Distribution and influencing factors of microeukaryote in different water layers of the southwestern Indian Ocean Ridge

Microeukaryotes play a vital role in shaping marine ecosystems,especially in marine productivity,the microbial food web,and carbon cycle.The Indian Ocean is one of the largest oligotrophic areas in the world,but little is known about the biodiversity of microeukaryotes in the area.The community composition and geographical distribution of microeukaryotes collected from the surface(SUR)and deep chlorophyll maximum(DCM)layers in the southwestern Indian Ocean were studied using high-throughput sequencing of the 18S rRNA gene.The metagenomic data helped quantify the impact of environmental factors on microeukaryotic communities.The relative abundance of different taxa groups exhibited distinct patterns between SUR and DCM layers,except for the most dominant Dinoflagellata that accounted for more than 40.6%abundance in each sample.Radiolaria was much more abundant in the nutrient-rich DCM layer than the SUR layer.The community similarity of microeukaryotes decreased with increasing of geographic distance,whereas the temperature and inorganic nitrogen were the most important environmental parameters to community structure.Abundant communities were more influenced by dispersal limitations and rare communities were more responsive to environmental factors.Correlation network analyses revealed strong biotic interactions indicative of parasitism,predation and competition,and their contribution to microeukaryotic population in diverse environments.Overall,this study provided insights into the biodiversity of microeukaryotes by characterizing the differences between water layers and identifying the driving factors in the ocean.

Contrasting physical mechanisms of yellowfin tuna fluctuations between the western and eastern Indian Ocean

As an economically critical pelagic migratory species,yellowfin tuna(Thunnus albacores,YFT)is very sensible to physical and environmental conditions,such as sea surface temperature(SST),ocean heat content(OHC),and the mixed layer depth(MLD).We investigated the impact of SST,OHC,and MLD on fluctuations of YFT catch in the western/eastern Indian Ocean using the long time series of 63-year environmental and YFT datasets.We found that the impact of SST on YFT was heavily overestimated in the past,and MLD plays a more critical role in the YFT catch fluctuation.When the MLD deepens(>34.8 m),SST was more influential in predicting the catches of YFT than OHC in the western Indian Ocean,and OHC was more critical to YFT than SST in the eastern Indian Ocean.However,when the MLD shallows(<34.8 m),MLD was more vital to predict the catch per unit effort(CPUE)of YFT than SST/OHC in the western.After 2000,there was an asynchronous pattern of YFT CPUE induced by higher frequency variations and ocean hiatus of SST/OHC signals in the western and eastern Indian Oceans basins.The impact of the subsurface hiatus may induce the decrease of YFT in the eastern Indian Ocean.The above findings clarified a non-stationary relationship between the environmental factors and catches of YFT and provided new insights into variations in YFT abundance.

Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

Environmental characteristics of trace metals in seawater from the Ninety East Ridge in the Indian Ocean

The Ninety East Ridge in the Indian Ocean has complex and unique characteristics.The concentrations and distribution characteristics of 10 trace metals(V,Cr,Mn,Fe,Co,Ni,Cu,Cd,Pb,and U)in seawater from the Ninety East Ridge in the Indian Ocean were investigated.Results show that the average concentrations of different trace metals in all the collected seawater samples were 1.134μg/L for V,0.158μg/L for Cr,0.489μg/L for Mn,0.427μg/L for Fe,0.011μg/L for Co,0.395μg/L for Ni,0.403μg/L for Cu,0.097μg/L for Cd,0.139μg/L for Pb,and 3.470μg/L for U.Differences in the horizontal and vertical distributions of all measured trace metals were revealed,and the occurrence of high concentrations was nonuniform.In addition,the significant differences in the concentration distribution of different trace metals in seawater on both sides of the Ninety East Ridge present regional segmentation in the area for various trace metals in deep sea water.This study provided basic data for future investigations on the environmental and ecological impact of trace metals in the Indian Ocean and the potential water mass transport mechanism.

