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.     

Formulation, implementation and examination of vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM)

A 1/8° global version of the Navy Coastal Ocean Model (NCOM) is described with details of its formulation, implementation, and configuration of the vertical coordinate. NCOM is a baroclinic, hydrostatic, Boussinesq, free-surface ocean model that allows its vertical coordinate to consist of σ coordinates for the upper layers and z-levels below a user-specified depth. This flexibility allows implementation of a hybrid σ–z coordinate system that is expected to mitigate some of the weaknesses that can be associated with either pure coordinate option. For the global NCOM application, the σ–z coordinate is used to allow terrain-following σ coordinates in the upper ocean, providing better resolution and topographic fidelity in shelf regions where flow is most sensitive to its representation. Including z coordinates for deeper regions efficiently maintains high near-surface vertical resolution in the open ocean. Investigation into the impact of the selected coordinate system focuses on differences between atmospherically-forced free-running (no assimilation) global solutions using σ–z and pure z coordinates. Comparisons with independent temperature observations indicate that global NCOM using the σ–z coordinate has improved skill relative to its z coordinate implementation. Among other metrics, we show that in comparison with time series of surface temperature from National Oceanic Data Center (NODC) buoys, mostly located in coastal regions, root mean squared differences (RMSD) improved for 63% and correlation improved for 71% of the stations when σ–z coordinates were used instead of pure z. For the exclusively open-ocean Tropical Atmosphere-Ocean (TAO) buoys, differences between the simulations were small, with the σ–z showing smaller RMSD for 45% of the stations and higher correlation for 65% of the stations. Additional comparisons using temperature profile observations further confirm a tendency for improved performance using the hybrid σ–z coordinates.     

Winter-to-winter recurrence of sea surface temperature, salinity and mixed layer depth anomalies

The mean seasonal cycle of mixed layer depth (MLD) in the extratropical oceans has the potential to influence temperature, salinity and mixed layer depth anomalies from one winter to the next. Temperature and salinity anomalies that form at the surface and spread throughout the deep winter mixed layer are sequestered beneath the mixed layer when it shoals in spring, and are then re-entrained into the surface layer in the subsequent fall and winter. Here we document this ‘re-emergence mechanism’ in the North Pacific Ocean using observed SSTs, subsurface temperature fields from a data assimilation system, and coupled atmosphere–ocean model simulations. Observations indicate that the dominant large-scale SST anomaly pattern that forms in the North Pacific during winter recurs in the following winter. The model simulation with mixed layer ocean physics reproduced the winter-to-winter recurrence, while model simulations with observed SSTs specified in the tropical Pacific and a 50 m slab in the North Pacific did not. This difference between the model results indicates that the winter-to-winter SST correlations are the result of the re-emergence mechanism, and not of similar atmospheric forcing of the ocean in consecutive winters. The model experiments also indicate that SST anomalies in the tropical Pacific associated with El Niño are not essential for re-emergence to occur.The recurrence of observed SST and simulated SST and SSS anomalies are found in several regions in the central North Pacific, and are quite strong in the northern (>50°N) part of the basin. The winter-to-winter autocorrelation of SSS anomalies exceed those of SST, since only the latter are strongly damped by surface fluxes. The re-emergence mechanism also has a modest influence on MLD through changes in the vertical stratification in the seasonal thermocline.     

Mixed Layer Depth and its Variability in the Eastern Equatorial Indian Ocean as Revealed by Observations and Model Simulations

Mixed layer depth (MLD) variability in the Eastern Equatorial Indian Ocean (EEIO) from a hindcast run of an Ocean General Circulation Model (OGCM) forced by daily winds and radiative fluxes from NCEP-NCAR reanalysis from 2004 to 2006 is investigated. Model MLD compares well with the ～20,000 observations from Argo floats and a TRITON buoy (1.5°S and 90°E) in the Indian Ocean. Tests with a one-dimensional upper ocean model were conducted to assess the impact on the MLD simulations that would result from the lack of the diurnal cycle in the forcing applied to the OGCM. The error was of the order of ～12 m. MLD at the TRITON buoy location shows a bimodal pattern with deep MLD during May–June and December–January. MLD pattern during fall 2006 was significantly different from the climatology and was rather shallow during December–January both in the model and observation. An examination of mixed layer heat and salt budget suggested salinity freshening caused by the advective and vertical diffusive mixing to be the cause of shallow MLD.     

