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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Landsat Derived Bathymetry of Lakes on the Arctic Coastal Plan of Northern Alaska

The Pleistocene sand sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by anancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, whichcover approximately 20 % of the Pleistocene sand sea, are relatively deep (up to 25 m). In addition to the nat-ural importance of ACP sand sea lakes for water storage, energy balance, and ecological habitat, the need forwinter water for industrial development and exploration activities makes lakes in this region a valuable resource.However, ACP sand sea lakes have received little prior study. Here, we collect in situ bathymetric data to test12 model variants for predicting sand sea lake depth based on analysis of Landsat-8 Operational Land Imager(OLI) images. Lake depth gradients were measured at 17 lakes in midsummer 2017 using a Humminbird 798ciHD SI Combo automatic sonar system. The field-measured data points were compared to red–green–blue (RGB)bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and map bathymetry. Exponential functions using a simple band ratio (withbands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For eachlake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetrieswere integrated with remotely sensed lake surface area to quantify lake water storage volumes, which rangedfrom 1.056×10−3to 57.416×10−3km3. Due to variations in depth maxima, substrate, and turbidity betweenlakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ datawith which to develop a regional model solution. Estimating lake water volumes using remote sensing will fa-cilitate better management of expanding development activities and serve as a baseline by which to evaluatefuture responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lakebathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) andhttps://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.

Claire E Simpson↗
NASA NTRS2019

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

The Arctic stratospheric response to El Niño (EN) and La Niña (LN) is evaluated in a 41 member ensemble of the period 1980 to 2009 in the Goddard Earth Observing System Chemistry-Climate Model. We consider whether the responses to EN and LN are equal in magnitude and opposite in sign, whether the responses to moderate and extreme events are proportion-ate, and if the response depends on whether sea surface temperature anomalies (SSTs) peak in the Eastern Pacific (EP) or Central Pacific (CP). There is no indication of any nonlinearities between EN and LN, though in ~ 15% of the ensemble members the stratospheric sudden warming (SSW) frequencies for EN and LN are similar, suggesting that a similar SSW frequency for EN and LN, as has occurred over the past ~ 60 years, can occur by chance. The response to extreme EN events is not proportionate to the amplitude of the underlying SST anomalies in spring. EP EN events preferentially increase zonal wavenumber 1 and decrease zonal wavenumber 2 as compared to CP EN events, however the zonal-mean Arctic stratospheric and subpolar surface response is generally little different between EP EN and CP EN once one accounts for the relative weakness of CP events. These differences between EP and CP events and between moderate and extreme EN events only emerge if at least 25 events are composited, however, due to the small signal-to-noise ratio, and hence these differences may be of little practical benefit.

Sea surface temperature anomalies (SSTs) peak in t↗
NASA NTRS2021

Climate Drivers of Arctic Tundra Variability and Change Using an Indicators Framework

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Uma S. Bhatt↗
NASA NTRS2021

Estimation of Thermodynamic and Dynamic Contributions to Sea Ice Growth in the Central Arctic Using ICESat-2 and MOSAiC SIMBA Buoy Data

The fine spatial resolution of the ICESat-2 (IS2) satellite altimeter allows monitoring the evolution of sea ice thickness with detailed dynamic information (e.g. ridges and leads). In this study, we first assess the ability of IS2 to estimate thermodynamic ice growth and dynamic thickening during the ice-growing season in the central Arctic Ocean. As an indicator of the thermodynamic ice growth, we use 10 thermistor string-based sea ice mass balance array (SIMBA) buoys deployed at a scale of ~ 50 km from the Icebreaker Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. We collect IS2 data within 20 km buffer distance from the individual buoys, and calculate the mode, median, and mean of the IS2-derived ice thickness. The IS2 modal thickness shows the least bias (−0.169 m) with the buoy ice thickness, representing level ice thickness. In addition, the increasing rate of the IS2 modal thickness is close to the thermodynamic ice growth with a small bias of −0.054 cm/day. However, the increasing rates of the IS2 median and mean thickness are greater than the thermodynamic ice growth by about 0.114 cm/day and 0.198 cm/day, respectively, because they also include ice growth caused by thickness redistribution during dynamic deformation. The dynamic contributions may account for 26.1 ± 10.3% and 34.4 ± 10.1% of the total increase of the IS2 median and mean thickness, respectively. Within a ~ 50 km radius area from the MOSAiC Central Observatory, IS2 measurements exhibit that the ridge fraction increased from <2% in November to ~ 4% in March (~0.029%/day of average increasing rate) and ridge height increased about 0.047 cm/day during the same period. However, lead formation does not show significant contributions to the dynamic ice thickening because leads are temporary features lasting only 2–3 days. Although there are considerable uncertainties in IS2 ice thickness estimation and IS2-buoy thickness comparison, this study emphasizes the importance of combining measurements by IS2 and SIMBA buoys to explain the regional sea ice mass balance with separating the thermodynamic and dynamic contributions.

satellite altimeter↗
NASA NTRS2021

Potential of Full-polarimetric P- and L-band Sar Data in Characterizing Post-fire Recovery of Arctic Tundra

