Development Of Assimilation Module For Ensemble Adjustment Kalman Filter And Its Applications In Ocean And Climate System Models

Since there is always exist some bias during the simulation of ocean and climate system, data assimilation is urgently needed to properly absorb the observed information into the numerical model in order to increase the precision of numerical simulation or forecast. Among all the methods of data assimilation used in the study of ocean and climate, ensemble adjustment Kalman filter (EAKF) is more suitable for application due to its significant advantage. In this method, perturbing of observation is avoided, the prior information from numerical model can be well preserved, and the cost of computation and requirement of storage are also relatively smaller. In this study, the basic theory and hypothesis of EAKF method is descripted in detail on the aspect of its implementation. After the discussion on the designing EAKF program in serial and parallel way, treatments of observations and ensemble sampling, the EAKF assimilation module is developed.Using the EAKF assimilation module, the Argo temperature and salinity profiles in 2005-2009 have been assimilated into a regional ocean general circulation model of the Northwest Pacific Ocean based on Princeton Ocean Model. Three numerical tests, including the control run (without data assimilation, which serves as the reference experiment; CTL), ensemble free run (without data assimilation; EnFR) and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The distribution of the correlation, which defined as the correlation between the SST referred to the point (135°E,25°N), shows that significant correlation occurs near the referred point and a character of high anisotropy can be found in the magnified distribution. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states and no ensemble inflation is necessary for this EAKF experiment. Different experiment results were compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated that a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85% and 80% respectively. The standard deviations of sea surface height were employed to examine the simulation ability and it indicated that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.The EAKF assimilation module is used to assimilate Argo profiles of 2008 in a global version of the Modular Ocean Model version 4 (MOM4). Four assimilation experiments are carried out to compare with the simulation without data assimilation, which serves as the control experiment. All experiment results are compared with dataset of GTSPP and satellite SST. The first experiment (Exp 1) is implemented by perturbing temperature of upper layers in the initial conditions (ICs) with an amplitude of 1.0℃ and no ensemble inflation. The results from Exp 1 show that the simulated temperature (salinity) deviation in the upper 400 m (500 m) is reduced through Argo data assimilation; however, these deviations are increased in deeper layers. The error reduction in SST is much greater during January to June than during the rest of the year. Three more experiments are designed to understand the responses in different layers and months. Two of them test model sensitivities to ICs by perturbing them vertically:one over the vertical extent of the whole water column (Exp 2) and the other employs smaller perturbation amplitude of 0.1℃ (Exp 3). Exp 2 shows that the simulated temperature and salinity deviations are systematically improved in the whole water column. Comparison between Exps 2 and 3 suggests that perturbation amplitude is important. Exp 4 tests the influence of the optimal inflation factor of 5%, which is determined by other set of numerical tests. Exp 4 improves assimilation performance much more than the other three experiments without inflation. Therefore, we conclude that the perturbation should be introduced to all model layers, proper perturbation amplitude is important for Ocean data assimilation using EAKF, and the ensemble inflation by an optimal inflation is critical to improve the skill of the EAKF analysis.Based on the Earth System Model (ESM) of FIO-ESM, a method of perturbing the initial ocean temperature by a tiny random value is used to set up the restarts for ensemble runs. The satellite SST and SLA data have been assimilated into FIO-ESM model. A climate system reanalysis data covering 1992-2013 have been reconstructed from the assimilated results in element models of ocean, atmosphere, ice, land surface, and ocean waves followed by an overall comparison of this dataset. The comparison of the experiments before and after assimilation of FIO-ESM indicated that the results are more reliable and capable to reproduce the evolution of climate system. In order to assessing the reconstructed dataset, the reanalyzing data of ERA-Interim, EN4 and GPCP, and the wave measurements by satellite altimeter provided by AVISO are employed to compare with the climate reanalysis data reconstructed by EAKF assimilation. The assessment shows that our reanalysis data can provide the reliable climate character for the upper ocean, motions and vapor distribution in atmosphere, ice evolution, and distribution of significant wave height. This dataset can be further used in the study of climate events, climate prediction and it is potentially helpful to improve our understanding on climate change.