SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The Arctic region plays a key role in the climate of the earth. The sea ice cover affects the radiative balance of the earth and radically changes the fluxes of heat between the atmosphere and the ocean. The observations of the Arctic made by the Electrically Scanning Microwave Radiometer (ESMR) on board the Nimbus 5 research satellite are summarized for the period 1973 through 1976.
Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 19822015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 19822015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 19992015 compared to 19821998.
The Arctic is a region in transformation. Warming in the region has been amplified, as expected from ice-albedo feedback effects, with the rate of warming observed to be approx. 0.60+/-0.07 C/decade in the Arctic (>64degN) compared to approx. 0.17 C/decade globally during the last three decades. This increase in surface temperature is manifested in all components of the cryosphere. In particular, the sea ice extent has been declining at the rate of approx. 3.8%/decade, whereas the perennial ice (represented by summer ice minimum) is declining at a much greater rate of approx.11.5%/decade. Spring snow cover has also been observed to be declining by −2.12%/decade for the period 1967-2012. The Greenland ice sheet has been losing mass at the rate of approx. 34.0Gt/year (sea level equivalence of 0.09 mm/year) during the period from 1992 to 2011, but for the period 2002-2011, a higher rate of mass loss of approx. 215 Gt/year has been observed. Also, the mass of glaciers worldwide declined at the rate of 226 Gt/year from 1971 to 2009 and 275 Gt/year from 1993 to 2009. Increases in permafrost temperature have also been measured in many parts of the Northern Hemisphere while a thickening of the active layer that overlies permafrost and a thinning of seasonally frozen ground has also been reported. To gain insight into these changes, comparative analysis with trends in clouds, albedo, and the Arctic Oscillation is also presented.
The Marginal sea Ice Zone (MIZ) and the sea ice edge are the most dynamic areas of the sea ice cover. Knowledge of the sea ice edge location is vital for routing shipping in the polar regions. The ice edge is the location of recurrent plankton blooms, and is the habitat for a number of animals, including several which are under severe ecological threat. Polar lows are known to preferentially form along the sea ice edge because of induced atmospheric baroclinicity, and the ice edge is also the location of both vertical and horizontal ocean currents driven by thermal and salinity gradients. Finally, sea ice is both a driver and indicator of climate change and monitoring the position of the ice edge accurately over long time periods enables assessment of the impact of global and regional warming near the poles. Several sensors are currently in orbit that can monitor the sea ice edge. These sensors, though, have different spatial resolutions, different limitations, and different repeat frequencies. Satellite passive microwave sensors can monitor the ice edge on a daily or even twice-daily basis, albeit with low spatial resolution - 25 km for the Special Sensor Microwave Imager (SSM/I) or 12.5 km for the Advanced Microwave Scanning Radiometer (AMSR-E). Although special methods exist that allow the detection of the sea ice edge at a quarter of that nominal resolution (PSSM). Visible and infrared data from the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily coverage at 1 km and 250 m, respectively, but the surface observations me limited to cloud-free periods. The Landsat 7 Enhanced Thematic Mapper (ETM+) has a resolution of 15 to 30 m but is limited to cloud-free periods as well, and does not provide daily coverage. Imagery from Synthetic Aperture Radar (SAR) instruments has resolutions of tens of meters to 100 m, and can be used to distinguish open water and sea ice on the basis of surface and volume scattering characteristics. The Canadian RADARSAT C-band SAR provides data that cover the Arctic Ocean and the MIZ every 3 days. A change-point detection approach was utilized to obtain an ice edge estimate from the RADARSAT data The Quickscat scatterometer provides ice edge information with a resolution of a few kilometers on a near-daily basis. During portions of March and April of 2003 a series of aircraft flights were conducted over the ice edge in the Bering Sea carrying the Polarimetric Scanning Radiometer (PSR), which provides spectral coverage identical with the AMSR-E instrument at a resolution of 500 meters. In this study we investigated these different data sets and analyzed differences in their definition of the sea ice edge and the marginal ice zone and how these differences as well as their individual limitations affect the monitoring of the ice edge dynamics. We also examined how the nature of the sea ice edge, including its location, compactness and shape, changes over the seasons. Our approach was based on calculation of distances between ice edges derived from the satellite and aircraft data sets listed above as well as spectral coherence methods and shape parameters such as tortuosity, curvature, and fractional dimension.
