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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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47 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Comment on "'Elevated Heat Pump' Hypothesis for the Aerosol-Monsoon Hydroclimate Link: 'Grounded' in Observations?" by S. Nigam and M. Bollasina

In their recent paper, Nigam and Bollasina [2010] (hereinafter NB) claimed to have found observational evidences that are at variance with the elevated heat pump (EHP) hypothesis regarding the possible impacts of absorbing aerosols on the South Asian summer monsoon [Lau et al., 2006; Lau and Kim, 2006]. We found NB's arguments and inferences against the EHP hypothesis flawed, stemming from their own out of context interpretation of the hypothesis. NB argued that the simultaneous negative correlation of aerosol with rainfall, and correlations with other quantities in May, are evidence against the EHP hypothesis. Their argument cannot be justified. First, Lau and Kim [2006] (hereinafter LK06) never stated that the main rainfall response to EHP is in May. Second, the EHP is about responses of the entire Indian monsoon system that are nonlocal in space and time with respect to the aerosol forcing. As shown in Figure 4 of LK06, while the aerosol anomalies are strongest in April-May, the strongest rainfall response is in June-July, with the enhanced rainfall fed by an induced thermally driven circulation which brings additional moisture from the ocean to the Indian subcontinent. Third, the increased rainfall over the Bay of Bengal as shown in Figure 1a of NB and the increased low-level convergence in Figure 1f of NB do not necessarily reflect responses associated with EHP but rather the large ]scale circulation that provides the buildup of the aerosols before the onset of the monsoon rainfall over India. Because aerosol can only accumulate where there is little or no washout by rain, the negative correlation is a necessary condition for increased atmospheric loading of aerosols. For the same reason, the spatial distributions of rainfall and aerosol generally are offset with each other, i.e., high aerosol in regions of low rainfall. This is evident in Figure 1, which shows the climatological mean of the MODIS aerosol optical depth (AOD), and TRMM rainfall over India in May. The maximum AOD is found over the Indo-Gangetic Plain and the desert regions of northwest India and Pakistan

Lau, K. M.↗
NASA NTRS2012

Mixing of Dust and NH3 Observed Globally over Anthropogenic Dust Sources

The global distribution of dust column burden derived from MODIS Deep Blue aerosol products is compared to NH3 column burden retrieved from IASI infrared spectra. We found similarities in their spatial distributions, in particular their hot spots are often collocated over croplands and to a lesser extent pastures. Globally, we found 22% of dust burden collocated with NH3, with only 1% difference between land-use databases. This confirms the importance of anthropogenic dust from agriculture. Regionally, the Indian subcontinent has the highest amount of dust mixed with NH3 (26 %), mostly over cropland and during the pre-monsoon season. North Africa represents 50% of total dust burden but accounts for only 4% of mixed dust, which is found over croplands and pastures in Sahel and the coastal region of the Mediterranean. In order to evaluate the radiative effect of this mixing on dust optical properties, we derive the mass extinction efficiency for various mixtures of dust and NH3, using AERONET sunphotometers data. We found that for dusty days the coarse mode mass extinction efficiency decreases from 0.62 to 0.48 square meters per gram as NH3 burden increases from 0 to 40 milligrams per square meter. The fine mode extinction efficiency, ranging from 4 to 16 square mters per gram, does not appear to depend on NH3 concentration or relative humidity but rather on mineralogical composition and mixing with other aerosols. Our results imply that a significant amount of dust is already mixed with ammonium salt before its long range transport. This in turn will affect dust lifetime, and its interactions with radiation and cloud properties

dust sources↗
NASA NTRS2014

Global Assessment of OMI Aerosol Single-scattering Albedo Using Ground-based AERONET and SKYNET Inversions

We compare the aerosol single-scattering albedo (SSA) retrieved by the near-UV two-channel algorithm (OMAERUV) applied to the Aura-Ozone Monitoring Instrument (OMI) measurements with an equivalent inversion made by the ground-based Aerosol Robotic Network (AERONET). This work is the first comprehensive effort to globally compare the OMI-retrieved SSA with that of AERONET using all available sites spanning the regions of biomass burning, dust, and urban pollution. An analysis of the co-located retrievals over 269 sites reveals that about 46 percent (69 percent) of OMI-AERONET matchups agree within the absolute difference of plus or minus 0.03 (plus or minus 0.05) for all aerosol types. The comparison improves to 52 percent (77 percent) when only 'smoke' and 'dust' aerosol types were identified by the OMAERUV algorithm. Regionally, the agreement between the two inversions was robust over the biomass burning sites of South America, Sahel, Indian subcontinent, and oceanic-coastal sites followed by a reasonable agreement over north-east Asia. Over the desert regions, OMI tends to retrieve higher SSA, particularly over the Arabian Peninsula. Globally, the OMI-AERONET matchups agree mostly within plus or minus 0.03 for the aerosol optical depth (440 nanometers) and UV-aerosol index larger than 0.4 and 1.0, respectively. We also compare the OMAERUV SSA against the inversion made by an independent network of ground-based radiometer called SKYNET with its operating sites in Japan, China, South-East Asia, India, and Europe. The advantage of the SKYNET database over AERONET is that it performs retrieval at near-UV wavelengths which facilitate the direct comparison of OMI retrievals with the equivalent ground-based inversion. Comparison of OMI and SKYNET over currently available sites reveals a good agreement between the two where more than 70 percent of matchups agree within the absolute difference of 0.05.

Aerosol Single-scattering Albedo↗
NASA NTRS2014

Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.

