Displays

Projections of Arctic sea ice area show substantial model spread in CMIP3, CMIP5 and early results from CMIP6. Here we assess how simulated seasonal transitions in Arctic sea ice may be contributing to the large inter-model spread. For this we make use of CMIP6 models, the CESM Large Ensemble and the new Arctic Sea Ice Seasonal Change and Melt/Freeze Climate Indicators satellite dataset. Spring ice loss and fall ice growth can be characterized by various metrics (melt onset, break-up, opening, freeze onset, freeze-up, closing). By assessing numerous metrics of seasonal sea ice transitions, we evaluate a range of ice loss and gain processes in CMIP6 models, as well as biases that may contribute to the large spread in model projections of Arctic sea ice. We show that model biases in seasonal sea ice transitions can compensate for other unrealistic aspects of the sea ice, such as very low ice thickness, resulting in acceptable September sea ice areas for the wrong reasons. Furthermore, we find that the metrics of seasonal sea ice change, while often used interchangeably, are not related to ice area and thickness in the same ways.

How to cite: Smith, A., Jahn, A., and Wang, M.: Seasonal transitions of Arctic sea ice over the satellite era in CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-897, https://doi.org/10.5194/egusphere-egu2020-897, 2020.

The lifetime and thickness of Arctic sea ice have markedly decreased in the recent past. This affects Arctic marine ecosystems and the biological pump, given that sea ice acts as platform and transport medium of marine and atmospheric nutrients. At the same time sea ice reduces light penetration to the Arctic Ocean and restricts ocean/atmosphere exchange. In order to understand the ongoing changes and their implications, reconstructions of source regions and drift trajectories of Arctic sea ice are imperative. Automated ice tracking approaches based on satellite-derived sea-ice motion products (e.g. ICETrack) currently perform well in dense ice fields, but provide limited information at the ice edge or in poorly ice-covered areas. Radiogenic neodymium (Nd) isotopes (ε_Nd) have the potential to serve as a chemical tracer of sea-ice provenance and thus may provide information beyond what can be expected from satellite-based assessments. This potential results from pronounced ε_Nd differences between the distinct marine and riverine sources, which feed the surface waters of the different sea-ice formation regions. We present the first dissolved (< 0.45 µm) Nd isotope and concentration data obtained from optically clean Arctic first- and multi-year sea ice (ice cores) collected from different ice floes across the Fram Strait during the RV POLARSTERN cruise PS85 in 2014. Our data confirm the preservation of the seawater ε_Ndsignatures in sea ice despite low Nd concentrations (on average ~ 6 pmol/kg) resulting from efficient brine rejection. The large range in ε_Nd signatures (~ -10 to -30) mirrors that of surface waters in various parts of the Arctic Ocean, indicating that differences between ice floes but also between various sections in an individual ice core reflect the origin and evolution of the sea ice over time. Most ice cores have ε_Nd signatures of around -10, suggesting that the sea ice was formed in well-mixed waters in the central Arctic Ocean and transported directly to the Fram Strait via the Transpolar Drift. Some ice cores, however, also revealed highly unradiogenic signatures (ε_Nd < ~ -15) in their youngest (bottom) sections, which we attribute to incorporation of meltwater from Greenland into newly grown sea ice layers. Our new approach facilitates the reconstruction of the origin and spatiotemporal evolution of isolated sea-ice floes in the future Arctic.

How to cite: Laukert, G., Bauch, D., Peeken, I., Krumpen, T., Werner, K., Hathorne, E., Gutjahr, M., Kassens, H., and Frank, M.: Dissolved Neodymium Isotopes Trace Origin and Spatiotemporal Evolution of Modern Arctic Sea Ice, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7782, https://doi.org/10.5194/egusphere-egu2020-7782, 2020.

In this study, satellite-derived observations of sea ice concentration, drift, and thickness are combined to provide a climatology and inter-annual monthly variability of the sea ice volume budget over the growth season for the CryoSat-2 period Octobre 2010 to April 2019. This allows the first wintertime observational decomposition of the dynamic (advection and divergence) and thermodynamic drivers of ice volume change.

Dynamic and thermodynamic processes will be separated by applying similar methods to Holland and Kwok (2012), which decomposed the governing equation of ice concentration, C:

[1] ∂C/∂t + ∇.(uC) = f_C - r

where u is ice drift motion, f_C is thermodynamic freezing or melting, and r is the concentration change from mass-conserving mechanical ice redistribution processes which convert ice area to ice thickness, such as ridging and rafting. ∂C/∂t represents ice intensification and ∇.(uC) represents ice flux divergence, the dynamic contribution to ice concentration.

Equation 1 can be rearranged and the dynamic contribution, ∇.(uC), expanded to show the contributions from advection, (-u.∇C) and divergence, (-C∇.u), determining four terms:

[2] ∂C/∂t = -u.∇C - C∇.u + f - r

The governing equation of the volume budget is of the same form but combines the thickness and concentration data:

[3] ∂Ch/∂t = -u.∇Ch - Ch∇.u + f_Ch

where h is the thickness of the ice and the resulting product of the two datasets, Ch, is the effective thickness. If Ch were to be multiplied by the grid cell area this would give the volume, V. It is not necessary to take this step because the area remains constant and does not influence the relative values of the terms. However, when deriving an Arctic-wide climatology spatial integration across the grid cell areas is required. Ridging is taken into account by effective thickness change, therefore, it is not included in the calculations.

The method is replicated using model simulations from the Centre for Climate Observation and Modelling (CPOM)-modified Los Alamos sea-ice model (CICE), providing a test of the model’s ability to calculate the volume budgets but also identifying unrealistic growth regimes in the CryoSat-2 observational datasets. Sensitivity to several observational datasets is performed to provide an estimated uncertainty of the budget calculations.

The observational results show ice gain in the central Arctic is dominated by ice freezing with contributions from convergence. Divergence at the coastlines of the Arctic form an ice sink where freezing generates new ice. Advection is shown to drive ice equatorward and induce melting at the ice edge where ice becomes thermodynamically unstable. The dynamic components are found to grow in influence throughout the growth season.

