- 1Southern University of Science and Technology, Shenzhen, China
- 2Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
- 3Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands
- 4Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong, China
- 5Department of Geodesy and Earth Observation, DTU Space—National Space Institute, Technical University of Denmark, Kongens Lyngby, Denmark
- 6National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Boulder, CO, USA
- 7School of Geosciences and Info-Physics, Central South University, Changsha, China
- 8MOE Key Laboratory of Geospace Environment and Geodesy, School of Geodesy and Geomatics, Wuhan University, Wuhan, China
- 9Hubei Luojia Laboratory, Wuhan University, Wuhan, China
- 10Division of Geodetic Science, School of Earth Sciences, Ohio State University, Columbus, OH, USA
- 11School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, China
- 12Department of Geography, University of Liège, Liège, Belgium
- 13Yellow River Research Institute, North China University of Water Resources and Electric Power, Zhengzhou, China
- 14Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, North China University of Water Resources and Electric Power, Zhengzhou, China
- 15Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- 16Research Institute for Land and Space, The Hong Kong Polytechnic University, Hong Kong, China
- 17Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China
- 18State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
- 19College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing, China
- 20Department of Geology and Geophysics, College of Mines and Earth Science, University of Utah, Salt Lake City, UT, USA
For the first time, we apply bedrock elastic deformation data to study meltwater transportation within the Greenland Ice Sheet (GrIS). We consider the vertical component of the deformations extracted from GPS data records acquired by the Greenland GNSS Network (GNET) stations. Data time-series from 22 stations distributed along the entire Greenland coast are analyzed. Various geophysical models are used to eliminate nuisance signals from the data. This concerns, among others, deformation associated with surface mass balance (SMB) processes. To quantify the effect of SMB processes, we use the estimates produced by regional climate models, such as RACMO2.3p2. The residual vertical deformations remaining after the subtraction of nuisance signals are fit to a simple analytic model, which allows us to quantify some parameters associated with buffered water storage (i.e., the temporal storage of meltwater on its way to the ocean). Among others, we quantify the average water storage time per station. We find that the average water storage time in Greenland is about 8 weeks. It is slightly larger along the northeast (9±2 weeks) and west (9±3 weeks) coasts. For the southeast coast, it is roughly halved (4.5±2 weeks). This is likely because the ablation zone in the southeast is relatively narrow and steep. Furthermore, we find that the water runoff estimated by regional climate models may require a down- or up-scaling, with the scaling factors being correlated with summer temperature anomalies. In the warmest summers the required runoff upscaling may reach 20%. Likely explanations are an underestimation of water melt or an overestimation of water retention in the firn (or both). The latter can happen if the model underestimates degradation of firn storage capacity caused by a reduction in the pore space and formation of impermeable ice layers. The finding that current regional climate models may require an adjustment in instances of high summer temperature is highly important in view of the ongoing climate warming. Summer temperatures that are considered high nowadays may become normal in the near future. Our study paves the way for more realistic projections of future GrIS meltwater production and its contribution to global sea level rise. Our results have been recently published in Nature (https://doi.org/10.1038/s41586-024-08096-3).
How to cite: Ran, J., Ditmar, P., van den Broeke, M. R., Liu, L., Klees, R., Khan, S. A., Moon, T., Li, J., Bevis, M., Zhong, M., Fettweis, X., Liu, J., Noël, B., Shum, C., Chen, J., Jiang, L., and Dam, T. V.: Application of bedrock elastic deformation data to study meltwater transportation in Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19370, https://doi.org/10.5194/egusphere-egu25-19370, 2025.
