Search NASA⌕ Search

SEARCH · Search NASA

Results for “CLIMATE MODELS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Differences between seasonal and mean annual energy balance model calculations of climate and climate sensitivity

The paper extends a simple Budyko-Sellers mean annual energy balance climate model with diffusive transport to include a seasonal cycle. In the model the latitudinal distribution of the zonal average surface temperature is represented by Legendre polynomials and its time-dependence by a Fourier sine-cosine series, and it has three parameters adjusted so that the observed amplitudes of the Northern Hemisphere zonal mean surface temperature are recovered. The seasonal model is used to reveal how the annual mean climate and the sensitivity to changes in incident radiation differ from the predictions obtained with the corresponding mean annual model. The distribution of the incident solar radiation in the models is shown to be insensitive to changes in the eccentricity and the longitude of perihelion and sensitive only to changes in the obliquity of the earth, and for past orbital changes both the seasonal and the mean annual model fail to produce glacial advances of the magnitude that are thought to have occurred.

North, G. R.↗
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
NASA NTRS2008

To What Extent Can Vegetation Mitigate Greenhouse Warming? A Modeling Approach

Climate models participating in the IPCC Fourth Assessment Report indicate that under a 2xCO2 environment, runoff would increase faster than precipitation overland. However, observations over large U.S watersheds indicate otherwise. This inconsistency suggests that there may be important feedbacks between climate and land surface unaccounted for in the present generation of models. We postulate that the increase in precipitation associated with the increase in CO2 is also increasing vegetation density, which may already be feeding back onto climate. Including this feedback in a climate model simulation resulted in precipitation and runoff trends consistent with observations and reduced the warming by 0.6OC overland. This unaccounted for missing water may be linked to about 10% of the missing land carbon sink. A recent compilation of outputs from 19 coupled atmosphere-ocean general circulation models used in the IPCC Fourth Assessment Report (AR4) shows projected increases in air temperature, precipitation and river discharge for 24 major rivers in the world in response to doubling CO2 by the end of the century (1). The ensemble mean from these models also indicates that, compared to their respective baselines overland, the global mean of the runoff change would increase faster (8.9% per year) than that of the precipitation (5% per year). We analyze century-scale observed annual runoff time-series (1901-2002) over 9 hydrological units covering large regions of the Eastern United States (Fig.1) compiled by the United States Geological Survey (USGS)(2). These regions were selected because they are the most forested; the least water-limited and are not under extensive irrigation. We compare these time-series to similar time-series of observed annual precipitation anomalies spanning the period 1900-1995 (3). Both time-series exhibit a positive longterm trend (Fig. 2); however, in contrast to the analysis of (I), these historic data records show that the rate of precipitation increase is 5.5 % per year, roughly double the rate of runoff increase of 3.1 % per year.

Bounoua, L.↗
NASA NTRS2021

The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations

The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.

Last Glacial Maximum↗
NASA NTRS1980

Spherical harmonic analysis of a synoptic climatology generated with a global general circulation model

Spherical harmonic analysis was used to analyze the observed climatological (C) fields of temperature at 850 mb, geopotential height at 500 mb, and sea level pressure. The spherical harmonic method was also applied to the corresponding "model climatological" fields (M) generated by a general circulation model, the "GISS climate model." The climate model was initialized with observed data for the first of December 1976 at 00. GMT and allowed to generate five years of meteorological history. Monthly means of the above fields for the five years were computed and subjected to spherical harmonic analysis. It was found from the comparison of the spectral components of both sets, M and C, that the climate model generated reasonable 500 mb geopotential heights. The model temperature field at 850 mb exhibited a generally correct structure. However, the meridional temperature gradient was overestimated and overheating of the continents was observed in summer.

