Arctic multiyear ice classification and summer ice cover using passive microwave satellite data

[1] Analysis of a satellite-derived record of sea ice age for 1980 through March 2011 shows continued net decrease in multiyear ice coverage in the Arctic Ocean, with particularly extensive loss of the oldest ice types. The fraction of total ice extent made up of multiyear sea ice in March decreased from about 75% in the mid 1980s to 45% in 2011, while the proportion of the oldest ice declined from 50% of the multiyear ice pack to 10%. These losses in the oldest ice now extend into the central Arctic Ocean and adjacent to the Canadian Archipelago; areas where the ice cover was relatively stable prior to 2007 and where long-term survival of sea ice through summer is considered to be most likely. Following record-minimum multiyear ice coverage in summer 2008, the total multiyear ice extent has increased to amounts consistent with the negative trend from 2001–2006, with an increasing proportion of older ice types. This implies some ability for the ice pack to recover from extreme conditions. This recovery has been weakest in the Beaufort Sea and Canada Basin though, with multiyear ice coverage decreasing by 83% from 2002 to 2009 in the Canada Basin, and with more multiyear ice extent now lost in the Pacific sector than elsewhere in the Arctic Ocean.

Decline of the Arctic summer minimum sea ice extent is characterized by large expanses of open water in the Siberian, Laptev, Chukchi, and Beaufort Seas, and introduces large fetch distances in the Arctic Ocean. Long waves can propagate deep into the pack ice, thereby causing flexural swell and failure of the sea ice. This process shifts the floe size diameter distribution smaller, increases floe surface area, and thereby affects sea ice dynamic and thermodynamic processes. The results of Radarsat-2 imagery analysis show that a flexural fracture event which occurred in the Beaufort Sea region on 6 September 2009 affected ∼40,000 km2. Open water fractional area in the area affected initially decreased from 3.7% to 2.7%, but later increased to ∼20% following wind-forced divergence of the ice pack. Energy available for lateral melting was assessed by estimating the change in energy entrainment from longwave and shortwave radiation in the mixed-layer of the ocean following flexural fracture. 11.54 MJ m−2 of additional energy for lateral melting of ice floes was identified in affected areas. The impact of this process in future Arctic sea ice melt seasons was assessed using estimations of earlier occurrences of fracture during the melt season, and is discussed in context with ocean heat fluxes, atmospheric mixing of the ocean mixed layer, and declining sea ice cover. We conclude that this process is an important positive feedback to Arctic sea ice loss, and timing of initiation is critical in how it affects sea ice thermodynamic and dynamic processes.

Accurate quantification and characterization of the Arctic summer ice cover are needed for mass balance, heat flux, and modeling studies in the region. A general assessment of the state and basic characteristics of the ice cover can best be done in summer because it is when the perennial component is fully revealed. The main source of summer ice information has been passive microwave and to a lesser degree active microwave data. However, the emissivity and backscatter of sea ice are abnormal and difficult to resolve during this time period, causing large uncertainties in the interpretation of satellite data. In this study we examined the state of the sea ice cover by using special scanning microwave imager (SSM/I), synthetic aperture radar (SAR), and advanced very high resolution radiometer (AVHRR) satellite data synergistically. The surface and radiative characteristics of the summer ice cover were evaluated in the context of three special events: onset of melt, melt ponding, and freeze‐up. These events affect the emissivity and backscatter and may alter the albedo and ice structure. Onset of melt is readily detectable and is shown to migrate rapidly to the north in June. Melt ponding is not directly observable but is postulated to be the main cause of the decreases in brightness temperatures and large discrepancies between the SSM/I and SAR ice concentration results in many areas. In these areas, SAR and AVHRR results show concentrations near 100%, while the SSM/I data were as low as 70%. During freeze‐up the ice signatures are still quite different from those of midwinter ice, but the ice concentrations from SSM/I generally agree well with those from SAR data. Our results show that, generally, the average ice concentration within the pack is usually greater than 90% during the summer, which is substantially larger than that inferred previously from passive microwave data. The use of combined SAR and SSM/I data may also provide melt‐ponding fraction and first‐order estimate of albedo in the Arctic region.

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.

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).

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%.

