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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Evidence for climate change in the satellite cloud record

Clouds substantially affect Earth’s energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space 1 . They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming 2, 3 . This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts 4, 5 . Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. Here, these results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.

58 GEOSCIENCES↗
DOE OSTI2021

COMBLE ARM Mobile Facility (AMF) Measurements of Ice Nucleating Particles Field Campaign Report

During the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), a project with the overarching goal to shed light on the dynamics and microphysical properties of clouds and precipitation in the high-latitude marine boundary layer during cold-air outbreaks (CAOs), we were tasked with providing and assisting the collection of aerosol filter samples for measuring ice nucleating particle (INP) concentrations. Little is known about the properties of CAO cloud systems, 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. Uncertainties in climate projections for the arctic and sub-arctic region are large, in part due to the lack of observational guidance to constrain the treatment of aerosol-cloud-precipitation linkages in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0

Simple climate models play an integral role in the policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v1.0, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, land, and ocean. The model's terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganic carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO 2 ], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (a well-known simple climate model), and models from the 5th Coupled Model Intercomparison Project. Hector's flexibility, open-source nature, and modular design will facilitate a broad range of research in various areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Assessing the Influence of Background State and Climate Variability on Tropical Cyclones Using Initialized Ensembles and Mesh Refinement in E3SM

The scientific goal of this project is to assess the influence of the background state and horizontal resolution of climate models on the simulation of extreme events like intense precipitation, tropical cyclones, atmospheric rivers, and heatwaves. The main results are: - Initialized ensembles of 4-week forecasts using a climate model (E3SM) show that dynamic (flow) and thermodynamic (moisture) biases asymptote to their climatological values at different rates—dynamic bias grows rapidly and approaches its climatological value within about 2 weeks whereas the thermodynamic bias takes about 4 weeks. - Moisture bias has a bigger impact on extreme events like tropical cyclones and intense precipitation than flow bias, and this impact is non-monotonic over bias evolution time. - Climate models are capable of simulating the flow features associated with unprecedented extreme events like the Western North America heatwave of June 2021, but moisture biases can weaken the surface manifestation of extreme heatwaves. Reducing model bias may be more important than increasing model resolution in improving the fidelity of heatwave simulations in climate models. - Climate models are capable of simulating weather patterns associated with “power droughts”, i.e., periods with low solar and wind power generation, provided model biases in the simulation of the background state can be corrected. - Regional mesh refinement can improve the simulation of extreme weather events but model biases may still persist.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Natural variability contributes to model–satellite differences in tropical tropospheric warming

Significance Climate models have, on average, simulated substantially more tropical tropospheric warming than satellite data, with few simulations matching observations. It has been suggested that this discrepancy arises because climate models are overly sensitive to greenhouse gas increases. Tropical tropospheric temperature trends from a large ensemble of simulations performed with a single climate model span a wide range that is solely due to natural climate variability. A subset of these simulations have warming rates in accord with satellite observations. Simulations with diminished tropical tropospheric warming due to climate variability exhibit characteristic patterns of surface warming that are similar to the observed record. Our results indicate that multidecadal variability can explain current model–observational differences in the rate of tropical tropospheric warming.

58 GEOSCIENCES↗
DOE OSTI2016

ENSO-Related Precipitation and Its Statistical Relationship with the Walker Circulation Trend in CMIP5 AMIP Models

Observational evidence shows that the Walker circulation (WC) in the tropical Pacific has strengthened in recent decades. In this study, we examine the WC trend for 1979–2005 and its relationship with the precipitation associated with the El Niño Southern Oscillation (ENSO) using the sea surface temperature (SST)-constrained Atmospheric Model Intercomparison Project (AMIP) simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. All of the 29 models show a strengthening of the WC trend in response to an increase in the SST zonal gradient along the equator. Despite the same SST-constrained AMIP simulations, however, a large diversity is found among the CMIP5 climate models in the magnitude of the WC trend. The relationship between the WC trend and precipitation anomalies (PRCPAs) associated with ENSO (ENSO-related PRCPAs) shows that the longitudinal position of the ENSO-related PRCPAs in the western tropical Pacific is closely related to the magnitude of the WC trend. Specifically, it is found that the strengthening of the WC trend is large (small) in the CMIP5 AMIP simulations in which the ENSO-related PRCPAs are located relatively westward (eastward) in the western tropical Pacific. Furthermore, the zonal shift of the ENSO-related precipitation in the western tropical Pacific, which is associated with the climatological mean precipitation in the tropical Pacific, could play an important role in modifying the WC trend in the CMIP5 climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Two-Way Integration of WRF and CCSM for Regional Climate Simulations

