The persistent biases that are associated with the mean state in coupled or climate models can cause simulation problems and degrade the results of seasonal to decadal climate predictions by affecting the variability of the model (
In this study, based on an ocean data assimilation system for the coupled climate model CAS-ESM-C, how to reasonably assimilate altimetry data are explored. In sea surface height (SSH) assimilations, the mean dynamic topography (MDT) is an important factor that can coordinate the observed sea level anomalies with the modeled SSH. The SSH assimilation results are first compared through assimilation experiments using three different MDTs, including the observed reference height, the MDT from model control run, and the MDT from the assimilation experiment in CAS-ESM-C, with the climatological World Ocean Atlas (WOA) temperature assimilated into the coupled model. The results show that the third one can significantly improve the ability of the ocean data assimilation system to assimilate the SSH observations. Using this MDT, the SSH assimilation scheme of the ocean data assimilation system was established for the CAS-ESM-C, and a long-term SSH assimilation experiment from 1994 to 2017 was carried out. The results show that the SSH assimilation performs much better than the SST assimilation in reproducing the ocean states and seasonal-interannual variability. The improvements of SSH assimilation compared with SST assimilation may be due to the different properties of the two ocean variables. While SST is a thermodynamic variable used to evaluate the thermal condition of the ocean surface layer, SSH is a dynamic variable linked to the dynamical information of the whole ocean layer. Thus, SSH assimilation can constrain the ocean model with observed dynamic information, which is more important to the state estimation and temporal evolution of the ocean. Plain Language Summary Altimetry data are widely used in ocean assimilation. Compared with standalone ocean model assimilation, assimilation in a coupled model framework is more realistic due to consideration of the air-sea interaction processes and draws more attention from the modeling community. To reasonably assimilate altimetry data, mean dynamic topography (MDT) is an essential factor that can coordinate the observed sea level anomalies with the modeled sea surface temperature (SSH). In this study, to choose a best MDT for altimetry data assimilation in the coupled model (CAS-ESM-C), three different MDTs were used in SSH assimilation in CAS-ESM-C. We concluded that the MDT derived from assimilating the climatological World Ocean Atlas (WOA) temperature into the coupled model can significantly improve the ability of the ocean data assimilation system to assimilate the SSH observations. Then, using this selected MDT scheme, long-term SSH assimilation experiments were conducted and the performances of SSH assimilation were compared with those of sea surface temperature (SST) assimilation. In comparison with SST assimilation, SSH assimilation achieves better performances in reproducing the mean states and seasonal-interannual variability of the ocean. DONG ET AL.
Currently, several ocean data assimilation methods have been adopted to increase the performance of air–sea coupled models, but inconsistent adjustments between the sea temperature with other oceanic fields can be introduced. In the coupled model CAS-ESM-C, inconsistent adjustments for ocean currents commonly occur in the tropical western Pacific and the eastern Indian Ocean. To overcome this problem, a new ensemble-based bias correction approach—a simple modification of the Ensemble Optimal Interpolation (EnOI) approach for multi-variable into a direct approach for a single variable—is proposed to minimize the model biases. Compared with the EnOI approach, this new approach can effectively avoid inconsistent adjustments. Meanwhile, the comparisons suggest that inconsistent adjustment mainly results from the unreasonable correlations between temperature and ocean current in the background matrix. In addition, the ocean current can be directly corrected in the EnOI approach, which can additionally generate biases for the upper ocean. These induced ocean biases can produce unreasonable ocean heat sinking and heat storage in the tropical western Pacific. It will generate incorrect ocean heat transmission toward the east, further amplifying the inconsistency introduced through the tropical air–sea interaction process.
Explosive extratropical cyclones (EECs) have long been a research focus for the meteorological society as they often cause serious economic losses and casualties. However, after a long period of research, there still remain some knowledge gaps about their rapid development. In this article, we conducted the first study by using both vorticity and kinetic energy (KE) budgets simultaneously on a typical EEC, which was the strongest EEC that affected the coastal areas of China in the last 3 years, to further the understanding of the mechanisms governing its rapid enhancement in rotation and wind speed. The vorticity budget shows that the lower-level convergence-related vertical stretching and the vertical transport of vorticity acted as the most and second most favorable factors for the increase in the cyclone's cyclonic vorticity, respectively, which were different from those findings based on the Zwack–Okossi vorticity budget. In contrast, the horizontal transport of vorticity and tilting mainly decelerated the EEC's development. Energetics features governing the rapid wind enhancement of the EEC were shown for the first time. It is found that the work on the rotational wind by the pressure gradient force and the net import transport of KE by the rotational wind contributed the largest and second largest to the cyclone's increase in wind speed. In contrast, the upward transport of KE and the cyclone's displacement mainly acted in an opposite way. Analysis based on the Green's theorem and rotational wind shows that the enhancement of the EEC's rotation and wind speed were linked to each other solidly.
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