Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7893-2026
https://doi.org/10.5194/gmd-19-7893-2026
Development and technical paper
 | 
25 Aug 2026
Development and technical paper |  | 25 Aug 2026

Paleoclimate data assimilation with adaptive observation error inflation and adaptive localization

Ge Luo, Yuefei Zeng, Feng Zhu, and Jiuwei Zhao

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Cited articles

Anderson, J. L.: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter, Physica D, 230, 99–111, 2007. a, b
Anderson, J. L.: Localization and sampling error correction in ensemble Kalman filter data assimilation, Mon. Weather Rev., 140, 2359–2371, 2012. a, b
Bishop, C. and Hodyss, D.: Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models, Tellus A, 61, 84–96, 2009a. a, b
Bishop, C. and Hodyss, D.: Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere, Tellus A, 61, 97–111, 2009b. a, b
Brady, E., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J., Otto-Bliesner, B. L., Tabor, C., Tomas, R., Wong, T., Zhang, J., and Zhu, J.: The connected isotopic water cycle in the Community Earth System Model version 1, J. Adv. Model. Earth Sy., 11, 2547–2566, 2019. a
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Reconstructing past temperatures from sparse coral records is challenging. We developed two adaptive methods to improve how model simulations are combined with proxy data. The first adjusts trust in each record based on model consistency; the second adapts how far each record influences the reconstruction. Our approach improved accuracy and better captured El Niño–Southern Oscillation patterns, especially in data‑poor regions. These strategies enable more reliable climate reconstructions
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