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1.
本研究发展了一个全球海洋资料同化系统ZFL_GODAS。该系统是一个短期气候数值预测业务系统的子系统,为短期气候预测海气耦合模式提供全球海洋初始场。系统能够同化的观测资料包括卫星高度计资料、卫星海表温度(SST)资料,以及Argo、XBT、TAO等各种不同来源的现场温盐廓线资料。系统使用的海洋模式为中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室开发的气候系统海洋模式LICOM1.0,同化方案为集合最优插值(EnOI)方案。系统使用一个由海洋模式自由积分得到的静态样本来估计背景场误差协方差。这样的基于集合样本的背景场误差协方差具有多变量协变、各向异性的特征,且能反映海洋物理过程固有的空间尺度特征。针对EnOI同化程序的特点,开发了一套特色鲜明、负载均衡、高效的并行化同化程序。本文通过与不同类型观测资料的比较,对同化系统的性能进行了评估。通过比较海表温度和海面高度的年际变率,海表温度异常随时间的变化,SST、海面高度异常(SLA)以及次表层温盐预报产品的均方根误差,5年平均温度偏差廓线、平均盐度廓线、平均纬向流速廓线等发现:系统工作正常、同化效果较好;经过同化以后,各变量都更加接近观测,误差更小,与观测场的相关性更好,可以为短期气候预测系统提供较好的海洋初始场,也可以为物理海洋学的研究提供有效的再分析资料。 相似文献
2.
The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used. 相似文献
3.
HAN Guijun LI Wei ZHANG Xuefeng LI Dong HE Zhongjie WANG Xidong WU Xinrong YU Ting MA Jirui 《大气科学进展》2011,28(3):682-690
A regional ocean reanalysis system for the coastal waters of China and adjacent seas has been developed by the National Marine Data and Information Service(NMDIS).It produces a dataset package called CORA (China ocean reanalysis).The regional ocean model used is based on the Princeton Ocean Model with a generalized coordinate system(POMgcs).The model is parallelized by NMDIS with the addition of the wave breaking and tidal mixing processes into model parameterizations.Data assimilation is a sequential three-dimensional variational(3D-Var) scheme implemented within a multigrid framework.Observations include satellite remote sensing sea surface temperature(SST),altimetry sea level anomaly(SLA),and temperature/salinity profiles.The reanalysis fields of sea surface height,temperature,salinity,and currents begin with January 1986 and are currently updated every year. Error statistics and error distributions of temperature,salinity and currents are presented as a primary evaluation of the reanalysis fields using sea level data from tidal gauges,temperature profiles,as well as the trajectories of Argo floats.Some case studies offer the opportunity to verify the evolution of certain local circulations.These evaluations show that the reanalysis data produced provide a good representation of the ocean processes and phenomena in the coastal waters of China and adjacent seas. 相似文献
4.
Argo大洋观测资料的同化及其在短期气候预测和海洋分析中的应用 总被引：3，自引：0，他引：3
国际Argo(Array for Real-time Geostrophic Oceanography)计划的实施,提供了前所未有的全球深海大洋0～2000 m水深范围内的海水温度和盐度观测资料,在大气和海洋科研业务中应用这一全新的资料,是深入认识大气和海洋变异、提高我国气候预测、海洋监测分析和预报能力的一个关键所在.通过开发非线性温—盐协调同化方案和利用同化高度计资料来调整模式的温度和盐度场,建立了可同化包括Argo等多种海洋观测资料的全球海洋资料变分同化系统,提高了对全球海洋的监测分析能力.实现了海洋资料同化系统与全球海气耦合模式的耦合,显著提高了短期气候预测水平.利用Argo资料改进了海洋动力模式中的物理过程参数化方案,有效提高了海洋模式对真实大洋的模拟能力和对厄尔尼诺/拉尼娜的预测能力.开发了利用Argo浮标漂流轨迹推算全球海洋表层和中层流的方法,提高了推算的全球表层流、中层流资料质量,有效弥补了洋流观测的匮乏. 相似文献
5.
