Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture

Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture Kun Xia, Ye Pu, Lijuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7877631/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate soil moisture simulation is essential for understanding regional hydroclimate variability and improving climate predictions. We evaluate the performance of the FGOALS-g3 model under the Atmospheric Model Intercomparison Project (AMIP) configuration in simulating surface soil moisture (SSM) over southeastern China during 1980–2014. Compared to the ERA5 and ESA-CCI reference datasets, the model exhibits a dry bias in its spatial distribution, seasonal cycle, and interannual variability. Replacing the model’s default soil texture with the Global Soil Dataset for Earth System Modeling (GSDE) significantly reduces this bias. GSDE promotes finer soil texture, reducing sand content by 41.25% and slightly increasing clay. This textural shift directly modifies hydraulic properties (such as increasing soil water suction and decreasing hydraulic conductivity), thereby enhancing the soil’s water retention capacity and leading to more accurate SSM simulations. However, SSM improvements have only a limited effect on latent heat flux and negligible impacts on precipitation. Although the weak response in precipitation is consistent with observational evidence of weak land–atmosphere coupling, the model incorrectly simulates strong coupling, indicating systematic biases in interaction mechanisms. These findings highlight that merely refining soil texture data is insufficient; more accurate physical parameterizations of land-atmosphere processes are essential for realistic hydroclimate simulations. Surface soil moisture Soil texture Dry bias Hydraulic properties Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Soil moisture, a key variable governing the exchange of water and energy at the surface, affects Earth’s climate system by modulating the surface energy balance and influencing the thermal contrast between land and sea (Berg and Sheffield, 2018 ). Meanwhile, soil moisture has a “memory” feature, and its abnormal signal can persist for a long time, making it a crucial indicator in seasonal climate prediction (Orth and Seneviratne, 2014 ; Zuo and Zhang, 2016 ). Numerous studies have highlighted the importance of soil moisture in southern China for climate predictions. For instance, Dong et al. ( 2022 ) identified a significant negative relationship between the soil moisture in July over southern China and the precipitation in August for the Huang-Huai River Basin (HHR). Specifically, when soil moisture is high in southern China during July, precipitation in HHR tends to decrease significantly in August, and vice versa. Zeng and Yuan ( 2018 ) demonstrated that southern China is a key zone for soil moisture-atmosphere interaction in August, linked to the subseasonal variations of the East Asian summer monsoon. Furthermore, Ning and Zhang ( 2023 ) found that on the interannual timescale, the start date of the East Asian subtropical summer monsoon is significantly correlated with premonsoon soil moisture anomalies in southeastern China. Climate system models are vital tools for climate prediction (Zhou et al., 2020 ), and accurate representation of soil moisture is essential for improving climate predictions. Considerable efforts have focused on improving soil moisture simulation in standalone land surface models, including refinements in parameterization schemes (Hu et al., 2023 ; Zhao et al., 2025 ), enhanced forcing data (Liu and Xie, 2013 ; Zeng et al., 2021 ), and advanced data assimilation methods (Nair and Indu, 2019 ; Shen et al., 2024 ). However, relatively few studies have focused on improving soil moisture simulations within climate system models. Existing evidence indicates that achieving such improvements remains challenging. For instance, increasing model spatial resolution does not consistently lead to better soil moisture simulations, as shown in multi-resolution CMIP6 model comparisons (Qiao et al., 2022 ). An improved representation of precipitation and soil moisture coupling is essential for better simulations of surface soil moisture in the BCC-CSM2-MR model (Sang et al., 2021 ). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-Point Version 3 (FGOALS-g3) is a major Chinese model contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Li et al., 2020b ). The model employs the Chinese Academy of Sciences Land Surface Model (CAS-LSM) as its land component (Xie et al., 2018 ), which was developed from the Community Land Model version 4.5 (CLM4.5). When run in offline mode, the standalone CAS-LSM has exhibited a dry bias in soil moisture simulations for the southern China region (Jia et al., 2021 ). However, a systematic evaluation of the soil moisture simulated by FGOALS-g3 in this region is still lacking. Furthermore, Wang et al. ( 2022 ) point out that soil texture representation is a major source of uncertainty in soil moisture simulations across contiguous China. Xia et al. ( 2024 ) found that incorporating new soil texture data can markedly reduce biases in the global mean surface soil moisture. Therefore, this study aims to (1) comprehensively evaluate the performance of FGOALS-g3 in simulating soil moisture over southern China against observational datasets, (2) improve its simulation performance by integrating new soil texture data, and (3) investigate the underlying mechanisms responsible for changes in model performance. 2 Model, Experiment and Data 2.1 Model and Experimental design The FGOALS-g3 model (2° horizontal resolution) is employed in this study. Its four component models (atmosphere, ocean, land, and sea ice) are coupled via a flux coupler, as described by Li et al. ( 2020b ). Within the FGOALS-g3, mineral soil texture is prescribed as a percentage of sand and clay content and translated into soil hydraulic properties using the pedotransfer functions outlined in Supplementary 1. The model’s default soil texture data are derived from the IGBP Global Soil Data Task dataset (IGBP, 2000 ), and feature ten soil layers, identical to the vertical configuration of CAS-LSM. The experiment conforms to the Atmospheric Model Intercomparison Project (AMIP) protocol and is forced by the CMIP6 datasets, which provides monthly prescribed sea surface temperature, sea ice concentrations, solar forcing, and other external forcings (Eyring et al., 2016 ). Two tests, referred to as Test1 and Test2, were conducted during 1975–2014, with the first five years designated as spin-up time; only results from January of 1980 to December 2014 were analyzed. Different soil texture data was employed in each test. Test1 uses the model’s default soil texture data, while Test2 incorporates the Global Soil Dataset for Earth System Model (GSDE) soil data (30 ’’ horizontal resolution) (Shangguan et al., 2014 ). The GSDE datasets is regarded as more representative of regional soil texture in China, since it uses significantly more soil profile data than the model’s default soil texture, and has been widely adopted in land surface model and weather forecast model (Dai et al., 2019 ; Li et al., 2020 ; Zhong et al., 2020 ). Although it follows the same vertical rules as the default model soil texture data, it is comprised of two fewer layers: specifically, the first and the tenth soil layers. To apply it, we assign the first layer of the GSDE to the model’s first and second soil layers, while the last layer corresponds to the model’s ninth and tenth soil layers. Meanwhile, the area-weighting method was employed to interpolate data to a 2-degree spatial resolution (Shangguan et al., 2014 ). 