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At least 307 records · Page 17

Infrared limb-darkening effects for the earth-atmosphere system

An infrared radiative transfer model has been developed for evaluating anisotropic functions in the longwave region (5-50 microns) due to limb-darkening effects in the earth's atmosphere. An accurate narrow-band model of absorption has been used for computing transmission functions of the atmosphere. Absorption due to all major and minor atmospheric constituents has been taken into account including the continuum absorption due to water vapor. Anisotropic functions have been calculated for several latitudinal and seasonal climatological-average model atmospheres. The effects of the variability of various meteorological parameters, e.g. surface temperature, surface relative humidity, and cloud-top height have been examined. It has been found that the variability of cloud parameters has the largest effect on the infrared anisotropic functions.

Gupta, S. K.↗

Reanalysis Activities at the NASA Global Modeling and Assimilation Office

This talk presents an overview of recent reanalysis activities at the NASA Global Modeling and Assimilation Office (GMAO) as part of a multi-faceted strategy towards an Integrated Earth System retrospective analysis, coupling components of the atmosphere, ocean, chemistry, land, and ice. While elements of the atmosphere-ocean coupled Goddard Earth Observing System (GEOS) model and data assimilation are being actively developed, a suite of reanalysis products is designed to provide further understanding of key aspects of Earth system coupling in a reanalysis context: The baseline atmospheric reanalysis, the GEOS Retrospective analysis for the early 21st Century (GEOS-R21C), features recent advances in the GEOS model and data assimilation, and targets the NASA’s Earth Observing System EOS and post-EOS satellite observations; GEOS-IT, a user-tailored low-resolution atmospheric reanalysis, serves as a second baseline to the NASA Instrument Teams for validation and calibration and drives a one-way coupled ocean reanalysis, GEOSIT-Ocean; PolarMERRA, a high-resolution downscaled product for the polar regions, focuses on improving the representation of polar atmospheric processes with an assessment of current cryospheric biases, and targeted improvements to surface sea ice and glacier conditions; Finally, R21C-Chem, an off-line atmospheric chemistry and composition reanalysis, includes both tropospheric and stratospheric trace gases. The diversity of these reanalysis activities presents unique opportunities for collaborations cross-teams/institutions, with new commercial data partners, and with end-user groups. This talk will discuss these opportunities and explore leveraging the lessons learned along the way on key drivers in Earth system interactions as we converge towards the next generation of the Modern-Era Retrospective analysis for Research and Applications (MERRA) suite.

Amal El Akkraoui↗

Development of Land‐River Two‐Way Hydrologic Coupling for Floodplain Inundation in the Energy Exascale Earth System Model

Abstract Floodplain inundation links river and land systems through significant water, sediment, and nutrient exchanges. However, these two‐way interactions between land and river are currently missing in most Earth System Models. In this study, we introduced the two‐way hydrological coupling between the land component, E3SM Land Model, and the river component, Model for Scale Adaptive River Transport, in Energy Exascale Earth System Model (E3SM) to study the impacts of floodplain inundation on land and river processes. We calibrated the river channel geometry and developed a new data‐driven inundation scheme to improve the simulation of inundation dynamics in E3SM. The new inundation scheme captures 96% of the spatial variation of inundation area in a satellite inundation product at global scale, in contrast with 7% when the default inundation scheme of E3SM was used. Global simulations including the new inundation scheme performed at resolution with and without land‐river two‐way coupling was used to quantify the impact of coupling. Comparisons show that two‐way coupling modifies the water and energy cycle in 20% of the global land cells. Specifically, riverine inundation is reduced by two‐way coupling, but inland inundation is intensified. Wetter periods are more impacted by the two‐way coupling at the global scale, while regions with different climates exhibit different sensitivities. The two‐way exchange of water between the land and river components of E3SM provides the foundation for enabling two‐way coupling of land‐river sediment and biogeochemical fluxes. These capabilities will be used to improve understanding of the interactions between water and biogeochemical cycles and their response to human perturbations.

