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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Analysis of the Warmest Arctic Winter, 2015-2016

December through February 2015-2016 defines the warmest winter season over the Arctic in the observational record. Positive 2m temperature anomalies were focused over regions of reduced sea ice cover in the Kara and Barents Seas and southwestern Alaska. A third region is found over the ice-covered central Arctic Ocean. The period is marked by a strong synoptic pattern which produced melting temperatures in close proximity to the North Pole in late December and anomalous high pressure near the Taymyr Peninsula. Atmospheric teleconnections from the Atlantic contributed to warming over Eurasian high-latitude land surfaces, and El Niño-related teleconnections explain warming over southwestern Alaska and British Columbia, while warm anomalies over the central Arctic are associated with physical processes including the presence of enhanced atmospheric water vapor and an increased downwelling longwave radiative flux. Preconditioning of sea ice conditions by warm temperatures affected the ensuing spring extent.

Barents and Kara Seas↗
NASA NTRS2017

A Probabilistic Method of Assessing Carbon Accumulation Rate at Imnavait Creek Peatland, Arctic Long Term Ecological Research Station, Alaska

Arctic peatlands are an important part of the global carbon cycle, accumulating atmospheric carbon as organic matter since the Late glacial. Current methods for understanding the changing efficiency of the peatland carbon sink rely on peatlands with an undisturbed stratigraphy. Here we present a method of estimating primary carbon accumulation rate from a site where permafrost processes have either vertically or horizontally translocated nearby carbon-rich sediment out of stratigraphic order. Briefly, our new algorithm estimates the probability of the age of deposition of a random increment of sediment in the core. The method assumes that if sediment age is measured at even depth increments, dates are more likely to occur during intervals of higher accumulation rate and vice versa. Multiplying estimated sedimentation rate by measured carbon density yields carbon accumulation rate. We perform this analysis at the Imnavait Creek Peatland, near the Arctic Long Term Ecological Research network site at Toolik Lake, Alaska. Using classical radiocarbon age modeling, we find unreasonably high rates of carbon accumulation at various Holocene intervals. With our new method, we find accumulation rate changes that are in improved agreement within the context of other sites throughout Alaska and the rest of the Circum-Arctic region.

carbon accumulation;Imnavait;peatlands;permafrost;↗
NASA NTRS2019

Inter-relationship Between Subtropical Pacific Sea Surface Temperature, Arctic Sea Ice Concentration, and North Atlantic Oscillation in Recent Summers

The inter-relationship between subtropical western–central Pacific sea surface temperatures (STWCPSST), sea ice concentrations in the Beaufort Sea (SICBS), and the North Atlantic Oscillation (NAO) in summer are investigated over the period 1980–2016. It is shown that the Arctic response to the remote impact of the Pacific SST is more dominant in recent summers, leading to a frequent occurrence of the negative phase of the NAO following the STWCPSST increase. Lag–correlations of STWCPSST positive (negative) anomalies in spring with the negative (positive) NAO and SICBS loss (recovery) in summer have increased over the last two decades, reaching r = 0.4–0.5 with significance at the 5 percent level. Both observations and the atmospheric general circulation model experiments suggest that the positive STWCPSST anomaly and subsequent planetary-scale wave propagation act to increase the Arctic upper-level geopotential heights and temperatures in the following season. This response extends to Greenland, providing favorable conditions for developing the negative phase of the NAO. Connected with this atmospheric response, SIC and surface albedo decrease with an increase in the surface net shortwave flux over the Beaufort Sea. Examination of the surface energy balance (radiative and turbulent fluxes) reveals that surplus energy that can heat the surface increases over the Arctic, enhancing the SIC reduction.

SST↗
NASA NTRS2019

Radiative Impacts of Future Arctic Sea Ice Melt

The dramatic retreat in Arctic sea ice in recent decades has reduced the top-of-atmosphere albedo over this region and added more solar energy to the climate system. Here we use two satellite datasets--17 years of CERES top-of-atmosphere albedo and 38 years of microwave sea ice concentration measurements-- to estimate the radiative impact of the loss of Arctic sea ice. We then use these observations to estimate the potential impact of a future complete loss of the Arctic sea ice. This presentation will also discuss the seasonal variation in the estimated warming, the dependence of the results on the potential cloud response to ice loss, and a comparison with CMIP5 model results.

