Interannual Variability Of Sea Ice Research Articles

CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.

Role of atmospheric and oceanic processes on interannual summertime (2016–2017) decrease of sea ice in the Antarctic regions of the Southern Ocean

In this article, the interannual variability of sea ice in the Antarctic sea ice regions between 2013–2018 is studied using a global ocean sea ice coupled model and satellite observation. The numerical model reasonably well simulates satellite observed interannual variability of sea ice concentration (SIC) and sea surface temperature (SST) in the Antarctic regions of Southern Ocean during all four austral seasons; summer (December–February), autumn (March–May), winter (June–August), and spring (September–November).A comparison of satellite and model shows that, during last two decades between 2001–2020, summertime of 2016–2017 had the lowest (highest) SIC (SST) across the Antarctic sea ice regions. Mixed layer heat budget analysis has been performed to comprehend how thermodynamic processes affect changes in SIC and SST in the Antarctic sea ice regions. The strong positive net atmospheric heat flux and the negative ocean vertical entrainment during summertime of 2016–2017 resulted in increased SST compared to other years, which lead to decreased SIC during above years. Also, loss of sea ice during summertime of 2016–2017 in the Antarctic sea ice regions are linked with significant decrease of wind stress magnitude and increase of wind stress curl.

A Deep Learning Method for Arctic Sea Ice Type Classification Based on Active-Passive Microwave Data

The Arctic-scale classification of sea ice type is important in the fields of macro-monitoring in the Arctic, climate change assessment, and long time-series interannual variability of sea ice. To date, a lack of deep learning-based algorithms for detecting Arctic sea ice types from microwave sensors is comparatively rare. In this study, a sea ice type classification algorithm based on the U-Net-CBAM deep learning semantic segmentation network was developed by carrying out research on sea ice type classification algorithms in the Arctic through the integrated use of ASCAT scatterometer and AMSR2 radiometer data. The results of the study show that the proposed method achieves an impressive average accuracy of 94.1% in extracting sea ice categories using AARI ice maps. The results of the algorithm show a high degree of agreement with the OSI-SAF sea ice type daily products.

Influence of sea-ice dynamics on coastal Antarctic benthos: A case study on lantern clams (Laternula elliptica) in Adélie Land

Polar regions are warming faster than the world average and are profoundly affected by changes in the spatio-temporal dynamics of sea ice, with largely unknown repercussions on the functioning of marine ecosystems. Here, we investigated the impacts of interannual sea-ice variability on coastal benthic communities in Antarctica, focusing on a close-to-pristine area (Adélie Land). We investigated shell growth of the circum-Antarctic bivalve Laternula elliptica, considered a key species in these soft bottom benthic communities. Chondrophores of live-collected clams were prepared using standard sclerochronological methods to study the interannual variability of shell growth from 1996 to 2015. Our results show that the master chronology varied with sea-ice dynamics. When sea ice breaks up too early, sympagic algae do not have time to accumulate sufficiently high biomass, thus strongly limiting the energy input to the benthos. This negatively affects the physiological performance of L. elliptica, thereby altering their population dynamics and hence the functioning of these soft-bottom ecosystems.

New insights into projected Arctic sea road: operational risks, economic values, and policy implications.

The online version contains supplementary material available at 10.1007/s10584-023-03505-4.

The Interannual Variability of Sea Ice Area, Thickness, and Volume in the Southern Sea of Okhotsk and Its Likely Factors

AbstractThe lowest latitude sea ice in the world (excluding coastal freezing) is in the southern Sea of Okhotsk (south of 46°N), where it has significant impacts on freshwater input and primary production. This region is subject to climate change, and accordingly the monitoring of sea ice conditions is important. However, the interannual variability of the region's sea ice is poorly understood due to its logistical challenges. Sea ice observations have been conducted in this region every winter for the period 1996–2020. The interannual variability of the ice conditions and the likely factors responsible for it were investigated using visual observations following the international ASPeCt protocol, combined with satellite SSM/I‐SSMIS ice concentration data (1988–2020). AMSR‐derived ice drift data sets and ERA5 meteorological reanalysis data sets were also analyzed to examine the effects of dynamic and thermodynamic processes. Our analysis revealed that (a) sea ice area in this region varies differently from that in the central and northern Sea of Okhotsk, where decreasing trends are reported, (b) sea ice volume has remarkable interannual variation and the peaks appeared much to more affected by dynamically deformed ice than freezing conditions, and (c) prominently deformed ice can be explained by taking shear components into account based on sea ice rheology. These results suggest the importance of including the proper sea ice rheology in numerical sea ice models to reproduce the realistic sea ice volume and deformation processes, for all seasonal ice zones.

