Abstract's details
Snow depth on sea ice from altimetry for 2013-2018 Arctic and Austral winters
CoAuthors
Event: 2019 Ocean Surface Topography Science Team Meeting
Session: Science IV: Altimetry for Cryosphere and Hydrology
Presentation type: Type Oral
Contribution: PDF file
Abstract:
Snow depth at the top of sea ice is a key parameter of the climate change
due to its isolation and albedo properties, which condition the sea ice
growth and melt, the heat absorption and the primary production under the
ice. Moreover, for several reasons that will be described in this
presentation, the lack of knowledge about the snow depth can impact sea ice
thickness retrieval using altimetry with an error that can reach up to 100%
in the worst cases.
Nevertheless, there exists currently no reliable snow depth product over
Arctic and Austral sea ice. Indeed, the Warren (1999) climatology,
frequently used to convert freeboard to sea ice thickness, has been built
with data obtained decades ago, before the first impacts of the climate
changes, and the meteorological re-analyses fail to faithfully reproduce
snow falls in polar regions.
A study published in [Guerreiro et al., 2016] has shown that the difference
of the scattering properties of the Ku-band and the Ka-band can provide a
good proxy of the snow depth using an adapted processing chain, Alti Snow
Depth (ASD). As a first demonstrator, the ASD chain has been applied on the
Ka measurements, issued from Saral/AltiKa, a French-Indian satellite, and
on the corresponding Ku measurements, issued from the European satellite
altimeter CryoSat-2/SIRAL. These two altimeters are based on different
technics and the ratio between the surface areas illuminated by each radar
is about one order of magnitude.
In order to make the AltiKa LRM measurements comparable to the CryoSat-2
SAR measurements, these last ones have been pre-processed by the CNES in a
degraded PLRM mode. The evaluation of the ASD product in regards with the
Operation Ice Bridge snow radar data shows a good correlation (R=0.67),
much better than all previous solutions. Nevertheless the results were
limited to two winter campaigns.
This talk will present the results for the period extended to the five
CryoSat-2/Saral common years (2013-2018) and to Austral sea ice, the GOP
product dedicated to ocean from ESA. Their performance in regards with
several in-situ datasets will be analysed.
Acknowledgement: this work has been supported by the CryoSeaNICE ESA
project
due to its isolation and albedo properties, which condition the sea ice
growth and melt, the heat absorption and the primary production under the
ice. Moreover, for several reasons that will be described in this
presentation, the lack of knowledge about the snow depth can impact sea ice
thickness retrieval using altimetry with an error that can reach up to 100%
in the worst cases.
Nevertheless, there exists currently no reliable snow depth product over
Arctic and Austral sea ice. Indeed, the Warren (1999) climatology,
frequently used to convert freeboard to sea ice thickness, has been built
with data obtained decades ago, before the first impacts of the climate
changes, and the meteorological re-analyses fail to faithfully reproduce
snow falls in polar regions.
A study published in [Guerreiro et al., 2016] has shown that the difference
of the scattering properties of the Ku-band and the Ka-band can provide a
good proxy of the snow depth using an adapted processing chain, Alti Snow
Depth (ASD). As a first demonstrator, the ASD chain has been applied on the
Ka measurements, issued from Saral/AltiKa, a French-Indian satellite, and
on the corresponding Ku measurements, issued from the European satellite
altimeter CryoSat-2/SIRAL. These two altimeters are based on different
technics and the ratio between the surface areas illuminated by each radar
is about one order of magnitude.
In order to make the AltiKa LRM measurements comparable to the CryoSat-2
SAR measurements, these last ones have been pre-processed by the CNES in a
degraded PLRM mode. The evaluation of the ASD product in regards with the
Operation Ice Bridge snow radar data shows a good correlation (R=0.67),
much better than all previous solutions. Nevertheless the results were
limited to two winter campaigns.
This talk will present the results for the period extended to the five
CryoSat-2/Saral common years (2013-2018) and to Austral sea ice, the GOP
product dedicated to ocean from ESA. Their performance in regards with
several in-situ datasets will be analysed.
Acknowledgement: this work has been supported by the CryoSeaNICE ESA
project