Ensemble Optimal Interpolation Research Articles

Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

Atlantic Meridional Overturning Circulation (AMOC) plays a central role in long-term climate variations through its heat and freshwater transports, which can collapse under a rapid increase of greenhouse gas forcing in climate models. Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations. In this work, with a low-resolution earth system model, the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse. Through a new optimization strategy, the extra freshwater flux (FWF) parameter is determined to be the dominant one affecting the AMOC's variability. The traditional ensemble optimal interpolation (EnOI) data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO2 forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC. The results show that, under an abrupt 4×CO2 forcing in millennial simulations, the AMOC will first collapse and then re-establish by the default FWF parameter slowly. However, during the parameter adjustment process, the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC, according to their physical relationship with FWF on the interdecadal timescale.

Comparison of 4-dimensional variational and ensemble optimal interpolation data assimilation systems using a Regional Ocean Modeling System (v3.4) configuration of the eddy-dominated East Australian Current system

Abstract. Ocean models must be regularly updated through the assimilation of observations (data assimilation) in order to correctly represent the timing and locations of eddies. Since initial conditions play an important role in the quality of short-term ocean forecasts, an effective data assimilation scheme to produce accurate state estimates is key to improving prediction. Western boundary current regions, such as the East Australia Current system, are highly variable regions, making them particularly challenging to model and predict. This study assesses the performance of two ocean data assimilation systems in the East Australian Current system over a 2-year period. We compare the time-dependent 4-dimensional variational (4D-Var) data assimilation system with the more computationally efficient, time-independent ensemble optimal interpolation (EnOI) system, across a common modelling and observational framework. Both systems assimilate the same observations: satellite-derived sea surface height, sea surface temperature, vertical profiles of temperature and salinity (from Argo floats), and temperature profiles from expendable bathythermographs. We analyse both systems' performance against independent data that are withheld, allowing a thorough analysis of system performance. The 4D-Var system is 25 times more expensive but outperforms the EnOI system against both assimilated and independent observations at the surface and subsurface. For forecast horizons of 5 d, root-mean-squared forecast errors are 20 %–60 % higher for the EnOI system compared to the 4D-Var system. The 4D-Var system, which assimilates observations over 5 d windows, provides a smoother transition from the end of the forecast to the subsequent analysis field. The EnOI system displays elevated low-frequency (>1 d) surface-intensified variability in temperature and elevated kinetic energy at length scales less than 100 km at the beginning of the forecast windows. The 4D-Var system displays elevated energy in the near-inertial range throughout the water column, with the wavenumber kinetic energy spectra remaining unchanged upon assimilation. Overall, this comparison shows quantitatively that the 4D-Var system results in improved predictability as the analysis provides a smoother and more dynamically balanced fit between the observations and the model's time-evolving flow. This advocates the use of advanced, time-dependent data assimilation methods, particularly for highly variable oceanic regions, and motivates future work into further improving data assimilation schemes.

Regional Comparison of Performance between EnKF and EnOI in the North Pacific

Abstract The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.

Representing uncertainty in limited-area data assimilating ocean models

A limited-area ocean prediction system is developed to acquire forecast error covariances related to uncertainty in atmospheric forcing and turbulent mixing using perturbed model parameters within an Ensemble Kalman Filter (EnKF). The system performs sequential data assimilation delivering realistic ocean state estimation and forecasts. It is initialised to observations using the EnKF, the hybrid-EnKF and Ensemble Optimal Interpolation (EnOI). It has higher resolution than the parent global model, includes tides and assimilates altimetric sea level anomaly to constrain the offshore mesoscale circulation. Dynamic ensemble spread introduced by parameter uncertainty shows agreement with error estimates obtained from forecast innovation statistics. Hybrid EnKF offers improvements to both EnKF and EnOI, having smaller analysis increments and forecast errors. The ensemble mean of the hybrid-EnKF is more accurate than any member due to the introduction of parameterised error which behaves as additive inflation in the hybrid EnKF system. This indicates the parameterisation acts as a non-linear filter for model forecast error growth. To demonstrate consistency, experiments are carried out for various regions that differ in their oceanographic situations. The hybrid EnKF data assimilation system enables practical use of dynamic ensembles that capture the flow dependent errors which are mixed with static or climatological covariances for situations where model error is systematically under-estimated.