A New Norm for Seasonal Sea Ice Advance Predictability in the Chukchi Sea: Rising Influence of Ocean Heat Advection

Abstract Predictability of seasonal sea ice advance in the Chukchi Sea has been investigated in the context of ocean heat transport from the Bering Strait; however, the underlying physical processes have yet to be fully clarified. Using the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS) reanalysis product (1979–2016), we examined seasonal predictability of sea ice advance in early winter (November–December) and its source using canonical correlation analysis. It was found that 2-month leading (September–October) surface heat flux and ocean heat advection is the major predictor for interannual variability of sea ice advance. Surface heat flux is related to the atmospheric cooling process, which has influenced sea ice area in the southeastern Chukchi Sea particularly in the 1980s and 1990s. Anomalous surface heat flux is induced by strong northeasterly winds related to the east Pacific/North Pacific teleconnection pattern. Ocean heat advection, which is related to fluctuation of volume transport in the Bering Strait, leads to decrease in the sea ice area in the northwestern Chukchi Sea. Diagnostic analysis revealed that interannual variability of the Bering Strait volume transport is governed by arrested topographic waves (ATWs) forced by southeasterly wind stress along the shelf of the East Siberian Sea. The contribution of ocean heat flux to sea ice advance has increased since the 2000s; therefore, it is suggested that the major factor influencing interannual variability of sea ice advance in early winter has shifted from atmospheric cooling to ocean heat advection processes. Significance Statement Predictability of sea ice advance in the marginal Arctic seas in early winter is a crucial issue regarding future projections of the midlatitude winter climate and marine ecosystem. This study examined seasonal predictability of sea ice advance in the Chukchi Sea in early winter using a statistical technique and historical model simulation data. We identified that atmospheric cooling and ocean heat transport are the two main predictors of sea ice advance, and that the impact of the latter has become amplified since the 2000s. Our new finding suggests that the precise information on wind-driven ocean currents and temperatures is crucial for the skillful prediction of interannual variability of sea ice advance under present and future climatic regimes.

Synoptic mode of Antarctic summer sea ice superimposed on interannual and decadal variability

In contrast to decreased Arctic sea ice extent, Antarctic sea ice extent shows a somewhat increased trend. There is a large interannual variability of Antarctic sea ice, especially in the Pacific sector of the Southern Ocean. The change and variability of Antarctic sea ice in synoptic timescales in the recent decades remain unclear. We identify synoptic modes of variability of Antarctic summer sea ice by applying the Self Organizing Map (SOM) technique to daily sea ice concentration data for the period 1979–2018. Nearly 40% of the variability is characterized by opposite changes between sea ice cover in the Bellingshausen, Amundsen and western Ross Seas and in the rest of the Antarctic seas, and another 30% by meridional asymmetry in the Weddell, Amundsen, and Ross Seas. Most of these spatial patterns may be explained by the dynamics and thermodynamic processes associated with anomalous atmospheric circulations related to the Southern Annular Mode (SAM) with a structure of strong zonal asymmetry. The interannual variability of the sea ice modes appears to have little connection to SAM, and only a weak relation to ENSO. The annual frequencies of SOM node occurrences also show a great decadal variability. Node 9 appears mainly prior to 1990; while node 1 occurs mainly after 1990. The decadal variability of nodes 1 and 9 is associated with the asymmetrical SAM, which results from two wavetrains excited over northern Australia and the southeastern Indian Ocean. These results further highlight the importance of understanding the role of southern mid-to-high latitude atmospheric intrinsic variability in predicting Antarctic summer sea ice variations from synoptic to decadal timescales.

Interannual Arctic sea ice variability and associated winter weather patterns: A regional perspective for 1979–2014

AbstractUsing Arctic sea ice concentration derived from passive microwave satellite observations in autumn and early winter over the 1979–2014 period, the Arctic region was objectively classified into several smaller regions based on the interannual sea ice variability through self‐organizing map analyses. The trend in regional sea ice extent (RSIE) in each region was removed using an adaptive, nonlinear, and nonstationary method called Ensemble Empirical Mode Decomposition, which captures well the accelerating decline of Arctic RSIEs in recent decades. Although the linear trend in RSIE is negative in all regions in both seasons, there are marked differences in RSIE trends and variability between regions, with the largest negative trends found during autumn in the Beaufort Sea, the Barents‐Kara Seas, and the Laptev‐East Siberian Seas. Winter weather patterns associated with the nonlinearly detrended RSIEs show distinct features for different regions and tend to be better correlated with the autumn than early winter RSIE anomalies. Sea ice losses in the Beaufort Sea and the Barents‐Kara Seas are both associated with a cooling of Eurasia, but in the former case the circulation anomaly is reminiscent of a Rossby wave train, whereas in the latter case the pattern projects onto the negative phase of the Arctic Oscillation. These results highlight the nonuniform changes in Arctic sea ice and suggest that regional sea ice variations may play a crucial role for the winter weather patterns.