The 2016/17 winter growth season budget shows reduced thermodynamic intensification and stronger dynamic tendencies which may be in response to thin initial ice and an exceptionally warm winter. Compared to the observed volume budget, the CICE model displays similar patterns of thermodynamic freezing, however, dynamic components in the central Arctic are significantly reduced whilst they are over-amplified at the ice edge.

How to cite: Tsamados, M., Racher, O., Holland, P., Kimura, N., Heorton, H., Feltham, D., Schroeder, D., Ridout, A., and Stroeve, J.: Winter Arctic Sea Ice Volume Budget Decomposition from Satellite Observations and Model Simulations over the CryoSat-2 period (2010-2019), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11615, https://doi.org/10.5194/egusphere-egu2020-11615, 2020.

Melt ponds appear during the Arctic summer on the sea ice cover when meltwater and liquid precipitation collect in the depressions of the ice surface. The albedo of the melt ponds is lower than that of surrounding ice and snow areas. Consequently, the melt ponds are an important factor for the ice-albedo feedback, a mechanism whereby a decrease in albedo results in greater absorption of solar radiation, further ice melt, and lower albedos 

To account for the effect of melt ponds on the climate, several numerical schemes have been introduced for Global Circulation Models. They can be classified into two groups. The first group makes use of an explicit relation to define the aspect ratio of the melt ponds. The scheme of Holland et al. (2012) uses a constant ratio of the melt pond depth to the fraction of sea ice covered by melt ponds. The second group relies on theoretical considerations to deduce the area and volume of the melt ponds. The scheme of Flocco et al. (2012) uses the ice thickness distribution to share the meltwater between the ice categories and determine the melt ponds characteristics.

Despite their complexity, current melt pond schemes fail to agree on the trends in melt pond fraction of sea ice area during the last decades. The disagreement casts doubts on the projected melt pond changes. It also raises questions on the definition of the physical processes governing the melt ponds in the schemes and their sensitivity to atmospheric surface conditions.

In this study, we aim at identifying 1) the conceptual difference of the aspect ratio definition in melt pond schemes; 2) the role of refreezing for melt ponds; 3) the impact of the uncertainties in the atmospheric reanalyses. To address these points, we have run the Louvain-la-Neuve Ice Model (LIM), part of the Nucleus for European Modelling of the Ocean (NEMO) version 3.6 along with two different atmospheric reanalyses as surface forcing sets. We used the reanalyses in association with Holland et al. (2012) and Flocco et al. (2012) melt pond schemes. We selected Holland et al. (2012) pond refreezing formulation for both schemes and tested two different threshold temperatures for refreezing. 

From the experiments, we describe the impact on Arctic sea ice and state the importance of including melt ponds in climate models. We attempt at disentangling the separate effects of the type of melt pond scheme, the refreezing mechanism, and the atmospheric surface forcing method, on the climate. We finally formulate a recommendation on the use of melt ponds in climate models. 

How to cite: Sterlin, J., Fichefet, T., Massonnet, F., Lecomte, O., and Vancoppenolle, M.: Modelling melt ponds in Global Circulation Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21530, https://doi.org/10.5194/egusphere-egu2020-21530, 2020.

In late spring small pools of melt water on the surface of Arctic sea ice begin to grow and coalesce to form large connected labyrinthine ponds. The fractal geometry of these iconic blue patterns is both beautiful to the eye and important to the evolution of sea ice albedo and its role in the climate system. Here we report on recent results in modeling the geometry of Arctic melt ponds. We consider two models, first where pond boundaries are the level curves of random surfaces representing snow topography, and then an Ising model, originally developed a century ago to understand ferromagnetic materials, adapted to describe melt ponds. Our melt pond Ising model requires only one measured input - a length scale from snow topography data. Then energy minimization produces realistic ponds whose sizes and transition in fractal dimension with increasing area agree closely with observations. Finally we examine how the random snow topography influences the evolution of pond fractal geometry and find that the saddle points of the surface play the critical role in transitional behavior.

How to cite: Golden, K., Bowen, B., Ma, Y., Moore, R., Strong, C., and Sudakov, I.: Modeling the geometry of melt ponds on Arctic sea ice, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5812, https://doi.org/10.5194/egusphere-egu2020-5812, 2020.

The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus processes like melt pond drainage, desalination and to some degree heat fluxes between the ocean and the atmosphere. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as it is difficult to measure in the field, observations of sea ice permeability are sparse and vary, even for similar porosity, over orders of magnitude. Here I present progress on this topic in three directions. First, I present results from numerical simulations of the permeability of young sea ice based on 3-d X-ray microtomographic images (XRT). These results provide a relationship between permeability and brine porosity of young columnar sea ice for the porosity range 2 to 25 %. The simulations also show that this ice type is permeable and electrically conducting down to a porosity of 2 %, considerably lower than what has been proposed in previous work. Second, the XRT-based simulations are compared to predictions based on a novel crystal growth modelling approach, finding good agreement. Third, the permeability model provides a relationship between sea ice growth velocity and permeability. Based on this relationshiop interesting aspects of the growth of permeable sea ice can be deduced: The predictions consistently explain observations of the onset of convection from growing sea ice. They also allow for an evaluation of expected permeability changes for a thinning sea ice cover in a warmer climate. As the model is strictly valid for growing and cooling sea ice, the results are mostly relevant for sea ice desalination processes during winter. Modelling permeability of summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a resonable starting point.

How to cite: Maus, S.: Permeability of growing sea ice - observations, modelling and some implications for thinning Arctic sea ice , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5863, https://doi.org/10.5194/egusphere-egu2020-5863, 2020.

The Arctic sea-ice decline is among the most emblematic manifestations of climate change and is occurring before we understand its ecological consequences. We investigated future changes in algal productivity combining a biogeochemical model for sympagic algae with sea-ice drivers from an ensemble of 18 CMIP5 climate models. Model projections indicate quasi-linear physical changes along latitudes but markedly nonlinear response of sympagic algae, with distinct latitudinal patterns. While snow cover thinning explains the advancement of algal blooms below 66°N, narrowing of the biological time windows yields small changes in the 66°N to 74°N band, and shifting of the ice seasons toward more favorable photoperiods drives the increase in algal production above 74°N. These diverse latitudinal responses indicate that the impact of declining sea ice on Arctic sympagic production is both large and complex, with consequent trophic and phenological cascades expected in the rest of the food web.

