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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Indian Ocean warming as key driver of long-term positive trend of Arctic Oscillation

Arctic oscillation (AO), which is the most dominant atmospheric variability in the Northern Hemisphere (NH) during the boreal winter, significantly affects the weather and climate at mid-to-high latitudes in the NH. Although a climate community has focused on a negative trend of AO in recent decades, the significant positive trend of AO over the last 60 years has not yet been thoroughly discussed. By analyzing reanalysis and Atmospheric Model Inter-comparison Project (AMIP) datasets with novel pacemaker experiments, we found that sea surface temperature warming in the Indian Ocean is conducive to the positive trend of AO from the late 1950s. The momentum flux convergence by stationary waves due to the Indian Ocean warming plays an important role in the positive trend of AO, which is characterized by a poleward shift of zonal-mean zonal winds. In addition, the reduced upward propagating wave activity flux over the North Pacific due to Indian Ocean warming also plays a role to strengthen the polar vortex, subsequently, it contributes to the positive trend of AO. Our results imply that the respective warming trend of tropical ocean basins including Indian Ocean, which is either anthropogenic forcing or natural variability or their combined effect, should be considered to correctly project the future AO’s trend.

Indian Ocean↗
NASA NTRS2017

Seasonal Clear-Sky Flux and Cloud Radiative Effect Anomalies in the Arctic Atmospheric Column Associated with the Arctic Oscillation and Arctic Dipole

The impact of the Arctic Oscillation (AO) and Arctic Dipole (AD) on the radiative flux into the Arctic mean atmospheric column is quantified. 3-month-averaged AO and AD indices are regressed with corresponding surface and top-of-atmosphere (TOA) fluxes from the CERES-SFC and CERES-TOA EBAF datasets over the period 2000-2014. An increase in clear-sky fluxes into the Arctic mean atmospheric column during fall is the largest net flux anomaly associated with AO, primarily driven by a positive net longwave flux anomaly (i.e. increase of net flux into the atmospheric column) at the surface. A decrease in the Arctic mean atmospheric column cloud radiative effect during winter and spring is the largest flux anomaly associated with AD, primarily driven by a change in the longwave cloud radiative effect at the surface. These prominent responses to AO and AD are widely distributed across the ice-covered Arctic, suggesting that the physical process or processes that bring about the flux change associated with AO and AD are distributed throughout the Arctic.

Hegyi, Bradley M.↗
NASA NTRS2017

The Regional Influence of the Arctic Oscillation and Arctic Dipole on the Wintertime Arctic Surface Radiation Budget and Sea Ice Growth

An analysis of 2000-2015 monthly Clouds and the Earth's Radiant Energy System-Energy Balanced and Filled (CERES-EBAF) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) data reveals statistically significant fall and wintertime relationships between Arctic surface longwave (LW) radiative flux anomalies and the Arctic Oscillation (AO) and Arctic Dipole (AD). Signifying a substantial regional imprint, a negative AD index corresponds with positive downwelling clear-sky LW flux anomalies (greater than10W m(exp -2)) north of western Eurasia (0 deg E-120 deg E) and reduced sea ice growth in the Barents and Kara Seas in November-February. Conversely, a positive AO index coincides with negative clear-sky LW flux anomalies and minimal sea ice growth change in October-November across the Arctic. Increased (decreased) atmospheric temperature and water vapor coincide with the largest positive (negative) clear-sky flux anomalies. Positive surface LW cloud radiative effect anomalies also accompany the negative AD index in December-February. The results highlight a potential pathway by which Arctic atmospheric variability influences the regional surface radiation budget over areas of Arctic sea ice growth.