The Role of Springtime Arctic Clouds in Determining Autumn Sea Ice Extent

AbstractRecent studies suggest that the atmosphere conditions arctic sea ice properties in spring in a way that may be an important factor in predetermining autumn sea ice concentrations. Here, the role of clouds in this system is analyzed using surface-based observations from Barrow, Alaska. Barrow is a coastal location situated adjacent to the region where interannual sea ice variability is largest. Barrow is also along a main transport pathway through which springtime advection of atmospheric energy from lower latitudes to the Arctic Ocean occurs. The cloud contribution is quantified using the observed surface radiative fluxes and cloud radiative forcing (CRF) derived therefrom, which can be positive or negative. In low sea ice years enhanced positive CRF (increased cloud cover enhancing longwave radiative forcing) in April is followed by decreased negative CRF (decreased cloud cover allowing a relative increase in shortwave radiative forcing) in May and June. The opposite is true in high sea ice years. In either case, the combination and timing of these early and late spring cloud radiative processes can serve to enhance the atmospheric preconditioning of sea ice. The net CRF (April and May) measured at Barrow from 1993 through 2014 is negatively correlated with sea ice extent in the following autumn (r2 = 0.33; p < 0.01). Reanalysis data appear to capture the general timing and sign of the observed CRF anomalies at Barrow and suggest that the anomalies occur over a large region of the central Arctic Ocean, which supports the link between radiative processes observed at Barrow and the broader arctic sea ice extent.

Clusters of interannual sea ice variability in the northern hemisphere

We determine robust modes of the northern hemisphere (NH) sea ice variability on interannual timescales disentangled from the long-term climate change. This study focuses on sea ice thickness (SIT), reconstructed with an ocean–sea-ice general circulation model, because SIT has a potential to contain most of the interannual memory and predictability of the NH sea ice system. We use the K-means cluster analysis—one of clustering methods that partition data into groups or clusters based on their distances in the physical space without the typical constraints of other unsupervised learning statistical methods such as the widely-used principal component analysis. To adequately filter out climate change signal in the Arctic from 1958 to 2013 we have to approximate it with a 2nd degree polynomial. Using 2nd degree residuals of SIT leads to robust K-means cluster patterns, i.e. invariant to further increase of the polynomial degree. A set of clustering validity indices yields K = 3 as the optimal number of SIT clusters for all considered months and seasons with strong similarities in their cluster patterns. The associated time series of cluster occurrences exhibit predominant interannual persistence with mean timescale of about 2 years. Compositing analysis of the NH surface climate conditions associated with each cluster indicates that wind forcing seem to be the key factor driving the formation of interannual SIT cluster patterns during the winter. Climate memory in SIT with such interannual persistence could lead to increased predictability of the Artic sea ice cover beyond seasonal timescales.

Ice–ocean coupled computations for sea-ice prediction to support ice navigation in Arctic sea routes

With the recent rapid decrease in summer sea ice in the Arctic Ocean extending the navigation period in the Arctic sea routes (ASR), the precise prediction of ice distribution is crucial for safe and efficient navigation in the Arctic Ocean. In general, however, most of the available numerical models have exhibited significant uncertainties in short-term and narrow-area predictions, especially in marginal ice zones such as the ASR. In this study, we predict short-term sea-ice conditions in the ASR by using a mesoscale eddy-resolving ice–ocean coupled model that explicitly treats ice floe collisions in marginal ice zones. First, numerical issues associated with collision rheology in the ice–ocean coupled model (ice–Princeton Ocean Model [POM]) are discussed and resolved. A model for the whole of the Arctic Ocean with a coarser resolution (about 25 km) was developed to investigate the performance of the ice–POM model by examining the reproducibility of seasonal and interannual sea-ice variability. It was found that this coarser resolution model can reproduce seasonal and interannual sea-ice variations compared to observations, but it cannot be used to predict variations over the short-term, such as one to two weeks. Therefore, second, high-resolution (about 2.5 km) regional models were set up along the ASR to investigate the accuracy of short-term sea-ice predictions. High-resolution computations were able to reasonably reproduce the sea-ice extent compared to Advanced Microwave Scanning Radiometer–Earth Observing System satellite observations because of the improved expression of the ice–albedo feedback process and the ice–eddy interaction process.Keywords: Arctic sea ice; numerical simulation; floe collision; Arctic sea routes.(Published: 23 November 2015)To access the supplementary material for this article, please see supplementary files in the column to the right (under Article Tools).Citation: Polar Research 2015, 34, 25008, http://dx.doi.org/10.3402/polar.v34.25008