54 ENVIRONMENTAL SCIENCES↗

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗

Developing a Model-based Capability to Analyze Requirements for the Climate Observing System

Models are foundational for estimating states of the earth's climate system, both as tools to extrapolate information in time and space, and as observation 'operators' used to relate what is analyzed and predicted to what is observed. Expanding the simulation approach further, observing system simulation experiments (OSSEs) are designed to mimic the complete process of analyzing the climate state by replacing real observations with entirely simulated ones determined from a model-based depiction of nature. OSSEs provide a framework to 'fly' simulated satellite instruments through a synthetic atmosphere and investigate the trade-spaces of measurements for various satellite configurations and sampling strategies, and assess their measurement impact on modeling and forecasting capabilities. Such a tool is a crucial but as yet unfulfilled need for future mission selection and design. The components of a state-of-the-art OSSE system are being assembled at the Global Modeling and Assimilation Office (GMAO, Code 610.1) at NASA/GSFC, leveraging on the GMAO's existing modeling and data assimilation infrastructure for numerical weather prediction (NWP). The OSSE framework is based on the GMAO's Goddard Earth Observing System atmospheric general circulation model, version 5 (GEOS-5) and the Gridpoint Statistical Interpolation (GSI) observational analysis scheme, combined with the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) model developed by the Atmospheric Chemistry and Dynamics Branch (Code 613.3). This system is an evolving, key component of Goddard's planned development of an Integrated Earth System Analysis (IESA) capability, which will bring together into a single, fully interactive system Goddard's modeling and assimilation efforts in atmosphere, ocean and chemistry and aerosols to provide a comprehensive analysis and prediction system for weather and climate In addition to providing a state-of-the-art capability for assimilating current observation types, GEOS-5, and the future IESA, provide the capability to identify the need for, and assess the potential impact of, future observing systems under consideration for improving weather and climate prediction.

Gelaro, Ronald↗

Near-Real Time Ocean-Atmosphere Skin Temperature as Part of NASA GMAO Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI).

SST↗

Synergistic and Collaborative Development Strategies for FV3 Powered Next Generation Unified Global Modeling System

The GFDL (Geophysical Fluid Dynamics Laboratory, NOAA - National Oceanic and Atmospheric Administration) Finite-­Volume Cubed-Sphere Dynamical Core (FV3) is a scalable and flexible dynamical core capable of both hydrostatic and non-hydrostatic atmospheric simulations. FV3 has been chosen as the dynamical core for the Next Generation Global Prediction System project (NGGPS), designed to upgrade the current NCEP (National Centers for Environmental Prediction, NOAA) operational Global Forecast System (GFS) to run as a unified, fully-coupled system in NOAA's Environmental Modeling System infrastructure. FV3 dynamic core has a long history of serving as the main engine for global atmospheric models at various Government and Academic Research Laboratories including NOAA: GFDL Climate Modeling Suite (AM4 (Atmosphere Modeling 4), CM4 (Climate Model 4) ESM4 (Earth System Model 4), Hiram (HIgh Resolution Atmospheric Model)); NASA: GMAO (Global Modeling and Assimilation Office) Goddard Earth Observing System Model (GEOS); and NCAR (National Center for Atmospheric Research) Community Earth System Model (CESM). The three primary stakeholders in FV3 (GFDL, GMAO, EMC (Environmental Modeling Center - NOAA)) have embarked on synergistic and collaborative strategies with focus on the advancement of non-hydrostratic dynamic core, physics, chemistry, and data assimilation efforts, leveraging collective strengths of each of the partnering agencies. This talk presents the ongoing plans for model advancements at GFDL, GMAO and EMC, with special emphasis on contributions towards developing community based unified global modeling system for operational and research applications at respective organizations. Future plans include extending the collaborations to the development of earth system components including GMAO's aerosol chemistry models (GOCART/MAM (Goddard Chemistry Aerosol Radiation and Transport / Modal Aerosol Model)), Land Information System (LIS), and advanced data assimilation techniques; and GFDL's Modular Ocean Model (MOM) and Sea Ice Simulator (SIS) for transition to operations at NCEP.

Putman, William↗

Full Implementation of Matrix Approach to Biogeochemistry Module of CLM5

Abstract Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented a matrix approach to the Community Land Model version 5 (CLM5) to represent carbon and nitrogen cycles. Specifically, we reorganized 18 balance equations each for carbon and nitrogen cycles among the 18 vegetation pools in the original CLM5 into two matrix equations. Similarly, 140 balance equations each for carbon and nitrogen cycles among the 140 soil pools were reorganized into two additional matrix equations. The vegetation carbon and nitrogen matrix equations are connected to soil matrix equations via litterfall. The matrix equations fully reproduce simulations of carbon and nitrogen dynamics by the original model. The computational cost for forwarding simulation of the CLM5 matrix model was 26% more expensive than the original model, largely due to calculation of additional diagnostic variables, but the spin‐up computational cost was significantly saved. We showed a case study on modeled soil carbon storage under two forcing data sets to illustrate the diagnostic capability that the matrix approach uniquely offers to understand simulation results of global carbon and nitrogen dynamics. The successful implementation of the matrix approach to CLM5, one of the most complex land models, demonstrates that most, if not all, the biogeochemical models can be reorganized into the matrix form to gain high modularity, effective diagnostics, and accelerated spin‐up.