2.2 Reference Datasets To minimize the uncertainty of soil moisture data, two datasets were utilized: the combined product from the European Space Agency’s Climate Change Initiative (ESA-CCI v06.1) and the ERA5 dataset (Hans et al., 2020 ). ERA5 spans the period 1979-present with a spatial resolution of 0.25 ∘ . The soil moisture data in this dataset is categorized into four vertical layers. ESA-CCI has the same spatial resolution as ERA5 and provides daily temporal resolution data from 1978 to 2019. However, as satellite-based soil moisture products, like those from ESA-CCI, can only measure soil moisture in the top 0-5cm, surface soil moisture (0-5cm) (SSM) was selected for validation in this study. The CN05.1 precipitation dataset, which has a 0.25 ∘ resolution and covers the period from 1961 to 2015 with monthly temporal resolution, was developed by interpolating data from more than 2000 gauge stations across China. This dataset was used to validate precipitation simulations (Wu and Gao, 2013 ). In addition, two sets of gridded energy flux products for the land-atmosphere system were employed to validate the latent heat flux. They were FLUXCOM with a 0.50 ∘ resolution during 1979–2013 (Jung et al., 2019 ) and DOLCE v2.1 with a spatial resolution of 0.25 ∘ from 1980–2018 (Hobeichi et al., 2018 ). The horizontal resolution of the reference datasets employed here is higher than that of the model, so the calibration data is interpolated to 2 ∘ ×2 ∘ . 3 Results and Discussions 3.1 Evaluation of climatologically annual mean surface soil moisture The spatial correlation coefficient of annual mean SSM over China between the ERA5 and ESACCI datasets is 0.97, indicating a high degree of consistency in their spatial patterns. Both datasets show higher annual mean SSM values in southern and northeastern China (Fig. 1 b, 1 c). However, the SSM simulated by Test1 exhibits a pervasive dry bias across almost the entire country when compared to either reference dataset. The most pronounced systematic dry bias is concentrated over southeastern China (Fig. 1 d, 1 e), a result consistent with previous evaluations of CMIP6 historical simulations (Wang et al., 2022 ). Therefore, we focus our subsequent analysis on southeastern China (SE, 21°N ~ 32°N, 106°E ~ 120°E). We further evaluate the seasonal and interannual variability of SSM over this region (Fig. 2 ). While ERA5 and ESACCI show only minor discrepancies during autumn and winter, their climatological annual cycles are strongly correlated (r = 0.94). In contrast, the Test1 simulation exhibits a significant negative bias throughout the entire seasonal cycle (Fig. 2 a). This dry anomaly is also robustly present at interannual scales. The simulated SSM time series from 1980 to 2014 consistently underestimates the observations (Fig. 2 b). The Test1 simulation systematically underestimates SSM over the SE region in terms of spatial distribution, seasonal variations, and interannual variability when validated against observational products. 3.2 Improvement of surface soil moisture We observed a significant increase in the simulated SSM over SE by replacing the default soil texture data with GSDE (Fig. 3 ). The mean negative bias with ERA5 was reduced from 0.14 to 0.10, representing a decrease of 28.57%. In comparison, the mean negative deviation with ESA-CCI declined from 0.12 to 0.08, indicating a reduction of 33.33%. Meanwhile, the root mean square error (RMSE) decreased by 22.22% and 30.77% separately, when compared to ERA5 and ESA-CCI. Furthermore, the time series variations from 1980 to 2014, as well as the annual cycle of simulated SSM by Test2 over the SE, are closer to the reference data than those from Test1, which shows a significant improvement (Fig. 2 ). 3.3 The mechanism behind the improved soil moisture simulation Mechanistically, the improved SSM simulation with the GSDE dataset results from changes in soil hydraulic properties due to its updated soil texture. Compared to the model’s default dataset, the sand content in GSDE decreased by an average of 41.25% for the entire soil layer across the SE regions, while for the clay content, a slight decrease is found at depths ranging from 0.2m to 1.7m below the surface (Fig. 4 ). However, at all other depths, clay content increased, although these increases were smaller than the reduction in sand content. As the soil hydraulic parameters used in the CAS-LSM are functions of sand or clay content (see Eq. 1-Eq5 in the Supplementary 1), this shift toward a finer texture directly modified key hydraulic properties (Table 1 ), ultimately enhancing the model’s water retention capacity: Table 1 The hydraulic parameters averaged over the SE region in Test1 and Test2, calculated based on the parameterization scheme in the Supplementary 1, and ψ is calculated by using the average 0-5cm layer volumetric soil water content. Ψ (mm) ksat (mm/s) B θsat (mm 3 /mm 3 ) θ0–5 (mm 3 /mm 3 ) θfc (mm 3 /mm 3 ) θwilt (mm 3 /mm 3 ) LH (W/m 2 ) Test1 -165.96 6.25×10-3 6.61 0.42 0.23 0.25 0.13 45.08 Test2 -313.90 2.80×10-3 6.90 0.45 0.27 0.28 0.17 49.38 (1) Soil hydraulic potential (ψ, unit: mm): It reflects the combined potential energies related to soil water within the soil. It is typically negative, and its absolute value represents soil water suction, which indicates the ability of soil particles to retain moisture. When the sand content decreases from Test1 to Test2, the clay content relatively increases, and the proportion of fine pores in the soil also increases. Consequently, the surface energy and the soil water suction increase, allowing the soil to retain more water in its pores. This is reflected in ψ becoming more negative in Test2, indicating that water is held more tightly within the finer soil pores. (2) Saturated hydraulic conductivity (k sat , unit: mm/s): It describes the ease with which the water moves through the pore space. In the model, k sa t decreases as the sand content decreases from Test1 to Test2. Physically, coarse-textured soils primarily consist of large particles with large pores between them, allowing rain or irrigation water to flow easily, resulting in a higher infiltration rate and conductivity. In contrast, fine-textured soils consist primarily of small particles with tiny pores in between, making it difficult for water to move through the soil. This suggests that much more water would be retained in the soil for Test2. (3) Saturated soil moisture ( θ sat ): Under the combined effect of the soil water potential and the saturated hydraulic conductivity, it is more beneficial for water to be retained in the soil when soil grain size decreases. So the saturated soil moisture ( θ sat ) increases. (4) Field capacity ( θ fc ) and wilting point ( θ wilt ): These parameters reflect the maximum amount of soil water accessible to most plants and the minimal point of soil moisture the plant requires not to wilting, respectively. Both of them in Test2 are larger than it in Test1, consistent with the shift to a finer texture. The revised soil texture enhances the soil’s ability to retain moisture by increasing water suction and reducing drainage conductivity, which is the fundamental reason for the alleviation of the dry bias of SSM in Test2. 3.4 Limited impacts on latent heat flux and precipitation We further investigated whether the improved SSM simulation led to better performance in latent heat flux and precipitation. Our results show that updating the soil texture data resulted in an increase in the simulated annual mean latent heat flux in the SE region, rising from 45.08 to 49.38, an approximate increase of 9% (Table 1 ). This adjustment reduced the model’s bias against the FluxCom and DOLCE reference products (Fig S1 a-e). In contrast, the impact on precipitation was minimal. Although Test2 simulated a slight increase in summer mean precipitation compared to Test1, a substantial dry bias relative to observations remained (Fig S1 f-h). This indicates that improvements in soil moisture did not effectively propagate to enhance moist convection and precipitation. 