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<p>Understanding the carbon cycle in Antarctic coastal systems is vital for evaluating the role of polar oceans in regulating atmospheric CO<sub>2</sub> and global climate feedbacks. However, these areas remain poorly sampled and are underrepresented in existing carbon flux models. This dataset offers high-resolution environmental observations collected in February 2025 from surface waters and inland stations in Deception Island, an active volcanic caldera in the South Shetland Islands, Antarctica. It includes measurements of surface seawater <i>p</i>CO<sub>2</sub>, temperature, salinity, wind speed, air temperature, solar radiation, tidal elevation, and seismic signals (long-period and tremor events), along with related spatiotemporal metadata. To enhance direct observations, we applied a data-driven modeling approach using deep learning techniques. A Bidirectional Long Short-Term Memory (Bi-LSTM) neural network was trained on multivariate sequences to estimate seawater <i>p</i>CO<sub>2</sub>, with model performance evaluated through five cross-validation folds. The final datasets contain both measured and Bi-LSTM-estimated <i>p</i>CO<sub>2</sub> values. All data and processing steps adhere to FAIR principles to support research on air-sea gas exchange, remote sensing calibration, and Antarctic carbon system dynamics.</p>

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Integrated meteocean and seismic dataset for AI-based seawater CO2 estimation at Deception Island, Antarctica

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月29日

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  • S. Flecha,
  • S. Amaya-Vías,
  • M. Medina,
  • A. Stips,
  • E. Fraile-Nuez,
  • V. Jiménez-Morales,
  • G. Contreras-Guijarro,
  • M. Díez-Minguito,
  • O. A. Caballero-Martínez,
  • G. Navarro,
  • A. Tovar-Sanchez,
  • I. E. Huertas

摘要

Understanding the carbon cycle in Antarctic coastal systems is vital for evaluating the role of polar oceans in regulating atmospheric CO2 and global climate feedbacks. However, these areas remain poorly sampled and are underrepresented in existing carbon flux models. This dataset offers high-resolution environmental observations collected in February 2025 from surface waters and inland stations in Deception Island, an active volcanic caldera in the South Shetland Islands, Antarctica. It includes measurements of surface seawater pCO2, temperature, salinity, wind speed, air temperature, solar radiation, tidal elevation, and seismic signals (long-period and tremor events), along with related spatiotemporal metadata. To enhance direct observations, we applied a data-driven modeling approach using deep learning techniques. A Bidirectional Long Short-Term Memory (Bi-LSTM) neural network was trained on multivariate sequences to estimate seawater pCO2, with model performance evaluated through five cross-validation folds. The final datasets contain both measured and Bi-LSTM-estimated pCO2 values. All data and processing steps adhere to FAIR principles to support research on air-sea gas exchange, remote sensing calibration, and Antarctic carbon system dynamics.