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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Crustal Deformation and Gravitational Effects From Dynamic Ocean Mass Redistribution Impact Projected Sea‐Level Change

As the climate warms, associated changes in ocean dynamics will redistribute sea‐water mass within the ocean, contributing to relative sea‐level change. This mass redistribution will cause additional sea‐level changes due to gravitational self‐attraction, deformation of the solid Earth, and shifts in the Earth's rotation axis (GRD), which are not incorporated in sea‐level projections. Using CMIP6 climate model output, we quantify relative sea‐level changes induced by GRD from ocean‐dynamic mass loading through 2100. These effects act to amplify projected ocean‐dynamic sea‐level patterns, causing sea‐level rise in coastal regions, particularly along wide continental shelves and at high latitudes. On average, the magnitude of such GRD‐induced sea‐level change is equivalent to ∼15% of the signal due to dynamic ocean mass redistribution. Although our results show substantial inter‐model spread, they reveal that GRD‐induced relative sea‐level changes from ocean mass redistribution represent a non‐negligible component of regional sea‐level change and should be considered in projections.

58 GEOSCIENCES↗
DOE OSTI2014

Hexavalent chromium is cytotoxic and genotoxic to hawksbill sea turtle cells

Sea turtles are a charismatic and ancient ocean species and can serve as key indicators for ocean ecosystems, including coral reefs and sea grass beds as well as coastal beaches. Genotoxicity studies in the species are absent, limiting our understanding of the impact of environmental toxicants on sea turtles. Hexavalent chromium (Cr(VI)) is a ubiquitous environmental problem worldwide, and recent studies show it is a global marine pollutant of concern. Thus, we evaluated the cytotoxicity and genotoxicity of soluble and particulate Cr(VI) in hawksbill sea turtle cells. Particulate Cr(VI) was both cytotoxic and genotoxic to sea turtle cells. Concentrations of 0.1, 0.5, 1, and 5 μg/cm{sup 2} lead chromate induced 108, 79, 54, and 7% relative survival, respectively. Additionally, concentrations of 0, 0.1, 0.5, 1, and 5 μg/cm{sup 2} lead chromate induced damage in 4, 10, 15, 26, and 36% of cells and caused 4, 11, 17, 30, and 56 chromosome aberrations in 100 metaphases, respectively. For soluble Cr, concentrations of 0.25, 0.5, 1, 2.5, and 5 μM sodium chromate induced 84, 69, 46, 25, and 3% relative survival, respectively. Sodium chromate induced 3, 9, 9, 14, 21, and 29% of metaphases with damage, and caused 3, 10, 10, 16, 26, and 39 damaged chromosomes in 100 metaphases at concentrations of 0, 0.25, 0.5, 1, 2.5, and 5 μM sodium chromate, respectively. These data suggest that Cr(VI) may be a concern for hawksbill sea turtles and sea turtles in general. - Highlights: • Particulate Cr(VI) is cytotoxic and clastogenic to hawksbill sea turtle cells. • Soluble Cr(VI) is cytotoxic and clastogenic to hawksbill sea turtle cells. • Cr(VI) may be a risk factor for hawksbill sea turtle health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Models

In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes compensation at low frequency, so the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. Finally, the response of Arctic sea ice thickness to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Is Ice-Rafted Sediment in a North Pole Marine Record Evidence for Perennial Sea-ice Cover?

Ice-rafted sediments of Eurasian and North American origin are found consistently in the upper part (13 Ma BP to present) of the Arctic Coring Expedition (ACEX) ocean core from the Lomonosov Ridge, near the North Pole (approximately 88 degrees N). Based on modern sea-ice drift trajectories and speeds, this has been taken as evidence of the presence of a perennial sea-ice cover in the Arctic Ocean from the middle Miocene onwards. However, other high latitude land and marine records indicate a long-term trend towards cooling broken by periods of extensive warming suggestive of a seasonally ice-free Arctic between the Miocene and the present. We use a coupled sea-ice slab-ocean model including sediment transport tracers to map the spatial distribution of ice-rafted deposits in the Arctic Ocean. We use 6 hourly wind forcing and surface heat fluxes for two different climates: one with a perennial sea-ice cover similar to that of the present day and one with seasonally ice-free conditions, similar to that simulated in future projections. Model results confirm that in the present-day climate, sea ice takes more than 1 year to transport sediment from all its peripheral seas to the North Pole. However, in a warmer climate, sea-ice speeds are significantly faster (for the same wind forcing) and can deposit sediments of Laptev, East Siberian and perhaps also Beaufort Sea origin at the North Pole. This is primarily because of the fact that sea-ice interactions are much weaker with a thinner ice cover and there is less resistance to drift. We conclude that the presence of ice-rafted sediment of Eurasian and North American origin at the North Pole does not imply a perennial sea-ice cover in the Arctic Ocean, reconciling the ACEX ocean core data with other land and marine records.