Under the support of the DOE award DE-SC0004670, we have successfully developed an integrated climate modeling system by nesting Weather Research and Forecasting (WRF) model within the Community Climate System Model (CCSM) and the ensuing new generation Community Earth System Model (CESM). The integrated WRF/CESM system is intended as one method of global climate modeling with regional simulation capabilities. It allows interactive dynamical regional downscaling in the computational flow of present or future global climate simulations. This capability substantially simplifies the process of dynamical downscaling by avoiding massive intermediate model outputs at high frequency that are typically required for offline regional downscaling. The inline coupling also has the advantage of higher temporal resolution for the interaction between regional and global model components. With the aid of the inline coupling, a capability has also been developed to ingest other global climate simulations (by CESM or other models), which otherwise may not have necessary intermediate outputs for regional downscaling, to realize their embedded regional details. It is accomplished by relaxing the global atmospheric state of the integrated model to that of the source simulations with an appropriate time scale. This capability has the potential to open a new venue for ensemble regional climate simulations using a single modeling system. Furthermore, this new modeling system provides an effective modeling framework for the studies of physical and dynamical feedbacks of regional weather phenomena to the large scale circulation. The projected uses of this capability include the research of up-scaling effect of regional weather system, and its use as an alternative physical representation of sub-scale processes in coarser-resolution climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Reconciling divergent trends and millennial variations in Holocene temperatures

Cooling during most of the past two millennia has been widely recognized1,2 and has been inferred to be the dominant global temperature trend of the past 11,700 years (the Holocene epoch)3. However, long-term cooling has been difficult to reconcile with global forcing4, and climate models consistently simulate long-term warming4. The divergence between simulations and reconstructions emerges primarily for northern mid-latitudes, for which pronounced cooling has been inferred from marine and coastal records using multiple approaches3. Here we show that temperatures reconstructed from sub-fossil pollen from 642 sites across North America and Europe closely match simulations, and that long-term warming, not cooling, defined the Holocene until around 2,000 years ago. The reconstructions indicate that evidence of long-term cooling was limited to North Atlantic records. Early Holocene temperatures on the continents were more than two degrees Celsius below those of the past two millennia, consistent with the simulated effects of remnant ice sheets in the climate model Community Climate System Model 3 (CCSM3)5. CCSM3 simulates increases in ‘growing degree days’—a measure of the accumulated warmth above five degrees Celsius per year—of more than 300 kelvin days over the Holocene, consistent with inferences from the pollen data. It also simulates a decrease in mean summer temperatures of more than two degrees Celsius, which correlates with reconstructed marine trends and highlights the potential importance of the different subseasonal sensitivities of the records. Despite the differing trends, pollen- and marine-based reconstructions are correlated at millennial-to-centennial scales, probably in response to ice-sheet and meltwater dynamics, and to stochastic dynamics similar to the temperature variations produced by CCSM3. Although our results depend on a single source of palaeoclimatic data (pollen) and a single climate-model simulation, they reinforce the notion that climate models can adequately simulate climates for periods other than the present-day. They also demonstrate that amplified warming in recent decades increased temperatures above the mean of any century during the past 11,000 years.

Science & Technology - Other Topics↗
DOE OSTI2014

WATER TRAPPING ON TIDALLY LOCKED TERRESTRIAL PLANETS REQUIRES SPECIAL CONDITIONS

Surface liquid water is essential for standard planetary habitability. Calculations of atmospheric circulation on tidally locked planets around M stars suggest that this peculiar orbital configuration lends itself to the trapping of large amounts of water in kilometers-thick ice on the night side, potentially removing all liquid water from the day side where photosynthesis is possible. We study this problem using a global climate model including coupled atmosphere, ocean, land, and sea ice components as well as a continental ice sheet model driven by the climate model output. For a waterworld, we find that surface winds transport sea ice toward the day side and the ocean carries heat toward the night side. As a result, nightside sea ice remains O(10 m) thick and nightside water trapping is insignificant. If a planet has large continents on its night side, they can grow ice sheets O(1000 m) thick if the geothermal heat flux is similar to Earth's or smaller. Planets with a water complement similar to Earth's would therefore experience a large decrease in sea level when plate tectonics drives their continents onto the night side, but would not experience complete dayside dessiccation. Only planets with a geothermal heat flux lower than Earth's, much of their surface covered by continents, and a surface water reservoir O(10%) of Earth's would be susceptible to complete water trapping.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM

The location, timing, and intermittency of precipitation in California make the state integrally reliant on winter-season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter-season snowpack has shown a net decline across the western United States over the past 50 years, resulting in major uncertainty in water-resource management heading into the next century. Cutting-edge tools are available to help navigate and preemptively plan for these uncertainties. This paper uses a next-generation modeling technique-variable-resolution global climate modeling within the Community Earth System Model (VR-CESM)-at horizontal resolutions of 0.125° (14 km) and 0.25° (28 km). VR-CESM provides the means to include dynamically large-scale atmosphere-ocean drivers, to limit model bias, and to provide more accurate representations of regional topography while doing so in a more computationally efficient manner than can be achieved with conventional general circulation models. This paper validates VR-CESM at climatological and seasonal time scales for Sierra Nevada snowpack metrics by comparing them with the "Daymet," "Cal-Adapt," NARR, NCEP, and North American Land Data Assimilation System (NLDAS) reanalysis datasets, the MODIS remote sensing dataset, the SNOTEL observational dataset, a standard-practice global climate model (CESM), and a regional climate model (WRF Model) dataset. Overall, given California's complex terrain and intermittent precipitation and that both of the VR-CESM simulations were only constrained by prescribed sea surface temperatures and data on sea ice extent, a 0.68 centered Pearson product-moment correlation, a negative mean SWE bias of < 7 mm, an interquartile range well within the values exhibited in the reanalysis datasets, and a mean December-February extent of snow cover that is within 7% of the expected MODIS value together make apparent the efficacy of the VR-CESM framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from the Weather Research Forecast (WRF) climate model to train deep neural networks (DNNs) and evaluate whether trained DNNs can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using DNNs for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are used in atmospheric models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics and turbulence, as well as velocity, temperature, and humidity profiles within the boundary layer are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical climate model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware DNN, which takes account of underlying domain structure (e.g., nonlocal mixing), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. Results also show that a single trained DNN from one location can produce predictions of wind speed, temperature, and water vapor profiles over nearby locations with similar terrain conditions with correlations higher than 0.9 when compared with the WRF simulations used as the training dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Quantification of marine aerosol subgrid variability and its correlation with clouds based on high‐resolution regional modeling

Abstract One limitation of most global climate models (GCMs) is that with the horizontal resolutions they typically employ, they cannot resolve the subgrid variability (SGV) of clouds and aerosols, adding extra uncertainties to the aerosol radiative forcing estimation. To inform the development of an aerosol subgrid variability parameterization, here we analyze the aerosol SGV over the southern Pacific Ocean simulated by the high‐resolution Weather Research and Forecasting model coupled to Chemistry. We find that within a typical GCM grid, the aerosol mass subgrid standard deviation is 15% of the grid‐box mean mass near the surface on a 1 month mean basis. The fraction can increase to 50% in the free troposphere. The relationships between the sea‐salt mass concentration, meteorological variables, and sea‐salt emission rate are investigated in both the clear and cloudy portion. Under clear‐sky conditions, marine aerosol subgrid standard deviation is highly correlated with the standard deviations of vertical velocity, cloud water mixing ratio, and sea‐salt emission rates near the surface. It is also strongly connected to the grid box mean aerosol in the free troposphere (between 2 km and 4 km). In the cloudy area, interstitial sea‐salt aerosol mass concentrations are smaller, but higher correlation is found between the subgrid standard deviations of aerosol mass and vertical velocity. Additionally, we find that decreasing the model grid resolution can reduce the marine aerosol SGV but strengthen the correlations between the aerosol SGV and the total water mixing ratio (sum of water vapor, cloud liquid, and cloud ice mixing ratios).