Pistone, Kristina↗
NASA NTRS2019

The Role of Cyclone Activity in Snow Accumulation on Arctic Sea Ice

Identifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

Webster, M. A.↗
NASA NTRS2019

Utilization of Airborne Observations to Assess Model Parametrizations of Critical RH Profiles in the Arctic Ocean

Observational paucity is a reality in the Arctic Ocean. This is especially true for near-surface variables such as temperature, moisture, heat fluxes and BL clouds. As a result, modeling has become one of the major avenues for understanding current and future Arctic trends. Reanalyses are frequently used to force global ocean circulation and sea-ice models. But in northern high latitudes, model integrations and reanalyses are known to have large uncertainties in temperature and humidity profiles, and in boundary layer cloudiness. These are common sources of error in the surface radiative budget terms.An important way to diagnose these biases spatially and temporally is by using satellite remote sensing data. However, remotely-sensed observations also have large uncertainties, especially in near-surface temperature and relative humidity profiles.In situ observational studies are important in bridging our knowledge gap in regions such as the Arctic Ocean. Here, we utilize airborne and ship observations from the ARISE, ACME-IV, and ASCOS campaigns to construct critical relative humidity (RH) profiles over the Beaufort Sea. Such profiles are used as parameterization inputs in the NASA GOES global model to derive the total water condensate in a model grid-box, which determines the cloud fraction. Currently, the critical RH profiles are derived by global AIRS data, relaying mostly on mid-latitude regions, which are not necessarily relevant to the Arctic.We derive campaign-wide mean, standard deviation, and critical RH values, for grid size of 50x50 km and altitude bins between 50 to 400 m, covering both open-ocean and sea-ice covered regions. We compare profiles over open ocean and sea-ice, and look at correlations between the observed critical RH values and water condensate (by cloud number concentration) from observations versus the modeled ones. We then input our calculated values of minimal critical RH values into a set of GEOS single column model (SCM) simulations over the ARISE and ASCOS regions and compare the differences between the predicted values of cloud liquid water path (LWP), ice water path (IWP) and surface fluxes with the observed ones under the range of input parameterizations. Finally, we discuss the implications on surface radiative budget predictions in this region.

Segal-Rozenhaimer, Michal↗
NASA NTRS2020

Winter Arctic Sea Ice Thickness from ICESat-2 Freeboards

NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.

Alek A Petty↗
NASA NTRSDate not supplied

A Community for Polar Early Career Researchers Engaging With the Interagency Arctic Research Policy Committee

The Interagency Arctic Research Policy Committee (IARPC) is tasked with implementation of the Arctic Research Plan (ARP) by coordinating representatives from federal and state agencies, academic, industry, and Indigenous partners with interests in Arctic research. IARPC is organized into several collaboration teams and discipline-specific communities of practice that interface on the IARPC Collaborations website. The Early Career Community of Practice (ECCoP) is one such community that was established to promote the research of early career researchers (ECR) and increase ECR engagement with the ARP, program managers, collaboration teams, and communities of practice. The ECCoP has grown to develop regular newsletters promoting recent ECR research publications ad sharing training, job, funding, and collaboration opportunities relevant to community members at all life stages that self-identify as ECRs. Within IARPC, the ECCoP continues to promote ECR participation in monthly community of practice meetings and webinars and to report on ECR efforts that contribute to deliverables of the ARP Biennial Implementation Plan. Recent activity of the ECCoP includes developing an annual survey of the ECR cohort on IARPC to follow change in demographics over time and to identify community needs to improve diversity and inclusion in the polar research space and to address gaps in training needs for ECRs.

Katy Smith↗
NASA NTRS2023

Passive Microwave Arctic Sea Ice Melt Onset Dates From the Advanced Horizontal Range Algorithm 1979 - 2022

The onset of the summer melt season is a key stage of the Arctic sea ice seasonal cycle and is an indicator of climate change. Surface melting of the bare or snow-covered sea ice is detected using passive microwave satellite observations. The data set presented here is a 44 year record of Arctic sea ice annual melt onset (MO) dates for 1979–2022 produced using an updated version of the Advanced Horizontal Range Algorithm (AHRA). This data product contains annual maps of the sea ice MO date and a set of descriptive statistics summarizing the data. This paper describes a new update of the AHRA methodology, now AHRA V5, including key changes to the algorithm starting date and sea ice mask methodology to improve estimates of early-season MO dates especially near the sea ice periphery. AHRA V5 data are suitable for monitoring trends in Arctic and regional sea ice MO dates and for process studies of atmosphere-sea ice interactions during the early spring and summer months.

Angela C. Bliss↗
NASA NTRS2010

Using the MicroASAR on the NASA SIERRA UAS in the Characterization of Arctic Sea Ice Experiment

The MicroASAR is a flexible, robust SAR system built on the successful legacy of the BYU microSAR. It is a compact LFM-CW SAR system designed for low-power operation on small, manned aircraft or UAS. The NASA SIERRA UAS was designed to test new instruments and support flight experiments. NASA used the MicroASAR on the SIERRA during a science field campaign in 2009 to study sea ice roughness and break-up in the Arctic and high northern latitudes. This mission is known as CASIE-09 (Characterization of Arctic Sea Ice Experiment 2009). This paper describes the MicroASAR and its role flying on the SIERRA UAS platform as part of CASIE-09.

