Iceberg production and characteristics around the Prince of Wales Icefield, Ellesmere Island, 1997-2015

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

Using a coastal ice core collected from Prince of Wales (POW) Icefield on Ellesmere Island, we investigate source regions of sea ice‐modulated chemical species (methanesulfonic acid (MSA) and chloride (Cl−)) to POW Icefield and the influence of large‐scale atmospheric variability on the transport of these marine aerosols (1979–2001). Our key findings are (1) MSA in the POW Icefield core is derived primarily from productivity in the sea ice zone of Baffin Bay and the Labrador Sea, with influence from waters within the North Water (NOW) polynya, (2) sea ice formation processes within the NOW polynya may be a significant source of sea‐salt aerosols to the POW core site, in addition to offshore open water source regions primarily in Hudson Bay, and (3) the tropical Pacific influences the source and transport of marine aerosols to POW Icefield through its remote control on regional winds and sea ice variability. Regression analyses during times of MSA deposition reveal sea level pressure (SLP) anomalies favorable for opening of the NOW polynya and subsequent oceanic dimethyl sulfide production. Regression analyses during times of Cl− deposition reveal SLP anomalies that indicate a broader oceanic region of sea‐salt sources to the core site. These results are supported by Scanning Multichannel Microwave Radiometer‐ and Special Sensor Microwave/Imager‐based sea ice reconstructions and air mass transport density analyses and suggest that the marine biogenic record may capture local polynya variability, while sea‐salt transport to the site from larger offshore source regions in Baffin Bay is likely. Regression analyses show a link to tropical dynamics via an atmospheric Rossby wave.

Sea ice conditions in the Canadian Arctic Archipelago (CAA) play a key role in the navigation of the Northwest Passage (NWP). Based on the observed and simulated sea ice concentration and thickness data, we studied the temporal and spatial characteristics of sea ice from 1979 to 2017 in the NWP of the CAA and evaluated the sea ice conditions along the southern and northern routes of the NWP. Against the background of the rapid retreat of Arctic sea ice, the 39-year observed sea ice concentration of the NWP exhibited a relatively large decreasing trend in summer and fall, while heavy sea ice conditions were maintained in winter and spring, with a slight increasing trend. Consistent with Arctic sea ice, the sea ice extent in the NWP displayed a decreasing trend of -2.34%/10a, with its minimum occurring in 2012. The sea ice thickness in most subregions of the NWP showed a decreasing trend, with the exception of Lancaster Sound. The decreasing trend of sea ice thickness in the NWP was estimated to -0.16 m/10a. Based on the sea ice concentration and thickness, however, the sea ice conditions were heavier along the northern route than the southern route. This study considered both of these routes, and we selected and evaluated more specific pathways. The correlation results between the sea ice and atmospheric and oceanic thermodynamic factors in the NWP suggested that the thermodynamic factors had a greater impact on sea ice in the summer and fall, and the variations of sea ice concentration were more closely correlated with the thermodynamic factors than sea ice thickness. The sea surface temperature (SST) had a higher correlation with sea ice concentration than surface air temperature (SAT), while SAT exhibited a higher correlation with sea ice thickness than SST. The residual amount of sea ice concentration and thickness in the fall, associated with the fall SAT and SST, contributed to the formation of sea ice in the following winter and spring.

Effect of Pacific warm and cold events on the sea ice behavior in the Indian sector of the Southern Ocean

The Arctic sea ice volume (SIV) was investigated by applying sea ice concentration (SIC) and multi‐source sea ice thickness (SIT) products from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation System (PIOMAS), Envisat and CryoSat‐2 (CS‐2) products. The SIV was estimated during the sea ice growth season (October–April) from October 2002 to December 2018. During the Envisat period (October 2002–April 2010), negative SIV trends were estimated by a hybrid Envisat and PIOMAS SIT dataset (defined as Envi‐PIO); the declining trends for both the maximum/minimum SIV were 360 and 177 km3⋅year−1, respectively; similar SIV trends were obtained by applying only the PIOMAS SIT data. During the CS‐2 period (October 2010–December 2018), no clear trends in the SIV were estimated by either CS‐2 or PIOMAS, except for clear increases in the SIV in northern Greenland and the Canadian Arctic Archipelago (CAA) using the CS‐2 SIT data. The age of sea ice plays an important role in SIV variability. For example, the SIV trend was found to be similar to the multi‐year ice trend between 2003 and 2007. The correlation coefficients between the monthly mean SIV and surface air temperature (SAT) and sea surface temperature (SST) were −0.60 and −0.82, respectively. The decreasing trend in the SIV during the Envisat period was influenced by the increase in the annual maximum SST and minimum SAT. The significant increase in the SIV in northern Greenland and the CAA during the CS‐2 period was related to ice deformation.

Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0 (CICE5.0) was introduced to the Beijing Climate Center Climate System Model (BCC_CSM) as a new alternative to the Sea Ice Simulator (SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation (AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further.

An ocean–atmosphere–sea ice model is developed to explore the time-dependent response of climate to Milankovitch forcing for the time interval 5–3 Myr BP. The ocean component is a zonally averaged model of the circulation in five basins (Arctic, Atlantic, Indian, Pacific, and Southern Oceans). The atmospheric component is a one-dimensional (latitudinal) energy balance model, and the sea-ice component is a thermodynamic model. Two numerical experiments are conducted. The first experiment does not include sea ice and the Arctic Ocean; the second experiment does. Results from the two experiments are used to investigate (1) the response of annual mean surface air and ocean temperatures to Milankovitch forcing, and (2) the role of sea ice in this response. In both experiments, the response of air temperature is dominated by obliquity cycles at most latitudes. On the other hand, the response of ocean temperature varies with latitude and depth. Deep water formed between 45°N and 65°N in the Atlantic Ocean mainly responds to precession. In contrast, deep water formed south of 60°S responds to obliquity when sea ice is not included. Sea ice acts as a time-integrator of summer insolation changes such that annual mean sea-ice conditions mainly respond to obliquity. Thus, in the presence of sea ice, air temperature changes over the sea ice are amplified, and temperature changes in deep water of southern origin are suppressed since water below sea ice is kept near the freezing point.

Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully polarimetric (FP) and simulated compact polarimetric (CP) C- (RADARSAT-2) and L- (ALOS-2 PALSAR-2) band SAR, in the 2018 melt season in the Canadian Arctic Archipelago, for stage-wise separation of first year ice (FYI) and multiyear ice (MYI). SAR scenes at both near- (19.1–28.3°) and far- (35.8–42.1°) range incidence angles and coincident high-resolution optical scenes were used to assess the impact of surface melt ponds on separability within a landfast ice zone of diverse ice thickness. C-band provided better separability between FYI and MYI during pond onset, while L-band was superior during pond drainage due to MYI volumetric scattering. CP parameters matched FP performance across the melt season. HH and HV, commonly offered in ScanSAR mode for both frequencies, presented good separability during pond onset and drainage. Using both C-band and L-band SAR along with constraining incidence angle ranges, enhances sea ice type identification and separability. Our results can support ice type classification and seasonal stage detection for climate studies and enhance existing frameworks for ice motion vector retrievals.

We present an overview of changes in Hudson Bay sea ice in the context of thermodynamic forcing due to increased surface air temperatures and dynamic wind and current forcing mechanisms. Examined in particular is the correspondence between sea ice extent, surface air temperatures, and atmospheric indices during spring and fall from 1980 to 2005. Changes in the timing of freeze-up and break-up over the last several decades were significant. In the spring, temperature trends were consistently positive with temperature increases of 0.23°C/decade from 1950 to 2005. With increasing temperatures in the Hudson Bay region, sea ice concentrations and sea ice extents have decreased significantly as well. Warmer surface air temperatures have also shifted the mean freeze-up and break-up dates by 0.8–1.6 weeks in each of the seasons. Dynamic forcing of sea ice is further explored using the concept of relative vorticity, or the tendency for sea ice to rotate clockwise (or counterclockwise) within Hudson Bay in response to changes in atmospheric circulation. Surface air temperatures and hence ice extent showed cyclical patterns over the time period studied and appear to be driven by large scale atmospheric circulation patterns. This cyclical behaviour has been previously associated with various hemispheric indices including the North Atlantic Oscillation. The implications of changing ice conditions in Hudson Bay for marine mammal habitat are discussed.

In this study, we use the National Center for Atmospheric Research Community Earth System Model to investigate the contribution of sea ice and land snow to the climate sensitivity in response to increased atmospheric carbon dioxide content. We focus on the overall effect arising from the presence or absence of sea ice and/or land snow. We analyze our results in terms of the radiative forcing and climate feedback parameter. We find that the presence of sea ice and land snow decreases the climate feedback parameter (and thus increases climate sensitivity). Adjusted radiative forcing from added carbon dioxide is comparatively less sensitive to the presence of sea ice or land snow. The effect of sea ice on the climate feedback parameter decreases as sea ice cover diminishes at higher CO2 concentration. However, the influence of both sea ice and land snow on the climate feedback parameter remains substantial under the CO2 concentration range considered here (to eight times preindustrial CO2 content). Approximately, one quarter of the effect of sea ice and land snow on the climate feedback parameter is a consequence of the effect of these components on longwave feedback that is mainly associated with cloud change. Polar warming in response to added CO2 is approximately doubled by the presence of sea ice and land snow. Relative to the case in which sea ice and land snow are absent in the model, in response to increased CO2 concentrations, the presence of sea ice and land snow results in an increase in global mean warming by over 40%.