The impact of bio-optical heating on the properties of the upper ocean: A sensitivity study using a 3-D circulation model for the Labrador Sea

The impact of bio-optical heating on the properties of the upper Labrador Sea water was investigated by considering changes in light attenuation in water associated with the seasonal change of chlorophyll distribution. The time- and depth-dependent attenuation coefficients were obtained from remotely sensed SeaWiFS ocean-colour data. Sea-surface temperature (SST) and mixed-layer depth (MLD) were computed from a three-dimensional ocean circulation model. The model was integrated from 1999 to 2003 with 6-hourly atmospheric forcing. The changes in SST and MLD attributable to bio-optical heating were determined by comparing the model results using the observed attenuation coefficients (chlorophyll) to those using a weak and constant attenuation (clear water). The model results show that bio-optical heating is controlled mainly by chlorophyll concentration and MLD. The increase in SST is around 1 °C in most parts of the Labrador Sea and the shelves, and up to 2.7 °C in areas of shallow MLD and high chlorophyll concentrations (the Grand Banks and Northeastern Newfoundland Shelf). The increase is much higher than that found in previous studies, which was typically a fraction of a degree. Bio-optical heating also can enhance the stratification of the upper ocean and reduce the MLD by 20–50%.     

Estimation of sea surface temperatures around Japan using the advanced very high resolution radiometer (AVHRR)/NOAA-11

The accuracy of sea surface temperatures (SSTs) derived from the Advanced Very High Resolution Radiometer (AVHRR)/NOAA-11 is examined by comparison with sea-truth SSTs obtained from ocean data buoys durings November 1988 through December 1989. We made a 122 point data set of buoy SSTs from the oceans around Japan and the corresponding brightness temperatures of channels 4 and 5 during cloud free periods. The satellite temperatures are corrected for atmospheric effects using the NOAA Multi-Channel SST (MCSST) and Cross Product SST (CPSST) algorithms. The two algorithms give similar results for our data set and result in biases of about –0.1°C with rms errors of about 0.6°C relative to buoy SSTs. It is found that MCSSTs and CPSSTs tend to be higher than SSTs from the buoy in the Japan Sea in summer. New coefficients for the MCSST equations suitable for our data set are determined and the resultant rms error is 0.49°C. If we eliminate the cluster of anomalous summer data in the Japan Sea, the rms error becomes 0.43°C.     

Ocean–wave coupled modeling in COAMPS-TC: A study of Hurricane Ivan (2004)

Tropical cyclone ocean–wave model interactions are examined using an ESMF – (Earth System Modeling Framework) based tropical cyclone (TC) version of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®¹). This study investigates Hurricane Ivan, which traversed the Gulf of Mexico (GOM) in September 2004. Several oceanic and wave observational data sets, including Acoustic Doppler Current Profilers (ADCPs), National Oceanic and Atmospheric Administration (NOAA) buoys, satellite altimeter data, and Scanning Radar Altimeter (SRA) data, allow for a unique analysis of the coupled atmosphere, ocean (Navy Coastal Ocean Model, NCOM), and wave (Simulating WAves Nearshore, SWAN) models in COAMPS-TC. To determine the feasibility of coupling NCOM to SWAN in high-wind conditions during Hurricane Ivan, near-surface currents in NCOM were first compared to near-surface ADCP observations. Recent modifications to SWAN, including new wind-to-wave energy input and wave-breaking energy dissipation source functions, as well as a new ocean surface drag coefficient formulation appropriate for high-wind conditions, significantly improved the forecast wave field properties, such as significant wave height (SWH), in TC conditions. Further results show that the ocean-to-wave model coupling, which allows for the strong, hurricane-induced, surface currents in NCOM to interact with SWAN, provided additional improvements to the forecast SWH field. Additionally, wave-to-ocean model coupling, which included the input of the Stokes Drift Current (SDC) calculated from the SWAN wave spectra to NCOM, is examined. The models indicate that the SDC was on the order of 10–25% of the near-surface Eulerian current during Ivan. Recent studies of the importance of the SDC and the resulting Langmuir turbulence on vertical ocean mixing in TCs is also discussed.     