A Model Study of the Relationship between Sea-Ice Variability and Surface and Intermediate Water Mass Properties in the Labrador Sea

Sea ice plays an important role in the variability of the Labrador Sea especially in its most western part adjacent to an important region of deep convection. Winter-to-winter re-emergence and propagation of both sea-ice concentration (SIC) and sea surface temperature anomalies have been observed following years of high SIC in this region. They have potentially important links to water mass properties and freshwater and heat transports in the subpolar North Atlantic. This article builds on the results of two precursor papers and presents results from a coupled sea-ice–ocean model study of the interannual variability of sea ice in the Labrador Sea. The relationships between SIC and water column properties in the subpolar North Atlantic are assessed. Winters with high SIC and strong surface cooling are found to be conducive to intensified convection. Surface and mid-depth temperature and salinity anomalies are observed in the Labrador Sea and the northwestern North Atlantic during winters with anomalous Labrador Sea SIC. These anomalies are found to propagate along the major circulation patterns in the subpolar North Atlantic and to persist for up to three years.

Satellite observations of Antarctic sea ice thickness and volume

We utilize satellite laser altimetry data from NASA's Ice, Cloud, and land Elevation Satellite (ICESat) combined with passive microwave measurements to analyze basin‐wide changes in Antarctic sea ice thickness and volume over a 5 year period from 2003–2008. Sea ice thickness exhibits a small negative trend while area increases in the summer and fall balanced losses in thickness leading to small overall volume changes. Using a 5 year time series, we show that only small ice thickness changes of less than −0.03 m/yr and volume changes of −266 km3/yr and 160 km3/yr occurred for the spring and summer periods, respectively. These results are in stark contrast to the much greater observed losses in Arctic sea ice volume and illustrate the different hemispheric changes of the polar sea ice covers in recent years. The uncertainties in the calculated thickness and volume trends are large compared to the observed basin‐scale trends. This masks the determination of a long‐term trend or cyclical variability in the sea ice cover. It is found that lengthening of the observation time series along with better determination of the interannual variability of sea ice and snow densities will allow for a more statistically significant determination of long‐term sea ice thickness and volume trends in the Southern Ocean.

Sea ice properties in the Bohai Sea measured by MODIS-Aqua: 2. Study of sea ice seasonal and interannual variability

During the 2009–2010 winter, the Bohai Sea experienced its most severe sea ice event in four decades, which caused significant economic losses, affected marine transportation and fishery, and impacted the entire marine ecosystem in the region. Measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from 2002 to 2010 and surface atmosphere temperature (SAT) data from the National Centers for Environmental Prediction (NCEP) are used to study and quantify the extreme sea ice event in the 2009–2010 winter and the interannual variability of the regional sea ice properties, as well as the relationship between sea ice and the climate variability in the Bohai Sea. The mean sea ice reflectance from MODIS-Aqua visible and near-infrared wavelengths are 9.33%, 13.26%, and 12.60% in the months of December 2009, January 2010, and February 2010, respectively, compared with the monthly average sea ice reflectance values (from 2002 to 2010) of 9.35%, 11.21%, and 11.41% in the same three winter months. The sea ice monthly average coverages are ~5427, ~27,414, and ~21,156km2 in these three winter months. These values are significantly higher than the averages of monthly sea ice coverage of ~2735, ~11,119, and ~10,287km2 in the Bohai Sea in December, January, and February between 2002 and 2010. Most of the sea ice coverage was located in the northern Bohai Sea. Both the intra-seasonal and interannual sea ice variability in the Bohai Sea is found to be related closely to SAT. The mechanism of anomalous SAT and intense sea ice severity are also discussed and attributed to large-scale climate changes due to the variability of the Arctic Oscillation (AO) and Siberian High (SH).

