Sea Ice Model Research Articles

Improving snow depth simulations on Arctic Sea ice by assimilating a passive microwave-derived record

The snow depth can modulate sea ice changes and is a necessary input parameter to obtain altimeter-derived sea ice thickness values. First, we determine the optimal model parameters for simulating snow depth based on a two-layer snow depth model. Then, we use the particle filter (PF) approach to assimilate satellite-derived snow depth observations to improve the snow depth simulations. Next, we generate daily snow depth estimates from 2012 to 2020 at a 50-km resolution. With the use of three airborne Operation IceBridge (OIB) datasets (i.e., the NSIDC OIB quick look product, NSIDC OIB L4 product and NOAA OIB product), this work reveals that compared to the original simulated snow depths, the snow depth estimates are improved, with root mean square error (RMSE) decreases of 0.8 cm (12%), 1.3 cm (22%) and 1.1 cm (15%), respectively, corresponding to the three OIB products. With the use of Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) snow buoy and Snow and Ice Mass Balance Array (SIMBA) buoy data, it can be verified that the application of PF improves snow depth simulations in the Central Arctic. The variations in the monthly and seasonal snow depth estimates retrieved from the proposed method agree well with those in the estimates retrieved from two other existing algorithms. Based on the presented snow depth estimates, we can perform long-term snow depth and sea ice analysis. Snow depth estimates improve the understanding of Arctic environmental change and promote the future development of sea ice models.

Modeling sea ice albedo and transmittance measurements with a fully-coupled radiative transfer model.

A rigorous treatment of the sea ice medium has been incorporated in the advanced Coupled Ocean-Atmosphere Radiative Transfer (COART) model. The inherent optical properties (IOPs) of brine pockets and air bubbles over the 0.25-4.0 µm spectral region are parameterized as a function of the sea ice physical properties (temperature, salinity and density). We then test the performance of the upgraded COART model using three physically-based modeling approaches to simulate the spectral albedo and transmittance of sea ice, and compare them with measurements collected during the Impacts of Climate on the Ecosystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaigns. The observations are adequately simulated when at least three layers are used to represent bare ice, including a thin surface scattering layer (SSL), and two layers to represent ponded ice. Treating the SSL as a low-density ice layer yields better model-observation agreement than treating it as a snow-like layer. Sensitivity results indicate that air volume (which determines the ice density) has the largest impact on the simulated fluxes. The vertical profile of density drives the optical properties but available measurements are scarce. The approach where the scattering coefficient for the bubbles is inferred in lieu of density leads to essentially equivalent modeling results. For ponded ice, the albedo and transmittance in the visible are mainly determined by the optical properties of the ice underlying the water layer. Possible contamination from light-absorbing impurities, such as black carbon or ice algae, is also implemented in the model and is able to effectively reduce the albedo and transmittance in the visible spectrum to further improve the model-observation agreement.

Evaluation of Vertical Profiles and Atmospheric Boundary Layer Structure Using the Regional Climate Model CCLM during MOSAiC

Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.

Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

In the last decades, the Arctic climate has changed dramatically, with the loss of multiyear sea ice one of the clearest consequences. These changes have occurred on relatively rapid timescales, and both accurate short-term Arctic prediction (e.g., 10 days to three months) and climate projection of future Arctic scenarios present ongoing challenges. Here we describe a representation of the Arctic ocean and sea ice in a ultrahigh resolution simulation in which the horizontal grid mesh reduces from 8 km at the equator to 2 km at the poles (UH8to2) for the years 2017–2020. We find the simulation reproduces observed distributions of seasonal sea-ice thickness and concentration realistically, although concentration is biased low in the spring and summer and low biases in thickness are found in the central and eastern basins in the fall. Volume, fresh water, and heat transports through key passages are realistic, lying within observationally determined ranges. Climatological comparisons reveal that the UH8to2 Atlantic Water is shallower, warmer, and saltier than the World Ocean Atlas 2018 climatology for 2005–2017 in the eastern basin. Our analysis suggests that these biases, combined with a lack of stratification in the upper 100 m of the simulated ocean, contribute to the winter biases in modeled sea ice thickness. This relationship between biases in the sea ice and ocean points to a potential positive feedback within the model, illuminating challenges for long term model predictive power in a changing Arctic climate.

