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Continuous Gap-filled Atmospheric N2O Record for the Past 800,000 Years Using Machine Learning Techniques: Dataset

Version 2 2025-06-12, 03:42
Version 1 2025-06-05, 02:35
dataset
posted on 2025-06-12, 03:42 authored by Nasrin Salehnia, Eunji ByunEunji Byun, Jinho Ahn, Kajal Kumari

This dataset includes a recent compilation of (1) the published Antarctic ice core greenhouse gas (GHG) records over the last 800,000 years and (2) a simulated continuous gap-filled nitrous oxide (N2O) record over the same period. Existing ice core records contain numerous gaps for the atmospheric N2O variations, particularly during glacial periods, while CO2 and CH4 data have relatively fewer gaps compared to N2O. Machine Learning (ML) models were applied to simulate the continuous time series of N2O based on existing CO2, CH4, and N2O data after carefully aligning their time scales. Several ML algorithms were compared and multiple simulations were iterated to decide the best-performing ML model and outcome for the final simulation of continuous time series. Examining the simulated gap-filled N2O data, we identified millennial-scale N2O variations associated with known climate orbital forcing in different periodicities, which could be a subject for future studies. The simulated records here, combined with existing measurements of N2O concentrations, present a benchmark for future studies to improve our understanding of atmospheric N2O control mechanisms.

Version history:

  • DataSet_20241016.xlsx: Original dataset submitted with the manuscript before peer review.
  • EDC_timescale.xlsx: Revised dataset reflecting changes made during peer review; used for all figures in the published article.
  • AICC2012_timescale.xlsx: Supplementary dataset applying the same gap-filling methods to GHG records, based on the updated AICC2012 timescale.
  • Apply_offset.xlsx: Contains the calculation of the CO₂ offset value used in the analysis.


Funding

The National Research Foundation of Korea (NRF) 2020H1D3A1A04081353

The National Research Foundation of Korea (NRF) 2020M1A5A1110607

The National Research Foundation of Korea (NRF) 2018R1A5A1024958

The National Research Foundation of Korea (NRF) RS-2023-00291696

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