Rapid Arctic sea ice retreat has fueled speculation about the possibility of threshold (or ‘tipping point’) behavior and irreversible loss of the sea ice cover. We test sea ice reversibility within a state-of-the-art atmosphere–ocean global climate model by increasing atmospheric carbon dioxide until the Arctic Ocean becomes ice-free throughout the year and subsequently decreasing it until the initial ice cover returns. Evidence for irreversibility in the form of hysteresis outside the envelope of natural variability is explored for the loss of summer and winter ice in both hemispheres. We find no evidence of irreversibility or multiple ice-cover states over the full range of simulated sea ice conditions between the modern climate and that with an annually ice-free Arctic Ocean. Summer sea ice area recovers as hemispheric temperature cools along a trajectory that is indistinguishable from the trajectory of summer sea ice loss, while the recovery of winter ice area appears to be slowed due to the long response times of the ocean near the modern winter ice edge. The results are discussed in the context of previous studies that assess the plausibility of sea ice tipping points by other methods. The findings serve as evidence against the existence of threshold behavior in the summer or winter ice cover in either hemisphere.

First estimates of sea ice exchange between the Arctic Ocean and the Canadian Arctic Archipelago (CAA) are obtained using six years (1997–2002) of RADARSAT ice motion. Over the period, the mean annual flux of sea ice area from the Amundsen Gulf (AG), M'Clure Strait (MS), and Queen Elizabeth Islands (QEI) are −85 ± 26 × 103, −20 ± 24 × 103, and 8 ± 6 × 103 km2. Positive/negative sign indicates Arctic outflow/inflow. Overall, net sea ice area is exported from the CAA; rough estimates suggest a mean annual volume flux of ∼102 km3 into the Canada Basin. Ice types are largely seasonal near the AG flux gate, mixed seasonal and multiyear at the MS gate, and primarily multiyear at the QEI gates. Sea ice at the QEI flux gates remains land fast for most of the year. Cross‐strait gradient in sea level pressure explains more than 50% of the variance in the area flux at these passages. The role of ice exchange during the anomalous decrease in multiyear ice coverage in the Sverdrup Basin during September 1998 is examined.

Arctic sea ice has declined rapidly. A record low of sea ice extent occurred in summer 2012 as observed over three decades of satellite data. The regime of the Arctic ice cover has shifted to a new state dominated by seasonal sea ice, as perennial sea ice has diminished drastically. Changes in the distribution of sea ice classes can precondition summer melt, alter the surface albedo, upset the Bowen ratio, and impact atmospheric photochemical processes. To investigate Arctic sea ice, environmental, and chemical changes, we conducted the Bromine, Ozone, and Mercury Experiment (BROMEX) around Barrow, Alaska, in March-April 2012, together with continuous measurements through the present. We used satellite active microwave scatterometer (AMS) and passive microwave radiometer (PMR) to observe the change in sea ice extent (SIE). A problem for sea ice observations in summer is that surface melt can impact PMR signatures of sea ice, causing large uncertainties in the measurement of SIE. We developed an AMS algorithm to determine SIE, primarily based on a quadruplet combination of relative AMS values rather than the absolute magnitudes of the signature and thus improving SIE accuracy. Then we took the SIE product from AMS in 2000–2009 to calibrate PMR results and reconstruct a SIE time series over three decades (1979–2013). To monitor sea surface temperature change caused by river discharge, we obtain results from two different algorithms using data acquired from the Moderate-resolution Imaging Spectroradiometer (MODIS) instruments that are currently in orbit on both the Terra and Aqua satellites, launched in 1999 and 2002, respectively. We present results from spring-summer 2012 over the Beaufort Sea where warm waters from the Mackenzie River surged onto the sea surface layer thereby accelerating sea ice melt. Additionally, we estimated the total annual heating power carried by 72 rivers into the Arctic Ocean. We found that Arctic rivers could provide an enormous amount of energy to melt sea ice. With their immense watersheds (several million square kilometers), the Arctic rivers are effective conduits that transport heat from the warm continents in summer to the Arctic Ocean, thus contributing to sea ice melt, and enhancing the sensitivity of Arctic sea ice to climate change. In stark contrast, Antarctica is a frozen continent without any warm rivers to affect sea ice in the Southern Ocean. Using satellite observations, we directly address the question: Why has Arctic sea ice decreased precipitously while Antarctic sea ice has been relatively stable? This topic draws tremendous attention from the cryospheric science community, and has been a challenging riddle in climate change research. The influence of river discharge into the Arctic Ocean helps to answer that puzzle.