How to cite: Tedesco, L., Vichi, M., and Scoccimarro, E.: Sea-ice algal phenology in a warmer Arctic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20328, https://doi.org/10.5194/egusphere-egu2020-20328, 2020.

Sea ice exists as individual units of ice called floes. These floes can vary by orders of magnitude in diameter over small spatial scales. They are better described by a floe size distribution (FSD) rather than by a single diameter. Observations of the FSD are frequently fitted to a power law with a negative exponent. Floe size can influence several sea ice processes including the lateral melt rate, momentum exchange between the sea ice, ocean and atmosphere, and sea ice rheology. There have been several recent efforts to develop a model of the floe size distribution to include within sea ice models to improve the representation of floe size beyond a fixed single value. Some of these involve significant approximations about the shape and variability of the distribution whereas others adopt a more prognostic approach that does not restrict the shape of the distribution.

In this study we compare the impacts of two alternative approaches to modelling the FSD within the CICE sea ice model. The first assumes floes follow a power law distribution with a constant exponent. Parameterisations of processes thought to influence the floe size distribution are expressed in terms of a variable FSD tracer. The second uses a prognostic floe size-thickness distribution. The sea ice area in individual floe size categories evolves independently such that the shape of distribution is an emergent behaviour rather than imposed. Here we compare the impact of the two modelling approaches on the thermodynamic evolution of the sea ice. We show that both predict an increase in lateral melt with a compensating reduction in basal melt. We find that the magnitude of this change is highly dependent on the form of the distribution for the smallest floes. We also explore the impact of both FSD models on the momentum exchange of the sea ice and find a large response in the spatial distribution of sea ice volume. Finally, we will discuss whether the results from the prognostic FSD model support the assumptions required to construct the power law derived FSD model.

How to cite: Bateson, A., Feltham, D., Schröder, D., Hosekova, L., Ridley, J., and Aksenov, Y.: A comparison of two alternative approaches to modelling the sea ice floe size distribution. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8546, https://doi.org/10.5194/egusphere-egu2020-8546, 2020.

Retrieval of sea ice depth from satellite altimetry relies on knowledge of snow depth in the conversion of freeboard measurements to sea ice thickness. This remains the largest source of uncertainty in calculating sea ice thickness. In order to go beyond the use of a seasonal snow climatology, namely the one by Warren created from measurements collected during the drifting stations in 1937 and 1954–1991, we have developed as part of an ESA Arctic+ project several novel snow on sea ice pan-Arctic products, with the ultimate goal to resolve for the first time inter-annual and seasonal snow variability.

Our products are inter-compared and calibrated with each other to guarantee multi-decadal continuity, and also compared with other recently developed snow on sea ice modelling and satellite based products. Quality assessment and uncertainty estimates are provided at a gridded level and as a function of sea ice cover characteristics such as sea ice age, and sea ice type.

We investigate the impact of the spatially and temporally varying snow products on current satellite estimates of sea ice thickness and provide an update on the sea ice thickness uncertainties. We pay particular attention to potential biases of the seasonal ice growth and inter-annual trends.

How to cite: Sallila, H., Buzzard, S., Rinne, E., and Tsamados, M.: The impact of snow products on detecting trends in sea ice thickness during the CryoSat-2 era, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18304, https://doi.org/10.5194/egusphere-egu2020-18304, 2020.

The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness.

Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy.

We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional “gradient ratio” method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths.

References

Wever, Nander, et al. "Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model." Geoscientific Model Development 13.1 (2020): 99-119.

Picard, Ghislain, Melody Sandells, and Henning Löwe. "SMRT: An active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0)." Geoscientific Model Development 11.7 (2018): 2763-2788.

Markus, Thorsten, and Donald J. Cavalieri. "Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data." Antarctic sea ice: physical processes, interactions and variability 74 (1998): 19-39.

How to cite: Mallett, R., Stroeve, J., Tsamados, M., and Liston, G.: Towards improving radiometry-derived snow depths with SNOWPACK and SMRT, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19061, https://doi.org/10.5194/egusphere-egu2020-19061, 2020.

In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations.

Large snow depth differences between data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest Spring snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest Spring snow depths. There is no significant trend for mean snow depth among all snow products since the 2000s, however, those in regional varies larhely. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model: NESOSIM, also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. Compared to climatology, snow density from SnowModel-LG is generally denser, whereas that from NESOSIM is less. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology.

Inconsistencies in the reconstructed snow parameters among the products, as well as differences and with in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

How to cite: Zhou, L., Stroeve, J., and Xu, S.: Inter-comparison of snow depth over sea ice from multiple methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19051, https://doi.org/10.5194/egusphere-egu2020-19051, 2020.

Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity, and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. Here, we develop a simple model of pre-melt ice surface topography that accurately describes snow cover on flat, undeformed ice. The model considers a surface that is a sum of randomly sized and placed ``snow dunes'' represented as Gaussian mounds. This model generalizes the "void model" of Popovic et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate. 

How to cite: Popovic, P., Finkel, J., Silber, M., and Abbot, D.: Snow topography on undeformed Arctic sea ice captured by an idealized model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12836, https://doi.org/10.5194/egusphere-egu2020-12836, 2020.