CASIE-09(CHARACTERIZATION OF ARCTIC SEA ICE EXPERI↗
NASA NTRS2000

Development of the polar vortex in the 1999-2000 Arctic winter stratosphere

The 1999-2000 Arctic stratospheric vortex was unusually cold, especially in the early winter lower stratosphere, with a larger area near polar stratospheric cloud formation temperatures in Dec and Jan, and much lower temperatures averaged over Nov-Jan, than any previously observed Arctic winter.

Ozone loss meteorology arctic stratospheric↗
NASA NTRS2014

Changes in Arctic Melt Season and Implications for Sea Ice Loss

The Arctic-wide melt season has lengthened at a rate of 5 days dec-1 from 1979 to 2013, dominated by later autumn freeze-up within the Kara, Laptev, East Siberian, Chukchi and Beaufort seas between 6 and 11 days dec(exp -1). While melt onset trends are generally smaller, the timing of melt onset has a large influence on the total amount of solar energy absorbed during summer. The additional heat stored in the upper ocean of approximately 752MJ m(exp -2) during the last decade, increases sea surface temperatures by 0.5 to 1.5 C and largely explains the observed delays in autumn freeze-up within the Arctic Ocean's adjacent seas. Cumulative anomalies in total absorbed solar radiation from May through September for the most recent pentad locally exceed 300-400 MJ m(exp -2) in the Beaufort, Chukchi and East Siberian seas. This extra solar energy is equivalent to melting 0.97 to 1.3 m of ice during the summer.

Season↗
NASA NTRS2013

Reducing Spread in Climate Model Projections of a September Ice-Free Arctic

This paper addresses the specter of a September ice-free Arctic in the 21st century using newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We find that large spread in the projected timing of the September ice-free Arctic in 30 CMIP5 models is associated at least as much with different atmospheric model components as with initial conditions. Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to approx. 1.7 million sq km in the mid 2040s and reach the ice-free state (defined as 1 million sq km) in 2054-2058. Under a medium-mitigation scenario, both approaches project a decrease to approx.1.7 million sq km in the early 2060s, followed by a leveling off in the ice extent.

simulation↗
NASA NTRS2014

Spatially Mapped Reductions in the Length of the Arctic Sea Ice Season

Satellite data are used to determine the number of days having sea ice coverage in each year 1979-2013 and to map the trends in these ice-season lengths. Over the majority of the Arctic seasonal sea ice zone, the ice season shortened at an average rate of at least 5 days/decade between 1979 and 2013, and in a small area in the northeastern Barents Sea the rate of shortening reached over 65 days/decade. The only substantial non-coastal area with lengthening sea ice seasons is the Bering Sea, where the ice season lengthened by 5-15 days/decade. Over the Arctic as a whole, the area with ice seasons shortened by at least 5 days/decade is 12.4 × 10(exp 6) square kilimeters, while the area with ice seasons lengthened by at least 5 days/decade is only 1.1 × 10(exp 6) square kilometers. The contrast is even greater, percentage-wise, for higher rates.

Arctic↗
NASA NTRS2014

Near-Surface Meteorology During the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of Reanalyses and Global Climate Models.

Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.

surface energy↗
NASA NTRS2015

Effect of Recent Sea Surface Temperature Trends on the Arctic Stratospheric Vortex

The springtime Arctic polar vortex has cooled significantly over the satellite era, with consequences for ozone concentrations in the springtime transition season. The causes of this cooling trend are deduced by using comprehensive chemistry-climate model experiments. Approximately half of the satellite era early springtime cooling trend in the Arctic lower stratosphere was caused by changing sea surface temperatures (SSTs). An ensemble of experiments forced only by changing SSTs is compared to an ensemble of experiments in which both the observed SSTs and chemically- and radiatively-active trace species are changing. By comparing the two ensembles, it is shown that warming of Indian Ocean, North Pacific, and North Atlantic SSTs, and cooling of the tropical Pacific, have strongly contributed to recent polar stratospheric cooling in late winter and early spring, and to a weak polar stratospheric warming in early winter. When concentrations of ozone-depleting substances and greenhouse gases are fixed, polar ozone concentrations show a small but robust decline due to changing SSTs. Ozone changes are magnified in the presence of changing gas concentrations. The stratospheric changes can be understood by examining the tropospheric height and heat flux anomalies generated by the anomalous SSTs. Finally, recent SST changes have contributed to a decrease in the frequency of late winter stratospheric sudden warmings.

Arctic↗
NASA NTRS2015

Revisiting the Potential of Melt Pond Fraction as a Predictor for the Seasonal Arctic Sea Ice Extent Minimum

The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state.

sea ice↗
NASA NTRS2016

Arctic Sea Ice Simulation in the PlioMIP Ensemble

Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate ice-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene sea ice extent. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive sea ice proxy data is highlighted, in order to better compare model performances.

simulation↗