Observation of Low-Level Jets in the Eastern Tropical Indian Ocean Based on Shipborne Coherent Doppler Lidar

In contrast to the Pacific and Atlantic Oceans,the Indian Ocean has lacked in-situ observations of wind profiles over open sea areas for decades.In 2021,a shipborne coherent Doppler lidar(CDL)was used to observe in-situ wind profiles in the eastern tropical Indian Ocean.This equipment successfully captured low-level jets(LLJs)in the region,and their characteristics were thoroughly analyzed.Results reveal that the observed wind speed of LLJs in the eastern Indian Ocean ranges from 6 m s^(-1) to 10 m s^(-1) during the boreal winter and spring seasons,showing a height range of 0.6 to 1 km and two peak times at 0800 and 2000 UTC.This wind shear is weaker than that in land or offshore areas,ranging from 0 s^(-1) to 0.006 s^(-1).Moreover,the accuracy of the CDL data is compared to that of ERA5 data in the study area.The results indicate that the zonal wind from ERA5 data significantly deviated from the CDL measurement data,and the overall ERA5 data are substantially weaker than the in-situ observations.Notably,ERA5 underestimates northwestward LLJs.

Synergistic Impacts of Indian Ocean SST and Indo-China Peninsula Soil Moisture on the 2020 Record-breaking Mei-yu

The Yangtze River basin(YRB)experienced a record-breaking mei-yu season in June‒July 2020.This unique long-lasting extreme event and its origin have attracted considerable attention.Previous studies have suggested that the Indian Ocean(IO)SST forcing and soil moisture anomaly over the Indochina Peninsula(ICP)were responsible for this unexpected event.However,the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event,especially their linkage with atmospheric circulation changes,remain unclear.By using observations and numerical simulations,this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020.Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds.The intensification of the warm condition further magnified the land thermal effects,which in turn facilitated the westward extension of the western North Pacific subtropical high(WNPSH)in June‒July.The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB,thereby contributing to the 2020 mei-yu.In contrast,the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture.The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model.Their synergistic impacts yield a notable 32%increase in YRB precipitation.Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.

Characteristics and triggering mechanisms of early negative Indian Ocean Dipole

Negative Indian Ocean Dipole(nIOD)can exert great impacts on global climate and can also strongly influence the climate in China.Early nIOD is a major type of nIOD,which can induce more pronounced climate anomalies in summer than La Niña-related nIOD.However,the characteristics and triggering mechanisms of early nIOD are unclear.Our results based on reanalysis datasets indicate that the early nIOD and La Niña-related nIOD are the two major types of nIOD,and the former accounts for over one third of all the nIOD events in the past six decades.These two types of nIODs are similar in their intensities,but are different in their spatial patterns and seasonal cycles.The early nIOD,which develops in spring and peaks in summer,is one season earlier than the La Niña-related nIOD.The spatial pattern of the wind anomaly associated with early nIOD exhibits a winter monsoon-like pattern,with strong westerly anomalies in the equatorial Indian Ocean and eastly anomalies in the northern Indian Ocean.Opposite to the triggering mechanism of early positve IOD,the early nIOD is induced by delayed Indian summer monsoon onset.The results of this study are helpful for improving the prediction skill of IOD and its climate impacts.

Corrections of shipboard GPS radiosonde soundings and applications on historical records in the eastern tropical Indian Ocean and South China Sea

Shipboard radiosonde soundings are important for detecting and quantifying the multiscale variability of atmosphere-ocean interactions associated with mass exchanges.This study evaluated the accuracies of shipboard Global Positioning System(GPS)soundings in the eastern tropical Indian Ocean and South China Sea through a simultaneous balloon-borne inter-comparison of different radiosonde types.Our results indicate that the temperature and relative humidity(RH)measurements of GPS-TanKong(GPS-TK)radiosonde(used at most stations before 2012)have larger biases than those of ChangFeng-06-A(CF-06-A)radiosonde(widely used in current observation)when compared to reference data from Vaisala RS92-SGP radiosonde,with a warm bias of 5℃and dry bias of 10%during daytimes,and a cooling bias of-0.8℃and a moist bias of 6%during nighttime.These systematic biases are primarily attributed to the radiation effects and altitude deviation.An empirical correction algorithm was developed to retrieve the atmospheric temperature and RH profiles.The corrected profiles agree well with that of RS92-SGP,except for uncertainties of CF-06-A in the stratosphere.These correction algorithms were applied to the GPS-TK historical sounding records,reducing biases in the corrected temperature and RH profiles when compared to radio occultation data.The correction of GPS-TK historical records illustrated an improvement in capturing the marine atmospheric structure,with more accurate atmospheric boundary layer height,convective available potential energy,and convective inhibition in the tropical ocean.This study contributes significantly to improving the quality of GPS radiosonde soundings and promotes the sharing of observation in the eastern tropical Indian Ocean and South China Sea.

