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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 37 records · Page 2
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NASA NTRS2021

Climate-driven Chemistry and Aerosol Feedbacks in CMIP6 Earth System Models

Feedbacks play a fundamental role in determining the magnitude of the response of the climate system to external forcing, such as from anthropogenic emissions. The latest generation of Earth system models includes aerosol and chemistry components that interact with each other and with the biosphere. These interactions introduce a complex web of feedbacks that is important to understand and quantify. This paper addresses multiple pathways for aerosol and chemical feedbacks in Earth system models. These focus on changes in natural emissions (dust, sea salt, dimethyl sulfide, biogenic volatile organic compounds (BVOCs) and lightning) and changes in reaction rates for methane and ozone chemistry. The feedback terms are then given by the sensitivity of a pathway to climate change multiplied by the radiative effect of the change. We find that the overall climate feedback through chemistry and aerosols is negative in the sixth Coupled Model Intercomparison Project (CMIP6) Earth system models due to increased negative forcing from aerosols in a climate with warmer surface temperatures following a quadrupling of CO2 concentrations. This is principally due to increased emissions of sea salt and BVOCs which are sensitive to climate change and cause strong negative radiative forcings. Increased chemical loss of ozone and methane also contributes to a negative feedback. However, overall methane lifetime is expected to increase in a warmer climate due to increased BVOCs. Increased emissions of methane from wetlands would also offset some of the negative feedbacks. The CMIP6 experimental design did not allow the methane lifetime or methane emission changes to affect climate, so we found a robust negative contribution from interactive aerosols and chemistry to climate sensitivity in CMIP6 Earth system models.

Aerosol feedbacks↗
NASA NTRS2009

Representations of the Stratospheric Polar Vortices in Versions 1 and 2 of the Goddard Earth Observing System Chemistry-Climate Model (GEOS CCM)

This study will document the behavior of the polar vortices in two versions of the GEOS CCM. Both versions of the model include the same stratospheric chemistry, They differ in the underlying circulation model. Version 1 of the GEOS CCM is based on the Goddard Earth Observing System, Version 4, general circulation model which includes the finite-volume (Lin-Rood) dynamical core and physical parameterizations from Community Climate Model, Version 3. GEOS CCM Version 2 is based on the GEOS-5 GCM that includes a different tropospheric physics package. Baseline simulations of both models, performed at two-degree spatial resolution, show some improvements in Version 2, but also some degradation, In the Antarctic, both models show an over-persistent stratospheric polar vortex with late breakdown, but the year-to-year variations that are overestimated in Version I are more realistic in Version 2. The implications of this for the interactions with tropospheric climate, the Southern Annular Mode, will be discussed. In the Arctic both model versions show a dominant dynamically forced variabi;ity, but Version 2 has a persistent warm bias in the low stratosphere and there are seasonal differences in the simulations. These differences will be quantified in terms of climate change and ozone loss. Impacts of model resolution, using simulations at one-degree and half-degree, and changes in physical parameterizations (especially the gravity wave drag) will be discussed.

Pawson, S.↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Climate Change Impact on Surface Ozone Based on CMIP6 Earth System Models

It is presented an analysis of the effect of climate change on surface ozone (O3) discussing the related penalties and benefits around the globe from the global modeling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. All models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions scenario (SSP3-7.0). Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration varying spatially from -0.2 to -2 ppbv oC-1, with strongest decline over tropical oceanic regions, which is mainly linked to the dominating role of enhanced ozone chemical loss with higher water vapour abudances under a warmer climate. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural Biogenic Volatile Organic Compounds (BVOC) emission sources with a rate ranging regionally from 0.2 to 2 ppbv oC-1, implying a regional surface ozone penalty due to global warming. The individual models show this robustly for south-eastern China and India as well as for regions of Africa but there are inter-model differences in areas within Europe and the United States (US) as well as in South America. The future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.

climate change↗
DOE OSTI2024

Atmospheric Radiation Measurement (ARM) Management Plan

Mission and Vision Statements for the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Mission The ARM Climate Research Facility, a DOE scientific user facility, provides the climate research community with strategically located in situ and remote-sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface. Vision To provide a detailed and accurate description of the Earth atmosphere in diverse climate regimes to resolve the uncertainties in climate and Earth system models toward the development of sustainable solutions for the nation's energy and environmental challenges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs

Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

ESD Reviews: Climate Feedbacks in the Earth System and Prospects for Their Evaluation

Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Climate feedbacks↗
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Land Boundary Conditions for the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling System: Recent Updates and Data File Descriptions

The Earths land surface boundary conditions in the Goddard Earth Observing System version 5 (GEOS-5) modeling system were updated using recent high spatial and temporal resolution global data products. The updates include: (i) construction of a global 10-arcsec land-ocean lakes-ice mask; (ii) incorporation of a 10-arcsec Globcover 2009 land cover dataset; (iii) implementation of Level 12 Pfafstetter hydrologic catchments; (iv) use of hybridized SRTM global topography data; (v) construction of the HWSDv1.21-STATSGO2 merged global 30 arc second soil mineral and carbon data in conjunction with a highly-refined soil classification system; (vi) production of diffuse visible and near-infrared 8-day MODIS albedo climatologies at 30-arcsec from the period 2001-2011; and (vii) production of the GEOLAND2 and MODIS merged 8-day LAI climatology at 30-arcsec for GEOS-5. The global data sets were preprocessed and used to construct global raster data files for the software (mkCatchParam) that computes parameters on catchment-tiles for various atmospheric grids. The updates also include a few bug fixes in mkCatchParam, as well as changes (improvements in algorithms, etc.) to mkCatchParam that allow it to produce tile-space parameters efficiently for high resolution AGCM grids. The update process also includes the construction of data files describing the vegetation type fractions, soil background albedo, nitrogen deposition and mean annual 2m air temperature to be used with the future Catchment CN model and the global stream channel network to be used with the future global runoff routing model. This report provides detailed descriptions of the data production process and data file format of each updated data set.

GEOS-5↗
DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Plant Physiology Increases the Magnitude and Spread of the Transient Climate Response to CO 2 in CMIP6 Earth System Models

Increasing concentrations of CO 2 in the atmosphere influence climate both through CO 2 ’s role as a greenhouse gas and through its impact on plants. Plants respond to atmospheric CO 2 concentrations in several ways that can alter surface energy and water fluxes and thus surface climate, including changes in stomatal conductance, water use, and canopy leaf area. These plant physiological responses are already embedded in most Earth system models, and a robust literature demonstrates that they can affect global-scale temperature. However, the physiological contribution to transient warming has yet to be assessed systematically in Earth system models. Here this gap is addressed using carbon cycle simulations from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP) to isolate the radiative and physiological contributions to the transient climate response (TCR), which is defined as the change in globally averaged near-surface air temperature during the 20-yr window centered on the time of CO 2 doubling relative to preindustrial CO 2 concentrations. In CMIP6 models, the physiological effect contributes 0.12°C ( σ : 0.09°C; range: 0.02°–0.29°C) of warming to the TCR, corresponding to 6.1% of the full TCR ( σ : 3.8%; range: 1.4%–13.9%). Moreover, variation in the physiological contribution to the TCR across models contributes disproportionately more to the intermodel spread of TCR estimates than it does to the mean. The largest contribution of plant physiology to CO 2 -forced warming—and the intermodel spread in warming—occurs over land, especially in forested regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

A11I-2104 Evaluation of the NASA GEOS Chemistry-Climate Model Coupled Atmosphere-Ocean Configuration for its Suitability to Simulations of Atmospheric Composition in a Changing Climate