ASCAT↗
NASA NTRS2020

Observation and Quantification of Aerosol Outflow from Southern Africa Using Spaceborne Lidar

Biomass burning in Africa provides a prolific source of aerosols that are transported from the source region to distant areas, as far away as South America and Australia. Models have long predicted the primary outflow and transport routes. Over time, field studies have validated the basic production and dynamics that underlie these transport patterns. In more recent years, the advancement of spaceborne active remote sensing techniques has allowed for more detailed verification of the models and, importantly, verification of the vertical distribution of the aerosols in the transport regions, particularly with respect to westerly transport over the Atlantic Ocean. The Cloud-Aerosol Transport System (CATS) lidar on the International Space Station has detection sensitivity that provides observations that support long-held theories of aerosol transport from the African subcontinent over the remote Indian Ocean and as far downstream as Australia.

Lidar↗
NASA NTRSDate not supplied

TPSAS-NF1676L-28151-DND

The proposed suborbital mission is to collect and analyze observations to help improve our ability to model and forecast these crucial monsoonal transports and their downstream effects. The objectives are 1) to observe and relate convective-scale to meso-scale changes in the three-dimensional circulation with the efficiency of the resulting convective transports (of air mass and heat) and the build-up of the large-scale anomalies, as they are modulated by the three-dimensional structure of the aerosol loading; and 2) to observe how fluctuations in the monsoon large-scale upper-level outflow affect downstream convection, especially within AEWs. The observations will be tailor-made to help understand the controls of the northward march of the monsoon isochrones, the initiation and magnitude of heat towers over the subcontinent during the monsoon, and how the mostly zonal land-sea differential heating interacts with the meridional progress of the Intertropical Convergence Zone (ITCZ) to constrain the intensities of the northern and western outflows. Unique contributions will be made by two airborne instruments, operated in concert: NASA-LARC's Doppler Aerosol Wind Lidar (DAWN) coherent-detection wind lidar which uses a pulsed laser with a wavelength of about two microns to measure vertical profiles of the three dimensional components of the wind field, and JPL's Airborne Second Generation Precipitation Radar (APR-2) precipitation radar which measures the three-dimensional structure of rain within a swath that is about10km wide (depending on the altitude of the plane) and extends from flight level down to the surface.

Upendra N Singh↗
NASA NTRSDate not supplied

The Climates of Earth’s next Supercontinent: Effects of Tectonics, Rotation Rate & Insolation

We investigate two possible deep future Earth climate scenarios using a 3-D GCM, 200 and 250 million years into the future when the next supercontinent phase is expected to take place. We use knowledge of the evolution of plate tectonics, solar luminosity, and rotation rate over this time period. In one scenario, a supercontinent forms at low latitudes. In the other scenario it forms at high northerly latitudes with an Antarctic subcontinent remaining at the south pole. The climates differences between these two scenarios are dramatic, with differences in mean surface temperatures approaching 4 degrees. The fractional habitability (where mean surface temperatures are between 0<T<100C year round) on land surfaces (as opposed to the ocean) is shown to differ up to 40\% between the two simulations. We believe these demonstrate that the community needs to consider alternative boundary conditions when simulating Earth-like exoplanetary climates. This work has recently been submitted to GRL and can be accessed at the ESSOAr repository.

Supercontinent↗
NASA NTRSDate not supplied

The Climates of Earth's Next Supercontinent: Effects of Tectonics, Rotation Rate, & Insolation

We investigate two possible deep future Earth climate scenarios using a 3-D GCM, 200 and 250 million years into the future when the next supercontinent phase is expect to take place. We use knowledge of the evolution of plate tectonics, solar luminosity, and rotation rate over this time period. In one scenario, a supercontinent forms at low latitudes. In the other scenario it forms at high northerly latitudes with an Antarctic subcontinent remaining at the south pole. The climates differences between these two scenarios are dramatic, with differences in mean surface temperatures approaching 4 degrees. The fractional habitability (where mean surface temperatures are between 0<T<100C year round) on land surfaces (as opposed to the ocean) is shown to differ up to 40% between the two simulations. We believe these demonstrate that the community needs to consider alternative boundary conditions when simulating Earth-like exoplanetary climates.

tectonics↗
NASA NTRSDate not supplied

Earth's next supercontinent climate: how tectonics, rotation rate, & insolation affect climate

We will explore two deep future Earth climate scenarios using a 3‐D GCM when the next supercontinent phase is expected to take place. In Amasia (Americas + Asia; forming ~200 Myr from now) the supercontinent forms at high northerly latitudes with the main continents 'squished' north of the equator while an Antarctic subcontinent remains at the south pole. In Aurica (All continents combined ~250 Myr) a supercontinent forms at low latitudes akin to that of a Sturtian topography. We utilize the forward evolution of plate tectonics, solar luminosity, rotation rate, and different potential topographic heights in our study. The climates between these scenarios can be dramatic, with differences in mean surface temperatures approaching several degrees. In particular, the topographic height of the Amasia supercontinent plays a critical role in the climate. These results demonstrate that using simple aquaplanet or modern Earth land/sea masks is insufficient to explore the possible variance in theoretical modeling of extrasolar planetary atmospheres.

supercontinent climate↗
NASA NTRS2021

The Climates of Earth's Next Supercontinent: Effects of Tectonics, Rotation Rate, and Insolation

We explore two possible Earth climate scenarios, 200 and 250 million years into the future, using projections of the evolution of plate tectonics, solar luminosity, and rotation rate. In one scenario, a supercontinent forms at low latitudes, whereas in the other it forms at high northern latitudes with an Antarctic subcontinent remaining at the south pole. The climates between these two end points are quite stark, with differences in mean surface temperatures approaching several degrees. The main factor in these differences is related to the topographic height of the high latitude supercontinents where higher elevations promote snowfall and subsequent higher planetary albedos. These results demonstrate the need to consider multiple boundary conditions when simulating Earth-like exoplanetary climates.

insolation↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