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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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26 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The impact of stratospheric aerosol intervention on the North Atlantic and Quasi-Biennial Oscillations in the Geoengineering Model Intercomparison Project (GeoMIP) G6sulfur experiment

As part of the Geoengineering Model Intercomparison Project a numerical experiment known as G6sulfur has been designed in which temperatures under a high-forcing future scenario (SSP5-8.5) are reduced to those under a medium-forcing scenario (SSP2-4.5) using the proposed geoengineering technique of stratospheric aerosol intervention (SAI). G6sulfur involves introducing sulfuric acid aerosol into the tropical stratosphere where it reflects incoming sunlight back to space, thus cooling the planet. Here, we compare the results from six Earth-system models that have performed the G6sulfur experiment and examine how SAI affects two important modes of natural variability, the northern wintertime North Atlantic Oscillation (NAO) and the Quasi-Biennial Oscillation (QBO). Although all models show that SAI is successful in reducing global mean temperature as designed, they are also consistent in showing that it forces an increasingly positive phase of the NAO as the injection rate increases over the course of the 21st century, exacerbating precipitation reductions over parts of southern Europe compared with SSP5-8.5. In contrast to the robust result for the NAO, there is less consistency for the impact on the QBO, but the results nevertheless indicate a risk that equatorial SAI could cause the QBO to stall and become locked in a phase with permanent westerly winds in the lower stratosphere.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

The Triple-Dip La Niña of 2020–22: Updates to the Correlation of ENSO With the Termination of Solar Cycles

The Sun provides the energy required to sustain life on Earth and drive our planet’s atmosphere. However, establishing a solid physical connection between solar and tropospheric variability has posed a considerable challenge across the spectrum of Earth-system science. Over the past few years a new picture to describe solar variability has developed, based on observing, understanding and tracing the progression, interaction and intrinsic variability of the magnetized activity bands that belong to the Sun’s 22-year magnetic activity cycle. A solar cycle’s fiducial clock does not run from the canonical min or max, instead resetting when all old cycle polarity magnetic flux is cancelled at the equator, an event dubbed the “termination” of that solar cycle, or terminator. In a recent paper, we demonstrated with high statistical significance, a correlation between the occurrence of termination of the last five solar cycles and the transition from El Niño to La Niña in the Pacific Ocean, and predicted that there would be a transition to La Niña in mid 2020. La Niña did indeed begin in mid-2020, and endured into 2023 as a rare “triple dip” event, but some of the solar predictions made did not occur until late 2021. This work examines what went right, what went wrong, the correlations between El Niño, La Niña and geomagnetic activity indices, and what might be expected for the general trends of large-scale global climate in the next decade.

Robert J Leamon↗
NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗
DOE OSTI2015

A Community Emissions Data System (CEDS) for Historical Emissions

Historical emission estimates for anthropogenic aerosol and precursor compounds are key data needed for Earth system models, climate models, and atmospheric chemistry and transport models; both for general analysis and assessment and also for model validation through comparisons with observations. Current global emission data sets have a number of shortcomings, including timeliness and transparency. Satellite and other earth-system data are increasingly available in near real-time, but global emission estimates lag by 5-10 years. The CEDS project will construct a data-driven, open source framework to produce annually updated emission estimates. The basic methodologies to be used for this system have been used for SO2 (Smith et al. 2011, Klimont, Smith and Cofala 2013), and are designed to complement existing inventory efforts. The goal of this system is to consistently extend current emission estimates both forward in time to recent years and also back over the entire industrial era. The project will produce improved datasets for global and (potentially) regional model, allow analysis of trends across time, countries, and sectors of emissions and emission factors, and facilitate improved scientific analysis in general. Consistent estimation of uncertainty will be an integral part of this system. This effort will facilitate community evaluation of emissions and further emission-related research more generally.

Smith, Steven J.↗
DOE OSTI2015

A Community Emissions Data System (CEDS) for Historical Emissions

Historical emission estimates for anthropogenic aerosol and precursor compounds are key data needed for Earth system models, climate models, and atmospheric chemistry and transport models; both for general analysis and assessment and also for model validation through comparisons with observations. Current global emission data sets have a number of shortcomings, including timeliness and transparency. Satellite and other earth-system data are increasingly available in near real-time, but global emission estimates lag by 5-10 years. The CEDS project will construct a data-driven, open source framework to produce annually updated emission estimates. The basic methodologies to be used for this system have been used for SO2 (Smith et al. 2011, Klimont, Smith and Cofala 2013), and are designed to complement existing inventory efforts. The goal of this system is to consistently extend current emission estimates both forward in time to recent years and also back over the entire industrial era. The project will produce improved datasets for global and (potentially) regional model, allow analysis of trends across time, countries, and sectors of emissions and emission factors, and facilitate improved scientific analysis in general. Consistent estimation of uncertainty will be an integral part of this system. This effort will facilitate community evaluation of emissions and further emission-related research more generally.

Smith, Steven J.↗
NASA NTRS2020

Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone

Observations taken over the last few decades indicate that dramatic changes are occurring in the ArcticBoreal Zone (ABZ), which are having significant impacts on ABZ inhabitants, infrastructure, flora and fauna, and economies. While suitable for detecting overall change, the current capability is inadequate for systematic monitoring and for improving processbased and largescale understanding of the integrated components of the ABZ, which includes the cryosphere, biosphere, hydrosphere, and atmosphere. Such knowledge will lead to improvements in Earth system models, enabling more accurate prediction of future changes and development of informed adaptation and mitigation strategies. In Duncan et al. (2020), we review the strengths and limitations of current spacebased observational capabilities for several important ABZ components and make recommendations for improving upon these current capabilities. We recommend an interdisciplinary and stepwise approach to develop a comprehensive ABZ Observing Network (ABZON), beginning with an initial focus on observing networks designed to gain processbased understanding for individual ABZ components and systems that can then serve as the building blocks for a comprehensive ABZON.

Artic-Boreal↗