Sea Ice Concentration Variability Research Articles

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.

Drivers of Laptev Sea interannual variability in salinity and temperature

Abstract. Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration.

Influence of sea ice concentration, sex and chick age on foraging flexibility and success in an Arctic seabird.

Declining sea ice and increased variability in sea ice dynamics are altering Arctic marine food webs. Changes in sea ice dynamics and prey availability are likely to impact pagophilic (ice-dependent and ice-associated) species, such as thick-billed murres (Uria lomvia), through changes in foraging behaviour and foraging success. At the same time, extrinsic factors, such as chick demand, and intrinsic factors, such as sex, are also likely to influence foraging behaviour and foraging success of adult murres. Here, we use 3 years of data (2017-2019) to examine the impacts of environmental conditions (sea ice concentration and sea surface temperature), sex and chick age (as a proxy for chick demand) on foraging and diving behaviour (measured via biologgers), energy expenditure (estimated from activity budgets) and foraging success (measured via nutritional biomarkers) of thick-billed murres during the incubation and chick-rearing stages at Coats Island, Nunavut. Murres only exhibited foraging flexibility to environmental conditions during incubation, which is also the only stage when ice was present. When more ice was present, foraging effort increased, murres foraged farther and made deeper dives, where murres making deeper dives had higher foraging success (greater relative change in mass). During incubation, murre behaviour was also influenced by sex of the individual, where males made more and shorter trips and more dives. During chick-rearing, murre behaviour was influenced primarily by the sex of the individual and chick age. Males made shallower dives and fewer dive bouts per day, and more dives. Birds made longer, deeper dives as chicks aged, likely representing increased intra-specific competition for prey throughout the season. Our results suggest variation in sea ice concentration does impact foraging success of murres; however, sex-specific foraging strategies may help buffer colony breeding success from variability in sea ice concentration.

SIPN South: six years of coordinated seasonal Antarctic sea ice predictions

Antarctic sea ice prediction has garnered increasing attention in recent years, particularly in the context of the recent record lows of February 2022 and 2023. As Antarctica becomes a climate change hotspot, as polar tourism booms, and as scientific expeditions continue to explore this remote continent, the capacity to anticipate sea ice conditions weeks to months in advance is in increasing demand. Spurred by recent studies that uncovered physical mechanisms of Antarctic sea ice predictability and by the intriguing large variations of the observed sea ice extent in recent years, the Sea Ice Prediction Network South (SIPN South) project was initiated in 2017, building upon the Arctic Sea Ice Prediction Network. The SIPN South project annually coordinates spring-to-summer predictions of Antarctic sea ice conditions, to allow robust evaluation and intercomparison, and to guide future development in polar prediction systems. In this paper, we present and discuss the initial SIPN South results collected over six summer seasons (December-February 2017-2018 to 2022-2023). We use data from 22 unique contributors spanning five continents that have together delivered more than 3000 individual forecasts of sea ice area and concentration. The SIPN South median forecast of the circumpolar sea ice area captures the sign of the recent negative anomalies, and the verifying observations are systematically included in the 10-90% range of the forecast distribution. These statements also hold at the regional level except in the Ross Sea where the systematic biases and the ensemble spread are the largest. A notable finding is that the group forecast, constructed by aggregating the data provided by each contributor, outperforms most of the individual forecasts, both at the circumpolar and regional levels. This indicates the value of combining predictions to average out model-specific errors. Finally, we find that dynamical model predictions (i.e., based on process-based general circulation models) generally perform worse than statistical model predictions (i.e., data-driven empirical models including machine learning) in representing the regional variability of sea ice concentration in summer. SIPN South is a collaborative community project that is hosted on a shared public repository. The forecast and verification data used in SIPN South are publicly available in near-real time for further use by the polar research community, and eventually, policymakers.

Chemical and biological vertical distributions within central Arctic (>82°N) sea ice during late summer

We assessed the distribution of biota (autotrophs and heterotrophs) and associated carbonate chemistry variables in Arctic sea ice at latitudes >82°N during late summer and early autumn 2018. The sampled sea ice was relatively thick (average 1.4 m) with variable snow cover (mean 7 cm) and low bulk salinities throughout. Most measured variables, including carbonate chemistry parameters, were low in the upper half of the ice cores, but increased with depth. Measurements of particulate organic carbon (POC), chlorophyll a (chl a), bacterial abundance, and particulate extracellular polysaccharide (pEPS) in the cores strongly suggested that detrital carbon was the major particulate organic pool. Near the ice-water interface, autotrophic material comprised ca. 50% of the total POC, whereas pEPS and bacterial carbon accounted for ca. 8 and 1% of the total POC, respectively. Under-ice water was nutrient poor, providing only a small input of nutrients to support autotrophic growth, at least during the time of our sampling. While the Arctic Ocean has substantial interannual variability in sea-ice concentration and thickness, these measurements enrich the available database and suggest that during years when autumn sea ice is >1 m thick, sea-ice biota are limited in activity and biomass.

Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean

AbstractAimInformation on the spatio‐temporal distribution of marine species is essential for developing proactive management strategies. However, sufficient information is seldom available at large spatial scales, particularly in polar areas. The Southern Ocean (SO) represents a critical habitat for various species, particularly migratory baleen whales. Still, the SO’s remoteness and sea ice coverage disallow obtaining sufficient information on baleen whale distribution and niche preference. Here, we used presence‐only species distribution models to predict the circumantarctic habitat suitability of baleen whales and identify important predictors affecting their distribution.LocationThe Southern Ocean (SO).MethodsWe used Maxent to model habitat suitability for Antarctic minke, Antarctic blue, fin and humpback whales. Our models employ extensive circumantarctic data and carefully prepared predictors describing the SO’s environment and two spatial sampling bias correction options. Species‐specific spatial‐block cross‐validation was used to optimize model complexity and for spatially independent model evaluation.ResultsModel performance was high on cross‐validation, with generally little predicted uncertainty. The most important predictors were derived from sea ice, particularly seasonal mean and variability of sea ice concentration and distance to the sea ice edge.Main conclusionsOur models support the usefulness of presence‐only models as a cost‐effective tool in the marine realm, particularly for studying the migratory whales’ distribution. However, we found discrepancies between our results and (within) results of similar studies, mainly due to using different species data quality and quantity, different study area extent and methodological reasons. We further highlight the limitations of implementing static distribution models in the highly dynamic marine realm. Dynamic models, which relate species information to environmental conditions contemporaneous to species occurrences, can predict near‐real‐time habitat suitability, necessary for dynamic management. Nevertheless, obtaining sufficient species and environmental predictors at high spatio‐temporal resolution, necessary for dynamic models, can be challenging from polar regions.

Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent

Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate.

Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.

Spatiotemporal Pattern of Ice Concentration in the Chukchi Sea

The seasonal and long-term variability of sea ice concentration in the Chukchi Sea in 1997–2016 was analyzed using all available satellite data on sea ice conditions. It is shown that the zone of maximum variability is situated in the areas with the ice concentration equal to (4–6)/10 and follows the displacements of this area: it moves to the north in the first half of the warm season (June–November) and moves back to the south in the second half of the period. The analysis of spectral components for the first three empirical orthogonal functions of sea ice concentration revealed quasiperiodic components with the periods of about 2–4 and 6–8 years.

Month-to-Month Variability of Winter Temperature over Northeast China Linked to Sea Ice over the Davis Strait–Baffin Bay and the Barents–Kara Sea

Abstract This study focuses on the month-to-month variability of winter temperature anomalies over Northeast China (NECTA), especially the out-of-phase change between December and January–February (colder than normal in December and warmer than normal in January–February, and vice versa), which accounts for 30% of the past 37 years (1980–2016). Our analysis shows that the variability of sea ice concentration (SIC) in the preceding November over the Davis Strait–Baffin Bay (SIC_DSBB) mainly affects NECTA in December, whereas the SIC over the Barents–Kara Sea (SIC_BKS) significantly impacts NECTA in January–February. A possible reason for the different effects of SIC_DSBB and SIC_BKS on NECTA is that the month-to-month increments (here called DM) of SIC over these two areas between October and November are different. A smaller DM of SIC_DSBB in November can generate eastward-propagating Rossby waves toward East Asia, whereas a larger DM of SIC_BKS can affect upward-propagating stationary Rossby waves toward the stratosphere in November. Less than normal SIC_DSBB in November corresponds to a negative phase of the sea surface temperature tripole pattern over the North Atlantic, which contributes to a negative phase of the North Atlantic Oscillation (NAO)-like geopotential height anomalies via the eddy-feedback mechanism, ultimately favoring cold conditions over Northeast China. However, positive November SIC_BKS anomalies can suppress upward-propagating Rossby waves that originate from the troposphere in November, strengthening the stratospheric polar vortex and leading to a positive phase of an Arctic Oscillation (AO)-like pattern in the stratosphere. Subsequently, these stratospheric anomalies propagate downward, causing the AO-like pattern in the troposphere in January–February, favoring warm conditions in Northeast China, and vice versa.

Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions

The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.

Variability of Arctic Sea Ice (1979–2016)

This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes.

Physical processes responsible for the interannual variability of sea ice concentration in Arctic in boreal autumn since 1979

Arctic sea ice concentration (ASIC) in boreal autumn exhibits prominent interannual variability since 1979. The physical mechanism responsible for the year-to-year variation of ASIC is investigated through observational data analyses and idealized numerical modeling. It is found that the ASIC interannual variability is closely associated with the anomalous meridional circulations over the Northern Hemisphere, which is further linked with the tropical sea surface temperature (SST) forcing. A tropics-wide SST cooling anomaly leads to an enhanced meridional SST gradient to the north of the equator in boreal summer, generating strengthened and northward shifting Hadley circulation over the Northern Hemisphere. Consequently, the meridional circulations are enhanced and pushed poleward, leading to an enhanced descending motion at the North Pole, surrounded by an ascending motion anomaly; the surface outflow turns into easterly anomalies, opposing the mean-state winds. As a result, positive cloudiness and weakened surface wind speed emerge, which reduce ASIC through changes in the surface latent heat flux and the downward longwave radiation.

Under the sea ice: Exploring the relationship between sea ice and the foraging behaviour of southern elephant seals in East Antarctica

Investigating ecological relationships between predators and their environment is essential to understand the response of marine ecosystems to climate variability and change. This is particularly true in polar regions, where sea ice (a sensitive climate variable) plays a crucial yet highly dynamic and variable role in how it influences the whole marine ecosystem, from phytoplankton to top predators. For mesopredators such as seals, sea ice both supports a rich (under-ice) food resource, access to which depends on local to regional coverage and conditions. Here, we investigate sex-specific relationships between the foraging strategies of southern elephant seals (Mirounga leonina) in winter and spatio-temporal variability in sea ice concentration (SIC) and coverage in East Antarctica. We satellite-tracked 46 individuals undertaking post-moult trips in winter from Kerguelen Islands to the peri-Antarctic shelf between 2004 and 2014. These data indicate distinct general patterns of sea ice usage: while females tended to follow the sea ice edge as it extended northward, the males remained on the continental shelf despite increasing sea ice. Seal hunting time, a proxy of foraging activity inferred from the diving behaviour, was longer for females in late autumn in the outer part of the pack ice, ∼150–370km south of the ice edge. Within persistent regions of compact sea ice, females had a longer foraging activity (i) in the highest sea ice concentration at their position, but (ii) their foraging activity was longer when there were more patches of low concentration sea ice around their position (either in time or in space; 30days & 50km). The high spatio-temporal variability of sea ice around female positions is probably a key factor allowing them to exploit these concentrated patches. Despite lack of information on prey availability, females may exploit mesopelagic finfishes and squids that concentrate near the ice-water interface or within the water column (from diurnal vertical migration) in the pack ice region, likely attracted by an ice algal autumn bloom that sustains an under-ice ecosystem. In contrast, male foraging effort increased when they remained deep within the sea ice (420–960km from the ice edge) over the shelf. Males had a longer foraging activity (i) in the lowest sea ice concentration at their position, and (ii) when there were more patches of low concentration sea ice around their position (either in time or in space; 30days & 50km) presumably in polynyas or flaw leads between land fast and pack ice. This provides access to zones of enhanced resources in autumn or in early spring such as polynyas, the Antarctic shelf and slope. Our results suggest that some seals utilized a highly sea ice covered environment, which is key for their foraging effort, sustaining or concentrating resources during winter.

Variability in sea ice cover and climate elicit sex specific responses in an Antarctic predator

Contrasting regional changes in Southern Ocean sea ice have occurred over the last 30 years with distinct regional effects on ecosystem structure and function. Quantifying how Antarctic predators respond to such changes provides the context for predicting how climate variability/change will affect these assemblages into the future. Over an 11-year time-series, we examine how inter-annual variability in sea ice concentration and advance affect the foraging behaviour of a top Antarctic predator, the southern elephant seal. Females foraged longer in pack ice in years with greatest sea ice concentration and earliest sea ice advance, while males foraged longer in polynyas in years of lowest sea ice concentration. There was a positive relationship between near-surface meridional wind anomalies and female foraging effort, but not for males. This study reveals the complexities of foraging responses to climate forcing by a poleward migratory predator through varying sea ice property and dynamic anomalies.

