Variability Of Sea Ice Extent Research Articles

Short‐term Impacts of Arctic Summer Cyclones on Sea Ice Extent in the Marginal Ice Zone

AbstractThe degree to which Arctic cyclones locally affect sea ice cover during the melt season is unclear. To address this, we use the ERA‐5 reanalysis to statistically analyze how surface energy fluxes and wind forcing from Arctic cyclones in the marginal ice zone between May and August (1999–2018) locally affect sea ice extent on 1–10 day time scales. In May and June, cyclones decelerate the local seasonal loss of sea ice extent compared to when no cyclone is present, which we hypothesize is due to cyclones reducing net shortwave radiative fluxes at the surface. By July and August, cyclones no longer decelerate the seasonal loss of sea ice extent, despite still reducing the net surface energy flux. Surface wind forcing across the ice edge only explains up to 13.5% of the variance in local sea ice extent in August, suggesting that processes other than wind‐induced drift and atmospheric energy fluxes drive late‐summer sea ice extent variability.

Modeling the annual cycle of daily Antarctic sea ice extent

Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to the multi-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation in daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016.

Southern Ocean carbon sink enhanced by sea-ice feedbacks at the Antarctic Cold Reversal

The Southern Ocean occupies some 14% of the planet’s surface and plays a fundamental role in the global carbon cycle and climate. It provides a direct connection to the deep ocean carbon reservoir through biogeochemical processes that include surface primary productivity, remineralisation at depth, and the upwelling of carbon-rich water masses. However, the role of these different processes in modulating past and future air-sea carbon flux remains poorly understood. A key period in this regard is the Antarctic Cold Reversal (ACR, 14.6-12.7 kyr BP), a period of mid- to high-latitude cooling that coincided with a sustained plateau in deglacial atmospheric rise in CO2 globally. Here we reconstruct high-latitude Southern Ocean surface productivity from marine derived aerosols captured in a highly-resolved horizontal ice core. Our multiproxy reconstruction reveals a coherent signal of enhanced productivity across the ACR. Transient climate modelling indicates this period coincided with maximum seasonal variability in sea-ice extent, implying that sea-ice biological feedbacks enhanced CO2 sequestration, creating a significant regional marine carbon sink that contributed to the sustained plateau in CO2 at the ACR. Our results highlights the role Antarctic sea ice plays in controlling global CO2, and demonstrates the need to incorporate such feedbacks in climate-carbon models.

The linkages between Antarctic sea ice extent and Indian summer monsoon rainfall

Teleconnection between the Antarctic sea ice and the tropical climate has been extensively investigated. This study examines the interannual relationship between the variability of sea ice extent in the Indian Ocean sector (20–90°E) and Indian summer monsoon rainfall under the influence of the Mascarene High. Sea ice extent during April-May-June (AMJ) appears to have a significant correlation with the summer monsoon rainfall over Peninsular India region during June-July-August-September from 1979 to 2013. Composites of mean sea level pressure (MSLP), 500 hPa geopotential height, and 850 hPa wind anomalies during high and low ice phases show a positive relation between the sea ice extent and the Mascarene High, revealing that high (low) ice phase corresponds respectively to the strengthening (weakening) of the Mascarene High as well as an increase (decrease) in Indian summer monsoon rainfall. During the respective high (low) ice phase years, positive (negative) MSLP anomalies were found, particularly over the Mascarene High region, associated with the eastwards (westwards) shifts of its climatology locations. Similar features were observed at 500 hPa geopotential height anomalies. In addition, strong anticyclonic (cyclonic) anomalies in the Mascarene High region were found in 850 hPa winds, which led to corresponding strong (weak) south westerlies and thus respective positive (negative) Indian summer monsoon rainfall anomalies.

An assessment of diatom assemblages in the Sea of Okhotsk as a proxy for sea-ice cover

