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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Cloud feedback mechanisms and their representation in global climate models

Cloud feedback—the change in top-of-atmosphere radiative flux resulting from the cloud response to warming—constitutes by far the largest source of uncertainty in the climate response to CO 2 forcing simulated by global climate models (GCMs). In this paper, we review the main mechanisms for cloud feedbacks, and discuss their representation in climate models and the sources of intermodel spread. Global-mean cloud feedback in GCMs results from three main effects: (1) rising free-tropospheric clouds (a positive longwave effect); (2) decreasing tropical low cloud amount (a positive shortwave [SW] effect); (3) increasing high-latitude low cloud optical depth (a negative SW effect). These cloud responses simulated by GCMs are qualitatively supported by theory, high-resolution modeling, and observations. Rising high clouds are consistent with the fixed anvil temperature (FAT) hypothesis, whereby enhanced upper-tropospheric radiative cooling causes anvil cloud tops to remain at a nearly fixed temperature as the atmosphere warms. Tropical low cloud amount decreases are driven by a delicate balance between the effects of vertical turbulent fluxes, radiative cooling, large-scale subsidence, and lower-tropospheric stability on the boundary-layer moisture budget. High-latitude low cloud optical depth increases are dominated by phase changes in mixed-phase clouds. Finally, the causes of intermodel spread in cloud feedback are discussed, focusing particularly on the role of unresolved parameterized processes such as cloud microphysics, turbulence, and convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of host climate model on contrail cirrus effective radiative forcing estimates

Estimates of aviation effective radiative forcing (ERF) indicate that contrail cirrus is currently its largest contributor, although with a substantial associated uncertainty of ∼ 70 %. Here, we implement the contrail parameterisation developed for the Community Atmosphere Model (CAM) in the UK Met Office Unified Model (UM), allowing us to compare, for the first time, the impact of key features of the host climate model on contrail cirrus ERF. We find that differences in background humidity between the models result in the UM-simulated contrail fractions being 2 to 3 times larger than in CAM. Additionally, the models show contrasting responses in overall global cloud fraction, with contrails increasing the total cloud fraction in the UM and decreasing it in CAM. Differences in the complexity of the cloud microphysics schemes lead to significant differences in simulated changes to cloud ice water content due to aviation. After compensating for the unrealistically low contrail optical depth in the UM, we estimate the 2018 contrail cirrus ERF to be 40.8 mW m−2 in the UM, compared to 60.1 mW m−2 in CAM. These values highlight the substantial uncertainty in contrail cirrus ERF due to differences in microphysics and radiation schemes between the two models. We also find a factor-of-8 uncertainty in contrail cirrus ERF due to existing uncertainty in contrail cirrus optical depth. Future research should focus on better representing microphysical and radiative contrail characteristics in climate models and on improved observational constraints.

Zhang, Weiyu [University of Leeds (United Kingdom)↗
DOE OSTI2016

Metrics for the Diurnal Cycle of Precipitation: Toward Routine Benchmarks for Climate Models

In this paper, metrics are proposed—that is, a few summary statistics that condense large amounts of data from observations or model simulations—encapsulating the diurnal cycle of precipitation. Vector area averaging of Fourier amplitude and phase produces useful information in a reasonably small number of harmonic dial plots, a procedure familiar from atmospheric tide research. The metrics cover most of the globe but down-weight high-latitude wintertime ocean areas where baroclinic waves are most prominent. This enables intercomparison of a large number of climate models with observations and with each other. The diurnal cycle of precipitation has features not encountered in typical climate model intercomparisons, notably the absence of meaningful “average model” results that can be displayed in a single two-dimensional map. Displaying one map per model guides development of the metrics proposed here by making it clear that land and ocean areas must be averaged separately, but interpreting maps from all models becomes problematic as the size of a multimodel ensemble increases. Global diurnal metrics provide quick comparisons with observations and among models, using the most recent version of the Coupled Model Intercomparison Project (CMIP). This includes, for the first time in CMIP, spatial resolutions comparable to global satellite observations. Finally, consistent with earlier studies of resolution versus parameterization of the diurnal cycle, the longstanding tendency of models to produce rainfall too early in the day persists in the high-resolution simulations, as expected if the error is due to subgrid-scale physics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Structure and Performance of GFDL's CM4.0 Climate Model

