Improving Arctic Sea Ice Prediction Though Freeboard Assimilation

Arctic sea ice variation in the Northwest Passage in 1979–2017 and its response to surface thermodynamics factors

The Arctic is the most rapidly warming region on Earth, and it is facing considerable environmental change. These changes are beginning to alter the way humanity uses and exploits the Arctic region, with commercial activities such as tourism, fishing, mineral and oil extraction, and shipping on the increase. Increased human activity in the Arctic has implications for safety and environmental conservation, and can present difficulties for local communities who rely on subsistence hunting and fishing, and community resupply. Environmental prediction for the Arctic is therefore becoming increasingly important, but our ability to accurately predict the Arctic atmosphere–sea-ice–ocean system is relatively immature, with short-term forecasts significantly less accurate than those produced for the mid-latitudes. There are numerous challenges with respect to forecasting systems in the Arctic: there is a relatively sparse in-situ observing network; some satellite-based observations are hard to use because of the snow and ice-covered surfaces; it is difficult and expensive to make detailed research-quality observations; and the dominant physical processes are different to those at mid-latitudes and over the tropics. To address these issues, the Year of Polar Prediction (YOPP1) was developed as one of the key elements of the WMO's Polar Prediction Project2. On Wednesday 21 November 2018, the Royal Meteorological Society hosted a meeting at the Society of Chemical Industry (SCI) in London to discuss recent advances and challenges associated with the understanding and prediction of key Arctic processes using numerical models and observations – including new insights provided by several field campaigns and modelling experiments carried out under the YOPP umbrella. The first talk was entitled: What are the challenges and priorities for improved prediction and climate monitoring of the Arctic? (Irina Sandu, ECMWF). Irina explained that in order to improve Arctic predictions and reanalyses, which constitute a great tool for Arctic climate monitoring, work is needed in three areas: (i) enhanced coupled modelling, (ii) data assimilation methods and (iii) the effective use of observations in the numerical weather prediction systems. Arctic regions pose specific challenges for each of these three areas because model errors are large, model uncertainty is not necessarily well represented, and in-situ observations are sparse. Moreover, while the Arctic is very rich in terms of remote sensing observations from polar orbiting satellites, some of these observations are difficult to use in data assimilation because of ambiguous signal properties, particularly over snow and sea-ice and in cloudy situations. Irina introduced the challenges and priorities in each of these three key areas and described recent and ongoing efforts to evaluate and improve predictions in the Arctic, and beyond, made in the framework of the YOPP modelling activities and the EU-APPLICATE3 project. She highlighted the fact that, compared to previous similar initiatives, YOPP puts an additional emphasis on numerical experimentation, in a concerted effort to exploit observations for model improvement and drive developments in data assimilation and the design of observing systems. The next talk presented was: What are the limitations of Arctic sea ice remote sensing products, and what opportunities can they provide for improving predictive skill of Arctic forecasts? (Ed Blockley, Met Office). Ed explained that, in response to declining sea-ice cover, human activity in the Arctic is increasing, and that accurate forecasts of Arctic sea ice – on a variety of different timescales – are therefore becoming increasingly important for the safety of human activities in the region. He then provided a brief overview of how sea ice has been traditionally initialised in short-range forecasting and seasonal prediction systems, and explained that most operational systems initialise sea ice purely using passive microwave sea ice concentration products. He gave an overview of the passive microwave remote sensing products typically used for initialisation of sea ice concentration, as well as the (relatively new) satellite-derived sea ice thickness products currently available. Information was provided about how these observations are derived, what the uncertainties and limitations are, and what new avenues of research are currently underway to improve measurements of Arctic sea ice from space. Ed also illustrated the potential impact of assimilating satellite sea ice thickness products into our operational prediction systems. Using the Met Office GloSea seasonal prediction system, he showed how sea ice thickness values inferred from the CryoSat-2 satellite altimetry could be used within the initialisation process to considerably improve forecasts of Arctic summer sea ice on seasonal timescales. Next up was: Chasing the source of the AMOC (Atlantic Meridional Overturning Circulation) atmosphere–ocean coupling in the Iceland and Greenland Seas (Ian Renfrew, University of East Anglia) Ian described a coordinated meteorological and oceanographic field campaign over the Iceland and southern Greenland Seas that took place in February and March 2018. The aim of the campaign was to characterise the atmospheric forcing and the ocean response of coupled atmosphere–ocean processes – in particular, cold-air outbreaks in the vicinity of the marginal-ice-zone, and their triggering of oceanic heat loss and the generation of dense waterbody body masses. He described how the team observed the spatial structure and variability of surface flux fields in the region, and