Coupled Model Research Articles

The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory

Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall on slope stability, focusing on the dynamic hydraulic behavior of unsaturated soils. By simulating the soil water content and slope stability under four different rainfall scenarios based on observational data and historical thresholds, this study reveals that higher rainfall intensity significantly increases the soil water content, leading to reduced slope stability. The results show a strong correlation between the soil water content and slope stability, with a 20 mm/h rainfall intensity threshold emerging as a reliable predictor of potential slope instability. This study contributes to a deeper understanding of slope stability dynamics and emphasizes the importance of proactive risk management in response to changing rainfall patterns while also validating current management practices and providing essential insight for improving early warning systems to effectively mitigate landslide risk.

Coupled CFD-DEM modeling of surface erosion in granular soils: Simulation of erosion function apparatus experiments

This study introduces a numerical modeling approach that couples the computational fluid dynamics (CFD) with the discrete element method (DEM) to simulate grain-scale soil erosion processes induced by water flows. In this modeling framework, CFD simulates fluid flows by solving the volume-averaged Navier-Stokes equations, and uses the k-ω turbulent model for turbulent flows. Simultaneously, DEM computes the displacement of solid particles by incorporating the fluid-particle interactions driven by fluid flows while adhering to Newton's laws of motion. These interactions encompass drag force, buoyancy force, pressure-gradient force, and viscous force exerted by fluid flows and acting on the particles. The coupled CFD-DEM modeling adeptly replicates soil erosion processes, demonstrating good alignment with results obtained from laboratory erosion function apparatus (EFA) tests. In particular, the DEM facilitates the estimation of shear stress acting on the soil surface based on fluid-particle interaction forces, which has been roughly approximated by empirical or semi-empirical models. This study underscores the capability of coupled CFD-DEM in providing valuable insights into the grain-scale behavior of soil particles subjected to fluid flows, with the potential for extension to address soil erosion and fines migration.

Land Space and High-Speed Transportation Coordinated Development Evaluation in the Beijing–Tianjin–Hebei Urban Agglomeration of China

There is a complex interaction between national land space and transportation. China is accelerating its strategy of building a strong transportation country and territorial spatial planning, which requires evaluating and strengthening the coordination relationship between transport and land space to promote development. Taking the Beijing–Tianjin–Hebei urban agglomeration in China as an example, this study analyze the high-speed transportation construction and land space development process, evaluates the coupling and coordination degree between high-speed transportation and land space, as well as the “development-protection” situation in the years 2000, 2010, and 2020 by means of the coupling and coordination model, spatial autocorrelation analysis, and GIS analysis. The result shows: (1) high-speed transportation construction and territorial space development have been active for a long time, and most of the cities have maintained a high level of increase in these two indicators. In terms of protection indicators, Tangshan and other coastal cities have been lagging behind for a long time without any improvement trend. (2) The coupling and coordination level between high-speed transportation and development is high, with slight weakening over time, with an average annual decrease of less than 0.09%. The change in the coupling coordination level with protection shows the wave dynamics of ‘low-middle-low’. (3) In the future, high-speed transportation will have a significant impact on land space in the country for a long time. In this regard, it is suggested to: (1) promote the construction of high-speed transportation for network, systematic, and serial development; (2) accelerate the extension of the high-speed transportation advantages of the existing growth poles of Beijing and Tianjin; and (3) develop Shijiazhuang and Langfang as the new growth poles. This study will promote the integration of transportation and urban planning, which will contribute to the development of high-quality synergistic urban agglomerations around the world.

Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow

Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970.

Projected North Equatorial Current/Countercurrent Towards an El Niño-Like Condition in the Western Pacific under Moderate and Worst-case CO2 Emission Scenarios

Abstract Validating the mean state and historical trends of the North Equatorial Current and Countercurrent (NEC and NECC) in 36 Coupled Model Intercomparison Project (CMIP) phase 5 models and 32 CMIP phase 6 models with those in ocean reanalysis products, this study extracts the most qualified 17 model outputs to explore the projected changes in NEC and NECC in the western Pacific Ocean under scenarios of moderate and worst-case CO2 emissions. NEC and NECC are robustly predicted to have an El Niño-like change, in which the NEC (NECC) tends to intensify with a poleward (equatorward) movement, during the 21st century under both scenarios. Meanwhile, the Kuroshio and Mindanao Current are projected to become weaker. Change in meridional gradient of basin-scale wind stress curl in the tropical Pacific Ocean is the major contributor to the centennial changes in NEC and NECC; however, anomalous wind stress curl dipole on the two sides of NEC off the eastern Philippines is the major factor in controlling the western boundary currents. This study not only represents a step forward in understanding the impact of global warming on the western tropical Pacific surface circulation, but also serves as a reference for downscaled modeling, which can better resolve regional circulation and western boundary currents, for selecting suitable inputs from a large number of CMIP models.