An observing system experiment framework for the tropical Indian Ocean salinity: A case study using a constellation of three satellites

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean.

Ensemble-based assimilation of wave model predictions: Contrasting the impact of assimilation in nearshore and offshore forecasting at different distances from assimilated data

Accurate wave predictions are crucial for many marine-related activities, including offshore operations, navigation, and marine renewable energy. To improve the forecast performance, this study presents a data assimilation system that utilizes the WAVEWATCH III wave model and the local Ensemble Optimal Interpolation scheme to assimilate significant wave heights data from HY-2B altimeter in the China Seas. The effects of assimilation are analyzed in both coastal and offshore regions at different distances from assimilated satellite data points during a forecast period. The results show that systematic bias in wave forecasting for both offshore and nearshore locations can be effectively reduced through the use of data assimilation, with localization employed to minimize the influence of random errors. Nearshore forecasting benefits from assimilation to a greater extent than offshore forecasting, with a more enduring improvement observed. Additionally, the decline in forecast improvement with increasing distance from assimilated data highlights the necessity of assimilating a greater quantity of observational satellite data. Furthermore, careful consideration should be given to the size of the ensemble when using a specific localization radius, as the improvement in skill may stagnate with larger ensembles. Finally, assimilating additional nearshore satellite data does not negatively impact the predictive precision.

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation.

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

An Improved Coupled Data Assimilation System with a CGCM Using Multi-Time-Scale High-Efficiency EnOI-Like Filtering

Abstract Coupled data assimilation (CDA), which combines coupled models and observations from multiple Earth system domains, plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. Traditional ensemble Kalman filter (EnKF) CDA algorithms, while convenient to implement in multiple DA components in a coupled system, are, however, expensive and lack sufficient representativeness for low-frequency background flows. Here, a multi-time-scale high-efficiency approximate filter with ensemble optimal interpolation (MSHea-EnOI) scheme has been implemented with a global fully coupled model. It consists of stationary, low-frequency, and high-frequency filters constructed from the time series of a single-model solution with improved representativeness for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnOI is evaluated in a biased twin experiment framework with synthetic “observations” produced by another coupled model, and a three-decade coupled reanalysis experiment with real observations. Results show that with increased representativeness on multiscale background flows, while computationally costing only a small fraction of ensemble-based CDA, the MSHea-EnOI shows the potential to improve CDA quality with synthetic observations. The coupled reanalysis experiment with real observations also shows reasonable fittings to observations and comparable results to other reanalysis products using different DA schemes. While reconstructing a close-to-rapid Atlantic meridional overturning circulation, the coupled reanalysis reproduces most of the atmosphere and ocean reanalysis signals such as the Hadley circulation and upper ocean heat content. The MSHea-EnOI could have good application potential in ensemble-based DA systems in terms of its multiscale property and computational efficiency.

Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions

Abstract. Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field PM2.5 and visibility forecasts were investigated. The results show that assimilation of PM2.5 reduces the uncertainty in the initial PM2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µg m−3, and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the time window of 24 h. The PM2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM2.5 further contributes to the improvement of the visibility forecast. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast.

Impact of satellite thickness data assimilation on bias reduction in Arctic sea ice concentration

