Different El Niño Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model

A previously developed hybrid coupled model(HCM)is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model(AGCM),denoted as HCMAGCM.In this study,different El Niño flavors,namely the Eastern-Pacific(EP)and Central-Pacific(CP)types,and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM.The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific,including the amplitude and spatial patterns of sea surface temperature(SST),zonal wind stress,and precipitation anomalies.An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events,respectively.Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events,the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter.In particular,the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere,while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American(PNA)pattern.As a result,different climatic impacts exist in North American regions,with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño,respectively.This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.

Hydrologic Response to Future Climate Change in the Dulong-Irra-waddy River Basin Based on Coupled Model Intercomparison Project 6

Within the context of the Belt and Road Initiative(BRI)and the China-Myanmar Economic Corridor(CMEC),the Dulong-Ir-rawaddy(Ayeyarwady)River,an international river among China,India and Myanmar,plays a significant role as both a valuable hydro-power resource and an essential ecological passageway.However,the water resources and security exhibit a high degree of vulnerabil-ity to climate change impacts.This research evaluates climate impacts on the hydrology of the Dulong-Irrawaddy River Basin(DIRB)by using a physical-based hydrologic model.We crafted future climate scenarios using the three latest global climate models(GCMs)from Coupled Model Intercomparison Project 6(CMIP6)under two shared socioeconomic pathways(SSP2-4.5 and SSP5-8.5)for the near(2025-2049),mid(2050-2074),and far future(2075-2099).The regional model using MIKE SHE based on historical hydrologic processes was developed to further project future streamflow,demonstrating reliable performance in streamflow simulations with a val-idation Nash-Sutcliffe Efficiency(NSE)of 0.72.Results showed that climate change projections showed increases in the annual precip-itation and potential evapotranspiration(PET),with precipitation increasing by 11.3%and 26.1%,and PET increasing by 3.2%and 4.9%,respectively,by the end of the century under SSP2-4.5 and SSP5-8.5.These changes are projected to result in increased annual streamflow at all stations,notably at the basin’s outlet(Pyay station)compared to the baseline period(with an increase of 16.1%and 37.0%at the end of the 21st century under SSP2-4.5 and SSP5-8.5,respectively).Seasonal analysis for Pyay station forecasts an in-crease in dry-season streamflow by 31.3%-48.9%and 22.5%-76.3%under SSP2-4.5 and SSP5-8.5,respectively,and an increase in wet-season streamflow by 5.8%-12.6%and 2.8%-33.3%,respectively.Moreover,the magnitude and frequency of flood events are pre-dicted to escalate,potentially impacting hydropower production and food security significantly.This research outlines the hydrological response to future climate change during the 21st century and offers a scientific basis for the water resource management strategies by decision-makers.

Assessment of earthquake-induced landslide hazard zoning using the physics-environmental coupled Model

In order to prevent and mitigate disasters,it is crucial to immediately and properly assess the spatial distribution of landslide hazards in the earthquake-affected area.Currently,there are primarily two categories of assessment techniques:the physical mechanism-based method(PMBM),which considers the landslide dynamics and has the advantages of effectiveness and proactivity;the environmental factor-based method(EFBM),which integrates the environmental conditions and has high accuracy.In order to obtain the spatial distribution of landslide hazards in the affected area with near realtime and high accuracy,this study proposed to combine the PMBM based on Newmark method with EFBM to form Newmark-Information value model(N-IV),Newmark-Logic regression model(N-LR)and Newmark-Support Vector Machine model(N-SVM)for seismic landslide hazard assessment on the Ludian Mw 6.2 earthquake in Yunnan.The predicted spatial hazard distribution was compared with the actual cataloged landslide inventory,and frequency ratio(FR),and area under the curve(AUC)metrics were used to verify the model's plausibility,performance,and accuracy.According to the findings,the model's accuracy is ranked as follows:N-SVM>N-LR>N-IV>Newmark.With an AUC value of 0.937,the linked N-SVM was discovered to have the best performance.The research results indicate that the physics-environmental coupled model(PECM)exhibits accuracy gains of 46.406%(N-SVM),30.625%(N-LR),and 22.816%(N-IV)when compared to the conventional Newmark technique.It shows varied degrees of improvement from 2.577%to 12.446%when compared to the single EFBM.The study also uses the Ms 6.8 Luding earthquake to evaluate the model,showcasing its trustworthy in forecasting power and steady generalization.Since the suggested PECM in this study can adapt to complicated earthquake-induced landslides situations,it aims to serve as a reference for future research in a similar field,as well as to help with emergency planning and response in earthquakeprone regions with landslides.

