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.

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.

The Tropical Pacific–Indian Ocean Associated Mode Simulated by LICOM2.0

Oceanic general circulation models have become an important tool for the study of marine status and change. This paper reports a numerical simulation carried out using LICOM2.0 and the forcing field from CORE. When compared with SODA reanalysis data and ERSST.v3 b data, the patterns and variability of the tropical Pacific–Indian Ocean associated mode(PIOAM) are reproduced very well in this experiment. This indicates that, when the tropical central–western Indian Ocean and central–eastern Pacific are abnormally warmer/colder, the tropical eastern Indian Ocean and western Pacific are correspondingly colder/warmer. This further confirms that the tropical PIOAM is an important mode that is not only significant in the SST anomaly field, but also more obviously in the subsurface ocean temperature anomaly field. The surface associated mode index(SAMI) and the thermocline(i.e., subsurface) associated mode index(TAMI) calculated using the model output data are both consistent with the values of these indices derived from observation and reanalysis data. However, the model SAMI and TAMI are more closely and synchronously related to each other.

Verification of an operational ocean circulation-surface wave coupled forecasting system for the China＇s seas

An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.

ENSO事件次表层海温的两个模态及其对大气环流的影响

利用SODA海洋同化资料和NCEP再分析大气资料,分析了热带太平洋次表层海温异常(subsurfaceoceantemperatureanomaly,SOTA)与厄尔尼诺与南方涛动(ElNi?o-SouthernOscillation,ENSO)循环的联系,及SOTA对大气环流的影响。回顾传统ENSO研究,指出存在的问题,提出了ENSO影响大气研究的新思路,得到以下结果:(1)以SOTA为基本资料的研究发现, ENSO事件有两个模态,主要出现在冬季的第一模态对冬季及夏季亚洲-北太平洋-北美地区上空中高纬大气环流有重要影响,主要出现在夏季的第二模态对该地区上空夏季热带和副热带大气系统有重要作用。(2)ENSO事件通过与ENSO相联系的热带太平洋海面温度异常(ENSO-relatedseasurface temperatureanomaly,RSSTA)对大气的异常热通量输送,强迫Walker环流和Hadley环流变化,导致热带和北太平洋及周边地区上空大气环流异常,进而影响相关地区冬季和夏季的气候。(3)海表面温度异常(seasurfacetemperatureanomaly,SSTA)包含RSSTA和大气异常导致的海温变化(sea temperature anomaly caused by atmospheric anomaly, STA)两部分, RSSTA是ENSO事件过程中海洋内部热动力结构调整导致的海面温度变化,在海洋对大气的热输送过程中,它随ENSO事件演变不断更新;STA是大气受RSSTA海洋异常加热后导致的大气环流异常对海面温度的影响,在海洋浅表层STA对RSSTA有重大影响。本文最后讨论了ENSO事件期间热带海洋对大气热输送过程,指出ENSO事件通过海洋内部热动力结构调整产生RSSTA,它直接对大气异常加热,导致大气环流和气候异常,局地海气之间负反馈过程产生STA,反过来抑制RSSTA。结果还指出,人们常用的SSTA变率实际上主要由秋冬季节RSSTA主导,丢失了春夏季ENSO信息,用SSTA研究ENSO事件存在局限性,这也可能是ENSO事件春季预报障碍的原因之一。

Mesoscale characteristics of Antarctic Intermediate Water in the South Pacific

The Argo float observations are used to investigate the mesoscale characteristics of the Antarctic Intermediate Water （AAIW） in the South Pacific in this paper. It is shown that a subsurface mesoscale phenomenon is probably touched by an Argo float during the float＇s ascent-descent cycles and is identified by the horizontal salinity gradient between the vertical temperature-salinity profiles. This shows that the transportation of the AAIW may be accompanied with the rich mesoscale characteristics. To derive the spatial length, time, and propagation characteristics of the mesoscale variability of the AAIW, the gridded temperature-salinity dataset ENACT/ENSEMBLE Version 3 constructed on the in-situ observations in the South Pacific since 2005 is used. The Empirical Mode Decomposition method is applied to decompose the isopycnal-averaged salinity anomaly from 26.8 cr0-27.4 ao, where the AAIW mainly resides, into the basin scale and two mesoscale modes. It is found that the first mesoscale mode with the length scale on the order of 1 000 km explains nearly 50% variability of the mesoscale characteristics of the AAIW. Its westward-propagation speeds are slower in the mid-latitude （around 1 cm/s） and faster in the low latitude （around 6 cm/s）, but with an increasing in the latitude band on 25^-30~S. The second mesoscale mode is of the length scale on the order of 500 km, explaining about 30% variability of the mesoscale characteristics of the AAIW. Its westward-propagation speed keeps nearly unchanged （around 0.5 cm/s）. These results presented the stronger turbulent motion of the subsurface ocean on the spatial scale, and also described the significant role of Argo program for the better understanding of the deep ocean.