Zhou Guangqing

Progress in the development and application of climate ocean models and ocean-atmosphere coupled models in China

A review is presented about the development and application of climate ocean models and oceanatmosphere coupled models developed in China as well as a review of climate variability and climate change studies performed with these models. While the history of model development is briefly reviewed, emphasis has been put on the achievements made in the last five years. Advances in model development are described along with a summary on scientific issues addressed by using these models. The focus of the review is the climate ocean models and the associated coupled models, including both global and regional models, developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The progress of either coupled model development made by other institutions or climate modeling using internationally developed models also is reviewed.

A three-dimensional variational ocean data assimilation system: Scheme and preliminary results

A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63°C and 0.34 psu.

Impacts of XBT, TAO, altimetry and ARGO observations on the tropical Pacific Ocean data assimilation

This study aims at assessing the relative impacts of four major components of the tropical Pacific Ocean observing system on assimilation of temperature and salinity fields. Observations were collected over a period between January 2001 through June 2003 including temperature data from the expendable bathythermographs (XBT), thermistor data from the Tropical Ocean Global Atmosphere Tropical Atmosphere-Ocean (TOGA-TAO) mooring array, sea level anomalies from the Topex/Poseidon and Jason-1 altimetry (T/P-J), and temperature and salinity profiles from the Array for Real-time Geostrophic Oceanography (ARGO) floats.An efficient three-dimensional variational analysis-based method was introduced to assimilate the above data into the tropical-Pacific circulation model. To evaluate the impact of the individual component of the observing system, four observation system experiments were carried out. The experiment that assimilated all four components of the observing system was taken as the reference. The other three experiments were implemented by withholding one of the four components. Results show that the spatial distribution of the data influences its relative contribution. XBT observations produce the most distinguished effects on temperature analyses in the off-equatorial region due to the large amount of measurements and high quality. Similarly, the impact of TAO is dominant in the equatorial region due to the focus of the spatial distribution. The Topex/Poseidon-Jason-1 can be highly complementary where the XBT and TAO observations are sparse. The contribution of XBT or TAO on the assimilated salinity is made by the model dynamics because no salinity observations from them are assimilated. Therefore, T/P-J, as a main source for providing salinity data, has been shown to have greater impacts than either XBT or TAO on the salinity analysis. Although ARGO includes the subsurface observations, the relatively smaller number of observation makes it have the smallest contribution to the assimilation system.

A Note on the Role of Meridional Wind Stress Anomalies and Heat Flux in ENSO Simulations

Four comparative experiments and some supplementary experiments were conducted to examine the role of meridional wind stress anomalies and heat flux variability in ENSO simulations by using a highresolution Ocean General Circulation Model (OGCM). The results indicate that changes in the direction and magnitude of meridional wind stress anomalies have little influence on ENSO simulations until meridional wind stress anomalies are unrealistically enlarged by a factor of 5.0. However, evidence of an impact on ENSO simulations due to heat flux variability was found. The simulated Niño-3 index without the effect of heat flux anomalies tended to be around 1.0° lower than the observed, as well as the control run, during the peak months of ENSO events.

A review of seasonal climate prediction research in China

The ultimate goal of climate research is to produce climate predictions on various time scales. In China, efforts to predict the climate started in the 1930s. Experimental operational climate forecasts have been performed since the late 1950s, based on historical analog circulation patterns. However, due to the inherent complexity of climate variability, the forecasts produced at that time were fairly inaccurate. Only from the late 1980s has seasonal climate prediction experienced substantial progress, when the Tropical Ocean and Global Atmosphere project of the World Climate Research program (WCRP) was launched. This paper, following a brief description of the history of seasonal climate prediction research, provides an overview of these studies in China. Processes and factors associated with the climate variability and predictability are discussed based on the literature published by Chinese scientists. These studies in China mirror aspects of the climate research effort made in other parts of the world over the past several decades, and are particularly associated with monsoon research in East Asia. As the climate warms, climate extremes, their frequency, and intensity are projected to change, with a large possibility that they will increase. Thus, seasonal climate prediction is even more important for China in order to effectively mitigate disasters produced by climate extremes, such as frequent floods, droughts, and the heavy frozen rain events of South China.

Ocean data assimilation with background error covariance derived from OGCM outputs

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.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.

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

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.

Modeling the Tropical Pacific Ocean Using a Regional Coupled Climate Model

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.

Recent advances in dynamical extra-seasonal to annual climate prediction at IAP/CAS

Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980–1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998–2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

Impact of Subsurface Entrainment on ENSO Prediction: 1997-98 El Niño

Abstract Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction. A new hybrid coupled model was used that considered the entrainment of sub-surface temperature anomalies into the sea surface. The results showed that predictions were improved significantly in the new coupled model. The predictive correlation skill increased by about 0.2 at a lead time of 9 months, and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general. A detailed analysis of the 1997-98 El Niño hindcast showed that the new model was able to predict the onset, peak (both time and amplitude), and decay of the 1997-98 strong El Nio event up to a lead time of one year, factors that are not represented well by many other forecast systems. This implies, in terms of prediction, that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles. Improving the presentation of such effects in models would increase the forecast skill.