Evaluation of East Asian Climatology as Simulated by Seven Coupled Models

Using observation and reanalysis data throughout 1961-1990, the East Asian surface air temperature, precipitation and sea level pressure climatology as simulated by seven fully coupled atmosphere-ocean models, namely CCSR/NIES, CGCM2, CSIRO-Mk2, ECHAM4/OPYC3, GFDL-R30, HadCM3, and NCAR-PCM, are systematically evaluated in this study. It is indicated that the above models can successfully reproduce the annual and seasonal surface air temperature and precipitation climatology in East Asia, with relatively good performance for boreal autumn and annual mean. The models' ability to simulate surface air temperature is more reliable than precipitation. In addition, the models can dependably capture the geographical distribution pattern of annual, boreal winter, spring and autumn sea level pressure in East Asia. In contrast, relatively large simulation errors are displayed when simulated boreal summer sea level pressure is compared with reanalysis data in East Asia. It is revealed that the simulation errors for surface air temperature, precipitation and sea level pressure are generally large over and around the Tibetan Plateau. No individual model is best in every aspect. As a whole, the ECHAM4/OPYC3 and HadCM3 performances are much better, whereas the CGCM2 is relatively poorer in East Asia. Additionally, the seven-model ensemble mean usually shows a relatively high reliability.

Improved ENSO Forecasts by Assimilating Sea Surface Temperature Observations into an Intermediate Coupled Model

A simple method for initializing intermediate coupled models （ICMs） using only sea surface temperature （SST） anomaly data is comprehensively tested in two sets of hindcasts with a new ICM. In the initialization scheme, both the magnitude of the nudging parameter and the duration of the assimilation are considered, and initial conditions for both atmosphere and ocean are generated by running the coupled model with SST anomalies nudged to the observations. A comparison with the observations indicates that the scheme can generate realistic thermal fields and surface dynamic fields in the equatorial Pacific through hindcast experiments. An ideal experiment is performed to get the optimal nudging parameters which include the nudging intensity and nudging time length. Twelve-month-long hindcast experiments are performed with the model over the period 1984-2003 and the period 1997-2003. Compared with the original prediction results, the model prediction skills are significantly improved by the nudging method especially beyond a 6-month lead time during the two different periods. Potential problems and further improvements are discussed regarding the new coupled assimilation system.

Fluid Flow and Solidification Simulation in Beam Blank Continuous Casting Process With 3D Coupled Model

Based on turbulent theory, a 3D coupled model of fluid flow and solidification was built using finite difference method and used to study the influence of superheating degree and casting speed on fluid flow and solidification, analyze the interaction between shell and molten steel, and compare the temperature distribution under different technological conditions. The results indicate that high superheating degree can lengthen the liquid-core depth and make the crack and breakout possible, so suitable superheating should be controlled within 35℃ according to the simulation results. Casting speed which is one of the most important technological parameters of improving production rate, should be controlled between 0. 85 m/min and 1.05 m/min and the caster has great potential in the improvement of blank quality.