3.5 The role of land-atmosphere coupling strength The limited effect of soil moisture improvement on precipitation may be related to land-atmosphere interactions. To explore this, we quantified the land-atmosphere coupling strength using the terrestrial coupling index (TCI) proposed by Dirmeyer ( 2011 ), which is defined as follows: \(TC{I_{SM - LE}}=\begin{array}{*{20}{c}} {\frac{{\partial (LE)}}{{\partial (SM)}}\sigma (LE)=\frac{{COV(SM,LE)}}{{\sigma (SM)}}}&{}&{}&{\begin{array}{*{20}{c}} {}&{}&{}&{\begin{array}{*{20}{c}} {\begin{array}{*{20}{c}} {\begin{array}{*{20}{c}} {}&{}&{} \end{array}}&{}&{}&{} \end{array}}&{} \end{array}} \end{array}} \end{array}\) Where SM is the soil moisture (mm 3 /mm 3 ), LE denotes the latent heat flux (W/m 2 ), COV is the covariance between SM and LE, and σ(SM) is the standard deviation of soil moisture. Due to the strong land-atmosphere coupling in the boreal warm season, this study focuses on the summer months (June to August) (Koster et al., 2004 ). The TCI, derived from summer averaged FluxCom/Dolce and ERA5 observations, consistently exhibits negative values across the SE region (Fig. 5 a, b), identifying it as an area of weak land–atmosphere coupling where atmospheric processes predominantly control the surface exchanges. This finding is consistent with previous studies (Zeng and Yuan, 2018 ). Observationally, precipitation in this region shows limited sensitivity to soil moisture variations. Superficially, the model simulations appear to capture this weak precipitation response. However, both Test1 and Test2 simulations produce positive TCI values across most of the SE region, suggesting strong land–atmosphere coupling, characterized by land surface anomalies driving atmospheric processes. This discrepancy reveals a systematic model bias in representing land–atmosphere interactions. Although the simulated precipitation response to soil moisture is similarly weak, it arises from an inaccurate physical framework rather than a realistic representation of local dynamics. The flawed representation of land–atmosphere coupling remains a major constraint on improving precipitation simulations in this region, suggesting that it requires future enhancements in boundary layer parameterizations and convective triggering mechanisms. Although updating soil texture reduces biases in SSM and latent heat flux, it doesn’t mitigate the model's structural deficiency in accurately representing land-atmosphere interactions across the SE region. 4 Conclusion This study evaluated the performance of the FGOALS-g3 model configured in an AMIP-style for simulating surface soil moisture (SSM) over southeast China from 1980 to 2014. The model demonstrates a pronounced dry bias in SSM when compared to ERA5 and ESA-CCI. This bias is consistently evident in its spatial distribution of the multi-year mean, the seasonal cycle, and the interannual variability. The GSDE dataset substantially reduces this dry bias by replacing the model’s default soil texture. The improvement is linked to a transition toward a finer soil texture in GSDE, which is characterized by a notable reduction in sand content (averaging 41.25% across the entire soil layer over the study region) and a slight increase in clay content. These changes in soil texture modify critical soil hydraulic properties, specifically by increasing soil water suction, decreasing saturated hydraulic conductivity, and raising both saturated moisture content and field capacity. Consequently, these changes enhance the soil’s water retention capacity. However, the improved SSM has only a limited effect on latent heat flux and a negligible impact on precipitation. This weak response of precipitation to soil moisture changes is consistent with observational evidence that identifies the region as weakly land-atmosphere coupled. Nevertheless, the model erroneously simulates a strong land–atmosphere coupling regime, indicating a systematic bias in representing interaction mechanisms. This deficiency likely arises from inaccuracies in boundary layer and convective parameterizations, which inhibit realistic soil moisture–precipitation feedback. In summary, adopting more realistic soil texture data enhances the model's capability in simulating soil moisture, but it only partially reduce the systematic dry bias. Refining soil texture alone is insufficient to fully improve the representation of soil moisture or the broader hydrological cycle. Future efforts should focus on improving the model’s physical parameterizations, particularly those governing land-atmosphere interactions, to achieve more accurate simulation of regional hydroclimate. Declarations Acknowledgements This work was supported by the National Key R&D Program of China (Grant 2022YFC3104804) and the National Natural Science Foundation of China (Grant 42230606). Financial interests: The authors declare they have no financial interests. Competing interests The authors declare that they have no competing interests. Author Contribution Kun Xia worte the main manuscript text and prepared all the figures, Ye Pu makes experiments in this text, and Lijuan Li reviewed the manusript. References Berg A, Sheffield J (2018) Soil moisture-evapotranspiration coupling in CMIP5 models: Relationship with simulated climate and prejections. J. Climate 31:4865-4878. doi:10.1175/JCLI-D-17-0757.1 Dai Y, Shangguan W, Wei N, Xin Q, Yuan H, Zhang S, Liu S, Lu X, Wang D, Yan F (2019) A review of the global soil property maps for Earth system models. Soil 5:137-158. Dirmeyer P (2011) The terrestrial segment of soil moisture-climate coupling. Geophys. Res. Lett. 38 (16). doi:10.1029/2011GL048268 Dong X, Zhou Y, Chen H, Zhou B, Sun S (2022) Lag impacts of the anomalous July soil moisture over Southern China on the August rainfall over the Huang–Huai River Basin. Clim Dyn 58:1737–1754. doi:10.1007/s00382-021-05989-1 Eyring V, Bony S, Meehl G, Senior C, Stevens B, Stouffer R, Taylor K (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9:1937–1958. Hans H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut J (2020) The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.146:1999–2049. Hobeichi, S, Abramowitz G, Evans J, Ukkola A (2018) Derived optimal linear combination evapotranspiration (DOLCE): A global gridded synthesis ET estimate. Hydrol. Earth Syst. Sci. 22:1317–1336. Hu W, Ma W, Yang Z, Ma Y, Xie Z (2023) Sensitivity analysis of the Noah-MP land surface model for soil hydrothermal simulations over the Tibetan Plateau. J. Adv. Model. Earth Sy. 15. doi:10.1029/2022MS003136 IGBP, 2000: Global Soil Data Task (IGBP-DIS, ISO-image of CD). International Geosphere-Biosphere Program, PANGAEA. (Available online at: https://doi.org/10.1594/PANGAEA.869912) Jia B, Wang L, Wang Y, Li R, Luo X, Xie J, Xie Z, Chen S, Qin P, Li L, Chen K (2021) CAS-LSM datasets for the CMIP6 land surface snow and soil moisture model intercomparison project. Adv. Atmos. Sci. 38:862-874. doi:10.1007/s00376-021-0293-x Jung M, Koirala S, Weber U, Ichii K, Gans F, Camps-Valls G, Papale D, Schwalm C, Tramontana G, Reichstein M (2019) The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6. doi:10.1038/s41597-019-0076-8 Koster R, Dirmeyer P, Guo Z, Bonan G, Chan E, Gordon C, Kanae S, Kowalczyk E, Lawrence D, Liu P, Lu C, Malyshev S, Mcavaney B, Mitchiell K, Mocko D, Oki T, Oleson K, Pitman A, Sud Y, Taylor C, Verseghy D, Vasic R, Xue Y, Yamada T (2004) Regions of strong coupling between soil moisture and precipitation. Science 305:1138-1140. doi:10.1126/science.1100217 Li L, Yu Y, Tang Y, Lin P, Xie J, Song M, Dong L, Zhou T, Liu L, Wang L, Pu Y, Chen X, Chen L, Xie Z, Liu H, Zhang L, Huang X, Feng T, Zheng W, Xia K, Liu H, Liu J, Wang Y, Wang L, Jia B, Xie F, Wang B, Zhao S, Yu Z, Zhao B, Wei J (2020b) The flexible global ocean-atmosphere-and system model grid-point version 3 (FGOALS-g3): description and evaluation. J. Adv. Model. Earth Sy. 12, e2019MS002012. doi:10.1029/2019MS002012 Li W, Wei N, Huang L, Shang G (2020) Impact of soil datasets on the global simulation of land surface processes. Climatic and Environmental Research (in Chinese) 25:555-574. Liu J, Xie Z (2013) Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach. Hydrol. Earth Syst. Sci. 17. doi:10.5194/hess-17-3355-2013 Nair A, Indu J (2019) Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products. J. Hydrol. 