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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Measurement of the Higgs boson mass in the H → ZZ*→ 4ℓ decay channel using 139 fb –1 of s = 13 TeV pp collisions recorded by the ATLAS detector at the LHC

The mass of the Higgs boson is measured in the H→ZZ * →4ℓ decay channel. The analysis uses proton–proton collision data from the Large Hadron Collider at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector between 2015 and 2018, corresponding to an integrated luminosity of 139 fb –1 . The measured value of the Higgs boson mass is 124.99 ± 0.18(stat.) ± 0.04(syst.) GeV. In final states with muons, this measurement benefits from an improved momentum-scale calibration relative to that adopted in previous publications. The measurement also employs an analytic model that takes into account the invariant-mass resolution of the four-lepton system on a per-event basis and the output of a deep neural network discriminating signal from background events. This measurement is combined with the corresponding measurement using 7 and 8 TeV pp collision data, resulting in a Higgs boson mass of 124.94 ± 0.17(stat.) ± 0.03(syst.) GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Room-Temperature Ferroelectric Epitaxial Nanowire Arrays with Photoluminescence

The development of large-scale, high-quality ferroelectric semiconductor nanowire arrays with interesting light-emitting properties can address limitations in traditional wide-bandgap ferroelectrics, thus serving as building blocks for innovative device architectures and next-generation high-density optoelectronics. Here, we investigate the optical properties of ferroelectric CsGeX 3 (X = Br, I) halide perovskite nanowires that are epitaxially grown on muscovite mica substrates by vapor phase deposition. Detailed structural characterizations reveal an incommensurate heteroepitaxial relationship with the mica substrate. Furthermore, photoluminescence that can be tuned from yellow-green to red emissions by varying the halide composition demonstrates that these nanowire networks can serve as platforms for future optoelectronic applications. In addition, the room-temperature ferroelectricity and ferroelectric domain structures of these nanowires are characterized using second harmonic generation (SHG) polarimetry. Finally, the combination of room-temperature ferroelectricity with photoluminescence in these nanowire arrays unlocks new avenues for the design of novel multifunctional materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Collapse of magnetized white dwarfs as site of heavy-element formation and kilonova signal

We present the first end-to-end calculation connecting the accretion-induced collapse (AIC) of a magnetized, rapidly rotating white dwarf to observable kilonova signatures, combining two-dimensional (2D) general-relativistic neutrino-magnetohydrodynamic simulations, followed by radiation hydrodynamics with in-situ nuclear network and 2D Monte Carlo radiative transfer with spatially resolved heating rates. Unlike all previous unmagnetized AIC models – which predicted proton-rich, $^{56}$Ni-dominated ejecta – strong magnetic fields eject ${\approx }\, 0.2\, \mathrm{ M}_\odot$ of neutron-rich material ($\langle Y_e \rangle \sim 0.24$) on dynamical time-scales, before neutrino irradiation can raise the electron fraction, enabling strong r-process nucleosynthesis up to and beyond the third peak. The resulting kilonova is lanthanide-rich ($X_{\rm lan} \approx 8~{{\ \rm per\ cent}}$) and dominated by near-infrared emission. We compute synthetic light curves in the Large Synoptic Survey Telescope and J ames Webb Space Telescope bands and find striking agreement, without parameter tuning, between the observations of AT 2023vfi/GRB 230307A and our broadband light curves for polar viewing angles. These results establish magnetized AIC as a viable channel for heavy r-process element production and a compelling progenitor candidate for long-duration gamma-ray bursts with kilonova signatures.

MHD↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

Nimbus 7 SMMR Derived Seasonal Variations in the Water Vapor, Liquid Water and Surface Winds over the Global Oceans

Monthly mean distributions of water vapor and liquid water contained in a vertical column of the atmosphere and the surface wind speed were derived from Nimbus Scanning Multichannel Microwave Radiometer (SMMR) observations over the global oceans for the period November 1978 to November 1979. The remote sensing techniques used to estimate these parameters from SMMR are presented to reveal the limitations, accuracies, and applicability of the satellite-derived information for climate studies. On a time scale of the order of a month, the distribution of atmospheric water vapor over the oceans is controlled by the sea surface temperature and the large scale atmospheric circulation. The monthly mean distribution of liquid water content in the atmosphere over the oceans closely reflects the precipitation patterns associated with the convectively and baroclinically active regions. Together with the remotely sensed surface wind speed that is causing the sea surface stress, the data collected reveal the manner in which the ocean-atmosphere system is operating. Prominent differences in the water vapor patterns from one year to the next, or from month to month, are associated with anomalies in the wind and geopotential height fields. In association with such circulation anomalies the precipitation patterns deduced from the meteorological network over adjacent continents also reveal anomalous distributions.

