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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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NASA NTRS2022

Constraining Clouds and Convective Parameterizations in A Climate Model Using Paleoclimate Data

Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof-of-concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere-only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present-day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid-Holocene (MH) and pre-industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non-stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

cloud and convective parameterization↗

NASA NTRS1990

Feedback Processes and Climate Response

The response of the climate system to an external perturbation--e.g., a change in solar irradiance or a change in atmospheric opacity due to an increase in CO2--depends rather strongly on feedback processes in the system, which either amplify or dampen the effects of the initial perturbation. A simple representation of the climate system is used to compare several important feedbacks, based upon general circulation model (GCM) simulations by various investigators. The models are in general agreement with respect to water vapor feedback, but in wide disagreement with respect to cloud feedback. Because of the arguments raised by Lindzen (1990)--that the processes which determine water vapor mixing ratio in the upper atmosphere are quite different from those which operate in the planetary boundary layer, and that upper tropospheric water vapor might actually decrease even when the boundary layer is getting warmer and more moist--researchers undertook a study to determine the sensitivity of climate to changes in water vapor at various levels in the troposphere. The result is that climate is just as sensitive to percentage changes in upper tropospheric water vapor, where the mixing ratio is very small, as it is to percentage changes in the boundary layer, which contains the bulk of total column water vapor.

Arking, Albert↗

NASA NTRS2016

Observation-Based Cloud Radiative Kernels from A-Train

We develop a method to empirically derive broadband and spectral cloud radiative kernels by cloud type from pixel-scale collocated A-Train observations and reanalysis, which does not require additional cloudy radiative transfer calculations nor cloud properties. This method is able to estimate the cloud feedback by maintaining the consistency between CRKs and cloud responses.

Yue, Qing↗

NASA NTRSDate not supplied

Cloud Climate Data Records from Geostationary Satellites to Support CERES: Challenges and Recent Progress

clouds↗

NASA NTRSDate not supplied

Development of a Consistent GEOsat Cloud Property Dataset for the CERES Climate Data Record

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. The GEOsat data analysis approach for the current CERES Edition-4 (Ed4) data products was focused on accuracy and consistency with MODIS by employing as much spectral information as possible from each satellite. However, the inconsistent use of spectral information across GEOsats led to marked discontinuities in the spatial and temporal record of cloud properties that had to be accounted for post facto in downstream CERES processing. This paper reports progress in developing a new GEOsat analysis system for the next CERES edition (Ed5) that has potential to improve cross-platform consistency and continuity. In this approach, the spectral channel complement is limited to just 3-channels during daytime, ~0.65 µm (VIS), ~3.9 µm (NIR), and ~10.8 µm (IR), common to nearly all of the satellites in the record. At night, a 2-channel approach is taken with the NIR and IR, and ~6.7 µm bands that includes a machine learning approach for optically thick cloud properties. A tradeoff is the potential for reduced accuracy particularly using data from the more advanced satellites that have more spectral channels (e.g. SEVIRI, AHI and ABI) that are known to help improve thin cirrus detection, cloud-aerosol discrimination and estimates in other difficult conditions that challenge cloud remote sensing. The new continuity approach is applied to one month of global GEOSat data for each year of the CERES record since 2000 and compared with the Ed4 GEO and MODIS cloud property time series in order to evaluate the level of improved consistency in the GEOsat record and to assess the accuracy impacts. Cloud fraction will also be assessed with CALIPSO data. Outstanding issues and challenges will be discussed. The results are expected to guide future work needed to develop a more robust GEOsat cloud data record for CERES.

CERES CDR↗

NASA NTRSDate not supplied

Is the high N_d of springtime Arctic water clouds an indication of ice algae – aerosol – cloud – Arctic melting feedback?