Abstract. Knowledge of past variations in sea-ice extent is crucial for understanding the relationship between climate change and changes in sea ice. Diatom assemblages could be applied as a proxy for paleo-sea-ice extent; this requires accurate information on the modern species that are indicative of sea ice. Scanning electron microscope observations were performed on modern diatom assemblages in sea ice, sinking particles, and surface sediments in the Sea of Okhotsk. A sea-ice sample was collected in the southwestern Sea of Okhotsk near Hokkaido island in February 2013. Fragilariopsis cylindrus was the dominant diatom species in the sea-ice sample, accounting for 87 % of the total diatom assemblage. Time-series sediment traps were deployed during 1998–2000 at two stations, M4 and M6, off Sakhalin island. Total diatom fluxes ranged from 105 to 108 valves m−2 d−1 with noticeable seasonality. During the sea-ice covering period, the total diatom flux decreased by 1 or 2 orders of magnitude. The highest diatom fluxes were observed in spring and summer. The diatom species composition in sinking particles also showed pronounced seasonal changes. During summer and fall, the Shionodiscus trifultus group and Neodenticula seminae were the major diatom taxa. During the sea-ice covering period, Fragilariopsis cylindrus and Bacterosira bathyomphala resting spores were abundant. Both the sea-ice-related species showed similar flux patterns except for the spring bloom after sea-ice retreat: F. cylindrus fluxes exhibited pronounced spring bloom peaks of 108 valves m−2 d−1; in contrast, the fluxes of Bacterosira bathyomphala resting spores during the spring bloom were 1 order of magnitude lower than those of F. cylindrus. Surface-sediment core XP98-MC4 was obtained near station M6 sediment-trap site off Sakhalin island. The relative abundance of Fragilariopsis cylindrus in the surface-sediment diatom assemblage was only 6.4 %, markedly lower than that in the sediment-trap samples (43.4 %). In the surface sediment, the relative abundances of diatom taxa with heavily silicified valves such as B. bathyomphala resting spores, Shionodiscus variantius, and Thalassionema nitzschioides were greater than their relative abundances in sinking particles.

Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill.

Arctic Sea‐Ice Variability During the Instrumental Era

AbstractArctic sea‐ice extent (SIE) has declined drastically in recent decades, yet its evolution prior to the satellite era is highly uncertain. Studies using SIE observations find little variability prior to the 1970s; however, these reconstructions are based on limited data, especially prior to the 1950s. We use ensemble Kalman filter data assimilation of surface air temperature observations with Last Millennium climate model simulations to create a fully gridded Arctic sea‐ice concentration reconstruction from 1850 to 2018 and investigate the evolution of Arctic SIE during this period. We find a decline of ∼1.25×106 km2 during the early 20th‐century warming (1910–1940). The 25‐year trends during this period are ∼33–38% smaller than the satellite era (1979–2018) but almost twice as large as previous estimates. Additionally, we find that variability of SIE on decadal timescales prior to the satellite era is ∼40% greater than previously estimated.

The observed recent surface air temperature development across Svalbard and concurring footprints in local sea ice cover

AbstractThe Svalbard archipelago in the Arctic North Atlantic is experiencing rapid changes in the surface climate and sea ice distribution, with impacts for the coupled climate system and the local society. This study utilizes observational data of surface air temperature (SAT) from 1980–2016 across the whole Svalbard archipelago, and sea ice extent (SIE) from operational sea ice charts to conduct a systematic assessment of climatologies, long‐term changes and regional differences. The proximity to the warm water mass of the West Spitsbergen Current drives a markedly warmer climate in the western coastal regions compared to northern and eastern Svalbard. This imprints on the SIE climatology in southern and western Svalbard, where the annual maxima of 50–60% area ice coverage are substantially less than 80–90% in the northern and eastern fjords. Owing to winter‐amplified warming, the local climate is shifting towards more maritime conditions, and SIE reductions of between 5 and 20% per decade in particular regions are found, such that a number of fjords in the west have been virtually ice‐free in recent winters. The strongest decline comes along with SAT forcing and occurs over the most recent 1–2 decades in all regions; while in the 1980s and 1990s, enhanced northerly winds and sea ice drift can explain 30–50% of SIE variability around northern Svalbard, where they had correspondingly lead to a SIE increase. With an ongoing warming it is suggested that both the meteorological and cryospheric conditions in eastern Svalbard will become increasingly similar to what is already observed in the western fjords, namely suppressed typical Arctic climate conditions.

Impacts of the Cryosphere and Atmosphere on Observed Microseisms Generated in the Southern Ocean

The Southern Ocean (in the region 60–180° E) south of the Indian Ocean, Australia, and the West Pacific is noted for the frequent occurrence and severity of its storms. These storms give rise to high‐amplitude secondary microseisms from sources, including the deep ocean regions, and primary microseisms where the swells impinge on submarine topographic features. A better understanding of the varying microseism wavefield enables improvements to seismic imaging and development of proxy observables to complement sparse in situ wave observations and hindcast models of the global ocean wave climate. We analyze 12–26 years of seismic data from 11 seismic stations either on the East Antarctic coast or sited in the Indian Ocean, Australia, and New Zealand. The power spectral density of the seismic wavefield is calculated to explore how the time‐changing microseism intensity varies with (i) sea ice coverage surrounding Antarctica and (ii) the Southern Annular Mode (SAM) climate index. Variations in sea ice extent are found to be the dominant control on the microseism intensity at Antarctic stations, which exhibit a seasonal pattern phase‐shifted by 4–5 months compared to stations in other continents. Peaks in extremal intensity at East Antarctic stations occur in March–April, with the highest peaks for secondary microseisms occurring during negative SAM events. This relationship between microseism intensity and the SAM index is opposite to that observed on the Antarctic Peninsula. This work informs the complexity of microseism amplitudes in the Southern Hemisphere and assists ongoing interdisciplinary investigations of interannual variability and long‐term trends.