We illustrate the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top-of-atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high-quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño-Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Alaska Daily Extreme Precipitation Processes in a Subset of CMIP5 Global Climate Models

We analyze physical processes leading to daily wintertime (December, January, and February) extreme precipitation events in Alaska between 1986 and 2005. This is done by applying self-organizing maps to environmental conditions corresponding to National Centers for Environmental Information precipitation, using the European Centre for Medium-Range Weather Forecasts reanalysis (ERA-Interim) and Coupled Model Intercomparison Project 5 (CMIP5) global climate selected Global climate model (GCM; selected GCMs). We focus on widespread extreme events, defined as the top 0.1% of daily precipitation occurring on at least six grid boxes on the same day. The self-organizing maps methodology allows identifying large-scale circulations conducive to extreme events. This methodology identifies distinctive circulation patterns conducive to producing extreme events with a trough west of Alaska leading to south or southwest flow across the state. Extreme events occur along the windward (southern) side of the Alaska Range due to uplift by the mountains in the ERA-Interim and in all models. In the National Centers for Environmental Information observations, precipitation rates are greater than in any of the selected GCMs. Simulated extreme precipitation decreases as model resolution decreases, and our study suggests that the smoothness of model topography is a reason for the scaling between model precipitation rate and model resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

In this work, we analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO 2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO 2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The Need for Urban‐Resolving Climate Modeling Across Scales

Abstract Increasing urbanization, evolving urban landscapes, and growing populations require an accurate representation of urban areas and urban processes at microscale, regional, and global scales and their feedback across the scales. Important urban processes should be represented in climate models, and current approaches used are discussed. We discuss strategies for urban‐resolving climate research to capture the effect of climate on urban regions and the non‐linear urban processes on climate. Advancing urban‐resolving climate modeling will take us closer to capabilities that can truly capture relevant natural and human components.

Sharma, Ashish↗
DOE OSTI2023

Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations

Abstract. Ice-nucleating particles (INPs) in the Southern Ocean (SO) atmosphere have significant impacts on cloud radiative and microphysical properties. Yet, INP prediction skill in climate models remains poorly understood, in part because of the lack of long-term measurements. Here we show, for the first time, how model-simulated INP concentrations compare with year-round INP measurements during the Macquarie Island Cloud Radiation Experiment (MICRE) campaign from 2017–2018. We simulate immersion-mode INP concentrations using the Energy Exascale Earth System Model version 1 (E3SMv1) by combining simulated aerosols with recently developed deterministic INP parameterizations and the native classical nucleation theory (CNT) for mineral dust in E3SMv1. Because MICRE did not collect aerosol measurements of super-micron particles, which are more effective ice nucleators, we evaluate the model's aerosol fields at other high-latitude sites using long-term in situ observations of dust and sea spray aerosol. We find that the model underestimates dust and overestimates sea spray aerosol concentrations by 1 to 2 orders of magnitude for most of the high-latitude sites in the Southern Hemisphere. We next compare predicted INP concentrations with concentrations of INPs collected on filter samples (typically for 2 or 3 d) and processed offline using the Colorado State University ice spectrometer (IS) in immersion freezing mode. We find that when deterministic parameterizations for both dust and sea spray INPs are used, simulated INPs are within a factor of 10 of observed INPs more than 60 % of the time during summer. Our results also indicate that the E3SM's current treatment of mineral dust immersion freezing in the SO is impacted by compensating biases – an underprediction of dust amount was compensated by an overprediction of its effectiveness as INPs. We also perform idealized droplet freezing experiments to quantify the implications of the time-dependent behavior assumed by the E3SM's CNT-parameterization and compare with the ice spectrometer observations. We find that the E3SM CNT 10 s diagnostic used in this study is a reasonable approximation of the exact formulation of CNT, when applied to ice spectrometer measurements in low-INP conditions similar to Macquarie Island. However, the linearized 10 s diagnostic underestimates the exact formula by an order of magnitude or more in places with high-INP conditions like the Sahara. Overall, our findings suggest that it is important to correct the biases in E3SM's simulated dust life cycle and update E3SM's INP parameterizations. INP prediction errors of 2 to 3 orders of magnitude can have considerable impacts on the simulated cloud and radiative properties in global climate models. On comparing INP concentrations during MICRE against ship-based campaigns, Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) and Antarctic Circumnavigation Expedition (ACE), we find that INPs from the latter are significantly higher only in regions closer to Macquarie Island. This alludes to the fact that physical, chemical and biological processes affecting INP concentrations as stimulated by the island could be partly responsible for the high INP concentrations observed at Macquarie Island during the MICRE campaign. Therefore, improvements to both aerosol simulation and INP parameterizations are required to adequately simulate INPs and their cloud impacts in E3SM. It will be helpful to include a parallel measurement of the size-resolved aerosol composition and explore opportunities for long-term measurement platforms in future field campaigns studying INP sources in remote marine regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Visualizing Life Zone Boundary Sensitivities Across Climate Models and Temporal Spans