Analysis of ecologically relevant sea ice and ocean variables for the Southern Ocean using a high-resolution model to inform ecosystem studies

Southern Ocean organisms are uniquely adapted to the extreme environmental conditions that characterise this region, making them especially vulnerable to climate change. Alterations to the physical environment have already been linked to alterations in the structure and functioning of entire ecosystems, and ecological disruptions are expected to continue to occur. Although our understanding of the physical processes driving ecological change in the Southern Ocean has improved in recent years, significant knowledge gaps remain largely as a result of insufficient observational data being available. High resolution ocean models are an important tool that can help us overcome data scarcity. However, models generally contain biases that may affect their ability to accurately represent environmental conditions in the region of interest. Thus, their outputs must be evaluated to understand if and how model outputs can be used to answer questions about ecological impacts. Here, we examined the suitability of ACCESS-OM2-01, a high-resolution coupled ocean–sea ice model, for ecological applications. Our results highlight the heterogeneous nature of the mean state of the environmental variables examined and their trends across the Southern Ocean. Our assessment shows that the ACCESS-OM2-01 model simulated well the observed seasonal cycle and broad baseline climatological conditions of the mixed layer depth and sea ice variables for the Southern Ocean over the past 50 years. However, the model performance varies across regions and at seasonal time scales: the model simulated much deeper winter mixed layer depth in the Weddell Sea and within the Antarctic Circumpolar Current, and the climatological sea-ice concentration for summer was lower in the model compared to available observations. Finally, we provide detailed Python-based scripts with all the code and steps for this analysis, which can be used as reference material to reapply elsewhere and evaluate the suitability of other model outputs for ecological applications.

Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.

Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models

Abstract. Atmospheric reanalyses are valuable datasets for driving ocean–sea ice general circulation models and for proposing multidecadal reconstructions of the ocean–sea ice system in polar regions. However, these reanalyses exhibit biases in these regions. It was previously found that the representation of Arctic and Antarctic sea ice in models participating in the Ocean Model Intercomparison Project Phase 2 (OMIP2, using the updated Japanese 55-year atmospheric reanalysis, JRA55-do) was significantly more realistic than in OMIP1 (forced by the atmospheric state from the Coordinated Ocean-ice Reference Experiments version 2, CORE-II). To understand why, we study the sea ice concentration budget and its relations to surface heat and momentum fluxes as well as the connections between the simulated ice drift and the ice concentration, the ice thickness and the wind stress in a subset of three models (CMCC-CM2-SR5, MRI-ESM2-0 and NorESM2-LM). These three models are representative of the ensemble and are the only ones to provide the surface fluxes and the tendencies of ice concentrations attributed to dynamic and thermodynamic processes required for the ice concentration budget analysis. The sea ice simulations of two other models (EC-Earth3 and MIROC6) forced by both CORE-II and JRA55-do reanalysis are also included in the analysis. It is found that negative summer biases in high-ice-concentration regions and positive biases in the Canadian Arctic Archipelago (CAA) and central Weddell Sea (CWS) regions are reduced from OMIP1 to OMIP2 due to surface heat flux changes. Net shortwave radiation fluxes provide key improvements in the Arctic interior, CAA and CWS regions. There is also an influence of improved surface wind stress in OMIP2 giving better winter Antarctic ice concentration and the Arctic ice drift magnitude simulations near the ice edge. The ice velocity direction simulations in the Beaufort Gyre and the Pacific and Atlantic sectors of the Southern Ocean in OMIP2 are also improved owing to surface wind stress changes. This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations. Our findings suggest that attention should be paid to the radiation fluxes and winds in atmospheric reanalyses in polar regions.

Time periodic solutions to Hibler’s sea ice model

It is shown that the viscous-plastic Hibler sea ice model admits a unique, strong T-time periodic solution provided the given T-periodic forcing functions are small in suitable norms. This is in particular true for time periodic wind forces and time periodic ice growth rates.