Accuracy assessment of global vertical displacement loading tide models for the equatorial and Indian Ocean

The three-dimensional displacements caused by ocean loading effects are significant enough to impact spatial geodetic measurements on sub-daily or longer timescales,particularly in the vertical direction.Currently,most tide models incorporate the distribution of vertical displacement loading tides;however,their accuracy has not been assessed for the equatorial and Indian Ocean regions.Global Positioning System(GPS)observations provide high-precision data on sea-level changes,enabling the assessment of the accuracy and reliability of vertical displacement tide models.However,because the tidal period of the K_(2) constituent is almost identical to the orbital period of GPS constellations,the estimation of the K_(2) tidal constituent from GPS observations is not satisfactory.In this study,the principle of smoothness is employed to correct the systematic error in K_(2) estimates in GPS observations through quadratic fitting.Using the adjusted harmonic constants from 31 GPS stations for the equatorial and Indian Ocean,the accuracy of eight major constituents from five global vertical displacement tide models(FES2014,EOT11a,GOT4.10c,GOT4.8,and NAO.99b)is evaluated for the equatorial and Indian Ocean.The results indicate that the EOT11a and FES2014 models exhibit higher accuracy in the vertical displacement tide models for the equatorial and Indian Ocean,with root sum squares errors of 2.29 mm and 2.34 mm,res-pectively.Furthermore,a brief analysis of the vertical displacement tide distribution characteristics of the eight major constituents for the equatorial and Indian Ocean was conducted using the EOT11a model.

Correlation of Rainfall Anomalies in Rwanda from September to December (SOND) with Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) Events

Understanding the relationship between rainfall anomalies and large-scale systems is critical for driving adaptation and mitigation strategies in socioeconomic sectors. This study therefore aims primarily to investigate the correlation between rainfall anomalies in Rwanda during the months of September to December (SOND) with the occurrences of Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) events. The study is useful for early warning and forecasting of negative effects associated with extreme rainfall anomalies across the country, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research (NCAR) reanalysis sea surface temperature and ERA5 reanalysis datasets, during the period of 1983-2021. Both empirical orthogonal function (EOF), correlation analysis and composite analysis were used to delineate variability, relationship and the related atmospheric circulation between Rwanda seasonal rainfall September to December (SOND) with Indian Ocean Dipole (IOD) and El-Nino Southern Oscillation (ENSO). The results for Empirical Orthogonal Function (EOF) for the reconstructed rainfall data set showed three modes. EOF-1, EOF-2 and EOF-3 with their total variance of 63.6%, 16.5% and 4.8%, Indian ocean dipole (IOD) events resulted to a strong positive correlation of rainfall anomalies and Dipole model index (DMI) (r = 0.42, p value = 0.001, DF = 37) significant at 95% confidence level. The composite analysis for the reanalysis dataset was carried out to show the circulation patterns during four different events correlated with September to December seasonal rainfall in Rwanda using T-test at 95% confidence level. Wind anomaly revealed that there was a convergence of south westerly winds and easterly wind over the study area during positive Indian Ocean Diploe (PIOD) and PIOD with El Nino concurrence event years. The finding of this study will contribute to the enhancement of SOND seasonal rainfall forecasting and the reduction of vulnerability during IOD (ENSO) event years.

The Relationship between Extreme Precipitation Events in East Africa during the Short Rainy Season and Indian Ocean Sea Surface Temperature

The East African short rainy season (October-November-December) is one of the major flood seasons in the East African region. The amount of rainfall during the short rainy season is closely related to the lives of the people and the socio-economic development of the area. By using precipitation data and sea surface temperature data, this study reveals the spatial and temporal variation patterns of extreme precipitation during the East African short rainy season. Key findings include significant rainfall variability, with Tanzania experiencing the highest amounts in December due to the southward shift of the Intertropical Convergence Zone (ITCZ), while other regions receive less than 100 mm. Extreme rainfall events (90th percentiles) are evenly distributed, averaging 2 to 10 days annually. Historical data shows maximum seasonal rainfall often peaks at 15 mm, with frequent occurrences of daily rainfall exceeding 10 mm during OND. Additionally, a positive correlation (0.48) between OND precipitation extremes and Indian Ocean Dipole (IOD) anomalies is statistically significant. These findings highlight the climatic variability and potential trends in extreme rainfall events in East Africa, providing valuable insights for regional climate adaptation strategies.