573:406-421. doi:10.1016/j.jhydrol.2019.03.088 Ning Z, Zhang R (2023) A diagnostic study of the influence of early spring soil moisture in southeastern China on interannual variability of the East Asian subtropical summer monsoon onset. J. Meteor. Res. 37:45-57. doi:10.1007/s13351-023-2083-0 Orth R, Seneviratne S (2014) Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe. Climate Dyn. 43:3403-3418. doi:10.1007/s00382-014-2112-x Qiao L, Zuo Z, Xiao D (2022) Evaluation of soil moisture in CMIP6 simulations. J. Clim. 35:779-800. doi:10.1175/JCLI-D-20-0827.1 Sang Y, Ren H, Shi X, Xu X, Chen H (2021) Improvement of soil moisture simulation in Eurasia by the Beijing Climate Center Climate System Model from CMIP5 to CMIP6. Adv. Atmos. Sci. 38:237-252. Shangguan W, Dai Y, Duan Q, Liu B, Yuan H (2014) A global soil data set for earth system modeling. J. Adv. Model. Earth Sy. 6:249-263. Shen W, Lin Z, Qin Z, Li J (2024) Development and preliminary validation of a land surface image assimilation system based on the Common Land Model. Geosci. Model Dev. 17:3447–3465. doi:10.5194/gmd-17-3447-2024 Wang A, Kong X, Chen Y, Ma X (2022) Evaluation of soil moisture in CMIP6 multimodel simulations over conterminous China. J. Geophys. Res. Atmos. 127, e2022JD037072. doi:10.1029/2022JD037072 Wu J, Gao X (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. 56:1102-1111. doi:10.6038/cjg20130406 Xia K, Pu Y, Li L, Wang B (2024) Effects of soil texture datasets on FGOALS-g3 global long-term simulations. SOLA 20:357-364. doi:10.215/sola.2024-047 Xie Z, Liu S, Zeng Y, Gao J, Qin P, Jia B, Xie J, Liu B, Li R, Wang Y, Wang L (2018) A high-resolution land model with groundwater lateral flow, water use and soil freeze–thaw front dynamics and its applications in an endorheic basin. J. Geophys. Res. Atmos. 123:7204–7222. Zeng D, Yuan X (2018) Multiscale land-atmosphere coupling and its application in assessing subseasonal forecasts over East Asia. J. Hydrometeor. 19:745-760. doi:10.1175/JHM-D-17-0215.s1 Zeng J, Yuan X, Ji P, Shi C (2021) Effects of meteorological forcings and land surface model on soil moisture simulation over China. J. Hydrol. 603,126978. doi:10.1016/j.jhydrol.2021.126978 Zhao X, Miao C, Hu J, Su J (2025) Improving land surface model accuracy in soil moisture simulations using parametric schemes and machine learning. J. Hydrol. 657. doi:10.1016/j.jhydrol.2025.133109 Zhong J, Lu B, Wang W, Huang C, Yang Y (2020): Impact of Soil Moisture on Winter 2-m Temperature Forecasts in Northern China. J. Hydrometeorol. 21:597-614. Zhou T, Chen Z, Zou L, Chen X, Yu Y, Wang B, Bao Q, Bao Y, Cao J, He B, Hu S, Li L, Li J, Lin Y, Ma L, Fang Q, Rong X, Song Z, Tang Y, Wu B, Wu T, Xin X, Zhang H, Zhang M (2020) Development of climate and earth system models in China: Past achievements and new CMIP6 results. J. Meteor. Res. 34(1):1-19. doi:10.1007/s13351-020-9164-0 Zuo Z, Zhang R (2016) Influence of soil moisture in eastern China on the East Asian summer monsoon. Adv. Atmos. Sci. 33:151-163. doi:10.1007/s00376-015-5024-8 Additional Declarations No competing interests reported. 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16:13:08","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92164,"visible":true,"origin":"","legend":"","description":"","filename":"4f4cad5ea0ec4935babb456e9ff44f961structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/97e6b9355ddae65afe26033c.xml"},{"id":94783752,"identity":"a8299dcf-e445-4f9f-886c-b4e865f254e6","added_by":"auto","created_at":"2025-10-30 16:13:08","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97317,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/321e2eb408b8ee451502ef70.html"},{"id":94783733,"identity":"a61be906-1fcf-456f-8d2c-51a472d2a7a7","added_by":"auto","created_at":"2025-10-30 16:13:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":262927,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial distribution of annual mean surface soil moisture (units: mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e) at 0-5cm in China for (a) FGOALS-g3 AMIP test results (Test1), (b) ERA5, (c) ESA, and the spatial distribution of the difference in surface soil moisture between (d) Test1 and ERA5, and between (e) Test1 and ESA.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/6ceddfccecfbd7dc89b32b79.png"},{"id":94824645,"identity":"5ee7ed4e-78db-4c7c-9170-95d12e5eead9","added_by":"auto","created_at":"2025-10-31 06:49:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160835,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Seasonal cycle characteristics of surface soil moisture (units: mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e) and (b) time series of surface soil moisture during 1980-2014 over SE (21°N-32°N, 106°E-120°E).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/bfc0ce6e37e3d8787be4d25e.jpeg"},{"id":94825658,"identity":"a5c0bcd2-dcfb-48ef-a541-5a5cc5ee02bb","added_by":"auto","created_at":"2025-10-31 06:50:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140600,"visible":true,"origin":"","legend":"\u003cp\u003eThe climatological annual cycle of surface soil moisture (units: mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e) over the regions (21°N-32°N, 106°E-120°E).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/08ab4f37a411f26a1fb54c4c.png"},{"id":94783734,"identity":"bedc9291-f4aa-4240-b5e4-bcf04ba23c5f","added_by":"auto","created_at":"2025-10-30 16:13:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30277,"visible":true,"origin":"","legend":"\u003cp\u003eThe variation of sand/clay content averaged over SE (21°-32°N, 106°-120°E) with soil depth variation; 1 and 2 indicate the data from the model default and GSDE separately.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/f9d5bddc197fd5a8bcddb599.png"},{"id":94825689,"identity":"64ceb414-4d39-4b2d-ab60-fe5d67a4027b","added_by":"auto","created_at":"2025-10-31 06:50:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":205392,"visible":true,"origin":"","legend":"\u003cp\u003eTCI calculated by using ERA5 and the observations of latent heat flux (a) FluxCom and (b) Dolce, and TCI simulated by (c) Test1 and (d) Test2.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/af0e128e5f539111cceb795c.png"},{"id":98203298,"identity":"771f47fd-833a-432d-bd23-5b731beaf397","added_by":"auto","created_at":"2025-12-15 08:10:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1202014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/0dff4727-5a3d-418e-b03c-d23a7b238065.pdf"},{"id":94825716,"identity":"f1d907c6-2319-4b43-81af-82fee3228dee","added_by":"auto","created_at":"2025-10-31 06:50:37","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":337920,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterialsubmissionSOLANEW.doc","url":"https://assets-eu.researchsquare.com/files/rs-7877631/v1/e2f81c03bcb88f5b22d4590e.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSoil moisture, a key variable governing the exchange of water and energy at the surface, affects Earth\u0026rsquo;s climate system by modulating the surface energy balance and influencing the thermal contrast between land and sea (Berg and Sheffield, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Meanwhile, soil moisture has a \u0026ldquo;memory\u0026rdquo; feature, and its abnormal signal can persist for a long time, making it a crucial indicator in seasonal climate prediction (Orth and Seneviratne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zuo and Zhang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Numerous studies have highlighted the importance of soil moisture in southern China for climate predictions. For instance, Dong et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified a significant negative relationship between the soil moisture in July over southern China and the precipitation in August for the Huang-Huai River Basin (HHR). Specifically, when soil moisture is high in southern China during July, precipitation in HHR tends to decrease significantly in August, and vice versa. Zeng and Yuan (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that southern China is a key zone for soil moisture-atmosphere interaction in August, linked to the subseasonal variations of the East Asian summer monsoon. Furthermore, Ning and Zhang (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that on the interannual timescale, the start date of the East Asian subtropical summer monsoon is significantly correlated with premonsoon soil moisture anomalies in southeastern China.