Hegyi, Bradley M.↗
NASA NTRS2012

Date of Snowmelt at High Latitudes as Determined from Visible Satellite Data and Relationship with the Arctic Oscillation

Spring snow cover across Arctic lands has, on average, retreated approximately five days earlier since the late 1980s compared to the previous twenty years. However, it appears that since about 1990, the date the snowline first retreats north during the spring has remained nearly unchanged--in the last twenty years, the date of snow disappearance has not been occurring noticeably earlier. Snowmelt changes observed in the 1980s was step-like in nature, unlike a more continuous downward trend seen in Arctic sea ice extent. At latitude 70 deg N, several latitudinal segments (of 10 degrees) show significant (negative) trends. However, only two latitudinal segments at 60 deg N show significant trends, one positive and one negative. These variations appear to be related to variations in the Arctic Oscillation (AO). Additional observations and modeling investigations are needed to better explain past and present spring melt characteristics and peculiarities.

Foster, James↗
NASA NTRS2022

Impact of the Arctic Oscillation from March on summertime sea ice

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

Arctic Oscillation↗
NASA NTRS2014

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

Arctic Oscillation↗
NASA NTRS2014

Comparison of the Impact of the Arctic Oscillation and East Atlantic - West Russia Teleconnection on Interannual Variation in East Asian Winter Temperatures and Monsoon

The large-scale impacts of the Arctic Oscillation (AO) and the East Atlantic/West Russia (EA/WR) teleconnection on the East Asian winter climate anomalies are compared for the past 34 winters focusing on 1) interannual monthly to seasonal temperature variability, 2) East Asian winter monsoon (EAWM), and 3) the Siberian high (SH) and cold surge. Regression analysis reveals warming by AO and EA/WR over mid-latitude East Asia during their positive phase and vice versa. The EA/WR impact is found to be comparable to the AO impact in affecting the East Asian temperature and monsoon. For example, warm (cold) months over mid-latitude East Asia during the positive (negative) AO are clearly seen when the AO and EA/WR are in the same phase. Near zero correlation is found between temperature and the AO phase when both teleconnections are in an opposite phase. The well-known negative relationship between SH and the AO phase is observed significantly more often when the AO is in the same phase with the EA/WR. Also, the indices of EAWM, cold surge, and SH are found to be more highly negative-correlated with the EA/WR rather than with the AO. The advective temperature change and associated circulation demonstrate that the anomalous large-scale field including the SH over the mid-latitude Asian inland is better represented by the EA/WR, influencing the East Asian winter climates. These results suggest that the impact of EA/WR should be considered more important than previously thought for a better understanding of East Asian winter temperature and monsoon variability.

Arctic Oscillation↗
NASA NTRS2013

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

This study assesses the prediction skill of the boreal winter Arctic Oscillation (AO) in the state-of-the-art dynamical ensemble prediction systems (EPSs): the UKMO GloSea4, the NCEP CFSv2, and the NASA GEOS-5. Long-term reforecasts made with the EPSs are used to evaluate representations of the AO, and to examine skill scores for the deterministic and probabilistic forecast of the AO index. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation according to the AO phase. Results demonstrate that all EPSs have better prediction skill than the persistence prediction for lead times up to 3-month, suggesting a great potential for skillful prediction of the AO and the associated climate anomalies in seasonal time scale. It is also found that the deterministic and probabilistic forecast skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).

Coupled Models↗
NASA NTRS2018

Dynamical Response of Mediterranean Precipitation to Greenhouse Gases and Aerosols

Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and, thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the beginning of the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare the modeled dynamical response of Mediterranean precipitation to individual forcing agents in a set of global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive sea level pressure (SLP) pattern similar to the North Atlantic Oscillation-Arctic Oscillation, characterized by higher SLP at midlatitudes and lower SLP at high latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901-2010, roughly one-third (31+/-17%) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs, whereas global scattering sulfate aerosols would have negligible impacts. Aerosol-cloud interactions appear to have minimal impacts on Mediterranean precipitation in these models, at least in part because many simulations did not fully include such processes; these merit further study. The findings from this study suggest that future BC and WMGHG emissions may significantly affect regional water resources, agricultural practices, ecosystems and the economy in the Mediterranean region.