The signatures of mesoscale eddies induced surface and subsurface changes have not been comprehensively quantified for the Bay of Bengal (BoB) region. This study quantifies the statistical properties and three-dimensional (3D) eddy structures in the BoB. To accomplish this, the satellite altimetry data combined with automated eddy detection and tracking algorithm is used. Horizontal distribution of surface characteristics of eddies is analyzed by using 24 years (1993–2016) of AVHRR infrared satellite sea surface temperature (SST) and 7 years (2010–2016) of sea surface salinity (SSS) of SMOS satellite data. Surface eddy centric composite analysis reveals the existence of warm (cold) and diverse SSS anomalies for anticyclonic (cyclonic) eddies. During winter, it is important to note that the eddy induced SST and SSS anomalies show the dipole patterns show opposite phases for the cyclonic and anticyclonic eddies. Observed diploe structures are consistent with the eddy rotation and background large-scale meridional gradient of temperature and salinity fields. The 3D structure of eddies is investigated by using the ARMOR3D and Argo float profiles. The horizontal distribution of temperature and salinity anomalies from ARMOR3D signify the monopole structure of eddies in the subsurface layers. Further, the analysis of composite averages of 241 (200) Argo temperature profiles indicates the core of anticyclonic (cyclonic) eddies centered at about ∼140 m (∼100 m). However, salinity profiles depict the existence of core at ∼65 m (∼50 m). This study have practical relevance to a variety of stakeholders and finds profound importance in the validation of eddy-resolving ocean models for the BoB region. 相似文献
6.
The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges. 相似文献
7.
The first version of a global ocean reanalysis over multiple decades (1979–2008) has been completed by the National Marine Data and Information Service within the China Ocean Reanalysis (CORA) project. The global ocean model employed is based upon the ocean general circulation model of the Massachusetts Institute of Technology. A sequential data assimilation scheme within the framework of 3D variational (3DVar) analysis, called multi-grid 3DVar, is implemented in 3D space for retrieving multiple-scale observational information. Assimilated oceanic observations include sea level anomalies (SLAs) from multi-altimeters, sea surface temperatures (SSTs) from remote sensing satellites, and in-situ temperature/salinity profiles. Evaluation showed that compared to the model simulation, the annual mean heat content of the global reanalysis is significantly approaching that of World Ocean Atlas 2009 (WOA09) data. The quality of the global temperature climatology was found to be comparable with the product of Simple Ocean Data Assimilation (SODA), and the major ENSO events were reconstructed. The global and Atlantic meridional overturning circulations showed some similarity as SODA, although significant differences were found to exist. The analysis of temperature and salinity in the current version has relatively larger errors at high latitudes and improvements are ongoing in an updated version. CORA was found to provide a simulation of the subsurface current in the equatorial Pacific with a correlation coefficient beyond about 0.6 compared with the Tropical Atmosphere Ocean (TAO) mooring data. The mean difference of SLAs between altimetry data and CORA was less than 0.1 m in most years. 相似文献
8.
A global ocean temperature and altimeter data assimilation system for studies of climate variability 总被引：4，自引：0，他引：4
An ocean data assimilation (ODA) system which can assimilate both temperature and altimeter observations has been applied
to the global ocean and tested between January 1993–October 1996. A statistical method has been used to convert sea surface
height (SSH) anomalies observations from TOPEX/POSEIDON into synthetic temperature profiles. The innovative aspect of this
method is the introduction of time dependency in the correlations used to transform the altimeter observations into temperature
corrections. The assimilation system is based on a univariate variational optimal interpolation scheme applied to assimilate
both in situ and synthetic temperature profiles. In addition, a longer global analysis for the upper-ocean temperature starting
from January 1979 and ending November 1997, has been produced to examine the skill of sea temperature assimilation with a
rather simple and practical method. The temperature analysis shows encouraging improvement over a corresponding ocean simulation
when compared to independent (not assimilated) temperature data both at seasonal and interannual time scales. However, the
univariate data assimilation of hydrographic data does not result in an improvement of the velocity field. In fact the assimilation
of sparse in situ data can introduce unrealistic spatial variability in the temperature field which affects the velocity field
in a negative way. This deficiency is partially overcome when we also assimilate altimeter observations since the coverage
is complete and uniform for this data. In particular, our study shows that temperature corrections due to the altimeter signal
have a positive impact on the current system in the tropical Pacific.
Received: 28 May 2000 / Accepted: 6 November 2000 相似文献
9.
The deep ocean below 2000 m is a large water body with the sparsest data coverage, challenging the closure of the sea-level budget and the estimation of the Earth's energy imbalance. Whether the deep ocean below 2000 m is warming globally has been debated in the recent decade. However, as the regional signals are generally larger than the global average, it is intriguing to investigate the regional temperature changes. Here, we adopt an indirect method that combines altimetry, GRACE, and Argo data to examine the global and regional deep ocean temperature changes below 2000 m. The consistency between high-quality conductivity-temperature-depth (CTD) data from repeated hydrographic sections and our results confirms the validity of the indirect method. We find that the deep oceans are warming in the Middle East Indian Ocean, the subtropical North and Southwest Pacific, and the Northeast Atlantic, but cooling in the Northwest Atlantic and Southern oceans from 2005 to 2015. 相似文献
10.
An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data. 相似文献