Palaeoclimate↗
NASA NTRS2017

Intercalibration of AMSR2 NASA Team 2 Algorithm Sea Ice Concentrations with AMSR-E Slow Rotation Data

Sea ice estimates from AMSR2 are intercalibrated with AMSR-E fields through a two-step process. First, slow rotation 2 r/min AMSR-E data is used to derive regression equations from colocated pairs of AMSR2 and AMSR-E brightness temperatures (Tb s). The regression equations are used to modify AMSR2 Tb s into AMSR-E equivalent Tb s that are then input into the NASA Team 2 (NT2) sea ice concentration algorithm used for the AMSR-E standard products. The regressed Tb s result in changes in sea ice concentration of a few percent compared to using the original un-regressed AMSR2 Tb s. Next, sea ice estimates from the F17 SSMIS sensor are used as a bridge to compare AMSR-E total sea ice extent estimates in 2010 with AMSR2 total sea ice extent estimates in 2013. Based on this comparison, a further adjustment is made to a weather filter threshold used in the NT2 algorithm to minimize the total extent bias between AMSR2 and AMSR-E using a double-differencing approach. The adjustments reduced apparent bias with AMSR-E from 200 000 km2 for the original unmodified AMSR2 Tb s to –700 and 4700 km2 for the Arctic and Antarctic, respectively. These differences are within the range of previous passive microwave sea ice intercalibrations. The adjusted AMSR2 sea ice fields provide a nearly 15-year time series of sea ice change; depending on the lifetime of AMSR2 and possible follow on sensors, AMSR2 has the potential to be part of a multidecadal record of sea ice change.

remote sensing↗
DOE OSTI2012

Biogeochemistry in Sea Ice: CICE model developments

Polar primary production unfolds in a dynamic sea ice environment, and the interactions of sea ice with ocean support and mediate this production. In spring, for example, fresh melt water contributes to the shoaling of the mixed layer enhancing ice edge blooms. In contrast, sea ice formation in the fall reduces light penetration to the upper ocean slowing primary production in marine waters. Polar biogeochemical modeling studies typically consider these types of ice-ocean interactions. However, sea ice itself is a biogeochemically active medium, contributing a significant and, possibly, essential source of primary production to polar regions in early spring and fall. Here we present numerical simulations using the Los Alamos Sea Ice Model (CICE) with prognostic salinity and sea ice biogeochemistry. This study investigates the relationship between sea ice multiphase physics and sea ice productivity. Of particular emphasis are the processes of gravity drainage, melt water flushing, and snow loading. During sea ice formation, desalination by gravity drainage facilitates nutrient exchange between ocean and ice maintaining ice algal blooms in early spring. Melt water flushing releases ice algae and nutrients to underlying waters limiting ice production. Finally, snow loading, particularly in the Southern Ocean, forces sea ice below the ocean surface driving an upward flow of nutrient rich water into the ice to the benefit of interior and freeboard communities. Incorporating ice microphysics in CICE has given us an important tool for assessing the importance of these processes for polar algal production at global scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. An ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. Finally, we discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Projections of winter polynyas and their biophysical impacts in the Ross Sea Antarctica

Abstract This study investigates winter polynyas in the southern Ross Sea, Antarctica where several polynyas are known to form. Coastal polynyas are areas of lower sea ice concentration and/or thickness along the coast that are otherwise surrounded by more extensive, thicker sea ice pack. Polynyas are also locations where organisms can exploit both the ice substrate and pelagic resources. Using a self organizing map algorithm, we identify polynya events in the Community Earth System Model Version 2 Large Ensemble (CESM2-LE). The neural network algorithm is able to identify polynya events without imposing an ice concentration or thickness threshold, as is often done when identifying polynyas. The CESM2-LE produces a wintertime polynya feature comparable in size and location to the Ross Sea polynya, and during polynya events there are large turbulent heat fluxes and export of sea ice from the Ross Sea. In the CESM2-LE polynya event frequency is projected to decrease sharply in the later twentyfirst century, leading to increasing sea ice concentrations and thicknesses in the region. The drivers of the polynya frequency decline are likely both large scale circulation changes and local atmosphere and ocean feedbacks. If declines in wintertime polynya frequency over the twentyfirst century do occur they may impact Antarctic Bottom Water formation and local net primary productivity. Thus, better understanding potential local and unexpected sea ice changes in the Ross Sea is important for both assessing climate system impacts and ecological impacts on the Ross Sea ecosystem, which is currently protected by an internationally recognized marine protected area.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Assessing the Robustness of Arctic Sea Ice Bi‐Stability in the Presence of Atmospheric Feedbacks