Understanding the distribution of snow depth on Antarctic sea ice is critical to estimating the sea ice thickness distribution from laser altimetry data, such as from Operation IceBridge or ICESat-2.  One major uncertainty in converting laser altimetry data to ice thickness is knowing the proportion of snow within the surface measurement. Snow redistributed by wind collects around areas of deformed ice, but it is not known how different surface morphologies affect this distribution. Here, we apply a textural segmentation algorithm to classify and group similar textures to infer the distribution of snow-ice ratios using snow surface freeboard measurements from Operation IceBridge (OIB) campaigns over the Weddell Sea. We find that texturally-similar regions have similar snow/ice ratios, but not similar snow depth measurements. This allows for the extrapolation of nadir-looking snow radar data using two-dimensional surface altimetry scans, providing a two-dimensional estimate of the snow depth. Using a convolutional neural network on an in-situ dataset, we find that local (~20 m) snow depth and sea ice thickness can be estimated with errors of < 20%, and that the learned convolutional filters imply that different surface morphologies have different proportions of snow/ice within the measured surface elevation. For the OIB data,  we show that at slightly larger scales (~180 m), snow depths can be estimated using the snow surface texture, and that the learned filters are comparable to standard textural segmentation filters. We also examine the statistical variability in the distribution of snow/ice ratios across different years to determine if snow distribution patterns on sea ice exhibit universal behaviour, or have significant interannual variations. These results suggest that surface morphological information can improve remotely-sensed estimates of snow depth, and hence sea ice thickness, compared to current methods. Such methods may be useful for reducing errors in Antarctic sea ice thickness estimates from ICESat-2.

How to cite: Mei, M. J. and Maksym, T.: A textural approach to snow depth distribution on Antarctic sea ice , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1167, https://doi.org/10.5194/egusphere-egu2020-1167, 2020.

The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = −0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53–0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4–23% and 5–8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05–2 m, respectively.

How to cite: Zhao, J., Cheng, B., Vihma, T., Yang, Q., Hui, F., Zhao, B., Hao, G., Shen, H., and Zhang, L.: Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1715, https://doi.org/10.5194/egusphere-egu2020-1715, 2020.

Arctic sea ice has experienced dramatic changes for the past few decades. Recent changes in the properties of Arctic sea ice have posed significant challenges to the research community to provide sea ice predictions. To improve our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere-sea ice-ocean model configured for the Arctic with sufficient flexibility. The Los Alamos sea ice model is coupled with the Weather Research and Forecasting (WRF) Model and the Regional Ocean Modeling System (ROMS) within the Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system. A series of sensitivity experiments with different physics options have been performed to determine the ‘optimal’ physics configuration that provides reasonable simulation of Arctic sea ice.

It is well known that dynamic models used to predict Arctic sea ice at short-term periods strongly depend on model initial conditions. Thus, a data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Parallel Data Assimilation Framework has been implemented into the new modeling system to assimilate SSMIS sea ice concentration, and CyroSat-2 and SMOS sea ice thickness using a localized error subspace transform ensemble Kalman filter (LESTKF). We have conducted Arctic sea ice predictions for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice states show reasonably accurate sea ice evolution and small biases in the minimum sea ice extent.

Storms-induced ocean surface waves are capable of breaking pack ice into smaller floes and changing the sea ice melting rate. We have also coupled the Simulating Wave Nearshore (SWAN) with above atmosphere-sea ice-ocean coupled system and examined the impacts of wave-ice interactions on sea ice simulation. Preliminary results suggest ocean waves have direct and indirect impacts on sea ice. Direct impacts are the fracturing of ice pack and indirect impacts the change of ocean thermo-structure through the wave breaking.

How to cite: Yang, C.-Y., Liu, J., and Xu, S.: A new coupled modeling system developed for Arctic sea ice simulation and prediction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4472, https://doi.org/10.5194/egusphere-egu2020-4472, 2020.

NMEFC provides sea ice services for the CHINARE since 2010, the products in the early stage (before 2017) include satellite-retrieved and numerical forecasts of sea ice concentration. Based on MITgcm and ensemble Kalman Filter data assimilation scheme,  the Arctic Ice-Ocean Prediction System (ArcIOPS v1.0), was established in 2017. ArcIOPS v1.0 assimilates available satellite-retrieved sea ice concentration and thickness data. Sea ice thickness forecasting products from ArcIOPS v1.0 are provided to the CHINARE8, and are believed to have played an important role in the successful passage of R/V XUELONG through the Central Arctic for the first time during the summer of 2017. In 2019, ArcIOPS v1.0 was upgraded to the latest version (ArcIOPS v1.1), which assimilates satellite-retrieved sea ice concentration, sea ice thickness, as well as sea surface temperature (SST) data in ice free areas. Comparison between outputs of the latest version of ArcIOPS and that of its previous version shows that the latest version has a substantial improvement on sea ice concentration forecasts. In the future, with more and more kinds of observations to be assimilated, the high-resolution version of ArcIOPS will be put into operational running and benefit Chinese scientific and commercial activities in the Arctic Ocean.

How to cite: Liang, X., Zhao, F., Li, C., and Zhang, L.: Development and application of NMEFC Arctic Ice-Ocean Prediction System (ArcIOPS) of CHINA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2497, https://doi.org/10.5194/egusphere-egu2020-2497, 2020.

The Arctic climate system is very sensitive to the state of sea ice due to its role in controlling heat and momentum exchanges between the atmosphere and the ocean. However, the representation of sea ice state, its past variability and future projections in modern Earth system models (ESMs) vary widely. One of the reasons for that is strong sensitivity of ESMs to sea ice related varying parameter space. Based on limited observations, those parameters typically have a range of possible values and / or are not constant in space and time, which is a source of model uncertainties.   

The Regional Arctic System Model (RASM) is a limited-domain fully coupled climate model used in this study to investigate sensitivity of sea ice states to limited set of parameters. It includes the atmospheric (Weather Research and Forecasting; WRF) and land hydrology (Variable Infiltration Capacity; VIC) components sharing a 50-km pan-Arctic grid. The sea ice (the version 6.0 of Los Alamos sea ice model, CICE) and ocean (Parallel Ocean Program, POP) components share a 1/12° pan-Arctic grid. In addition, a river routing scheme (RVIC) is used to represent the freshwater flux from land to ocean. All components are coupled at high frequency via the Community Earth System Model (CESM) coupler version CPL7.