Tang, Tao↗
NASA NTRS2000

A Footprint of Solar Variability Variability on Climate

An 11-year variation is shown here to exist in an index of the atmospheric variability defined as the difference between the North Atlantic Oscillation and Arctic Oscillation indices.

UV North Atlantic Oscillation El Nino↗
NASA NTRS2003

Modeling, Simulation, and Forecasting of Subseasonal Variability

A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

Waliser, Duane↗
NASA NTRS2018

Nasa GEOS Model for Sub-Seasonal to Seasonal Predictions: The Major Teleconnections, Tropical Cyclone Activity, and ENSO

Ability of the NASA GEOS seasonal prediction model system (the latest version) in predicting the large-scale teleconnections, tropical cyclone (TC) activity, and ENSO are validated. The major large-scale teleconnections include the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), and the Pacific North American (PNA) that span vast geographical area across the North Pacific/Atlantic and North America. Predictive skill of TC activity is assessed by Genesis potential index (GPI). Anomaly correlations are greater than 0.5 for winter teleconnections at up to 2 month lead and for the first four month (June- July-August-September) GPIs over the North Atlantic and the Western Pacific. Correlation for the long-range prediction of the ENSO (Nino3.4 SST) maturity reaches 0.9 at 6 month lead and 0.8 at 9 month lead.

Lim, Young-Kwon↗
NASA NTRS2022

Seasonal Variability in the Mechanisms Behind the 2020 Siberian Heatwaves

Record-breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier-than-usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record-breaking heatwaves in Siberia in the spring of 2020.

Allison B. Marquardt Collow↗
NASA NTRS2005

Sea Ice

Sea ice covers vast areas of the polar oceans, with ice extent in the Northern Hemisphere ranging from approximately 7 x 10(exp 6) sq km in September to approximately 15 x 10(exp 6) sq km in March and ice extent in the Southern Hemisphere ranging from approximately 3 x 10(exp 6) sq km in February to approximately 18 x 10(exp 6) sq km in September. These ice covers have major impacts on the atmosphere, oceans, and ecosystems of the polar regions, and so as changes occur in them there are potential widespread consequences. Satellite data reveal considerable interannual variability in both polar sea ice covers, and many studies suggest possible connections between the ice and various oscillations within the climate system, such as the Arctic Oscillation, North Atlantic Oscillation, and Antarctic Oscillation, or Southern Annular Mode. Nonetheless, statistically significant long-term trends are also apparent, including overall trends of decreased ice coverage in the Arctic and increased ice coverage in the Antarctic from late 1978 through the end of 2003, with the Antarctic ice increases following marked decreases in the Antarctic ice during the 1970s. For a detailed picture of the seasonally varying ice cover at the start of the 21st century, this chapter includes ice concentration maps for each month of 2001 for both the Arctic and the Antarctic, as well as an overview of what the satellite record has revealed about the two polar ice covers from the 1970s through 2003.

Parkinson, Claire L.↗
NASA NTRSDate not supplied

The Impact of Large Scale Atmospheric and Cyclone Variability on the Sea-Ice Edge in the Labrador Sea

We study the temporal variability of the wintertime Labrador Sea ice area. The driving factors of these intraseasonal and interannual variations are related to large scale atmospheric variability and cyclone variability both of which can be characterized by the Arctic Oscillation (AO) index. We observe negative trends in the maximum sea-ice area over the past 40 years, and a positive correlation between the AO index and Labrador Sea ice area. Using satellite-derived daily ice area along with reanalysis-derived cyclones, turbulent flux, wind, humidity, air and sea temperature fields, we delve into the physical coupling mechanisms by which cyclones influence the position of the ice edge in the Labrador Sea throughout the winter.

Sea ice↗
NASA NTRS2014

Climate Trends in the Arctic as Observed from Space

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.

arctic↗