The NASA Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM) is a full tropospheric-stratospheric chemistry enabled configuration of the NASA GEOS Earth system model. Among the objectives for research performed with GEOSCCM is to understand the impacts of climate change on recovery of the stratospheric ozone layer over the twenty-first century. Simulations performed in the past with GEOSCCM did not have an interactive ocean and were driven with projected sea surface temperature and sea ice boundary conditions provided by external models. This method limits the application of GEOSCCM for climate study because it doesn’t allow the feedback of composition changes to ocean. For a full representation of the chemistry-climate feedbacks in the Earth System, we have developed a configuration of GEOSCCM that makes use of the MOM5 ocean general circulation model coupled to the GEOS atmospheric general circulation model. This configuration leverages development of the GEOS Sub-seasonal-to-Seasonal (S2S) prediction system, and includes interactive, radiatively coupled aerosols and two-moment, aerosol-aware cloud microphysics. W e have assessed the baseline model climate sensitivity with an Monday, 11 December 2023 08:30 - 12:50 Poster Hall A-C - South (Exhibition Level, South, MC) experiment performed under pre-industrial (e.g., year 1850) greenhouse gas conditions and a second experiment under 4xCO2 conditions, similar to the typical CMIP protocol. The GEOS model is demonstrated to have a climate sensitivity of a 2.6 K increase in global mean surface temperature for an equivalent doubling of CO2 from pre-industrial conditions, in line with current CMIP models. This configuration of the GEOS model is suitable for application to multidecadal to century-long simulations of climate system response to changing greenhouse gas levels. We report here our evaluation of the climate diagnostics of this configuration and discuss future directions for work with this model configuration, including an ongoing twenty-first century projection experiment based on the Chemistry-Climate Model Intercomparison project protocol.

models↗
DOE OSTI2025

SCILLA Scanning Electrical Mobility Spectrometer (SEMS) airborne data

The Scanning Electrical Mobility Spectrometer (SEMS) provides measurements of bulk aerosol size distributions, which are crucial for studying aerosol-cloud interactions. The instrument sampled through two different inlets: an isokinetic inlet and a counter-flow virtual impactor (CVI) inlet. Data were collected to help improve climate and earth systems models, characterize atmospheric conditions, and validate satellite observations and climate simulations.

Scanning Electrical Mobility Spectrometer↗
DOE OSTI2022

Application-specific optimal model weighting of global climate models: A red tide example

Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

The Earth System Model

The Earth System Model is the natural evolution of current climate models and will be the ultimate embodiment of our geophysical understanding of the planet. These models are constructed from components - atmosphere, ocean, ice, land, chemistry, solid earth, etc. models and merged together through a coupling program which is responsible for the exchange of data from the components. Climate models and future earth system models will have standardized modules, and these standards are now being developed by the ESMF project funded by NASA. The Earth System Model will have a variety of uses beyond climate prediction. The model can be used to build climate data records making it the core of an assimilation system, and it can be used in OSSE experiments to evaluate. The computing and storage requirements for the ESM appear to be daunting. However, the Japanese ES theoretical computing capability is already within 20% of the minimum requirements needed for some 2010 climate model applications. Thus it seems very possible that a focused effort to build an Earth System Model will achieve succcss.

Schoeberl, Mark↗
DOE OSTI2020

Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models

For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO 2 ) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO 2 doubling in a 1% per year CO 2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ice‐Nucleating Particles That Impact Clouds and Climate: Observational and Modeling Research Needs

Abstract Atmospheric ice‐nucleating particles (INPs) play a critical role in cloud freezing processes, with important implications for precipitation formation and cloud radiative properties, and thus for weather and climate. Additionally, INP emissions respond to changes in the Earth System and climate, for example, desertification, agricultural practices, and fires, and therefore may introduce climate feedbacks that are still poorly understood. As knowledge of the nature and origins of INPs has advanced, regional and global weather, climate, and Earth system models have increasingly begun to link cloud ice processes to model‐simulated aerosol abundance and types. While these recent advances are exciting, coupling cloud processes to simulated aerosol also makes cloud physics simulations increasingly susceptible to uncertainties in simulation of INPs, which are still poorly constrained by observations. Advancing the predictability of INP abundance with reasonable spatiotemporal resolution will require an increased focus on research that bridges the measurement and modeling communities. This review summarizes the current state of knowledge and identifies critical knowledge gaps from both observational and modeling perspectives. In particular, we emphasize needs in two key areas: (a) observational closure between aerosol and INP quantities and (b) skillful simulation of INPs within existing weather and climate models. We discuss the state of knowledge on various INP particle types and briefly discuss the challenges faced in understanding the cloud impacts of INPs with present‐day models. Finally, we identify priority research directions for both observations and models to improve understanding of INPs and their interactions with the Earth System.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

54 ENVIRONMENTAL SCIENCES↗