacific STMW subducted and advected southward from the formation region south of the KE also controlled the overlying stratification through the uplifting effect on decadal timescales. STMWs in other basins also show interannual to decadal variations (60)(61)(62). In addition, the North Atlantic STMW has exhibited a notable decline in recent years (63), which might have increased upper-ocean heat content and hurricane intensity (64).…”

Section: Discussionmentioning

confidence: 98%

Subtropical Mode Water south of Japan impacts typhoon intensity

Oka,

Sugimoto,

Kobashi

et al. 2023

Sci. Adv.

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Subtropical Mode Water (STMW), characterized by vertically uniform temperature of ~17°C, is distributed horizontally over 5000 kilometers at the 100- to 500-meter depths in the subtropical North Pacific Ocean. Its formation and spreading fluctuate in relation to the Pacific Decadal Oscillation and the Kuroshio path variation, but the feedback from STMW on the sea surface temperature (SST) and the overlying atmosphere remains unclear. Using Argo profiling float data, we show that STMW south of Japan, whose thickness varies decadally, modulates the overlying thermal structure throughout the year by increasing isotherm uplift with increasing thickness. The STMW-induced decadal temperature change has a magnitude of up to ~1°C and is large in the warm season in the presence of the seasonal thermocline. Furthermore, 50-year observations, together with numerical simulation, show that SST, upper ocean heat content, and typhoon intensification rate have been significantly lower in years with thicker STMW and higher in years with thinner STMW.

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Section: Discussionmentioning

confidence: 98%

Subtropical Mode Water south of Japan impacts typhoon intensity

Oka,

Sugimoto,

Kobashi

et al. 2023

Sci. Adv.

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“…3 and 7 ). For instance, in the Subtropical Mode Water layer, we separately define regional Subtropical Mode Waters at the subtropical South Atlantic 26 , 47 , 78 , 79 (SA-STMW), North Atlantic 26 , 47 , 80 – 82 (NA-STMW, also known as the North Atlantic’s Eighteen Degree Water), western South Pacific 26 , 47 , 83 , 84 (SP-WSTMW), eastern South Pacific 26 , 47 , 85 (SP-ESTMW), western North Pacific 26 , 47 , 86 – 88 (NP-STMW), eastern North Pacific 26 , 47 , 86 , 88 (NP-ESTMW), and Indian Ocean 26 , 47 , 89 , 90 (IO-STMW), by further using a set of more specific density and geographic limit (Figs. 3 , 7 , and 8b, e ).…”

Section: Methodsmentioning

confidence: 99%

Recent acceleration in global ocean heat accumulation by mode and intermediate waters

Li,

England,

Groeskamp

2023

Nat Commun

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The ocean absorbs >90% of anthropogenic heat in the Earth system, moderating global atmospheric warming. However, it remains unclear how this heat uptake is distributed by basin and across water masses. Here we analyze historical and recent observations to show that ocean heat uptake has accelerated dramatically since the 1990s, nearly doubling during 2010–2020 relative to 1990–2000. Of the total ocean heat uptake over the Argo era 2005–2020, about 89% can be found in global mode and intermediate water layers, spanning both hemispheres and both subtropical and subpolar mode waters. Due to anthropogenic warming, there are significant changes in the volume of these water-mass layers as they warm and freshen. After factoring out volumetric changes, the combined warming of these layers accounts for ~76% of global ocean warming. We further decompose these water-mass layers into regional water masses over the subtropical Pacific and Atlantic Oceans and in the Southern Ocean. This shows that regional mode and intermediate waters are responsible for a disproportionate fraction of total heat uptake compared to their volume, with important implications for understanding ongoing ocean warming, sea-level rise, and climate impacts.

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“…Ocean temperature, one of the most important variables of seawater, plays a significant role in marine disasters, marine ecosystems, ocean dynamics, and climate changes [1,2], for example, it is closely related to the formation of many high impact thermos-dynamical ocean-atmospheric processes, such as the marine heatwaves [3,4], thermocline layer formation [5], El Niño evolution [6,7], or deep-water formation [8,9]. To better understand the role of ocean temperature in marine disaster and ecosystems, ocean circulation and climate changes, it is essential to identify the ocean subsurface thermal structure (OSTS) accurately.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Liu

Chi

et al. 2022

Remote Sensing

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Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

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Variability of the South Pacific Western Subtropical Mode Water and Its Relationship With ENSO During the Argo Period

Cited by 7 publications

References 53 publications

Subtropical Mode Water south of Japan impacts typhoon intensity

Subtropical Mode Water south of Japan impacts typhoon intensity

Recent acceleration in global ocean heat accumulation by mode and intermediate waters

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

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