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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Improvement in Simulation of Eurasian Winter Climate Variability with a Realistic Arctic Sea Ice Condition in an Atmospheric GCM

The present study investigates how much a realistic Arctic sea ice condition can contribute to improve simulation of the winter climate variation over the Eurasia region. Model experiments are set up using different sea ice boundary conditions over the past 24 years (i.e., 1988-2011). One is an atmospheric model inter-comparison (AMIP) type of run forced with observed sea-surface temperature (SST), sea ice, and greenhouse gases (referred to as Exp RSI), and the other is the same as Exp RSI except for the sea ice forcing, which is a repeating climatological annual cycle (referred to as Exp CSI). Results show that Exp RSI produces the observed dominant pattern of Eurasian winter temperatures and their interannual variation better than Exp CSI (correlation difference up to approx. 0.3). Exp RSI captures the observed strong relationship between the sea ice concentration near the Barents and Kara seas and the temperature anomaly across Eurasia, including northeastern Asia, which is not well captured in Exp CSI. Lagged atmospheric responses to sea ice retreat are examined using observations to understand atmospheric processes for the Eurasian cooling response including the Arctic temperature increase, sea-level pressure increase, upper-level jet weakening and cold air outbreak toward the mid-latitude. The reproducibility of these lagged responses by Exp RSI is also evaluated.

sea-ice↗
NASA NTRS2004

ICESat Observations of Arctic Sea Ice: A First Look

Analysis of near-coincident ICESat and RADARSAT imagery shows that the retrieved elevations from the laser altimeter are sensitive to new openings (containing thin ice or open water) in the sea ice cover as well as to surface relief of old and first-year ice. The precision of the elevation estimates, measured over relatively flat sea ice, is approx. 2 cm. Using the thickness of thin-ice in recent openings to estimate sea level references, we obtain the sea-ice freeboard along the altimeter tracks. This step is necessitated by the large uncertainties in the sea surface topography compared to that required for accurate determination of freeboard. Unknown snow depth introduces the largest uncertainty in the conversion of freeboard to ice thickness. Surface roughness is also derived, for the first time, from the variability of successive elevation estimates along the altimeter track. Overall, these ICESat measurements provide an unprecedented view of the Arctic Ocean ice cover at length scales at and above the spatial dimension of the altimeter footprint of approx. 70 m.

Arctic Ocean↗
NASA NTRS2004

ICESat Observations of Arctic Sea Ice: A First Look

Analysis of near-coincident ICESat and RADARSAT imagery shows that the retrieved elevations from the laser altimeter are sensitive to new openings (containing thin ice or open water) in the sea ice cover as well as to surface relief of old and first-year ice. The precision of the elevation estimates, measured over relatively flat sea ice, is approx. 2 cm Using the thickness of thin-ice in recent openings to estimate sea level references, we obtain the sea-ice free-board along the altimeter tracks. This step is necessitated by the large uncertainties in the time-varying sea surface topography compared to that required for accurate determination of free-board. Unknown snow depth introduces the largest uncertainty in the conversion of free-board to ice thickness. Surface roughness is also derived, for the first time, from the variability of successive elevation estimates along the altimeter track Overall, these ICESat measurements provide an unprecedented view of the Arctic Ocean ice cover at length scales at and above the spatial dimension of the altimeter footprint.

Kwok, Ron↗
NASA NTRS2016

Satellite Sea Ice Measurements in the Arctic Ocean

Satellite observations sustained since 1979 have been the primary source of information to reveal Arctic Ocean sea ice extent is diminishing rapidly. Sea ice is also thinning based on historical surface and near-surface records and more recent satellite retrievals. There is a new “normal” environment in the Arctic environment with substantial socio-economic impacts. A key question is: What are the spatial and temporal characteristics of the sea ice thickness distribution throughout the annual cycle, and what is the evolving inter-annual trend? Arctic Ocean sea ice conditions have been determined with passive microwave radiometer, active scatterometer and other satellite instrument measurements recorded by a continuing series of satellites. An important feature of the continuous time series measurements has been the overlap of each new satellite dataset with ongoing measurements so as to provide adequate time intervals for calibration and validation. Satellite instrument diversity has provided both coarse spatial resolution measurements over the entire Arctic Ocean for long-period time series and limited-duration fine spatial resolution data over selected regions for navigation and other applications. Satellite observations of Arctic Ocean sea ice will continue to increase in importance because predictability of sea ice is poor and societal interest is great. Unfortunately, however the sustainability of some critical elements of the current Arctic Ocean satellite measurement suite beyond 2020 remains uncertain, e.g., after the CryoSat-2 and ICESat-2 missions have concluded.