Constraining Uncertainties in CMIP5 Projections of September Arctic Sea Ice Extent with Observations

AbstractIn agreement with observations, Earth system models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulate a decline in September Arctic sea ice extent (SIE) over the past decades. However, the spread in their twenty-first-century SIE projections is large and the timing of the first ice-free Arctic summer ranges from 2020 to beyond 2100. The uncertainties arise from three sources (internal variability, model uncertainty, and scenario uncertainty), which are quantified in this study for projections of SIE. The goal is to narrow uncertainties by applying multiple diagnostic ensemble regression (MDER). MDER links future projections of sea ice extent to processes relevant to its simulation under present-day conditions using data covering the past 40 years. With this method, we can reduce model uncertainty in projections of SIE for the period 2020–44 by 30%–50% (0.8–1.3 million km2). Compared to the unweighted multimodel mean, the MDER-weighted mean projects an about 20% smaller SIE and an earlier near-disappearance of Arctic sea ice by more than a decade for a high–greenhouse gas scenario. We also show that two different methods estimating internal variability in SIE differ by 1 million km2. Regardless, the total uncertainties in the SIE projections remain large (up to 3.5 million km2, with irreducible internal variability contributing 30%) so that a precise time estimate of an ice-free Arctic proves impossible. We conclude that unweighted CMIP5 multimodel-mean projections of Arctic SIE are too optimistic and mitigation strategies to reduce Arctic warming need to be intensified.

Evaluation of the effect of insolation factor on sea ice extent variability in the Russian Arctic.

Evaluation of the effect of insolation factor on sea ice extent variability in the Russian Arctic.

Оценка роли инсоляционного фактора в изменениях площади морских льдов в российской Арктике

Оценка роли инсоляционного фактора в изменениях площади морских льдов в российской Арктике

Possible influence of the tropospheric polar vortex on the Barents Sea ice extent in winter

The Arctic seas are mainly located inside the polar vortex under conditions of low temperature. Sea ice extent in these seas in the winter-spring period reaches maximum values with negligible interannual changes. The exception is the Barents Sea, where interannual variability of sea ice extent increases from December to February. In winter, the vortex edge is often located near the Barents Sea, which leads to an increase in surface temperature and a decrease in sea ice extent. We show that the Barents Sea ice extent in winter can be determined by the shape and location of the tropospheric polar vortex.

Radiative Control of the Interannual Variability of Arctic Sea Ice

AbstractOn top of a declining trend driven by global warming, the Arctic sea ice extent (SIE) exhibits considerable interannual variations. In this study, we analyze the interannual anomalies of September SIE in relation to the surface radiation anomalies. We find that the accumulation of radiation energy in the early months (June, July, and August) very well explains the September SIE variability (R2 = 0.81). In Particular, strong correlations are found between September SIE and June radiation anomalies, which in the shortwave is due to cloud and surface albedo changes and in the longwave due to atmospheric warming. The results show that monitoring the radiation anomalies affords a potential means to improve the prediction of the late summer sea ice.

Seasonal forecast of sea ice extent in the Barents sea

Earlier, the authors established a close relationship between the temperature of water coming from the North Atlantic and the sea ice extent (SIE) in the Barents Sea, which accounts for up to 75 % of the inter-annual variability of the monthly SIE from January to June. In turn, temperature variations of the incoming Atlantic water are affected from anomalies of sea surface temperature (SST) in the low latitudes of the North Atlantic. These dependences served as the basis for the development of a forecast method. The empirical orthogonal functions decomposition of the SIE set from January to June for 1979–2014 was used. The main component of decomposition reflects 83 % of the inter-annual variability of SIE from January to June. Regression model of forecast is based on the relation of the main component with SST anomalies taking into account the delay. Comparison of prognostic and actual values of the climatic component for each of the 6 months showed the correctness of forecasts with a lead time of 27 to 32 months is 83 %, and for the prediction of the initial values of SIE 79 %. Appealing to the second predictor — SST anomalies in the Norwegian Sea allowed to improve the quality of the forecast of the observed values of SIE. At the same time, the forecast advance time was reduced to 9–14 months.