We have selected four parameters out of the set evaluated by Urrego-Blanco et al. (2016) and subject to their potential impact on sea ice and coupling across the atmosphere-sea ice-ocean interface. The total of 96 sensitivity simulations have been completed with fully coupled and forced RASM configurations, varying each parameter within its respective acceptable range. Using sea ice volume as a measure of sensitivity, the thermal conductivity of snow (ksno) parameter has produced the most sensitivity, in qualitative agreement with Urrego-Blanco et al. (2016). However, using dynamics related metrics, such as sea ice drift or deformation, other parameters, i.e. controlling the sea ice roughness and frictional energy dissipation, have been shown more important. Finally, different quantitative sensitivities to the same parameter have been diagnosed between fully-coupled and forced RASM simulations, as well as compared to the stand alone sea ice results.

How to cite: Osinski, R., Maslowski, W., Lee, Y., Craig, A., Clement-Kinney, J., and Niciejewski, J.: Sensitivity of Arctic sea ice to variable model parameter space in Regional Arctic System Model simulations., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13039, https://doi.org/10.5194/egusphere-egu2020-13039, 2020.

A sub-grid scale sea ice thickness distribution (ITD) is a key parameterization to enable a large-scale sea ice model to simulate winter ice growth and sea ice ridging processes realistically. Recent sophisticated developments, e.g. a melt pond model, a form drag parameterization, a floe-size distribution model, fundamentally depend on the ITD. In spite of its importance, knowledge is poor about the accuracy of the simulated ITD. Here, we derive the ITD from individual Arctic sea ice thickness estimates available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We bin the CS2 data into 5 ice thickness categories used by the sea ice component CICE of HadGEM3 climate simulations: (1) ice thickness h < 60 cm, (2) 60 cm < h < 1.4 m, (3) 1.4 m < h < 2.4 m, (4) 2.4 m < h < 3.6 m, (5) h > 3.6 m. Our analysis includes historical simulations and future projections with the HadGEM3-GC31 model as well as forced ocean-ice and standalone ice simulations with the same model components NEMO v3.6 and CICE v5.1.2. The most striking difference occurs regarding the annual cycle of area fraction of ice in the thickest category (> 3.6 m). According to CS2, in the Central Arctic the fraction is below 2% in October and increases to 15-40% in April. In contrast the annual cycle is weak in all simulations. The magnitude of the area fraction differs between the simulations. For simulations which agree best with CS2 for grid cell mean ice thickness, the area fraction of thick ice is around 5% constantly throughout the whole year. Potential reasons for the discrepancy are discussed and sensitivity experiments presented to study the impact of sea ice settings on the simulated ITD, e.g. ice strength parameter, parameter for participating in ridging, heat transfer coefficients.

How to cite: Schroeder, D., Feltham, D., and Tsamados, M.: Using CryoSat-2 estimates to analyse sub-grid scale sea ice thickness distribution in HadGEM3 simulations for CMIP6, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18134, https://doi.org/10.5194/egusphere-egu2020-18134, 2020.

Evaluating the performance and consistency of passive microwave (PM) sea ice concentration (SIC) products derived from different algorithms is critical since a good knowledge of the quality of the satellite SIC products is essential for their application and improvement. To comprehensively evaluate the performance of satellite SIC in long time series and the whole polar regions (both Arctic and Antarctic), in the study we examined the spatial and temporal distribution of the discrepancy between four PM satellite SIC products with the ERA-Interim sea ice fraction dataset (ERA SIC) during the period of 2015-2018. The four PM SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2) at a spatial resolution of 12.5 km.

The results show the spatial patterns of PM SIC products are generally in good agreement with ERA SIC. The comparison of monthly and annual SIC shows that the largest bias and root mean square difference (RMSD) for the PM SIC products mainly occur in summer and the marginal ice zone, indicating that there are still many uncertainties in PM SIC products in such period and region. Meanwhile, the daily sea ice extent (SIE) and sea ice area (SIA) derived from the four PM SIC products can generally well reflect the variation trend of SIE and SIA in Arctic and Antarctic. The largest bias of SIE and SIA are above 4×10⁶ km² when the sea ice reaches the maximum and minimum value, and the daily bias of SIE and SIA vary seasonally and regionally, which is mainly concentrated from June to October in Arctic. In general, among the four PM SIC products, the SSMIS/ASI product performs the best compared with ERA SIC though it usually underestimates SIC with a negative bias. The FY3B/NT2 and FY3C/NT2 products show more significant discrepancy with higher RMSD and bias in Arctic and Antarctic compared with the SSMIS/ASI and AMSR2/BT. The AMSR2/BT product performs much better in Antarctic than in Arctic and it always overestimates ERA SIC with a positive bias. The consistency of the four PM products concerning ERA SIC in the Antarctic region is generally superior to that in Arctic region.

How to cite: Liang, S., Zeng, J., and Li, Z.: Comparison of sea ice concentrations from ASI, BT and NT2 algorithms with ERA-Interim dataset in the Arctic and Antarctic regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4531, https://doi.org/10.5194/egusphere-egu2020-4531, 2020.

The floe size distribution (FSD) is important to the physical and biological processes in the marginal ice zone (MIZ). The FSD is controlled by ice advection, thermodynamics (lateral melting), and dynamics (winds, tides, currents and ocean swell). These thermodynamic and dynamic conditions are different between the western Arctic (e.g., Chukchi and Beaufort Seas) and the eastern Arctic (e.g., Fram Strait). For example, the MIZ in the western Arctic is strongly influenced by a warm ocean due to enhanced sea-ice albedo feedback, while the MIZ in the eastern Arctic is strongly influenced by ocean swell. We hypothesise that this regional difference can affect the FSD differently between the two regions. To address the hypothesis, we analysed the FSD data derived the images from MEDEA and synthetic aperture radar (SAR) TerraSAR-X in Chukchi Sea, East Siberian Sea and Fram Strait. Our results show that the FSD in Chukchi Sea the most dynamic as it contains a larger percentage of smaller floes and undergoes a greater interannual variability in the FSD compared to East Siberian Sea and Fram Strait. In particular, the FSD in Chukchi Sea shows a notable change before and after 2012. This change is likely attributed to the severe storm occurred in early August 2012 and the presence of thinner ice in this region.

How to cite: Wang, Y., Hwang, B., Basu, R., and Ren, J.: Regional differences in processes controlling Arctic sea ice floe size distribution in Chukchi Sea, East Siberian and Fram Strait during pre-ponding season , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5208, https://doi.org/10.5194/egusphere-egu2020-5208, 2020.

