Snow height and air temperature on sea ice from Snow Buoy measurements

Nicolaus, Marcel; Hoppmann, Mario; Arndt, Stefanie; Hendricks, Stefan; Katlein, Christian; König-Langlo, Gert; Nicolaus, Anja; Rossmann, Leonard; Schiller, Martin; Schwegmann, Sandra; Langevin, Danielle; Bartsch, Annekathrin

doi:10.1594/pangaea.875638

Cited by 6 publications

(4 citation statements)

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“…Future work could explore including additional measurements of snow depth and density in the MCMC calibration, such as snow depth retrieved from passive microwave measurements, which can provide daily observations at a 25 km resolution (Brucker & Markus, 2013;Rostosky et al, 2018), and local-scale (∼100 m) in-situ depth and density measurements can provide information when aggregated to the nearest model grid square for each observation site (King et al, 2020). Additional buoy observations, such as those from the Alfred Wegener Institute snow buoys, may also be used (Nicolaus et al, 2017). Recent campaigns such as MOSAiC can also provide useful measurements for such a calibration (Wagner et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Automated Calibration of a Snow‐On‐Sea‐Ice Model

Cabaj

Kushner

Petty

2023

Earth and Space Science

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Snow on Arctic sea ice has many, contrasting effects on ice thickness and extent. Furthermore, estimates of snow depth on Arctic sea ice are a key input for ice thickness estimates from satellite altimeters such as ICESat‐2. Models such as the NASA Eulerian Snow on Sea Ice Model (NESOSIM) have been recently utilized by the sea ice community to provide time‐varying basin‐wide estimates of snow depth and density on Arctic sea ice. NESOSIM is a two‐snow‐layer model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Two free parameters in NESOSIM, which dictate the bulk effect of wind packing (densification) and blowing snow processes, lack direct observational constraints. We present an indirect calibration of these parameters using a Markov Chain Monte Carlo (MCMC) approach. NESOSIM output is calibrated to observations of snow depth from Operation IceBridge and CRREL‐Dartmouth buoys, and density from historical drifting stations. OIB measurements alone are found to more strictly constrain the blowing snow parameter, and including additional observations yields more physically reasonable density estimates. The MCMC‐calibrated model output is further used to estimate sea ice thickness and uncertainty from model parameter uncertainty using ICESat‐2 freeboard measurements. Despite visible differences in density, the change in ice thickness is minimal. We also find that the model is relatively insensitive to parameter variations, and hence, the snow model uncertainty contribution to ice thickness is small compared to the systematic uncertainty from snow in the current ICESat‐2 thickness product.

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Section: Discussionmentioning

confidence: 99%

Automated Calibration of a Snow‐On‐Sea‐Ice Model

Cabaj

Kushner

Petty

2023

Earth and Space Science

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“…MOSAiC, IMB and SIMBA data can be downloaded from https://data.meereisportal.de/ relaunch/buoymosaic.php?lang=en (Alfred Wegener Institute, 2021), http://imb-crrel-dartmouth.org (Perovich et al, 2023) and https://doi.org/10.1594/PANGAEA.938244 (Lei et al, 2021). The AWI data are available at https://doi.org/10.1594/PANGAEA.875638 (Nicolaus et al, 2017). Two AR products are available on the UCLA Dataverse (https://doi.org/10.25346/S6/YO15ON) (Guan, 2022)…”

Section: Data Availability Statementmentioning

confidence: 99%

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

Li,

Ke,

Shen

et al. 2024

Geophysical Research Letters

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The state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

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“…To decide which reanalysis to use for our region, we compared four reanalysis data sets to buoy measurements: ERA5 (Hersbach et al, 2020), the Japanese 55-year Reanalysis (JRA55) (Kobayashi et al, 2015), NCEP Climate Forecast System Version 2 (CFSv2) (Saha et al, 2014), and the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) (Gelaro et al, 2017). The daily snow and automatic weather station buoy measurements (Nicolaus et al, 2017a(Nicolaus et al, , 2017b provide daily sea level pressure (SLP), 2 m air temperature, and relative humidity that we compared to the reanalyses. We selected the buoys that had (a) been deployed in the Weddell Sea and (b) survived at least one austral winter (40 buoys in total, Figure S2 in Supporting Information S1).…”

Section: Turbulent Fluxes From Reanalysesmentioning

confidence: 99%

Sea Ice Production in the 2016 and 2017 Maud Rise Polynyas

2023

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Polynyas are regions of low sea ice concentration (SIC) or thickness where the comparatively warm ocean is exposed to the cold atmosphere. Coastal polynyas are mainly driven by katabatic winds, while sensible heat convection results in open-ocean/offshore polynyas (Morales Maqueda et al., 2004). Morales Maqueda et al. ( 2004) (and references therein) summarized that air-ice-ocean interactions in polynyas could impact: (a) the moisture and heat exchange between atmosphere and ocean and, therefore, the local wind dynamics; (b) the upper ocean properties and vertical flows of dense saline water through brine rejection and buoyancy loss; (c) the biogeochemical fluxes, for example, acting as the sinks of atmospheric CO 2 , between air and sea; and (d) the local ecosystems with extra phytoplankton and zooplankton, serving as "oases" in a polar ocean desert. In the Southern Ocean, open-ocean polynyas rarely occur but can reach a large extent. Such an open-ocean polynya, the Maud Rise polynya, opened in the vicinity of the Maud Rise seamount (centered on 64°S and 3°E)

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Snow height and air temperature on sea ice from Snow Buoy measurements

Cited by 6 publications

References 0 publications

Automated Calibration of a Snow‐On‐Sea‐Ice Model

Automated Calibration of a Snow‐On‐Sea‐Ice Model

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

Sea Ice Production in the 2016 and 2017 Maud Rise Polynyas

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