the weather systems that dictate these fluxes, through the first meteorological field campaign in the Iceland Sea. This was done as part of a coupled atmosphere–ocean field campaign in winter 2018 involving a rare wintertime research cruise, airborne observations and a host of ocean and atmosphere observing systems. In-situ observations of air–sea interaction processes were made from several platforms. Ian presented some highlights from the field campaign and introduced some early findings from the post-campaign analysis – most notably a comprehensive set of observations of a marine cold air outbreak which were obtained via radiosonde releases and the research aircraft. Mixing it up: What connects Arctic clouds and sea ice? was a talk presented by Ian Brooks (University of Leeds). Ian explained that the single largest source of uncertainty in climate models arises from the representation of clouds; this is particularly true in the Arctic where, for much of the year, low-level boundary-layer clouds dominate the surface energy budget. The time-evolution and properties of these clouds are intimately linked their interactions with the surface and boundary layer structure. The properties of both clouds and boundary layer (and indeed the surface) are essentially sub-grid-scale in climate and weather forecast models, and thus many important features are poorly represented by current parameterisations. Ian emphasised that observations show that the summertime boundary-layer over sea ice is often decoupled below cloud base, inhibiting turbulent mixing – and hence the transport of aerosols and water vapour – between cloud and the surface. This decoupling is not reproduced by models, and the controls on this turbulent structure are poorly understood. He provided a review of the state of knowledge, alongside some of the recent research on Arctic boundary layer processes and their effects on clouds and the surface energy budget. A unique feature of polar boundary layers is the occurrence of a humidity inversion, where absolute humidity increases across the top of the boundary layer. This allows boundary-layer clouds to extend into the temperature inversion and results in a weakening of turbulence generated by cloud-top radiative cooling. Finally, he showed how recent field measurements have improved the parameterisation of surface turbulent fluxes over marginal sea ice, which will ultimately affect the model representation of the evolution of sea ice. The view from above Arctic snow at 89-325GHz: What can surface emissivity on these channels tell us about snowpack stratigraphy? was the next talk by Chawn Harlow ( Met Office). Chawn introduced Measurements of Arctic Cloud, Snow and Sea Ice in the Marginal Ice ZonE (MACSSIMIZE) – an airborne campaign carried out by the Met Office in March 2018 that focussed on evaluation of snow microwave emission models in the 89–325GHz frequency range. He described how the campaign included a ground-based component to collect snow pit measurements co-located with the airborne radiometric measurements, and that these were now being used to improve snow physical models for use in future Met Office numerical weather prediction systems. An explanation of the main motivation for MACSSIMIZE followed, both from the point of view of snow remote sensing and for improving atmospheric data assimilation, along with a description of the emissivity retrievals obtained during the campaign at 89, 118, 157, 183.3, 243 and 325GHz. Chawn then described how the MACSSIMIZE observations will be used to evaluate the snow thermodynamic and emissivity models used at the Met Office in the short term, and how they will eventually be used to improve assimilation of microwave atmospheric sounding data over snow-covered surfaces in the polar regions. Finally, Richard Essery (University of Edinburgh) presented: Why tundra snow is upside down in models, and why it matters?. Richard described how Earth System and Numerical Weather Prediction models are beginning to use more sophisticated representations of terrestrial snow, drawing on snow physics models that were first developed for forecasting avalanche risk. He explained that, having been developed for deep mid-latitude mountain snow, these models neglect important physical processes occurring in shallow tundra snow subjected to high winds and low temperatures. Understanding these processes is important for representing the thermal properties of Arctic snow and exploiting information from microwave remote sensing over snow-covered surfaces. Richard provided an overview of the ground-based component of the YOPP-endorsed MACSSIMIZE campaign, carried out at Trail Valley Creek in northwestern Canada. He presented results obtained from the snow pit measurements within MACSSIMIZE and drew attention to the existence of a granular depth hoar layer located below the wind-slab, which is a common feature of Arctic tundra snow. The resulting vertical density profile – with low-density depth hoar below a high-density wind-slab layer – is not reproduced by current snow models. Field instruments that can replace visual and subjective measurements of snow structure are now becoming available and can provide data for improving models. With speakers focussing on operational forecasting, Earth observation, and on observation and modelling of key Arctic processes such as clouds, sea ice and snow, this meeting illustrated the broad range of the UK's expertise and interest in Arctic prediction, and highlighted the significant contribution that scientists in the UK are making to YOPP. This RMetS meeting was convened by Ed Blockley and Chawn Harlow (Met Office) and was chaired by Ed Blockley (Met Office), with Jonathan Day (ECMWF) as rapporteur. The event was sponsored by the Centre for Polar Observation and Modelling (CPOM).