Satellite and aircraft observations have revealed that remarkable changes in the Earth s polar ice cover have occurred in the last decade. The impacts of these changes, which include dramatic ice loss from ice sheets and rapid declines in Arctic sea ice, could be quite large in terms of sea level rise and global climate. NASA's Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), currently planned for launch in 2015, is specifically intended to quantify the amount of change in ice sheets and sea ice and provide key insights into their behavior. It will achieve these objectives through the use of precise laser measurements of surface elevation, building on the groundbreaking capabilities of its predecessor, the Ice Cloud and Land Elevation Satellite (ICESat). In particular, ICESat-2 will measure the temporal and spatial character of ice sheet elevation change to enable assessment of ice sheet mass balance and examination of the underlying mechanisms that control it. The precision of ICESat-2's elevation measurement will also allow for accurate measurements of sea ice freeboard height, from which sea ice thickness and its temporal changes can be estimated. ICESat-2 will provide important information on other components of the Earth System as well, most notably large-scale vegetation biomass estimates through the measurement of vegetation canopy height. When combined with the original ICESat observations, ICESat-2 will provide ice change measurements across more than a 15-year time span. Its significantly improved laser system will also provide observations with much greater spatial resolution, temporal resolution, and accuracy than has ever been possible before.
The weather systems, cyclones, and anticyclones, along with air trajectories and cloud forms, are compared to past studies of the Arctic to assess compatibility of the four month study of the Arctic Cloud Experiment flights of the First ISCCP Regional Experiment (FIRE/ACE) with past climatologies. The frequency and movement of cyclones (lows) and anticyclones (highs) followed the general eastward and northeastward directions indicated by past studies. Most cyclones (lows) came from eastern Siberia and the Bering Sea to the south and moved north across the Bering Straight or Alaska into the Arctic Ocean. They generally weakened in central pressure as they moved poleward. Anticyclones (highs) were most common in the eastern Beaufort Sea near Canada in June and July as predicted from previous studies. However, many cyclones and anticyclones moved in westward directions which is rare in other latitudes. Erratic changes in shape and intensity on a daily basis also were observed. The National Center for Environmental Prediction (NCEP) analysis generally reflected the Surface Heat Budget in the Arctic (SHEBA) Ship World Meteorological Organization (WMO) observations which it used. However, NCEP temperatures were biased warm by 1.0 to 1.5 C in April and early May. In July when the surface temperature were at the freezing/thawing point, the NCEP analysis changed to a cold bias of -1.0 C. Dew points had smaller biases except for July where they were biased cold by -1.4 C. Wind speeds had a -2 m/s low bias for the six windiest days. Surface barometric pressures had consistently low biases from -1.2 to -2.8 hPa in all four months. Air parcel historical trajectories were mainly from the south or from local anticyclonic gyres in the Beaufort Sea. Most air came to the SHEBA Ship from the north Pacific Ocean or from Alaska and Canada and occasionally from eastern Siberia. Very few trajectories traced back across the pole to Europe and Central Asia. Cloud cover was high, as expected, from 69-86% of the time. Satellite data also indicate frequent stratus, altostratus, and cirrus clouds (occurring 61% of the time) above the expected boundary layer fog and Arctic stratus clouds.
The variability of stratospheric meridional winds is examined in both space and time. Height-latitude sections for January along 70 deg E and 90 deg W show a divergence zone above 50 km near 60 deg N and an intense convergence zone 40 km near 50 deg N over North America. This latter structure, with southward winds in the Arctic and northward winds at mid-latitudes over North America, persists from October through April. Tidal winds dominate all other circulation features in summer at all latitudes, and throughout the year at low latitudes. To help understand the observed patterns of variability, long-term periodic features are analyzed. The quasi-biennial oscillation, annual wave, and four-month wave have amplitudes of about 10, 20, and 10 m/sec respectively in the Arctic near 45 km. The phase of the annual wave changes by nearly 180 deg in a narrow zone near 45 deg N. The semiannual wave has an amplitude of 10 m/sec. 50 deg N above 50 km equinoctial phase dates in the region of maximum amplitude. This polar semiannual wave corresponds closely to that previously found in the zonal wind.
Long-term periodic features in the meridional wind between 20 and 65 km attitude are analyzed. No appreciable periodic waves are found in the tropics. The quasi-biennial oscillation, annual wave, and four-month wave have maximum amplitudes of about 10, 20, and 10 m/s respectively in the arctic near 45 km. The phase of the annual wave changes by nearly 180 deg in a narrow zone near 45 deg N. The semiannual wave has an amplitude of 10 m/s near 50 deg N above 50 km with equinoctial phase dates in the region of maximum amplitude. The location of this polar semiannual wave corresponds closely to that previously found in the zonal wind.