Current operational sea ice models solve primitive equations on a grid and treat sea ice as a continuum with smoothly varying properties. This is the same method that is used in ocean models. The continuum assumption is unrealistic for sea ice which consists of separate rigid ice floes. The assumption works best for length scales much larger than typical floe size, and worst for very small length scales.

Winter shipping in finnish ports depends on timely sea ice information on the Baltic Sea. Due to climate change, the yearly ice covered area and thermodynamic ice growth are decreasing. However, sea ice is also becoming more mobile and dynamic, especially in the Bay of Bothnia which lies in the north end of the Baltic Sea.

A particle-based granular approach is more realistic in the length scales of individual ice floes. Such models have been developed (eg. by Mark Hopkins and Agnieszka Herman) and used successfully in limited scales, such as fjords. For larger horizontal scales, they have been computationally too expensive. Using modern GPU acceleration techniques, discrete element simulation of sea ice is becoming possible in the scale required for Baltic sea basins.

This work presents an ongoing project for building a granular sea ice model for forecasting ice dynamics. This includes ice movement and deformation and describes ridge and lead formation and similar phenomena. Existing accelerated solvers are examined, and the most suitable is adapted for Baltic sea ice and applied for the Bay of Bothnia.

How to cite: Lehtiranta, J.: Basin-scale granular ice dynamics modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18117, https://doi.org/10.5194/egusphere-egu2020-18117, 2020.

Sea ice, as a rule, is covered with a heat-insulating snow cover, consisting of an ice skeleton and air-saturated pores. However, the temperature difference between the sea and the atmosphere during the cold season provides favorable conditions for macroscopic air movement, which significantly reduces the thermal resistance of snow and, thereby, affects the thermal and dynamic interaction of the atmosphere with the upper layers of the sea.

Actual snow cover accumulating on the surface of sea ice has significant heterogeneity and anisotropy of geometric and thermophysical characteristics conditioned by snow density stratification. Our work is aimed to studying the occurrence of convective instability in a system of two porous layers with permeable common boundary for boundary conditions taking into account the oceanographic aspect of the problem. The analytical solution of the problem in the Darcy-Boussinesq approximation is obtained by the Galerkin method, by selecting approximations of the vertical amplitudes of dimensionless temperature and velocity perturbations that satisfy the boundary conditions of the problem. A qualitative originality of the problem is revealed in comparison with a similar problem for a homogeneous porous layer. It is shown that the stability criteria (critical filtering Rayleigh numbers) due to the difference in the thermophysical and structural properties (coefficients of thermal conductivity, porosity and air permeability) of the layers can significantly differ from each other. According to detailed measurements of the thermal structure and metric characteristics of the fixed snow-ice cover in Amba Bay (Shokalsky Strait, Severnaya Zemlya Archipelago) during Winter 2015-2016, as well as calculations of its thermodynamic evolution, the values and temporal variability of the Rayleigh numbers are estimated. By comparing the observational and modeling data, the reality of the existence of a convective heat transfer regime in the snow cover is revealed. It is concluded that it is necessary to take into account its contribution to the thermal and mass balance of sea ice during winter period.

How to cite: Bogorodskii, P., Kustov, V., and Laurila, T.: Thermal convection of air in a two-layers snow cover of immobile sea ice , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6994, https://doi.org/10.5194/egusphere-egu2020-6994, 2020.

Atmospheric blocking events in the Northern Hemishpere have been related to regional Arctic sea ice decline. During blocking events, pulses of warm and moist air enhance the radiative forcing on the sea ice in winter due to the increased longwave radiation associated with clouds. Several studies have shown that such events are related to regional sea ice concentration decline. Daily sea ice output with the latest version of CICE from the coupled Regional Arctic System model is used to study sea ice tendencies during January-February 2014. In this period there was a follow-up of a Atlantic warm moist air insturion and a Pacific warm moist air intrusion associated with surface air temperature perturbations up to 20 degrees locally.

A decline in sea ice concentration during wintertime does not neccesarily mean that ice melt has occurred. The goal of this case study is to distinguish the sea ice response between a dynamic and a thermodynamic component. In this way, we learn how much of the sea ice is advected into another region during such an event and how much the sea ice is lost due to the enhanced forcing and temperature increase.

How to cite: Dekker, E.: The impact of a warm moist air intrusion on dynamic and thermodynamic sea ice tendencies in the Arctic., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8418, https://doi.org/10.5194/egusphere-egu2020-8418, 2020.

Sea ice is a porous mushy layer composed of ice crystals and interstitial brine. The dense brine tends to sink through the ice, driving convection. Downwelling at the edge of convective cells leads to dissolution of the ice matrix and the development of narrow, entirely liquid brine channels. The channels provide an efficient pathway for drainage of the cold, saline brine into the underlying ocean. This brine rejection provides an important buoyancy forcing for the polar oceans, and causes variation of the internal structure and properties of sea ice on seasonal and shorter timescales. This process is inherently multiscale, with simulations requiring resolution from O(mm) brine-channel scales to O(m) mushy-layer dynamic scales.

We present new, fully 3-dimensional numerical simulations of ice formation and convective brine rejection that model flow through a reactive porous ice matrix with evolving porosity. To accurately resolve the wide range of dynamical scales, our simulations exploit Adaptive Mesh Refinement using the Chombo framework. This allows us to integrate over several months of ice growth, providing insights into mushy-layer dynamics throughout the winter season. The convective desalination of sea ice promotes increased internal solidification, and we find that convective brine drainage is restricted to a narrow porous layer at the ice-ocean interface. This layer evolves as the ice grows thicker over time. Away from this interface, stagnant sea ice consists of a network of previously active brine channels that retain higher solute concentrations than the surrounding ice. We investigate the response of ice growth and brine drainage to varying atmospheric cooling conditions, and consider the potential implications for ice-ocean brine fluxes, nutrient transport, and sea ice ecology.