[1] The Canadian Ice Service Digital Archive (CISDA) is a compilation of weekly ice charts covering Canadian waters from the early 1960s to present. The main sources of uncertainty in the database are reviewed and the data are validated for use in climate studies before trends and variability in summer averaged sea ice cover are investigated. These data revealed that between 1968 and 2008, summer sea ice cover has decreased by 11.3% ± 2.6% decade−1 in Hudson Bay, 2.9% ± 1.2% decade−1 in the Canadian Arctic Archipelago (CAA), 8.9% ± 3.1% decade−1 in Baffin Bay, and 5.2% ± 2.4% decade−1 in the Beaufort Sea with no significant reductions in multiyear ice. Reductions in sea ice cover are linked to increases in early summer surface air temperature (SAT); significant increases in SAT were observed in every season and they are consistently greater than the pan-Arctic change by up to ∼0.2°C decade−1. Within the CAA and Baffin Bay, the El Nino-Southern Oscillation index correlates well with multiyear ice coverage (positive) and first-year ice coverage (negative) suggesting that El Nino episodes precede summers with more multiyear ice and less first-year ice. Extending the trend calculations back to 1960 along the major shipping routes revealed significant decreases in summer sea ice coverage ranging between 11% and 15% decade−1 along the route through Hudson Bay and 6% and 10% decade−1 along the southern route of the Northwest Passage, the latter is linked to increases in SAT. Between 1960 and 2008, no significant trends were found along the northern western Parry Channel route of the Northwest Passage.

This review concerns crustaceans that associate with sea ice. Particular emphasis is placed on comparing and contrasting the Arctic and Antarctic sea ice habitats, and the subsequent influence of these environments on the life history strategies of the crustacean fauna. Sea ice is the dominant feature of both polar marine ecosystems, playing a central role in physical processes and providing an essential habitat for organisms ranging in size from viruses to whales. Similarities between the Arctic and Antarctic marine ecosystems include variable cover of sea ice over an annual cycle, a light regimen that can extend from months of total darkness to months of continuous light and a pronounced seasonality in primary production. Although there are many similarities, there are also major differences between the two regions: The Antarctic experiences greater seasonal change in its sea ice extent, much of the ice is over very deep water and more than 80% breaks out each year. In contrast, Arctic sea ice often covers comparatively shallow water, doubles in its extent on an annual cycle and the ice may persist for several decades. Crustaceans, particularly copepods and amphipods, are abundant in the sea ice zone at both poles, either living within the brine channel system of the ice-crystal matrix or inhabiting the ice-water interface. Many species associate with ice for only a part of their life cycle, while others appear entirely dependent upon it for reproduction and development. Although similarities exist between the two faunas, many differences are emerging. Most notable are the much higher abundance and biomass of Antarctic copepods, the dominance of the Antarctic sea ice copepod fauna by calanoids, the high euphausiid biomass in Southern Ocean waters and the lack of any species that appear fully dependent on the ice. In the Arctic, the ice-associated fauna is dominated by amphipods. Calanoid copepods are not tightly associated with the ice, while harpacticoids and cyclopoids are abundant. Euphausiids are nearly absent from the high Arctic. Life history strategies are variable, although reproductive cycles and life spans are generally longer than those for temperate congeners. Species at both poles tend to be opportunistic feeders and periods of diapause or other reductions in metabolic expenditure are not uncommon.