Abstract. Parameters in sea ice-ocean coupled models greatly affect the simulated evolution of the ocean and sea ice, and are typically tuned to bring the model state close to observations. Using an adjoint method, spatiotemporally varying parameters of an Arctic sea ice-ocean coupled model are optimized simultaneously with the initial conditions and atmospheric forcing by assimilating satellite and in-situ observations. The assimilation results show that the joint state and parameter estimation (SPE) substantially improves the sea ice concentration simulations. Particularly in October, when the ocean surface starts to refreeze, SPE reduces the lead closing parameter Ho (which determines the minimum ice thickness formed in the open water), thereby increasing the sea ice growth and facilitating the seasonal rapid sea ice recovery in the Arctic's Pacific sector. Comparisons with sea ice thickness observations from the moored upward-looking sonars and Ice Mass Balance buoys demonstrate that incorporating optimized model parameters into the coupled model also leads to better sea ice thickness estimation. Given that the optimal set of sea ice parameters may evolve alongside the thinning of Arctic sea ice, the adjoint-based SPE scheme has the potential to more accurately reconstruct the histical Arctic ocean and sea ice changes covering the satellite era, supporting research on Arctic sea ice and ocean variability.

Abstract. Sea ice thickness is an essential climate variable, which is often derived from satellite altimetry freeboard estimates, e.g., by CryoSat-2. In order to convert freeboard to sea ice thickness, assumptions are needed for snow thickness, snow density, sea ice density and water density. These parameters are difficult to observe when co-located in time and space with the satellite-derived freeboard measurements. For this reason, most available CryoSat-2 sea ice thickness products rely on climatologies based on outdated observations and empirical values. Model- and observation-based alternatives to sea ice density and snow thickness values have been suggested in recent years, but their combined influence on the freeboard to sea ice thickness conversion has not been analyzed. This study evaluates model-based spatially varying snow thickness, sea ice density and water density with in situ observations and the associated parameters used in the classical CryoSat-2 sea ice thickness production. The observations used for the comparison are a snow thickness product from Ku- and Ka-band radar, sea ice density observations from airborne campaigns and ice core measurements as well as water density from a large variety of observation platforms included in the World Ocean Atlas. Furthermore, this study calculates the mean sea ice thickness differences resulting from substituting the parameters used in a classical CryoSat-2 sea ice thickness product with model-based values. The evaluation shows that the model-derived snow thickness, sea ice density and water density compare better to observations than the associated parameters used in the CryoSat-2 sea ice thickness product. The parameters were compared to the weekly CryoSat-2 sea ice thickness (SIT) product from the Alfred Wegener Institute, which uses similar values for snow thickness, sea ice density and water density to other available CryoSat-2 SIT products. Furthermore, we find that the model-based snow thickness and sea ice density separately lead to the largest sea ice thickness differences but that, to some extent, their differences cancel out when both parameters are used in combination. For the water density, we find the average and maximum sea ice thickness difference to be small in comparison to the sea ice thickness differences introduced by the snow thickness and sea ice density, but this is not negligible, as currently stated in most studies. We find that the origin of the assumption that water density is negligible in the freeboard to sea ice thickness conversion originates from a study investigating the seasonal Arctic sea ice density variability, not taking into account the spacial variability. Based on our findings, we recommend using either a water density climatology or an uncertainty value of 2.6 kg m−3 instead of the commonly used value of 0 to 0.5 kg m−3 in CryoSat-2 freeboard to sea ice thickness conversion.

