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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Arctic Sea ice by passive microwave observations from the Nimbus-5 Satellite

The results of a dynamic/thermodynamic numerical model of Arctic sea ice are compared with satellite images from the Nimbus 5 electrically scanning microwave radiometer. The model combines aspects of two previous sea ice models those of Parkinson and Washington and Ling, Rasmussen, and Campbell. A solid/fluid model basically follows the formulation of the Parkinson and Washington model with the addition of the constitutive equation and equation of state from the Ling model. The Parkinson and Washington model simulates the seasonal cycle of sea ice thicknesses and concentrations with a horizontal resolution of roughly 200 km and a timestep of 8 hours. The thermodynamics are calculated through energy balances at the interfaces between ice and air, water and ice, and water and air. The ice dynamics are calculated through a momentum equation balancing air stress, water stress, dynamic topography, and Coriolis force, with an adjustment for internal ice resistance.

Campbell, W. J.↗
NASA NTRS1994

Observation of melt onset on multiyear Arctic sea ice using the ERS 1 synthetic aperture radar

We present nearly coincident observations of backscattering from the Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and of near-surface temperature from six drifting buoys in the Beaufort Sea, showing that the onset of melting in snow on multiyear sea ice is clearly detectable in the SAR data. Melt onset is marked by a clean, steep decrease in the backscattering cross section of multiyear ice at 5.3 GHz and VV polarization. We investigate the scattering physics responsible for the signature change and find that the cross section decrease is due solely to the appearance of liquid water in the snow cover overlying the ice. A thin layer of moist snow is sufficient to cause the observed decrease. We present a prototype algorithm to estimate the date of melt onset using the ERS 1 SAR and apply the algorithm first to the SAR data for which we have corresponding buoy temperatures. The melt onset dates estimated by the SAR algorithm agree with those obtained independently from the temperature data to within 4 days or less, with the exception of one case in which temperatures oscillated about 0 C for several weeks. Lastly, we apply the algorithm to the entire ERS 1 SAR data record acquired by the Alaska SAR Facility for the Beaufort Sea north of 73 deg N during the spring of 1992, to produce a map of the dates of melt onset over an area roughly 1000 km on a side. The progression of melt onset is primarily poleward but shows a weak meridional dependence at latitudes of approximately 76 deg-77 deg N. Melting begins in the southern part of the study region on June 13 and by June 20 has progressed to the northermost part of the region.

Winebrenner, D. P.↗
NASA NTRS1995

On treatments of fetch and stability sensitivity in large-area estimates of sensible heat flux over sea ice

Bulk transfer coefficients estimated as a function of atmospheric stability and sea-ice lead width are combined with Arctic meteorological observations and ice thickness data to calculate the sensitivity of turbulent flux estimates to changes in lead width, wind speed, air temperature, and ice thickness for a high-concentration ice pack. These results are considered in terms of bulk transfer parameterizations that use a fixed transfer coefficient r that address atmospheric stability only. On the basis of the fetch-sensitive parameterizations considered here, differences in lead width for widths up to about 200 m can exert a substantial influence on sensible heat transfer coefficients and heat flux from leads under typical Arctic conditions. Fluxes from an open water lead decrease by 34% if fetch increases from 10 m to 100 m. This effect is greatest for open water leads, decreases considerably as leads refreeze, and is negligible for ice thicker than about 0.3 m. If open or newly refrozen leads make up 2% of the ice cover, than an increase in mean fetch from 10 m to 100 m yields a decrease of about 2 W/sq m in areally averaged flux from the ice pack. Calculations using observed and theoretical lead width distributions suggest that parameterizing lead widths in a sea ice model can be done effectively using a single, representative lead width rather than requiring a full distribution of widths. When coupled to the lowest atmospheric boundary layer using a bulk similarity theory model, this sensitivity of heat transfer to fetch results in substantially higher near-surface air temperatures over narrow leads, with equilibrium air temperatures decreasing by about 50% as fetch increases from 10 to 100 m.