The orbit manoeuvre, known as CRYO2ICE, that periodically aligned CryoSat-2 with ICESat-2, allows for unprecedented near-coincident radar and lidar observations targeted the polar regions. This is of particular interest to sea ice thickness studies, since snow on sea ice remains the largest contributor to altimetry-derived sea ice thickness uncertainties. To date, snow depth estimates from space have been acquired from passive microwave radiometers, and by using dual-frequency observations (Ku- and Ka-band, or laser and Ku-band). However, until now, dual-frequency observations have only been based on monthly averaged estimates at basin-scales, and while passive microwave-derived snow estimates are provided daily, they are only reliable over first-year ice. CRYO2ICE presents the possibility of investigating along-track snow depth on sea ice using observations at two different frequencies, along with an opportunity for further investigation of penetration capabilities and footprint-related issues of high interest to the altimetry community.Some of the most noticeable differences between CryoSat-2 and ICESat-2 are found in their measurement configuration and sampling rates. This difference in measurement configuration between retrieving surface elevation using conventional ways, such as re-tracking of the synthetic aperture radar (SAR) waveform of CryoSat-2 in comparison to re-tracking the surface from high-density photon clouds of ICESat-2, as well as the difference in sampling rates, presents additional challenges. Here, we investigate the challenge of binning these different type of observation strategies into comparable observations and what we can expect from the CRYO2ICE observations over sea ice. We examine near-coincident radar and laser freeboards from CryoSat-2 and ICESat-2 (CRYO2ICE observations) and the resulting snow depth observations in the Arctic. We utilise three CryoSat-2 products (Baseline-D, CCI and LARM) representing a variety of re-trackers used in sea ice altimetry studies, and the ATL10 product from ICESat-2. Our focus is on how the CryoSat-2 and ICESat-2 derived freeboards respond along-track to various ice and snow conditions, and how this affects the possibility to retrieve snow depth.This study investigates the freeboards and the derived snow depth in relation to changes in surface roughness, sea ice concentration and sea ice lead identifications. Here, we find inconsistencies in the radar freeboard estimates across the changing ice conditions. By comparison with sea ice concentration, identified leads and roughness estimates, the inconsistencies relate to retrieval methodology of CryoSat-2 (re-tracking to a backscattering interface using a threshold-based re-tracker or a physical re-tracker) and binning methodology (posing the question of when CRYO2ICE observations are truly comparable). Other inconsistencies relate to how the condition of the surface impacts the radar signal. We also present comparisons of radar and laser freeboards with daily estimates of snow depth based on passive-microwave observations (AMSR-2) and snow evolution models (SnowModel-LG). Here, large discrepancies are observed: AMSR-2 observations show little variation over first-year ice, compared to both estimates from SnowModel-LG and CRYO2ICE observations. CRYO2ICE snow depths are comparable across the used CryoSat-2 products but shows significantly larger variation compared to SnowModel-LG estimates. Future work includes delving more into the changing ice conditions and their impact on the radar signal.

Autumn Arctic sea ice has been declining since the beginning of the era of satellite sea ice observations. In this study, we examined the factors contributing to the decline of autumn sea ice concentration. From the Beaufort Sea to the Barents Sea, autumn sea ice concentration has decreased considerably between 1982 and 2020, and the rates of decline were the highest around the Beaufort Sea. We calculated the correlation coefficients between sea ice extent (SIE) anomalies and anomalies of sea surface temperature (SST), surface air temperature (SAT) and specific humidity (SH). Among these coefficients, the largest absolute value was found in the coefficient between SIE and SAT anomalies for August to October, which has a value of −0.9446. The second largest absolute value was found in the coefficient between SIE and SH anomalies for September to November, which has a value of −0.9436. Among the correlation coefficients between SIE and SST anomalies, the largest absolute value was found in the coefficient for August to October, which has a value of −0.9410. We conducted empirical orthogonal function (EOF) analyses of sea ice, SST, SAT, SH, sea level pressure (SLP) and the wind field for the months where the absolute values of the correlation coefficient were the largest. The first EOFs of SST, SAT and SH account for 39.07%, 63.54% and 47.60% of the total variances, respectively, and are mainly concentrated in the area between the Beaufort Sea and the East Siberian Sea. The corresponding principal component time series also indicate positive trends. The first EOF of SLP explains 41.57% of the total variance. It is mostly negative in the central Arctic. Over the Beaufort, Chukchi and East Siberian seas, the zonal wind weakened while the meridional wind strengthened. Results from the correlation and EOF analyses further verified the effects of the ice–temperature, ice–SH and ice–SLP feedback mechanisms in the Arctic. These mechanisms accelerate melting and decrease the rate of formation of sea ice. In addition, stronger meridional winds favor the flow of warm air from lower latitudes towards the polar region, further promoting Arctic sea ice decline. Citation :Li S Y, Cui H Y, Xu J L, et al. Factors contributing to rapid decline of Arctic sea ice in autumn. Adv Polar Sci, 2021, 32(2): 96-104, doi: 10.13679/j.advps.2020.0039