A Regional Air-Sea Coupled Model and Its Application over East Asia in the Summer of 2000

A regional air-sea coupled model, comprising the Regional Integrated Environment Model System （RIEMS） and the Princeton Ocean Model （POM） was developed to simulate summer climate features over East Asia in 2000. The sensitivity of the model＇s behavior to the coupling time interval （CTI）, the causes of the sea surface temperature （SST） biases, and the role of air-sea interaction in the simulation of precipitation over China are investigated. Results show that the coupled model can basically produce the spatial pattern of SST, precipitation, and surface air temperature （SAT） with five different CTIs respectively. Also, using a CTI of 3, 6 or 12 hours tended to produce more successful simulations than if using 1 and 24 hours. Further analysis indicates that both a higher and lower coupling frequency result in larger model biases in air-sea heat flux exchanges, which might be responsible for the sensitivity of the coupled model＇s behavior to the CTI. Sensitivity experiments indicate that SST biases between the coupled and uncoupled POM occurring over the China coastal waters were due to the mismatch of the surface heat fluxes produced by the RIEMS with those required by the POM. In the coupled run, the air-sea feedbacks reduced the biases in surface heat fluxes, compared with the uncoupled RIEMS, consequently resulted in changes in thermal contrast over land and sea and led to a precipitation increase over South China and a decrease over North China. These results agree well observations in the summer of 2000.

Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction

A four-dimensional variational （4D-Var） data assimilation method is implemented in an improved intermediate coupled model （ICM） of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The ＂observation＂ of the SST anomaly, which is sampled from a ＂truth＂ model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

A Hybrid Coupled Model for the Pacific Ocean–Atmosphere System.Part I: Description and Basic Performance

A hybrid coupled model （HCM） is constructed for El Nifio-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 coupled model of TiN inclusion growth in GCr15SiMn during solidification in the electroslag remelting process

TiN inclusions observed in an ingot produced by electroslag remelting （ESR） are extremely harmful to GCrl5SiMn steel. Therefore, accurate predictions of the growth size of these inclusions during steel solidification are significant for clean ESR ingot production. On the basis of our previous work, a coupled model of solute microsegregation and TiN inclusion growth during solidification has been established. The results demonstrate that compared to a non-coupled model, the coupled model predictions of the size of TiN inclusions are in good agreement with experimental results using scanning electron microscopy with energy disperse spectroscopy （SEM-EDS）. Because of high cooling rate, the sizes of TiN inclusions in the edge area of the ingots are relatively small compared to the sizes in the center area. During the ESR process, controlling the content of Ti in the steel is a feasible and effective method of decreasing the sizes of TiN inclusions.

Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model

The initial errors constitute one of the main limiting factors in the ability to predict the E1 Nino-Southem Oscillation （ENSO） in ocean-atmosphere coupled models. The conditional nonlinear optimal perturbation （CNOP） approach was em- ployed to study the largest initial error growth in the E1 Nino predictions of an intermediate coupled model （ICM）. The optimal initial errors （as represented by CNOPs） in sea surface temperature anomalies （SSTAs） and sea level anomalies （SLAs） were obtained with seasonal variation. The CNOP-induced perturbations, which tend to evolve into the La Nifia mode, were found to have the same dynamics as ENSO itself. This indicates that, if CNOP-type errors are present in the initial conditions used to make a prediction of E1 Nino, the E1 Nino event tends to be under-predicted. In particular, compared with other seasonal CNOPs, the CNOPs in winter can induce the largest error growth, which gives rise to an ENSO amplitude that is hardly ever predicted accurately. Additionally, it was found that the CNOP-induced perturbations exhibit a strong spring predictability barrier （SPB） phenomenon for ENSO prediction. These results offer a way to enhance ICM prediction skill and, particularly, weaken the SPB phenomenon by filtering the CNOP-type errors in the initial state. The characteristic distributions of the CNOPs derived from the ICM also provide useful information for targeted observations through data assimilation. Given the fact that the derived CNOPs are season-dependent, it is suggested that seasonally varying targeted observations should be implemented to accurately predict ENSO events.

Mean Climatic Characteristics in High Northern Latitudes in an Ocean-Sea Ice-Atmosphere Coupled Model

Emphasizing the model's ability in mean climate reproduction in high northern latitudes, results from an ocean-sea ice-atmosphere coupled model are analyzed. It is shown that the coupled model can simulate the main characteristics of annual mean global sea surface temperature and sea level pressure well, but the extent of ice coverage produced in the Southern Hemisphere is not large enough. The main distribution characteristics of simulated sea level pressure and temperature at 850 hPa in high northern latitudes agree well with their counterparts in the NCEP reanalysis dataset, and the model can reproduce the Arctic Oscillation (AO) mode successfully. The simulated seasonal variation of sea ice in the Northern Hemisphere is rational and its main distribution features in winter agree well with those from observations. But the ice concentration in the sea ice edge area close to the Eurasian continent in the inner Arctic Ocean is much larger than the observation. There are significant interannual variation signals in the simulated sea ice concentration in winter in high northern latitudes and the most significant area lies in the Greenland Sea, followed by the Barents Sea. All of these features agree well with the results from observations.