The impact of ocean data assimilation on seasonal predictions based on the National Climate Center climate system model

An ensemble optimal interpolation (EnOI) data assimilation method is applied in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the “truth”. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Ni?o was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.     

中国海及邻近海域卫星观测资料同化试验

利用1个基于POMgcs海洋模式和多重网格三维变分同化方法建立的中国海及邻近海域海面高与三维温盐流数值预报模型,通过一系列数值试验,研究了同化卫星测高和卫星遥感海面温度观测资料对该模型预报能力的影响。试验结果表明,同化卫星测高资料可明显改善海面高度与三维温度和盐度的分析预报效果,使1 200 m以上的温度预报误差减小0.16℃,并能有效提高对海洋中尺度现象的预报能力;同化卫星遥感海面温度对100 m以上的温度和盐度的预报效果有所改善,可使海面温度的预报误差减小10%。     

利用热带太平洋TAO浮标阵列评估全球高分辨率浪-潮-流耦合模式同化结果

In order to evaluate the assimilation results from a global high resolution ocean model, the buoy observations from tropical atmosphere ocean(TAO) during August 2014 to July 2015 are employed. The horizontal resolution of wave-tide-circulation coupled ocean model developed by The First Institute of Oceanography(FIOCOM model) is 0.1°×0.1°, and ensemble adjustment Kalman filter is used to assimilate the sea surface temperature(SST), sea level anomaly(SLA) and Argo temperature/salinity profiles. The simulation results with and without data assimilation are examined. First, the overall statistic errors of model results are analyzed. The scatter diagrams of model simulations versus observations and corresponding error probability density distribution show that the errors of all the observed variables, including the temperature, isotherm depth of 20°C(D20), salinity and two horizontal component of velocity are reduced to some extent with a maximum improvement of 54% after assimilation. Second, time-averaged variables are used to investigate the horizontal and vertical structures of the model results. Owing to the data assimilation, the biases of the time-averaged distribution are reduced more than70% for the temperature and D20 especially in the eastern Pacific. The obvious improvement of D20 which represents the upper mixed layer depth indicates that the structure of the temperature after the data assimilation becomes more close to the reality and the vertical structure of the upper ocean becomes more reasonable. At last,the physical processes of time series are compared with observations. The time evolution processes of all variables after the data assimilation are more consistent with the observations. The temperature bias and RMSE of D20 are reduced by 76% and 56% respectively with the data assimilation. More events during this period are also reproduced after the data assimilation. Under the condition of strong 2014/2016 El Ni?o, the Equatorial Undercurrent(EUC) from the TAO is gradually increased during August to November in 2014, and followed by a decreasing process. Since the improvement of the structure in the upper ocean, these events of the EUC can be clearly found in the assimilation results. In conclusion, the data assimilation in this global high resolution model has successfully reduced the model biases and improved the structures of the upper ocean, and the physical processes in reality can be well produced.     

The response of the upper ocean to tropical cyclone Viyaru over the Bay of Bengal

Better forecast of tropical cyclone(TC) can help to reduce risk and enhance management. The TC forecast depends on the scientific understanding of oceanic processes, air-sea interaction and finally, the atmospheric process. The TC Viyaru is taken as an example, which is formed at the end of 11 May 2013 and sustains up to 17 May 2013 during pre-monsoon season. Argo data are used to investigate ocean response processes by comparing pre-and post-conditions of the TC. Eight oceanic parameters including the sea surface temperature(SST), the sea surface salinity(SSS), and the barrier layer thickness(BLT), the 26°C isotherm depth in the ocean(D26), the isothermal layer depth(ILD), the mixed layer depth(MLD), the tropical cyclone heat potential(TCHP) and the effective oceanic layer for cyclogenesis(EOLC) are chosen to evaluate the pre-and post-conditions of the TC along the track of Viyaru. The values of the SST, D26, BLT, TCHP and EOLC in the pre-cyclonic condition are higher than the post-cyclonic condition, while the SSS, ILD and MLD in the post-cyclonic condition are higher than the pre-cyclonic condition of the ocean due to strong cyclonic winds and subsurface upwelling. It is interesting that the strong intensity of the TC reduces less SST and vice versa. The satisfied real time Argo data is not available in the northern Bay of Bengal especially in the coastal region. A weather research and forecasting model is employed to hindcast the track of Viyaru, and the satellite data from the National Center Environmental Prediction are used to assess the hindcast.     

