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The internal and ENSO-forced modes of the Indian Ocean sea surface temperature (SST) are investigated using a high-resolution regional coupled model. Five different model simulations were performed by controlling atmospheric and oceanic boundary conditions (BCs), which are lateral walls of the model domain. In the internal run performed by prescribing the climatological mean oceanic and atmospheric BCs, the first and second empirical orthogonal functions (EOF1 and EOF2) of internal mode are similar to the observed Indian Ocean basin (IOB) and dipole (IOD) modes with relatively weak amplitudes, respectively. In the control run with observed BCs, those EOFs are much amplified with their power spectrums significantly changed, and their spatial patterns are modified, particularly for the EOF2. Three ENSO runs with combinations of ENSO-related and climatological mean BCs show that the modification of spatial pattern of EOFs is mainly due to ENSO forcing. Furthermore, ENSO forcing determines the major 4-yr period of IOB mainly through the atmosphere, whereas the major 3-yr period of IOD is determined by both ENSO atmosphere and ocean forcings. It is also found that IOB and IOD exhibit a significant seasonally dependent relationship in both internal and ENSO-forced simulations. Most importantly, by applying the empirical singular vector method to both observed and modeled data, it is found that the IOD–IOB relationship is associated with an unstable mode of Indian Ocean SST anomalies, evolving from boreal fall to the next spring. This unstable mode is intrinsic within the Indian Ocean but is substantially amplified by the ENSO.

The major data of "Temperature variation and trend of the Kuroshio in the East China Sea" includes following parts: 1 The horizontal linear trends (℃ yr-1) and standard deviation (℃) of temperature during 1982-2014, 1982-1998 and 1999-2014 from multi-source merged observational data. Data is saved as a matlab file of “Temp_horizontal.mat” with variables of longitude, latitude, linear trends and standard deviation of temperature. 2 The vertical linear trends (℃ yr-1) of temperature anomaly, the temperature gradient with depth (i.e., ∂T/∂z, ℃ m-1) and the temperature variation along the Kuroshio axis (∂T/∂h). Data is saved as a matlab file of “Trend_vertical.mat” with variables of depth, time, vertical linear trends of temperature anomaly 3 The time series of area-averaged temperature anomaly (℃) from multi-source merged observational data and OISST. Data is saved as a matlab file of “Temp_time_series.mat” with variables of time and temperature anomaly. 4 Variation of Kuroshio volume transport (KVT, Sv) during 1982-2014. Data is saved as a matlab file of “KVT.mat” with variables of time and KVT.

The nutrients availability is crucial to the primary production in the eastern Equatorial Indian Ocean, which is largely regulated by the climate change, e.g., the Indian Ocean Dipole (IOD). The in situ observations were conducted across the eastern Equatorial Indian Ocean during the positive IOD in 2019 and neutral conditions in 2017, to reveal the biogeochemical and biological responses. The datasets includes: (1) Temperature, salinity, nitrate, and Chl a concentrations of discrete water samples during 2019 and 2017 in the eastern EIO. (2) The high-resolution nitrate profiles obtained by deep-Suna during 2019 in the eastern EIO. (3) Turbulent mixing and diapycal nitrate flux in 2019. The dataset is archived in .xlsx data format, consists of three data files with data size of 570 KB.

Sediment oxygen consumption (SOC) is important in modulating the oxygen budget in the East China Sea where seasonal hypoxia occurs. Porewater advection, molecular diffusion and bioturbation supply oxygen for sedimentary organic matter degradation. A pelagic‒benthic coupled model was applied to quantify the SOC. A comparison with observations showed good model performance in reproducing the hydrographic and ecological environments, particularly for the interannual variation in the hypoxic zone. Simulation results show that porewater-advection-induced flux is the predominant component of the SOC in sandy areas on the Changjiang bank and outer shelves, while the bioturbation‒induced flux is predominant at mud depocenters. By comparing SOC to the water oxygen consumption (WOC) below the pycnocline, the contribution of SOC is generally below ∼40% in the hypoxic zone. The spatial distribution of SOC in summer is relatively steady from year to year, while the high WOC patches explain more about the interannual variation in the hypoxic zone. WOC rather than SOC drives the variation of hypoxia. Particularly on the Changjiang bank, milder hydrodynamics are favorable for both the higher WOC and bioturbation-induced benthic oxygen flux but substantially suppress the porewater advective flux, which results in the net lower contribution of SOC to hypoxia. This finding may shed light on other pelagic‒benthic coupling processes in coastal shelf seas where hypoxia occurs on permeable sediments.

The major data of "Cross-shore currents in the East China Sea" includes following parts: 1 Variations of wind speed (m/s) from 01-Nov-2013 to 28-Feb-2014. Data is save in a matlab file of “Wind.mat” with variables of time and wind speed. 2 Variations of WCC (m/s) from observed and simulated data. Data is save in a matlab file of “WCC_stations.mat” with variables of time and WCC. 3 Distributions of WCC (m/s) from simulated data. Data is save in a matlab file of “WCC_distributions.mat” with variables of longitude, latitude, mesoscale WCC and submesoscale WCC. 4 The raw measured data series from observations and simulations. Eight text files (S1_obs.txt, S2_obs.txt, S3_obs.txt, S4_obs.txt, S1_sim.txt, S2_sim.txt, S3_sim.txt and S4_sim.txt) present velocity observations and simulations in stations S1-4, which were the major measured data series used in the paper. The time format is “yyyymmddHHMM”. 5 The SST data of remote sensing and of simulation, as well as the associated velocities data of simulation. The SST data is saved as a three-column data file of longitude, latitude and temperature. The velocity data is saved as a four-column data file of longitude, latitude, U and V.