Halpern, David↗
DOE OSTI2023

Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic

To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as a heat and humidity feeding mechanism which can modify Arctic cloud properties. Cloud microphysical properties in the central Arctic are analysed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. The Cloudnet classification algorithm is used to characterize the clouds based on remote sensing observations and the atmospheric thermodynamic state from the observatory on board the research vessel (RV) Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapour transport (WVT) being conveyed towards RV Polarstern has been utilized as a mechanism to associate upwind sea ice conditions with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer. Only a conical sub-sector of sea ice concentration (SIC) and the lead fraction (LF) centred on the RV Polarstern location and extending up to 50 km in radius and with an azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT and selected by LF regimes. Liquid water path of low-level clouds is found to increase as a function of LF, while the ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to generally have a lower cloud base and larger thickness than decoupled clouds. Thermodynamically, for coupled cases the cloud-top temperature is warmer and accompanied by a temperature inversion at the cloud top, whereas the decoupled cases are found to be closely compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface–atmosphere processes. With this formulation, long-term observations can be analysed to enforce the statistical significance of the asymmetries. Furthermore, our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Antarctic Sea Ice Variability and Trends, 1979-2010

In sharp contrast to the decreasing sea ice coverage of the Arctic, in the Antarctic the sea ice cover has, on average, expanded since the late 1970s. More specifically, satellite passive-microwave data for the period November 1978 - December 2010 reveal an overall positive trend in ice extents of 17,100 +/- 2,300 square km/yr. Much of the increase, at 13,700 +/- 1,500 square km/yr, has occurred in the region of the Ross Sea, with lesser contributions from the Weddell Sea and Indian Ocean. One region, that of the Bellingshausen/Amundsen Seas, has, like the Arctic, instead experienced significant sea ice decreases, with an overall ice extent trend of -8,200 +/- 1,200 square km/yr. When examined through the annual cycle over the 32-year period 1979-2010, the Southern Hemisphere sea ice cover as a whole experienced positive ice extent trends in every month, ranging in magnitude from a low of 9,100 +/- 6,300 square km/yr in February to a high of 24,700 +/- 10,000 square km/yr in May. The Ross Sea and Indian Ocean also had positive trends in each month, while the Bellingshausen/Amundsen Seas had negative trends in each month, and the Weddell Sea and Western Pacific Ocean had a mixture of positive and negative trends. Comparing ice-area results to ice-extent results, in each case the ice-area trend has the same sign as the ice-extent trend, but differences in the magnitudes of the two trends identify regions with overall increasing ice concentrations and others with overall decreasing ice concentrations. The strong pattern of decreasing ice coverage in the Bellingshausen/Amundsen Seas region and increasing ice coverage in the Ross Sea region is suggestive of changes in atmospheric circulation. This is a key topic for future research.

Parkinson, C. L.↗
NASA NTRS1979

A large-scale numerical model of sea ice

The described large-scale sea ice model, which is capable of coupling with atmospheric and oceanic models of comparable resolution, simulates the yearly cycle of ice in both the Northern and Southern Hemispheres. Model results for the yearly cycle of sea ice thickness and extent in both the Arctic and the Antarctic are presented. Horizontally the model resolution is approximately 200 km, while vertically four layers - ice, snow, ocean, and atmosphere - are considered. Thermodynamic processes based on energy balances at the various interfaces and dynamic processes based on wind stress, water stress, Coriolis force, internal ice resistance, and the stress from the tilt of the sea surface are incorporated. It is assumed that the ice within a given grid square is of uniform thickness, although each square has a variable percentage of its area ice free.