Response of subsurface waters in the eastern Arabian Sea to tropical cyclones

Thermister chain data at different depths for June 1998 cyclone in the Arabian Sea at a location (69.2 E,15.5 N) which is about 60 km to the left of the cyclone track indicates subsurface warming below 60 m and inertial oscillations of temperature with a periodicity of about 2 days. The oscillations continued for ∼15 days even after the cyclone crossed the coast. The analysis of the buoy, DS1 located at the same position also suggests a stabilized southward flow after about two weeks of the cyclone crossed the coast. Analysis of the buoy data for May 1999 cyclone in the same region also indicates similar pattern. In order to investigate the effect of cyclone–ocean interaction and primarily to understand the process for the subsurface warming, 3-dimensional Princeton Ocean Model is configured for the eastern part of the Arabian Sea. The model uses high horizontal resolution of about 6 km near the coast and a terrain following sigma coordinate in the vertical with 26 levels. The study focuses on surface cooling and temperature rise in the underlying waters and explains its mechanism through upwelling and downwelling respectively. The simulations in concurrence with the observations suggest that the occurrence of subsurface warming precedes the surface cooling with a lag of ˜a day as the cyclone advances DS1. The simulations also demonstrate local temperature stratification plays an important role for cooling of the upper ocean and warming of the subsurface waters and extent of warming is directly related to the depth of the thermocline.     

发展基于集合样本估计的SST张驰逼近方法用于气候模式的次表层海温场订正

An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature(SST) observations into the Max Planck Institute(MPI) climate model,ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year(1960–2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the qualitycontrolled subsurface ocean temperature objective analyses(EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square(RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m(OHT100 m)is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT100 m convergent to one value after several times iteration,indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.     

基于最优插值方法的微波-红外辐射计数据融合全球海面温度网格产品

A new 0.1° gridded daily sea surface temperature(SST) data product is presented covering the years 2003–2015. It is created by fusing satellite SST data retrievals from four microwave(Wind Sat, AMSR-E, ASMR2 and HY-2 A RM)and two infrared(MODIS and AVHRR) radiometers(RMs) based on the optimum interpolation(OI) method. The effect of including HY-2 A RM SST data in the fusion product is studied, and the accuracy of the new SST product is determined by various comparisons with moored and drifting buoy measurements. An evaluation using global tropical moored buoy measurements shows that the root mean square error(RMSE) of the new gridded SST product is generally less than 0.5℃. A comparison with US National Data Buoy Center meteorological and oceanographic moored buoy observations shows that the RMSE of the new product is generally less than 0.8℃. A comparison with measurements from drifting buoys shows an RMSE of 0.52–0.69℃. Furthermore, the consistency of the new gridded SST dataset and the Remote Sensing Systems microwave-infrared SST dataset is evaluated, and the result shows that no significant inconsistency exists between these two products.     

A comparison of two global ocean-ice coupled models with different horizontal resolutions

A global eddy-permitting ocean-ice coupled model with a horizontal resolution of 0.25 by 0.25 is established on the basis of Modular Ocean Model version 4 (MOM4) and Sea Ice Simulator (SIS). Simulation results are compared with those of an intermediate resolution ocean-ice coupled model with a horizontal resolution of about 1 by 1 . The results show that the simulated ocean temperature, ocean current and sea ice concentration from the eddy-permitting model are better than those from the intermediate resolution model. However, both the two models have the common problem of ocean general circulation models (OGCMs) that the majority of the simulated summer sea surface temperature (SST) is too warm while the majority of the simulated subsurface summer temperature is too cold. Further numerical experiments show that this problem can be alleviated by incorporating the non-breaking surface wave-induced vertical mixing into the vertical mixing scheme for both eddy-permitting and intermediate resolution models.     