It is pointed out that Arctic sea ice and its environment undergo a number of changes during the summer period. Some of these changes affect the ice cover properties and, in turn, their response to thermal and mechanical forcing throughout the year. The main objective of this investigation is related to the development of a method for estimating the areal coverage of exposed ice, melt ponds, and leads, which are the basic surface variables determining the local surface albedo. The study is based on data obtained in a field investigation conducted from Mould Bay (NWT), Nimbus 5 satellite data, and Seasat data. The investigation demonstrates that microwave data from satellites, especially microwave brightness temperature, provide good data for estimating important characteristics of summer sea ice cover.
This project created a workbook which teaches Earth Science to undergraduate and graduate students through guided in-class activities and take-home assignments organized around climate topics which use GIS to teach key geospatial analysis techniques and cartography skills. The workbook is structured to the White House's Data.gov climate change themes, which include Coastal Flooding, Ecosystem Vulnerability, Energy Infrastructure, Arctic, Food Resilience, Human Health, Transportation, Tribal Nations, and Water. Each theme provides access to framing questions, associated data, interactive tools, and further reading (e.g. The US Climate Resilience Toolkit and National Climate Assessment). Lessons make use of the respective theme's available resources. The structured thematic approach is designed to encourage independent exploration. The goal is to teach climate concepts and concerns, GIS techniques and approaches, and effective cartographic representation and communication results; and foster a greater awareness of publicly available resources and datasets. To reach more audiences more effectively, a two level approach was used. Level 1 serves as an introductory study and relies on only freely available interactive tools to reach audiences with fewer resources and less familiarity. Level 2 presents a more advanced case study, and focuses on supporting common commercially available tool use and real-world analysis techniques.
The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAPs L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.The SMAP soil moisture, freeze/taw state and SSSprovide a synergistic view of water cycle. For example, Fig.7 illustrates the transition of freeze/thaw state, change of soilmoisture near the pole and SSS in the Arctic Ocean fromApril to October in 2015 and 2016. In April, most parts ofAlaska, Canada, and Siberia remained frozen. Melt onsetstarted in May. Alaska, Canada, and a big part of Siberia havebecome thawed at the end of May; some freshwater dischargecould be found near the mouth of Mackenzie in 2016, but notin 2015. The soil moisture appeared to be higher in the Oband Yenisei river basins in Siberia in 2015. As a result,freshwater discharge was more widespread in the Kara Seanear the mouths of both rivers in June 2015 than in 2016. TheNorth America and Siberia have become completely thawedin July. After June, the freshwater discharge from other riversinto the Arctic, indicated by blue, also became visible. Thefreeze-up started in September and the high latitude regionsin North America and Eurasia became frozen. Comparing thespread of freshwater in August 2015 and 2016 suggests thatthere was more discharge from Ob and Yenisei in 2015,which appeared to correspond to a higher soil moisturecontent in the Ob and Yenisei basins. In contrast, Mackenzieappeared to have more discharge in September 2016.
Recent Arctic warming outpaces global warming and is characterized by dramatic changes in sea ice (record sea ice loss, thinner and more seasonal ice cover), warming SSTs, and a lengthening melt season. These changes impact the surface energy budget and the sensitivity of sea ice to radiative forcing, namely an increased sensitivity of thinner ice to local radiative forcings and a weaker buffering of surface-atmosphere exchanges of heat and moisture. However, these processes are a large source of uncertainty in global climate models with prior studies finding that poor representations of downwelling longwave radiation and sea ice albedo lead to model spread in sea ice conditions. A limitation of prior work is examining sea ice-atmosphere interactions through an Eulerian framework. This study utilizes a sea ice parcel dataset in a LaGrangian framework, allowing for tracking of surface energy budget-sea ice interactions over the lifespan of individual sea ice parcels; this framework also allows for the quantification of the sea ice response to episodic weather events along with seasonal drivers, including the drivers of rapid growth and melt events. First, lag-lead relationships between relevant surface energy budget terms and sea ice thickness changes are explored to understand the atmospheric conditions preceding rapid growth and melt. The impact of episodic events- moisture intrusion, warm air outbreaks, cyclones- on sea ice growth are also investigated by stratifying first-year parcels by sea ice thickness at the end of the growth season and analyzing the effect of atmospheric variability (number and intensity of episodic events) on parcel thickness change. Lastly, parcel growth and melt and the associated radiative anomalies are compared to snowfall events/snow thickness to determine how snowfall modifies the interaction between the local surface energy budget and sea ice mass balance.
