Ice Thickness Anomalies Research Articles

Predictability of the Minimum Sea Ice Extent from Winter Fram Strait Ice Area Export: Model versus Observations

Abstract Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

The role of oceanic heat flux in reducing thermodynamic ice growth in Nares Strait and promoting earlier collapse of the ice bridge

Abstract. The ice bridge in Nares Strait is a well-known phenomenon that affects the liquid and solid freshwater flux from the Arctic Ocean through the strait and controls the downstream North Open Water polynya in northern Baffin Bay. Recently, the ice bridge has been in a state of decline, either breaking up earlier in the year or not forming at all and thereby increasing the sea ice export out of the Arctic Ocean. The decline in the ice bridge has been ascribed to thinner and therefore weaker ice from the Arctic Ocean entering Nares Strait; however, local forcing also affects the state of the ice bridge and thereby influences when it breaks up. Using a variety of remotely sensed data we examine the spatial patterns of sea ice thickness within the ice bridge, highlighting the presence of negative ice thickness anomalies on both the eastern and western sides of the strait and identifying a recurrent sensible heat polynya that forms within the ice bridge near Cape Jackson in northwestern Greenland. Using the sea ice–ocean model FESOM2, we then attribute these ice thickness anomalies to the heat from warmer subsurface waters of Pacific and Atlantic origin that reduce thermodynamic ice growth throughout winter on the western and eastern sides, respectively. The consequently weaker and thinner areas within the ice bridge are then suggested to promote instability and earlier breakup. This work provides new insight into the structure of the Nares Strait ice bridge and highlights that warming of the modified Atlantic and/or Pacific waters that enter the strait may contribute to its further decline.

A Regional Seasonal Forecast Model of Arctic Minimum Sea Ice Extent: Reflected Solar Radiation versus Late Winter Coastal Divergence

AbstractThinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum sea ice extent (SIE). The current generation of forced or fully coupled models, however, has difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is coastal divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in reflected solar radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE), and CD are compared as predictors using a regional seasonal sea ice forecast model for 1 July, 1 June, and 1 May forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface; that is, June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June melt pond fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate the presence of ice or open water, which is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF, and the minimum SIE. A hindcast model shows that for a 1 May forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

Seasonal Prediction and Predictability of Regional Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

Subseasonal Predictability of Arctic Ocean Sea Ice Conditions: Bering Strait and Ekman-Driven Ocean Heat Transport

AbstractWe use ocean observations and reanalyses to investigate the subseasonal predictability of summer and fall sea ice area (SIA) in the western Arctic Ocean associated with lateral ocean heat transport (OHT) through Bering Strait and vertical OHT along the Alaskan coastline from Ekman divergence and upwelling. Results show predictive skill of spring Bering Strait OHT anomalies in the Chukchi Sea and eastern East Siberian Sea for June and July SIA, followed by a sharp drop in predictive skill in August, September, and October and a resurgence of the correlation in November during freeze-up. Fall upwelling of Pacific Water along the Alaskan coastline—a mechanism that was proposed as a preconditioner for lower sea ice concentration (SIC) in the Beaufort Sea the following summer—shows minimal predictive strength on both local and regional scales for any months of the melt season. A statistical hindcast based on May Bering Strait OHT anomalies explains 77% of July Chukchi Sea SIA variance. Using OHT as a predictor of SIA anomalies in the Chukchi Sea improves hindcasts from the simple linear trend by 35% and predictions from spring sea ice thickness anomalies by 24%. This work highlights the importance of ocean heat anomalies for melt season sea ice prediction and provides observational evidence of subseasonal changes in forecast skill observed in model-based forecasts of the Chukchi Sea.