Christidis, Z. D.↗
NASA NTRS2014

Uncertainties in the Modelled CO2 Threshold for Antarctic Glaciation

frequently cited atmospheric CO2 threshold for the onset of Antarctic glaciation of approximately780 parts per million by volume is based on the study of DeConto and Pollard (2003) using an ice sheet model and the GENESIS climate model. Proxy records suggest that atmospheric CO2 concentrations passed through this threshold across the Eocene-Oligocene transition approximately 34 million years. However, atmospheric CO2 concentrations may have been close to this threshold earlier than this transition, which is used by some to suggest the possibility of Antarctic ice sheets during the Eocene. Here we investigate the climate model dependency of the threshold for Antarctic glaciation by performing offline ice sheet model simulations using the climate from 7 different climate models with Eocene boundary conditions (HadCM3L, CCSM3, CESM1.0, GENESIS, FAMOUS, ECHAM5 and GISS_ER). These climate simulations are sourced from a number of independent studies, and as such the boundary conditions, which are poorly constrained during the Eocene, are not identical between simulations. The results of this study suggest that the atmospheric CO2 threshold for Antarctic glaciation is highly dependent on the climate model used and the climate model configuration. A large discrepancy between the climate model and ice sheet model grids for some simulations leads to a strong sensitivity to the lapse rate parameter.

simulation↗
DOE OSTI2022

Probabilistic Modeling of Climate Change Impacts on Renewable Energy and Storage Requirements for NM's Energy Transition Act (SAND Report)

This report provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico’s Integrated Resource Plan for 2020 – 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~25,000 – 30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

Global climate modeling is one of the grand chalenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting the future climate change.

Optimization Ocean Modeling Parallel Computations↗
NASA NTRS1995

Development, improvement and application of atmospheric radiative transfer models for prediction of the climatic effects of clouds and aerosols

The demands of accurate predictions of radiative transfer for climate applications are well-documented. While much effort is being devoted to evaluating the accuracy of the GCM radiative transfer schemes, the problem of developing accurate, computationally efficient schemes for climate models still remains. This paper discusses our efforts in developing accurate and fast computational methods for global and regional climate models.

Bergstrom, Robert W.↗
DOE OSTI2019

Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) Science Implementation Plan

Boreal regions have experienced warming faster than the rest of the Earth, and the arctic ice cover has declined at a rate faster than predicted by most climate models. Uncertainties in climate projections for this region are large, in part due to the lack of observational guidance to constrain the treatment of aerosol-cloud-precipitation linkages in climate models. To remedy this shortfall, field campaigns have been conducted to collect integrated, comprehensive measurements under specific atmospheric regimes. While considerable efforts have been devoted to subtropical marine boundary-layer clouds, as well as to clouds in the stable arctic environment, very few targeted observations exist of convective boundary-layer clouds that form over open water when cold airmasses are advected off ice-covered regions or boreal continents. There is an urgent need to shed light on the dynamics and microphysical properties of clouds and precipitation in the high-latitude marine boundary layer during cold-air outbreaks. Despite the common occurrence of linear and cellular cloud patterns that typify cold-air outbreaks, little is known about the properties of these clouds; how they vary with surface, environmental, and aerosol conditions; the role of cold-air outbreaks in the global atmospheric and ocean circulation; and the accuracy of the treatment of this atmospheric regime in climate models. Thus, we proposed to conduct the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), to focus on marine boundary-layer clouds during cold-air outbreaks. COMBLE will deploy the first ARM Mobile Facility (AMF1) and an AMF “satellite” station in the far North Atlantic in January–May 2020. COMBLE will take advantage of the synergy with several coincident campaigns, notably MOSAIC, which will characterize the source airmasses of cold-air outbreaks over the arctic ice, and (AC) 3 , which will operate several aircraft between northern Scandinavia and the MOSAIC deployment to document evolution of these airmasses. COMBLE and its sister campaigns will compose a four-member array of supersites between northern Scandinavia and the Arctic, each with profiling and in situ cloud, precipitation, radiation, and aerosol measurements. COMBLE will be guided by six science themes. The first five deal with boundary-layer convection in cold-air outbreaks: (1) the fetch-dependent mesoscale organization of clouds and precipitation, including linear and cellular convection; (2) surface heat and momentum fluxes and vertical profiles of temperature, humidity, wind, and turbulence; (3) vertical structure of clouds and precipitation; (4) the sources and sinks of aerosol, including ice-nucleating particles, and the role of cloud-active aerosol on cloud processes and radiative fluxes; and (5) the influence of these four themes on polar cyclogenesis and polar low vertical structure. The overarching sixth theme is that COMBLE will provide integrated data sets of dynamical, thermodynamic, and cloud microphysical characteristics of marine boundary-layer convection in cold-air outbreaks, including cloud and aerosol properties, that will enable constraining high-resolution numerical simulations, developing process-level understanding, and, subsequently, evaluating and improving representations of shallow convection in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exploring ship track spreading rates with a physics-informed Langevin particle parameterization