Modelling the evolution of Arctic multiyear sea ice over 2000–2018

Abstract. Multiyear sea ice (MYI) cover in the Arctic has been monitored for decades using increasingly sophisticated remote sensing techniques, and these have documented a significant decline in MYI over time. However, such techniques are unable to differentiate between the processes affecting the evolution of the MYI. Further, estimating the thickness and thus the volume of MYI remains challenging. In this study we employ a sea ice–ocean model to investigate the changes to MYI over the period 2000–2018. We exploit the Lagrangian framework of the sea ice model to introduce a new method of tracking MYI area and volume which is based on identifying MYI during freeze onset each autumn. The model is found to successfully reproduce the spatial distribution and evolution of observed MYI extent. We discuss the balance of the processes (melt, ridging, export, and replenishment) linked to the general decline in MYI cover. The model suggests that rather than one process dominating the losses, there is an episodic imbalance between the different sources and sinks of MYI. We identify those key to the significant observed declines in 2007 and 2012; while melt and replenishment are important in 2012, sea ice dynamics play a significant role in 2007. Notably, the model suggests that in years such as 2007, convergence of the ice, through ridging, can result in large reductions in MYI area without a corresponding loss of MYI volume. This highlights the benefit of using models alongside satellite observations to aid interpretation of the observed MYI evolution in the Arctic.

Improving numerical accuracy for the viscous-plastic formulation of sea ice

Accurate modeling of sea ice dynamics is critical for predicting environmental variables and is important in applications such as navigating ice breaker ships, and as such there have been numerous investigations on modeling sea ice dynamics. The 1979 viscous-plastic (VP) sea ice model introduced by W.D. Hibler remains the most widely accepted. Due to its highly nonlinear features, the Hibler model is intrinsically challenging for computational solvers. This study therefore focuses on improving the numerical accuracy of the VP sea ice model. Since the poor convergence observed in existing numerical simulations stems from the nonlinear nature of the VP formulation, this investigation proposes using the celebrated weighted essentially non-oscillatory (WENO) scheme – as opposed to the frequently employed centered difference (CD) scheme – for the spatial derivatives in the VP sea ice model. We then proceed to numerically demonstrate that WENO yields higher-order convergence for smooth solutions, and that furthermore it is able to resolve the discontinuities in the sharp features of sea ice covers – something that is not possible using CD methods. Finally, our proposed framework introduces a potential function method that utilizes the phase field method that naturally incorporates the physical restrictions of ice thickness and ice concentration in transport equations, resulting in modified transport equations which include additional forcing terms. Our method does not require post-processing, thereby avoiding the possible introduction of discontinuities and corresponding negative impact on the solution behavior. Numerical experiments are provided to demonstrate the efficacy of our new methodology.

Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020

Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve state estimates and consequent forecasts. We interface the deterministic ensemble Kalman filter (DEnKF) to the Lagrangian neXt generation Sea Ice Model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble–DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble–DA. As a solution, we perform the DEnKF analysis on a fixed and regular reference mesh, on which model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea ice observations on the model's forecast skills of the Arctic sea ice by comparing satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvements of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. For most of the winter, the correlation between SIT and SIC is weaker, which results in little cross-inference between the two variables in the assimilation step. Overall, the ensemble–DA system based on the stand-alone sea ice model demonstrates the feasibility of winter Arctic sea ice prediction with good computational efficiency. These results open the path toward operational implementation and the extension to multi-year assimilation.