Maslanik, J. A.↗
NASA NTRS1995

Arctic sea ice leads from advanced very high resolution radiometer images

A large number of advanced very high resolution radiometer (AVHRR) images from throughout 1989 are analyzed to determine lead characteristics. The units of analysis are square 200-km cells, and there are 270 such cells in the data set. Clouds are masked manually. Leads determine from images of the potential open water delta, a scaled version of the surface temperature or albedo that weights thin ice by its thermal or brightness impact. The lead fraction is determined as the mean delta, the monthly mean lead fraction ranges from 0.02 in winter to 0.06 in summer in the central Arctic and is near 0.08 in the winter in the peripheral seas. A method of accounting for lead width sampling errors due to the finite sample areas is introduced. In the central Arctic the observed mean lead width for a threshold of delta = 0.1 ranges from 2 or 3 km (near the resolution of the instrument) in the winter to 6 km in the summer. In the peripheral seas it is about 5 km in the winter. Width distributions are often more heavily weighted in the tail than exponential distributions and are well approximated by a power law. The along-track, number density power law N = aw(exp -6) has a mean exponent of b = 1.60 (standard deviation 0.18) and shows some seasonal variability. Mean floe widths in the central Arctic are 40 to 50 km in the winter, dropping to about 10 km in the summer. For floes the power law has a mean exponent of 0.93 and exhibits a clearer annual cycle. Lead orientation is determined with a method based on the direction of maximum extent.

Lindsay, R. W.↗
NASA NTRS1992

Rayleigh/raman Greenland Lidar Observations of Atmospheric Temperature During a Major Arctic Stratospheric Warming Event

Between Jan. 22 1991 to Feb. 5 1991, we made numerous observations of atmospheric temperature profiles between 10 and 70 km by using the combination of Rayleigh and Raman lidar systems contained in the PL Mobile Lidar Facility located at the National Science Foundation Incoherent Radar Facility of Sondrestrom in Greenland. The purpose of these measurements was to observe the dynamics of the winter Arctic stratosphere and mesosphere regions during a winter period from the succession of temperature profiles obtained in our campaign observations. Various aspects of this investigation are presented.

Meriwether, John W.↗
NASA NTRSDate not supplied

An Observationally-Based Determination of the Arctic Sea Ice-Cloud Feedback Since 2000: Isolating the Arctic Cloud Response to Sea Ice Loss

Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.

Patrick C Taylor↗
NASA NTRS2001

An Assessment of the Ozone Loss During the 1999-2000 SOLVE Arctic Campaign

Ozone observations from ozonesondes, the lidars aboard the DC-8, in situ ozone measurements from the ER-2, and satellite ozone measurements from Polar Ozone and Aerosol Measurement III (POAM) were used to assess ozone loss during the Sage III Ozone Loss and Validation Experiment (SOLVE) 1999-2000 Arctic campaign. Two methods of analysis were used. In the first method a simple regression analysis is performed on the ozonesonde and POAM measurements within the vortex. In the second method, the ozone measurements from all available ozone data were injected into a free running diabatic trajectory model and carried forward in time from December 1 to March 15. Vortex ozone loss was then estimated by comparing the ozone values of those parcels initiated early in the campaign with those parcels injected later in the campaign. Despite the variety of observational techniques used during SOLVE, the measurements provide a fairly consistent picture. Over the whole vortex, the largest ozone loss occurs between 550 and 400 K potential temperatures (approximately 23-16 km) with over 1.5 ppmv lost by March 15, the end of the SOLVE mission period. An ozone loss rate of 0.04-0.05 ppmv/day was computed for March 15. Ozonesondes launched after March 15 suggest that an additional 0.5 ppmv or more ozone was lost between March 15 and April 1. The small disagreement between ozonesonde and POAM analysis of January ozone loss is found to be due to biases in vortex sampling. POAM makes most of its solar occultation measurements at the vortex edge during January 2000 which bias samples toward air parcels that have been exposed to sunlight and likely do experience ozone loss. Ozonesonde measurements and the trajectory technique use observations that are more distributed within the interior of the vortex. Thus the regression analysis of the POAM measurements tends to overestimate mid-winter vortex ozone loss. Finally, our loss calculations are broadly consistent with other loss computations using ER-2 tracer data and MLS satellite data, but we find no evidence for the 1992 high mid-January loss reported using the Match technique.

