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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 541 records · Page 30
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NASA NTRS2020

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (i) benefited less from an increase in atmospheric [CO2], (ii) was less affected by higher temperature or decreasing rainfall and (iii) was more affected by increased rainfall because N leaching was more critical. The model inter-comparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation practices across SSA, because the impact of climate change will be modified if farmers intensify maize production with more mineral fertilizer.

Crop simulation model↗
DOE OSTI2020

Numerically Relevant Timescales in the MG2 Microphysics Model

Climate models rely on parameterizations of a variety of processes in the atmospheric physics, but a common concern is that the temporal resolution is too coarse to consistently resolve the behavior that individual parameterizations are designed to capture. This study examines timescales numerically derived from the Morrison-Gettelman (MG2) microphysics as implemented within the Energy Exascale Earth System Model, version 1 (E3SMv1). Numerically relevant timescales in MG2 are derived by computing the eigenspectrum of its Jacobian. These timescales are found to often be smaller than the default 5 min time step used for MG2. The fast timescales are then heuristically connected to individual microphysics processes. By substepping a few particular rain processes within MG2, the time discretization error for those processes was considerably reduced with minimal additional expense to the overall microphysics. While this improvement has a substantial effect on the target processes and on the vertical distribution of stratiform-derived rain within E3SMv1, the overall model climate is found to not be sensitive to MG2 time step. Here, we hypothesize that this is because the surface climate does not depend strongly on certain process rates, especially MG2's rain evaporation rate.

58 GEOSCIENCES↗
DOE OSTI2018

Reducing uncertainties in climate models

Here, we report radiative forcing is a fundamental quantity for understanding both anthropogenic and natural changes in climate. It measures the extent to which human activities [such as the emission of carbon dioxide (CO 2 ), see the image] and natural events (such as volcanic eruptions) perturb the flow of energy into and out of the climate system. This perturbation initiates all other changes of the climate in response to external forcings. Inconsistencies in the calculation of radiative forcing by CO 2 introduce uncertainties in model projections of climate change, a problem that has persisted for more than two decades. The explicit calculation of radiative forcing and a careful vetting of radiative transfer parameterizations provide a straightforward means to substantially reduce these uncertainties and improve the projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Why are cloud and surface albedo feedbacks anti-correlated in climate models?

A strong anti-correlation exists between global-mean cloud and surface albedo feedbacks in current general circulation models (GCMs), without which spread in climate sensitivity would be greater. This anti-correlation raises questions about the nature of the physical connection between the two feedbacks. Here, we examine the idea that the relationship between the two feedbacks arises from the fact that each is responding primarily to regional warming, but is defined by globally-averaged warming. Take the example of a model with larger-than-average tropical warming due to a strongly positive local cloud feedback: It may have a smaller conventionally-defined surface albedo feedback simply because the planetary heating due to surface albedo reductions is normalized by a larger globally-averaged warming. We test this idea in a one-dimensional energy balance model (EBM) under an external forcing. This EBM includes three feedbacks similar to those in GCMs: a positive cloud feedback confined to low latitudes, a positive surface albedo feedback confined to high latitudes, and a uniform Planck feedback. We perform a suite of EBM runs by varying the strengths of the cloud and surface albedo feedbacks, which operate on local warming. Their strengths are regionally-defined and have little correlation across the runs. However, the conventionally-defined cloud and surface albedo feedbacks diagnosed from the runs end up being strongly anti-correlated, as in GCMs. Thus the anticorrelation reflects the regional nature of the feedbacks rather than a globalscale teleconnection between them. In this sense, the choice to define the feedbacks according to global temperature change may be misleading.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Accelerated Climate Modeling for Energy (ACME) Final Scientific/Technical Report

Seven Department of Energy (DOE) national laboratories, Universities, and Kitware, undertook a coordinated effort to build an Earth system modeling capability tailored to meet the climate change research strategic objectives of the DOE Office of Science, as well as the broader climate change application needs of other DOE programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Simulation of the transport, vertical distribution, optical properties and radiative impact of smoke aerosols with the ALADIN regional climate model during the ORACLES-2016 and LASIC experiments

Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent ( ~0.7 over continental sources and ~0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m -2 over the SAO (20 ° S–10 ° N and 10 ° W–20 ° E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes ( -25 W m -2 ) and surface land temperature ( -1.5 ° C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ~200 m.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models

The CFMIP Diagnostic Codes Catalogue assembles cloud metrics, diagnostics and methodologies, together with programs to diagnose them from general circulation model (GCM) outputs written by various members of the CFMIP community. This aims to facilitate use of the diagnostics by the wider community studying climate and climate change. Here, this paper describes the diagnostics and metrics which are currently in the catalogue, together with examples of their application to model evaluation studies and a summary of some of the insights these diagnostics have provided into the main shortcomings in current GCMs. Analysis of outputs from CFMIP and CMIP6 experiments will also be facilitated by the sharing of diagnostic codes via this catalogue. Any code which implements diagnostics relevant to analysing clouds – including cloud–circulation interactions and the contribution of clouds to estimates of climate sensitivity in models – and which is documented in peer-reviewed studies, can be included in the catalogue. We very much welcome additional contributions to further support community analysis of CMIP6 outputs.

