Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K‐Transect (2000–2014). Issue 17 (31st August 2021)

• Record Type: Journal Article
• Authors: Navari, Mahdi; Margulis, Steven A.; Tedesco, Marco; Fettweis, Xavier; van de Wal, Roderik S. W.
• Is Part Of: Geophysical research letters. Volume 48:Issue 17(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-31
• Subjects: Greenland ice sheet -- surface mass balance -- data assimilation -- particle batch smoother -- K‐transect stations; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL094602
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea‐level rise. Given the limitations of in‐situ measurements, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS surface mass balance (SMB). This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow model Crocus and a 16‐day albedo product derived from satellite remote sensing data. Comparison of the results against in‐situ SMB measurements shows that the assimilation of the albedo product reduces the root mean square error of the posterior estimates of SMB by 51% and reduces bias by 95%. Plain Language Summary: Greenland ice sheet (GrIS) is losing mass through ice discharge from outlet glaciers and surface processes (e.g., meltwater runoff, sublimation, and evaporation). Recent studies suggest that meltwater runoff will be the dominant mass loss process over the GrIS in the future as it will increase under climate warming. Accurate estimates of the GrIS surface mass balance (SMB) are a critical objective, which, despite its importance, continues to contain large uncertainties from significant errors in forcing data as well as model errors. This work uses a data assimilation framework (which has not been used in estimation of the GrIS SMB) and a satellite‐derived 16‐day albedo product to produce a reanalysis estimates of SMB along the Kangerlussuaq transect (K‐transect) stations in west Greenland. We used the K‐transect in‐situ SMB measurements to validate our results over the 2000–2014 hydrological year. The data assimilation technique (i.e., particle batch smoother) reduces the spatial root‐mean‐square error of SMB over the K‐transect stations by 51% from 858 millimeter water equivalent (mmWE) to 423 mmWE and the bias in the estimates by 95%, from −70 to 3.5 mmWE. It was shown that this methodology has the potential to resolve the spatial variability of the surface processes along the K‐transect stations and in particular of the bare ice surface albedo that is not resolved by the model at a resolution of 25 km (i.e., the model uses a constant bare ice albedo). The results suggest that the methodology can be applied over the entire GrIS using MODIS albedo observations to generate an improved reanalysis of SMB estimates. Key Points: A data assimilation method was used to generate a reanalysis estimates of the surface mass balance of the Greenland ice sheet along the K‐transect stations A particle batch smoother technique was used to condition the prior estimates of surface mass balance on 16‐day MODIS albedo Results show that the assimilation of albedo reduces the root mean square error of the surface mass balance estimates by 51%