Abstract The accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant ( p < 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (<11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation.
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Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance.
Assimilation of remote-sensing products of sea ice
thickness (SIT) into sea ice–ocean models has been shown to
improve the quality of sea ice forecasts. Key open questions
are whether assimilation of lower-level data products such
as radar freeboard (RFB) can further improve model performance
and what performance gains can be achieved through
joint assimilation of these data products in combination with
a snow depth product. The Arctic Mission Benefit Analysis
system was developed to address this type of question. Using
the quantitative network design (QND) approach, the system
can evaluate, in a mathematically rigorous fashion, the observational
constraints imposed by individual and groups of
data products. We demonstrate the approach by presenting
assessments of the observation impact (added value) of different
Earth observation (EO) products in terms of the uncertainty
reduction in a 4-week forecast of sea ice volume (SIV)
and snow volume (SNV) for three regions along the Northern
Sea Route in May 2015 using a coupled model of the
sea ice–ocean system, specifically the Max Planck Institute
Ocean Model. We assess seven satellite products: three real
products and four hypothetical products. The real products
are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived
from CryoSat-2 by the AlfredWegener Institute. These
are complemented by two hypothetical monthly laser freeboard
(LFB) products with low and high accuracy, as well as
two hypothetical monthly snow depth products with low and
high accuracy.
On the basis of the per-pixel uncertainty ranges provided
with the CryoSat-2 SIT, SIFB, and RFB products, the SIT
and RFB achieve a much better performance for SIV than
the SIFB product. For SNV, the performance of SIT is only
low, the performance of SIFB is higher and the performance
of RFB is yet higher. A hypothetical LFB product with low
accuracy (20 cm uncertainty) falls between SIFB and RFB
in performance for both SIV and SNV. A reduction in the
uncertainty of the LFB product to 2 cm yields a significant
increase in performance.
Combining either of the SIT or freeboard products with
a hypothetical snow depth product achieves a significant
performance increase. The uncertainty in the snow product
matters: a higher-accuracy product achieves an extra performance
gain. Providing spatial and temporal uncertainty correlations
with the EO products would be beneficial not only
for QND assessments, but also for assimilation of the products.
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- Award ID(s):
- 1749081
- NSF-PAR ID:
- 10082161
- Date Published:
- Journal Name:
- The cryosphere
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 1994-0416
- Page Range / eLocation ID:
- 2569-2594
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded
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Abstract. Arctic rain on snow (ROS) deposits liquid water onto existing snowpacks. Upon refreezing, this can form icy crusts at the surface or within the snowpack. By altering radar backscatter and microwave emissivity, ROS over sea ice can influence the accuracy of sea ice variables retrieved from satellite radar altimetry, scatterometers, and passive microwave radiometers. During the Arctic Ocean MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, there was an unprecedented opportunity to observe a ROS event using in situ active and passive microwave instruments similar to those deployed on satellite platforms. During liquid water accumulation in the snowpack from rain and increased melt, there was a 4-fold decrease in radar energy returned at Ku- and Ka-bands. After the snowpack refroze and ice layers formed, this decrease was followed by a 6-fold increase in returned energy. Besides altering the radar backscatter, analysis of the returned waveforms shows the waveform shape changed in response to rain and refreezing. Microwave emissivity at 19 and 89 GHz increased with increasing liquid water content and decreased as the snowpack refroze, yet subsequent ice layers altered the polarization difference. Corresponding analysis of the CryoSat-2 waveform shape and backscatter as well as AMSR2 brightness temperatures further shows that the rain and refreeze were significant enough to impact satellite returns. Our analysis provides the first detailed in situ analysis of the impacts of ROS and subsequent refreezing on both active and passive microwave observations, providing important baseline knowledge for detecting ROS over sea ice and assessing their impacts on satellite-derived sea ice variables.
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Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.more » « less
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Accepted Manuscript1.0
- Journal Article:
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