High-Resolution Global and Basin-Scale Ocean Analyses and Forecasts

In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do.During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases.We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation.

Globally, 39% of the world's population lives within 100 km of the coast. Eight of the top ten largest cities in the world are located along the coast. In South Korea, 27% of the population also resides in coastal areas. Ocean waves, induced by sea winds, potentially endanger offshore infrastructure and threaten low-lying ecosystems and communities due to coastal erosion and flooding. More accurate prediction of ocean wave patterns, or sea states, is crucial for informed decision-making in mitigating these risks. Recently, many National Meteorological and Hydrological Services (NMHs) have been endeavoring to enhance the accuracy of ocean wave predictions by assimilating in-situ and remote sensing observations. The Korea Meteorological Administration (KMA) also initiated an ocean wave data assimilation system in 2021. However, this system has thus far only been adapted to a global-scale model. This study aims to install and evaluate the impact of ocean wave data assimilation on a regional scale. The regional ocean wave data assimilation system employs optimal interpolation based on WAVEWATCH-Ⅲ version 6.07 with a spatial resolution of 1/30° targeted for the East Asian region. Significant wave height data from five satellites in polar orbit, ocean data buoys, and coastal wave buoys were utilized. Numerical experiments for summer (2023JJA) and winter (2023/24DJF) reveal that the use of data assimilation reduced the root mean square error by 49.2% and 38.6%, respectively, for the initial fields of regional wave models. The assimilated initial fields improved ocean wave predictions by 12 hours in the KMA regional ocean wave model, which is consistent with previous research in the field. Particularly during Typhoon KHANUN in August 2023, there was a tendency to overestimate sea winds, which are input variables of the ocean wave model. Despite the use of overestimated sea winds, the regional ocean wave model utilizing data assimilation reduced the error by up to 94 cm.

Ocean acoustic propagation models used for sonar performance prediction often rely on global or regional ocean modeling and data assimilation, or monthly climatologic averages, for estimating the 4D ocean temperature and salinity (T/S) field. Ocean models rely heavily on satellite-based ocean surface measurements, e.g., sea surface temperature (SST) and height (SSH), plus typically vastly less resolved in space and time vertical water column measurements derived using ship-based and/or ARGO float instruments. Systems also exist that create 3D synthetic T/S fields from satellite SST/SSH measurements, e.g., the Modular Ocean Data Assimilation System (MODAS) and the more recent Improved Synthetic Ocean Profile (ISOP) system. It is unclear, however, how well these synthetic T/S fields capture significant ocean ducting conditions. Similarly, climatologic averages combine historical vertical T/S profile data in spatial cells by month; but the averaging process tends to blur out important features. This talk will address the use of 2021 New England Shelf Break Acoustics (NESBA) experiment and ARGO vertical profile data for: (1) testing the degree to which ocean models or climatology capture important acoustic features under a range of conditions; and (2) providing more realistic sonar performance prediction means and uncertainties. [Work supported by the Office of Naval Research].

The coupled data assimilation (CDA) system consisting of ocean, sea‐ice, and atmosphere data assimilation components with the Beijing Climate Center (BCC) Climate System Model has been developed to provide reliable analyses of the atmosphere, ocean, and sea‐ice states. It incorporates ocean temperature/salinity profiles, sea surface temperature, sea level height, and sea‐ice concentration observations at a daily frequency, and atmosphere reanalysis at a 6‐hourly frequency. Results show that the system is capable of realistically reproducing the climatology and variability of ocean, sea‐ice, and atmosphere. The performances in analyzing ocean component are comparable to those of well‐known ocean reanalyses that were once used to initialize the BCC model for climate predictions. A series of experiments with and without sea‐ice observations in the CDA framework are designed to explore the role of sea‐ice data assimilation (DA). The addition of sea‐ice DA exerts very small influence to the analysis of upper ocean temperature over the Arctic area, but leads to an evident reduction of temperature error in the upper 1000 m of ocean south of 60°S. Particularly, only the inclusion of sea‐ice DA can make the ocean/ocean‐atmosphere DA effective in providing skillful analysis in the high‐latitude Southern Ocean. Furthermore, we find that only on the basis of ocean DA, can the addition of sea‐ice concentration assimilation improve the analysis of tropical tropospheric atmosphere, along with a better analysis of mid‐ and high‐latitude stratospheric atmosphere. These results address the importance of coordination of sea‐ice observations and ocean observations in CDA.

