Sea Ice Data Research Articles

Comparative Analysis of Sea Ice Between the Antarctic and the Arctic

Polar regions have a profound impact on global climate change, because polar regions are one of the world’s largest sources of cold. Sea ice is the main influencing factor in the polar regions, which has a great influence on the heat transfer, which in turn affects the global climate system. In the past two decades, Arctic sea ice has decreased year by year, while Antarctic sea ice has increased. The use of passive satellite remote sensing data for long-term observation and analysis of polar sea ice will provide an important theoretical basis for studying global climate change and an important data source for polar sea ice research. This study uses the data of the Antarctic and Arctic sea ice density data from 1998 to 2018 to compare and analyze the changes in time and space to find out the changes of the sea ice in the south and Arctic. Using the SSM / I data of the US National Defense Meteorological Satellite and the NASA TEAM algorithm, the sea ice data from 1998 to 2018 was compared and analyzed. The specific process is to use the NASA TEAM algorithm to invert the Arctic and Arctic sea ice, obtain multi-year ice, one-year ice, and overall sea ice density data, and then determine the sea ice area. Through comparative analysis, it is found that the area of Antarctic sea ice is significantly larger than that of the North Pole, and the change trend of the two is similar. The downward trend is greater than that of the South Pole; the total annual ice volume of the South Pole is greater than that of the North Pole, both of which have an upward trend, and the Arctic has a greater increase; the overall sea ice has fallen, and the multi-year ice has decreased while the annual ice has increased. Sea ice has a tendency to transform from multi-year ice to one-year ice; Antarctic multi-year ice is mainly distributed in the Weddell Sea, with stable areas accounting for 60% of the multi-year ice area; Antarctica is dominated by one-year ice, and one-year ice stable area accounts for one 80.1% of the total annual ice area; Arctic multi-year ice mainly exists in the central area of the Arctic Ocean. The Arctic multi-year ice stable area only accounts for 35.06%; the Arctic one-year ice is mainly distributed around the multi-year ice periphery, and the stable existence area accounts for the total annual ice area 48.5%.

Spatial and Temporal Variations of Arctic Sea Ice during 1992-2021

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. The arctic is a region in transition to a warmer climate and one of the most visible signs of that change is in the declining sea ice cover. Sea ice in the arctic is one of the most rapidly changing components of the global climate system. The extent of area covered by arctic sea ice is an important indicator of changes in global climate because warmer air and water temperatures are reducing the amount of sea ice present. Sea ice extent plays an important role in reflecting sunlight back into space, regulating ocean and air temperatures, circulating ocean water, maintaining animal habitats. Similarly, sea ice concentration helps to determine the number of other important climate variables. In this paper, the sea ice data from different institutes were used to analyze the spatial and temporal variation of arctic sea ice concentration (SIC) and sea ice extent (SIE) over the 30-year period 1992-2021. During this period, the results shows that these two variables were increased in the arctic during 2021 due to the decrease of temperatures particularly near Chukchi, Kara, Beaufort, East Siberian and Greenland Seas.

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.

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

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

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Optimized sea ice simulation in MITgcm-ECCO2 forced by ERA5

Coupled ice–ocean models require an accurate performance of sea ice dynamics and thermodynamics to realistically simulate sea ice circulation in the polar regions. However, atmospheric surface variables from reanalysis products that force the models differ in spatial and temporal resolutions, and there are also substantial inconsistencies in the distributions and magnitudes of these variables between the products. Therefore, several key parameters in the models need to be adjusted for the models forced by different atmospheric reanalysis products. This study compares the sea ice properties simulated by the previously optimized MITgcm-ECCO2 model forced by JRA25 and forced by the recently released ERA5. The comparisons with high-resolution satellite sea ice data reveal that the simulations forced by these two datasets significantly overestimate the Arctic sea ice concentration in summer, whereas the simulation forced by ERA5 underestimates the winter Antarctic sea ice. It is likely that the downward longwave radiation and wind stress regime in ERA5 is responsible for the underestimated Antarctic sea ice cover. We then optimize MITgcm-ECCO2 model forced by ERA5 by using Green functions. We consider six key parameters: sea ice strength, ice-atmospheric drag coefficient, ice–ocean drag coefficient, ice and snow albedos, and sea ice salinity. The optimized simulation significantly improves the sea ice distribution in both regions using a more realistic set of sea ice parameters. The most significant improvement is in the Antarctic, where winter sea ice extent misfit decreases by 48%. The optimized simulation reveals that ERA5 reproduces a closer representation of the Arctic sea ice extent and thickness than JRA25. In the southern hemisphere, ERA5 simulates the sea ice drift better, whereas the JRA25 have the closest match to the sea ice extent. Despite the improvements, further work is necessary for the Antarctic, where the lack of reliable observational data and limitations of the ocean model hinders the optimization.