The impact of assimilating satellite-retrieved Arctic sea ice thickness (SIT) on simulating sea ice concentration (SIC) climatology in CICE5 is examined using a data assimilation (DA) system based on the ensemble optimal interpolation. The DA of the SIT satellite data of CryoSat-2 and SMOS during 2011–2019 significantly reduces the climatological bias of SIC and SIT in both sea ice melting and growing seasons. Moreover, the response of SIC to SIT change is strongly dependent on the seasons and latitudinal locations. The SIT in the inner ice zone thickens due to the SIT DA during the boreal winter wherein the SIT observation is available; the ice melting throughout the subsequent seasons is attenuated to increase SIC during the boreal summer to reduce the simultaneous SIC bias. In marginal ice zones, the positive SIT bias depicted in the control simulation is significantly reduced by SIT DA, which reduces the positive SIC bias. The idealized experiments of reducing the SIT show that the enhanced ice bottom melting process plays a crucial role in reducing the SIC; the prescribed SIT thinning increases the ice bulk salinity due to the weak gravity drainage of brine and increases the ice bulk temperature due to the decrease of the sea ice albedo. The augmentation of the ice salinity and temperature contributes to the shrinkage of the ice enthalpy, boosting the bottom melting process, which leads to SIC decrease.

Improvements of ocean fronts forecasting in the South China Sea by using ensemble optimal interpolation assimilation

Based on the operational ocean forecasting system in the South China Sea (SCS) runned by National Marine Environmental Forecasting Center (NMEFC), current Ensemble Optimal Assimilation (EnOI) system are updated by developing the dynamic coordination of multi-source data assimilation system, and has carried on the systematic evaluation. The diagnostic analysis of ocean fronts in the SCS is carried out using the daily average hindcast results in 2018, and the improvement effect of data assimilation on ocean mesoscale processes was evaluated. The statistical analysis results show that the occurrence frequency and diagnostic parameters of the ocean front after assimilation are closer to the observation. The comparison of the scores of the ocean front prediction skills before and after assimilation shows that the assimilated model has higher front prediction skills.

The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction.

Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations

Abstract. The ocean carbon store plays a vital role in setting the carbon response to emissions and variability in the carbon cycle. However, due to the ocean's strong regional and temporal variability, sparse carbon observations limit our understanding of historical carbon changes. Ocean temperature and salinity profiles are more widespread and rapidly expanding due to autonomous programmes, and so we explore how temperature and salinity profiles can provide information to reconstruct ocean carbon inventories with ensemble optimal interpolation. Here, ensemble optimal interpolation is used to reconstruct ocean carbon using synthetic Argo temperature and salinity observations, with examples for both the top 100 m and top 2000 m carbon inventories. When considering reconstructions of the top 100 m carbon inventory, coherent relationships between upper-ocean carbon, temperature, salinity, and atmospheric CO2 result in optimal solutions that reflect the controls of undersaturation, solubility, and alkalinity. Out-of-sample reconstructions of the top 100 m show that, in most regions, the trend in ocean carbon and over 60 % of detrended variability can be reconstructed using local temperature and salinity measurements, with only small changes when considering synthetic profiles consistent with irregular Argo sampling. Extending the method to reconstruct the upper 2000 m reveals that model uncertainties at depth limit the reconstruction skill. The impact of these uncertainties on reconstructing the carbon inventory over the upper 2000 m is small, and full reconstructions with historical Argo locations show that the method can reconstruct regional inter-annual and decadal variability. Hence, optimal interpolation based on model relationships combined with hydrographic measurements can provide valuable information about global ocean carbon inventory changes.

Analog ensemble data assimilation in a quasigeostrophic coupled model

AbstractThe ensemble forecast dominates the computational cost of many data assimilation methods, especially for high‐resolution and coupled models. In situations where the cost is prohibitive, one can either use a lower‐cost model or a lower‐cost data assimilation method, or both. Ensemble optimal interpolation (EnOI) is a classical example of a lower‐cost ensemble data assimilation method that replaces the ensemble forecast with a single forecast and then constructs an ensemble about this single forecast by adding perturbations drawn from climatology. This research develops lower‐cost ensemble data assimilation methods that add perturbations to a single forecast, where the perturbations are obtained from analogs of the single model forecast. These analogs can either be found from a catalog of model states, constructed using linear combinations of model states from a catalog, or constructed using generative machine‐learning methods. Four analog ensemble data assimilation methods, including two new ones, are compared with EnOI in the context of a coupled model of intermediate complexity: Q‐GCM. Depending on the method and on the physical variable, analog methods can be up to 40% more accurate than EnOI.