Parkinson, C. L.↗
NASA NTRS1998

A Modified NASA Team Sea Ice Algorithm for the Antarctic

A recent comparative study of the NASA Team and Bootstrap passive microwave sea ice algorithms revealed significantly different sea ice concentration retrievals in some parts of the Antarctic. The study identified potential reasons for the discrepancies including the influence of sea ice temperature variability on the Bootstrap retrievals and the influence of ice surface reflectivity on the horizontally polarized emissivity in the NASA Team retrievals. In this study, we present a modified version of the NASA Team algorithm which reduces the error associated with the use of horizontally polarized radiance data, while retaining the relative insensitivity to ice temperature variations provided by radiance ratios. By retaining the 19 GHz polarization as an independent variable, we also maintain a relatively large dynamic range in sea ice concentration. The modified algorithm utilizes the 19 GHz polarization (PR19) and both gradient ratios, GRV and GRH defined by (37V-19V)/(37V+19V) and (37H-19H)/(37H+19H), respectively, rather than just GRV used in the current NASA Team algorithm. A plot of GRV versus GRH shows that the preponderance of points lie along a quadratic curve, whereas those points affected by surface reflectivity anomalies deviate from this curve. This serves as a method of identifying the problems points. The 19H brightness temperature of these problem points is increased so they too fall along quadratic curve. Sea ice concentrations derived from AVHRR imagery illustrate the extent to which this method reduces the error associated with surface layering.

Cavalieri, Donald J.↗
DOE OSTI2021

AMOC stability and diverging response to Arctic sea ice decline in two climate models

This study compares the impacts of Arctic sea ice decline on the Atlantic meridional overturning circulation (AMOC) in two configurations of the Community Earth System Model with different horizontal resolution. In a suite of model experiments, we impose radiative imbalance at the ice surface, replicating a loss of sea ice cover comparable to that observed during 1979–2014, and we find dramatic differences in the AMOC response between the two models. In the lower-resolution configuration, the AMOC weakens by about one-third over the first 100 years, approaching a new quasi-equilibrium. By contrast, in the higher-resolution configuration, the AMOC weakens by ~10% during the first 20–30 years followed by a full recovery driven by invigorated deep water formation in the Labrador Sea and adjacent regions. We investigate these differences using a diagnostic AMOC stability indicator, which reflects the AMOC freshwater transport in and out of the basin and hence the strength of the basin-scale salt-advection feedback. This indicator suggests that the AMOC in the lower-resolution model is less stable and more sensitive to surface perturbations, as confirmed by hosing experiments mimicking Arctic freshening due to sea ice decline. Differences between the models’ mean states, including the Atlantic Ocean mean surface freshwater fluxes, control the differences in AMOC stability. Our results demonstrate that the AMOC stability indicator is indeed useful for evaluating AMOC sensitivity to perturbations. We emphasize that, despite the differences in the long-term adjustment, both models simulate a multidecadal AMOC weakening caused by Arctic sea ice decline, relevant to climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.

sea ice↗
NASA NTRS1993

Recent variations of sea ice and air temperature in high latitudes

A gridded sea-ice database, for which the record length is now approaching four decades for the Arctic and two decades for the Antarctic, is summarized here. The sea-ice fluctuations derived from the dataset are characterized by temporal scales of several seasons to several years and spatial scales of 30-180 deg of longitude. The ice data are examined in conjunction with air temperature data for evidence of recent climate change in the polar regions. The arctic sea-ice variations over the past several decades are compatible with the corresponding air temperatures, which show a distinct warming that is strongest over northern land areas during the winter and spring. Statistically significant decreases of the summer extent of arctic ice are apparent in the sea-ice data, and new summer minima have been achieved three times in the past 15 years. There is no significant trend if ice extent in the Arctic during winter or in the Antarctic during any season.

Chapman, William L.↗
NASA NTRS2019

A 40-Y Record Reveals Gradual Antarctic Sea Ice Increases Followed by Decreases at Rates Far Exceeding the Rates Seen in the Arctic

Following over 3 decades of gradual but uneven increases in sea ice coverage, the yearly average Antarctic sea ice extents reached a record high of 12.8 by 10 (sup 6) square kilometers in 2014, followed by a decline so precipitous that they reached their lowest value in the 40-year 1979-2018 satellite multichannel passive-microwave record, 10.7 by 10 (sup 6) square kilometers, in 2017. In contrast, it took the Arctic sea ice cover a full 3 decades to register a loss that great in yearly average ice extents. Still, when considering the 40-year record as a whole, the Antarctic sea ice continues to have a positive overall trend in yearly average ice extents, although at 11,300 plus or minus 5,300 square kilometers per year, this trend is only 50 percent of the trend for 1979-2014, before the precipitous decline. Four of the 5 sectors into which the Antarctic sea ice cover is divided all also have 40-year positive trends that are well reduced from their 2014-2017 values. The one anomalous sector in this regard,the Bellingshausen/Amundsen Seas, has a 40-year negative trend, with the yearly average ice extents decreasing overall in the first 3 decades, reaching a minimum in 2007, and exhibiting an overall upward trend since 2007 (i.e., reflecting a reversal in the opposite direction from the other 4 sectors and the Antarctic sea ice cover as a whole).