An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.

An ocean-land-atmosphere coupled model for tropical cyclone landfall processes: The multi-layer ocean model and its verification

POM (Princeton ocean model) tentatively taken as the ocean part of an ocean-land atmosphere coupled modcl is verified for the ultimate purpose of studying the landfall process of tropical cyclone (TC) in the western North Pacific. The POM is tested with monthly mean wind stress in the summer and given lateral boundary conditions. The results indicate that the equilibrium state of the ocean is in accordance with the climate mean, with the error in sea surface temperature (salinity) less than 0.5 ℃ (0.5). The simulated occan currents are reasonable as well.Several numerical experiments are designed to verify the oceanic response to a stationary or moving TC. It is found that the results agree fairly well with the previous work, including both the drop magnitude and the distribution ofsca temperature. Compared with the simple two-layer ocean model used by some other studies, the response of the ocean to a TC is more logical here. The model is also verified in a real case with a TC passing the neighborhood of a buoy station. It is shown that the established ocean model can basically reproduce the sea surface temperature change as observed.

Seasonal and Intraseasonal Variations of East Asian Summer Monsoon Precipitation Simulated by a Regional Air-Sea Coupled Model

The performance of a regional air-sea coupled model, comprising the Regional Integrated Environment Model System （RIEMS） and the Princeton Ocean Model （POM）, in simulating the seasonal and intraseasonal variations of East Asian summer monsoon （EASM） rainfall was investigated. Through comparisons of the model results among the coupled model, the uncoupled RIEMS, and observations, the impact of air-sea coupling on simulating the EASM was also evaluated. Results showed that the regional air sea coupled climate model performed better in simulating the spatial pattern of the precipitation climatology and produced more realistic variations of the EASM rainfall in terms of its amplitude and principal EOF modes. The coupled model also showed greater skill than the uncoupled RIEMS in reproducing the principal features of climatological intraseasonal oscillation （CISO） of EASM rainfall, including its dominant period, intensity, and northward propagation. Further analysis indicated that the improvements in the simulation of the EASM rainfall climatology and its seasonal variation in the coupled model were due to better simulation of the western North Pacific Subtropical High, while the improvements of CISO simulation were owing to the realistic phase relationship between the intraseasonal convection and the underlying SST resulting from the air-sea coupling.

Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific

Regional coupled modeling is one of the frontiers of regional climate modeling,but intercomparison has not been well coordinated.In this study,a community regional climate model,WRF4,with a resolution of 15 km,was coupled with a high-resolution(0.1°)North Pacific Ocean model(LICOM_np).The performance of the regional coupled model,WRF4_LICOM,was compared to that of another regional coupled model,RegCM4_LICOM,which was a coupling of version 4 of the Regional Climate Model(RegCM4)with LICOM_np.The analysis focused on the 2005 western North Pacific summer monsoon rainfall.The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well.Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables.RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall,while WRF4_LICOM better captured the tropical cyclone(TC)intensity,the percentage contributions of rainfall induced by TCs to the total rainfall,and the diurnal cycle of rainfall and stratiform percentages,especially over land areas.The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high(WNPSH).The stronger(weaker)WNPSH in WRF4_LICOM(RegCM4_LICOM)was driven by overestimated(underestimated)diabatic heating,which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes.Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.

A COUPLED MODEL FOR MUILTIPHASE FLUID FLOW AND SEDIMENTATION DEFORMATION IN OIL RESERVOIR AND ITS NUMERICAL SIMULATION

A mathematical model for coupled multiphase fluid flow and sedi- mentation deformation is developed based on fluid-solid interaction mechanism.A finite difference-finite element numerical approach is presented.The results of an example show that the fluid-solid coupled effect has great influence on multiphase fluid flow and reservoir recovery performances,and the coupled model has practical significance for oilfield development.