Evaluations of threshold and curvature mixed layer depths by various mixing schemes in the Mediterranean Sea

Predictive ability of five different embedded turbulent mixing models that range from second-order turbulent closure to bulk mixing parameterization is examined in the Mediterranean Sea. Each is embedded in the HYbrid Coordinate Ocean Model (HYCOM). Mixed layer depth (MLD), which is one of the most important upper ocean variables, is used to evaluate the treatment of turbulent processes in each model run. In addition to overall spatial and temporal variability, analyses of MLD are presented using an extensive set (3976) of temperature and salinity profiles from various data sources during 2003–2006. Results obtained from simulations (with no data assimilation and relaxation only to salinity) for the five mixing models are compared with observed MLDs obtained from in situ temperature and salinity profile observations. To ensure the robustness of the validation statistics MLD is computed using both curvature and threshold based methodologies. Results indicate that while all mixing schemes represent the MLD well, the bulk mixing models have substantial accuracy deficiencies relative to the higher order mixing models. The modeled MLDs are slightly deeper than observed MLDs with the mean bias error ∼10 m for the higher order mixing models while the bulk mixing model bias error is 15 m or more. The RMS error for the higher order mixing models is ∼40 m while it is ∼50 m for the bulk mixing models. The bulk mixing models had substantially larger errors particularly for the curvature MLD definition.     

盐度对变化2014年东北太平洋“暖泡”的作用

A significant strong, warm "Blob"(a large circular water body with a positive ocean temperature anomaly)appeared in the Northeast Pacific(NEP) in the boreal winter of 2013–2014, which induced many extreme climate events in the US and Canada. In this study, analyses of the temperature and salinity anomaly variations from the Array for Real-time Geostrophic Oceanography(Argo) data provided insights into the formation of the warm"Blob" over the NEP. The early negative salinity anomaly dominantly contributed to the shallower mixed layer depth(MLD) in the NEP during the period of 2012–2013. Then, the shallower mixed layer trapped more heat in the upper water column and resulted in a warmer sea surface temperature(SST), which enhanced the warm"Blob". The salinity variability contributed to approximately 60% of the shallowing MLD related to the warm"Blob". The salinity anomaly in the warm "Blob" region resulted from a combination of both local and nonlocal effects. The freshened water at the surface played a local role in the MLD anomaly. Interestingly, the MLD anomaly was more dependent on the local subsurface salinity anomaly in the 100–150 m depth range in the NEP.The salinity anomaly in the 50–100 m depth range may be linked to the anomaly in the 100–150 m depth range by vertical advection or mixing. The salinity anomaly in the 100–150 m depth range resulted from the eastward transportation of a subducted water mass that was freshened west of the dateline, which played a nonlocal role.The results suggest that the early salinity anomaly in the NEP related to the warm "Blob" may be a precursor signal of interannual and interdecadal variabilities.     

基于神经网络的全球三维温盐场重构技术研究

文章利用果蝇优化广义回归神经网络算法FOAGRNN (fruit fly optimization algorithm, FOA; generalized regression neural network, GRNN)对SODA (simple ocean data assimilation)再分析数据进行训练, 构建海表温度、盐度、海面高度与次表层温盐场之间的投影关系模型, 并在全球范围使用SODA和卫星遥感数据评估了模型的应用性能。首先, 利用独立的2016年SODA海表数据作为模型输入进行理想重构试验, 结果显示全球重构温、盐平均均方根误差(MRMSE)分别为0.36℃和0.08‰, 与世界海洋图集WOA13资料相比减小约50%和60%。然后, 利用卫星观测的海表信息作为模型输入进行实际应用试验, 并与Argo观测剖面进行比较评估。试验结果表明, 重构模型能有效表征海水温、盐特征, 其中重构温、盐MRMSE分别为0.79℃和0.16‰, 相比WOA气候态减小27%和11%。误差的垂向分布显示, 重构温度RMSE从海表向下迅速增大, 至100m达到峰值1.35℃, 而后又迅速回落,至250m处为0.81℃, 跃层往下不断减小; 重构盐度RMSE基本随深度增大而减小, 误差峰值位于25m附近, 约为0.25‰。此外, Argo浮标跟踪分析和区域水团统计结果也表明模型能够较好地刻画海洋三维温盐场的内部结构特征。