A Balanced Atmospheric Ensemble Forcing for Sea Ice Modeling in Southern Ocean

AbstractTo deal with the great challenges in simulating the highly non‐linear physics of Antarctic sea ice, a multivariate balanced atmospheric ensemble forcing is developed based on the high‐resolution component of ERA5, which considers the relationship between different variables and adjacent times. To validate the performance of this new forcing, experiments were conducted from 1 January 2016 to 28 February 2017. Compared to simulations forced with the ensemble component of ERA5, a more reasonable ensemble of simulations is produced by the atmospheric ensemble forcing developed in this study, which suppresses the sea‐ice concentration (SIC) simulation errors and produces a better estimation of SIC simulation uncertainties. Further sea‐ice thickness budget analysis reveals that this impact of atmospheric ensemble forcing on sea ice simulation is due to a modulation of atmosphere‐ocean and sea ice‐ocean thermodynamic processes. These results lay the foundation for further improvements in Antarctic sea ice data assimilation and probabilistic prediction.

Increase in the wave power caused by decreasing sea ice over the Sea of Okhotsk in winter

Increasing ocean surface waves owing to decreasing sea ice in the Sea of Okhotsk (SO) is a major concern. However, long-term trends of ocean surface waves in the SO have not yet been investigated. Therefore, the long-term trends of wave power ({P}_{mathrm{w}}) in the SO were investigated using the 40-year (from the 1980s) simulations and based on the wave model (WAVEWATCH III) enforced by three reanalyses and one satellite product. Three wave model simulations were conducted using the original (hourly or daily) reanalysis or satellite sea ice data. In addition, to quantify the responses of the long-term {P}_{mathrm{w}} trends to surface winds and sea ice, three wave model simulations were performed with climatological sea ice data, for a total of six model simulations. The model results of the original sea ice data corresponded well with the buoy observations. Moreover, {P}_{mathrm{w}} increased (~ 12–15% per decade) remarkably during winter (December–February). The increased {P}_{mathrm{w}} could be attributed to the strengthened surface winds and reduced sea ice (i.e., reduction of direct wave decay by sea ice). Moreover, a horizontal gradient of sea level pressure enhanced by the reduced sea ice strengthened the surface winds. These results suggested that the reduction of sea ice is the most important factor responsible for the positive {P}_{mathrm{w}} trend over the SO during winter.

Causes and evolution of winter polynyas north of Greenland

Abstract. During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.

Wind- and eddy-driven upwelling over submarine canyons inshore of the northern Agulhas Current

The iSimangaliso Wetland Park, located along the coastline of northern KwaZulu-Natal, hosts South Africa’s only subtropical coral reef system. Episodic upwelling events are observed inshore and at the heads of Diepgat, Leadsman and Leven canyons within the iSimangaliso Wetland Park and may assist in larval dispersal and act as a buffer against coral bleaching. Over a 12-month period (1 June 2018-31 May 2019), two cooling events were identified from surface and subsurface temperature measurements extracted from the Sea Surface Temperature (SST) product of the Operational Sea Surface Temperature and Ice Analysis (OSTIA) satellite and data collected from underwater temperature recorders, respectively. Lagged correlations between subsurface temperatures confirmed that the anomalous cooling events were upwelling events (of 2-7 hours). The approximate upwelling rate was greatest at Diepgat Canyon, which suggests that upwelling events are enhanced by the physical structure of the canyon head and its shallow termination point which may influence the affected ecosystems. Wind measurements from the Mbazwana weather station and satellite sea-level anomalies were used to investigate the respective roles of wind stress and mesoscale ocean variability as driving forces of the upwelling events. The results indicate that the less intense and shorter cooling event was driven by a cyclonic eddy event, but that the stronger cooling event was driven by a combination of both forcing mechanisms.

A Comparison of Top‐Of‐Atmosphere Radiative Fluxes From CERES and ARISE

AbstractUncertainty in Arctic top‐of‐atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth's Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation‐IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave (SW), within 2σ uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2σ uncertainty, both approaches show a ∼−10 W m−2 average CERES‐aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration data set. Switching from imager‐based to passive microwave‐based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long‐term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short‐term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

Mapping of Active Frazil and Sea Ice Production in the Northern Hemisphere, With Comparison to the Southern Hemisphere