Interseasonal Connections between the Timing of the Stratospheric Final Warming and Arctic Sea Ice

AbstractConnections across seasons in atmospheric circulation and sea ice have long been sought to advance seasonal prediction. This study presents a link between the springtime stratosphere and Arctic sea ice in summer through autumn. The polar stratospheric vortex dominates the winter stratosphere before breaking down each spring, which is called the stratospheric final warming, as solar radiation returns to the pole. Interannual variability of this breakdown is dynamically driven, leading to different springtime tropospheric and surface circulation patterns. To examine the different impacts of delayed and early final warmings, a multimodel composite was generated from selected CMIP5 models. Additionally, regressions were performed on JRA-55 against an index of springtime polar vortex strength. In both the multimodel composites and reanalysis regressions, significant anomalies in sea ice thickness persist several months following an anomalous timing of the final warming. A later final warming or stronger springtime polar stratospheric vortex leads to negative sea ice thickness anomalies in the East Siberian Sea and positive anomalies in the Beaufort Sea in comparison with an earlier final warming or weaker polar vortex. The spring polar stratospheric vortex is related to spring polar surface circulation patterns. The winds associated with this pattern induce anomalous sea ice motion, moving ice from the East Siberian Sea toward the Beaufort Sea. Reduced sea ice in the East Siberian Sea is linked to anomalous warmth over this region in autumn. Our results suggest that the timing of the stratospheric final warming exerts an influence on the tropospheric circulation and sea ice through autumn, which has implications for seasonal climate prediction.

On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

Characteristics of Arctic Sea-Ice Thickness Variability in GCMs

Abstract Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

Decrease of sea ice thickness at Hopen, Barents Sea, during 1966–2007

Seasonal fast ice thickness at the island of Hopen (Barents Sea) was monitored over 40 years. Sea ice thickness variability as a climate indicator provides more quantitative information on the state of the ice cover than solely sea ice extent. Usually, starting to form just before December Hopen fast ice reaches maximum thickness in May (on average 0.99 m), before the ice starts to decay. Swell, currents, and winds interrupt the fast ice development at Hopen during several of the winters observed, leading to ice removal and new ice formation. Since 2000, no ice thicker than 1.0 m was observed. We find a trend in the ice thickness anomalies of −0.11 m per decade, coinciding with decreasing seasonal maximum ice thickness, and an increase in local surface air and water temperatures. This is consistent with the decreasing sea ice extent in the Barents Sea and the entire Arctic.

Variability of Fram Strait sea ice export: causes, impacts and feedbacks in a coupled climate model

Analyses of a 500-year control integration of the global coupled atmosphere–sea ice–ocean model ECHAM5.0/MPI-OM show a high variability in the ice export through Fram Strait on interannual to decadal timescales. This variability is mainly determined by variations in the sea level pressure gradient across Fram Strait and thus geostrophic wind stress. Ice thickness anomalies, formed at the Siberian coast and in the Chukchi Sea, propagate across the Arctic to Fram Strait and contribute to the variability of the ice export on a timescale of about 9 years. Large anomalies of the ice export through Fram Strait cause fresh water signals, which reach the Labrador Sea after 1–2 years and lead to significant changes in the deep convection. The associated anomalies in ice cover and ocean heat release have a significant impact on air temperature in the Labrador Sea and on the large-scale atmospheric circulation. This affects the sea ice transport and distribution in the Arctic again. Sensitivity studies, simulating the effect of large ice exports through Fram Strait, show that the isolated effect of a prescribed ice/fresh water anomaly is very important for the climate variability in the Labrador Sea. Thus, the ice export through Fram Strait can be used for predictability of Labrador Sea climate up to 2 years in advance.

Temporal and spatial variation of shore-fast ice in the Kara Sea

The present study of temporal and spatial variations of shore-fast ice in the Kara Sea includes utilization of aircraft observations by the Arctic and Antarctic Research Institute (AARI) from 1953 to 1990, and surface brightness temperature from SSM/I from 1987 to 2001. Analyses of a time series of spatially distributed SSM/I brightness temperatures provide estimates of the shore-fast ice extent. The present work shows spatial distributions of frequencies of shore-fast ice coverage in the Kara Sea for each month, and it gives a time series of its area. The fast ice extent in early spring typically reaches a stable configuration, which falls into one of two different categories (modes) with significantly different area. The smaller of these two modes tends to appear with increased frequency since 1980. This gives, for this period, a negative trend in fast ice area during spring. There is a correlation above 0.5 between the average winter temperature and maximum ice thickness anomalies and fast ice area. Analysis of surface wind data and sea level pressure indicates that wind during winter controls much of the shore-fast ice development. Specially strong westerly and southwesterly winds tend to give a smaller fast ice extent than otherwise. Variations in river discharge tend only to have an observable effect on the ice at the river mouths.