<p>Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.</p>

This study was motivated by the potential development of offshore oil exploration leases in the Canadian Southern Beaufort Sea, an area within the Inuvialuit Settlement Region. Sea ice concentration, extent, motion, and thickness data are vital to the success of potential oil operations in this region, and relevant data cannot be gleaned from larger-scale hemispheric studies. We therefore undertook regionally specific sea ice analyses in the southern Beaufort Sea during the summer drilling season (July, August, and September) in 2009 and over the long-term (1996 – 2010). On average, the Canadian oil lease areas contain mostly old sea ice during the drilling season and have not experienced significant decreasing trends in total or old sea ice. The average sea ice motion in the region for the period was anti-cyclonic at 20 – 25 cm·s-1, acting to transport sea ice southward toward the lease areas. Summer 2009 was used as a case study of regional ice concentration, motion, and thickness and to compare September sea ice thickness measurements to data collected in April 2009. In the summer of 2009, old sea ice was the predominant ice type in the lease areas. Sea ice motion was anti-cyclonic and faster than the long-term average, reaching 60 cm·s-1 west of Banks Island and across the north end of the lease areas. September 2009 sea ice thickness (mean = 1.03 m, σ = 0.97 m) was modal about the 0.20 – 0.29 m thickness bin. The sea ice thickness distri­bution was spatially variable, with the thickest ice occurring at the north end of the study area, in an area dominated by high old ice concentrations. Ice thicknesses greater than 10 m (the upper limit our instruments could measure) were encountered. Thinner sea ice predominated at the periphery of the core Beaufort Sea multi-year pack. Near the oil lease areas, the sea ice thickness distributions were shifted left on the histogram in comparison to those farther north, resulting in a greater proportion of relatively thick sea ice due to the thermodynamic loss of thinner (< 1.5 m) first-year ice during its southward movement. After enduring a summer’s melt, however, this thicker ice at the south end of the study region had thinned in comparison to the ice at the north end.

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean–sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P*. These show that higher values of P* generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P* is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6).

The Arctic region has been warming at a rate significantly faster than the global average, leading to an accelerated decline in sea ice. This trend is expected to continue, potentially resulting in a "low-ice regime," which could make sea ice conditions more unpredictable. Anticipating changes in Arctic sea ice and climate states is therefore crucial for guiding various human activities, from natural resource management to risk assessment decisions. While global climate and Earth system models project continuous sea ice decline over decadal time scales, achieving reliable seasonal forecasts remains challenging. To address this, we apply dynamical downscaling with the state-of-the-art Regional Arctic System Model (RASM), which enables us to forecast Arctic sea ice on time scales ranging from weeks to six months. RASM is a fully coupled regional climate model that integrates components for the atmosphere, ocean, sea ice, and land, interconnected through the flux coupler of the Community Earth System Model. In our study, we simulate RASM at a horizontal resolution of 1/12 degree (approximately 9 km) for both the ocean and sea ice, with 45 vertical levels in the ocean and five thickness categories for sea ice. The atmosphere is configured on a 50-km grid with 40 vertical levels, dynamically downscaled from the NOAA/NCEP Climate Forecasting System version 2 (CFSv2) at 72-hour intervals for the upper half of the atmosphere. Monthly ensemble forecasts extending up to six months are generated using initial conditions derived from a fully-coupled RASM hindcast simulation without bias correction and assimilation. This presentation highlights results for September sea ice predictions initialized on April 1, May 1, June 1, July 1, August 1, and September 1, covering pan-Arctic and regional sea ice spatio-temporal conditions from 2012 to 2021. Specifically, we examine how lead time and initial conditions affect the quantitative skill of seasonal predictability for Arctic sea ice and demonstrate skillful predictions of September sea ice up to six months in advance. Overall, our study underscores that enhancing model physics and obtaining more realistic initial conditions are crucial for achieving skillful sub-seasonal to seasonal predictions.