Abstract. The rate at which aerosols spread from a point source injection, such as from a ship or other stationary pollution source, is critical for accurately representing subgrid plume spreading in a climate model. Such climate model results will guide future decisions regarding the feasibility and application of large-scale intentional marine cloud brightening (MCB). Prior modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers and shear. In this study, we apply a Lagrangian particle model (PM-ABL v1.0), governed by a Langevin stochastic differential equation, to create a simplified framework for predicting the rate of spreading from a ship-injected aerosol plume in sheared, precipitating, and non-precipitating boundary layers. The velocity and position of each stochastic particle is predicted with the acceleration of each particle being driven by the turbulent kinetic energy, dissipation rate, momentum variance, and mean wind. These inputs to the stochastic particle velocity equation are derived from high-fidelity large-eddy simulations (LES) equipped with a prognostic aerosol–cloud microphysics scheme (UW-SAM) to simulate an aerosol injection from a ship into a cloud-topped marine boundary layer. The resulting spreading rate from the reduced-order stochastic model is then compared to the spreading rate in the LES. The stochastic particle velocity representation is shown to reasonably reproduce spreading rates in sheared, precipitating, and non-precipitating cases using domain-averaged turbulent statistics from the LES.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Understanding Differences in the Response to Composition Change as Simulated by CCMVal Models

Chemistry climate models (CCMs) have a common conceptual basis. Differences in implementation lead to differences in the stratospheric ozone response to changes in composition and climate. Although evaluation by CCMVal-2 identified strengths and weaknesses of participant models, the evaluation results were not used to discriminate among projections for future ozone evolution, at least in part because the overall diagnostic evaluation did not cleanly relate to the differences in CCM response. Here we use a subset of CCMVal diagnostics and additional analysis to understand the differences in response. In the upper stratosphere, differences in simulated temperature and total odd nitrogen prior to increases in chlorine loading explain the large differences in CCM sensitivity. In the lower atmosphere, there are two principle contributions to differences in CCM sensitivity to chlorine and climate change. First, differences in the lower stratospheric ClO affect simulated sensitivity to chlorine. CCMs with best transport performance match NDACC column HCl measurements at a broad range of latitudes. Other CCMs disagree with observations due to differences in total inorganic chlorine, partitioning between HCl and ClONO2, or both. Differences in ClONO2 are directly related to differences in simulated ClO. Second, although all CCMs predict increased tropical upwelling, the rate of increase varies and contributes to differences in tropical ozone and the 60N-60S column average.

Douglass, Anne R.↗
DOE OSTI2022

Scrutinizing entrainment and mass flux closures in shallow cumulus parameterizations using cloud-radar observations and large-eddy simulation (Final Report)

Representing warm, shallow clouds is a challenge for Global Climate Models, which leads to uncertainty in model projections of future climate. Our study was particularly concerned with how climate models represent the effects of shallow, non-precipitating cumulus clouds. Climate models must make certain assumptions when representing low clouds such as how much dry air from outside the cloud is mixed into the warm, moist cloud updraft. This process is called “entrainment” and tends to weaken the cloud updraft and dry out the cloud, compared to a theoretical cloud with no mixing. This project was a joint observational and modeling study to derive estimates of entrainment from field observations and use high-resolution modeling to evaluate the assumptions used in climate model representations of shallow cumulus. Our research took advantage of a wide variety of Department of Energy measurements from fixed climate-monitoring locations over the Southern Great Plains of the U.S. and the in the Eastern North Atlantic, along with short-term field projects (MAGIC, CAP–MBL, ACE–ENA). During the project, the PI maintained regular collaborations with Department of Energy Scientists at Brookhaven and Argonne National Laboratories. The project funded two students, who both obtained their M.S. in Atmospheric Science and are currently in the Atmospheric Science Ph.D. program. Both were lead authors on research papers funded by this grant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Changes in Precipitation From North Atlantic Tropical Cyclones Under RCP Scenarios in the Variable-Resolution Community Atmosphere Model

