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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 289 records · Page 16
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NASA NTRS2011

An Estimate of Low-Cloud Feedbacks from Variations of Cloud Radiative and Physical Properties with Sea Surface Temperature on Interannual Time Scales

Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000 - February 2005) of Clouds and the Earth s Radiant Energy System (CERES) -- Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud fraction, and cloud optical depth with SST anomaly. The major findings are as follows. First, the low-cloud amount (-1.9% to -3.4% /K) and the logarithm of low-cloud optical depth (-0.085 to -0.100/K) tend to decrease while the net cloud radiative effect (3.86 W/m(exp 2)/ K) becomes less negative as SST anomalies increase. These results are broadly consistent with previous observational studies. Second, after the changes in cloud and radiative properties with SST anomaly are separated into dynamic, thermodynamic, and residual components, changes in the dynamic component (taken as the vertical velocity at 700 hPa) have relatively little effect on cloud and radiative properties. However, the estimated inversion strength decreases with increasing SST, accounting for a large portion of the measured decreases in cloud fraction and cloud optical depth. The residual positive change in net cloud radiative effect (1.48 W/m(exp 2)/ K) and small changes in low-cloud amount (-0.81% to 0.22% /K) and decrease in the logarithm of optical depth (-0.035 to -0.046/ K) with SST are interpreted as a positive cloud feedback, with cloud optical depth feedback being the dominant contributor. Last, the magnitudes of the residual changes differ greatly among the six low-cloud regions examined in this study, with the largest positive feedbacks (approximately 4 W/m(exp 2)/ K) in the southeast and northeast Atlantic regions and a slightly negative feedback (-0.2 W/m(exp 2)/ K) in the south-central Pacific region. Because the retrievals of cloud optical depth and/or cloud fraction are difficult in the presence of aerosols, the transport of heavy African continental aerosols may contribute to the large magnitudes of estimated cloud feedback in the two Atlantic regions.

Eitzen, Zachary A.↗
NASA NTRS2011

From Space Weather Toward Space Climate Time Scales: Substorm Analysis from 1993 to 2008

Magnetic activity in the Northern Hemisphere auroral region was examined during solar cycles 22 and 23 (1993- 2008). Substorms were identified from ground-based magnetic field measurements by an automated search engine. On average, 550 substorms were observed per year, which gives in total about 9000 substorms. The interannual, seasonal and solar cycle-to-cycle variations of the substorm number (R(sub ss)), substorm duration (T(sub ss)), and peak amplitude (A(sub ss)) were examined. The declining phases of both solar cycles 22 and 23 were more active than the other solar cycle phases due to the enhanced solar wind speed. The spring substorms during the declining solar cycle phase (absolute value of A(sub ss,decl)) - 500 nT) were 25% larger than the spring substorms during the ascending solar cycle years ((absolute value of A(sub ss,asc) = 400 nT). The following seasonal variation was found: the most intense substorms occurred during spring and fall, the largest substorm frequency in the Northern Hemisphere winter, and the longest-duration substorms in summer. Furthermore, we found a winter-summer asymmetry in the substorm number and duration. which is speculated to be due to the variations in the ionospheric conductivity. The solar cycle-Io-cycle variation was found in the yearly substorm number and peak amplitude. The decline from the peak substorm activity in 1994 and 2003 to the following minima took 3 years during solar cycle 22, while it took 6 years during solar cycle 23.

Tanskanen, E. I.↗
NASA NTRS2016

How Much Global Burned Area Can Be Forecast on Seasonal Time Scales Using Sea Surface Temperatures?

Large-scale sea surface temperature (SST) patterns influence the interannual variability of burned area in many regions by means of climate controls on fuel continuity, amount, and moisture content. Some of the variability in burned area is predictable on seasonal timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire season. Here we systematically evaluated the degree to which annual burned area from the Global Fire Emissions Database version 4 with small fires (GFED4s) can be predicted using SSTs from 14 different ocean regions. We found that about 48 of global burned area can be forecast with a correlation coefficient that is significant at a p < 0.01 level using a single ocean climate index (OCI) 3 or more months prior to the month of peak burning. Continental regions where burned area had a higher degree of predictability included equatorial Asia, where 92% of the burned area exceeded the correlation threshold, and Central America, where 86% of the burned area exceeded this threshold. Pacific Ocean indices describing the El Nino-Southern Oscillation were more important than indices from other ocean basins, accounting for about 1/3 of the total predictable global burned area. A model that combined two indices from different oceans considerably improved model performance, suggesting that fires in many regions respond to forcing from more than one ocean basin. Using OCI-burned area relationships and a clustering algorithm, we identified 12 hotspot regions in which fires had a consistent response to SST patterns. Annual burned area in these regions can be predicted with moderate confidence levels, suggesting operational forecasts may be possible with the aim of improving ecosystem management.

Chen, Yang↗
NASA NTRSDate not supplied

Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRSDate not supplied

Satellite Remote Sensing of Atmospheric and Surface Properties on Daily and Decadal Time Scales

Satellite and airborne remote sensors can provide high-quality atmospheric and surface products such as atmospheric temperature, water vapor, trace gas, and aerosol vertical profiles, cloud, and surface properties. In this presentation, I will give an overview of various advanced forward and inversion models developed to retrieve these quantities. The satellite data used in this study includes hyperspectral thermal infrared and solar remote sensors such as AIRS, CrIS, IASI, CPF, and EMIT. I will also show some results of aerosol microphysical property retrievals using lidar and polarimeters.

Xu Liu↗