: The goal of this project is to improve the forecast performance of shallow-water and limited-area ocean forecast systems through improvement in ocean data analysis and assimilation techniques. The Shallow Water Analysis and Forecast System (SWAFS) is the operational ocean nowcast/forecast system at the Naval Oceanographic Office (NAVOCEANO). The objective of this project is to improve the forecast skill of SWAFS by integrating into the NAVOCEANO system the ocean data assimilation component developed for the Coupled Ocean Atmosphere Mesoscale Prediction System (COAMPS). The ocean data quality control, analysis, and assimilation components of SWAFS will be upgraded.

The fusion of real-time remote sensing imagery with numerical ocean circulation models provides a unique capability for monitoring the coastal ocean. A real-time ocean monitoring system is currently operating in the Gulf of Mexico. Dynamic changes in the ocean environment occur on scales of hours, days, weeks and years, which influence biological, ecological and physical processes. By monitoring these processes at different spatial resolutions, new enhanced capability is available to coastal managers and researchers for making decisions. Monitoring of physical processes and bio-optical responses are currently being done for the Gulf of Mexico. Open ocean eddies and loop current interact with coastal processes such as river plumes and tides and have significant impact on the biological and ecological processes along our shores. Until now we had no capability to routinely monitor these ocean conditions without insitu sensors and observations. Now the “weather” in the Gulf of Mexico is being monitored daily and provides coastal managers and researchers an edge in tracking or pin-pointing events. This information assists the decision maker in identifying how physical events are influencing the coastal ecosystem. How is this being done? Improved bio-optical algorithms have been developed and applied to ocean color satellite imagery from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) providing estimates of water constituents such as absorption properties from phytoplankton, color dissolved organic matter (CDOM), and detritus and backscattering properties related to sediment concentration. In addition, advances in numerical circulation modeling (Navy Coastal Ocean Model — NCOM) provide nowcasts and forecasts of coastal processes (currents, sea surface height, salinity and temperature). Physical ocean properties of temperature, salinity, and currents from NCOM are combined with daily latest pixel composite products of phytoplankton, CDOM, and detritus absorption, backscattering and sea surface temperature (SST). We demonstrate an advanced environmental monitoring capability available operationally that highlights the fusion of physical (model) and bio-optical properties (satellite). The entire Gulf conditions are demonstrated daily in addition to higher resolution monitoring of coastal conditions. We will focus on Mississippi and Louisiana Coast using high resolution (250 meter resolution) MODIS imagery. We will demonstrate a capability of supporting coastal manager's and researchers integrating satellite bio-optical products, model circulation (current vectors), sea surface height (contours), salinity (contours) and temperature (contours) layers into Google Earth. In addition, we will demonstrate promising new research using satellite optics and numerical models to create surface optical forecast (hourly increments up to 48 hours) and a 3-D optical volume of the ocean derived from a physical (model) / optical (satellite) Gaussian relationship optimized using insitu profiles. By using these methods of fusing the satellite and ocean circulation model it will provide coastal zone managers with a new capability to assess, predict and track Harmful Algal Blooms, Hypoxia, and sediment discharge.

Modern Approaches to Data Assimilation in Ocean Modeling

A coupled atmosphere–ocean data assimilation system, the Meteorological Research Institute‐Coupled Data Assimilation System Version 1 (MRI‐CDA1), was developed based on the coupled atmosphere–ocean general circulation model and separate atmosphere and ocean analysis routines operated by the Japan Meteorological Agency (JMA). To implement coupled atmosphere–ocean data assimilation, 6‐hr data assimilation cycles with the coupled model in the outer loop are adopted in atmospheric data assimilation, whereas incremental analysis updates with 10‐day data assimilation cycles are adopted in ocean data assimilation. A coupled data assimilation (reanalysis) experiment (CDA‐Exp) is conducted using MRI‐CDA1 along with an uncoupled reanalysis experiment (UCPL‐Exp) in which the same atmospheric component of the coupled model is forced by prescribed sea surface temperature (SST) similarly to the JMA reanalysis, JRA‐55. Climatological precipitation and variations in precipitation and sea surface air temperature (SAT) are better represented in CDA‐Exp than in JRA‐55, which is brought about by the modification of the atmosphere model physics of the coupled model to better represent climate states. In CDA‐Exp, the SST adjustment to the atmosphere amplifies the lead/lag correlations between subseasonal variations of SST and precipitation. The atmosphere–ocean coupling generates SST variations associated with tropical instability waves, and the SAT field responds to SST variations. The SST–precipitation and SST–SAT relationships on the weather timescale are also recovered in CDA‐Exp, although they are hardly seen in UCPL‐Exp. The coupled model physics generates weather‐timescale SST variations consistent with the atmospheric state, and the atmospheric parameters respond to the SST variations through the coupled model physics. This study suggests that the benefits of coupled data assimilation would be more evident if the ability of MRI‐CDA1 to represent SST variations and its interaction with the atmosphere are improved.