The Deep Arctic Ocean and Fram Strait in CMIP6 Models

Abstract Arctic sea ice loss has become a symbol of ongoing climate change, yet climate models still struggle to reproduce it accurately, let alone predict it. A reason for this is the increasingly clear role of the ocean, especially that of the “Atlantic layer,” on sea ice processes. We here quantify biases in that Atlantic layer and the Arctic Ocean deeper layers in 14 representative models that participated in phase 6 of the Climate Model Intercomparison Project. Compared to observational climatologies and hydrographic profiles, the modeled Atlantic layer core is on average too cold by −0.4°C and too deep by 400 m in the Nansen Basin. The Atlantic layer is too thick, extending to the seafloor in some models. Deep and bottom waters are in contrast too warm by 1.1° and 1.2°C. Furthermore, the modeled properties hardly change throughout the Arctic. We attribute these biases to an inaccurate representation of shelf processes: only three models seem to produce dense water overflows, at too few locations, and these do not sink deep enough. No model compensates with open ocean deep convection. Therefore, the properties are set by the inaccurate volume fluxes through Fram Strait, biased low by up to 6 Sv (1 Sv ≡ 106 m3 s−1), but coupled to a too-warm Fram Strait, resulting in a somewhat accurate heat inflow. These fluxes are related to biases in the Nordic seas, themselves previously attributed to inaccurate sea ice extent and atmospheric modes of variability, thus highlighting the need for overall improvements in the different model components and their coupling. Significance Statement Coupled climate models are routinely used for climate change projection and adaptation, but they are only as good as the data used to create them. And in the deep Arctic, those data are scarce. We determine how biased 14 of the most recent models are regarding the deep Arctic Ocean and the Arctic’s only deep gateway, Fram Strait (between Greenland and Svalbard). These models are very biased: too cold where they should be warm, too warm where they should be cold, not stratified enough, not in contact with the surface as they should, moving the wrong way around the Arctic, etc. Some problems are induced by biases in regions outside of the Arctic and/or from the sea ice models.

Ocean–Ice Sheet Coupling in the Totten Glacier Area, East Antarctica: Analysis of the Feedbacks and Their Response to a Sudden Ocean Warming

We coupled together high-resolution versions of the ocean–sea ice model NEMO and the ice sheet model BISICLES configured to the Totten Glacier area and ran a series of simulations over the recent past (1995–2014) and under warming conditions (2081–2100; SSP4-4.5) with NEMO in stand-alone mode and with the coupled model to assess the effects of the coupling. During the recent past, the ocean–ice sheet coupling has increased the time-averaged value of the basal melt rate in both the Totten and Moscow University ice shelf cavities by 6.7% and 14.2%, respectively. The relationship between the changes in ice shelf thickness and ice shelf basal melt rate suggests that the effect of the coupling is not a linear response to the melt rate but rather a more complex response, driven partly by the dynamical component of the ice sheet model. The response of the ice sheet–ocean coupling due to the ocean warming is a 10% and 3% basal melt rate decrease in the Totten and Moscow University ice shelf cavities, respectively. This indicates that the ocean–ice sheet coupling under climate warming conditions dampens the basal melt rates. Our study highlights the importance of incorporating ocean–ice sheet coupling in climate simulations, even over short time periods.

Linking scales of sea ice surface topography: evaluation of ICESat-2 measurements with coincident helicopter laser scanning during MOSAiC

Abstract. Information about sea ice surface topography and related deformation is crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), part of the National Aeronautics and Space Administration (NASA) Earth Observing System, has been on orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of sea ice surface topography are required. During the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS), achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution centimeter-scale measurements of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography are represented in different ICESat-2 products as well as how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here, we compare the ALS measurements with ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small, narrow, and only partly open cracks that appear to be overlooked by ATL07.

Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

Abstract. In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness, will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen Fjord (Svalbard), we observe the spatial clustering of icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial seismic sources, we were able to derive a power law of icequake energy and to relate this energy to the size of the cracks that generate the icequakes.

Effects of including the adjoint sea ice rheology on estimating Arctic Ocean–sea ice state

Abstract. The adjoint assimilation method has been applied to coupled ocean and sea ice models for sensitivity studies and Arctic state estimations. However, the accuracy of the adjoint model is degraded by simplifications of the adjoint of the sea ice model, especially the adjoint sea ice rheologies. As part of ongoing developments in coupled ocean and sea ice estimation systems, we incorporate and approximate the adjoint of viscous-plastic sea ice dynamics (adjoint-VP) and compare it with the adjoint of free-drift sea ice dynamics (adjoint-FD) through assimilation experiments. Using the adjoint-VP results in a further cost reduction of 7.9 % in comparison to adjoint-FD, with noticeable improvements in the ocean temperature over the open water and the intermediate layers of the Arctic Ocean. Adjoint-VP adjusts the model input more efficiently than adjoint-FD does by involving different sea ice retreat processes. For instance, adjoint-FD melts the sea ice up to 1.0 m in the marginal seas from May to June by overadjusting air temperature (>8 ∘C); adjoint-VP reproduces the sea ice retreat with smaller adjustments to the atmospheric state within their prior uncertainty range. These developments of the adjoint model here lay the foundation for further improving Arctic Ocean and sea ice estimations by comprehensively adjusting the initial conditions, atmospheric forcings, and parameters of the model.