Assessment of Atmospheric Reanalysis Data Based on Buoy Observations over the Tropical Western Indian Ocean in 2019

Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

Circulation Patterns Linked to the Positive Sub-Tropical Indian Ocean Dipole

The positive phase of the subtropical Indian Ocean dipole(SIOD)is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa.This paper examines austral summer rain-bearing circulation types(CTs)in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship.Four austral summer rain-bearing CTs were obtained.Among the four CTs,the CT that featured(i)enhanced cyclonic activity in the southwest Indian Ocean;(ii)positive widespread rainfall anomaly in the southwest Indian Ocean;and(iii)low-level convergence of moisture fluxes from the tropical South Atlantic Ocean,tropical Indian Ocean,and the southwest Indian Ocean,over the south-central landmass of Africa,was found to be related to the positive SIOD climatic mode.The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT.The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa,where the SIOD is already known to significantly influence its rainfall variability.Hence,through the large-scale patterns of atmospheric circulation associated with the CT,the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence.

The Relationship Between Indian Ocean SST and Tropical Cyclone Genesis Frequency over North Indian Ocean in May

Tropical cyclone(TC)activities in the North Indian Ocean(NIO)peak in May during the pre-monsoon period,but the TC frequency shows obvious inter-annual variations.By conducting statistical analysis and dynamic diagnosis of long-term data from 1948 to 2016,the relationship between the inter-annual variations of Indian Ocean SST and NIO TC genesis frequency in May is analyzed in this paper.Furthermore,the potential mechanism concerning the effect of SST anomaly on TC frequency is also investigated.The findings are as follows:1)there is a broadly consistent negative correlation between NIO TC frequency in May and SST in the Indian Ocean from March to May,with the key influencing area located in the southwestern Indian Ocean(SWIO);2)the anomalies of SST in SWIO(SWIO-SST)are closely related to a teleconnection pattern surrounding the Indian Ocean,which can significantly modulate the high-level divergence,mid-level vertical motion and other related environmental factors and ultimately influence the formation of TCs over the NIO;3)the increasing trend of SWIO-SST may play an essential role in the downward trend of NIO TC frequency over the past 69 years.

Assessment of Sea Surface Temperature Warming in the Tropical Indian Ocean Simulated by CMIP Models

The tropical Indian Ocean is an important region that affects local and remote climate systems,and the simulation of longterm trends in sea surface temperature(SST)is a major focus of climate research.This study presents a preliminary assessment of multiple model simulations of tropical Indian Ocean SST warming from 1950 to 1999 based on outputs from the 20 Coupled Model Intercomparison Project(CMIP)Phase 5(CMIP5)models and the 36 CMIP 6(CMIP6)models to analyze and compare the warming patterns in historical simulations.Results indicate large discrepancies in the simulations of tropical Indian Ocean SST warming,especially for the eastern equatorial Indian Ocean.The multimodel ensemble mean and most of the individual models generally perform well in reproducing basin-wide SST warming.However,the strength of the SST warming trends simulated by the CMIP5 and CMIP6 models are weaker than those observed,especially for the CMIP6 models.In addition to the general warming trend analysis,decadal trends are also assessed,and a statistical method is introduced to measure the near-term variability in an SST time series.The simulations indicate large decadal variability over the entire tropical Indian Ocean,differing from observations in which significant decadal trend variability is observed only in the southeastern Indian Ocean.In the CMIP model simulations,maximum decadal variability occurs in boreal autumn,but the observations display the minimum and maximum variability in boreal autumn and spring,respectively.

Subthermocline eddies carrying the Indonesian Throughflow water observed in the southeastern tropical Indian Ocean