We used the airborne full polarimetric L-band and P-band SAR (PolSAR) data collected from recent NASA ABoVE Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign to understand the sensitivity of longwave radar backscatter intensity and phase to post-fire recovery process of Arctic tundra. The 2007 Anaktuvuk River fire was used as a case study. At 10-year post-fire, we observed a strong increase (>~4 dB) in both the P- and L-band radar backscatter in the severely burned areas, in contrast to limited backscatter differences (VV, VH) between burned and unburned areas at C-band. The polarimetric target decomposition analysis indicated a general trend towards more random surface scattering, and strong increases of the double-bounce and volumetric scattering power at both P- and L-band in the burned areas. Large differences were also observed in the Pauli phase angle and the dominant-scattering-type Touzi phase angle between the burned and unburned areas. The above changes are likely caused by increasing surface roughness and microtopography due to thermokarst development and ice degradation, and increasing subsurface scattering due to an overall drier and deeper active layer in the burned areas

Miller, Charles E.↗
NASA NTRSDate not supplied

Defining Burned Area in the Arctic: Examples from Small Fires in the Southeastern United States

Burning of small fires in the Arctic is important to climate change related processes and in the past the importance has been overlooked. Similarly, small fire burning in the Southeast United states has been underestimated and overlooked, but has just as much burned area as wildfires in the Western United States. Using FIREX-AQ campaign data, we are looking at the ability of satellites to detected the fires. Possible similarities in small fires between the Central and Southeastern US and the Arctic give ideas for collaboration and quantification of burned area.

Emily Gargulinski↗
NASA NTRS2022

The Radiative Effect on Cloud Microphysics from the Arctic to the Tropics

Cloud representation is one of the largest uncertainties in the current weather and climate models. In this article, the observations and modeling of the radiative effect on (cloud) microphysics (REM) from the Arctic to the Tropics are overviewed, providing a new direction to meet the challenge of cloud representation. REM deals with the radiation-induced temperature difference between cloud particles and air. It leads to two common phenomena observed at the surface—dew and frost—and impacts clouds aloft significantly, which is noticed via the wide occurrence of horizontally oriented ice crystals (HOICs). However, REM has been overlooked by all of the operational weather and climate models. Based on the bin model of REM and the global distribution of radiative cooling/warming, the observations of REM from several platforms (e.g., aircrafts, field campaigns, and satellites) are coordinated in this article, yielding a global picture on REM. As a result, the picture is compatible with the global distribution of HOICs and other ice crystal characteristics obtained from various clouds on the globe, such as diamond dust (or clear-sky precipitation) in the Arctic, sub-visual cirrus clouds in the tropical tropopause layer, and other cirrus clouds from the low to high latitudes. In addition, ice crystals possess relatively strong REM compared to liquid drops because their aspect ratio is usually not one. The global picture on REM can be used by the weather and climate modelers to diagnose their cloud representation biases. It can also be used to improve the atmospheric ice retrieval algorithm from satellite observations.

cloud microphysics↗
NASA NTRS2022

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

Madison M. Smith↗
NASA NTRS2023

Summer Atmospheric Circulation Over Greenland in Response to Arctic Amplification and Diminished Spring Snow Cover

The exceptional atmospheric conditions that have accelerated Greenland Ice Sheet mass loss in recent decades have been repeatedly recognized as a possible dynamical response to Arctic amplification. Here, we present evidence of two potentially synergistic mechanisms linking high-latitude warming to the observed increase in Greenland blocking. Consistent with a prominent hypothesis associating Arctic amplification and persistent weather extremes, we show that the summer atmospheric circulation over the North Atlantic has become wavier and link this wavier flow to more prevalent Greenland blocking. While a concomitant decline in terrestrial snow cover has likely contributed to this mechanism by further amplifying warming at high latitudes, we also show that there is a direct stationary Rossby wave response to low spring North American snow cover that enforces an anomalous anticyclone over Greenland, thus helping to anchor the ridge over Greenland in this wavier atmospheric state.

Atmospheric dynamics↗
NASA NTRS2023

A 1D Model for Nucleation of Ice From Aerosol Particles: An Application to a Mixed-Phase Arctic Stratus Cloud Layer

Mixed-phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long-lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice-nucleating particle (INP) reservoir dynamics governing immersion-mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol-cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time-independent (singular) number- and surface area-based descriptions and a time-dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT-based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.

aerosol↗
NASA NTRSDate not supplied

Supporting Arctic Research, Engagement, and Policy With GMAO’s Next Generation Reanalysis & Prediction Systems

Modeling efforts at NASA aim to advance scientific understanding of the Earth system and its response to natural and human-induced changes and to improve our ability to predict climate, weather, and natural hazards. These efforts are particularly relevant for communities, scientists and stakeholders working and living in the Arctic. Here, we highlight current and planned Earth system prediction and data assimilation products from the Global Modeling and Assimilation Office (GMAO). We invite feedback and discussion on GMAO’s model and reanalysis development in support of Arctic research, engagement, and policy.

Lauren C Andrews↗
NASA NTRS2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

land data assimilation↗
NASA NTRS1997

Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978-September 1995 User's Guide

Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea ice covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea ice concentration data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily ice concentrations (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and Ice Data Center in Boulder, CO.

Sea ice concentration↗