Generally, the sea ice prediction skills can be improved via assimilating available observations of the sea ice concentration (SIC) and the sea ice thickness (SIT) into a numerical forecast model to update the initial fields of the model. However, due to the lack of SIT satellite observations in the melting season, only SIC fields in the forecast model can be directly updated, which will bring about the dynamical mismatch between SIC and SIT to affect the model prediction accuracy. In order to solve this problem, a statistically based bivariate regression model of SIT, named as BRMT, is tentatively established based on the grid reanalysis data of SIC and SIT, to reconstruct the daily Arctic sea ice thickness data. Both BRMT-constructed SIT and several popular reanalysis datasets are compared to each other and validated based on available SIT observations in situ. Results show that BRMT can effectively reproduce the spatial and temporal changes of ice thickness in the melting season, and BRMT-constructed SIT is more accurate in capturing the change trend of ice thickness over a period of time, also the reconstructed SIT of one-year ice and multi-year ice types in the central Arctic and E Greenland Sea are closer to the observations. Further, as SIT from BRMT and SIC from satellite remote sensing are jointly assimilated into the ice-sea coupled numerical model, the prediction accuracy of SIC and SIT in the Arctic melting season is significantly improved, especially the SIC in the marginal ice zone and SIT in the central Arctic.

Because of its persistence on seasonal time scales, Arctic sea ice thickness (SIT) is a potential source of predictability for summer sea ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea ice volume anomalies is studied using a 700-yr control integration and a suite of initialized ensemble forecasts from a fully coupled global climate model. This analysis is focused on the September sea ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea ice concentration field, melt-onset date, snow coverage, and ice thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice–albedo feedback processes in seasonal forecast systems.

Climate warming has enabled the Arctic region to achieve seasonal navigation, and sea ice concentration is an important factor affecting the navigation of the Arctic waterways. This article uses the Arctic sea ice concentration data of the three highest temperatures in 2016, 2019, and 2020, combined with the Arctic summer sea level pressure, wind field, temperature, temperature anomaly, ice age, and sea ice movement data to analyze the spatial and temporal variation of sea ice and connectivity in the Northeast Passage (NEP) of the Arctic in Summer in three hot years, and summarizes the causes of sea ice anomalies. The results show that: (1) the summer Arctic sea ice extent in 2016, 2019 and 2020 were all lower than the multi-year average sea ice extent, and the summer sea ice extent in 2020 had the largest change trend; (2) in October of these three years, the sea ice was all negative anomalies, extending the opening time of the NEP; (3) when the sea ice concentration was 30% as the threshold, the navigation period of the NEP in 2016 was from mid-August to late October, 2019 was from the beginning of August to mid-October, 2020 was from the end of July to the end of October, and 2020 was the longest year since the opening of the NEP; (4) when the sea ice concentration was 10% as the threshold, the navigation period of the NEP in 2016 was from the end of August to the end of October, 2019 was from early August to mid-October, and 2020 was from the beginning of August to the end of October; (5) the key navigable areas of the NEP in the past three years were the central waters of the East Siberian Sea, the New Siberian Islands and the Vilkitsky Strait; (6) the navigation period of the NEP in 2016, 2019 and 2020 was longer. The main reasons were that the temperature of the NEP in the past 3 years was relatively high, the wind was weak, the sea ice movement had little effect, and the sea ice age in the key navigable areas was first year ice, which was easy to melt, which greatly promoted the opening of the NEP.

Numerical experiments with the ECHAM5 atmospheric general circulation model have been performed in order to simulate the influence of changes in the ocean surface temperature (OST) and sea ice concentration (SIC) on climate characteristics in regions of Eurasia. The sensitivity of winter and summer climates to OST and SIC variations in 1998-2006 has been investigated and compared to those in 1968-1976. These two intervals correspond to the maximum and minimum of the Atlantic Long-Period Oscillation (ALO) index. Apart from the experiments on changes in the OST and SIC global fields, the experiments on OST anomalies only in the North Atlantic and SIC anomalies in the Arctic for the specified periods have been analyzed. It is established that temperature variations in Western Europe are explained by OST and SIC variations fairly well, whereas the warmings in Eastern Europe and Western Siberia, according to model experiments, are substantially (by a factor of 2-3) smaller than according to observational data. Winter changes in the temperature regime in continental regions are controlled mainly by atmospheric circulation anomalies. The model, on the whole, reproduces the empirical structure of changes in the winter field of surface pressure, in particular, the pressure decrease in the Caspian region; however, it substantially (approximately by three times) underestimates the range of changes. Summer temperature variations in the model are characterized by a higher statistical significance than winter ones. The analysis of the sensitivity of the climate in Western Europe to SIC variations alone in the Arctic is an important result of the experiments performed. It is established that the SIC decrease and a strong warming over the Barents Sea in the winter period leads to a cooling over vast regions of the northern part of Eurasia and increases the probability of anomalously cold January months by two times and more (for regions in Western Siberia). This effect is caused by the formation of the increased-pressure region with a center over the southern boundary of the Barents Sea during the SIC decrease and an anomalous advection of cold air masses from the northeast. This result indicates that, to estimate the ALO actions (as well as other long-scale climatic variability modes) on the climate of Eurasia, it is basically important to take into account (or correctly reproduce) Arctic sea ice changes in experiments with climatic models.

