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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend

Abstract Although numerous studies have projected changes in freezing rain under future climate conditions, the internal variability of freezing rain remains poorly quantified. Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. By employing multivariate quantile mapping, we decompose the projected freezing rain trend into contributions from changes in temperature, relative humidity, and precipitation, which helps separate the forced response from internal climate variability. Our finding reveals a notable decrease in freezing rain occurrence in most areas. Despite a substantial temperature increase, internal variability overshadows climate forcing across a large portion of the eastern United States until about 2050. This insight has implications for practitioners, suggesting that the observed freezing rain frequency climatology continues to provide a relevant baseline for decision‐making in the near term. However, longer‐term design and adaptation plans should consider the projected changes in these regions.

Zhuang, Haoyu (Richard) [Department of Atmospheric↗
DOE OSTI2024

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Response of U.S. West Coast Mountain Snowpack to Local Sea Surface Temperature Perturbations: Insights from Numerical Modeling and Machine Learning

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. Furthermore, these findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

New efforts in improving numerical aspects of atmospheric physics parameterizations

This is an invited article for GEWEX Quarterly, the newsletter of the Global Energy and Water Exchanges (GEWEX) program which is a core project of the World Climate Research Programme (https://www.gewex.org/resources/gewex-news/). The article introduces recent research results and ongoing activities being carried out in a SciDAC project funded by DOE that focuses on identifying and addressing numerical issues in physics parameterizations in global atmospheric general circulation models.

global atmospheric model↗
DOE OSTI2025

Improving the QBO Forcing by Resolved Waves With Vertical Grid Refinement in E3SMv2

Abstract The quasi‐biennial oscillation (QBO) is the dominate mode of variability in the tropical stratosphere and plays an important role in stratospheric dynamics and chemistry. The QBO is notably deficient in many climate models, including the Energy Exascale Earth System Model (E3SM) developed by the US Department of Energy. In this work, we refine the lower stratospheric vertical grid spacing from roughly 1 km to 500 m to facilitate more realistic equatorial wave activity in the lower stratosphere in E3SM version 2. The refinement results in a simulated QBO with a reasonable amplitude and easterly‐westerly transition in both directions, but still has a longer period than observed, slower easterly downward propagation speed, and shallower vertical depth. Similar refinement in the multi‐scale modeling framework configuration of E3SM yields similar improvements. By analyzing the forcing contributions from different wave types, we find that most of the QBO forcing still comes from parameterized gravity wave drag from convection. The improved QBO forcing contributions from resolved waves, especially equatorial Kelvin waves and resolved small scale waves, can be attributed to the grid refinement.

E3SM↗
DOE OSTI2020

Computational modelling and experimental tank testing of the multi float WaveSub under regular wave forcing

A submerged wave device generates energy from the relative motion of floating bodies. In WaveSub, three floats are joined to a reactor; each connected to a spring and generator. Electricity generated damps the orbital movements of the floats. The forces are non-linear and each float interacts with the others. Tuning to the wave climate is achieved by changing the line lengths, so there is a need to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design. to understand the performance trade-offs for a large number of configurations. This requires an efficient, large displacement, multidirectional, multi-body numerical scheme. Results from a 1/25 scale wave basin experiment are described. Here, we show that a time domain linear potential flow formulation (Nemoh, WEC-Sim) can match the tank testing provided that suitably tuned drag coefficients are employed. Inviscid linear potential models can match some wave device experiments; however, additional viscous terms generally provide better accuracy. Scale experiments are also prone to mechanical friction, and we estimate friction terms to improve the correlation further. The resulting error in mean power between numerical and physical models is approximately 10%. Predicted device movement shows a good match. Overall, drag terms in time domain wave energy modelling will improve simulation accuracy in wave renewable energy device design.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Investigating Aerosol Hygroscopicity in the Subcloud Transition Zone and at the Surface in the Southern Great Plains

Aerosols beneath a cloud base, a subcloud transition zone (SCTZ), are key to understand both the aerosol-cloud interaction and aerosol-radiation interactions. Lidars have been the primary means of observing aerosols in the SCTZ by virtue of enhanced light scattered by aerosol particles. The enhanced light maybe caused by several factors: the aerosol swelling effect due to hygroscopicity under high relative humidity, cloud 3-dimensional (3D) effect, aerosol nucleation into cloud droplets, etc. While each factor and process has been known, their relative contributions are much poorly quantified. This study explores the hygroscopicity and optical properties of aerosols in the SCTZ and at ground level in the Southern Great Plains (SGP) region. Utilizing comprehensive observational data from the U.S. Department of Energy's Atmospheric Radiation Measurement at the Oklahoma SGP site, including ground-based aerosol measurements and Raman lidar profiles from April 2021 to April 2022, this study extensively analyzes the influence of aerosol hygroscopic growth and cloud fragments on aerosol optical properties. Distinct seasonal variations in aerosol hygroscopic characteristics are revealed. At the ground level, aerosols in autumn and winter exhibit stronger hygroscopicity due to a higher proportion of inorganic content than summer. In the SCTZ, aerosols during summer show enhanced backscatter due to strong cloud fragmentation effects, with numerous cloud fragments elevating hygroscopicity beyond that observed in autumn and winter. These insights are crucial for understanding the interactions between aerosols at the surface and cloud layers, evaluating cloud condensation nuclei beneath clouds, and their implications for atmospheric radiation and climate modeling.