\u003c/p\u003e\u003cp\u003eClimate system models are vital tools for climate prediction (Zhou et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and accurate representation of soil moisture is essential for improving climate predictions. Considerable efforts have focused on improving soil moisture simulation in standalone land surface models, including refinements in parameterization schemes (Hu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), enhanced forcing data (Liu and Xie, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and advanced data assimilation methods (Nair and Indu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, relatively few studies have focused on improving soil moisture simulations within climate system models. Existing evidence indicates that achieving such improvements remains challenging. For instance, increasing model spatial resolution does not consistently lead to better soil moisture simulations, as shown in multi-resolution CMIP6 model comparisons (Qiao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An improved representation of precipitation and soil moisture coupling is essential for better simulations of surface soil moisture in the BCC-CSM2-MR model (Sang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Flexible Global Ocean-Atmosphere-Land System Model, Grid-Point Version 3 (FGOALS-g3) is a major Chinese model contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). The model employs the Chinese Academy of Sciences Land Surface Model (CAS-LSM) as its land component (Xie et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which was developed from the Community Land Model version 4.5 (CLM4.5). When run in offline mode, the standalone CAS-LSM has exhibited a dry bias in soil moisture simulations for the southern China region (Jia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, a systematic evaluation of the soil moisture simulated by FGOALS-g3 in this region is still lacking. Furthermore, Wang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) point out that soil texture representation is a major source of uncertainty in soil moisture simulations across contiguous China. Xia et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that incorporating new soil texture data can markedly reduce biases in the global mean surface soil moisture.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to (1) comprehensively evaluate the performance of FGOALS-g3 in simulating soil moisture over southern China against observational datasets, (2) improve its simulation performance by integrating new soil texture data, and (3) investigate the underlying mechanisms responsible for changes in model performance.\u003c/p\u003e"},{"header":"2 Model, Experiment and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Model and Experimental design\u003c/h2\u003e\n \u003cp\u003eThe FGOALS-g3 model (2\u0026deg; horizontal resolution) is employed in this study. Its four component models (atmosphere, ocean, land, and sea ice) are coupled via a flux coupler, as described by Li et al. (\u003cspan class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Within the FGOALS-g3, mineral soil texture is prescribed as a percentage of sand and clay content and translated into soil hydraulic properties using the pedotransfer functions outlined in Supplementary 1. The model\u0026rsquo;s default soil texture data are derived from the IGBP Global Soil Data Task dataset (IGBP, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), and feature ten soil layers, identical to the vertical configuration of CAS-LSM.\u003c/p\u003e\n \u003cp\u003eThe experiment conforms to the Atmospheric Model Intercomparison Project (AMIP) protocol and is forced by the CMIP6 datasets, which provides monthly prescribed sea surface temperature, sea ice concentrations, solar forcing, and other external forcings (Eyring et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Two tests, referred to as Test1 and Test2, were conducted during 1975\u0026ndash;2014, with the first five years designated as spin-up time; only results from January of 1980 to December 2014 were analyzed. Different soil texture data was employed in each test. Test1 uses the model\u0026rsquo;s default soil texture data, while Test2 incorporates the Global Soil Dataset for Earth System Model (GSDE) soil data (30\u003csup\u003e\u0026rsquo;\u0026rsquo;\u003c/sup\u003e horizontal resolution) (Shangguan et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The GSDE datasets is regarded as more representative of regional soil texture in China, since it uses significantly more soil profile data than the model\u0026rsquo;s default soil texture, and has been widely adopted in land surface model and weather forecast model (Dai et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhong et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although it follows the same vertical rules as the default model soil texture data, it is comprised of two fewer layers: specifically, the first and the tenth soil layers. To apply it, we assign the first layer of the GSDE to the model\u0026rsquo;s first and second soil layers, while the last layer corresponds to the model\u0026rsquo;s ninth and tenth soil layers. Meanwhile, the area-weighting method was employed to interpolate data to a 2-degree spatial resolution (Shangguan et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cspan\u003e\n \u003ch2\u003e\u003cstrong\u003e2.2 Reference Datasets\u003c/strong\u003e\u003c/h2\u003e\n \u003c/span\u003e\n \u003cp\u003eTo minimize the uncertainty of soil moisture data, two datasets were utilized: the combined product from the European Space Agency\u0026rsquo;s Climate Change Initiative (ESA-CCI v06.1) and the ERA5 dataset (Hans et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). ERA5 spans the period 1979-present with a spatial resolution of 0.25\u003csup\u003e∘\u003c/sup\u003e. The soil moisture data in this dataset is categorized into four vertical layers. ESA-CCI has the same spatial resolution as ERA5 and provides daily temporal resolution data from 1978 to 2019. However, as satellite-based soil moisture products, like those from ESA-CCI, can only measure soil moisture in the top 0-5cm, surface soil moisture (0-5cm) (SSM) was selected for validation in this study.\u003c/p\u003e\n \u003cp\u003eThe CN05.1 precipitation dataset, which has a 0.25\u003csup\u003e∘\u003c/sup\u003e resolution and covers the period from 1961 to 2015 with monthly temporal resolution, was developed by interpolating data from more than 2000 gauge stations across China. This dataset was used to validate precipitation simulations (Wu and Gao, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, two sets of gridded energy flux products for the land-atmosphere system were employed to validate the latent heat flux. They were FLUXCOM with a 0.50\u003csup\u003e∘\u003c/sup\u003e resolution during 1979\u0026ndash;2013 (Jung et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and DOLCE v2.1 with a spatial resolution of 0.25\u003csup\u003e∘\u003c/sup\u003e from 1980\u0026ndash;2018 (Hobeichi et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe horizontal resolution of the reference datasets employed here is higher than that of the model, so the calibration data is interpolated to 2\u003csup\u003e∘\u003c/sup\u003e\u0026times;2\u003csup\u003e∘\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results and Discussions","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Evaluation of climatologically annual mean surface soil moisture\u003c/h2\u003e\u003cp\u003eThe spatial correlation coefficient of annual mean SSM over China between the ERA5 and ESACCI datasets is 0.97, indicating a high degree of consistency in their spatial patterns. Both datasets show higher annual mean SSM values in southern and northeastern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). However, the SSM simulated by Test1 exhibits a pervasive dry bias across almost the entire country when compared to either reference dataset. The most pronounced systematic dry bias is concentrated over southeastern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), a result consistent with previous evaluations of CMIP6 historical simulations (Wang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, we focus our subsequent analysis on southeastern China (SE, 21\u0026deg;N\u0026thinsp;~\u0026thinsp;32\u0026deg;N, 106\u0026deg;E\u0026thinsp;~\u0026thinsp;120\u0026deg;E).