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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Skillful Spring Forecasts of September Arctic Sea Ice Extent Using Passive Microwave Data

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

melt onset↗
NASA NTRS2019

Changes in Vegetation Cover of the Arctic National Wildlife Refuge Estimated from MODIS Greenness Trends, 2000 to 2018

The Arctic National Wildlife Refuge (ANWR) was established in 1980 and covers 19 million acres (77,000 km2) in northeast Alaska. Wildlife habitats in the ANWR are vulnerable to long-lasting effects from any disturbance, in part because short growing seasons in the arctic provide limited time for species to recover. Trends and transitions in the growing season MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in all ecoregions of the ANWR. Statistical analysis of changes in each MODIS pixel NDVI time series was conducted using the "Breaks for Additive Seasonal and Trend" method (BFAST) to map regional change rates. Results suggested that most negative NDVI anomalies in the tundra-covered river drainages of the Brooks Range Mountains and coastal plain have been associated with early spring thawing and elevated levels of surface moisture in low elevation drainages of the northern ANWR ecoregions.

Arctic National Wildlife Refuge↗
NASA NTRS2019

The Salience of Nonlinearities in the Boreal Winter Response to ENSO: Arctic Stratosphere and Europe

The salience of nonlinearities in the Arctic stratospheric response to El Niño - Southern Oscillation (ENSO) is evaluated in a 41 member ensemble of the period 1980 to 2009 in the NASA Goddard Earth Observing System Chemistry-Climate Model. Central Pacific El Niño (CP EN) events preferentially increase zonal wavenumber 2, while Eastern Pacific El Niño (EP EN) events preferentially increase zonal wavenumber 1, though these nonlinearities become statistically robust only after more than 45 events are considered. This leads to a difference in the morphology of SSW events preferentially forced by each ENSO flavor. While the zonal-mean Arctic stratospheric response is little different between EP and CP EN once one accounts for the relative weakness of CP events, the tropospheric subpolar negative North Atlantic Oscillation response is stronger for CP events. Other nonlinearities are even less salient: there is no indication of any nonlinearities in the wave driving between moderate EP EN and extreme EP EN, nor between EN and La Niña (LN). However in approximately 15% of the integrations within our ensemble, there is no difference in SSW frequency between EN and LN, suggesting that a similar SSW frequency for both EN and LN, as has occurred over the past 60 years, can occur by chance. The intra-ensemble variability in LN SSW frequency can be related back to intra-ensemble variability in the tropospheric response to LN.

Arctic↗
NASA NTRS2017

Meteorological Inventory of Rain-On-Snow Events in the Canadian Arctic Archipelago and Satellite Detection Assessment Using Passive Microwave Data

The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.

Canadian Arctic Archipelago (CAA)↗
NASA NTRS1998

Arctic Sea Ice Extent and Melt Onset from NSCAT Observations

Arctic sea ice extent and the timing of melt onset are important parameters in the study of polar processes and climate changes. NSCAT Ku-band active microwave observations of the Arctic sea ice cover are described in this article with emphasis on their potential use for estimating ice extent and detection of spring melt.