Sea ice is considered an important indicator of climate change. Interest has focused on the trend towards reduced sea ice cover in the Arctic Ocean, particularly during summer months. In the Southern Hemisphere, sea ice has also received attention due to a trend towards increasing cover, in opposition to the Northern Hemisphere and to climate model projections. This all changed in the austral spring 2016, when the Southern Ocean sea ice cover dropped dramatically, and has remained consistently at below average values up to the present. Here I review some of the issues and perceived causes of the 2016 sea ice reduction. Several studies have suggested a change in surface winds, which would allow warmer waters to move upwards in the water column and towards the surface. Results from the GMAO (Global Modeling and Assimilation Office) ocean data assimilation system are consistent with this hypothesis, and indicate a significant warming of subsurface waters occurred in the winter of 2016, and has remained in place.
The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×10^6 sq.km, compared to 6.46×10^6 sq.km for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×10^6 sq.km for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 %/yr) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.
The extent and concentration of the Summer minima provide indirect information about the long term ability of the perennial portion of the ice pack to survive the Arctic atmosphere and ocean system. Both active and passive microwave data were used with some success for monitoring the ice cover during the Summer, but they both suffer from similar problems caused by the presence of meltponding, surface wetness, flooding, and freeze/thaw cycles associated with periodic changes in surface air temperatures. A comparative analysis of ice conditions in the Arctic region using coregistered ERS-1 SAR (Synthetic Aperture Radar) and SSM/I (Special Sensor Microwave/Imager) data was made. The analysis benefits from complementary information from the two systems, the good spatial resolution of SAR data, and the good time resolution of and global coverage by SSM/I data. The results show that in many areas ice concentrations derived from SAR data are significantly different (usually higher) than those derived from passive microwave data. Additional insights about surface conditions can be inferred depending on the nature of the discrepancies.
The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300 over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were 50 smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq))/ and various biomass burning tracers (BBt/ across the multi-campaign dataset, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACI, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be 0.12 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/ cu m) and very high aerosol concentrations (2000-3000 cu m) in the most polluted clouds, the estimated ACI value was only 0.06. In this case, competition for water vapor by the high concentration of CCN strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease shortwave radiative flux by 2 and 4 W/sq or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. We lastly show evidence to suggest that numerous northern latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei. However, the influence of background particles on smoke-driven indirect effects is currently unclear.
Reanalyses in the Arctic are widely used for model evaluation and for understanding contemporary climate change. Nevertheless, differences among reanalyses in fundamental meteorological variables including surface air temperature are large. For example, the 1980-2009 mean surface air temperature for the north polar cap (70ÂdegN-90ÂdegN) among global reanalyses span a range of 2.4 K, which approximates the average warming trend from these reanalyses over the 30-year period of 2.1 K. Understanding these differences requires evaluation over the three principal surface domains of the Arctic: glaciated land, the unglaciated terrestrial surface, and sea ice/ocean. An examination is conducted of contemporary global reanalyses of the ECMWF Interim project, NASA MERRA, MERRA-2, JRA-55, and NOAA CFSR using available in situ data and assessments of the surface energy budget. Overly-simplistic representations of the Greenland Ice Sheet surface are found to be associated with local warm air temperature biases in winter. A review of progress made in the development of the MERRA-2 land-ice representation is presented. Large uncertainty is also found in temperatures over the Arctic tundra and boreal forest zone. But a key focus of temperature differences for northern high latitudes is the Arctic Ocean. Near-surface air temperature differences over the Arctic Ocean are found to be related to discrepancies in sea ice and sea surface temperature boundary data, which are severely compromised in current reanalyses. Issues with the modeled representation of sea ice cover are an additional factor in reanalysis temperature trends. Differences in the representation of the surface energy budget among the various reanalyses are also reviewed.
This paper describes the thermal analysis and test design performed in preparation for the ATLAS thermal vacuum test. NASA's Advanced Topographic Laser Altimeter System (ATLAS) will be flown as the sole instrument aboard the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). It will be used to take measurements of topography and ice thickness for Arctic and Antarctic regions, providing crucial data used to predict future changes in worldwide sea levels. Due to the precise measurements ATLAS is taking, the laser altimeter has very tight pointing requirements. Therefore, the instrument is very sensitive to temperature-induced thermal distortions. For this reason, it is necessary to perform a Structural, Thermal, Optical Performance (STOP) analysis not only for flight, but also to ensure performance requirements can be operationally met during instrument-level thermal vacuum testing. This paper describes the thermal model created for the chamber setup, which was used to generate inputs for the environmental STOP analysis. This paper also presents the results of the STOP analysis, which indicate that the test predictions adequately replicate the thermal distortions predicted for flight. This is a new application of an existing process, as STOP analyses are generally performed to predict flight behavior only. Another novel aspect of this test is that it presents the opportunity to verify pointing results of a STOP model, which is not generally done. It is possible in this case, however, because the actual pointing will be measured using flight hardware during thermal vacuum testing and can be compared to STOP predictions.