Schoeberl, Mark R.↗
NASA NTRS2004

Response of Sea Ice and Marine Ecosystems to the Observed Warming Trends in the Arctic

Our third (final) year activities have focused on two elements: 1) Creating and evaluating panarctic weekly climatologies of ocean color products for 1998 through 2002, 2) Interacting to assess image databases, submit and revise publications. Comiso and his programmer have principal responsibility for assembling and archiving the monthly and weekly image database at GSFC. There are now five year's (1998-2002) of climatologies. In addition to email correspondence, the PIs have met to prepare a series of publications ranging from panarctic physical-biological interactions to more focused regional studies.

Cota, Glenn F.↗
NASA NTRS2017

Aerosols at the Poles: An Aerocom Phase II Multi-Model Evaluation

Atmospheric aerosols from anthropogenic and natural sources reach the polar regions through long-range transport and affect the local radiation balance. Such transport is, however, poorly constrained in present-day global climate models, and few multi-model evaluations of polar anthropogenic aerosol radiative forcing exist. Here we compare the aerosol optical depth (AOD) at 550 nm from simulations with 16 global aerosol models from the AeroCom Phase II model intercomparison project with available observations at both poles. We show that the annual mean multi-model median is representative of the observations in Arctic, but that the intermodel spread is large. We also document the geographical distribution and seasonal cycle of the AOD for the individual aerosol species: black carbon (BC) from fossil fuel and biomass burning, sulfate, organic aerosols (OAs), dust, and sea-salt. For a subset of models that represent nitrate and secondary organic aerosols (SOAs), we document the role of these aerosols at high latitudes. The seasonal dependence of natural and anthropogenic aerosols differs with natural aerosols peaking in winter (seasalt) and spring (dust), whereas AOD from anthropogenic aerosols peaks in late spring and summer. The models produce a median annual mean AOD of 0.07 in the Arctic (defined here as north of 60 degrees N). The models also predict a noteworthy aerosol transport to the Antarctic (south of 70 degrees S) with a resulting AOD varying between 0.01 and 0.02. The models have estimated the shortwave anthropogenic radiative forcing contributions to the direct aerosol effect (DAE) associated with BC and OA from fossil fuel and biofuel (FF), sulfate, SOAs, nitrate, and biomass burning from BC and OA emissions combined. The Arctic modelled annual mean DAE is slightly negative (-0.12 W m(exp. -2), dominated by a positive BC FF DAE in spring and a negative sulfate DAE in summer. The Antarctic DAE is governed by BC FF. We perform sensitivity experiments with one of the AeroCom models (GISS modelE) to investigate how regional emissions of BC and sulfate and the lifetime of BC influence the Arctic and Antarctic AOD. A doubling of emissions in eastern Asia results in a 33 percent increase in Arctic AOD of BC. A doubling of the BC lifetime results in a 39 percent increase in Arctic AOD of BC. However, these radical changes still fall within the AeroCom model range.

Sand, Maria↗
NASA NTRSDate not supplied

Towards a More Realistic Representation of Surface Albedo in NASA CERES Satellite Products: A Comparison with MOSAiC Field Campaign

Observing the Arctic from space is one of the most challenging tasks in climate science. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other scene type and comes from several sources. The one-year long MOSAiC expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes has been conducted using in-situ measurements from MOSAiC flux stations. The CERES SYN1deg product overestimates the SW_down flux by 11.40 Wm-2 and underestimates the SW_up flux by −15.70 Wm-2 and LW_down fluxes by −13.30 Wm-2 at the surface during summertime. In addition, large differences are found in the LW_up flux (~320 Wm-2) when the surface reaches melting point (~0℃). The large negative bias in upwelling shortwave flux can be attributed to the underestimation of surface albedo (−0.15) in SYN1deg. In addition to direct comparison, a series of perturbation experiments with a radiative transfer model are performed to estimate the contributions to the differences. By correcting both cloud and albedo inputs, the biases in SW_net flux and LW_net flux can be reduced to less than half of the control run biases to +19.90% and −10.53%, respectively. Furthermore, a compensating effect between underestimation of broadband albedo and overestimation of spectral albedo in visible and mid-infrared bands in SYN1deg datasets is found and contributes to the shortwave flux differences. The difference in CERES broadband albedo (~20 Wm 2) contributes to larger uncertainty in SW_up flux than spectral albedo shape (~3 Wm 2). The results of this study inform the future development of CERES products and will ultimately reduce uncertainties in Arctic surface radiation budget derived from satellite measurements.

