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June 18, 2026Open Access

Extension and sensitivity analysis of the EKS-based inversion method for reconstructing historical ground surface temperatures from permafrost borehole data

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This research aims to improve the reconstruction of historical ground surface temperatures from permafrost borehole data using an enhanced Bayesian inversion method.
Introduced a multiple-likelihood framework using several temperature-depth profiles.
Developed a data-driven prior for temperature histories based on air temperature data.
Conducted a systematic sensitivity analysis on reconstruction results based on measurement noise and profile selection.
Combination of multiple likelihoods and ERA-based prior reduced posterior uncertainty.
Pearson correlation improved the accuracy in reconstructing temporal patterns of ground surface temperature.
RMSE indicates that increasing the noise scale can lead to higher reconstruction errors, affecting absolute accuracy.

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Randomized trial investigates temperature reconstructions from permafrost boreholes, suggesting improved accuracy over time.

Shorif Alamgir (2026) studied this question.

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