\u003c/p\u003e\u003cp\u003eWe further evaluate the seasonal and interannual variability of SSM over this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While ERA5 and ESACCI show only minor discrepancies during autumn and winter, their climatological annual cycles are strongly correlated (r\u0026thinsp;=\u0026thinsp;0.94). In contrast, the Test1 simulation exhibits a significant negative bias throughout the entire seasonal cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This dry anomaly is also robustly present at interannual scales. The simulated SSM time series from 1980 to 2014 consistently underestimates the observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe Test1 simulation systematically underestimates SSM over the SE region in terms of spatial distribution, seasonal variations, and interannual variability when validated against observational products.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Improvement of surface soil moisture\u003c/h2\u003e\u003cp\u003eWe observed a significant increase in the simulated SSM over SE by replacing the default soil texture data with GSDE (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean negative bias with ERA5 was reduced from 0.14 to 0.10, representing a decrease of 28.57%. In comparison, the mean negative deviation with ESA-CCI declined from 0.12 to 0.08, indicating a reduction of 33.33%. Meanwhile, the root mean square error (RMSE) decreased by 22.22% and 30.77% separately, when compared to ERA5 and ESA-CCI. Furthermore, the time series variations from 1980 to 2014, as well as the annual cycle of simulated SSM by Test2 over the SE, are closer to the reference data than those from Test1, which shows a significant improvement (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The mechanism behind the improved soil moisture simulation\u003c/h2\u003e\u003cp\u003eMechanistically, the improved SSM simulation with the GSDE dataset results from changes in soil hydraulic properties due to its updated soil texture.\u003c/p\u003e\u003cp\u003eCompared to the model\u0026rsquo;s default dataset, the sand content in GSDE decreased by an average of 41.25% for the entire soil layer across the SE regions, while for the clay content, a slight decrease is found at depths ranging from 0.2m to 1.7m below the surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, at all other depths, clay content increased, although these increases were smaller than the reduction in sand content. As the soil hydraulic parameters used in the CAS-LSM are functions of sand or clay content (see Eq.\u0026nbsp;1-Eq5 in the Supplementary 1), this shift toward a finer texture directly modified key hydraulic properties (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), ultimately enhancing the model\u0026rsquo;s water retention capacity:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe hydraulic parameters averaged over the SE region in Test1 and Test2, calculated based on the parameterization scheme in the Supplementary 1, and ψ is calculated by using the average 0-5cm layer volumetric soil water content.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΨ\u003c/p\u003e\u003cp\u003e(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eksat\u003c/p\u003e\u003cp\u003e(mm/s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eθsat (mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eθ0\u0026ndash;5\u003c/p\u003e\u003cp\u003e(mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eθfc\u003c/p\u003e\u003cp\u003e(mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eθwilt\u003c/p\u003e\u003cp\u003e(mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLH\u003c/p\u003e\u003cp\u003e(W/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-165.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e\u003cp\u003e6.25\u0026times;10-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e45.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-313.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e\u003cp\u003e2.80\u0026times;10-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e49.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(1) Soil hydraulic potential (ψ, unit: mm): It reflects the combined potential energies related to soil water within the soil. It is typically negative, and its absolute value represents soil water suction, which indicates the ability of soil particles to retain moisture. When the sand content decreases from Test1 to Test2, the clay content relatively increases, and the proportion of fine pores in the soil also increases. Consequently, the surface energy and the soil water suction increase, allowing the soil to retain more water in its pores. This is reflected in ψ becoming more negative in Test2, indicating that water is held more tightly within the finer soil pores.\u003c/p\u003e\u003cp\u003e(2) Saturated hydraulic conductivity (k\u003csub\u003esat\u003c/sub\u003e, unit: mm/s): It describes the ease with which the water moves through the pore space. In the model, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003esa\u003c/em\u003et\u003c/sub\u003e decreases as the sand content decreases from Test1 to Test2. Physically, coarse-textured soils primarily consist of large particles with large pores between them, allowing rain or irrigation water to flow easily, resulting in a higher infiltration rate and conductivity. In contrast, fine-textured soils consist primarily of small particles with tiny pores in between, making it difficult for water to move through the soil. This suggests that much more water would be retained in the soil for Test2.\u003c/p\u003e\u003cp\u003e(3) Saturated soil moisture (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003esat\u003c/em\u003e\u003c/sub\u003e): Under the combined effect of the soil water potential and the saturated hydraulic conductivity, it is more beneficial for water to be retained in the soil when soil grain size decreases. So the saturated soil moisture (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003esat\u003c/em\u003e\u003c/sub\u003e) increases.\u003c/p\u003e\u003cp\u003e(4) Field capacity (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003efc\u003c/em\u003e\u003c/sub\u003e) and wilting point (\u003cem\u003eθ\u003c/em\u003e\u003csub\u003e\u003cem\u003ewilt\u003c/em\u003e\u003c/sub\u003e): These parameters reflect the maximum amount of soil water accessible to most plants and the minimal point of soil moisture the plant requires not to wilting, respectively. Both of them in Test2 are larger than it in Test1, consistent with the shift to a finer texture.\u003c/p\u003e\u003cp\u003eThe revised soil texture enhances the soil\u0026rsquo;s ability to retain moisture by increasing water suction and reducing drainage conductivity, which is the fundamental reason for the alleviation of the dry bias of SSM in Test2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Limited impacts on latent heat flux and precipitation\u003c/h2\u003e\u003cp\u003eWe further investigated whether the improved SSM simulation led to better performance in latent heat flux and precipitation.