Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

The ensemble optimal interpolation （EnOI） is applied to the regional ocean modeling system （ROMS） with the ability to assimilate the along-track sea level anomaly （TSLA）. This system is tested with an eddy-resolving system of the South China Sea （SCS）. Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation （RMSD） of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error （AGE） perform better than no AGE error （NoAGE） when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models.

Altimeter significant wave height data assimilation in the South China Sea using Ensemble Optimal Interpolation

The application of ensemble optimal interpolation in wave data assimilation in the South China Sea is presented. A sampling strategy for a stationary ensemble is first discussed. The stationary ensemble is constructed by sampling from 24-h-interval significant wave height differences of model outputs over a long period,and is validated with altimeter significant wave height data,indicating that the ensemble errors have nearly the same probability distribution function. The background error covariance fields expressed by the ensemble sampled are anisotropic. Updating the static samples by season,the seasonal characteristics of the correlation coefficient distribution are reflected. Hindcast experiments including assimilation and control runs are conducted for the summer of 2010 in the South China Sea. The effect of ensemble optimal interpolation assimilation on wave hindcasts is validated using different satellite altimeter data(Jason-1 and 2 and ENVISAT) and buoy observations. It is found that the ensemble-optimal-interpolation-based wave assimilation scheme for the South China Sea achieves improvements similar to those of the previous optimal-interpolation-based scheme,indicating that the practical application of this computationally cheap ensemble method is feasible.

Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods

The effects of sea surface temperature（SST） data assimilation in two regional ocean modeling systems were examined for the Yellow Sea（YS）. The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis（OSTIA） were assimilated. The National Marine Environmental Forecasting Center（NMEFC） modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University（KNU） modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error（RMSE） of the SST from 1.78°C（1.46°C） to 1.30°C（1.21°C） for the NMEFC（KNU） modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature（OISST） SST dataset. A comparison with the buoy SST data indicated a 41%（31%） decrease in the SST error for the NMEFC（KNU） modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.

An Ocean Reanalysis System for the Joining Area of Asia and Indian-Pacific Ocean

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.

An Ocean Data Assimilation System in the Indian Ocean and West Pacific Ocean

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.

Evaluation of Ocean Data Assimilation in CAS-ESM-C:Constraining the SST Field

A weakly coupled assimilation system, in which SST observations are assimilated into a coupled climate model （CAS- ESM-C） through an ensemble optimal interpolation scheme, was established. This system is a useful tool for historical climate simulation, showing substantial advantages, including maintaining the atmospheric feedback, and keeping the oceanic tields from drifting far away from the observation, among others. During the coupled model integration, the bias of both surface and subsurface oceanic fields in the analysis can be reduced compared to unassimilated fields. Based on 30 model years of ot.tput fiom the system, the climatology and imerannual variability of the climate system were evaluated. The results showed that the system can reasonably reproduce the climatological global precipitation and SLP, bul it still sutters from the double ITCZ problem. Besides, the ENSO footprint, which is revealed by ENSO-related surface air temperature, geopotential height and precipitation during El Nifio evolution, is basically reproduced by the system. The system can also simulate the observed SST-rainfall relationships well on both interannual and intraseasonal timescales in the western North Pacific region, in which atmospheric feedback is crucial for climate simulation.

Establishment and tests of EnOI assimilation module for WAVEWATCH Ⅲ

In this paper, we propose a parallel data assimilation module based on ensemble optimal interpolation （EnOI）. We embedded the method into the full-spectral third-generation wind-wave model, WAVEWATCH III Version 3.14, producing a wave data assimilation system. We present our preliminary experiments assimilating altimeter significant wave heights （SWH） using the EnOI-based wave assimilation system. Waters north of 15°S in the Indian Ocean and South China Sea were chosen as the target computational domain, which was two-way nested into the global implementation of the WAVEWATCH III. The wave model was forced by six-hourly ocean surface wind velocities from the cross-calibrated multi-platform wind vector dataset. The assimilation used along-track SWH data from the Jason-2 altimeter. We evaluated the effect of the assimilation on the analyses and hindcasts, and found that our technique was effective. Although there was a considerable mean bias in the control SWHs, a month-long consecutive assimilation reduced the bias by approximately 84% and the root mean-square error （RMSE） by approximately 65%. Improvements in the SWH RMSE for both the analysis and hindcast periods were more significant in July than January, because of the monsoon climate. The improvement in model skill persisted for up to 48 h in July. Furthermore, the SWH data assimilation had the greatest impact in areas and seasons where and when the sea-states were dominated by swells.

The Impact of "Bad" Argo Profiles on Ocean Data Assimilation

Recent studies have found cold biases in a fraction of Argo profiles (hereinafter referred to as bad Array for Real-time Geostrophic Oceanography (Argo) profiles) due to the pressure drifts during 2003 and 2006. These bad Argo profiles have had an important impact on in situ observation-based global ocean heat content esti- mates. This study investigated the impact of bad Argo profiles on ocean data assimilation results that were based on observations from diverse ocean observation systems, such as in situ profiles (e.g., Argo, expendable bathy- thermograph (XBT), and Tropical Atmosphere Ocean (TAO), remote-sensing sea surface temperature products and satellite altimetry between 2004 and 2006. Results from this work show that the upper ocean heat content analysis is vulnerable to bad Argo profiles and demon- strate a cooling trend in the studied period despite the multiple independent data types that were assimilated. When the bad Argo profiles were excluded from the as- similation, the decreased heat content disappeared and a warming occurred. Combination of satellite altimetry and mass variation data from gravity satellite demonstrated an increase, which agrees well with the increased heat con- tent. Additionally, when an additional Argo profile quality control procedure was utilized that simply removed the profiles that presented static unstable water columns, the results were very similar to those obtained when the bad Argo profiles were excluded from the assimilation. This indicates that an ocean data assimilation that uses multiple data sources with improved quality control could be less vulnerable to a major observation system failure, such as a bad Argo event.