Simulation of a Persistent Snow Storm over Southern China with a Regional Atmosphere-Ocean Coupled Model

A regional atmosphere-ocean coupled model, RegCM3-POM, was developed by coupling the regional climate model （RegCM3） with the Princeton Ocean Model （POM）. The performance of RegCM3-POM in simulating a persistent snow storm over southern China and the impact of the Madden Julian oscillation （MJO） on this persistent snow storm were investigated. Compared with the stand-alone RegCM3, the coupled model performed better at reproducing the spatial-temporal evolution and intensity of the precipitation episodes. The power spectral analysis indicated that the coupled model successfully captured the dominant period between 30 and 60 days in the precipitation field, leading to a notable improvement in simulating the magnitude of intraseasonal precipitation variation, and further in enhancing the intensity of the simulated precipitation. These improvements were mainly due to the well-simulated low-frequency oscillation center and its eastward propagation characteristics in each MJO phase by RegCM3-POM, which improved the simulations of MJO-related low-frequency vertical motions, water vapor transport, and the deep inversion layer that can directly influence the precipitation event and that further improved the simulated MJOprecipitation relationship. Analysis of the phase relationship between convection and SST indicated that RegCM3-POM exhibits a near-quadrature relation between the simulated convection and SST anomalies, which was consistent with the observations. However, such a near-quadrature relation was not as significant when the stand-alone RegCM3 was used. This difference indicated that the inherent coupled feedback process between the ocean and atmosphere in RegCM3-POM played an important part in reproducing the features of the MJO that accompanied the snow storm.

Biases of the Arctic climate in a regional ocean-sea iceatmosphere coupled model: an annual validation

The Coupling of three model components, WRF/PCE （polar climate extension version of weather research and forecasting model （WRF））, ROMS （regional ocean modeling system）, and CICE （community ice code）, has been implemented, and the regional atmosphere-ocean-sea ice coupled model named WRF/PCE- ROMS-CICE has been validated against ERA-interim reanalysis data sets for 1989. To better understand the reasons that generate model biases, the WRF/PCE-ROMS-CICE results were compared with those of its components, the WRF/PCE and the ROMS-CICE. There are cold biases in surface air temperature （SAT） over the Arctic Ocean, which contribute to the sea ice concentration （SIC） and sea surface temperature （SST） biases in the results of the WRF/PCE-ROMS-CICE. The cold SAT biases also appear in results of the atmo- spheric component with a mild temperature in winter and similar temperature in summer. Compared to results from the WRF/PCE, due to influences of different distributions of the SIC and the SST and inclusion of interactions of air-sea-sea ice in the WRF/PCE-ROMS-CICE, the simulated SAT has new features. These influences also lead to apparent differences at higher levels of the atmosphere, which can be thought as responses to biases in the SST and sea ice extent. There are similar atmospheric responses in feature of distribution to sea ice biases at 700 and 500 hPa, and the strength of responses weakens when the pressure decreases in January. The atmospheric responses in July reach up to 200 hPa. There are surplus sea ice ex- tents in the Greenland Sea, the Barents Sea, the Davis Strait and the Chukchi Sea in winter and in the Beau- fort Sea, the Chukchi Sea, the East Siberian Sea and the Laptev Sea in summer in the ROMS-CICE. These differences in the SIC distribution can all be explained by those in the SST distributions. These features in the simulated SST and SIC from ROMS-CICE also appear in the WRF/PCE-ROMS-CICE. It is shown that the performance of the WRF/PCE-ROMS-CICE is determined to a large extent by its components, the WRF/PCE and the ROMS-CICE.

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational （4D-Vat） data assimilation system was developed for an intermediate coupled model （ICM） and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer （Te）, which is empirically and explicitly related to sea level （SL） variation. The strength of the thermocline effect on SST （referred to simply as ＂the thermocline effect＂） is represented by an introduced parameter, （l＇Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Vat data assimilation system implemented in the ICM are also discussed.