Greenland ice sheet mass balance from 1840 through next week

The mass of the Greenland ice sheet is declining as mass gain from snowfall is exceeded by mass loss from surface meltwater runoff, marine-terminating glacier calving and submarine melting, and basal melting. Here we use the input/output (IO) method to estimate mass change from 1840 through next wee...

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Bibliographic Details
Main Authors: Mankoff, Kenneth D., Fettweis, Xavier, Langen, Peter L., Stendel, Martin, Kjledsen, Kristian K., Karlsson, Nanna B., Noël, Brice, Broeke, Michiel R., Colgan, Wiliam, Simonsen, Sebastian B., Box, Jason E., Solgaard, Anne, Ahlstrøm, Andreas P., Andersen, Signe Bech, Fausto, Robert S.
Format: Text
Language:English
Published: 2021
Subjects:
Online Access:https://doi.org/10.5194/essd-2021-131
https://essd.copernicus.org/preprints/essd-2021-131/
Description
Summary:The mass of the Greenland ice sheet is declining as mass gain from snowfall is exceeded by mass loss from surface meltwater runoff, marine-terminating glacier calving and submarine melting, and basal melting. Here we use the input/output (IO) method to estimate mass change from 1840 through next week. Mass gains come from three regional climate models (RCMs; HIRHAM/HARMONIE, MAR, and RACMO) and a semi-empirical surface mass balance (SMB) model. Mass losses come from the RCMs, a statistical SMB model, ice discharge at marine terminating glaciers, and ice melted at the base of the ice sheet. From these products we provide an annual estimate of GIS mass balance from 1840 through 1985 and a daily estimate at sector and region scale from 1986 through next week. Compared to other mass balance estimates, this product updates daily, has higher temporal resolution, and is the first IO product to include the basal mass balance which is a source of an additional ~8 % mass loss. Our results demonstrate an accelerating GIS-scale mass loss and general agreement among six other products. Results from this study are available at https://dataverse01.geus.dk/privateurl.xhtml?token=d09976c4-4f89-43ef-8f91-173d269806a4 (Mankoff et al., 2021).