The Impact of Stratospheric Circulation Extremes on Minimum Arctic Sea Ice Extent

AbstractGiven the rapidly changing Arctic climate, there is an urgent need for improved seasonal predictions of Arctic sea ice. Yet, Arctic sea ice prediction is inherently complex. Among other factors, wintertime atmospheric circulation has been shown to be predictive of summertime Arctic sea ice extent. Specifically, many studies have shown that the interannual variability of summertime Arctic sea ice extent (SIE) is anticorrelated with the leading mode of extratropical atmospheric variability, the Arctic Oscillation (AO), in the preceding winter. Given this relationship, the potential predictive role of stratospheric circulation extremes and stratosphere–troposphere coupling in linking the AO and Arctic SIE variability is examined. It is shown that extremes in the stratospheric circulation during the winter season, namely, stratospheric sudden warming (SSW) and strong polar vortex (SPV) events, are associated with significant anomalies in sea ice concentration in the Barents Sea in spring and along the Eurasian coastline in summer in both observations and a fully coupled, stratosphere-resolving general circulation model. Consistent with previous work on the AO, it is shown that SSWs, which are followed by the negative phase of the AO at the surface, result in sea ice growth, whereas SPVs, which are followed by the positive phase of the AO at the surface, result in sea ice loss, although the mechanisms in the Barents Sea and along the Eurasian coastline are different. The analysis suggests that the presence or absence of stratospheric circulation extremes in winter may play a nontrivial role in determining total September Arctic SIE when combined with other factors.

Spatiotemporal dependence of Antarctic sea ice variability to dynamic and thermodynamic forcing: a coupled ocean–sea ice model study

Satellite-derived Antarctic sea ice extent has displayed a slight upward since 1979, but with strong temporal and regional variability—the drivers of which are poorly understood. Here, we conduct numerical experiments with a circum-Antarctic ocean–sea ice–ice shelf model driven by realistic atmospheric surface boundary conditions to examine the factors responsible for the temporal and spatial patterns in observed Antarctic sea ice variability. The model successfully reproduces observed seasonal and interannual variability in total sea ice extent and the temporal/spatial patterns of sea ice concentration and seasonality (days of advance and retreat and actual ice days) for 1979–2014. Sensitivity experiments are performed, in which the interannual variability in wind stress or thermodynamic surface forcing is ignored, to delineate their contributions to Antarctic sea ice fields. The results demonstrate that: (1) thermodynamic forcing plays a key role in driving interannual variability in sea ice extent and seasonality in most Antarctic sectors; (2) only in the Ross Sea the wind stress does become the main driver of sea ice extent variability; (3) thermodynamic forcing largely regulates interannual variability in the timing of sea ice advance, while wind stress largely controls the timing of the sea ice retreat; and (4) although both wind stress and thermodynamic forcing contribute to variability in total sea ice volume, the wind stress plays a dominant role in regulating sea ice volume variability in the near-coastal zone.

Variation of sea ice extent in different regions of the Arctic Ocean

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979–2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4–6 years. The year of 2003–2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature.

The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20×106 km2 for the first time during the satellite era. The positive trend is confirmed with a newly reprocessed sea ice data that addressed inconsistency issues in the time series. The variability in sea ice extent and ice area was studied alongside surface ice temperature for the 34-year period starting 1981 and the result of the analysis show a strong correlation of -0.94 during the growth season and -0.86 during the melt season. The correlation coefficients are even stronger with a one-month lag in surface temperature at -0.96 during the growth season and -0.98 during the melt season suggesting that the trend in sea ice cover is strongly influenced by the trend in surface temperature. The correlation with atmospheric circulation as represented by the Southern Annular Mode (SAM) index appears to be relatively weak. A case study comparing the record high in 2014 with a relatively low ice extent in 2015 also shows strong sensitivity to changes in surface temperature. The results suggest that the positive trend is a consequence of the spatial variability of global trends in surface temperature and that the ability of current climate models to forecast sea ice trend can be improved through better performance in reproducing observed surface temperatures in the Antarctic region.

Sea salt sodium record from Talos Dome (East Antarctica) as a potential proxy of the Antarctic past sea ice extent

Antarctic sea ice has shown an increasing trend in recent decades, but with strong regional differences from one sector to another of the Southern Ocean. The Ross Sea and the Indian sectors have seen an increase in sea ice during the satellite era (1979 onwards). Here we present a record of ssNa+ flux in the Talos Dome region during a 25-year period spanning from 1979 to 2003, showing that this marker could be used as a potential proxy for reconstructing the sea ice extent in the Ross Sea and Western Pacific Ocean at least for recent decades. After finding a positive relationship between the maxima in sea ice extent for a 25-year period, we used this relationship in the TALDICE record in order to reconstruct the sea ice conditions over the 20th century. Our tentative reconstruction highlighted a decline in the sea ice extent (SIE) starting in the 1950s and pointed out a higher variability of SIE starting from the 1960s and that the largest sea ice extents of the last century occurred during the 1990s.