Human-induced changes in the global meridional overturning circulation are emerging from the Southern Ocean

In a warming climate, the Global Meridional Overturning Circulation (GMOC) is expected to change significantly with a risk of disrupting the global redistribution of ocean properties that sustains marine ecosystems, carbon cycle, and others. Here we make a novel attempt to utilize a diagnostic ocean & sea-ice model to estimate the GMOC and its interdecadal changes since the mid-1950s that are consistent with historical hydrographic observations. We find that significant changes in the GMOC have already occurred, most notably in the upper and lower overturning cells in the Southern Ocean. The former has expanded poleward and into denser water and strengthened by 3–4 Sv since the mid-1970s, while the latter has contracted and weakened by a similar rate during the same period. These changes are driven by the increasing Southern Hemisphere (SH) Ferrel cell and associated increases in the westerlies and the surface buoyancy loss over its sinking branch, and the increasing Antarctic meltwater discharge, in response to ozone depletion in the SH stratosphere and increasing atmospheric CO2. A large-scale readjustment of the GMOC seems to be underway in the South Atlantic and Indo-Pacific Oceans since the mid-2000s in response to the Southern Ocean changes.

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

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

Light Under Arctic Sea Ice in Observations and Earth System Models

AbstractThe intensity and spectrum of light under Arctic sea ice, key to the energy budget and primary productivity of the Arctic Ocean, are tedious to observe. Earth System Models (ESMs) are instrumental in understanding the large‐scale properties and impacts of under‐ice light. To date, however, ESM parameterizations of radiative transfer have been evaluated with a few observations only. From observational programs conducted over the past decade at four locations in the Northern Hemisphere sea ice zone, 349 observational records of under‐ice light and coincident environmental characteristics were compiled. This data set was used to evaluate seven ESM parameterizations. Snow depth, melt pond presence and, to some extent, ice thickness explain the observed variance in light intensity, in agreement with previous work. The effects of Chlorophyll‐a are also detected, with rather low intensity. The spectral distribution of under‐ice light largely differs from typical open ocean spectra but weakly varies among the 349 records except for a weak effect of snow depth on the blue light contribution. Most parameterizations considered reproduce variations in under‐ice light intensity. Large errors remain for individual records, on average by a factor of ∼3, however. Skill largely improves if more predictors are considered (snow and ponds in particular). Residual errors are attributed to missing physics in the parametrizations, inconsistencies in the model‐observation comparison protocol, and measurement errors. We provide recommendations to improve the representation of light under sea ice in the ice‐ocean model NEMO, which may also apply to other ESMs and help improve next‐generation ESMs.

Impact of Cyclonic Wind Anomalies Caused by Massive Winter Sea Ice Retreat in the Barents Sea on Atlantic Water Transport Toward the Arctic: A Model Study

AbstractThe Arctic is warming much faster than the global average. This is known as Arctic Amplification and is caused by feedbacks in the local climate system. In this study, we explore a previously proposed hypothesis that an associated wind feedback in the Barents Sea could play an important role by increasing the warm water inflow into the Barents Sea. We find that the strong recent decrease in Barents Sea winter sea ice cover causes enhanced ocean‐atmosphere heat flux and a local air temperature increase, thus a reduction in sea level pressure and a local cyclonic wind anomaly with eastward winds in the Barents Sea Opening. By investigating various reanalysis products and performing high‐resolution perturbation experiments with the ocean and sea ice model FESOM2.1, we studied the impact of cyclonic atmospheric circulation changes on the warm Atlantic Water import into the Arctic via the Barents Sea and Fram Strait. We found that the observed wind changes do not significantly affect the warm water transport into the Barents Sea, which rejects the wind‐feedback hypothesis. At the same time, the cyclonic wind anomalies in the Barents Sea increase the amount of Atlantic Water recirculating westwards in Fram Strait by a downslope shift of the West Spitsbergen Current, and thus reduce Atlantic Water reaching the Arctic basin via Fram Strait. The resulting warm‐water anomaly in the Greenland Sea Gyre drives a local anticyclonic circulation anomaly.