Satellite Earth Observations↗
NASA NTRS2019

A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic

Following over 3 decades of gradual but uneven increases in sea ice coverage, the yearly average Antarctic sea ice extents reached a record high of 12.8 × 106(6) sq. km in 2014, followed by a decline so precipitous that they reached their lowest value in the 40-y 1979– 2018 satellite multichannel passive-microwave record, 10.7 × 10^(6) sq. km, in 2017. In contrast, it took the Arctic sea ice cover a full 3 decades to register a loss that great in yearly average ice extents. Still, when considering the 40-y record as a whole, the Antarctic sea ice continues to have a positive overall trend in yearly average ice extents, although at 11,300 ± 5,300 sq. km/y, this trend is only 50% of the trend for 1979–2014, before the precipitous decline. Four of the 5 sectors into which the Antarctic sea ice cover is divided all also have 40-y positive trends that are well reduced from their 2014–2017 values. The one anomalous sector in this regard, the Bellingshausen/Amundsen Seas, has a 40-y negative trend, with the yearly average ice extents decreasing overall in the first 3 decades, reaching a minimum in 2007, and exhibiting an overall upward trend since 2007 (i.e., reflecting a reversal in the opposite direction from the other 4 sectors and the Antarctic sea ice cover as a whole).

sea ice↗
NASA NTRS1987

A microwave radiometer weather-correcting sea ice algorithm

A new algorithm for estimating the proportions of the multiyear and first-year sea ice types under variable atmospheric and sea surface conditions is presented, which uses all six channels of the SMMR. The algorithm is specifically tuned to derive sea ice parameters while accepting error in the auxiliary parameters of surface temperature, ocean surface wind speed, atmospheric water vapor, and cloud liquid water content. Not only does the algorithm naturally correct for changes in these weather conditions, but it retrieves sea ice parameters to the extent that gross errors in atmospheric conditions propagate only small errors into the sea ice retrievals. A preliminary evaluation indicates that the weather-correcting algorithm provides a better data product than the 'UMass-AES' algorithm, whose quality has been cross checked with independent surface observations. The algorithm performs best when the sea ice concentration is less than 20 percent.

Walters, J. M.↗
NASA NTRS1988

Arctic Sea ice studies with passive microwave satellite observations

The objectives of this research are: (1) to improve sea ice concentration determinations from passive microwave space observations; (2) to study the role of Arctic polynyas in the production of sea ice and the associated salinization of Arctic shelf water; and (3) to study large scale sea ice variability in the polar oceans. The strategy is to analyze existing data sets and data acquired from both the DMSP SSM/I and recently completed aircraft underflights. Special attention will be given the high resolution 85.5 GHz SSM/I channels for application to thin ice algorithms and processes studies. Analysis of aircraft and satellite data sets is expected to provide a basis for determining the potential of the SSM/I high frequency channels for improving sea ice algorithms and for investigating oceanic processes. Improved sea ice algorithms will aid the study of Arctic coastal polynyas which in turn will provide a better understanding of the role of these polynyas in maintaining the Arctic watermass structure. Analysis of satellite and archived meteorological data sets will provide improved estimates of annual, seasonal and shorter-term sea ice variability.

Cavalieri, D. J.↗
NASA NTRS2014

Arctic Sea Ice in Transformation: A Review of Recent Observed Changes and Impacts on Biology and Human Activity

Sea ice in the Arctic is one of the most rapidly changing components of the global climate system. Over the past few decades, summer areal extent has declined over 30, and all months show statistically significant declining trends. New satellite missions and techniques have greatly expanded information on sea ice thickness, but many uncertainties remain in the satellite data and long-term records are sparse. However, thickness observations and other satellite-derived data indicate a 40 decline in thickness, due in large part to the loss of thicker, older ice cover. The changes in sea ice are happening faster than models have projected. With continued increasing temperatures, summer ice-free conditions are likely sometime in the coming decades, though there are substantial uncertainties in the exact timing and high interannual variability will remain as sea ice decreases. The changes in Arctic sea ice are already having an impact on flora and fauna in the Arctic. Some species will face increasing challenges in the future, while new habitat will open up for other species. The changes are also affecting peoples living and working in the Arctic. Native communities are facing challenges to their traditional ways of life, while new opportunities open for shipping, fishing, and natural resource extraction.

Sea Ice↗