Interannual Covariation of the North American and African Summer Monsoons

ABSTRACTThe North American summer monsoon (NASM) and the North African summer monsoon (NAFSM) are two vital subsystems of the global monsoon. To date, the potential inter‐monsoon relationship between the NASM and NAFSM has not been fully understood. To fill this gap, we investigate the NASM–NAFSM relationship on the interannual timescale during the period of 1979–2022. Based on statistical methods (including correlation, empirical orthogonal function and cross wavelet analyses), we identify a noteworthy interannual covariation of the NASM and NAFSM. This observed NASM–NAFSM covariation can be explained by atmospheric circulation anomalies associated with sea surface temperature (SST) anomalies in the tropical central‐eastern Pacific and tropical Atlantic, suggesting the critical roles of tropical Pacific–Atlantic SST anomalies in shaping the NASM–NAFSM covariation. The results of Coupled Model Intercomparison Project Phase 6 (CMIP6) models indicate that a model's ability to simulate the NASM–NAFSM covariation tends to be related to its ability to reproduce the modulating effects of the tropical Pacific–Atlantic SST anomalies. These results have potential implications for seasonal forecasts of the NASM and NAFSM variations, suggesting that the NASM and NAFSM can be considered simultaneously in climate predictions.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Importance of Experiments That Can Test Theories Critically.

General dynamic and thermodynamic properties of complex materials, including amorphous polymers and molecular glass-formers, have been established from the wealth of experimental data accumulated over the years. Naturally, these general properties attract researchers to construct theories and models to address and explain them. Often more than one theory with contrasting or even conflicting theoretical bases can equally explain a general property rather well. The correct explanation becomes unclear, and progress is stopped. The resolution of the problem comes when an innovative experiment is performed with insightful results that can critically test the premise and assumptions of each theory. This important role played by experimentalists is exemplified by the contributions of Mark Ediger in several general properties considered in this paper: (1) dynamics of the components in binary polymer blends; (2) breakdown of the Stokes-Einstein and the Debye-Stokes-Einstein relations; (3) enhancement of surface mobility and in relation to formation of ultrastable glasses; and (4) the Johari-Goldstein β-relaxation in ultrastable glasses. Different theories proposed to explain these properties are discussed, including the Coupling Model of the author.

Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models

Abstract. Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Detection and Attribution of Changes in Precipitation Extremes in China and Its Different Climate Zones

Abstract Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.

Combining Temperature and Precipitation to Constrain the Aerosol Contribution to Observed Climate Change

Abstract Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP. Significance Statement Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.

Research on Temperature–Pressure Coupling Model of Gas Storage Well during Injection Production

Periodic changes in wellbore temperature and pressure caused by the cyclic injecting and producing of gas storage wells affect wellbore integrity. To explore the distribution and influencing factors of wellbore temperature and pressure during gas storage well injection-production processes, based on energy conservation, momentum theorem, and the transient heat transfer mechanism of the wellbore, a temperature and pressure coupling model for gas storage injection-production wellbores was established, and a piecewise iterative method was used to solve the model equations. Compared with the field data, the predicted relative errors of the wellhead temperature and pressure were 2.30% and 2.07%, respectively, indicating that the coupling model has a high predictive accuracy. The influences of the injection-production conditions, tubing diameter, and overall heat transfer coefficient on the wellbore temperature and pressure distributions were analyzed through an example. When the gas injection flow rate increased by 1.5 times, the bottomhole temperature decreased by 37%. Doubling the overall heat transfer coefficient resulted in a 10% rise in the bottomhole temperature. An increase of 0.3 times in the gas injection pressure led to a 31% increase in bottomhole pressure. With a 1.5-fold increase in the gas production flow rate, the wellhead temperature rose by 28%, and the wellhead pressure dropped by 20%. The research in this paper can serve as a guide for the optimization design and safe operation of gas storage wells.

Numerical investigation of thermal influence on debonding behavior in composite structures through a friction and cohesive coupled model

AbstractThis study conducts an in-depth numerical analysis of debonding behavior in composite structures, with a particular focus on the critical role of thermal effects. Utilizing a frictional contact cohesive zone model, the research characterizes the debonding process while sequentially integrating thermal analysis to assess the impact of thermal loading. A thermomechanical coupled model is developed and implemented using the finite element software ABAQUS. The model's accuracy is validated through the Double Cantilever Beam (DCB) test, ensuring reliable results. The methodology involves a detailed finite element analysis, where thermal loads are applied to composite specimens, followed by mechanical loading to simulate debonding. The frictional contact cohesive zone model accurately captures the interface behavior under varying thermal conditions. Quantitative results indicate that thermal loading significantly affects the debonding process, with a noticeable increase in debonding initiation and propagation rates, highlighting the critical influence of thermal effects on structural integrity.

On the present and future changes in Indian summer monsoon precipitation characteristics under different SSP scenarios from CMIP6 models

AbstractMonsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results.