Explore the source record for details and available documents.

clouds↗

NASA NTRS2012

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

Small boundary-layer clouds are ubiquitous over many parts of the globe and strongly influence the Earths radiative energy balance. However, our understanding of these clouds is insufficient to solve pressing scientific problems. For example, cloud feedback represents the largest uncertainty amongst all climate feedbacks in general circulation models (GCM). Several issues complicate understanding boundary-layer clouds and simulating them in GCMs. The high spatial variability of boundary-layer clouds poses an enormous computational challenge, since their horizontal dimensions and internal variability occur at spatial scales much finer than the computational grids used in GCMs. Aerosol-cloud interactions further complicate boundary-layer cloud measurement and simulation. Additionally, aerosols influence processes such as precipitation and cloud lifetime. An added complication is that at small scales (order meters to 10s of meters) distinguishing cloud from aerosol is increasingly difficult, due to the effects of aerosol humidification, cloud fragments and photon scattering between clouds.

boundary layer↗

NASA NTRS1984

Cloud optical thickness feedbacks in the CO2 climate problem

A radiative-convective equilibrium model is developed and applied to study cloud optical thickness feedbacks in the CO2 climate problem. The basic hypothesis is that in the warmer and moister CO2-rich atmosphere, cloud liquid water content will generally be larger too. For clouds other than thin cirrus the result is to increase the albedo more than to increase the greenhouse effect. Thus, the sign of the feedback is negative: cloud optical properties act as a thermostat and alter in such a way as to reduce the surface and tropospheric warming caused by the addition of CO2. This negative feedback can be substantial. When observational estimates of the temperature dependence of cloud liquid water content are employed in the model, the surface temperature change caused by doubling CO2 is reduced by about one half. This result is obtained for global and annual average conditions, no change in cloud amount or altitude, and constant relative humidity. These idealizations, together with other simplifications typical of one-dimensional radiative-convective climate models, render the result tentative. Further study of cloud optical property feedbacks is warranted, however, because the climate is apparently so sensitive to them.

Somerville, R. C. J.↗

NASA NTRS1986

Cloud radiative forcing in a general circulation model

Preliminary results are reported from a simulation carried out with the UCLA/Goddard GCM to investigate cloud radiative forcing phenomena. Of particular interest was cloud feedback, i.e., the changes in forcing brought about by climate change. Early results indicate that clouds cool the tropical oceans and land surface and warm the tropical atmosphere.

Randall, D. A.↗

NASA NTRS2016

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300% over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were approx. 40- 60% smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq)/ and various biomass burning tracers (BBt/ across the multi-campaign data set, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACIs, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be approx. 0.16 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/cu m and very high aerosol concentrations (2000- 3000/ cu cm in the most polluted clouds, the estimated ACI value was only 0.05. In this case, competition for water vapor by the high concentration of cloud condensation nuclei (CCN) strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease local summertime short-wave radiative flux by between 2 and 4 W/sq m or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic.We lastly explore evidence suggesting that numerous northern-latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei.

cloud microphysics↗

NASA NTRS2016

Aircraft-Measured Indirect Cloud Effects from Biomass Burning Smoke in the Arctic and Subarctic

The incidence of wildfires in the Arctic and subarctic is increasing; in boreal North America, for example, the burned area is expected to increase by 200-300 over the next 50-100 years, which previous studies suggest could have a large effect on cloud microphysics, lifetime, albedo, and precipitation. However, the interactions between smoke particles and clouds remain poorly quantified due to confounding meteorological influences and remote sensing limitations. Here, we use data from several aircraft campaigns in the Arctic and subarctic to explore cloud microphysics in liquid-phase clouds influenced by biomass burning. Median cloud droplet radii in smoky clouds were 50 smaller than in background clouds. Based on the relationship between cloud droplet number (N(liq))/ and various biomass burning tracers (BBt/ across the multi-campaign dataset, we calculated the magnitude of subarctic and Arctic smoke aerosol-cloud interactions (ACI, where ACI = (1/3) x dln(N(liq))/dln(BBt)) to be 0.12 out of a maximum possible value of 0.33 that would be obtained if all aerosols were to nucleate cloud droplets. Interestingly, in a separate subarctic case study with low liquid water content (0.02 gm/ cu m) and very high aerosol concentrations (2000-3000 cu m) in the most polluted clouds, the estimated ACI value was only 0.06. In this case, competition for water vapor by the high concentration of CCN strongly limited the formation of droplets and reduced the cloud albedo effect, which highlights the importance of cloud feedbacks across scales. Using our calculated ACI values, we estimate that the smoke-driven cloud albedo effect may decrease shortwave radiative flux by 2 and 4 W/sq or more under some low and homogeneous cloud cover conditions in the subarctic, although the changes should be smaller in high surface albedo regions of the Arctic. We lastly show evidence to suggest that numerous northern latitude background Aitken particles can interact with combustion particles, perhaps impacting their properties as cloud condensation and ice nuclei. However, the influence of background particles on smoke-driven indirect effects is currently unclear.

