El Nino-Southern Oscillation (ENSO) is the strongest interannual signal that is produced by basinscale processes in the tropical Pacific, with significant effects on weather and climate worldwide. In the past, extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies. A hierarchy of coupled ocean-atmosphere models has been formulated; in terms of their complexity, they can be categorized into intermediate coupled models (ICMs), hybrid coupled models (HCMs), and fully coupled general circulation models (CGCMs). ENSO modeling has made signifiscant progress over the past decades, reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance. Meanwhile, ENSO exhibits great diversity and complexity as observed in nature, which still cannot be adequately captured by current state-of-the-art coupled models, presenting a challenge to ENSO modeling. We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China; some selected representative examples are presented here to review the current status of ENSO model developments and applications, which have been actively pursued with noticeable progress being made recently. As ENSO simulations are very sensitive to model formulations and process representations etc., dedicated efforts have been devoted to ENSO model developments and improvements. Now, different ocean-atmosphere coupled models have been available in China, which exhibit good model performances and have already had a variety of applications to climate modeling, including the Coupled Model Intercomparison Project Phase 6 (CMIP6). Nevertheless, large biases and uncertainties still exist in ENSO simulations and predictions, and there are clear rooms for their improvements, which are still an active area of researches and applications. Here, model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models, pinpointing to the areas where they need to be further improved for ENSO studies. These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

A hybrid coupled model (HCM) is constructed for El Nino-Southern Oscillation (ENSO)-related modeling studies over almost the entire Pacific basin. An ocean general circulation model is coupled to a statistical atmospheric model for interannual wind stress anomalies to represent their dominant coupling with sea surface temperatures. In addition, various relevant forcing and feedback processes exist in the region and can affect ENSO in a significant way; their effects are simply represented using historical data and are incorporated into the HCM, including stochastic forcing of atmospheric winds, and feedbacks associated with freshwater flux, ocean biology-induced heating (OBH), and tropical instability waves (TIWs). In addition to its computational efficiency, the advantages of making use of such an HCM enable these related forcing and feedback processes to be represented individually or collectively, allowing their modulating effects on ENSO to be examined in a clean and clear way. In this paper, examples are given to illustrate the ability of the HCM to depict the mean ocean state, the circulation pathways connecting the subtropics and tropics in the western Pacific, and interannual variability associated with ENSO. As satellite data are taken to parameterize processes that are not explicitly represented in the HCM, this work also demonstrates an innovative method of using remotely sensed data for climate modeling. Further model applications related with ENSO modulations by extratropical influences and by various forcings and feedbacks will be presented in Part II of this study.

A hybrid coupled model for the pacific ocean-atmosphere system. Part I: Description and basic performance

The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.

Ocean data assimilation with background error covariance derived from OGCM outputs

A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution. A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features. From 1990 to 2000, the average of HR’s RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97°C compared to a RMSE of 0.56°C for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.

A comparison study of tropical Pacific ocean state estimation: Low-resolution assimilation vs. high-resolution simulation.

A high-resolution tropical Pacific general circulation model (GCM) coupled to a global atmospheric GCM is described in this paper. The atmosphere component is the 5° × 4° global general circulation model of the Institute of Atmospheric Physics (IAP) with 9 levels in the vertical direction. The ocean component with a horizontal resolution of 0.5°, is based on a low-resolution model (2° × 1° in longitude-latitude). Simulations of the ocean component are first compared with its previous version. Results show that the enhanced ocean horizontal resolution allows an improved ocean state to be simulated: this involves (1) an apparent decrease in errors in the tropical Pacific cold tongue region, which exists in many ocean models, (2) more realistic large-scale flows, and (3) an improved ability to simulate the interannual variability and a reduced root mean square error (RMSE) in a long time integration. In coupling these component models, a monthly “linear-regression” method is employed to correct the model’s exchanged flux between the sea and the atmosphere. A 100-year integration conducted with the coupled GCM (CGCM) shows the effectiveness of such a method in reducing climate drift. Results from years 70 to 100 are described. The model produces a reasonably realistic annual cycle of equatorial SST. The large SSTA is confined to the eastern equatorial Pacific with little propagation. Irregular warm and cold events alternate with a broad spectrum of periods between 24 and 50 months, which is very realistic. But the simulated variability is weaker than the observed and is also asymmetric in the sense of the amplitude of the warm and cold events.

Modeling the tropical Pacific Ocean using a regional coupled climate model

On the basis of Zeng's theoretical design, a coupled general circulation model (CGCM) is developed with its characteristics different from other CGCMs such as the unified vertical coordinates and subtraction of the standard stratification for both atmosphere and ocean, available energy consideration, and so on. The oceanic component is a free surface tropical Pacific Ocean GCM between 30°N and 30°S with horizontal grid spacing of 1° in latitude and 2° in longitude, and with 14 vertical layers. The atmospheric component is a global GCM with low—resolution of 4° in latitude and 5° in longitude, and two layers of equal mass in the vertical between the surface and 200 hPa. The atmospheric GCM includes comprehensive physical processes. The coupled model is subjected to seasonally-varying cycle. Several coupling experiments, ranging from straight forward coupling without flux correction to one with flux correction, and to so—called predictor—corrector monthly coupling (PCMC), are conducted to show the existence and final controlling of the climate drift in the coupled system. After removing the climate drift with the PCMC scheme, the coupled model is integrated for more than twenty years. The results show reasonable simulations of the annual mean and its seasonal cycle of the atmospheric and oceanic circulation. The model also produces the coherent interannual variations of the climate system, manifesting the observed El Nino / Southern Oscillation (ENSO).

Zhou Guangqing

Papers

A Coupled General Circulation Model for the Tropical Pacific Ocean and Global Atmosphere