Formation and propagation of great salinity anomalies

North Atlantic/Arctic ocean and sea ice variability for the period 1948–2001 is studied using a global Ocean General Circulation Model coupled to a dynamic/thermodynamic sea ice model forced by daily NCEP/NCAR reanalysis data [Kalnay et al., 1996]. Variability of Arctic sea ice properties is analysed, in particular the formation and propagation of sea ice thickness anomalies that are communicated via Fram Strait into the North Atlantic. These export events led to the Great Salinity Anomalies (GSA) of the 1970s, 1980s and 1990s in the Labrador Sea (LS). All GSAs were found to be remotely excited in the Arctic, rather than by local atmospheric forcing over the LS. Sea ice and fresh water exports through the Canadian Archipelago (CAA) are found to be only of minor importance, except for the 1990s GSA. Part of the anomalies are tracked to the Newfoundland Basin, where they enter the North Atlantic Current. The experiments indicate only a minor impact of a single GSA event on the strength of the North Atlantic Thermohaline Circulation (THC).

Investigating the variability of the Arctic sea ice thickness in response to a stochastic thermodynamic atmospheric forcing

The natural low frequency variability of the sea-ice thickness in the Arctic is investigated based on a 10 000 years simulation with a one-dimensional thermodynamic sea-ice model forced by random perturbations of the air surface temperature and solar radiation. The simulation results suggest that atmospheric random perturbations are integrated by the sea-ice. Moreover those perturbations occurring at the onset of ice melting force the largest ice thickness anomalies, which are successively amplified in summer by the albedo feedback and damped in winter by the feedback of the heat conduction through the ice. They also result in a global shift of the melting season which, in the mean annual cycle, leads to earlier melting as compared to the mean climatological cycle. The power spectrum of the ice anomalies suggests that the thickness of the perennial ice should vary preferentially on a time scale of approximately 20 years. The shape of the spectrum is consistent with that of a first order Markov process in which the characteristic time scale of the ice fluctuations would be the relaxation time scale associated with the linear feedback. The equivalent Markov model is constructed by linearizing the ice growth rate anomaly equations and allows us to derive an analytical expression of the feedback and of the forcing of the anomalies. The characteristic time scale depends explicitly on those model parameters involved in the atmosphere-ice interaction but also on the mean seasonal characteristics of the forcing and of the ice thickness.

Arctic sea ice variability: Model sensitivities and a multidecadal simulation

A dynamic‐thermodynamic sea ice model is used to illustrate a sensitivity evaluation strategy in which a statistical model is fit to the output of the ice model. The statistical model response, evaluated in terms of certain metrics or integrated features of the ice model output, is a function of a selected set of d (= 13) prescribed parameters of the ice model and is therefore equivalent to a d‐dimensional surface. The d parameters of the ice model are varied simultaneously in the sensitivity tests. The strongest sensitivities arise from the minimum lead fraction, the sensible heat exchange coefficient, and the atmospheric and oceanic drag coefficients. The statistical model shows that the interdependencies among these sensitivities are strong and physically plausible. A multidecadal simulation of Arctic sea ice is made using atmospheric forcing fields from 1960 to 1988 and parametric values from the approximate midpoints of the ranges sampled in the sensitivity tests. This simulation produces interannual variations consistent with submarine‐derived data on ice thickness from 1976 and 1987 and with ice extent variations obtained from satellite passive microwave data. The ice model results indicate that (1) interannual variability is a major contributor to the differences of ice thickness and extent over timescales of a decade or less, and (2) the timescales of ice thickness anomalies are much longer than those of ice‐covered areas. However, the simulated variations of ice coverage have less than 50% of their variance in common with observational data, and the temporal correlations between simulated and observed anomalies of ice coverage vary strongly with longitude.