Lin, Guangxing↗
DOE OSTI2023

Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Abstract Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating and Improving Convective Parameterization for GCMs Using ARM Observations. Final report

This technical report summarizes the achievements during the project period funded by the Atmospheric System Research (ASR) program. The general goal of the project is to improve the representation of atmospheric deep convection in global climate models. By using DOE Atmospheric Radiation Measurement (ARM) program observations and a hierarchy of models from cloud-resolving to global climate models, we evaluated and improved many aspects of convection parameterization schemes, such as the trigger function for convection and closure condition to determine the amount of convection. We also incorporated the stochasticity of convection into a convection scheme. These improvements, when implemented into the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) and the DOE Exascale Energy Earth System Model (E3SM), led to improved simulation of precipitation characteristics, including precipitation intensity, Intertropical Convergence Zone, and Madden-Julian oscillation. The project also improved the representation of microphysical processes in convective clouds. It enables the interaction of cloud microphysics with aerosols in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved representation of black carbon mixing structures suggests stronger direct radiative heating

Black carbon significantly influences the Earth system because of its strong solar radiation absorption. However, its direct radiative effect remains poorly understood in current climate models, partly because current climate models oversimplify the diverse structures formed when black carbon mixes with other atmospheric components. Here we show that incorporating more realistic, multi-mixing-structure representations of black carbon increases the direct radiative effect. We find that aged black carbon particles, with thicker coatings and higher embedded fractions, enhance the direct radiative effect more efficiently. Using machine learning alongside the Community Earth System Model, we show that the direct radiative effect at the top of the atmosphere in regions with heavy black carbon pollution is 31.6% greater when multi-mixing structures are considered. These findings highlight the importance of modeling complex mixing structures of particle-resolved black carbon to accurately capture their warming impacts on global atmosphere, particularly in highly polluted regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Observations and Projections of Heat Waves in South America

Although Heat Waves (HWs) are expected to increase due to global warming, they are a regional phenomenon that demands for local analyses. In this paper, we assess four HW metrics (HW duration, HW frequency, HW amplitude, and number of HWs per season) as well as the share of extremely warm days (TX95, according to the 95th percentile) in South America (SA). Our analysis included observations as well as simulations from global and regional models. In particular, Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX), and Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to project both TX95 estimates and HW metrics according to two representative concentration pathways (RCP4.5 and RCP8.5). We found that in recent decades the share of extremely warm days has at least doubled over the period December–January–February (DJF) in northern SA; less significant increases have been observed in southern SA. We also found that by midcentury, under the RCP4.5 scenario, extremely warm DJF days (as well as the number of HWs per season) are expected to increase by 5–10 times at locations close to the Equator and in the Atacama Desert. Increases are expected to be less pronounced in southern SA. Projections under the RCP8.5 scenario are more striking, particularly in tropical areas where half or more of the days could be extremely warm by midcentury.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Solar Radiation Using NA-CORDEX and the NSRDB

The current state-of-art for estimating long-term PV production uses long-term estimates of solar radiation variables, such as global horizontal irradiance (GHI), from previous years. This data is used in models such as the System Advisor Model (SAM) or PYSyst to predict annual production for a PV plant. This information is then used to estimate the production over the next 20 years (a typical plant lifetime) under the assumption that the variability over the current period is representative of the future. As the PV industry moves to extend plant lifetimes to 50 years the current assumptions of representativeness of weather may not be appropriate. This is especially true as our climate changes rapidly. To assess long-term PV production, future projections for solar radiation based on projected carbon emissions are readily available in regional and global climate models. However, climate model projections contain inherent biases that may need to be corrected for accurate analysis of future projections of climate variables. Several studies have analyzed projections of solar radiation for future years, however the accuracy of the model output compared to current and historic data has not been widely studied. Chen (2021) showed that available climate models do not accurately represent solar radiation in some cases, over-projecting GHI at the surface while under-projecting its obstructions, such as clouds and aerosols. This works aims to (1) increase understanding of the accuracy of solar radiation currently available in global and regional climate models and (2) implement bias correction through linear models based on reanalysis data compared to observed solar radiation. The latter aim will be conducted using available observed solar radiation data and modeled data from several regional climate models (RCMs). The bias correction method will be applied to projections of solar radiation resulting in a more accurate representation of the future of solar production.

climate data↗