A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses

Stroeve, Julienne; Liston, Glen E.; Buzzard, Sammie; Zhou, Lu; Mallett, Robbie; Barrett, A. P.; Tschudi, M. A.; Tsamados, Michel; Itkin, Polona; Stewart, J.E.

doi:10.1029/2019jc015900

Cited by 49 publications

(65 citation statements)

References 80 publications

(139 reference statements)

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“…This date was chosen solely because this OIB observation year covered the most area with its flight lines. Late‐winter snow property distributions are similar for the other 37 years (Stroeve et al, 2020); the details are different, but the general conclusions are the same as those revealed by looking at the 1 April 2014 data. Domain‐average quantities from the Figure 2 panels are provided in Table 2.…”

Section: Resultssupporting

confidence: 52%

“…These SnowModel‐LG simulations are validated against snow depth and snow density field observations in Part II of this paper (Stroeve et al, 2020). Part II also compares the model outputs with other Arctic snow‐related data sets, including those from other OIB data sets, passive microwave products, and two climatologies, and analyzes the long‐term trends in the simulated snow properties.…”

Section: Discussionmentioning

confidence: 84%

“…This assimilation used the OIB waveform fitting QuickLook product (GSFC‐NK) described by Kwok et al (2017) (NSIDC, 2016). See Stroeve et al (2020) for additional details.…”

Section: Methodsmentioning

confidence: 99%

“…This paper (Part I) describes the model, the modeling approach, the Lagrangian simulation method, the input data sets, and the general model results. A companion paper (Part II; Stroeve et al, 2020) compared the model outputs with snow depth estimates from field campaigns, ice mass balance buoys, passive microwave remote sensing, and various snow climatologies. Part II also evaluated the snow‐related differences between the two reanalyses, the simulated interannual variability, and the 38‐year trends in key snow properties.…”

Section: Introductionmentioning

confidence: 99%

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A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

et al. 2020

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A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

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Section: Resultssupporting

confidence: 52%

Section: Discussionmentioning

confidence: 84%

“…This assimilation used the OIB waveform fitting QuickLook product (GSFC‐NK) described by Kwok et al (2017) (NSIDC, 2016). See Stroeve et al (2020) for additional details.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

et al. 2020

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“…Data availability. Data are available at the NERC data center (https://doi.org/10.5285/5FB5FBDE-7797-44FA-AFA6-4553B122FDEF, Stroeve et al, 2020b). On 1 January 2023, all MOSAiC data will be made publicly available with a citable DOI in a certified data repository following the FAIR Data Principles.…”

Section: Resultsmentioning

confidence: 99%

Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

et al. 2020

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Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

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Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

Sandven¹,

Spreen

Heygster

et al. 2023

Surv Geophys

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Sea ice monitoring by polar orbiting satellites has been developed over more than four decades and is today one of the most well-established applications of space observations. This article gives an overview of data product development from the first sensors to the state-of-the-art regarding retrieval methods, new products and operational data sets serving climate monitoring as well as daily operational services including ice charting and forecasting. Passive microwave data has the longest history and represents the backbone of global ice monitoring with already more than four decades of consistent observations of ice concentration and extent. Time series of passive microwave data is the primary climate data set to document the sea ice decline in the Arctic. Scatterometer data is a valuable supplement to the passive microwave data, in particular to retrieve ice displacement and distinguish between firstyear and multiyear ice. Radar and laser altimeter data has become the main method to estimate sea ice thickness and thereby fill a gap in the observation of sea ice as an essential climate variable. Data on ice thickness allows estimation of ice volume and masses as well as improvement of the ice forecasts. The use of different altimetric frequencies also makes it possible to measure the depth of the snow covering the ice. Synthetic Aperture Radar (SAR) has become the work horse in operational ice observation on regional scale because high-resolution radar images are delivered year-round in nearly all regions where national ice services produce ice charts. Synthetic Aperture Radar data are also important for sea ice research because the data can be used to observe a number of sea ice processes and phenomena, like ice type development and sea ice dynamics, and thereby contribute to new knowledge about sea ice. The use of sea ice data products in modelling and forecasting services as well as in ice navigation is discussed. Finally, the article describes future plans for new satellites and sensors to be used in sea ice observation.

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A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses

Cited by 49 publications

References 80 publications

A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets

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