\u003c/p\u003e\u003cp\u003eOur results show that updating the soil texture data resulted in an increase in the simulated annual mean latent heat flux in the SE region, rising from 45.08 to 49.38, an approximate increase of 9% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This adjustment reduced the model\u0026rsquo;s bias against the FluxCom and DOLCE reference products (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-e). In contrast, the impact on precipitation was minimal. Although Test2 simulated a slight increase in summer mean precipitation compared to Test1, a substantial dry bias relative to observations remained (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ef-h). This indicates that improvements in soil moisture did not effectively propagate to enhance moist convection and precipitation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.5 The role of land-atmosphere coupling strength\u003c/h2\u003e\u003cp\u003eThe limited effect of soil moisture improvement on precipitation may be related to land-atmosphere interactions. To explore this, we quantified the land-atmosphere coupling strength using the terrestrial coupling index (TCI) proposed by Dirmeyer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is defined as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(TC{I_{SM - LE}}=\\begin{array}{*{20}{c}} {\\frac{{\\partial (LE)}}{{\\partial (SM)}}\\sigma (LE)=\\frac{{COV(SM,LE)}}{{\\sigma (SM)}}}\u0026amp;{}\u0026amp;{}\u0026amp;{\\begin{array}{*{20}{c}} {}\u0026amp;{}\u0026amp;{}\u0026amp;{\\begin{array}{*{20}{c}} {\\begin{array}{*{20}{c}} {\\begin{array}{*{20}{c}} {}\u0026amp;{}\u0026amp;{} \\end{array}}\u0026amp;{}\u0026amp;{}\u0026amp;{} \\end{array}}\u0026amp;{} \\end{array}} \\end{array}} \\end{array}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere SM is the soil moisture (mm\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e), LE denotes the latent heat flux (W/m\u003csup\u003e2\u003c/sup\u003e), COV is the covariance between SM and LE, and σ(SM) is the standard deviation of soil moisture. Due to the strong land-atmosphere coupling in the boreal warm season, this study focuses on the summer months (June to August) (Koster et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe TCI, derived from summer averaged FluxCom/Dolce and ERA5 observations, consistently exhibits negative values across the SE region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b), identifying it as an area of weak land\u0026ndash;atmosphere coupling where atmospheric processes predominantly control the surface exchanges. This finding is consistent with previous studies (Zeng and Yuan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Observationally, precipitation in this region shows limited sensitivity to soil moisture variations. Superficially, the model simulations appear to capture this weak precipitation response.\u003c/p\u003e\u003cp\u003eHowever, both Test1 and Test2 simulations produce positive TCI values across most of the SE region, suggesting strong land\u0026ndash;atmosphere coupling, characterized by land surface anomalies driving atmospheric processes. This discrepancy reveals a systematic model bias in representing land\u0026ndash;atmosphere interactions. Although the simulated precipitation response to soil moisture is similarly weak, it arises from an inaccurate physical framework rather than a realistic representation of local dynamics. The flawed representation of land\u0026ndash;atmosphere coupling remains a major constraint on improving precipitation simulations in this region, suggesting that it requires future enhancements in boundary layer parameterizations and convective triggering mechanisms.\u003c/p\u003e\u003cp\u003eAlthough updating soil texture reduces biases in SSM and latent heat flux, it doesn\u0026rsquo;t mitigate the model's structural deficiency in accurately representing land-atmosphere interactions across the SE region.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study evaluated the performance of the FGOALS-g3 model configured in an AMIP-style for simulating surface soil moisture (SSM) over southeast China from 1980 to 2014. The model demonstrates a pronounced dry bias in SSM when compared to ERA5 and ESA-CCI. This bias is consistently evident in its spatial distribution of the multi-year mean, the seasonal cycle, and the interannual variability.\u003c/p\u003e\u003cp\u003eThe GSDE dataset substantially reduces this dry bias by replacing the model\u0026rsquo;s default soil texture. The improvement is linked to a transition toward a finer soil texture in GSDE, which is characterized by a notable reduction in sand content (averaging 41.25% across the entire soil layer over the study region) and a slight increase in clay content. These changes in soil texture modify critical soil hydraulic properties, specifically by increasing soil water suction, decreasing saturated hydraulic conductivity, and raising both saturated moisture content and field capacity. Consequently, these changes enhance the soil\u0026rsquo;s water retention capacity.\u003c/p\u003e\u003cp\u003eHowever, the improved SSM has only a limited effect on latent heat flux and a negligible impact on precipitation. This weak response of precipitation to soil moisture changes is consistent with observational evidence that identifies the region as weakly land-atmosphere coupled. Nevertheless, the model erroneously simulates a strong land\u0026ndash;atmosphere coupling regime, indicating a systematic bias in representing interaction mechanisms. This deficiency likely arises from inaccuracies in boundary layer and convective parameterizations, which inhibit realistic soil moisture\u0026ndash;precipitation feedback.\u003c/p\u003e\u003cp\u003eIn summary, adopting more realistic soil texture data enhances the model's capability in simulating soil moisture, but it only partially reduce the systematic dry bias. Refining soil texture alone is insufficient to fully improve the representation of soil moisture or the broader hydrological cycle. Future efforts should focus on improving the model\u0026rsquo;s physical parameterizations, particularly those governing land-atmosphere interactions, to achieve more accurate simulation of regional hydroclimate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (Grant 2022YFC3104804) and the National Natural Science Foundation of China (Grant 42230606).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no financial interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKun Xia worte the main manuscript text and prepared all the figures, Ye Pu makes experiments in this text, and Lijuan Li reviewed the manusript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerg A, Sheffield J (2018) Soil moisture-evapotranspiration coupling in CMIP5 models: Relationship with simulated climate and prejections. J. Climate 31:4865-4878. doi:10.1175/JCLI-D-17-0757.1\u003c/li\u003e\n\u003cli\u003eDai Y, Shangguan W, Wei N, Xin Q, Yuan H, Zhang S, Liu S, Lu X, Wang D, Yan F (2019) A review of the global soil property maps for Earth system models. Soil 5:137-158.\u003c/li\u003e\n\u003cli\u003eDirmeyer P (2011) The terrestrial segment of soil moisture-climate coupling. Geophys. Res. Lett. 38 (16). doi:10.1029/2011GL048268\u003c/li\u003e\n\u003cli\u003eDong X, Zhou Y, Chen H, Zhou B, Sun S (2022) Lag impacts of the anomalous July soil moisture over Southern China on the August rainfall over the Huang\u0026ndash;Huai River Basin. Clim Dyn 58:1737\u0026ndash;1754. doi:10.1007/s00382-021-05989-1\u003c/li\u003e\n\u003cli\u003eEyring V, Bony S, Meehl G, Senior C, Stevens B, Stouffer R, Taylor K (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9:1937\u0026ndash;1958.\u003c/li\u003e\n\u003cli\u003eHans H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R, H\u0026oacute;lm E, Janiskov\u0026aacute; M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I, Vamborg F, Villaume S, Th\u0026eacute;paut J (2020) The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.146:1999\u0026ndash;2049. \u003c/li\u003e\n\u003cli\u003eHobeichi, S, Abramowitz G, Evans J, Ukkola A (2018) Derived optimal linear combination evapotranspiration (DOLCE): A global gridded synthesis ET estimate. Hydrol. Earth Syst. Sci. 22:1317\u0026ndash;1336.