How to cite: Wells, A., Parkinson, J., Martin, D., and Katz, R.: Three-dimensional convection, phase change, and solute transport in mushy sea ice, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12685, https://doi.org/10.5194/egusphere-egu2020-12685, 2020.

Snow plays an important role in the Arctic climate system, modulating heat transfer in terrestrial and marine environments and controlling feedbacks. Changes in snow depth over Arctic sea ice, particularly in spring, have a strong impact on the surface energy budget, influencing ocean heat loss, ice growth and surface ponding. Snow conditions are sensitive to the phase (solid or liquid) of deposited precipitation. However, variability and potential trends of rain-on snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. Time series of surface observations at Utqiagvik, Alaska, reveal rapid reduction in snow depth linked to late-spring rain-on-snow events. Liquid precipitation is critical in preconditioning and triggering snow ablation through reduction in surface albedo as well as latent heat release determined by rainfall amount, supported by field observations beginning in 2000 and model results. Rainfall was found to accelerate warming and ripening of the snowpack, with even small amounts (such as 0.3mm recorded on 24 May 2017) triggering the transition from the warming phase into the ripening phase. Subsequently, direct heat input drives snowmelt, with water content of the snowpack increasing until meltwater output occurs, with an associated rapid decrease in snow depth. Rainfall during the ripening phase can further raise water content in the snow layer, prompting onset of the meltwater output phase in the snowpack. First spring rainfall in Utqiagvik has been observed to shift to earlier dates since the 1970s, in particular after the mid-1990s. Early melt season rainfall and its fraction of total annual precipitation also exhibit an increasing trend. These changes of precipitation over sea ice may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.

How to cite: Dou, T.: The impacts of liquid precipitation on sea ice surface ablation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20971, https://doi.org/10.5194/egusphere-egu2020-20971, 2020.

Landfast sea ice is a dominant sea ice feature of the Arctic coastal region. As a part of Arctic sea ice cover, landfast ice is an important part of coastal ecosystem, it provides functions as a climate regulator and platform for human activity. Recent changes in sea ice conditions in the Arctic have also affected landfast ice regime. At the same time, industrial interest in the Arctic shelf seas continue to increase. Knowledge on local landfast ice conditions are required to ensure safety of on ice operations and accurate forecasting.  In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.  An automatic sea ice tracking algorithm was applied to the sequential SAR images during the development stage of landfast ice cover. The analysis of resultant time series of sea ice drift allows to classify homogeneous sea ice drift fields and timing of their attachment to the landfast ice. In addition, the drift data allows to locate areas of formation of grounded sea ice accumulation called stamukha. This information сan be useful for local landfast ice stability assessment. The study is supported by the Russian Foundation for Basic Research (RFBR) grant 19-35-60033.

How to cite: Selyuzhenok, V., Demchev, D., and Krumpen, T.: Landfast ice zoning from SAR imagery, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22541, https://doi.org/10.5194/egusphere-egu2020-22541, 2020.

The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science
community. A majority of climate analyses performed to date have used monthly or seasonal data. Here,
however, we analyze daily sea ice data for 1979–2016 using the self-organizing map (SOM) method to further
examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice
variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara
Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more
than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be
associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation
(AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the
Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the
Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave
radiation associated with the advection are consistent with the melt-season sea ice anomalies observed
in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice
on multiple (synoptic, intraseasonal, and interannual) time scales.

How to cite: Yu, L. and Zhong, S.: Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-373, https://doi.org/10.5194/egusphere-egu2020-373, 2020.

Over the past decades, Arctic sea ice has declined in thickness and extent and is shifting toward a seasonal ice regime. These rapid changes have widespread implications for ecological and human activities as well as the global climate, and accurate predictions could benefit a wide range of stakeholders, from local residents to governmental policy makers. However, many aspects of the polar transient climate response remain poorly understood, particularly in regard to the response of Arctic sea ice to increasing atmospheric CO₂ concentration and warming temperatures. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a useful framework for understanding this response, and the participating climate model simulations are a powerful tool for advancing our understanding of present and future changes in the Arctic climate system.

Here we explore the current and future states of Arctic sea ice in the Community Earth System Model version 2 (CESM2), the latest generation of the CESM and NCAR’s contribution to CMIP6. We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the “low-top” configuration with limited chemistry (CESM2-CAM) and the “high-top” configuration with interactive chemistry (CESM2-WACCM). We find that the two experiments show large differences in their simulation of Arctic sea ice over the historical period. The CESM2-CAM winter ice thickness distribution is skewed thin, with an insufficient amount of ice thicker than 3 m. This leads to a lower summer ice extent compared to the CESM2-WACCM and observations. In both experiments, the timing of first ice-free conditions is insensitive to the choice of future emissions scenario (known as the shared socioeconomic pathways, or SSPs, in CMIP6), an alarming result that points to the current vulnerable state of Arctic sea ice. However, if global warming stays below 1.5°C, the probability of an ice-free summer remains low, consistent with other recent studies. By the end of the 21^st century, both experiments exhibit an accelerated decline in winter ice extent under the high emissions scenario (SSP5-8.5), leading to ice-free conditions for up to 8 months and an open-water period of 220 days or more depending on the region. Initial results show that the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying the formation of sea ice and leading to lower winter ice extent. Given that the CESM2 reaches a higher atmospheric CO₂ concentration and thus warmer global and Arctic temperatures by 2100, these results suggest the presence of emerging processes associated with a state of the Arctic climate that has never been sampled before.

How to cite: DeRepentigny, P., Jahn, A., Holland, M., and Smith, A.: Arctic Sea Ice in the Community Earth System Model version 2 (CESM2) over the 20th and 21st Centuries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-576, https://doi.org/10.5194/egusphere-egu2020-576, 2020.

