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Glacier observational data assimilation for mass balance modelling in Svalbard

Authors

Cao,  Wenxue
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Schmidt,  Louise Steffensen
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Aalstad,  Kristoffer
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Westermann,  Sebastian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Schuler,  Thomas V.
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Cao, W., Schmidt, L. S., Aalstad, K., Westermann, S., Schuler, T. V. (2023): Glacier observational data assimilation for mass balance modelling in Svalbard, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3280


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019747
Abstract
Accurate estimation of glacier mass balance is vital in several fields, including climate change impact assessment and water resource management. However, classical modelling approaches on a regional scale are usually hampered by uncertainties in forcing data, model structure, and parameter values. Data assimilation is a method to incorporate observations into modellings and to effectively reduce the uncertainty of results. So far, it has been rarely used in glacier mass balance modelling. Here, we test different assimilation methods, including ensemble Kalman filters and smoothers, to incorporate albedo derived from MODIS data and stake readings from in-situ mass-balance measurements into an energy balance model applied to Svalbard glaciers. The overall objective is to improve the accuracy of glacier mass balance reconstruction and forecasting in Svalbard by combining observations and models. In a range of experiments, we analyze the performance of different assimilation methods and different observation products. One of the research challenges is to identify the information content of different observations and determine at which level they influence the model behaviour. We compare the prior and posterior states to help disentangle which process or forcing has the most impact on the uncertainty of the model’s results.