Decreasing climate models' grid spacing improves the representation of tropical cyclones at decadal time scales. In this study, a variable-resolution (VR) version of the Community Atmosphere Model 5 (CAM5-VR) is utilized to study North Atlantic tropical cyclone climatology in ensemble historical climate simulations and under two Representative Concentration Pathway (RCP) projections (RCP4.5 and RCP8.5). Basin-wide tropical cyclone counts decrease in the RCP simulations, although landfalling storm counts do not show as straightforward of a pattern, especially when focusing on regional changes. Lifetime maximum intensity metrics suggest that tropical cyclones increase in strength in the RCP ensembles. However, despite increases in tropical cyclone-related precipitation rates and the amount of precipitation produced per storm with warming, the annual average Rx5day from tropical cyclones over the eastern United States decreases due to less landfalling storms. This work is part of a continued effort to quantify how tropical cyclone-induced hazards may change in future climates.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Biological production models as elements of coupled, atmosphere-ocean models for climate research

Process models of phytoplankton production are discussed with respect to their suitability for incorporation into global-scale numerical ocean circulation models. Exact solutions are given for integrals over the mixed layer and the day of analytic, wavelength-independent models of primary production. Within this class of model, the bias incurred by using a triangular approximation (rather than a sinusoidal one) to the variation of surface irradiance through the day is computed. Efficient computation algorithms are given for the nonspectral models. More exact calculations require a spectrally sensitive treatment. Such models exist but must be integrated numerically over depth and time. For these integrations, resolution in wavelength, depth, and time are considered and recommendations made for efficient computation. The extrapolation of the one-(spatial)-dimension treatment to large horizontal scale is discussed.

Platt, Trevor↗
NASA NTRS2015

TPSAS-NF1676L-22800-DND

This lecture will provide an overview of an advanced turbulence closure scheme used in cloud-resolving models (CRMs) and climate modeling effort with the superparameterization approach. The CRM is embedded into each atmospheric column of a climate model as a super parameterization. It replaces the traditional subgrid-scale physical parameterizations in a conventional climate model. Two sensitivity simulations with doubling of CO2 and an increase of sea surface temperature, respectively, are performed and compared to similar simulations without this advanced turbulence closure in CRM to better understand the role of low-level clouds in climate sensitivity.

Kuan-Man Xu↗
NASA NTRS1998

An Improved Radiative Transfer Model for Climate Calculations

This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications, we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols.

Bergstrom, Robert W.↗
DOE OSTI2022

Observations Indicate That Clouds Amplify Mechanisms of Southern Ocean Heat Uptake

Abstract The Southern Ocean has absorbed most of the excess heat associated with anthropogenic greenhouse gas emissions. Since Southern Ocean observations are sparse in certain regions and seasons, much of our knowledge of ocean heat uptake is based on climate model simulations. However, climate models still inadequately represent some properties of Southern Ocean clouds, and they have not identified the mechanisms by which clouds may affect Southern Ocean heat uptake (SOHU). Here, we use the ERA5 and JRA‐55 reanalyses to assess the influence of clouds and other atmospheric processes on SOHU from 1979 to 2020. We find that years with the highest SOHU between 45° and 65°S are dominated by ocean heat uptake anomalies during winter and spring, but not during summer or fall. Winter and spring cloud cover are up to 7% higher when SOHU is up to 5.5 W/m 2 higher than the climatological seasonal mean, with the largest increases in the South Pacific Ocean. Clouds also contain more liquid water. These changes in cloud properties increase downwelling longwave radiation, amplifying ocean heat uptake. Cloud changes are also concomitant with a more stable lower atmosphere, which suppresses turbulent heat fluxes out of the surface. Overall, we find that SOHU is likely not mediated by enhanced surface shortwave absorption over the observational time period. A better understanding of how atmospheric processes impact ocean heat uptake may help improve our understanding of ocean heat uptake mechanisms in the current generation of climate models.

54 ENVIRONMENTAL SCIENCES↗