Prabhakara, C.↗
NASA NTRS2000

Infrastructure for Training and Partnershipes: California Water and Coastal Ocean Resources

The purpose of this project was to advance the existing ICESS/Bren School computing infrastructure to allow scientists, students, and research trainees the opportunity to interact with environmental data and simulations in near-real time. Improvements made with the funding from this project have helped to strengthen the research efforts within both units, fostered graduate research training, and helped fortify partnerships with government and industry. With this funding, we were able to expand our computational environment in which computer resources, software, and data sets are shared by ICESS/Bren School faculty researchers in all areas of Earth system science. All of the graduate and undergraduate students associated with the Donald Bren School of Environmental Science and Management and the Institute for Computational Earth System Science have benefited from the infrastructure upgrades accomplished by this project. Additionally, the upgrades fostered a significant number of research projects (attached is a list of the projects that benefited from the upgrades). As originally proposed, funding for this project provided the following infrastructure upgrades: 1) a modem file management system capable of interoperating UNIX and NT file systems that can scale to 6.7 TB, 2) a Qualstar 40-slot tape library with two AIT tape drives and Legato Networker backup/archive software, 3) previously unavailable import/export capability for data sets on Zip, Jaz, DAT, 8mm, CD, and DLT media in addition to a 622Mb/s Internet 2 connection, 4) network switches capable of 100 Mbps to 128 desktop workstations, 5) Portable Batch System (PBS) computational task scheduler, and vi) two Compaq/Digital Alpha XP1000 compute servers each with 1.5 GB of RAM along with an SGI Origin 2000 (purchased partially using funds from this project along with funding from various other sources) to be used for very large computations, as required for simulation of mesoscale meteorology or climate.

Siegel, David A.↗
NASA NTRS2005

Weak-signal Phase Calibration Strategies for Large DSN Arrays

The NASA Deep Space Network (DSN) is studying arrays of large numbers of small, mass-produced radio antennas as a cost-effective way to increase downlink sensitivity and data rates for future missions. An important issue for the operation of large arrays is the accuracy with which signals from hundreds of small antennas can be combined. This is particularly true at Ka band (32 GHz) where atmospheric phase variations can be large and rapidly changing. A number of algorithms exist to correct the phases of signals from individual antennas in the case where a spacecraft signal provides a useful signal-to-noise ratio (SNR) on time scales shorter than the atmospheric coherence time. However, for very weak spacecraft signals it will be necessary to rely on background natural radio sources to maintain array phasing. Very weak signals could result from a spacecraft emergency or by design, such as direct-to-Earth data transmissions from distant planetary atmospheric or surface probes using only low gain antennas. This paper considers the parameter space where external real-time phase calibration will be necessary, and what this requires in terms of array configuration and signal processing. The inherent limitations of this technique are also discussed.

interferometry↗
NASA NTRS2017

The South/Southeast Asia Research Initiative (SARI) Update and Meeting Objectives

Land Use/Cover Change (LU/CC) is one of the most important types of environmental change in South and Southeast Asian countries. Several studies suggest that LU/CC in these countries is in large part driven by population growth and economic development. In the region, changes that are most common include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. To address the research needs and priorities in the region, a regional initiative entitled South Southeast Asia Regional Initiative (SARI) has been developed involving US and regional scientists. The initiative is funded by NASA Land Cover, Land Use Change program. The goal of SARI is to integrate state-of-the-art remote sensing, natural sciences, engineering and social sciences to enrich LU/CC science in South Southeast Asian countries. In the presentation, LU/CC change research in SARI countries will be highlighted including the drivers of change. For example, in South Asia, forest cover has been increasing in countries like India, Nepal and Bhutan due to sustainable afforestation measures; whereas, large-scale deforestation in Southeast Asian countries is still continuing, due to oil palm plantation expansion driven by the international market demand in Malaysia and Indonesia. With respect to urbanization, South and Southeast Asian countries contain 23 megacities, each with more than 10 million people. Rapid urbanization is driving agricultural land loss and agricultural intensification has been increasing due to less availability of land for growing food crops such as in India, Vietnam, and Thailand. The drivers of LUCC vary widely in the region and include such factors as land tenure, local economic development, government policies, inappropriate land management, land speculation, improved road networks, etc. In addition, variability in the weather, climate, and socioeconomic factors also drive LU/CC resulting in disruptions of biogeochemical cycles, radiation and the surface energy balance of the atmosphere. The presentation will also highlight SARI collaborative activities with space agencies, universities and non-government organizations including data sharing mechanisms in the region.