Yiyi Huang↗
NASA NTRS2001

Volcanoes, Polar Clouds and Arctic Ozone

Satellite observations and model calculations show 5 to 10% local column ozone loss in some tropical and mid latitude locations, following El Chichon and Mount Pinatubo eruptions. The rapid deepening of the Antarctic ozone hole in the early 1980s has also been partially attributed to chemistry on volcanic aerosols from a number of large eruptions. Here the effects of volcanoes on Arctic polar processes are explored. Large polar stratospheric cloud particles that cause denitrification cannot form in a volcanically perturbed environment. Denitrification can increase Arctic ozone loss by up to 30% in a future colder climate. However, we show that enhanced chemical processing on volcanic aerosols can increase Arctic ozone loss in a cold year by about 60% independent of denitrification. A coupled chemistry-microphysics model is used to show that widespread distribution of volcanic aerosols in 2000 could have caused severe springtime ozone depletion in the Arctic stratosphere. While, volcanic aerosols can strongly affect the current Arctic column ozone abundance in a cold year, denitrification effects on ozone can only become important in a much colder lower stratosphere.

Tabazadeh, Azadeh↗
NASA NTRSDate not supplied

Process Drivers, Inter-model Spread, and a Path Forward: What Do We Know About Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Patrick Charles Taylor↗
NASA NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Arctic amplification↗
NASA NTRSDate not supplied

Process drivers, inter-model spread, and a path forward: What do we know about Arctic Amplification?

Over the recent decades, rapid changes have been observed in the Arctic resulting from enhanced surface warming in the Arctic relative to the rest of the globe, a phenomenon called Arctic Amplification. While surface-based amplification of Arctic warming in response to increased CO2 has been established. The driving mechanisms, relevant processes, and the relative roles of local and remote mechanisms to Arctic Amplification remain unclear. Research over the last decade has made it clear that coupling between the atmosphere, sea ice, and ocean play a key role in Arctic Amplification. This presentation summarizes recent research on the fundamental role that atmosphere-sea ice-ocean interactions and energy exchanges play in driving Arctic Amplification, how these interactions manifest as radiative climate feedbacks, and how these processes influence the differences in the projected Arctic climate change across CMIP5 and 6. We hypothesize that models that simulate larger warming do so by generating a larger local atmospheric circulation response that more effectively redistributes energy drawn from the ocean in sea ice retreat regions Arctic-wide and contributing to a larger increase in downwelling long wave radiation. This process is also fundamentally tied to the Arctic lapse rate feedback.

Patrick C. Taylor↗
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
NASA NTRS1986

SAM 2 measurements of the polar stratospheric aerosol, volume 2

The Stratospheric Aerosol Measurement (SAM) 2 sensor aboard Nimbus 7 is providing extinction measurements of Antarctic and Arctic stratospheric aerosols with a vertical resolution of 1 km. Representative examples and weekly averages including corresponding temperature profiles provided by NOAA for the time and place of each SAM 2 measurement (Oct. 1981 - Apr. 1982) are presented. Contours of aerosol extinction as a function of altitude and longitude or time are plotted, and aerosol optical depths are calculated for each week. Typical values of aerosol extinction at 1.0 micron in the main lower stratospheric aerosol layer for this time period are 2 to 4 times 10 to the -4 power/km. for the Antarctic region and 0.5 to 1 times 10 to the -3 power/km. for the Arctic region. Stratospheric optical depths are about 0.001 to 0.004 for the Antarctic region and 0.003 to 0.004 at the beginning to about 0.006 at the end of the time period for the Arctic region. Polar stratospheric clouds (PSC's) were observed during the Arctic winter, as expected. This report provides, in a ready-to-use format, a representative sample of the seventh semester of data to be used in atmospheric and climatic studies.

Mccormick, M. P.↗