Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Earth System 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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Leaf respiration ( GlobResp ) - global trait database supports Earth System Models

Here we detail how Atkin and his colleagues compiled a global database (GlobResp) that details rates of leaf dark respiration and associated traits from sites that span Arctic tundra to tropical forests. This compilation builds upon earlier research (Reich et al., 1998; Wright et al., 2006) and was supplemented by recent field campaigns and unpublished data.In keeping with other trait databases, GlobResp provides insights on how physiological traits, especially rates of dark respiration, vary as a function of environment and how that variation can be used to inform terrestrial biosphere models and land surface components of Earth System Models. Although an important component of plant and ecosystem carbon (C) budgets (Wythers et al., 2013), respiration has only limited representation in models. Seen through the eyes of a plant scientist, Atkin et al. (2015) give readers a unique perspective on the climatic controls on respiration, thermal acclimation and evolutionary adaptation of dark respiration, and insights into the covariation of respiration with other leaf traits. We find there is ample evidence that once large databases are compiled, like GlobResp, they can reveal new knowledge of plant function and provide a valuable resource for hypothesis testing and model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Energy Exascale Earth System Model Version 3: 1. Overview of the Atmospheric Component

This paper describes the atmospheric component of the US Department of Energy's Energy Exascale Earth System Model (E3SM) version 3. Significant updates have been made to the atmospheric physics compared to earlier versions. Specifically, interactive gas chemistry has been implemented, along with improved representations of aerosols and dust emissions. A new stratiform cloud microphysics scheme more physically treats ice processes and aerosol‐cloud interactions. The deep convection parameterization has been largely improved with sophisticated microphysics for convective clouds, making model convection sensitive to large‐scale dynamics, and incorporating the dynamical and physical effects of organized mesoscale convection. Improvements in aerosol wet removal processes and parameter re‐tuning of key aerosol and cloud processes have improved model aerosol radiative forcing. The model's vertical resolution has increased from 72 to 80 layers with the extra eight layers added in the lower stratosphere to better simulate the Quasi‐Biennial Oscillation. These improvements have enhanced E3SM's capability to couple aerosol, chemistry, and biogeochemistry and reduced some long‐standing biases in simulating tropical variability. Compared to its predecessors, the model shows a much stronger signal for the Madden‐Julian Oscillation, Kelvin waves, mixed Rossby‐gravity waves, and eastward inertia‐gravity waves. Aerosol radiative forcing has been considerably reduced and is now better aligned with community best estimates, leading to significantly improved skill in simulating historical temperature records. Its simulated mean‐state climate is largely comparable to E3SMv2, but with some notable degradation in shortwave cloud radiative effect, precipitable water, and surface wind stress, which will be addressed in future updates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Improving the representation of hydrologic processes in Earth System Models

Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale models, such as the land components of Earth System Models (ESMs), do not yet represent the terrestrial water cycle in a fully integrated manner or resolve the finer-scale processes that can dominate large-scale water budgets. This paper reviews the current representation of hydrologic processes in ESMs and identifies the key opportunities for improvement. This review suggests that (1) the development of ESMs has not kept pace with modeling advances in hydrology, both through neglecting key processes (e.g., groundwater) and neglecting key aspects of spatial variability and hydrologic connectivity; and (2) many modeling advances in hydrology can readily be incorporated into ESMs and substantially improve predictions of the water cycle. Accelerating modeling advances in ESMs requires comprehensive hydrologic benchmarking activities, in order to systematically evaluate competing modeling alternatives, understand model weaknesses, and prioritize model development needs. This demands stronger collaboration, both through greater engagement of hydrologists in ESM development and through more detailed evaluation of ESM processes in research watersheds. Advances in the representation of hydrologic process in ESMs can substantially improve energy, carbon and nutrient cycle prediction capabilities through the fundamental role the water cycle plays in regulating these cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗
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↗
DOE OSTI2023

Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

Emulation through pattern scaling is a well-established method of rapidly producing climate fields (like temperature or precipitation) from existing Earth System Model (ESM) output that, while inaccurate, is often useful for a variety of downstream purposes. Conducting pattern scaling has historically been a laborious process, in large part due to the increasing volume of ESM output data that has often required downloading and storing locally to train on. Here we describe the Pangeo-Enabled ESM Pattern Scaling (PEEPS) dataset, a repository of trained annual and monthly patterns from CMIP6 outputs. This manuscript describes and validates these updated patterns so that users can save effort calculating and reporting error statistics in manuscripts focused on the use of patterns. The trained patterns are available as NetCDF files on Zenodo for ease of use in the impact community, and are reproducible with the code provided via GitHub in both Jupyter notebook and Python script formats. Because all training data for the PEEPS data set is cloud-based, users do not need to download and house the ESM output data to reproduce the patterns in the zenodo archive, should that be more efficient. Validating the PEEPS data set on the CMIP6 archive for annual and monthly temperature, precipitation, and near-surface relative humidity, pattern scaling performs well over a variety of future scenarios except for regions in which there are strong, potentially nonlinear climate feedbacks. Although pattern scaling is normally conducted on annual mean ESM output data, it works equally well on monthly mean ESM output data. We identify several downstream applications of the PEEPS data set, including impacts assessment and evaluating certain types of Earth system uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Downscaled Earth System Model Data for Resilient Energy System Planning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2024

Assessing Acetone for the GISS ModelE2.1 Earth System Model

Acetone is an abundant volatile organic compound (VOC) in the atmosphere, with important influences on ozone and oxidation capacity. Direct sources include chemical production from other VOCs and anthropogenic emissions, terrestrial vegetation, biomass-burning emissions, and ocean production. Sinks include chemical loss, deposition onto the land surface, and ocean uptake. Acetone also has a lifetime that is long enough to allow transport and reactions with other compounds remote from its sources. The NASA Goddard Institute for Space Studies (GISS) Earth system model ModelE2.1 simulates a variety of Earth system interactions. Previously, acetone had a very simplistic representation in the ModelE chemical scheme. This study assesses a more sophisticated acetone scheme in which acetone is a full 3-dimensional tracer with explicit sources, sinks, and atmospheric transport. We first evaluate the new global acetone budget in the context of past literature. Estimated source and sink fluxes fall within the range of previous models, although total atmospheric burden and lifetime are at the lower end of the published literature. Acetone's new representation in ModelE2.1 also results in more realistic spatial and vertical distributions, which we compare against previous models and field observations. The seasonality of acetone-related processes was also studied in conjunction with field measurements, and these comparisons show promising agreement but also shortcomings at high-emission urban locations, where the model's resolution is too coarse to capture the true behavior. Finally, we conduct a variety of sensitivity studies that explore the influence of key parameters on the acetone budget and its global distribution. An impactful finding is that the production of acetone from precursor hydrocarbon oxidation has strong leverage on the overall chemical source, indicating the importance of accurate molar yields. Overall, our implementation is one that corroborates with previous studies and marks a significant improvement in the development of the acetone tracer in GISS ModelE2.1.

Acetone↗
DOE OSTI2020

PCMDI - An Earth System Model Evaluation Project Lawrence Livermore National Laboratory Annual Report

This annual report describes the activities of PCMDI – An Earth System Model Evaluation Project for the period November 1, 2019 – June 30, 2020. PCMDI is a project at Lawrence Livermore National Laboratory that is funded by the Climate and Environmental Sciences Division (CESD) of the Department of Energy. PCMDI is funded by two efforts within CESD: The Regional and Global Model Analysis program area (Dr. Renu Joseph, program manager) and the Atmospheric Systems Research program (Dr. Shaima Nasiri and Jeffrey Stehr, program managers). As instructed by Drs. Joseph, Nasiri, and Stehr, we provide an annual report addressing sections 4 (Progress and Plans) and 5 (Staffing and Budget Summary) of the SFA Program Management and Performance Reporting Criteria.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Evaluation of SO2, SO4(2−) and an Updated SO2 Dry Deposition Parameterization in the United Kingdom Earth System Model

In this study we evaluate simulated surface SO2 and sulphate (SO4(2-)) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground based measurement networks in the USA and Europe for the period 1987 to 2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO4(2-) in both Europe and the USA over the period 1987 to 2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3, while under-predicting surface SO4(2-) concentrations by 25-35%. In the cleaner western USA, the model over-predicts both surface SO2 and SO4(2-) concentrations by a factor of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO4(2-) concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model’s global performance, finding that UKESM1 over predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model’s SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO(sup 2-)(sub 4). In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the models over prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations the simulated surface SO4(2-) concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