Research↗
DOE OSTI2024

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗
DOE OSTI2020

Chapter 14: Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation

In this chapter we demonstrate how supervised deep learning techniques can be used to construct models for the filtered progress variable source term necessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling strategy was devised to ensure that a uniform representation of all the states observed in the filtered DNS data are equally present in the training dataset. A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.

combustion LES models↗
DOE OSTI2023

Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets

Recent advances in modeling large-scale, complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex systems can require significant computational resources. Similarly, acquiring experimental datasets can prove difficult. For these systems, often computationally inexpensive, but in general inaccurate models, known as the low-fidelity models, are available. Here in this paper, we propose a bi-fidelity modeling approach for complex physical systems, where we model the discrepancy between the true system's response and a low-fidelity response in the presence of a small training dataset from the true system's response using a deep operator network, a neural network architecture suitable for approximating nonlinear operators. We apply the approach to systems that have parametric uncertainty and are partially unknown. Three numerical examples are used to show the efficacy of the proposed approach to model uncertain and partially unknown physical systems.

MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Maximum strength and dislocation patterning in multi–principal element alloys

Multi–principal element alloys (MPEAs) containing three or more components in high concentrations render a tunable chemical short-range order (SRO). Leveraging large-scale atomistic simulations, we probe the limit of Hall-Petch strengthening and deformation mechanisms in a model CrCoNi alloy and unravel chemical ordering effects. The presence of SRO appreciably increases the maximum strength and lowers the propensity for faulting and structure transformation, accompanied by intensification of planar slip and strain localization. Deformation grains exhibit notably different microstructures and dislocation patterns that prominently depend on their crystallographic orientation and the number of active slip planes. Grain of single-planar slip attains the highest volume fraction of deformation-induced structure transformation, and grain with double-slip planes develops the densest dislocation network. These results advancing the fundamental understanding of deformation mechanisms and dislocation patterning in MPEAs suggest a mechanistic strategy for tuning mechanical behavior through simultaneously tailoring grain texture and local chemical order.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Crystal ball gazing

Over the last seven years, the CPU on my desk has increased speed by two orders of magnitude, from around 1 MIP to more than 100 MIPS; more important is that it is about as fast as any uniprocessor of any type available for any price, for compute bound problems. Memory on the system is also about 100 times as big, while disk is only about 10 times as big. Local network and I/O performance have increased greatly, though not quite at the same rate as processor speed. More important, I will argue, is that the CPU's address space is 64 bits, rather than 32 bits, allowing us to rethink some time honored presumptions. The Internet has gone from a few hundred machines to a million, and now have grown to span the entire globe, and wide area networks have now becoming commercial services. 'PC's' are now real computers, bringing what was top of the line computing capability to the masses only a few years behind the leading edge. So even a year or two from now, we can anticipate commonplace desktop machines running at speeds hundreds of MIPS, with main memories in the hundreds of megabytes to a gigabyte, able to draw millions of vectors/second, and all capable of some reasonable 3D graphics. And only a few years later, this will be the $1500 PC. So the 1990's certainly brings: 64 bit processors becoming standard; BIP/BFLOP class uniprocessors; large scale multiprocessors for special purpose applications; I/O as the most significant computer engineering problem; Hierarchical data servers in everyday use; routine access to archived data around the world; and what else? What do systems such as those we will have this decade imply to those building data analysis systems today? Many of the presumptions of the 1970's and 1980's need to be reexamined in the light of 1990's technology.