We observed a subthermocline eddy(STE)with a cold and fresh core during an observation cruise along a transect of 10°S in the southeastern tropical Indian Ocean(SETIO)in December 2017.The vertical scale,speed radius,and maximum swirl velocity of the STE were about 200 m,55 km,and 0.5 m/s,respectively.The mean Rossby number and Burger number of the STE were then estimated to be about−0.7 and 2.4,indicating the STE was a submesoscale coherent vortex.The STE core water had characteristics of the Indonesian Throughflow(ITF)water and was distinct from that of surrounding areas.By examining Argo float data,another STE was well captured by five successive profiles of the same Argo float.Both STEs showed significant temperature and salinity anomalies at theσ0=26.0-26.5 kg/m3 surfaces.With the assumption that the low-salinity ITF water parcels could be carried only by surface eddies and the STEs,the Argo profiles,which detected low-salinity ITF water and were located outside a surface eddy,were believed to be inside an STE and were used to analyze the distribution,origin,and generation mechanism of the STE.The results suggested that the STEs carrying ITF water may be generated under topography-current interaction at the eastern coastal waters or under front-induced subduction in the area away from coastal waters.Those STEs may be widely distributed in the SETIO and may play a role in ITF water parcel transport.

The Subsurface and Surface Indian Ocean Dipoles and Their Association with ENSO in CMIP6 models

This study assesses the reproducibility of 31 historical simulations from 1850 to 2014 in the Coupled Model Intercomparison Project phase 6(CMIP6) for the subsurface(Sub-IOD) and surface Indian Ocean Dipole(IOD) and their association with El Ni?o-Southern Oscillation(ENSO). Most CMIP6 models can reproduce the leading east-west dipole oscillation mode of heat content anomalies in the tropical Indian Ocean(TIO) but largely overestimate the amplitude and the dominant period of the Sub-IOD. Associated with the much steeper west-to-east thermocline tilt of the TIO, the vertical coupling between the Sub-IOD and IOD is overly strong in most CMIP6 models compared to that in the Ocean Reanalysis System 4(ORAS4). Related to this, most models also show a much tighter association of Sub-IOD and IOD events with the canonical ENSO than observations. This explains the more(less) regular Sub-IOD and IOD events in autumn in those models with stronger(weaker) surface-subsurface coupling in TIO. Though all model simulations feature a consistently low bias regarding the percentage of the winter–spring Sub-IOD events co-occurring with a Central Pacific(CP) ENSO, the linkage between a westward-centered CP-ENSO and the Sub-IOD that occurs in winter–spring, independent of the IOD, is well reproduced.

Spatial Distribution and Seasonal Variation of Hypoxic Zone in the Eastern Equatorial Indian Ocean

The spatial distribution and seasonal variations of the hypoxic zone in the eastern equatorial Indian Ocean were investigated using survey data collected from four cruises from 2013 to 2018.Results showed that hypoxic zone occurred all year round in the eastern equatorial Indian Ocean,and it spread southward in the shape of a double tongue at two depths with one at subsurface centered at a depth of 150 m and the other in intermediate water centered at a depth of 800 m.The southward expansion and maximum thickness of the hypoxic zone were greatest in the spring inter-monsoon and least in the summer monsoon.The hypoxic zone originated from the southward expansion of the hypoxic water in the Bay of Bengal and its spatial distribution was driven by southward output flux of mid-deep(100–1000 m)hypoxic water from the Bay of Bengal.The hypoxia southward expansion was blocked near the equator in the subsurface layer,because of mixing with multiple zonal circulations(e.g.,Wyrtki Jets and the equatorial undercurrent),which meant that the hypoxic zone extended over a smaller area than in the intermediate water.These new findings will contribute to an improved understanding of the hypoxic zone and will contribute to circulation research,particularly about intermediate circulation in the eastern equatorial Indian Ocean.

Mixed layer warming by the barrier layer in the southeastern Indian Ocean

The southeastern Indian Ocean is characterized by the warm barrier layer(BL)underlying the cool mixed layer water in austral winter.This phenomenon lasts almost half a year and thus provides a unique positive effect on the upper mixed layer heat content through the entrainment processes at the base of the mixed layer,which has not been well evaluated due to the lack of proper method and dataset.Among various traditional threshold methods,here it is shown that the 5 m fixed depth difference can produce a reliable and accurate estimate of the entrainment heat flux(EHF)in this BL region.The comparison between the daily and monthly EHF warming indicates that the account for high-frequency EHF variability almost doubles the warming effect in the BL period,which can compensate for or even surpass the surface heat loss.This increased warming is a result of stronger relative rate of the mixed layer deepening and larger temperature differences between the mixed layer and its immediate below in the daily-resolving data.The interannual EHF shows a moderately increasing trend and similar variabilities to the Southern Annular Mode(SAM),likely because the mixed layer deepening under the positive SAM trend is accompanied by enhanced turbulent entrainment and thus increases the BL warming.