Life zones are a convenient and quantifiable method for delineating areas with similar plant and animal communities based on bioclimatic conditions. Such ecoregionalization techniques have proved useful for defining habitats and for studying how these habitats may shift due to environmental change. The ecological impacts of climate change are of particular interest. Here we show that visualizations of the geographic projection of life zones may be applied to the investigation of potential ecological impacts of climate change using the results of global climate model simulations. Using a multi-factor classification scheme, we show how life zones change over time based on quantitative model results into the next century. Using two straightforward metrics, we identify regions of high sensitivity to climate changes from two global climate simulations under two different greenhouse gas emissions scenarios. Finally, we identify how preferred human habitats may shift under these scenarios. We apply visualization methods developed for the purpose of displaying multivariate relationships within data, especially for situations that involve a large number of concurrent relationships. Our method is based on the concept of multivariate classification, and is implemented directly in VisIt, a production quality visualization package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine learning-based bias-corrected future projections of ozone concentrations from a chemistry-climate model

Reliable projection of future near-surface ozone is crucial for air quality management and health risk assessment. However, potential biases in spatial distribution, magnitude and trends in ozone concentrations simulated by global chemistry-climate models limit their applicability in regional-scale evaluations. In this study, LightGBM, a machine learning (ML) algorithm is applied to correct biases in CESM2-simulated ozone concentrations over China, the United States and Europe and calibrate future ozone projections under two diverse Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) scenarios from 2020 to 2060. The ML-based correction significantly improves the spatial distribution and reduces the model bias by 40%–60%. It also reverses the potentially incorrect trend of ozone change under SSP1-2.6 in eastern China. When applying ML-based bias correction to CESM2 future projections, warm season mean ozone concentrations decrease across China, the United States, and Europe by –13.5, –17.9, and –13.7 µg/m³, respectively, between 2020 and 2060 in SSP1-2.6, while they increase by 9.4, 2.0, and 5.2 µg/m³ in SSP5-8.5. Decomposition analysis show that changes in anthropogenic emissions dominate future ozone changes in both scenarios, while strong climate penalty from ozone changes occurs in polluted eastern China and climate benefit is found in western China, the United States and Europe under SSP5-8.5. These findings demonstrate the value of combining ML with chemistry-climate models to produce more accurate air quality projections, thereby informing more effective and region-specific environmental protection strategies.

Chemistry Model↗
DOE OSTI2025

A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models

Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly averaged cloud fraction partitioned by cloud droplet effective radius (r e ) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed -0.34 ± 0.23, -0.22 ± 0.13, and -0.09 ± 0.11 W m -2 , respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r e –LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Atmospheric System Research Treatment of Convection in Next-Generation Climate Models: Challenges and Opportunities Workshop Report