Modeling sea ice microwave emissivities at large scales presents challenges, due to complex interactions between the microwave signal and the sea ice environment. For the preparation of the Copernicus Imaging Microwave Radiometer mission (CIMR) that focusses on the monitoring of polar regions, a pragmatic parameterization of the sea ice emissivity over the Arctic in winter is proposed, providing consistent emissivity parameterizations between 1.4 and 36 GHz, for both orthogonal polarizations. Satellite‐derived microwave emissivities are calculated from the Advanced Microwave Scanning Radiometer 2, Soil Moisture Active Passive, and Soil Moisture Ocean Salinity observations, subtracting the atmospheric contributions and the surface temperature modulation using ERA5 meteorological reanalysis. The resulting Arctic sea ice emissivities are analyzed, alongside sea ice geophysical parameters from neXtSIM model outputs and ERA5, to identify the variables for the emissivity parameterization. Sea Ice Thickness emerges as a crucial factor, particularly at 18 and 36 GHz. A training database of coincident satellite‐derived emissivities and geophysical parameters is set up, to develop a Neural Network parameterization of the emissivities based on the geophysical parameters. This pragmatic methodology establishes a direct link between calculated emissivities and physical sea ice properties, eliminating the need for a priori assumptions. Promising emissivity results are obtained, with Root Mean Square Error below 0.03 for most channels, and reaching 0.04 at 36 GHz. Part of the error is expected to come from uncertainties in the input geophysical parameters. The emissivity frequency dependence is checked, and the emissivity angular variation of the 1.4 GHz is calculated from SMOS‐derived emissivities.

This report summarises visual shipboard ice observations carried out during leg ARK 17/2 of RV Polarstern in August and September 2001, operating along the Gakkel Ridge and at the North Pole. Data on general ice conditions, navigational information as well as photographs taken from the ships bridge are presented. Although most data are subject to large uncertainties due to the different experience of observers, they provide a general and quite representative overview of recent summer conditions in the Transpolar Drift, as seen from a ship. The data and photographs might be of interest as background information for discussions of recent changes of Arctic sea ice, and for comparisons with observations performed in other years. For those who have not seen a sea ice landscape so far, this report might yield first impressions of what the Arctic sea ice cover looks like. For scientists working on remote sensing, modelling, or other aspects of sea ice, the report provides some ground-truth and boundary conditions for their work in the summer of 2001.Ice conditions were characterised by very easily penetrable ice in the first half of the cruise, west of 30°E. There were many large leads with ice concentrations ranging between only 60 and 90%. Only from late August onwards, and east of 30°E, narrower leads and ice concentrations above 90% were observed. At that time, also new ice started to form on the leads. In the late period, sometimes the ship became beset in convergent ice conditions. Initially, melt ponds were observed to cover 10 to 30% of the ice surface. The ponds were ice covered already when we entered the ice in early August. However, the pond ice cover was thick enough to step on (>0.05 m) only after about August 20. Melt ponds became snow covered for some period, before they were visible again. Only after mid-September air temperatures permanently dropped below 0°C, and no snow or surface melting was observed any more.

Global, Arctic, and Antarctic sea ice volume predictions using score-driven threshold climate models

In a 600-year control run simulation of the Community Climate System Model, version 3 (CCSM3) of the National Center for Atmospheric Research, three events of extraordinary minimum September sea ice extent are identified, from which the system recovers within a few years. The control simulation is run in a stable climate mode using constant 1990 solar, trace gas, and aerosol forcing. The identified events are all of similar magnitude to the observed 2007 record low Arctic ice cover. In the first event (simulation year 451), the record low ice extent coincides with an extreme, cyclonic phase of atmospheric circulation and follows a 10-year period of steady decline in Northern Hemisphere sea ice volume that is associated with increased winter ice export. The ice extent recovered the following year when both the ice export and summer melt returned to more normal values. However, Arctic ice volume did not fully recover until 20 years after the event. In the second event (model year 490), the system was again preconditioned by a 10-year decline in Arctic sea ice volume because of larger than normal ice export. The event itself was associated with a coincident peak in early summer melt and ice export. During the recovery phase, the ice extent again rapidly returned to normal while the sea ice volume continued to decline for another 10 years, to its lowest value of the entire simulation. The recovery in Northern Hemisphere sea ice volume is associated with periods of reduced wintertime ice export through Fram Strait. The third event (model year 556―558) is characterized by a longer-lived anomaly (3 years). The system was not preconditioned by low ice volume, indicating that redistribution of sea ice (rather than ice mass loss) played an important role. Recovery for the third event is associated with a period of low ice export associated with a rapid return of an expanded Beaufort Gyre, which serves to recirculate sea ice within the Arctic Ocean. Notwithstanding known climate model deficiencies, the control simulation suggests that Arctic sea ice extent anomalies of the magnitude observed in the September 2007 minimum are plausible under 1990 forcing conditions. It is reasonable to conclude that more severe anomalies are achievable with more acute forcing conditions that are in excess of those of 1990.