Gettys, Jim↗
NASA NTRS2001

A Model for the Breakup of Comet Linear (C/1999 S4)

We propose a mechanism based on the rubble-pile hypothesis of the cometary nucleus (Weissman 1986) to explain the catastrophic breakup of comet LINEAR (C/1999 S4) observed during July-August 2000. We suggest that a solid nucleus made up of 10-100 m "cometesimals" (Weidenschilling 1997) contains a network of inter-connected voids in the inter-cometesimal regions. The production of super-volatile (i.e., species more volatile than water) gases into these voids occurs due to the thermal wave propagating through the nucleus and associated phase transitions of water ice. The network of voids provides an efficient pathway for rapid propagation of these gases within the nucleus resulting in gas pressure caused stresses over a wide regime of the nucleus. This provides a mechanism for catastrophic breakups of small cometary nuclei such as comet LINEAR (C/1999 S4) as well as for some observed cometary outbursts including those that occur at large heliocentric distances (e.g., West et al. 1991). We emphasize the importance of techniques such as radar reflection tomography and radiowave transmission tomography (e.g., Kofman et al. 1998) aboard cometary missions to determine the three dimensional structure of the nucleus in particular the extent of large scale voids.

Samarasinha, Nalin H.↗
DOE OSTI2020

Insights of Molecular Mechanism of Xylem Development in Five Black Poplar Cultivars

Black poplar (Populus deltoides, P. nigra, and their hybrids) is the main poplar cultivars in China. It offers interesting options of large-scale biomass production for bioenergy due to its rapid growth and high yield. Poplar wood properties were associated with chemical components and physical structures during wood formation. In this study, five poplar cultivars, P. euramericana ‘Zhonglin46’ (Pe1), P. euramericana ‘Guariento’ (Pe2), P. nigra ‘N179’ (Pn1), P. deltoides ‘Danhong’ (Pd1), and P. deltoides ‘Nanyang’ (Pd2), were used to explore the molecular mechanism of xylem development. We analyzed the structural differences of developing xylem in the five cultivars and profiled the transcriptome-wide gene expression patterns through RNA sequencing. The cross sections of the developing xylem showed that the cell wall thickness of developed fiber in Pd1 was thickest and the number of xylem vessels of Pn1 was the least. A total of 10,331 differentially expressed genes were identified among 10 pairwise comparisons of the five cultivars, most of them were related to programmed cell death and secondary cell wall thickening. K-means cluster analysis and Gene Ontology enrichment analysis showed that the genes highly expressed in Pd1 were related to nucleotide decomposition, metabolic process, transferase, and microtubule cytoskeleton; whereas the genes highly expressed in Pn1 were involved in cell wall macromolecule decomposition and polysaccharide binding processes. Based on a weighted gene co-expression network analysis, a large number of candidate regulators for xylem development were identified. And their potential regulatory roles to cell wall biosynthesis genes were validated by a transient overexpression system. This study provides a set of promising candidate regulators for genetic engineering to improve feedstock and enhance biofuel conversion in the bioenergy crop Populus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Abstract. Recent advances in deep convolutional neural network (CNN)-based super resolution can be used to downscale atmospheric chemistry simulations with substantially higher accuracy than conventional downscaling methods. This work both demonstrates the downscaling capabilities of modern CNN-based single image super resolution and video super-resolution schemes and develops modifications to these schemes to ensure they are appropriate for use with physical science data. The CNN-based video super-resolution schemes in particular incur only 39 % to 54 % of the grid-cell-level error of interpolation schemes and generate outputs with extremely realistic small-scale variability based on multiple perceptual quality metrics while performing a large (8×10) increase in resolution in the spatial dimensions. Methods are introduced to strictly enforce physical conservation laws within CNNs, perform large and asymmetric resolution changes between common model grid resolutions, account for non-uniform grid-cell areas, super-resolve lognormally distributed datasets, and leverage additional inputs such as high-resolution climatologies and model state variables. High-resolution chemistry simulations are critical for modeling regional air quality and for understanding future climate, and CNN-based downscaling has the potential to generate these high-resolution simulations and ensembles at a fraction of the computational cost.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Drought Relief and Reversal Over North America from 1500 to 2016

Season-to-season persistence of soil moisture drought varies across North America. Such inter-seasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool (December-April) and warm (May-July) season precipitation series and offers new insights on the intra-annual changes in drought for up to 2,000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and three-month long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the 20th century and are used to estimate relief and reversals for the most skillfully reconstructed past 500-years. Sub-continental to continental scale reversals of cool season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the Mid-Atlantic region and the US Southwest. Drought relief at the continental scale may arise in part from macroscale ocean-atmosphere processes, while the smaller scale regional reversals may reflect land-surface feedbacks and stochastic variability.

Drought↗