58 GEOSCIENCES↗
NASA NTRS1993

Calibration of radiation codes in climate models: Comparison of calculations with observations from the SPECtral Radiation Experiment (SPECTRE)

The primary goal of SPECTRE is to: close the loopholes by which longwave radiation models have eluded incisive comparisons with measurements. Likewise, the experimental approach was quite simple in concept, namely: accurately measure the zenith infrared radiance at high spectral resolution while simultaneously profiling the radiatively important atmospheric properties with conventional and remote sensing devices. The field phase of SPECTRE was carried out as part of FIRE Cirrus II, and detailed spectra of the down welling radiance were obtained by several interferometers simultaneous to the measurement of the optical properties of the atmosphere. We are now well along in the process of analyzing the data and calibrating radiation codes so that they may be used more effectively in climate related studies. The calibration is being done with models ranging from the most detailed (line-by-line) to the broad-band parameterizations used in climate models. This paper summarizes our progress in the calibration for clear-sky conditions. When this stage is completed, we will move on to the calibration for cirrus conditions.

Ellingson, R. G.↗
DOE OSTI2022

Impact of Tropical Cyclone Wind Forcing on the Global Climate in a Fully Coupled Climate Model

Tropical cyclones (TCs) alter upper-ocean temperature and influence ocean heat content via enhanced turbulent mixing. A better understanding of the role of TCs within the climate system requires a fully coupled modeling framework, where TC-induced ocean responses feed back to the atmosphere and subsequently to the climate mean state and variability. Here, we investigate the impacts of TC wind forcing on the global ocean and the associated feedbacks within the climate system using the fully coupled Community Earth System Model version 1.3 (CESM1.3). Using the low-resolution version of CESM1.3 (1° atmosphere and ocean grid spacing) with no intrinsic TCs, we conduct a suite of sensitivity experiments by inserting TC winds extracted from a high-resolution (0.25° atmosphere grid spacing) TC-permitting simulation into the low-resolution model. Results from the low-resolution TC experiment are compared to a low-resolution control simulation to diagnose TCs’ impact. We found that the added TC winds can increase ocean heat content by affecting ocean vertical mixing, air–sea enthalpy fluxes, and cloud amount. Here, the added TCs can influence mean SST, precipitation, ocean subsurface temperature, and ocean mixed layer depth. We found a strengthening of the wind-driven subtropical cells and a weakening of the Atlantic meridional overturning circulation due to the changes of surface buoyancy fluxes. TCs in the model cause anomalous equatorward ocean heat convergence in the deep tropics and an increase of poleward ocean heat transport out of the subtropics. Our modeling results provide new insights into the multiscale interactions between TCs and the coupled climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implications of a Pervasive Climate Model Bias for Low‐Cloud Feedback

Abstract How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low‐cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low‐cloud feedback (0.45 W , 90% range 0.18–0.72 W ), about twice the mean value (0.22 W ) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean‐state bias: models underestimate the low‐cloud response to warming because (a) they systematically underestimate present‐day tropical marine low‐cloud amount, and (b) the low‐cloud sensitivity to warming is proportional to this present‐day low‐cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections.

58 GEOSCIENCES↗
DOE OSTI2016

Collaborative Project: Building improved optimized parameter estimation algorithms to improve methane and nitrogen fluxes in a climate model