SO2↗
NASA NTRS2023

Earth-System-Model Evaluation of Cloud and Precipitation Occurrence for Supercooled and Warm Clouds Over the Southern Ocean's Macquarie Island

Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

cloud feedbacks↗
DOE OSTI2016

Towards improved and more routine Earth system model evaluation in CMIP

The Coupled Model Intercomparison Project (CMIP) has successfully provided the climate community with a rich collection of simulation output from Earth system models (ESMs) that can be used to understand past climate changes and make projections and uncertainty estimates of the future. Confidence in ESMs can be gained because the models are based on physical principles and reproduce many important aspects of observed climate. More research is required to identify the processes that are most responsible for systematic biases and the magnitude and uncertainty of future projections so that more relevant performance tests can be developed. At the same time, there are many aspects of ESM evaluation that are well established and considered an essential part of systematic evaluation but have been implemented ad hoc with little community coordination. Given the diversity and complexity of ESM analysis, we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently and consistently. We provide a perspective and viewpoint on how a more systematic, open, and rapid performance assessment of the large and diverse number of models that will participate in current and future phases of CMIP can be achieved, and announce our intention to implement such a system for CMIP6. Accomplishing this could also free up valuable resources as many scientists are frequently "re-inventing the wheel" by re-writing analysis routines for well-established analysis methods. A more systematic approach for the community would be to develop and apply evaluation tools that are based on the latest scientific knowledge and observational reference, are well suited for routine use, and provide a wide range of diagnostics and performance metrics that comprehensively characterize model behaviour as soon as the output is published to the Earth System Grid Federation (ESGF). The CMIP infrastructure enforces data standards and conventions for model output and documentation accessible via the ESGF, additionally publishing observations (obs4MIPs) and reanalyses (ana4MIPs) for model intercomparison projects using the same data structure and organization as the ESM output. This largely facilitates routine evaluation of the ESMs, but to be able to process the data automatically alongside the ESGF, the infrastructure needs to be extended with processing capabilities at the ESGF data nodes where the evaluation tools can be executed on a routine basis. Efforts are already underway to develop community-based evaluation tools, and we encourage experts to provide additional diagnostic codes that would enhance this capability for CMIP. And, at the same time, we encourage the community to contribute observations and reanalyses for model evaluation to the obs4MIPs and ana4MIPs archives. The intention is to produce through the ESGF a widely accepted quasi-operational evaluation framework for CMIP6 that would routinely execute a series of standardized evaluation tasks. Over time, as this capability matures, we expect to produce an increasingly systematic characterization of models which, compared with early phases of CMIP, will more quickly and openly identify the strengths and weaknesses of the simulations. This will also reveal whether long-standing model errors remain evident in newer models and will assist modelling groups in improving their models. Finally, this framework will be designed to readily incorporate updates, including new observations and additional diagnostics and metrics as they become available from the research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM)

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire-aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997-2016. Furthermore, we distributed fire emissions vertically using smoke plume heights from Multi-angle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg y -1 of primary particulate organic matter and 3.9 Tg y -1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14 ± 7% and contribute to a reduction in net shortwave radiation at the surface (-2.3 ± 0.5 W m -2 ). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17 ± 0.15°C, relative humidity to increase by 0.4 ± 0.3%, and diffuse light fraction to increase by 0.5 ± 0.3%. In response, GPP declines by 2.8 Pg C y -1 , as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light) nearly cancelling out large negative drivers (decreases shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs)

Abstract This paper provides a comprehensive derivation of the total energy equations for the atmospheric components of Earth System Models (ESMs). The assumptions and approximations made in this derivation are motivated and discussed. In particular, it is emphasized that closing the energy budget is conceptually challenging and hard to achieve in practice without resorting to ad hoc fixers. As a concrete example, the energy budget terms are diagnosed in a realistic climate simulation using a global atmosphere model. The largest total energy errors in this example are spurious dynamical core energy dissipation, thermodynamic inconsistencies (e.g., coupling parameterizations with the host model) and missing processes/terms associated with falling precipitation and evaporation (e.g., enthalpy flux between components). The latter two errors are not, in general, reduced by increasing horizontal resolution. They are due to incomplete thermodynamic and dynamic formulations. Future research directions are proposed to reconcile and improve thermodynamics formulations and conservation principles.

54 ENVIRONMENTAL SCIENCES↗