This study provides 6 years of high‐resolution underway measurements of the sea surface partial pressure of CO2 (pCO2sw), sea surface temperature, and salinity across the Canadian Arctic Archipelago (CAA). Observed pCO2sw varied regionally, with the northern and central channels of the CAA undersaturated in pCO2sw (with respect to the atmosphere), while the western regions were typically saturated to supersaturated in pCO2sw. This apparent spatial variability was caused to some extent by the timing of our ship transit through the CAA, as we also found a general seasonal trend of pCO2sw being undersaturated in the early summer, followed by saturation to supersaturation in late summer, and a return to undersaturation during the autumn. Sea surface temperature was significantly correlated with pCO2sw at various locations across the CAA, but we also observed the effects of other regional processes like upwelling, primary production, riverine input, and sea ice melt. These processes are linked to each other, and hence, it is impossible to pinpoint only one dominant factor controlling pCO2sw variability in the CAA. However, we found that sea ice dominates the seasonal cycle of all these processes, thus making the timing of sea ice breakup a useful predictor of pCO2sw variability in the CAA. We calculated an average net oceanic sink of 14 mmol CO2 · m−2 · day−1 for the CAA during the summer and autumn seasons, but caution that a more rigorous budgeting approach is required to fully account for biases in dates and locations of our measurements.

An idealized planetary flat-bottom geostrophic ice–ocean model is constructed with boundaries at latitudes 5° and 65° N and longitudes 50° W and 10° E in order to approximate the North Atlantic. The model is driven by fixed zonally averaged wind, surface air temperatures and surface ocean salinity. A dynamic thermodynamic sea-ice model is coupled to the ocean model. Only the thermodynamic insulating effects of the sea ice are considered, and no salt fluxes due to melting and freezing are included. Four equilibrium simulations of about 5000 years each are performed: two with interactive sea ice with and without ice dynamics, and two control simulations with either a fixed or no ice cover. In the two simulations including interactive sea ice, characteristic oscillations in the ice thickness and ocean temperature are found to occur. The oscillations are smaller when sea-ice dynamics are included. The dominant oscillation occurs at about a 5 year period, with the key feature being that the presence of sea ice tends to insulate the ocean and hence allows an oceanic warming. This warming in turn eventually causes a melt-back of the ice and a subsequent cool-down of the ocean. Oscillations at longer periods of about 20 years in the thermohaline circulation are also observed. These longer-period oscillations are particularly pronounced in the northward surface water transport.

An idealized planetary flat-bottom geostrophic ice–ocean model is constructed with boundaries at latitudes 5° and 65° N and longitudes 50° W and 10° E in order to approximate the North Atlantic. The model is driven by fixed zonally averaged wind, surface air temperatures and surface ocean salinity. A dynamic thermodynamic sea-ice model is coupled to the ocean model. Only the thermodynamic insulating effects of the sea ice are considered, and no salt fluxes due to melting and freezing are included. Four equilibrium simulations of about 5000 years each are performed: two with interactive sea ice with and without ice dynamics, and two control simulations with either a fixed or no ice cover.In the two simulations including interactive sea ice, characteristic oscillations in the ice thickness and ocean temperature are found to occur. The oscillations are smaller when sea-ice dynamics are included. The dominant oscillation occurs at about a 5 year period, with the key feature being that the presence of sea ice tends to insulate the ocean and hence allows an oceanic warming. This warming in turn eventually causes a melt-back of the ice and a subsequent cool-down of the ocean. Oscillations at longer periods of about 20 years in the thermohaline circulation are also observed. These longer-period oscillations are particularly pronounced in the northward surface water transport.