58 GEOSCIENCES↗

Effects of Rapid Permafrost Thaw on CO2 and CH4 Fluxes in a Warmer and Wetter Future (Final Technical Report)

When ice-rich permafrost thaws, the ground subsides, creating thermokarst landscapes with dramatically different soil conditions and carbon fluxes than the original ecosystem. Roughly 20% of the northern permafrost region is susceptible to thermokarst formation (Olefeldt et al., 2016). Thermokarst formation is often rapid; tens of meters of permafrost can thaw within a few years (Schuur et al., 2015). In topographically low areas, thermokarst thaw converts boreal forest or tundra dry shrub ecosystems into sedge or Sphagnum moss wetlands (Olefeldt et al., 2016). While this type of landscape transformation releases carbon stored in permafrost into the atmosphere, on longer time scales, it facilitates sequestration of atmospheric carbon in plant biomass because permafrost thaw releases plant-available nutrients and wetlands are highly productive (M. C. Jones et al., 2017). However, wetlands also generate methane, which is a potent greenhouse gas. Methane emissions from thermokarst wetlands can cause these carbon-sequestering systems to have a positive global warming potential (Johansson et al., 2006; Turetsky et al., 2007). Our project objective was to improve Earth System and environmental predictability by advancing understanding of how CO 2 and CH 4 flux in permafrost thaw-induced wetlands (thermokarst) will change in the future as temperatures and climate conditions shift. Northern latitudes are expected to get warmer and wetter (IPCC 2013), and initiation and expansion of thermokarst thaw is expected to increase (Jorgenson et al. 2006; Zhang et al. 2017). Given these expected changes, our work sought to address three broad questions: Q1) How will northern latitude CO2 and CH4 emissions respond to warming temperatures? Q2) What is the impact of precipitation on permafrost thaw and carbon emissions? Q3) How do CO2 and CH4 emissions change as wetlands age after permafrost thaw? To answer these questions, we took both a modeling and measurement approach. Modeling work was conducted with DOE’s Energy Exascale Earth System Model (E3SM) land model (ELM). Empirical work took place in two primary locations. The first was a thermokarst site near Fairbanks, AK. The site is part of the Bonanza Creek Long Term Ecological Research program and is well instrumented (Neumann et al., 2019). The second was an isolated thawing permafrost wetland located on Kenai Peninsula — Brown’s Lake bog (B. M. Jones et al., 2016) — where the current climate is representative of what is expected at higher latitudes in the future. This site provides an ideal opportunity to test our hypotheses about how thermokarst wetland dynamics will respond to future environmental conditions. In addition, the project collaborated with researchers asking similar questions who were collecting measurements in high latitude post-glacial lakes located in collaborators tackling similar questions in Sweden (Stordalen Mire) (Emerson et al., 2021). Project efforts directly aligned with the stated goal of the funding opportunity announcement (FOA), which was “to improve the understanding and representation of terrestrial ecosystems in ways that advance Earth system model parameterizations and capabilities... thereby improving the quality of Earth and environmental model projections and providing the scientific foundation needed to support DOE’s science and energy missions.” Specifically, the project improved sophistication and accuracy of the Energy Exascale Earth System Model (E3SM), which is being developed primarily at DOE National Laboratories to support scientific research and decision-making.

54 ENVIRONMENTAL SCIENCES↗

Atmospheric river representation in the Energy Exascale Earth System Model (E3SM) version 1.0

Abstract. The Energy Exascale Earth System Model (E3SM) project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project developed by the US Department of Energy (DOE). With an emphasis on supporting the DOE's energy mission, understanding and quantifying how well the model simulates water cycle processes is of particular importance. Here, we evaluate E3SM version 1.0 (v1.0) for its ability to represent atmospheric rivers (ARs), which play significant roles in water vapor transport and precipitation. The characteristics and precipitation associated with global ARs in E3SM at standard resolution (1∘ × 1∘) are compared to the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). Global patterns of AR frequencies in E3SM show high degrees of correlation (≥0.97) with MERRA2 and low mean absolute errors (MAEs; <1 %) annually, seasonally, and across different ensemble members. However, some large-scale condition biases exist, leading to AR biases – most significant of which are the double intertropical convergence zone (ITCZ), a stronger and/or equatorward-shifted subtropical jet during boreal and austral winters, and enhanced Northern Hemisphere westerlies during summer. By comparing atmosphere-only and fully coupled simulations, we attribute the sources of the biases to the atmospheric component or to a coupling response. Using relationships revealed in Dong et al. (2021), we provide evidence showing the stronger North Pacific jet in winter and the enhanced Northern Hemisphere westerlies during summer, associated with E3SM's double ITCZ and related weaker Atlantic meridional overturning circulation (AMOC), respectively, which are significant sources of the AR biases found in the coupled simulations.