The ice arches that usually develop at the northern and southern ends of Nares Strait play an important role in modulating the export of multi-year sea ice out of the Arctic Ocean.   As a result of global warming, the Arctic Ocean is evolving towards an ice pack that is younger, thinner and more mobile and the fate of its multi-year ice is becoming of increasing interest to both the scientific and policy communities.  Here, we use sea ice motion retrievals derived from Sentinel-1 imagery to report on recent behaviour of these ice arches and the associated ice flux. In addition to the previously identified early collapse of the northern ice arch in May 2017, we report that this arch failed to develop during the winters of 2018 and 2019.  In contrast, we report that the southern ice arch was only present for a short period of time during the winter of 2018.  We also show that the duration of arch formation has decreased over the past 20 years as ice in the region has thinned, while the ice area and volume fluxes have both increased.  These results suggest that a transition is underway towards a state where the formation of these arches will become atypical with a concomitant increase in the export of multi-year ice accelerating the transition towards a younger and thinner Arctic ice pack.

How to cite: Moore, K., Howell, S., Brady, M., Xu, X., and McNeil, K.: Recent behavior of the Nares Strait ice arches: anomalous collapses and enhanced export of multi-year ice from the Arctic Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2425, https://doi.org/10.5194/egusphere-egu2020-2425, 2020.

The ice arches that ring the northern Canadian Arctic Archipelago have historically blocked the inflow of Arctic Ocean sea ice for the majority of the year. However, annual average air temperature in northern Canada has increased by more than 2°C over the past 65+ years and a warmer climate is expected to contribute to the deterioration of these ice arches, which in turn has implications for the overall loss of Arctic Ocean sea ice. We investigated the effect of warming on the Arctic Ocean ice area flux into the Canadian Arctic Archipelago using a 22-year record (1997-2018) of ice exchange derived from RADARSAT-1 and RADARSAT-2 imagery. Results indicated that there has been a significant increase in the amount of Arctic Ocean sea ice (10³ km²/year) entering the northern Canadian Arctic Archipelago over the period of 1997-2018. The increased Arctic Ocean ice area flux was associated with reduced ice arch duration but also with faster (thinner) moving ice and more southern latitude open water leeway as a result of the Canadian Arctic Archipelago’s long-term transition to a younger and thinner ice regime. Remarkably, in 2016, the Arctic Ocean ice area flux into the Canadian Arctic Archipelago (161x10³ km²) was 7 times greater than the 1997-2018 average (23x10³ km²) and almost double the 2007 ice area flux into Nares Strait (87x10³ km²). Indeed, Nares Strait is known to be an important pathway for Arctic Ocean ice loss however, the results of this study suggest that with continued warming, the Canadian Arctic Archipelago may also become a large contributor to Arctic Ocean ice loss.

How to cite: Howell, S. and Brady, M.: Increased Arctic Ocean sea ice loss through the Canadian Arctic Archipelago under a warmer climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2728, https://doi.org/10.5194/egusphere-egu2020-2728, 2020.

Abstract

Arctic sea ice is a critical component in the global climate system. It affects the climate system by radiating incident heat back into space and regulating ocean-atmosphere heat and momentum. Satellite altimetry such as CryoSat-2 serves as the primary approach for observing sea ice thickness. Nevertheless, the thickness retrieval with CryoSat-2 mainly depends on the height of the ice surface above the sea level, which leads to significant uncertainties over thin ice regimes. The sea ice at the north of Greenland is considered one of the oldest and thickest in the Arctic. However, during late February - early March 2018, a polynya formed north to Greenland due to extra strong southern winds. We focus on the retrieval of sea ice thickness and snow conditions with CryoSat-2 and SMOS during the formation of the polynya. Specifically, we investigate the uncertainty of CryoSat-2 and carry out inter- comparison of sea ice thickness retrieval with SMOS and CryoSat-2/SMOS synergy. Besides, further discussion of retrieval with CryoSat-2 is provided for such scenarios where the mélange of thick ice and newly formed thin ice is present.

How to cite: Zhu, W., Zhou, L., and Xu, S.: Sea Ice in the Greenland Polynya in 2018 - A Study with CryoSat-2 and SMOS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5495, https://doi.org/10.5194/egusphere-egu2020-5495, 2020.

The landfast ice (LFI) is an important component of the Arctic environment, especially in regions of shallow shelfs North of Alaska and Siberia. Its presence affects the transfer of energy between the atmosphere and the ocean. Its outer edge continuously interacts with the moving pack ice. One of the mechanisms of LFI formation – grounded ice keels, acting as anchor points – was parametrized in the version 6 of Los Alamos sea ice model (CICE) Consortium.  The parametrization is based on the bathymetry data, ice concentration and the mean ice thickness in a grid cell. It enables determination of the critical thickness, required for large ice keels to reach the bottom and calculation of the basal stress. A series of experiments using the Regional Arctic System Model (RASM) with CICEv6 has been conducted. In addition to sea ice model, RASM includes the atmosphere (WRF), ocean (POP), land hydrology (VIC), and river routing scheme (RVIC) components controlled by a flux coupler (CPL). LFI simulations using two different rheologies: elastic-visous-plast (EVP) and elastic-anisotropic-plastic (EAP) have been evaluated in the fully coupled and forced sea ice - ocean configurations.  Also, sensitivity studies with varying values of the LFI free parameters have been performed. Results are compared against landfast ice extent data from the National Snow & Ice Data Center. In the optimal configuration, including the basal stress parameterization, the model reproduces observed landfast ice in East Siberian, Laptev Sea, and along the coast of Alaska. However, some areas continue to be problematic – like the Kara Sea where LFI is underestimated and the area around the New Siberian Islands, where landfast ice growth is too high. In the former case, the ice arching might be the major landfast ice formation mechanism there, whereas in the latter case the model internal stress distribution might not be adequate to allow realistic sea ice drift between the islands.

How to cite: Niciejewski, J., Osinski, R., Maslowski, W., and Craig, A.: Evaluation of landfast ice simulations with basal stress parameterization using the Regional Arctic System Model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8086, https://doi.org/10.5194/egusphere-egu2020-8086, 2020.

Snow on Arctic sea ice plays multiple—and sometimes contrasting—roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.

In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat’s near-polar orbit enables it to make measurements at latitudes up to 82°N, with a 16-day repeat cycle, over the time period from 2006-2016.

We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

How to cite: Cabaj, A., Kushner, P., Petty, A., Howell, S., and Fletcher, C.: Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11436, https://doi.org/10.5194/egusphere-egu2020-11436, 2020.