AbstractFrazil ice in the coastal polynyas has been increasingly recognized as important for efficient sea ice production, associated dense water formation, transport of particulate matter and consequent biological production. However, it has not been well understood where and to what degree active‐frazil areas occur in the Northern Hemisphere polynyas. We presented the first mapping of active frazil in the Northern Hemisphere, using sea ice data during September–May for 2002/2003–2010/2011, created from an Advanced Microwave Scanning Radiometer for the Earth Observing System thin ice algorithm that can detect active frazil areas. The North Water, Northeast Water, Anadyr, and St. Lawrence Island polynyas were found to have a relatively high occurrence rate of active frazil compared with other polynyas. We improved the estimation of sea ice production by discriminating between active frazil and thin solid ice and found that previous algorithms tended to overestimate sea ice production, particularly for polynyas dominated by thin solid ice such as the Okhotsk Northwestern polynya. We also made the first global comparison of polynya characteristics for all the major coastal polynyas. The annual ice production rate is 3–4 m yr−1 in the Northern Hemisphere polynyas and 6–9 m yr−1 in the Antarctic coastal polynyas. This large difference is mainly explained by the less frequent occurrence of active frazil and thin ice areas in the Northern Hemisphere polynyas owing to a lower occurrence of strong wind events.

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

Most remote sensing sea ice classification methods use single-source remote sensing data, such as synthetic aperture radar (SAR) data and optical remote sensing data. SAR data contain rich sea ice texture information, but the data are relatively single, making it difficult to distinguish detailed sea ice categories. Optical data include abundant spatial-spectral information, but they are often affected by clouds, fog, and severe weather. Hence, given the limitations of single-source data, the remote sensing sea ice classification accuracy cannot be further improved. A sea ice classification method based on deep learning and multisource remote sensing data fusion is proposed utilizing an improved densely connected convolutional neural network (DenseNet) to mine and fuse the multilevel features of sea ice. According to the characteristics of SAR data and optical data, a dual-branch network structure based on an improved DenseNet is employed for feature extraction, and a squeeze-and-excitation attention mechanism is introduced to weight the fusion features to further enhance the feature weights that can effectively distinguish different types of sea ice. The fully connected network is used to perform the deep fusion of features and classify sea ice. To verify the effectiveness of the proposed method, two sets of sea ice data are utilized for sea ice classification. The experimental results show that the proposed method fully excavates and fuses the multilevel characteristics of heterogeneous data using the improved dual-branch network structure, leverages the complementary characteristics of SAR data and optical data, significantly increases the sea ice classification accuracy, and effectively improves the influence of cloud cover on the sea ice classification accuracy. Compared with the typical single-source data classification method and other heterogeneous data fusion classification methods, the proposed method achieves superior overall classification accuracy (98.49% and 98.58%).

A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters

Icebreaker assistance is a common but complex operation in ice-infested regions. Currently, the operational decision-making and the decisions regarding the safety indicators are primarily based on expert knowledge, resulting in subjectivity and the ad hoc nature of icebreaker assistance. This can negatively impact both the navigational efficiency of icebreaker services and the productivity of port services. This paper proposes a data mining method to automatically identify icebreaker assistance cases from big data. The identified cases are then used to statistically analyze the safety indicators. The data used in the paper include navigational data obtained from the Automatic Identification System (AIS) and sea ice data in the Baltic Sea area. A multi-step clustering method is adopted to cluster similar trajectories of merchant vessels and icebreakers, identifying assistance events automatically. The results show that the proposed method can automatically identify icebreaker assistance cases with an accuracy of 99.6%, precision of 87%, and recall of 78.3%. The automatic identification along with the statistical analysis can assist in the development of an intelligent decision-making system for safe and efficient winter navigation.

Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data

ABSTRACT The potential of Arctic routes (ARs) has attracted global attention, and exploiting the Arctic has become an important strategy for many countries. However, there are still some challenges for ships sailing in Arctic ice zones, including harsh marine environments and the insufficient service capacity of sea ice information service systems. To better understand the route changes in the Arctic and extract real-time ship navigation routes, we developed an online interactive route planning system (RouteView) for ships sailing in the Arctic based on big Earth data. RouteView includes two main features: (1) an online calculation interface is provided for optimal routes along the Arctic Northeast Passage (NEP) 60 days into the future by utilizing reinforcement learning (RL) based on sea ice and meteorological data, and (2) an online ice-water classification is established based on synthetic aperture radar (SAR) data and deep learning to help users extract the sea ice distribution in real time. This work can potentially enhance the safety of shipping navigation along the NEP and improve information extraction methods for ARs.

