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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Petty, Alek A.↗
NASA NTRS1997

A Laboratory Study of the Effect of Frost Flowers on C Band Radar Backscatter from Sea Ice

C band images of Arctic sea ice taken by the ERS 1 synthetic aperture radar show transitory regions of enhanced radar backscatter from young sea ice. Published field observations associate this increase with frost flower growth and the capture of blowing snow by the flowers. To investigate the first part of this phenomenon, we carried out a laboratory experiment on the response of C band radar backscatter to frost flowers growing on the surface of newly formed saline ice. The experiment took place in a 5 m by 7 m by 1.2 m deep saline water pool located in a two-story indoor refrigerated facility at the Cold Regions Research and Engineering Laboratory. Sodium chloride ice was grown in this pool at an air temperature of -28 C. The frost flowers first appeared on the ice surface as dendrites and then changed to needles as the ice sheet grew thicker and the surface temperatures became colder. The frost flowers reached to a height of 10-15 mm, and beneath each cluster of frost flowers a slush layer formed to a thickness of approximately 4 mm. Far-field radar measurements of the backscatter from the ice were made at incident angles from 20 to 40 deg and at approximately 6-hour intervals throughout the 3-day period of the experiment. A backscatter minimum occurred early in the flower growth at the time coincident with an abrupt doubling in the ice surface salinity. Once the full flower coverage was achieved, we removed first the crystal flowers and then the slush layer from the ice surface. The results for these cases show that the crystals have little impact on the backscatter, while the underlying slush patches yield a backscatter increase of 3-5 dB over that of bare ice. The laboratory results suggest that this relative backscatter increase of approximately 5 dB can be used as an index to mark the full area coverage of frost flowers.

Nghiem, S. V.↗
NASA NTRS1997

A Laboratory Study of the Effect of Frost Flowers on C Band Radar Backscatter from Sea Ice

C band images of Arctic sea ice taken by the ERS 1 synthetic aperture radar show transitory regions of enhanced radar backscatter from young sea ice. Published field observations associate this increase with frost flower growth and the capture of blowing snow by the flowers. To investigate the first part of this phenomenon, we carried out a laboratory experiment on the response of C band radar backscatter to frost flowers growing on the surface of newly formed saline ice. The experiment took place in a 5 m by 7 m by 1.2 m deep saline water pool located in a two-story indoor refrigerated facility at the Cold Regions Research and Engineering Laboratory. Sodium chloride ice was grown in this pool at an air temperature of -28 C. The frost flowers first appeared on the ice surface as dendrites and then changed to needles as the ice sheet grew thicker and the surface temperatures became colder. The frost flowers reached to a height of 10-15 mm, and beneath each cluster of frost flowers a slush layer formed to a thickness of approximately 4 mm. Far-field radar measurements of the backscatter from the ice were made at incident angles from 20 C to 40 C and at approximately 6-hour intervals throughout the 3-day period of the experiment. A backscatter minimum occurred early in the flower growth at the time coincident with an abrupt doubling in the ice surface salinity. Once the full flower coverage was achieved, we removed first the crystal flowers and then the slush layer from the ice surface. The results for these cases show that the crystals have little impact on the backscatter, while the underlying slush patches yield a backscatter increase of 3-5 dB over that o f bare ice. The laboratory results suggest that this relative backscatter increase of approximately 5 dB can be used as an index to mark the full areal coverage of frost flowers.

Nghiem, S. V.↗
NASA NTRS2004

Modeling and Observational Study of the Global Atmospheric Impacts of Antarctic Sea Ice Anomalies

A combined observational and modeling study considers the linkage between Antarctic sea ice and the climate of non-local latitudes. The observational component is based upon analyses of monthly station observations and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis (NNR). The modeling component consists of simulations of the NCAR Community Climate Model versions 2 (CCM2) and 3 (CCM3) and the recent Community Atmosphere Model (CAM2). A convenient mechanism for communication between the Antarctic region (particularly the Ross Sea area) and the tropics and Northern Hemisphere is examined. The first evidence of this teleconnection came from CCM2 simulations performed during an earlier NASA supported project. Annual-cycle simulations with and without Antarctic sea ice show statistically- significant responses in monsoon precipitation over central and northern China during the month of September. The changes in monsoon precipitation are physically consistent with an intensified southwest Pacific (Northern Hemisphere) subtropical high in response to all Antarctic sea ice being removed and replaced with open water at -1.9"C. The intensified high is the northernmost component of three primary anomalies. The southernmost anomaly includes the Ross Sea area, where sea ice has been removed. An earlier study by Peng and Domros had also found a link between Antarctic sea ice and the East Asian monsoon circulation. The current project has helped to understand the teleconnection.

