Understanding changes in glacier mass balances is essential for investigating climate changes. However, glacier-wide mass balances determined from geodetic observations do not provide a relevant climatic signal as they depend on the dynamic response of the glaciers. In situ point mass balance measurements provide a direct signal but show a strong spatial variability that is difficult to assess from heterogeneous in situ measurements over several decades. To address this issue, we propose a nonlinear statistical model that takes into account the spatial and temporal changes in point mass balances. To test this model, we selected four glaciers in different climatic regimes (France, Bolivia, India, and Norway) for which detailed point annual mass balance measurements were available over a large elevation range. The model extracted a robust and consistent signal for each glacier. We obtained explained variances of 87.5, 90.2, 91.3, and 75.5% on Argentière, Zongo, Chhota Shigri, and Nigardsbreen glaciers, respectively. The standard deviations of the model residuals are close to measurement uncertainties. The model can also be used to detect measurement errors. Combined with geodetic data, this method can provide a consistent glacier-wide annual mass balance series from a heterogeneous network. This model, available to the whole community, can be used to assess the impact of climate change in different regions of the world from long-term mass balance series.
Bibliographical noteFunding Information:
This study was funded by Observatoire des Sciences de l'Univers de Grenoble (OSUG), Institut de Recherche pour le Dévelppement (IRD), and Institut des Sciences de l'Univers (INSU) in the framework of the French “GLACIOCLIM (Les GLACIers un Observatoire du CLIMat)” project. The monitoring of Nigardsbreen was funded by the Statkraft AS and data were made available to NVE. The monitoring of Chhota Shigri glacier was funded by Department of Science and Technology (DST), Government of India, IFCPAR/CEFIPRA project 3900-W1, and INDICE project funded by the Norwegian Research Council (2013 to 2015). M. F. A. acknowledges the research grant from INSPIRE Faculty award (IFA-14-EAS-22) from DST (India). This work has been also supported by INDICE funded by the Norwegian Research Council from 2013 to 2015. Data and model are available via public repository. The full process and documentation to access this repository can be found at the website of the GLACIOCLIM program (https://glacioclim.osug.fr; data access) as well as a tutorial on how to use the executable codes. E. B. acknowledges support from the French Space Agency (CNES) through the TOSCA program and the Programme National de Télédétection Spatiale through PNTS-2016-01. R. B. thanks the Centro de Estudios Científicos (CECs) funded by the Base Financing Program of CONICYT-Chile. We thank the Editor in Chief B. Hubbard, Associate Editor O. Sergienko, and three anonymous reviewers whose thorough comments and suggestions improved the quality of the manuscript.
©2018. The Authors.