A meso- to micro-scale coupled model under typhoon conditions considering vortex structure and coriolis effect for wind farms

Accurate simulations of typhoon-induced wind speeds on wind farms are crucial for the refined assessment of typhoon risks in wind turbines. Southeastern coastal area in China is rich in wind resources but is also severely threatened by typhoons. As an extremely destructive weather system with complex structures, the refined near-surface wind fields of typhoons are difficult to simulate by meso-scale models for risk assessment. Therefore, a coupled meso- and micro-scale model under typhoon conditions is proposed to simulate typhoon-induced winds on a wind farm. The Coriolis force is considered in the coupled model to reflect the rotating effect of typhoons caused by the vortex structure. A coupling algorithm based on objective analysis is proposed to construct boundary conditions at the coupling interface, to consider the variation of inflow variables in the vertical and cross-wind directions. Model constants of the two-equation turbulence model are calibrated based on typhoon field observations, to more reasonably simulate typhoons. A validation study is conducted, and the results show that the maximum wind speed on the wind farm simulated by the coupled model exhibits an increased accuracy compared with that of the meso-scale model, where the absolute error decreases by 34%.

Multifactor Coupled Evaluation Model of Fracability Based on a Combinatorial Weighting Method: A Case Study of the Low-Permeability Sandstone Reservoirs of Yanchang Formation, Southwestern Ordos Basin, China

Multifactor Coupled Evaluation Model of Fracability Based on a Combinatorial Weighting Method: A Case Study of the Low-Permeability Sandstone Reservoirs of Yanchang Formation, Southwestern Ordos Basin, China

Research Progress of Three-dimensional Geological Modeling and Multi-field Coupling of Fractured Oil and Gas Reservoirs

Fractured oil and gas reservoirs play an important role in oil and gas exploration and development due to their complex fracture network structure. As the main fluid channel in the reservoir, fractures have a significant impact on the migration, accumulation and exploitation efficiency of oil and gas. This paper summarizes the key geological characteristics of fractured reservoirs, including fracture types, distribution rules and main controlling factors of fracture development, and discusses the influence of fractures on reservoir physical properties. Furthermore, the deterministic and stochastic modeling methods of three-dimensional geological modeling of fractured reservoirs and the latest progress of discrete fracture network modeling technology are introduced in detail. In addition, this paper also discusses in detail the application of multi-field coupling model in the numerical simulation of fluid flow in fractured reservoirs, including continuous medium model and discrete medium model, and their advantages and disadvantages in simulating fluid flow in fractured reservoirs. Finally, this paper summarizes the research progress of multi-physics coupling model of fractured reservoirs, and points out the direction and challenges of future research. Through these studies, it can provide theoretical support and technical guidance for oil and gas exploration and development, in order to achieve more effective development and management of fractured reservoirs.

Research on modeling method for connectors in the train-track-bridge system based on multi-timestep explicit-implicit co-simulation

For the response of the train-track-bridge system under extreme environment, an increasing number of studies employ co-simulation strategies for detailed modeling and calculation of the structure. However, there is a lack of research on the module connectors, which significantly impact simulation accuracy and stability. In this paper, the theoretical differences between weak, moderate, and strong coupling methods in dealing with co-simulation module interface connectors are discussed. An explicit-implicit multi-timestep co-simulation platform is established using MATLAB and OPENSEES software. Taking China's CRTS II slab ballastless track, a simply supported beam bridge, and a train as examples, a three-dimensional train-track-bridge coupling model is created. The three coupling methods are then verified and compared. Finally, two coupling methods are used to model and calculate the train-track-bridge system under earthquake. It is concluded that when calculating the train-track-bridge under earthquake, using the fastener with less damage as the co-simulation partition boundary can avoid complex nonlinear calculation. On this basis, it is feasible to use weak, moderate and strong coupling methods to deal with fastener elements. Different co-simulation software can adopt different coupling methods according to the difficulty of modeling. In the MATLAB-OPENSEES multi-timestep explicit-implicit co-simulation platform, the weak and moderate methods can achieve better calculation accuracy, and can be used to calculate the response of train-track-bridge system under earthquake.

Electromechanical coupling modeling and fractional-order control of the seeker stabilization platform

To address the electromechanical coupling and multi-source disturbance problems of the seeker stabilized platform, this paper constructs an electromechanical coupling model of the seeker stabilized platform based on the Lagrange-Maxwell equation. To mitigate the influence of electromechanical coupling on the control performance of the seeker, a super-twisting controller based on a fractional-order terminal sliding mode surface (FOSTSMC) is proposed. Additionally, to handle various disturbances in the system, this paper introduces a method that combines the extended state observer (ESO) with the proposed controller to enhance the system’s stability and anti-disturbance performance. The Lyapunov function is designed to prove that the proposed controller can reach a convergence state within finite time. Finally, the proposed control method is compared with PID control, fuzzy PID control, linear sliding mode control, and super spiral control combined with a disturbance observer (DOB). Multiple simulation experiments demonstrate that, under the influence of electromechanical coupling and multi-source disturbance, the FOSTSMC-ESO significantly improves the stability and anti-disturbance performance of the seeker stabilization platform.