Bromwich, David H.↗
NASA NTRS1984

The sensitivity of a one-dimensional thermodynamic sea ice model to changes in cloudiness

A thermodynamic sea ice-lead model is used to assess the importance of cloud cover changes to modeled ice thickness. For regions of either permanent multiyear ice or seasonal sea ice, the cloud amount variations have relatively little impact. However, for regions where the presence of summer ice is variable from year to year, the predicted ice thickness is strongly dependent on cloud cover. In general, with a snow covered surface, decreased cloud leads to surface cooling while increased cloud gives rise to a surface warming. For a melting bare ice surface, the reverse occurs. The ice model response time is too long for interannual variations in cloud amount to explain interannual variations in ice thickness and extent. Nevertheless, the implication of the results is that numerical modeling of sea ice distribution requires accurate cloud data or cloud prediction and that trends in cloud cover may lead to significant perturbations in sea ice extent and thickness.

Shine, K. P.↗
NASA NTRS1988

Late Pleistocene variations in Antarctica sea ice. I - Effect of orbital isolation changes. II - Effect of interhemispheric deep-ocean heat exchange

A dynamic-thermodynamic sea-ice model is presently used to ascertain the effects of orbitally-induced insolation changes on Antarctic sea-ice cover; the results thus obtained are compared with modified CLIMAP reconstructions of sea-ice 18,000 years ago. The minor influence exerted by insolation on Pleistocene sea-ice distributions is attributable to a number of factors. In the second part of this investigation, variations in the production of warm North Atlantic Deep Water are proposed as a mechanism constituting the linkage between climate fluctuations in the Northern and Southern hemispheres during the Pleistocene; this hypothesis is tested by examining the sensitivity of the dynamic-thermodynamic model for Antarctic sea-ice changes in vertical ocean heat flux, and comparing the simulations with modified CLIMAP sea-ice maps for 18,000 years ago.

Crowley, Thomas J.↗
NASA NTRS1995

Measurement characteristics and Kinematics of Sea Ice Using Microwave Satellite Data

Changes in sea ice conditions have major effects on the ocean and on weather, but visual observations miss periods of cloudiness and darkness. Microwave radar satellites are the only uninterrupted source of high resolution (30 m) data. Recent developments will allow high-resolution global sea-ice tracking at intervals of a few days.

remote sensing sea ice synthetic aperture radar SA↗
NASA NTRS2002

A Rapidly Declining Arctic Perennial Sea Ice Cover

The perennial sea ice cover in the Arctic is shown to be declining at -8.9 plus or minus 2.0% per decade, using 22 years of satellite data. A sustained decline at this rate would mean the disappearance of the multiyear ice cover during this century and drastic changes in the seasonal characteristics of the Arctic ice cover. An apparent increase in the fraction of second year ice in the 1990s is also inferred suggesting an overall thinning of the ice cover while co-registered satellite surface temperatures show a warming trend of 0.8 plus or minus 0.6 K per decade in summer and a good correlation with the perennial ice data.

Comiso, Josefino C.↗
NASA NTRS1985

Active microwave measurements of Arctic sea ice under summer conditions

Radar provides a valuable tool in the study of sea-ice conditions and the solution of sea-ice operational problems. For this reason, the U.S. and Canada have conducted studies to define a bilateral synthetic aperture radar (SAR) satellite program. The present paper is concerned with work which has been performed to explore the needs associated with the study of sea-ice-covered waters. The design of a suitable research or operational spaceborne SAR or real aperture radar must be based on an adequate knowledge of the backscatter coefficients of the ice features which are of interest. In order to obtain the needed information, studies involving the use of a helicopter were conducted. In these studies L-C-X-Ku-band calibrated radar data were acquired over areas of Arctic first-year and multiyear ice during the first half of the summer of 1982. The results show that the microwave response in the case of sea ice is greatly influenced by summer melt, which produces significant changes in the properties of the snowpack and ice sheet.

Onstott, R. G.↗
NASA NTRS2015

Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed −1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

Arctic Sea↗
NASA NTRS2017

Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice

Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice-ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.

sea ice↗
NASA NTRS1993

Modeling of topographic effects on Antarctic sea ice using multivariate adaptive regression splines

The role of seafloor topography in the spatial variations of the southern ocean sea ice cover as observed (every other day) by the Nimbus 7 scanning multichannel microwave radiometer satellite in the years 1980, 1983, and 1984 is studied. Bottom bathymetry can affect sea ice surface characteristics because of the basically barotropic circulation of the ocean south of the Antarctic Circumpolar current. The main statistical tool used to quantify this effect is a local nonparametric regression model of sea ice concentration as a function of the depth and its first two derivatives in both meridional and zonal directions. First, we model the relationship of bathymetry to sea ice concentration in two sudy areas, one over the Maud Rise and the other over the Ross Sea shelf region. The multiple correlation coefficient is found to average 44% in the Maud Rise study area and 62% in the Ross Sea study area over the years 1980, 1983, and 1984. Second, a strategy of dividing the entire Antarctic region into an overlapping mosaic of small areas, or windows is considered. Keeping the windows small reduces the correlation of bathymetry with other factors such as wind, sea temperature, and distance to the continent. We find that although the form of the model varies from window to window due to the changing role of other relevant environmental variables, we are left with a spatially consistent ordering of the relative importance of the topographic predictors. For a set of three representative days in the Austral winter of 1980, the analysis shows that an average of 54% of the spatial variation in sea ice concentration over the entire ice cover can be attributed to topographic variables. The results thus support the hypothesis that there is a sea ice to bottom bathymetry link. However this should not undermine the considerable influence of wind, current, and temperature which affect the ice distribution directly and are partly responsible for the observed bathymetric effects.