Abstract. The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

&amp;lt;p&amp;gt;Ocean reanalyses (ORAs) are used extensively in polar research, hence their realism should be assessed regularly. Here the ORAs performance in the Antarctic region is analyzed with specific emphasis on sea ice concentration and thickness. We used four global ocean-sea ice products: C-GLORSv7, FOAM-GLOSEA5v13, GLORYS2v4, and ORAS5, and their ensemble mean GREP (provided by CMEMS) within the 1993 to 2018 period. All ORAs use the NEMO ocean model in a global eddy-permitting configuration (1/4&amp;amp;#176; horizontal resolution and 75 vertical levels) and are forced by the ECMWF ERA-Interim atmospheric reanalysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we examine the ability of ORAs to reproduce sea ice properties in the Southern Ocean taking into account regional characteristics and sea ice types. Seasonal and interannual variability of sea ice concentration (SIC) and sea ice thickness (SIT) is examined in the hemispheric domain and in five sub-regions for three different sea ice classes: pack ice (SIC &amp;amp;#8805; 80%), marginal ice zone (MIZ) (15% &amp;amp;#8804; SIC &amp;lt; 80%), and sparse ice (0 &amp;lt; SIC &amp;lt;15%). &amp;amp;#160;Modeled sea ice properties are compared to a set of satellite products: NSIDC CDR, Ifremer/CERSAT, and EUMETSAT OSI-SAF for SIC and Envisat and CryoSat-2 for SIT, together with PIOMAS and GIOMAS reanalyses. We revealed shortcomings of reanalysis systems to be improved in the future representation of Antarctic sea ice. Additionally, we focused on the assessment of the GREP ensemble mean product. We found that for certain metrics GREP minimizes the single errors and outperforms individual members. The evidence from this study implies that GREP can be a feasible product for a number of applications.&amp;lt;/p&amp;gt;

China launched its Arctic research program and organized the first Chinese National Arctic Research Expedition (CHINARE-Arctic) in 1999. By 2016, six further expeditions had been conducted using the R/V Xuelong. The main region of the expeditions has focused on the Pacific sector of the Arctic Ocean for sea ice observations. The expeditions have used icebreaker, helicopter, boat, floe, and buoy platforms to perform these observations. Some new technologies have been developed, in particular, the underway auto-observing system for sea ice thickness using an electromagnetic instrument. The long-term measurement systems, e.g., the sea ice mass balance buoy, allow observations to extend from summer to winter. Some international cooperation projects have been involved in CHINARE-Arctic, especially the “Developing Arctic Modeling and Observing Capabilities for Long-Term Environmental Studies” project funded by the European Union during the International Polar Year. Arctic sea ice observations have been used to verify remote sensing products, identify changes in Arctic sea ice, optimize the parameterizations of sea ice physical processes, and assess the accessibility of ice-covered waters, especially around the Northeast Passage. Recommendations are provided as guidance to future CHINARE-Arctic projects. For example, a standardized operation system of sea ice observations should be contracted, and the observations of sea ice dynamics should be enhanced. The upcoming launch of a new Chinese icebreaker will allow increased ship time in support of future CHINARE Arctic oceanographic investigations. Citation: Lei R B, Zhang Z H, Li Z J, et al. Review of research on Arctic sea ice physics based on the Chinese National Arctic Research Expedition. Adv Polar Sci, 2017, 28 (2): 100-110, doi:10.13679/j.advps.2017.2.00100