A workshop sponsored by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program was held at the Pacific Northwest National Laboratory (PNNL) on 3-5 February 2016. This workshop was a response to the rapid approach of climate models with cloud-permitting (1- to 10-km grid spacing) resolutions. The expansion of computational resources and advances in numerical methods are making such models a reality. The workshop participants considered specifically the new set of challenges for representing convective cloud processes in this generation of models. The challenges arise from the fact that some aspects of convection are partly resolved. Short- and long-term strategies that would lead to accurately representing convection in these models were discussed, and opportunities for the ASR program to effectively leverage DOE’s unique capabilities, high-performance computing resources, and Atmospheric Radiation Measurement (ARM) Climate Research Facility observational facilities to this end were identified. The workshop was organized around the premise that a strategy to accurately treat convection in the next generation of climate models comprises addressing challenges on three core themes and their integration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortages with uncertainty-aware and physics-informed machine learning

Focal Area(s): The focus area is predictive modeling through the use of AI techniques and AI-derived model components with a particular emphasis on extreme weather in Atmospheric Science and power outages and shortages in Energy Science. Science Challenge: Predicting weather extremes (e.g., heavy precipitation, strong wind, and large hailstones), and weather-related power system outages and shortages can mitigate economic losses, save lives, support renewables integration, and improve power system resiliency. However, currently, the poor reliability and large uncertainty associated with the weather extreme prediction in the current climate models make the problem intractable. The key challenges are: (1) physical factors like green-house gases (GHGs), aerosols, and land use and land cover (LULC) can significantly impact extreme storms, but the understanding of these impacts is limited, particularly globally; (2) the convective permitting resolutions needed to model severe convective storms and their impacts are computationally prohibitive with global climate models (GCMs); (3) interactions between weather extremes and power system outages are complex and subject to great uncertainty. Current outage prediction models are short lead (~ 3 days), which do not allow for long-time planning of energy production and distribution. Moreover, we have limited capacity to predict weather events leading to sustained shortages in a renewable-energy-dominated power system. These challenges drive motivation for mechanistic understanding and reliable and efficient predictive modeling of extremes and their impacts from the sub-seasonal to long term projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

The accuracy of climate models' simulated season lengths and the effectiveness of grid scale correction factors

Global climate change is expected to impact biological populations through a variety of mechanisms including increases in the length of their growing season. Climate models are useful tools for predicting how season length might change in the future. However, the accuracy of these models tends to be rather low at regional geographic scales. Here, I determined the ability of several atmosphere and ocean general circulating models (AOGCMs) to accurately simulate historical season lengths for a temperate ectotherm across the continental United States. I also evaluated the effectiveness of regional-scale correction factors to improve the accuracy of these models. I found that both the accuracy of simulated season lengths and the effectiveness of the correction factors to improve the model's accuracy varied geographically and across models. These results suggest that regional specific correction factors do not always adequately remove potential discrepancies between simulated and historically observed environmental parameters. As such, an explicit evaluation of the correction factors' effectiveness should be included in future studies of global climate change's impact on biological populations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

Current climate models cannot resolve individual convective clouds, and hence parameterizations are needed. The primary goal of convective parameterization is to represent the bulk impact of convection on the gridbox-scale variables. Spectral convective parameterizations also aim to represent the key features of the subgrid-scale convective cloud field such as cloud-top-height distribution and in-cloud vertical velocities in addition to precipitation rates. Ground-based radar retrievals of these quantities have been made available at Darwin, Australia, permitting direct comparisons of internal parameterization variables and providing new observational references for further model development. A spectral convective parameterization [the convective cloud field model (CCFM)] is discussed, and its internal equation of motion is improved. Results from the ECHAM–HAM model in single-column mode using the CCFM and the bulk mass flux Tiedtke–Nordeng scheme are compared with the radar retrievals at Darwin. The CCFM is found to outperform the Tiedtke–Nordeng scheme for cloud-top-height and precipitation-rate distributions. Radar observations are further used to propose a modified CCFM configuration with an aerodynamic drag and reduced entrainment parameter, further improving both the convective cloud-top-height distribution (important for large-scale impact of convection) and the in-cloud vertical velocities (important for aerosol activation). This study provides a new development in the CCFM, improving the representation of convective cloud spectrum characteristics observed in Darwin. This is a step toward an improved representation of convection and ultimately of aerosol effects on convection. It also shows how long-term radar observations of convective cloud properties can help constrain parameters of convective parameterization schemes.

54 ENVIRONMENTAL SCIENCES↗