A global gridded ocean salinity dataset with 0.5° horizontal resolution since 1960 for the upper 2000 m

A gridded salinity dataset with high resolution is essential for investigating global ocean salinity variability and understanding its role in climate and the ocean ecosystem. In this study, a new version of the Institute of Atmospheric Physics gridded salinity dataset with a higher resolution (0.5° by 0.5°) is provided by using a revised ensemble optimal interpolation scheme with a dynamic ensemble. The performance of this dataset is evaluated using “subsample test” and the high-resolution satellite-based data. Compared with the previous 1° by 1° resolution IAP product, the new dataset is more capable of representing regional salinity changes with the meso-scale and small-scale signals (i.e., in the coastal and boundary currents regions), meanwhile, maintains the large-scale structure and variability. Therefore, the new dataset complements the previous data product. Besides, the new dataset is compared with in situ observations and several international salinity products for the salinity multiscale variabilities and patterns. The comparison shows the smaller magnitude of mean difference and Root-mean-square deviation (RMSD) in basin scale for the new dataset, some differences in strength and fine structure of the “fresh gets fresher, salty gets saltier” surface and subsurface salinity pattern amplification trends from 1980 to 2017, a broad similarity for the salinity changes associated with El Niño-Southern Oscillation (ENSO) and a consistent salinity dipole mode in the tropical Indian Ocean (S-IOD). These results support the future use of gridded salinity data.

Impact of satellite and regional in-situ profile data assimilation on a high-resolution ocean prediction system in the Northwest Pacific

The impacts of observation data sets on the high-resolution (1/24°) Northwest Pacific prediction system were investigated with the model sensitivity tests. We compared the model experiments assimilating the different combinations of the observation data sets, such as the sea surface height derived from satellite altimetry, sea surface temperature, and in-situ profiles, based on the Ensemble Optimal Interpolation. Pseudo-profiles constructed by the method of Cooper and Haines (1996, CH96) were assimilated into the model to assimilate sea surface height data. CH96 applied a conservation principle to derive pseudo-profiles by rearranging preexisting profiles. The comparison of the model experiments suggests that each observation data set enhances the model performance. Especially, the assimilation of the sea surface height reduces the model error by more than 9.81% and 6.44%, respectively, in terms of the root-mean-square error of the ocean temperature and salinity in the subsurface layer. It is interesting that the assimilation of the in-situ temperature profiles in the Korean marginal seas contributes to improving the reproducibility of the subsurface temperature and salinity in the East/Japan Sea (EJS) as well as Kuroshio-Kuroshio Extension (K-KE) regions. The improvement in the K-KE region seems to be related to the reproducibility of the Kuroshio axis. As the water mass in the EJS flows into the Pacific Ocean through the Tsugaru Strait, it affects the front of the Sanriku confluence, and it seems to eventually control the Oyashio Current and Kuroshio axis. In conclusion, this study evaluated the contribution of each observation component to ocean analysis in the KOOS-OPEM and confirmed the role of the existing observation networks. This study also suggests that greater attention should be paid to the role of regional ocean observation networks to improve the forecast skill of the ocean prediction system not only in the region but also in the open ocean, such as the Pacific Ocean.

ВЛИЯНИЕ СПОСОБОВ УСВОЕНИЯ СПУТНИКОВЫХ ДАННЫХ O ТЕМПЕРАТУРЕ ПОВЕРХНОСТИ МОРЯ НА ВОСПРОИЗВЕДЕНИЕ ГИДРОФИЗИЧЕСКИХ ПОЛЕЙ ЧЕРНОГО, АЗОВСКОГО И МРАМОРНОГО МОРЕЙ В МОДЕЛИ INMOM