Hu, Rong [Beijing Normal University, Beijing (Chin↗
DOE OSTI2023

Assumptions and advantages of diagnosing climate change at global warming levels

Scientists increasingly assess climate changes at specific levels of global warming rather than at specific times in the future. This has numerous advantages over other methods of diagnosing future climate changes and allows for a more direct connection with various climate mitigation targets like the 1.5° or 2°C Paris agreement goals. A key assumption inherent to global warming levels (GWLs) is that all climate models and all future experiments that cross a given global temperature change threshold can be averaged together – that the response of the climate at a given GWL is largely independent of the emissions scenario or the sensitivity to carbon dioxide in the models comprising the average. This assumption has not been systematically vetted in the literature. Here we mine the entire CMIP5 and CMIP6 archives to determine the extent to which the climate response of several extreme climate indices at various GWLs is sensitive to the future emissions scenario and to the models’ climate sensitivities. We quantify this sensitivity to see whether using GWLs as a control in climate projections is justified. This information will be valuable for establishing the extent to which constraints on climate sensitivity can have tangible benefits for regional climate resiliency planning.

58 GEOSCIENCES↗
DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Importance of ice nucleation and precipitation on climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)

Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigation of ventilation-coupled high energy density sensible thermal energy storage

Low-cost energy storage will play an important role in supporting the decarbonization of the energy sector. Here, a novel approach to thermal energy storage for buildings is proposed, in which a tank of antifreeze liquid can be used to heat outdoor air in mechanical ventilation systems. If heated to a high enough temperature, the fluid can potentially undergo temperature swings of nearly 100°C during discharge in cold climates. Because the energy density of sensible storage systems scales proportionally to the system temperature change, this concept has the potential to offer higher energy densities than other liquid-based sensible storage devices used in building applications. A one-dimensional numerical model of the storage system was developed and experimentally validated using 30 wt% (wt%) potassium acetate as an aqueous antifreeze solution. Using a commercial hot water tank, energy densities of 47.0 kW-hour per cubic meter (kWh/m 3 ) were demonstrated in a laboratory setting. Material energy densities of approximately 74.7 (kWh/m 3 ) were measured. Design improvements may boost this energy storage density even further. Because of the system's relative simplicity, ventilation-coupled sensible storage has the potential to be an easily deployable, low-cost energy storage solution for building systems.

25 ENERGY STORAGE↗
DOE OSTI2020

A Holistic Algorithmic Approach to Improving Accuracy, Robustness, and Computational Efficiency for Atmospheric Dynamics

Atmospheric weather and climate models must perform simulations very quickly to be useful. Therefore, modelers have traditionally focused on reducing computations as much as possible. However, in our new era of increasingly compute-capable hardware, data movement is now the prohibiting expense. This study examines the computational benefits of a new algorithmic approach to modeling atmospheric dynamics on scales relevant to weather and climate simulation. Rather than minimizing computations, this new approach considers the larger problem more holistically, including spatial accuracy, temporal accuracy, robustness (i.e., oscillations), on-node efficiency, and internode data transfers together at once. Numerical experiments demonstrate how computations can be strategically increased to simultaneously address each of these constraints while reducing data movement to adapt to modern accelerated hardware. The new algorithm can achieve at times up to 80% peak floating point throughput in single precision on the Nvidia Tesla V100 GPU, where the traditional approach is shown to only achieve single-digit floating point efficiency. Further, the new algorithm is twice as fast as a standard Runge--Kutta time integrator, and high-order accuracy with Weighted Essentially Non-Oscillatory (WENO) limiting came at less than 30% additional runtime cost on a GPU, thus increasing the accuracy per degree of freedom.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Bayesian Deep Learning Approach to Near-Term Climate Prediction

Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model. While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of machine learning models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying structural errors in cloud condensation nuclei activity from reduced representation of aerosol size distributions

Aerosol effects on clouds and radiation are the dominant contribution to uncertainty in radiative forcing relative to the pre-industrial atmosphere. While previous studies have assessed the impact of parametric uncertainty on modeled forcing, structural errors from the numerical representation of particle distributions have not been well quantified. Here we present a framework for quantifying error in aerosol size distributions and cloud condensation nuclei activity, which we apply to the widely used 4-mode version of the Modal Aerosol Module (MAM4). Box model predictions from the MAM4 are evaluated against the Particle Monte Carlo Model for Simulating Aerosol Interactions and Chemistry (PartMC-MOSAIC), a benchmark model that tracks the evolution of individual particles. We show that size distributions simulated by MAM4 diverge from those simulated by PartMC-MOSAIC after only a few hours of aging by condensation and coagulation in polluted conditions, which leads to large errors in modeled cloud condensation nuclei concentrations. We find that differences between MAM4 and PartMC-MOSAIC are largest under polluted conditions, where the size distribution evolves rapidly though aging by condensation of semi-volatile substances and coagulation among particles. These findings suggest that structural error in modeled aerosol properties contributes to the large inter-model variability in aerosol radiative forcing.