Arctic↗

NASA NTRS2023

Dominant Cloud Controlling Factors for Low-Level Cloud Fraction: Subtropical Versus Extratropical Oceans

To improve cloud feedback understanding and simulation, observations have been used to quantify the rate of change of cloud radiative properties as a function of specific environmental metrics (or cloud controlling factors; CCFs). The study focuses on low-level cloud dominated regions during 2006–2010. For each ocean gridpoint, Spearman's rank correlation coefficients of daily mean observed cloud fraction versus (a) 10-m wind, (b) sensible heat flux (SHF), (c) sea surface temperature, (d) estimated inversion strength (EIS), (e) 850 hPa vertical velocity, and (f) the M parameter (Δ𝜃 800hPa skin ) are sorted to identify the dominant CCFs in both extratropics (30°–60°N/S) and subtropics (30°S–30°N). A novel map for visualizing dominant CCFs for low-level cloud fraction reveals that: Δ𝜃 800hPa skin dominates in the subtropical stratocumulus regions while 10-m winds dominate in shallow cumulus regions but in the extratropics, a different inversion structure diagnostic (EIS) dominates, while SHF dominates in western boundary current areas.

cloud controlling factor↗

NASA NTRS2016

Spectral Longwave Cloud Radiative Forcing as Observed by AIRS

AIRS V6 products contain the spectral contributions to Outgoing Longwave Radiation (OLR), clear-sky OLR (OLR(sub CLR)), and Longwave Cloud Radiative Forcing (LWCRF) in 16 bands from 100 cm(exp -1) to 3260 cm(exp -1). We show climatologies of selected spectrally resolved AIRS V6 products over the period of September 2002 through August 2016. Spectrally resolved LWCRF can better describe the response of the Earth system to cloud and cloud feedback processes. The spectral LWCRF enables us to estimate the fraction of each contributing factor to cloud forcing, i.e.: surface temperature, mid to upper tropospheric water vapor, and tropospheric temperature. This presentation also compares the spatial characteristics of LWCRF from AIRS, CERES_EBAF Edition-2.8, and MERRA-2. AIRS and CERES LWCRF products show good agreement. The OLR bias between AIRS and CERES is very close to that of OLR(sub CLR). This implies that both AIRS and CERES OLR products accurately account for the effect of clouds on OLR.

Cloud radiative forcing↗

NASA NTRSDate not supplied

The Atmosphere Observing System (AOS): Synergistic Aerosol, Cloud, Convection and Precipitation Measurement and Modeling Systems

The 2017 Decadal Survey (DS) highlighted Earth System Science themes, science and application questions, and several high priority objectives that have led to the inclusion of Aerosols (A) and Clouds-Convection-Precipitation (CCP) as Designated Observables (DOs). On June 1, 2018, several NASA centers (GSFC, LaRC, JPL, MSFC, GRC and ARC) submitted a joint Study Plan to the NASA Earth Science Division for the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Pre-formulation Study (ACCP), with the ACCP Study concluding in early 2021. The new mission now in pre-phase A is being referred to as the Atmosphere Observing System (AOS), an integral part of NASA’s Earth System Observatory (ESO) strategy. The DS and the ACCP team recognized the science merit in combining the A and CCP DOs for both enhancing the ability to address a number of science objectives and also to provide an expanded capability to address additional objectives beyond those of the individual DOs. A critical element of the ACCP observing strategy is to make extensive use of new passive and active sensors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires comprehensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, atmospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data for advancing operational predictions and to generate expanded hindcasts and reconstruction of the climate record.