De Veaux, Richard D.↗
NASA NTRS1983

Antartic sea ice, 1973 - 1976: Satellite passive-microwave observations

Data from the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite are used to determine the extent and distribution of Antarctic sea ice. The characteristics of the southern ocean, the mathematical formulas used to obtain quantitative sea ice concentrations, the general characteristics of the seasonal sea ice growth/decay cycle and regional differences, and the observed seasonal growth/decay cycle for individual years and interannual variations of the ice cover are discussed. The sea ice data from the ESMR are presented in the form of color-coded maps of the Antarctic and the southern oceans. The maps show brightness temperatures and concentrations of pack ice averaged for each month, 4-year monthly averages, and month-to-month changes. Graphs summarizing the results, such as areas of sea ice as a function of time in the various sectors of the southern ocean are included. The images demonstrate that satellite microwave data provide unique information on large-scale sea ice conditions for determining climatic conditions in polar regions and possible global climatic changes.

Zwally, H. J.↗
NASA NTRS2015

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

SIPN↗
NASA NTRS1997

ERS satellite microwave radar observations of Antarctic sea-ice dynamics

ERS-1 and ERS-2 scatterometer and synthetic aperture radar (SAR) data are used to monitor and track large and small scale sea ice dynamics in the Southern Ocean, and in particular in the Weddell Sea, Antarctica. Sea ice formation in the Weddell Sea regulates vertical and horizontal thermohaline circulation and influences bottom water production rates. Significant seasonal to interannual variability is observed in the sea ice drift dynamics. Coupled model simulations reproduce this variability and indicate that there is significant interannual variability in Weddelll Sea ice formation, drifts and extent on the El Nino southern oscillation (ENSO) timescale, with a period of approximately eight years. Changes in ice dynamics on these timescales regulate the amount of sea ice divergence and polynya formation. Anomalies in the timing and duration of the opening of the Ronne ice shelf polynya system are closely related to the variability in outflow of Weddell Sea bottom water measured at Joinville Island.

Drinkwater, Mark R.↗
NASA NTRS2014

Spatially Mapped Reductions in the Length of the Arctic Sea Ice Season

Satellite data are used to determine the number of days having sea ice coverage in each year 1979-2013 and to map the trends in these ice-season lengths. Over the majority of the Arctic seasonal sea ice zone, the ice season shortened at an average rate of at least 5 days/decade between 1979 and 2013, and in a small area in the northeastern Barents Sea the rate of shortening reached over 65 days/decade. The only substantial non-coastal area with lengthening sea ice seasons is the Bering Sea, where the ice season lengthened by 5-15 days/decade. Over the Arctic as a whole, the area with ice seasons shortened by at least 5 days/decade is 12.4 × 10(exp 6) square kilimeters, while the area with ice seasons lengthened by at least 5 days/decade is only 1.1 × 10(exp 6) square kilometers. The contrast is even greater, percentage-wise, for higher rates.

Arctic↗
NASA NTRS2015

An Assessment of Southern Ocean Water Masses and Sea Ice During 1988-2007 in a Suite of Interannual CORE-II Simulations

We characterise the representation of the Southern Ocean water mass structure and sea ice within a suite of 15 global ocean-ice models run with the Coordinated Ocean-ice Reference Experiment Phase II (CORE-II) protocol. The main focus is the representation of the present (1988-2007) mode and intermediate waters, thus framing an analysis of winter and summer mixed layer depths; temperature, salinity, and potential vorticity structure; and temporal variability of sea ice distributions. We also consider the interannual variability over the same 20 year period. Comparisons are made between models as well as to observation-based analyses where available. The CORE-II models exhibit several biases relative to Southern Ocean observations, including an underestimation of the model mean mixed layer depths of mode and intermediate water masses in March (associated with greater ocean surface heat gain), and an overestimation in September (associated with greater high latitude ocean heat loss and a more northward winter sea-ice extent). In addition, the models have cold and fresh/warm and salty water column biases centred near 50 deg S. Over the 1988-2007 period, the CORE-II models consistently simulate spatially variable trends in sea-ice concentration, surface freshwater fluxes, mixed layer depths, and 200-700 m ocean heat content. In particular, sea-ice coverage around most of the Antarctic continental shelf is reduced, leading to a cooling and freshening of the near surface waters. The shoaling of the mixed layer is associated with increased surface buoyancy gain, except in the Pacific where sea ice is also influential. The models are in disagreement, despite the common CORE-II atmospheric state, in their spatial pattern of the 20-year trends in the mixed layer depth and sea-ice.

Sea ice↗
NASA NTRS2015

Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.

Taylor, Patrick C.↗