Abstract Arctic sea‐ice loss is influenced by multiple positive feedbacks, sparking concerns of accelerated loss in the coming years or even a tipping point, where a sea‐ice equilibrium disappears at a given CO 2 value and sea ice rapidly evolves to a new steady state. Such a tipping point would imply a bi‐stability of the Arctic climate—where multiple steady‐state Arctic climates are possible at the same CO 2 value. Previous works have sought to establish the existence of bi‐stability using a range of models, from zero‐dimensional sea ice thermodynamic models to fully coupled global climate models, with conflicting results. Here, we present a new model of the Arctic that includes both sea‐ice thermodynamics and key atmospheric feedbacks in a simple framework. We exploit the model's simplicity to identify physical mechanisms that control the timing and extent of sea‐ice bi‐stability, and the abruptness of ice loss. We show that longwave radiation feedbacks can have a strong influence on Arctic surface climate from atmospheric temperature increases alone, even without major contributions from clear‐sky moisture or convective clouds suggested previously. While winter sea‐ice bi‐stability is robust to changes in uncertain model parameters in this study, summer sea ice is more sensitive. Finally, our model indicates that positive feedbacks may modulate the CO 2 threshold of sea‐ice loss and the width of bi‐stability much more strongly than the abruptness of loss. These results lead to a comprehensive understanding of the conditions that favor Arctic sea‐ice bi‐stability, particularly the role of atmospheric feedbacks, in both future and past climates.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗
DOE OSTI2022

OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds

Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm -3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by -0.36 W m -2 in the global annual mean and contributes more than -3.5 W m -2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about -4 W m -2 around 50–60°S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC

Abstract. Improved quantification of sea spray aerosol concentration and size is important for determining aerosol effects on clouds and the climate, though attempts to accurately capture the size distribution of the sea spray mode remain limited by the availability of supermicron size distributions. In this work, we introduce a new approach to retrieving lognormal mode fit parameters for a sea spray aerosol mode by combining submicron size distributions with supermicron scattering measurements using a Mie inversion. Submicron size distributions were measured by an ultra-high-sensitivity aerosol spectrometer (UHSAS), and supermicron scattering was taken as the difference between <10 µm and <1 µm three-wavelength integrating nephelometer measurements (NEPH). This UHSAS-NEPH method was applied during background marine periods of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (November 2016–May 2017), when the contribution of sea spray aerosol was expected to represent a large fraction of the aerosol mass and total scattering. Lognormal sea spray modal parameters were retrieved from comparisons between nephelometer measurements and a lookup table of Mie theory-simulated scattering coefficients for low-error solutions that minimized the 0.4–1 µm residual in the UHSAS size distribution. We evaluated the UHSAS-NEPH method with a set of clean marine measurements in the North Atlantic that included supermicron size and chemical measurements, showing that measured supermicron size distributions are needed to constrain the sea spray number concentration but that mass concentration was reasonably characterized using supermicron scattering. For LASIC, the UHSAS-NEPH method retrieved sea spray mode properties for approximately 88 % of the background marine times when the scattering variability and total particle concentration were low (<± 5 Mm−1 and <400 cm−3, respectively), with mass mean diameter ranging from 0.6 to 1.9 µm (1.47 ± 0.17 µm), modal width ranging from 1.1 to 3.97 (2.4±0.3), and mass concentration ranging from 0.18 to 23.0 µg m−3 (8.37. ± 4.1 µg m−3). The measured nephelometer scattering at three wavelengths was found to constrain the mode width marginally at the largest particle sizes in the absence of additional size and chemical measurements for defining parameters for the Mie solutions. Comparing UHSAS-NEPH retrievals to those of a fitting algorithm applied only to the submicron UHSAS number size distribution showed that correlations between retrieved mass concentration and the available mass-based sea spray tracers (coarse scattering, wind speed, and chloride) are low when supermicron measurements are not considered. This work demonstrates the added value of supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available observationally constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Spatial patterns in the length of the sea ice season in the Southern Ocean, 1979-1986