Arlindo da Silva↗

NASA NTRS2021

Evaluating Observational Constraints on Intermodel Spread in Cloud, Temperature, and Humidity Feedbacks

Uncertainty in climate feedbacks is the primary source of the spread in projected surface temperature responses to anthropogenic forcing. Cloud feedback persistently appears as the main source of disagreement in future projections while the combined lapse-rate plus water vapor (LR+WV) feedback is a smaller (30%), but non-trivial source of uncertainty in climate sensitivity. Here we attempt to observationally constrain the feedbacks in an effort to reduce the irintermodel uncertainties. The observed interannual variation provides a useful constraint on the long-term cloud feedback, as evidenced by the consistency of global-mean values and regional contributions to the intermodal spread on both interannual and long-term timescales. However, interannual variability does not serve to constrain the long-term LR+WV feedback spread, which we find is dominated by the varying tropical relative humidity (RH) response to interhemispheric warming differences under clear-sky conditions and the RH-fixed LR feedback under all-sky conditions

radiative feedbacks↗

NASA NTRS1985

Cloud optical thickness feedbacks in the CO2 climate problem

Somerville, R. C. J.↗

NASA NTRS1999

Climatic Implications of the Observed Temperature Dependence of the Liquid Water Path of Low Clouds

The uncertainty in the global climate sensitivity to an equilibrium doubling of carbon dioxide is often stated to be 1.5-4.5 K, largely due to uncertainties in cloud feedbacks. The lower end of this range is based on the assumption or prediction in some GCMs that cloud liquid water behaves adiabatically, thus implying that cloud optical thickness will increase in a warming climate if the physical thickness of clouds is invariant. Satellite observations of low-level cloud optical thickness and liquid water path have challenged this assumption, however, at low and middle latitudes. We attempt to explain the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurements (ARM) Cloud And Radiation Testbed (CART) site in the Southern Great Plains. We find that low cloud liquid water path is insensitive to temperature in winter but strongly decreases with temperature in summer. The latter occurs because surface relative humidity decreases with warming, causing cloud base to rise and clouds to geometrically thin. Meanwhile, inferred liquid water contents hardly vary with temperature, suggesting entrainment depletion. Physically, the temperature dependence appears to represent a transition from higher probabilities of stratified boundary layers at cold temperatures to a higher incidence of convective boundary layers at warm temperatures. The combination of our results and the earlier satellite findings imply that the minimum climate sensitivity should be revised upward from 1.5 K.

DelGenio, Anthony↗

NASA NTRSDate not supplied

Observing System Simulations for the AOS Mission

The Earth System Observatory (ESO) is NASA’s response to the recommendations of the 2017 Earth Sciences Decadal Survey conducted by the US National Academy of Sciences, Engineering and Medicine. The ESO is being conceived as a set of fully integrated missions addressing 4 main Earth science focus areas including aerosols, clouds, convection and precipitation (jointly re-ferred to as AOS, the Atmosphere Observing System). ESO ground breaking observations will provide critical measurements to address societally relevant problems in climate change, natural hazard mitiga-tion, fighting forest fires, and improving real-time agricultural processes. A critical element of the AOS observing strategy is to make extensive use of new passive and active sen-sors as well as of the so-called Program-of-Record (PoR), complemented by a fully integrated sub-orbital component. In order to achieve maximum benefit, all these observations need to be integrated into comprehensive observing and modeling/data assimilation systems. Such an approach requires compre-hensive model-data synthesis capabilities that needs to be conceived in conjunction with the space-based and suborbital components of AOS. In this presentation we will summarize the major science goals of AOS including cloud feedbacks, at-mospheric convection, emphasizing aerosol processes and aerosol radiative effects, and the synergistic aspects of clouds-precipitation-aerosol interactions. We will describe examples of the observing system simulation capabilities being developed for AOS, including global storm resolving nature runs, detailed instrument and retrieval simulators, as well as fast retrieval emulators for instrumenting climate models. This simulation environment, being developed under NASA’s open-source science initiative, will permit us to explore how AOS data will be used across space and time to better initialize forecasts and train modeling systems, and to infuse models and data assimilation systems with AOS data, well before launch.

Arlindo da Silva↗