The results are analyzed of the simulation of hydrophysical fields of the Black, Azov, and Marmara seas with the Institute of Numerical Mathematics Ocean Model (INMOM) implemented with a spatial resolution of 4 km, with various assimilation technique for sea surface temperature (SST) data from the SEVIRI sensor installed on MSG satellites. The relaxation (so called nudging) and the ensemble optimal interpolation (EnOI) with various assimilation time frequencies (3-, 6-, 12-, and 24-hour assimilation windows) were used as assimilation methods. It was shown that the assimilation of SST data via the EnOI made it possible to reproduce hydrophysical fields more accurately than via nudging or without assimilation at all. Even with the assimilation of SST data irregularly distributed over space and time, a decrease in the calculation error was observed over the entire sea area, and the structures of the zones of temperature rise or drop were more correctly simulated. The best results were achieved with via the EnOI assimilation with an increasing assimilation frequency over time. When SST data were assimilated using the EnOI, the mean error decreased from 0.16°C (24-hour assimilation window) to 0.08°C (3-hour assimilation window); accordingly, the absolute mean error decreased from 1.03 to 0.33°C, and the standard deviation decreased from 1.33 to 0.42°C. In addition, assimilation using the EnOI 3-hour window improved the reproduction of SST during the period of convection cooling. The assimilation of SST data also led to changes in the structure of the surface sea circulation. In some areas, the direction of currents varied within 5°-10°, and the velocity modulus changed by 3-5%. The assimilation of SST data only slightly reduced errors in the structure of the model vertical temperature profile, which can reach 2°C at depths of 30-40 m. At depths greater than 100 m, deviations did not exceed 0.05°C.

Evaluation of an Ocean Reanalysis System in the Indian and Pacific Oceans

This paper describes an ocean reanalysis system in the Indian and Pacific oceans (IPORA) and evaluates its quality in detail. The assimilation schemes based on ensemble optimal interpolation are employed in the hybrid coordinate ocean model to conduct a long-time reanalysis experiment during the period of 1993–2020. Different metrics including comparisons with satellite sea surface temperature, altimetry data, observed currents, as well as other reanalyses such as ECCO and SODA are used to validate the performance of IPORA. Compared with the control experiment without assimilation, IPORA greatly reduces the errors of temperature, salinity, sea level anomaly, and current fields, and improves the interannual variability. In contrast to ECCO and SODA products, IPORA captures the strong signals of SLA variability and reproduces the linear trend of SLA very well. Meanwhile, IPORA also shows a good consistence with observed currents, as indicated by an improved correlation and a reduced error.

A Simple Bias Correction Scheme in Ocean Data Assimilation

The mode bias is present and time-dependent due to imperfect configurations. Data assimilation is the process by which observations are used to correct the model forecast, and is affected by the bias. How to reduce the bias is an important issue. This paper investigates the roles of a simple bias correction scheme in ocean data assimilation. In this scheme, the misfits between modeled and monthly temperature and salinity with interannual variability from the Met Office Hadley Centre subsurface temperature and salinity data set (EN4.2.2) are used for the innovations in assimilation via the Ensemble Optimal Interpolation method. Two assimilation experiments are implemented to evaluate the impacts of bias correction. The first experiment is a data assimilation system without bias correction. In the second experiment, the bias correction is applied in assimilation. For comparison, the nature run with no assimilation and no bias correction is also conducted. When the bias correction is not applied, the assimilation alone leads to a rising trend in the heat and salt content that is not found in the observations. It is a spurious temporal variability due to the effect of the bias on the data assimilation. Meanwhile, the assimilation experiment without bias correction also produces significant negative impacts on the subsurface salinity. The experiment with bias correction performs best with notable improvements over the results of the other two experiments.

A Frequency-Dependent Assimilation Algorithm: Ensemble Optimal Smoothing

Motivated by the need for a simple and effective assimilation scheme that could be used in a relocatable ocean model, a new assimilation algorithm called ensemble optimal smoothing (EnOS) was developed. This scheme was a straightforward extension of the ensemble optimal interpolation (EnOI) by involving time correlation information in the Kalman gain. The main advantage of this scheme was the ability to estimate the present state from the time history of observation. We first examined the new scheme in an ideal ocean model using simulated observations. Further applying these two assimilation schemes to the Chinese offshore and adjacent waters, the root-mean-square error (RMSE) of the EnOS scheme was reduced by 6.4% relative to EnOI. The results showed that the EnOS was more efficient and effective in eliminating model errors when compared to the EnOI scheme.