Bellingshausen and western Antarctic Peninsula region: Pigment biomass and sea-ice spatial/temporal distributions and interannual variabilty

The Palmer Long-Term Ecological Research (LTER) program seeks to obtain a comprehensive understanding of various components of the Antarctic marine ecosystem—the assemblage of plants, microbes, animals, ocean, and sea-ice south of the Antarctic Polar Front. A central hypothesis of the Palmer LTER is that the seasonal and interannual variability of sea ice affects all levels of the Antarctic marine ecosystem, from the timing and magnitude of seasonal primary production to the breeding success and survival of apex predators. In the context of this high-latitude ecosystem we use satellite imagery to examine physical forcing and possible mechanisms influencing the distribution of phytoplankton biomass in the region to the west of the Antarctic Peninsula. We evaluate the spatial and temporal variability of pigment biomass (estimated as chlorophyll-a concentrations using SeaWiFS data) in response to the spatial and temporal variability of sea-ice extent (estimated from passive microwave satellite data). While the ocean-color data record is relatively short (7 years) and contains high interannual variability, there are persistent spatial patterns of phytoplankton biomass that indicate important regional-scale physical mechanisms including: the marginal ice zone and its impact on the mixed-layer depth, the timing of spring sea-ice retreat, the importance of the southern Antarctic Circumpolar front, and teleconnections with sub-polar regions. The SeaWIFS imagery presented here provides the most complete synoptic space/time views of phytoplankton biomass within this region to date. These observations suggest that the southern Antarctic Circumpolar front may have a more profound influence on the western Antarctic Peninsula ecosystem than previously thought.

Seasonally dependent interannual variability of sea ice in the Bering Sea and its relation to atmospheric fluctuations

Interannual variability of sea ice in the Bering Sea and its relationship to atmospheric variability is analyzed using a singular value decomposition (SVD) analysis of sea ice concentrations (SICs) and 1000 hPa wind speeds in winter and spring seasons. The statistically significant first and second SVD modes, explaining 76.3% and 17.6% in winter and 54.6% and 29.6% in spring of the squared covariance between the two fields, are identified for SICs both in the winter and spring seasons with 1 month leading wind speeds. The spatial structures show that the first (second) SVD mode explains the SIC variability in the northeastern (northwestern) Bering Sea, related to the local northwesterly (northerly) wind anomalies for the positive SIC anomalies both in the winter and spring seasons. A comparison of the first SVD modes between the winter and spring seasons suggests that the difference of dominant patterns of wind anomalies results in the difference of SIC anomaly distributions between two seasons. The relationship between sea ice and atmospheric circulation anomalies indicates that one mode of the leading two SVD modes in each season is related to large‐scale atmospheric circulation associated with the Aleutian low and the other mode is related to relatively local atmospheric fluctuations related with pressure anomalies over Alaska. Furthermore, a slight difference of 700 hPa geopotential height anomalies results in the substantially different sea ice anomalies. These results suggest that in order to know the interannual sea ice variability in the Bering Sea, a better understanding of the wind anomalies over the Bering Sea are important.

Variations in Sea Ice in the Hudson Strait: 1971-1999

Variations in the timing and characteristics of sea ice formation and retreat in the Hudson Strait are examined, in order to identify both the primary controls on sea ice formation in the Strait and statistically significant trends. No statistically significant trend in the timing of sea ice formation and retreat was observed for the entire data set (1971-1999), although a potentially significant trend has developed since ∽1990. Interannual variability of sea ice represents an autoregressive process in the thermal properties of the Strait. Statistically significant correlations between the length of the ice-free and ice-covered seasons of one year to the next suggests that preconditioning of Hudson Strait waters play a dominant role in determining subsequent ice formation and duration. Further interannual variability depends on the average air temperature during the ice-free season.

Modelling inter-annual sea-ice variability off eastern Canada

Inter-annual sea-ice variability north of 55°N in eastern Canada was explored with a box model incorporating annually invariant, bathymetrically dependent ocean heat fluxes and near-surface currents. Using inputs of ice concentrations, regional surface temperatures and geostrophic winds at external model boundaries, ice composition was simulated in seven thickness categories at 10 day intervals during three annual ice seasons. Comparisons indicated good reproduction of observed inter-annual differences in regional ice volumes during critical January-March periods. Additional simulations of artificial cutoffs in southward ice fluxes showed that advective influence decreases with latitude and dominates the development of spring ice conditions in areas south of 60°N.

Modelling inter-annual sea-ice variability off eastern Canada

Inter-annual sea-ice variability north of 55°N in eastern Canada was explored with a box model incorporating annually invariant, bathymetrically dependent ocean heat fluxes and near-surface currents. Using inputs of ice concentrations, regional surface temperatures and geostrophic winds at external model boundaries, ice composition was simulated in seven thickness categories at 10 day intervals during three annual ice seasons. Comparisons indicated good reproduction of observed inter-annual differences in regional ice volumes during critical January-March periods. Additional simulations of artificial cutoffs in southward ice fluxes showed that advective influence decreases with latitude and dominates the development of spring ice conditions in areas south of 60°N.