Arctic↗
NASA NTRS2022

Impact of the Arctic Oscillation from March on summertime sea ice

Current understanding of the cold season Arctic oscillation (AO) impact on the summertime sea ice is revisited in this study by analyzing the role from each month. Earlier studies examined the prolonged AO impact using a smooth average over 1–2 seasons (e.g. December–March, December–April, March–May), ignoring large month-to-month AO variability. This study finds that the March AO is most influential on the summertime sea ice loss. First, the March AO is most highly negative-correlated with the AO in summer. Secondly, surface energy budget, sea level pressure, and low-tropospheric circulation exhibit that their time-lagged responses to the positive (negative) phase of the March AO grow with time, transitioning to the patterns associated with the negative (positive) phase of the AO that induces sea ice decrease (increase) in summer. Time evolution of the surface energy budget explains the growth of the sea ice concentration anomaly in summer, and a warming-to-cooling transition in October. The regional difference in sea ice anomaly distribution can be also explained by circulation and surface energy budget patterns. The sea ice concentration along the pan-Arctic including the Laptev, East Siberian, Chukchi, and Beaufort Sea decreases (increases) in summer in response to the positive (negative) phase of the March AO, while the sea ice to the northeast of Greenland increases (decreases). This sea ice response is better represented by the March AO than by the seasonally averaged winter AO, suggesting that the March AO can play more significant role. This study also finds that the sea ice decrease in response to the positive AO is distinctively smaller in the 20th century than in the 21st century, along with the opposite sea ice response over the Canada Basin due to circulation difference between the two periods.

Arctic Oscillation↗
NASA NTRS2017

The Footprints of 16 Year Trends of Arctic Springtime Cloud and Radiation Properties on September Sea Ice Retreat

The most prominent September Arctic sea ice decline over the period of 2000–2015 occurs over the Siberian Sea, Laptev Sea, and Kara Sea. The satellite observed and retrieved sea ice concentration (SIC) and cloud/radiation properties over the Arctic (70°–90°N) have been used to investigate the impact of springtime cloud and radiation properties on September SIC variation. Positive trends of cloud fractions, cloud water paths, and surface downward longwave flux at the surface over the September sea ice retreat areas are found over the period of 1 March to 14 May, while negative trends are found over the period of 15 May to 28 June. The spatial distributions of correlations between springtime cloud/radiation properties and September SIC have been calculated, indicating that increasing cloud fractions and downward longwave flux during springtime tend to enhance sea ice melting due to strong cloud warming effect. Surface downward and upward shortwave fluxes play an important role from May to June when the onset of sea ice melting occurs. The comparison between linearly detrended and nondetrended of each parameter indicates that significant impact of cloud and radiation properties on September sea ice retreat occurs over the Chukchi/Beaufort Sea at interannual time scale, especially over the period of 31 March to 29 April, while strongest climatological trends are found over the Laptev/Siberian Sea.

Arctic sea ice trend↗
NASA NTRS2023

Top‐Down Regulation by a Reindeer Herding System Limits Climate‐Driven Arctic Vegetation Change at a Regional Scale

Warming-driven growth of tall woody vegetation in the Arctic has the potential to accelerate climate change through multiple positive feedbacks. Local-scale evidence suggests that large herbivores limit this vegetation shift, but there is uncertainty at larger, regional scales whether current herbivory pressure is a major top-down control on ecosystem structure and functioning. Across a 67,000 km2 region of the Yamal Peninsula in West Siberia, we integrated satellite remote sensing with a novel data set mapping the migrations of herds comprising 151,000 domesticated reindeer. Where reindeer numbers varied over space, higher reindeer herbivory pressure was consistently linked with lower coverage of tall woody vegetation. Within areas dominated by this vegetation type, productivity and climate were increasingly decoupled where reindeer density was higher. Our spaceborne fingerprint detection suggests that large herbivores, at current population densities, counteract Arctic vegetation responses to climate change over large spatial scales.

Reindeer Herding System↗
NASA NTRS2023

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance.

hyperspectral field sensor↗
NASA NTRS1970

The remote sensing needs of Arctic geophysics

The application of remote sensors for obtaining geophysical information of the Arctic regions is discussed. Two significant requirements are to acquire sequential, synoptic imagery of the Arctic Ocean during all weather and seasons and to measure the strains in the sea ice canopy and the heterogeneous character of the air and water stresses acting on the canopy. The acquisition of geophysical data by side looking radar and microwave sensors in military aircraft is described.

Campbell, W. J.↗
NASA NTRS1972

Microwave emission of Arctic Sea ice

The feasibility of carrying microwave radiometers on Nimbus E and F missions for sea ice surveys was studied in the arctic using aircraft. It was found that passive microwave signatures of Arctic Sea ice relate specifically to the structure and type of ice. It is concluded that a new tool for conducting ice surveys from aircraft and satellites with remote sensors was discovered.