In this study, the accuracy of ocean analysis data, which are produced from the Korea Meteorological Administration (KMA) Nucleus for European Modelling of the Ocean/Variational Data Assimilation (NEMO/NEMOVAR, hereafter NEMO) system and the HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA, hereafter HYCOM) system, was evaluated using various oceanic observation data from March 2015 to February 2016. The evaluation was made for oceanic thermal environments in the tropical Pacific, the western North Pacific, and the Korean peninsula. NEMO generally outperformed HYCOM in the three regions. Particularly, in the tropical Pacific, the RMSEs (Root Mean Square Errors) of NEMO for both the sea surface temperature and vertical water temperature profile were about 50% smaller than those of HYCOM. In the western North Pacific, in which the observational data were not used for data assimilation, the RMSE of NEMO profiles up to 1000 m (0.49℃) was much lower than that of HYCOM (0.73℃). Around the Korean peninsula, the difference in RMSE between the two models was small (NEMO, 0.61oC; HYCOM, 0.72oC), in which their errors show relatively big in the winter and small in the summer. The differences reported here in the accuracy between NEMO and HYCOM for the thermal environments may be attributed to horizontal and vertical resolutions of the models, vertical coordinate and mixing scheme, data quality control system, data used for data assimilation, and atmosphere forcing. The present results can be used as a basic data to evaluate the accuracy of NEMO, before it becomes the operational model of the KMA providing real-time ocean analysis and prediction data.

This paper compares the performance of operational short-range ocean forecast systems developed under the Global Ocean Data Assimilation Experiment (GODAE) – an international effort to demonstrate the feasibility of operational ocean forecasting. ‘Best estimates’ from four different operational forecast systems (either analyses, hindcasts, or nowcasts) are inter-compared for the Tasman and Coral Seas, off eastern Australia. Systems considered include those developed in Australia, France, the USA, and the UK. Each system is compared to observations of along-track sea-level anomaly, sea-surface temperature, near-surface velocity, and sub-surface temperature and salinity. All have their strengths and weaknesses, and each system out-performs all others in one aspect or another. With few exceptions, all systems demonstrate signal-to-noise ratios greater than one for all variables. Due to the Australian focus, in addition to the best estimates from the operational systems, operational forecasts and a delayed-mode reanalysis are also inter-compared using the Australian system. The Australian system generally performs the best for sea-level anomaly; the French system is best for near-surface velocities; the USA system generally performs the best for sea-surface temperature; and the UK system is best for sub-surface temperature and salinity. These findings provide useful indicators of deficiencies in each system and clear metrics by which future developments should be assessed. Based on these results and other practical considerations the adoption of multimodel consensus forecasting, using all available forecasts from all systems, is promoted as the most robust approach for the user community. Such developments are being pursued under GODAE OceanView – the successor to GODAE. The results show the success of GODAE in demonstrating the feasibility of operational oceanography.

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC GODAS2.0) has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC GODAS2.0 for the user community. BCC GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational (3DVAR) data assimilation, and global ocean circulation model [Modular Ocean Model 4 (MOM4)]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC GODAS2.0 reanalysis are compared and assessed with observations for 1990–2011. The climatology of the mixed layer depth of BCC GODAS2.0 is generally in agreement with that ofWorld Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC GODAS2.0 are evaluated. The standard deviation of the SST in BCC GODAS2.0 agrees well with observations in the tropical Pacific. BCC GODAS2.0 is able to capture the main features of El Ni˜no Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of El Ni˜no Modoki are also reproduced.

: Sea surface temperature (SST) measurements are vital to global weather prediction, climate change studies, fisheries management, and a wide range of other applications. Measurements are taken by several satellites carrying infrared and microwave radiometers, moored buoys, drifting buoys, and ships. Collecting all these measurements together and producing global maps of SST has been a difficult endeavor due in part to different data formats, data location and accessibility, and lack of measurement error estimates. The need for a uniform approach to SST measurements and estimation of measurement errors resulted in the formation of the international Global Ocean Data Assimilation Experiment (GODAE) High Resolution SST Pilot Project (GHRSST-PP). Projects were developed in Japan, Europe, and Australia. Simultaneously, in the United States, the Multi-sensor Improved SST (MISST) project was initiated. Five years later, the MISST project has produced satellite SST data from nine satellites in an identical format with ancillary information and estimates of measurement error. Use of these data in global SST analyses has been improved through research into modeling of the ocean surface skin layer and upper ocean diurnal heating. These data and research results have been used by several groups within MISST to produce high-resolution global maps of SSTs, which have been shown to improve tropical cyclone prediction. Additionally, the new SSTs are now used operationally for marine weather warnings and forecasts.