\u003c/li\u003e\n\u003cli\u003eHu W, Ma W, Yang Z, Ma Y, Xie Z (2023) Sensitivity analysis of the Noah-MP land surface model for soil hydrothermal simulations over the Tibetan Plateau. J. Adv. Model. Earth Sy. 15. doi:10.1029/2022MS003136\u003c/li\u003e\n\u003cli\u003eIGBP, 2000: Global Soil Data Task (IGBP-DIS, ISO-image of CD). International Geosphere-Biosphere Program, PANGAEA. (Available online at: https://doi.org/10.1594/PANGAEA.869912)\u003c/li\u003e\n\u003cli\u003eJia B, Wang L, Wang Y, Li R, Luo X, Xie J, Xie Z, Chen S, Qin P, Li L, Chen K (2021) CAS-LSM datasets for the CMIP6 land surface snow and soil moisture model intercomparison project. Adv. Atmos. Sci. 38:862-874. doi:10.1007/s00376-021-0293-x\u003c/li\u003e\n\u003cli\u003eJung M, Koirala S, Weber U, Ichii K, Gans F, Camps-Valls G, Papale D, Schwalm C, Tramontana G, Reichstein M (2019) The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6. doi:10.1038/s41597-019-0076-8\u003c/li\u003e\n\u003cli\u003eKoster R, Dirmeyer P, Guo Z, Bonan G, Chan E, Gordon C, Kanae S, Kowalczyk E, Lawrence D, Liu P, Lu C, Malyshev S, Mcavaney B, Mitchiell K, Mocko D, Oki T, Oleson K, Pitman A, Sud Y, Taylor C, Verseghy D, Vasic R, Xue Y, Yamada T (2004) Regions of strong coupling between soil moisture and precipitation. Science 305:1138-1140. doi:10.1126/science.1100217\u003c/li\u003e\n\u003cli\u003eLi L, Yu Y, Tang Y, Lin P, Xie J, Song M, Dong L, Zhou T, Liu L, Wang L, Pu Y, Chen X, Chen L, Xie Z, Liu H, Zhang L, Huang X, Feng T, Zheng W, Xia K, Liu H, Liu J, Wang Y, Wang L, Jia B, Xie F, Wang B, Zhao S, Yu Z, Zhao B, Wei J (2020b) The flexible global ocean-atmosphere-and system model grid-point version 3 (FGOALS-g3): description and evaluation. J. Adv. Model. Earth Sy. 12, e2019MS002012. doi:10.1029/2019MS002012\u003c/li\u003e\n\u003cli\u003eLi W, Wei N, Huang L, Shang G (2020) Impact of soil datasets on the global simulation of land surface processes. Climatic and Environmental Research (in Chinese) 25:555-574.\u003c/li\u003e\n\u003cli\u003eLiu J, Xie Z (2013) Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach.\u003cem\u003e \u003c/em\u003eHydrol. Earth Syst. Sci. 17. doi:10.5194/hess-17-3355-2013 \u003c/li\u003e\n\u003cli\u003eNair A, Indu J (2019) Improvement of land surface model simulations over India via data assimilation of satellite-based soil moisture products. J. Hydrol. 573:406-421. doi:10.1016/j.jhydrol.2019.03.088\u003c/li\u003e\n\u003cli\u003eNing Z, Zhang R (2023) A diagnostic study of the influence of early spring soil moisture in southeastern China on interannual variability of the East Asian subtropical summer monsoon onset. J. Meteor. Res. 37:45-57. doi:10.1007/s13351-023-2083-0\u003c/li\u003e\n\u003cli\u003eOrth R, Seneviratne S (2014) Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe. Climate Dyn. 43:3403-3418. doi:10.1007/s00382-014-2112-x\u003c/li\u003e\n\u003cli\u003eQiao L, Zuo Z, Xiao D (2022) Evaluation of soil moisture in CMIP6 simulations. J. Clim. 35:779-800. doi:10.1175/JCLI-D-20-0827.1\u003c/li\u003e\n\u003cli\u003eSang Y, Ren H, Shi X, Xu X, Chen H (2021) Improvement of soil moisture simulation in Eurasia by the Beijing Climate Center Climate System Model from CMIP5 to CMIP6. Adv. Atmos. Sci. 38:237-252.\u003c/li\u003e\n\u003cli\u003eShangguan W, Dai Y, Duan Q, Liu B, Yuan H (2014) A global soil data set for earth system modeling. J. Adv. Model. Earth Sy. 6:249-263.\u003c/li\u003e\n\u003cli\u003eShen W, Lin Z, Qin Z, Li J (2024) Development and preliminary validation of a land surface image assimilation system based on the Common Land Model. Geosci. Model Dev. 17:3447\u0026ndash;3465. doi:10.5194/gmd-17-3447-2024\u003c/li\u003e\n\u003cli\u003eWang A, Kong X, Chen Y, Ma X (2022) Evaluation of soil moisture in CMIP6 multimodel simulations over conterminous China. J. Geophys. Res. Atmos. 127, e2022JD037072. doi:10.1029/2022JD037072 \u003c/li\u003e\n\u003cli\u003eWu J, Gao X (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. 56:1102-1111. doi:10.6038/cjg20130406\u003c/li\u003e\n\u003cli\u003eXia K, Pu Y, Li L, Wang B (2024) Effects of soil texture datasets on FGOALS-g3 global long-term simulations. SOLA 20:357-364. doi:10.215/sola.2024-047 \u003c/li\u003e\n\u003cli\u003eXie Z, Liu S, Zeng Y, Gao J, Qin P, Jia B, Xie J, Liu B, Li R, Wang Y, Wang L (2018) A high-resolution land model with groundwater lateral flow, water use and soil freeze\u0026ndash;thaw front dynamics and its applications in an endorheic basin. J. Geophys. Res. Atmos. 123:7204\u0026ndash;7222.\u003c/li\u003e\n\u003cli\u003eZeng D, Yuan X (2018) Multiscale land-atmosphere coupling and its application in assessing subseasonal forecasts over East Asia.\u003cem\u003e \u003c/em\u003eJ. Hydrometeor. 19:745-760. doi:10.1175/JHM-D-17-0215.s1\u003c/li\u003e\n\u003cli\u003eZeng J, Yuan X, Ji P, Shi C (2021) Effects of meteorological forcings and land surface model on soil moisture simulation over China. J. Hydrol. 603,126978. doi:10.1016/j.jhydrol.2021.126978\u003c/li\u003e\n\u003cli\u003eZhao X, Miao C, Hu J, Su J (2025) Improving land surface model accuracy in soil moisture simulations using parametric schemes and machine learning. J. Hydrol. 657. doi:10.1016/j.jhydrol.2025.133109\u003c/li\u003e\n\u003cli\u003eZhong J, Lu B, Wang W, Huang C, Yang Y (2020): Impact of Soil Moisture on Winter 2-m Temperature Forecasts in Northern China. J. Hydrometeorol. 21:597-614.\u003c/li\u003e\n\u003cli\u003eZhou T, Chen Z, Zou L, Chen X, Yu Y, Wang B, Bao Q, Bao Y, Cao J, He B, Hu S, Li L, Li J, Lin Y, Ma L, Fang Q, Rong X, Song Z, Tang Y, Wu B, Wu T, Xin X, Zhang H, Zhang M (2020) Development of climate and earth system models in China: Past achievements and new CMIP6 results. J. Meteor. Res. 34(1):1-19. doi:10.1007/s13351-020-9164-0\u003cem\u003e \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eZuo Z, Zhang R (2016) Influence of soil moisture in eastern China on the East Asian summer monsoon. Adv. Atmos. Sci. 33:151-163. doi:10.1007/s00376-015-5024-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Surface soil moisture, Soil texture, Dry bias, Hydraulic properties","lastPublishedDoi":"10.21203/rs.3.rs-7877631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7877631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate soil moisture simulation is essential for understanding regional hydroclimate variability and improving climate predictions. We evaluate the performance of the FGOALS-g3 model under the Atmospheric Model Intercomparison Project (AMIP) configuration in simulating surface soil moisture (SSM) over southeastern China during 1980\u0026ndash;2014. Compared to the ERA5 and ESA-CCI reference datasets, the model exhibits a dry bias in its spatial distribution, seasonal cycle, and interannual variability. Replacing the model\u0026rsquo;s default soil texture with the Global Soil Dataset for Earth System Modeling (GSDE) significantly reduces this bias. GSDE promotes finer soil texture, reducing sand content by 41.25% and slightly increasing clay. This textural shift directly modifies hydraulic properties (such as increasing soil water suction and decreasing hydraulic conductivity), thereby enhancing the soil\u0026rsquo;s water retention capacity and leading to more accurate SSM simulations. However, SSM improvements have only a limited effect on latent heat flux and negligible impacts on precipitation. Although the weak response in precipitation is consistent with observational evidence of weak land\u0026ndash;atmosphere coupling, the model incorrectly simulates strong coupling, indicating systematic biases in interaction mechanisms. These findings highlight that merely refining soil texture data is insufficient; more accurate physical parameterizations of land-atmosphere processes are essential for realistic hydroclimate simulations.\u003c/p\u003e","manuscriptTitle":"Improving Surface Soil Moisture Simulation in FGOALS-g3 over Southeastern China: The Role of Soil Texture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 16:13:03","doi":"10.21203/rs.3.rs-7877631/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7ee4271-a1a7-47a0-8871-8dc6b607bb09","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T08:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 16:13:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7877631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7877631","identity":"rs-7877631","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}