Gloersen, P.↗
NASA NTRS1972

Arctic ice measurements with microwave radiometers

The application of microwave radiometers for measuring Arctic ice is discussed. The acquisition of Arctic ice data simultaneously on the surface with conventional instruments and remotely with microwave and infrared radiometers, photography, and a laser geodolite is described. A multifrequency view of a large multilayer ice floe is presented. The significance of the data recordings is explained.

Gloersen, P.↗
NASA NTRS1973

Use of ERTS data for mapping Arctic sea ice

This investigation is to evaluate the application of ERTS data for detecting and mapping Arctic sea ice. The specific objectives are to determine the spectral bands most suitable for detecting ice, to measure the scale and types of ice features that can be detected, and to develop interpretive techniques for differentiating ice from clouds and for mapping ice concentrations. The ERTS data are being analyzed primarily for three Arctic areas, the eastern Beaufort Sea, Baffin Bay, and the Greenland Sea.

Barnes, J. C.↗
NASA NTRS1975

SEASAT economic assessment. Volume 6: Arctic operations case study and generalization

The hypothetical development and transportation of Arctic oil and other resources by ice breaking super tanker fleets to the continental East Coast are discussed. The utilization of SEASAT ice mapping data is shown to contribute to a more effective transportation operation through the Arctic ice by reducing transportation costs as a consequence of reduced transit time per voyage.

Source record↗
NASA NTRS1976

Arctic odyssey - Five years of data buoys in AIDJEX

The Arctic Ice Dynamics Joint Experiment of 1975-1976 used data bouys in conjunction with tracking satellites to study the interaction of Arctic Sea ice with the environment, and, more specifically, to define the motion of ice on the perimeter of the area of interest and to measure surface barometeric pressure over the same area. Charts are presented, indicating the position of the buoy array along with a table detailing buoy characteristics. The position fix accuracy of RAMS (Random Access Measurement System) buoys is discussed together with position errors due to the along-track motion of the observing satellites. Polar satellite data collection and tracking efficiency is assessed together with satellite system flexibility concepts.

Martin, P.↗
NASA NTRS1978

Time-dependence of sea-ice concentration and multiyear ice fraction in the Arctic Basin

Microwave images of sea ice obtained by Nimbus-5 and the NASA CV-990 airborne laboratory are used to determine the time variation of the sea-ice concentration and multiyear ice fraction within the pack ice in the Arctic Basin. The images, constructed from data acquired from the electrically scanned microwave radiometer, are analyzed for four seasons during 1973-1975. Observations indicate significant variations in the sea-ice concentration in the spring, late fall, and early winter. Sea-ice concentrations as low as 50% were detected in large areas in the interior of the Arctic polar sea-ice pack. The applicability of passive-microwave remote sensing for monitoring the time dependence of sea-ice concentration is considered.

Gloersen, P.↗
NASA NTRS1980

Applications of radar imagery to arctic and subarctic problems

Radar imagery provides year around data acquisition of areas in the Arctic and the Subarctic. The foremost factor influencing the choice of radar imagery as the major data source was the demand for neotric data. The weather is so adverse in parts of Alaska that radar imagery was the only remote sensing technique which could meet the demand. The major map products derived from radar imagery are landform maps and lineament maps. These maps are used to make environmental assessments of areas and to reconstruct the geomorphic history of certain regions or features. Since radar imagery provides information about geologic structure and geomorphic features, it can be used to determine the relationship which exists between geologic structure and geomorphology. Important geologic information related to surface roughness can be obtained through a dry snow cover. Radar imagery is the only remote sensing technique which can provide information needed about sea ice through a cloud cover and dry snow, during strong wind conditions, and throughout the Arctic night.

Cannon, P. J.↗