Impact of the selected boundary layer schemes and enhanced horizontal resolution on the Weather Research and Forecasting model performance on James Ross Island, Antarctic Peninsula

The output of the various Weather Research and Forecasting (WRF) model configurations was compared with ground-based observations in the northern part of James Ross Island, Antarctic Peninsula. In this region, a network of automatic weather stations deployed at ice-free sites (as well as small glaciers) is operated by the Czech Antarctic Research Programme. Data from these stations provide a unique opportunity to evaluate the WRF model in a complex terrain of James Ross Island. The model was forced by the ERA5 reanalysis data and the University of Bremen sea ice data. The model configurations include a novel Three-Dimensional Scale-Adaptive Turbulent Kinetic Energy (3D TKE) planetary boundary layer scheme and a more traditional Quasi-Normal Scale Elimination (QNSE) scheme. Impact of model horizontal resolution was evaluated by running simulations in both 700 m and 300 m. The validation period, 25 May 2019 to 12 June 2019, was selected to cover different stratification regimes of air temperature and a significant snowfall event. Air temperature was simulated well except for strong low-level inversions. These inversions occurred in 44% of all cases and contributed to a higher mean bias (2.0–2.9°C) at low-elevation sites than at high altitude sites (0.2–0.6°C). The selection of the 3D TKE scheme led to improvement at low-elevation sites; at high altitude sites, the differences between model configurations were rather small. The best performance in wind speed simulation was achieved with the combination of the 3D TKE scheme and 300 m model resolution. The most important improvement was decrease of bias at a coastal Mendel Station from 3.5 m·s‑1 with the QNSE scheme on the 700 m grid to 1.2 m·s‑1 with the 3D TKE scheme on the 300 m grid. The WRF model was also proven to simulate a large snowfall event with a good correspondence with the observed snow height.

Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period

The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.

Simulating Dynamics and Ecology of the Sea Ice of the White Sea by the Coupled Ice–Ocean Numerical Model

In this paper, a numerical model of the White Sea is presented. The White Sea is a small shallow sea with strong tidal currents and complex ice behavior. The model is the only comprehensive numerical model for the White Sea. It consists of several coupled submodels (for water, ice, pelagic, and sympagic ecology). In this work, the focus is on the dynamics of sea ice and its ecosystem. The model is described and its results are compared to available sea–ice data, mostly satellite data. The spatial resolution of the model is 3 km. High current velocities require the time step of 3 min. The model is shown to reproduce sea–ice concentration well; in particular, timing of the sea ice is perfect. The dynamics of the sea–ice ecosystem also looks reasonable. Chlorophyll-a content agrees well with measurements, and the ratio of algal, bacterial, and faunal biomass is correct. Sympagic biomass is underestimated. Light is limiting at the early stage of sympagic bloom, nutrient limitation is for the second half. We show that sympagic component influences the spring bloom (in terms of timing and height of the peaks) but has little effect on the dynamics during the warm period of the year.

Seasonal extrema of sea surface temperature in CMIP6 models

Abstract. CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly used climatologies (WOA18, WOCE-Argo Global Hydrographic climatology (WAGHC) and the Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST)). The biases in SST seasonal extrema are largely consistent with the annual mean SST biases. However, the amplitude and spatial pattern of SST bias vary seasonally in the 20 CMIP6 models assessed. Large seasonal variations in the SST bias occur in eastern boundary upwelling regions, polar regions, the North Pacific and the eastern equatorial Atlantic. These results demonstrate the importance of evaluating model performance not simply against annual mean properties. Models with greater vertical resolution in their ocean component typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. No significant relationship of SST seasonal extrema with horizontal ocean model resolution is found.

Spatiotemporal Analysis and Modeling of Sea Ice Growth Rate in Nella Fjord, Antarctica: Based on Observations During the 36th Chinese National Antarctic Research Expedition in 2020

Obtaining field sea ice data is crucial for the validation and improvement of satellite data and numerical models. During the 36th Chinese National Antarctic Research Expedition, seven field observations covering the whole area were conducted in Nella Fjord, and large amounts of in situ sea ice and snow thickness data were acquired. Then, the influences of snow cover, topography, and air temperature on the sea ice growth rate were examined. Based on the relationship between the sea ice growth rate and air temperature, an empirical model of sea ice thickness was developed. The results show that sea ice started to freeze in early March, and the peak growth rate was reached in mid-June, with a mean growth rate of 0.62 cm/d. Between mid-June and mid-October, the mean growth rate was 0.36–0.4 cm/d; between mid-October and mid-November, the mean growth rate decreased to 0.15 cm/d. After mid-November, ice growth stopped, and the ice thickness decreased. The growth rate and thickness of sea ice near the shore were generally greater than those closer to the center of the fjord, with differences of 0.25 cm/d in mean growth rate and 7.16 cm in mean thickness. Because of the large exchanges of heat between the atmosphere and the ocean, the sea ice thickness increased rapidly during the early stage of freeze-up. When the air temperature was between -12 and −15°C, the ice growth rate was between 0.36 and 0.4 cm/d. However, when the ice thickness reached 0.5 m, the sea ice growth rate became less sensitive to air temperature. In addition, we found that the proposed model has high simulation accuracy for the growth rate of sea ice. Based on the model, we estimated that the growth rates become zero at an air temperature of approximately −4°C. Our study highlights the inverse relationship between the sea ice growth rate and the snow thickness and the fact that the sea ice thickness is mainly affected by air temperature.