What are we doing?
Welcome to CACEE Lab @ Texas A&M University!
Our group broadly uses data-driven and model-driven approaches to quantify the patterns of element flux and isotope behavior involved in the global carbon and biogeochemical cycles, especially under periods of climatic perturbations.
Extensive data mining, data assimilation, large-scale spatial-temporal statistical analysis, and machine learning are frequently used in our research projects. We hope geo-statistics and machine learning could reveal the intrinsic patterns of nature’s processes that are sometimes extremely difficult to be captured by classical physical process models. With that being said, in areas where data is extremely limited or data-driven approaches are not suitable, numerical modeling (e.g., modeling the global carbon cycle) also serves as a critical tool in our research.
Our group broadly uses data-driven and model-driven approaches to quantify the patterns of element flux and isotope behavior involved in the global carbon and biogeochemical cycles, especially under periods of climatic perturbations.
Extensive data mining, data assimilation, large-scale spatial-temporal statistical analysis, and machine learning are frequently used in our research projects. We hope geo-statistics and machine learning could reveal the intrinsic patterns of nature’s processes that are sometimes extremely difficult to be captured by classical physical process models. With that being said, in areas where data is extremely limited or data-driven approaches are not suitable, numerical modeling (e.g., modeling the global carbon cycle) also serves as a critical tool in our research.
Recent News
2025-07-14
Dr. Shuang Zhang contributes to a new paper in Journal of Atmospheric and Oceanic Technology. This study develops a deep learning–based pipeline to accurately measure active whitecap fractions, overcoming the subjectivity of traditional image-based methods. A new horizon detection algorithm and a U-Net model enable reliable identification of active whitecaps, even under sun glint conditions. Applied to 48 hours of Gulf of Mexico video data, the results reveal greater variability with wind speed than previously reported. Random forest analysis shows that sea surface temperature, swell, and wave age also strongly influence active whitecap formation, with further ANOVA results indicating a positive correlation between sea surface temperature and active whitecap fraction. Yang, X., Potter, H., Zhang, S., Lyu, M. Remote Measurement of Active Whitecaps Using Deep Learning. Journal of Atmospheric and Oceanic Technology. https://doi.org/10.1175/JTECH-D-24-0057.1
Dr. Shuang Zhang contributes to a new paper in Journal of Atmospheric and Oceanic Technology. This study develops a deep learning–based pipeline to accurately measure active whitecap fractions, overcoming the subjectivity of traditional image-based methods. A new horizon detection algorithm and a U-Net model enable reliable identification of active whitecaps, even under sun glint conditions. Applied to 48 hours of Gulf of Mexico video data, the results reveal greater variability with wind speed than previously reported. Random forest analysis shows that sea surface temperature, swell, and wave age also strongly influence active whitecap formation, with further ANOVA results indicating a positive correlation between sea surface temperature and active whitecap fraction. Yang, X., Potter, H., Zhang, S., Lyu, M. Remote Measurement of Active Whitecaps Using Deep Learning. Journal of Atmospheric and Oceanic Technology. https://doi.org/10.1175/JTECH-D-24-0057.1
2025-07-11
Graduate student Yating Li participated in the Community Earth System Model (CESM) Tutorial in Boulder, Colorado from July 7–11. The CESM Tutorial, hosted annually by the NSF National Center for Atmospheric Research, covered CESM fundamentals, model configuration, and data analysis, with financial support available to attendees. For more information, visit here.
Graduate student Yating Li participated in the Community Earth System Model (CESM) Tutorial in Boulder, Colorado from July 7–11. The CESM Tutorial, hosted annually by the NSF National Center for Atmospheric Research, covered CESM fundamentals, model configuration, and data analysis, with financial support available to attendees. For more information, visit here.
2025-06-23
Shihan Li and Dr. Shuang Zhang contributes to a new paper in Proceedings of the National Academy of Sciences (PNAS). This study reconstructs marine redox evolution during the Late Paleozoic Ice Age (310–290 Ma), a time of peak atmospheric O2 and relatively low CO2. Using high-resolution uranium and carbon isotope records from South China, combined with biogeochemical modeling, the authors document repeated CO2-driven episodes of marine anoxia at the 10⁵-year scale. Results suggest that even under oxygen-rich conditions, moderate seafloor anoxia (4–12%) could develop, potentially disrupting marine biodiversity. The work highlights that widespread anoxia can arise under CO2 levels similar to present-day or near-future projections. Chen, J., Li, S., Zhang, S., Isson, T., Dahl, T. W., Planavsky, N. J., et al. (2025). Repeated occurrences of marine anoxia under high atmospheric O2 and icehouse conditions. Proceedings of the National Academy of Sciences, 122(26), e2420505122. https://doi.org/10.1073/pnas.2420505122
Shihan Li and Dr. Shuang Zhang contributes to a new paper in Proceedings of the National Academy of Sciences (PNAS). This study reconstructs marine redox evolution during the Late Paleozoic Ice Age (310–290 Ma), a time of peak atmospheric O2 and relatively low CO2. Using high-resolution uranium and carbon isotope records from South China, combined with biogeochemical modeling, the authors document repeated CO2-driven episodes of marine anoxia at the 10⁵-year scale. Results suggest that even under oxygen-rich conditions, moderate seafloor anoxia (4–12%) could develop, potentially disrupting marine biodiversity. The work highlights that widespread anoxia can arise under CO2 levels similar to present-day or near-future projections. Chen, J., Li, S., Zhang, S., Isson, T., Dahl, T. W., Planavsky, N. J., et al. (2025). Repeated occurrences of marine anoxia under high atmospheric O2 and icehouse conditions. Proceedings of the National Academy of Sciences, 122(26), e2420505122. https://doi.org/10.1073/pnas.2420505122