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.
more »
« less
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.
more »
« less
- Award ID(s):
- 1749081
- 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
More Like this
-
-
Abstract The Arctic region is experiencing significant changes due to climate change, and the resulting decline in sea ice concentration and extent is already impacting ocean dynamics and exacerbating coastal hazards in the region. In this context, numerical models play a crucial role in simulating the interactions between the ocean, land, sea ice, and atmosphere, thus supporting scientific studies in the region. This research aims to evaluate how different sea ice products with spatial resolutions varying from 2 to 25 km influence a phase averaged spectral wave model results in the Alaskan Arctic under storm conditions. Four events throughout the Fall to Winter seasons in 2019 were utilized to assess the accuracy of wave simulations generated under the dynamic sea ice conditions found in the Arctic. The selected sea ice products used to parameterize the numerical wave model include the National Snow and Ice Data Center (NSIDC) sea ice concentration, the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis (ERA5), the HYbrid Coordinate Ocean Model‐Community Ice CodE (HYCOM‐CICE) system assimilated with Navy Coupled Ocean Data Assimilation (NCODA), and the High‐resolution Ice‐Ocean Modeling and Assimilation System (HIOMAS). The Simulating WAves Nearshore (SWAN) model's accuracy in simulating waves using these sea ice products was evaluated against Sea State Daily Multisensor L3 satellite observations. Results show wave simulations using ERA5 consistently exhibited high correlation with observations, maintaining an accuracy above 0.83 to the observations across all events. Conversely, HIOMAS demonstrated the weakest performance, particularly during the Winter, with the lowest correlation of 0.40 to the observations. Remarkably, ERA5 surpassed all other products by up to 30% in accuracy during the selected storm events, and even when an ensemble was assessed by combining the selected sea ice products, ERA5's individual performance remained unmatched. Our study provides insights for selecting sea ice products under different sea ice conditions for accurately simulating waves and coastal hazards in high latitudes.more » « less
-
Abstract. We conducted a series of perfect model experiments using Icepack, a one-dimensional single-column sea ice model, to assess the potential of data assimilation (DA) to improve predictions of the mean sea ice state through the incorporation of sea ice albedo (SIAL) observations in addition to sea ice concentration (SIC) and sea ice thickness (SIT) observations. One ensemble member is designated as the TRUTH, and synthetic observations drawn from it are assimilated into the remaining ensemble members. DA is carried out using the Data Assimilation Research Testbed (DART) Quantile Conserving Ensemble Filtering Framework (QCEFF), which accounts for the bounded nature of sea ice variables. Icepack ensembles were spun-up for five Arctic locations based on small perturbations to atmospheric forcing. Results show that assimilating SIAL has the potential to improve reanalysis products when concurrently assimilated with the more commonly assimilated observables SIC and SIT at three of the five discrete points examined in the Arctic Ocean, when observational uncertainty in SIAL is reduced below current literature estimates. These findings underscore the value of leveraging existing SIAL observations and expanding their temporal and spatial coverage in the Arctic. Furthermore, the study highlights the critical need to better constrain the observational uncertainty of SIAL. Enhanced observational networks would provide the necessary validation data, enabling more accurate uncertainty characterization and improving sea ice forecasts in a rapidly evolving polar climate.more » « less
-
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 yieldedr2values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had ar20.15 or lower.r2for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.more » « less
-
Abstract The Canada Basin (CB) has seen significant sea‐ice loss in recent decades. We use output from the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System to examine the 1979–2023 evolution of seasonal sea‐ice volume (SIV) changes in the CB partitioned into advective and thermodynamic changes. In winter, some years show net convergence into the region that is of comparable magnitude to the SIV change attributed to sea‐ice growth. In summer, melt/ablation dominates the change each year. In both seasons, 44 year trends in seasonal SIV changes are driven primarily by thermodynamic processes. The inferred thermodynamic growth each year is nearly equal to the inferred melt consistent with SIV at the end of the melt season declining more rapidly than SIV at the end of the growth season. Increased melt season atmospheric heating of the ice‐ocean system over 1979–2023, estimated from ERA5 reanalysis, is consistent with the ice‐albedo feedback. In the growth season, net cumulative atmospheric heat release from the ice‐ocean system shows no trend, suggesting increases in inferred thermodynamic ice growth can be attributed to more rapid growth of thinner ice. In each season, cumulative atmospheric heat input exceeds that required for ice melt/growth resulting in a residual that influences ocean heat content (OHC). Seasonal OHC changes, inferred from ocean observations, are equal to approximately one‐third of this residual, although limited ocean observations leave the total heat budget poorly constrained, highlighting a need for more water column observations.more » « less
An official website of the United States government