Potential ocean–atmosphere preconditioning of late autumn Barents-Kara sea ice concentration anomaly

Many recent studies have revealed the importance of the climatic state in November on the seasonal climate of the subsequent winter. In particular, it has been shown that interannual variability of sea ice concentration (SIC) over the Barents-Kara (BK) seas in November is linked to winter atmospheric circulation anomaly that projects on the North Atlantic Oscillation. Understanding the lead–lag processes involving the different components of the climate system from autumn to winter is therefore important. This note presents dynamical interpretation for the ice-ocean–atmosphere relationships that can affect the BK SIC anomaly in late autumn. It is found that cyclonic (anticyclonic) wind anomaly over the Arctic in October, by Ekman drift, can be responsible for positive (negative) SIC in the BK seas in November. The results also suggest that ocean heat transport via the Barents Sea Opening in September and October can contribute to BK SIC anomaly in November.

On the link between Barents‐Kara sea ice variability and European blocking

AbstractThis study examines the connection between the variability of sea ice concentration in the Barents and Kara (B‐K) seas and winter European weather on an intraseasonal time scale. Low sea ice regimes in autumn and early winter over the B‐K seas are shown to affect the strength and position of the polar vortex, and increase the frequency of blocking regimes over the Euro‐Atlantic sector in late winter. A hypothesis is presented on the mechanism that links sea ice over the B‐K seas and circulation regimes in the North Atlantic, and is investigated considering 34 years of European Centre for Medium‐Range Weather Forecasts reanalysis data. Four key steps have been identified, starting from a local response of the near‐surface fluxes and modification of the upper tropospheric wave pattern, to the stratospheric adjustment and the tropospheric response in the North Atlantic. The proposed mechanism explains the delayed, late winter response of the North Atlantic Oscillation to the late autumn sea ice reduction, which has been found both in observations and model experiments. It also provides valuable insights on how the reduction of Arctic sea ice can influence the position of the tropospheric jet in the Euro‐Atlantic sector.

Investigating the effects of ocean layering and sea ice cover on acoustic propagation in the Beaufort Sea

Over the past several decades, the Beaufort Gyre has experienced changes in sea-ice freshwater accumulation and ocean stratification which has implications for long-range acoustic propagation. In this talk, acoustic propagation from the Canadian Basin to the Alaskan Beaufort Shelf is modeled using measurements of physical oceanography and sea ice. Water masses that impact acoustic propagation and stratification in the basin include the warm, saline Atlantic Water (AW), which is overlain by cooler, less-saline Pacific Winter Water (PWW). Oceanographic observations reveal intrusions of a warmer, fresher water mass called Pacific Summer Water (PSW). This water mass resides below the surface mixed layer, but above the PWW and reduces acoustic interaction with the sea-ice canopy for source depths located in the halocline duct. Oceanographic data indicate that on the continental shelf, the PSW intrusion can be absent in the ice-covered months resulting in an upward refracting sound speed profile. Using measurements from ice-tethered profilers in the basin and oceanographic moorings on the shelf, we model the temporal and spatial variability of the acoustic field. The effect of scattering from the ice cover is included, with consideration given to the seasonal variability of sea-ice concentration, thickness, and acoustic properties. [Work supported by ONR.]

Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

Reemergence Mechanisms for North Pacific Sea Ice Revealed through Nonlinear Laplacian Spectral Analysis*

Abstract This paper studies spatiotemporal modes of variability of sea ice concentration and sea surface temperature (SST) in the North Pacific sector in a comprehensive climate model and observations. These modes are obtained via nonlinear Laplacian spectral analysis (NLSA), a recently developed data analysis technique for high-dimensional nonlinear datasets. The existing NLSA algorithm is modified to allow for a scale-invariant coupled analysis of multiple variables in different physical units. The coupled NLSA modes are utilized to investigate North Pacific sea ice reemergence: a process in which sea ice anomalies originating in the melt season (spring) are positively correlated with anomalies in the growth season (fall) despite a loss of correlation in the intervening summer months. It is found that a low-dimensional family of NLSA modes is able to reproduce the lagged correlations observed in sea ice data from the North Pacific Ocean. This mode family exists in both model output and observations and is closely related to the North Pacific gyre oscillation (NPGO), a low-frequency pattern of North Pacific SST variability. Moreover, this mode family provides a mechanism for sea ice reemergence in which summer SST anomalies store the memory of spring sea ice anomalies, allowing for sea ice anomalies of the same sign to appear in the fall season. Lagged correlations in model output and observations are significantly strengthened by conditioning on the NPGO mode being active, in either positive or negative phase. Another family of NLSA modes, related to the Pacific decadal oscillation (PDO), is found to capture a winter-to-winter reemergence of SST anomalies.