Snowmelt processes on Antarctic sea ice observed by satellite scatterometers

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. While there is strong surface melt on Arctic sea ice, snowmelt on Antarctic sea ice is weak with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perenni...

Full description

Bibliographic Details
Main Authors: Arndt, Stefanie, Haas, Christian
Format: Conference Object
Language:unknown
Published: 2018
Subjects:
Online Access:https://epic.awi.de/id/eprint/48745/
https://epic.awi.de/id/eprint/48745/1/201806_polar2018_scatterometer_poster_sarndt.pdf
https://hdl.handle.net/10013/epic.5688ca25-a43d-4241-b9a9-0c662335ef86
https://hdl.handle.net/
Description
Summary:Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. While there is strong surface melt on Arctic sea ice, snowmelt on Antarctic sea ice is weak with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with average pre-melt and snowmelt onset dates of 29 November and 10 December, respectively, without any significant trends over the study period. Related to different signal frequencies, we show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. As different signal frequencies result in different penetration depths, we hypothesize that the different sensors respond to typical snowmelt processes in different depths within the snow cover.