The length of the sea ice season summarizes in one number the ice coverage conditions for an individual location for an entire year. It becomes a particularly valuable variable when mapped spatially over a large area and examined for regional and interannual differences, as is done here for the Southern Ocean over the years 1979-1986, using the satellite passive microwave data of the Nimbus 7 scanning multichannel microwave radiometer. Three prominent geographic anomalies in ice season lengths occur consistently in each year of the data set, countering the general tendency toward shorter ice seasons from south to north: (1) in the Weddell Sea the tendency is toward shorter ice seasons from southwest to northeast, reflective of the cyclonic ice/atmosphere/ocean circulations in the Weddell Sea region. (2) Directly north of the Ross Ice Shelf anomalously short ice seasons occur, lasting only 245-270 days, in contrast to the perennial ice coverage at comparable latitudes in the southern Bellingshausen and Amundsen Seas and in the western Weddell Sea. The short ice season off the Ross Ice Shelf reflects the consistently early opening of the ice cover each spring, under the influence of upwelling along the continental slope and shelf and atmospheric forcing from winds blowing off the Antarctic continent. (3) In the southern Amundsen Sea, anomalously short ice seasons occur adjacent to the coast, owing to the frequent existence of coastal polynyas off the many small ice shelves bordering the sea. Least squares trends in the ice season lengths over the 1979-1986 period are highly coherent spatially, with overall trends toward shorter ice seasons in the northern Weddell and Bellingshausen seas and toward longer ice seasons in the Ross Sea, around much of East Antarctica, and in a portion of the south central Weddell Sea.

Parkinson, Claire L.↗
NASA NTRS1995

Polarimetric signatures of sea ice. 2: Experimental observations

Experimental observations of polarimetric signatures are presented for sea ice in the Beaufort Sea under cold winter conditions and interpreted with the composite model developed in Part 1. Polarimetric data were acquired in March 1988 with the Jet Propulsion Laboratory multifrequency airborne synthetic aperture radar (SAR) during the Beaufort Sea Flight Campaign. The experimental area was located near 75 N latitude and spanned 140 deg-145 deg W longitude. Selected sea ice scenes contain various ice types, including multiyear, thick first-year, and thin lead ice. Additionally, the C band SAR on the first European Remote Sensing Satellite provides supplementary backscattering data of winter Beaufort Sea ice for small incident angles (20 deg-26 deg) at vertical polarization. Sea ice characterization and environment data used in the model were collected at the Applied Physics Laboratory drifting ice station to the northeast of Prudhoe Bay; additional data from field and laboratory experiments are also utilized in this analysis. The model related sea ice polarimetric backscattering signatures to physical, structural, and electromagnetic properties of sea ice. Scattering mechanisms contributing to sea ice signatures are explained, and sensitivies of polarimetric signatures to sea ice characterization parameters are studied.

Nghiem, S. V.↗
NASA NTRS1999

Variability of Arctic Sea Ice as Determined from Satellite Observations

The compiled, quality-controlled satellite multichannel passive-microwave record of polar sea ice now spans over 18 years, from November 1978 through December 1996, and is revealing considerable information about the Arctic sea ice cover and its variability. The information includes data on ice concentrations (percent areal coverages of ice), ice extents, ice melt, ice velocities, the seasonal cycle of the ice, the interannual variability of the ice, the frequency of ice coverage, and the length of the sea ice season. The data reveal marked regional and interannual variabilities, as well as some statistically significant trends. For the north polar ice cover as a whole, maximum ice extents varied over a range of 14,700,000 - 15,900,000 sq km, while individual regions experienced much greater percent variations, for instance, with the Greenland Sea having a range of 740,000 - 1,110,000 sq km in its yearly maximum ice coverage. In spite of the large variations from year to year and region to region, overall the Arctic ice extents showed a statistically significant, 2.80% / decade negative trend over the 18.2-year period. Ice season lengths, which vary from only a few weeks near the ice margins to the full year in the large region of perennial ice coverage, also experienced interannual variability, along with spatially coherent overall trends. Linear least squares trends show the sea ice season to have lengthened in much of the Bering Sea, Baffin Bay, the Davis Strait, and the Labrador Sea, but to have shortened over a much larger area, including the Sea of Okhotsk, the Greenland Sea, the Barents Sea, and the southeastern Arctic.

Parkinson, Claire L.↗
NASA NTRS2001

Satellite Remote Sensing: Passive-Microwave Measurements of Sea Ice

Satellite passive-microwave measurements of sea ice have provided global or near-global sea ice data for most of the period since the launch of the Nimbus 5 satellite in December 1972, and have done so with horizontal resolutions on the order of 25-50 km and a frequency of every few days. These data have been used to calculate sea ice concentrations (percent areal coverages), sea ice extents, the length of the sea ice season, sea ice temperatures, and sea ice velocities, and to determine the timing of the seasonal onset of melt as well as aspects of the ice-type composition of the sea ice cover. In each case, the calculations are based on the microwave emission characteristics of sea ice and the important contrasts between the microwave emissions of sea ice and those of the surrounding liquid-water medium.

Parkinson, Claire L.↗
NASA NTRS2006

March 2003 EOS Aqua AMSR-E Arctic Sea Ice Field Campaign

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.

Cavalieri, Donald J.↗