Soils in natural and managed ecosystems and wetlands are well known sources of methane, nitrous oxides, and reactive nitrogen gases, but the magnitudes of gas flux to the atmosphere are still poorly constrained. Thus, the reasons for the large increases in atmospheric concentrations of methane and nitrous oxide since the preindustrial time period are not well understood. The low atmospheric concentrations of methane and nitrous oxide, despite being more potent greenhouse gases than carbon dioxide, complicate empirical studies to provide explanations. In addition to climate concerns, the emissions of reactive nitrogen gases from soils are important to the changing nitrogen balance in the earth system, subject to human management, and may change substantially in the future. Thus improved modeling of the emission fluxes of these species from the land surface is important. Currently, there are emission modules for methane and some nitrogen species in the Community Earth System Model’s Community Land Model (CLM-ME/N); however, there are large uncertainties and problems in the simulations, resulting in coarse estimates. In this proposal, we seek to improve these emission modules by combining state-of-the-art process modules for emissions, available data, and new optimization methods. In earth science problems, we often have substantial data and knowledge of processes in disparate systems, and thus we need to combine data and a general process level understanding into a model for projections of future climate that are as accurate as possible. The best methodologies for optimization of parameters in earth system models are still being developed. In this proposal we will develop and apply surrogate algorithms that a) were especially developed for computationally expensive simulations like CLM-ME/N models; b) were (in the earlier surrogate optimization Stochastic RBF) demonstrated to perform very well on computationally expensive complex partial differential equations in earth science with limited numbers of simulations; and, c) will be (as part of the proposed research) significantly improved both by adding asynchronous parallelism, early truncation of unsuccessful simulations, and the improvement of both serial and parallel performance by the use of derivative and sensitivity information from global and local surrogate approximations S(x) . The algorithm development and testing will be focused on the CLM-ME/N model application, but the methods are general and are expected to also perform well on optimization for parameter estimation of other climate models and other classes of continuous multimodal optimization problems arising from complex simulation models. In addition, this proposal will compile available datasets of emissions of methane, nitrous oxides and reactive nitrogen species and develop protocols for site level comparisons with the CLM-ME/N. Once the model parameters are optimized against site level data, the model will be simulated at the global level and compared to atmospheric concentration measurements for the current climate, and future emissions will be estimated using climate change as simulated by the CESM. This proposal combines experts in earth system modeling, optimization, computer science, and process level understanding of soil gas emissions in an interdisciplinary team in order to improve the modeling of methane and nitrogen gas emissions. This proposal thus meets the requirements of the SciDAC RFP, by integrating state-of-the-art computer science and earth system to build an improved earth system model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Climate Modeling and Prediction at NSIPP

The talk will review modeling and prediction efforts undertaken as part of NASA's Seasonal to Interannual Prediction Project (NSIPP). The focus will be on atmospheric model results, including its use for experimental seasonal prediction and the diagnostic analysis of climate anomalies. The model's performance in coupled experiments with land and atmosphere models will also be discussed.

Suarez, Max↗
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↗
DOE OSTI2022

Mechanisms behind the Springtime North Pacific ENSO Teleconnection Bias in Climate Models

Previous studies have shown that models overestimate the strength of ENSO teleconnections to the North Pacific during springtime, but the underlying reasons for this bias remain unknown. Here, in this work, the relative contributions from basic-state and thermodynamic/dynamic forcing factors are disentangled through idealized experiments with the Community Earth System Model and a range of stationary wave modeling experiments. It is revealed that in CESM1 the diabatic heating biases over the tropical Indian Ocean and tropical central-western Pacific jointly favor a cyclonic (anticyclonic) circulation bias to occur in the North Pacific during the springtime of El Niño (La Niña) events. On one hand, the difference in the modeled and observed climatological basic state does not lead to the bias formation directly, as the diabatic heating biases are the primary cause. On the other hand, the springtime basic state is conducive to a more vigorous stationary wave response to the biased diabatic heating than the wintertime state, and this explains why the teleconnection bias occurs during springtime but not in winter. An iterative bias-correction approach is then implemented in the atmospheric model component of CESM1 to verify the linkage between the tropical diabatic heating bias and the teleconnection bias. Moreover, this explanation is shown to be relevant in other models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) as a strong relationship is found between biases in ENSO-related tropical central-western Pacific/Indian Ocean precipitation and North Pacific circulation across models in spring.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Climate Modeling of a Potential ExoVenus

The planetary mass and radius sensitivity of exoplanet discovery capabilities has reached into the terrestrial regime. The focus of such investigations is to search within the Habitable Zone where a modern Earth-like atmosphere maybe a viable comparison. However, the detection bias of the transit and radial velocity methods lies close to the host star where the received flux at the planet may push the atmosphere into a runaway greenhouse state. One such exoplanet discovery, Kepler-1649b, receives a similar flux from its star as modern Venus does from the Sun, and so was categorized as a possible exoVenus. Here we discuss the planetary parameters of Kepler-1649b in relation to Venus to establish its potential as a Venus analog. We utilize the general circulation model ROCKE-3D (Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics) to simulate the evolution of the surface temperature of Kepler-1649b under various assumptions, including relative atmospheric abundances. We show that in all our simulations the atmospheric model rapidly diverges from temperate surface conditions toward a runaway greenhouse with rapidly escalating surface temperatures. We calculate transmission spectra for the evolved atmosphere and discuss these spectra within the context of the James Webb Space Telescope Near-Infrared Spectrograph capabilities. We thus demonstrate the detectability of the key atmospheric signatures of possible runaway greenhouse transition states and outline the future prospects of characterizing potential Venus analogs.

Astrobiology↗