This is the model data from the submittedmanuscript entitled "Stormsurgeriskunderstrengtheningandacceleratingtrendsoflandfallingtropicalcyclones". The major data in this manuscript includes the following four parts: (1) the track, maximum wind speed (MWS), forward speed (FS), and maximum wind radius (MWR) of tropical cyclones were from the Joint Typhoon Warning Center (JTWC) of the U.S. Naval Pacific Meteorology Oceanography Center in Hawaii. (2) Four typical constructed windspeeds during Tropical Storm Nida, Typhoon Vicente, Severe Typhoon Hato, and Supertyphoon Mangkhut are from the improved Emanuel [2004] model [Emanuel and Rotunno, 2011]. (3) two long-term observation stations in the PRE and Daya Bay are from the National Marine Data Center (http://mds.nmdis.org.cn/). (4) 8 statistics of large-ensemble data were simulated by finite-volume community ocean model (FVCOM, Chen et al., [2003]). &nbsp

Eddy-induced heat transport (EHT) in the South China Sea (SCS) is important for the heat budget. However, knowledge of its variability is limited owing to discrepancies arising from the limitation of the down-gradient method and uncertainties arising from numerical models. Herein, we investigated the spatiotemporal variability and dynamics of EHT using a well-validated assimilated model. In particular, to the southeast of Vietnam (SEV) and west of Luzon Strait (WLS), significant values of annual mean EHT are observed and most EHT is confined in the upper 400 m. EHT also exhibits significant seasonality, and the largest EHT amplitude in autumn at SEV is mainly driven by the wind stress curl, while that in winter at WLS is mainly related to the Kuroshio intrusion. Energy budget analysis reveals that both the barotropic and baroclinic instabilities increase the eddy kinetic energy in autumn at SEV, whereas only the barotropic instability contributes to the eddy kinetic energy at WLS in winter. Specially, an up-gradient EHT is observed at WLS in all four seasons, characterized by the same directions between EHT and mean temperature gradient. The up-gradient EHT at WLS is induced by the baroclinic instability through an inverse energy transfer, which is generated by the interaction between the Kuroshio intrusion and topography below the surface layer. Moreover, the most significant up-gradient EHT in winter shows a wave-like southwestward propagating pattern in the subsurface layer.

Combining satellite altimetry and in situ hydrographic measurement, we estimated the cross-shelf transport (CST) and its spatial and temporal variations across 200 m isobath in the East China Sea (ECS) from 1993 to 2014. The vertically-integrated CST can be dynamically divided into three parts: surface Ekman transport, geostrophic transport, and bottom Ekman transport. The results show that the 22-year-mean, sectionally-integrated CST to be 1.7±2.0 Sv (positive in the on-shelf direction), comprised of bottom and surface Ekman transports of 2.7±1.0 Sv and 0.6±0.6 Sv, respectively, that are partially offset by a geostrophic transport of -1.5±1.7 Sv. The sectionally-integrated CST shows significantly high power at roughly annual period from 1999-2001, with lower power at intra-annual period. The time-averaged, vertically-integrated CST to the northeast of Taiwan is the main source of sectionally-integrated CST. The vertically-integrated CST also shows significant variations in the 6–15 month period band to the northeast of Taiwan as well. The temporal variations of the sectionally-integrated and vertically-integrated CST are both controlled primarily by geostrophic transport and modulated by bottom Ekman transport. In the upper 50 m, the geostrophic current to the northeast of Taiwan exhibits large mean and significant variability. The empirical orthogonal function analysis of vertical structure of geostrophic current shows two significant modes with strong annual signal. The first mode is associated with the migration of Kuroshio axis near Taiwan, while the second mode is associated with the variation of the meander of the Kuroshio to the northeast of Taiwan. This article is protected by copyright. All rights reserved.

The seasonal mean and synoptic fluctuation of the wintertime Taiwan Warm Current (TWC) were investigated using a well validated finite volume community ocean model. The spatial distribution and dynamics of the synoptic fluctuation were highlighted. The seasonal mean of the wintertime TWC has two branches: an inshore branch between the 30 and 100 m isobaths and an offshore branch between the 100 and 200 m isobaths. The Coriolis term is much larger than the inertia term and is almost balanced by the pressure gradient term in both branches, indicating the geostrophic balance of the mean current. Two areas with significant fluctuations of the TWC were identified during wintertime. One of the areas is located to the north of Taiwan with velocities varying in the cross-shore direction. These significant cross-shore fluctuations are driven by barotropic pressure gradients associated with the intrusion of the Taiwan Strait Current (TSC). When a larger TSC intrudes north of Taiwan, the isobaric slope tilts downward from south to north, leading to a cross-shore current from the coastal area to the offshore area. When the TSC intrusion is weak, the cross-shore current to the north of Taiwan is directed from offshore to inshore. The other area of significant fluctuation is located in the inshore area, extending in the region between the 30 and 100 m isobaths. The fluctuations are generally strong in the alongshore direction, in particular at the latitudes 26.5° N and 28° N where they are important for the local cross-shore transports. Wind affects the synoptic fluctuation through episodic events. When the northeasterly monsoon prevails, the southward Zhe-Min Coastal Current dominates the inshore area associated with a deepening of the mixed layer. When the winter monsoon is weakened or the southerly wind prevails, the northward TWC dominates in the inshore area.