Fierce, Laura M.↗
DOE OSTI2024

Should We Conserve Entropy or Energy when Computing CAPE with Mixed-Phase Precipitation Physics?

Abstract The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from nonequilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution-dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1%–3% as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 and 250 m using explicit first-order and second-order integration schemes, respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast to energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution, whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. Significance Statement Convective available potential energy (CAPE) is a measure of instability in the atmosphere that helps forecasters and researchers understand when and where thunderstorms will form. The purpose of this article is to identify the most efficient and accurate methods for computing CAPE. Two methods are considered here, one that relates to the entropy (a measure of thermodynamic disorder) of an air parcel and one that relates to the energy of an air parcel. Results indicate that the entropy method is most accurate and insensitive to the resolution of the data used for the calculation (which can vary considerably), whereas the energy method uses the least computation time.

Peters, John M.↗
DOE OSTI2023

Insights into ENSO Diversity from an Intermediate Coupled Model. Part I: Uniqueness and Sensitivity of the ENSO Mode

Abstract The basic dynamics of the spatiotemporal diversity for El Niño–Southern Oscillation (ENSO) has been the subject of extensive research and, while several hypotheses have been proposed, remains elusive. One promising line of studies suggests that the observed eastern Pacific (EP) and central Pacific (CP) ENSO may originate from two coexisting leading ENSO modes. We show that the coexistence of unstable EP-like and CP-like modes in these studies arises from contaminated linear stability analysis due to unnoticed numerical scheme caveats. In this two-part study, we further investigate the dynamics of ENSO diversity within a Cane–Zebiak-type model. We first revisit the linear stability issue to demonstrate that only one ENSO-like linear leading mode exists under realistic climate conditions. This single leading ENSO mode can be linked to either a coupled recharge-oscillator (RO) mode favored by the thermocline feedback or a wave-oscillator (WO) mode favored by the zonal advective feedback at the weak air–sea coupling end. Strong competition between the RO and WO modes for their prominence in shaping this ENSO mode into a generalized RO mode makes it sensitive to moderate changes in these two key feedbacks. Modulations of climate conditions yield corresponding modulations in spatial pattern, amplitude, and period associated with this ENSO mode. However, the ENSO behavior undergoing this linear climate condition modulations alone does not seem consistent with the observed ENSO diversity, suggesting the inadequacy of linear dynamics in explaining ENSO diversity. A nonlinear mechanism for ENSO diversity will be proposed and discussed in Part II.

Geng, Licheng↗
DOE OSTI2026

Measurements of TRACER pre-convective conditions and mesoscale circulations using small unmanned aircraft systems (sUAS)

Improved comprehension of the physical processes governing convective cloud formation and lifecycle are of critical importance for understanding and predicting future climate states. The influence of these clouds on the planetary energy budget, including on precipitation, is significant. Things are particularly complex in coastal regimes, where gradients in aerosol particle properties, localized circulations such as sea breezes, and large population centers are found. To date, numerical models struggle to accurately represent these critical clouds and are therefore challenged to provide a realistic view on the planetary energy budget. Through the proposed research, we deployed two uncrewed aircraft systems (UAS) equipped with a variety of instruments alongside sensors deployed by the US Department of Energy Atmospheric Radiation Measurement (ARM) program for the TRACER (Tracking Aerosol Convection Interactions Experiment) field campaign. The two small UAS platforms consisted of a CU RAAVEN fixed-wing airplane and an OU CopterSonde system, with the copter collecting frequent vertical profiles of thermodynamic and kinematic variables such as temperature, pressure, wind and humidity. At the same time, the fixed-wing captured horizontal gradients of these quantities and aerosol size distribution. These systems were deployed south of the Houston metro area, in an area that is impacted by the Gulf of Mexico sea breeze on a daily basis. These observations offer enhanced and complementary perspectives to those provided by the DOE ARM Mobile Facility (AMF) which is was deployed in southeast Houston, and an ancillary site in a more rural location west of the urban Houston area. Quality-controlled versions of the UAS data were collected and posted on the DOE ARM data archive after the conclusion of the campaign where they are accessible by the research community and general public. The UAS perspective offers revolutionary insight into key spatial and temporal effects that have not been evaluated previously.

54 ENVIRONMENTAL SCIENCES↗