A global ocean general circulation model, called LASG/IAP Climate system ocean model (LICOM), is employed to study the influence of climate change on the uptake and storage of anthropogenic CO2 in the global ocean. Two simulations were made: the control run (RUN1) with the climatological daily mean forcing data, and the climate change run (RUN2) with the interannually varying daily mean forcing data from the NCEP (National Centers for Environmental Prediction) of the US. The results show that the simulated distributions and storages of anthropogenic dissolved inorganic carbon (anDIC) from both runs are consistent with the data-based results. Compared with the data-based results, the simulations generate higher anDIC concentrations in the upper layer and lower storage amount of anDIC between the subsurface and 1000-m depth, especially in RUN1. A comparison of the two runs shows that the interannually varying forcing can enhance the transport of main water masses, so the rate of interior transport of anDIC is increased. The higher transfer rate of anDIC in RUN2 decreases its high concentration in the upper layer and increases its storage amount below the subsurface, which leads to closer distributions of anDIC in RUN2 to the data-based results than in RUN1. The higher transfer rate in RUN2 also induces larger exchange flux than in RUN1. It is estimated that the global oceanic anthropogenic CO2 uptake was 1.83 and 2.16 Pg C yr−1 in the two runs in 1995, respectively, and as of 1994, the global ocean contained 99 Pg C in RUN1 and 107 Pg C in RUN2 of anDIC, indicating that the model under the interannually varying forcing could take up 8.1% more anthropogenic carbon than the model under the climatological forcing. These values are within the range of other estimates based on observation and model simulation, while the estimates in RUN1 are near the low bound of other works. It is estimated that the variability of root mean square of the global air-sea anthropogenic carbon flux from the simulated monthly mean results of RUN2 with its seasonal cycle and long-term trend removed is 0.1 Pg C yr−1. The most distinct anomalies appear to be in the tropical Pacific Ocean and the Southern Ocean.

A previous study by Rosati et al. (1997) has concluded that the specification of an adequate thermocline structure along the equatorial Pacific ocean is most crucial for El Niño forecasts. In that paper, the oceanic initial condition was generated by a data assimilation (DA) system (Derber and Rosati, 1989). However, the initial condition for the atmospheric part was taken from the National Meteorological Center's (NMC) operational analysis, which was simply attached to the oceanic part for the coupled model forecasts. In the present paper, both the atmospheric and oceanic initial conditions are generated by a coupled DA system applied to a coupled air-sea general circulation model (GCM). The assimilation for the ocean is performed by the same system as mentioned above, in which the SST (sea surface temperature) and the subsurface temperatures are injected into a 15 vertical level oceanic GCM. The Upper boundary condition, such as surface wind stress, is specified by the atmospheric DA. The assimilation for the atmosphere is performed by the continuous injection method of Stern and Ploshay (1992), using an 18 vertical level atmospheric GCM. The lower boundary condition, such as SST, is specified by the oceanic DA. The coupled model assimilations are carried out by switching the DA processes alternately every 6 hours between the ocean and the atmosphere. The emphases of this study are: firstly, the effect of coupled air-sea model DA on the performance of subsequent forecasts; secondly, the impact of the coupled assimilation on improvement of the “spin-up” behaviour of forecasts, i.e. to see whether a smooth start to the forecast is achieved by the coupled model DA process; and thirdly, investigation of the effect that the “spring barrier” has on predictability in the coupled GCM system. Preliminary results indicate that, in order to answer these questions, ensemble forecasts are necessary. Besides, the coupled assimilation could be important in improving the overall behaviour of El Niño and La Niña forecasts.

Data assimilation is a method for combining and synthesizing data to enhance the interpretation of the data [Winguth et al., 1999], to build a consistent picture of a system [Schlitzer, 2002], to estimate model parameters [Spitz et al., 1998] and to enable the extrapolation of observations in space and time [Eknes and Evensen, 2002]. The field of data assimilation is well known in both meteorology and physical oceanography where it routinely provides the tool to make short-term forecasts of weather and upper ocean currents [Ghil and Malanotte-Rizzoli, 1991, Thacker, 1988; Tziperman and Thacker, 1989a]. At present, a comprehensive international program (Global Ocean Data Assimilation Experiment) is being developed and implemented to build a real time ocean prediction system [http://www.usgodae.fnmoc.navy.mil/]. The ocean prediction system is well advanced for predicting ocean ocean circulation with a comprehensive plan to collect physical data from in situ instruments CARGO profiling floats) and satellites, and to assimilate the data into models.