58 GEOSCIENCES↗

Our changing planet: The FY 1993 US global change research program. A supplement to the US President's fiscal year 1993 budget

An improved predictive understanding of the integrated Earth system, including human interactions, will provide direct benefits by anticipating and planning for possible impacts on commerce, agriculture, energy, resource utilization, human safety, and environmental quality. The central goal of the U.S. Global Change Research Program (USGCRP) is to help establish the scientific understanding and the basis for national and international policymaking related to natural and human-induced changes in the global Earth system. This will be accomplished through: (1) establishing an integrated, comprehensive, long-term program of documenting the Earth system on a global scale; (2) conducting a program of focused studies to improve our understanding of the physical, geological, chemical, biological, and social processes that influence the Earth system processes; and (3) developing integrated conceptual and predictive Earth system models.

Source record↗

Global changes in biogeochemical cycles in response to human activities

The main objective of our research was to characterize biogeochemical cycles at continental and global scales in both terrestrial and aquatic ecosystems. This characterization applied to both natural ecosystems and those disturbed by human activity. The primary elements of interest were carbon and nitrogen and the analysis sought to quantify standing stocks and dynamic cycling processes. The translocation of major nutrients from the terrestrial landscape to the atmosphere (via trace gases) and to fluvial systems (via leaching, erosional losses, and point source pollution) were of particular importance to this study. Our aim was to develop the first generation of Earth System Models. Our research was organized around the construction and testing of component biogeochemical models which treated terrestrial ecosystem processes, aquatic nutrient transport through drainage basins, and trace gas exchanges at the continental and global scale. A suite of three complementary models were defined within this construct. The models were organized to operate at a 1/2 degree latitude by longitude level of spatial resolution and to execute at a monthly time step. This discretization afforded us the opportunity to understand the dynamics of the biosphere down to subregional scales, while simultaneously placing these dynamics into a global context.

Moore, Berrien, III↗

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗

Solar dynamic heat receiver thermal characteristics in low earth orbit

A simplified system model is under development for evaluating the thermal characteristics and thermal performance of a solar dynamic spacecraft energy system's heat receiver. Results based on baseline orbit, power system configuration, and operational conditions, are generated for three basic receiver concepts and three concentrator surface slope errors. Receiver thermal characteristics and thermal behavior in LEO conditions are presented. The configuration in which heat is directly transferred to the working fluid is noted to generate the best system and thermal characteristics. as well as the lowest performance degradation with increasing slope error.

Wu, Y. C.↗

Advanced Information Systems Technology (AIST) Overview & Current and Recent Projects

"NASA's Advanced Information Systems Technology (AIST) Program identifies, develops, and supports adoption of software and information systems, as well as novel computer science technologies expected to be needed by the Earth Science Division in the 5-10-year timeframe. Overall, the AIST Program is focusing on advanced technologies and innovative concepts with three main objectives: O1. Enable new observation measurements and new observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing; O2. Enable agile science analyses that fully utilize the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments, and that interact seamlessly with relevant observing systems; O3. Enable the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. This thrust will provide technology for enabling near- and long-term science and policy decisions (""""science decisions"""" including planning for the acquisition of new measurements; the development of new models or science analysis; the integration of Earth observations in novel ways; applications to inform choices, support decisions, and guide actions for societal benefit; etc.). AIST objectives aim at optimizing Earth Science mission return – NOS from an observation point of view and ACF/ESDT from an information analysis and utilization point of view. The assets and data accessed and utilized in these systems may come from NASA and non-NASA sources. "

Earth Science Remote Sensing; Information Systems↗

NASA's New Wildland Fire Earth Observation Science & Applications Programmatic Developments

In 2021, the U.S. National Aeronautics & Space Administration (NASA) initiated new programmatic elements within the Science Mission Directorate (SMD) and the Aeronautics Research Mission Directorate (ARMD) focused on supporting wildland fire science and applications improvements, employing the vast array of NASA scientific knowledge, airborne and space-borne Earth Observations (EO) capabilities, technology development (sensor systems, etc.), and large framework modeling efforts. Within the Science Mission Directorate, the NASA Earth Science Division (ESD) will focus on improving our understanding of wildland fire through EO tools and applying rigorous-tested modeling and results of that research into operational use. The ESD Wildfire strategy is to invest in new technology and to better integrate NASA’s satellite, airborne, and ground-based observations with wildfire models to provide the wildfire stakeholders with the information they need to make informed decisions about the pre-, active-, and post-fire conditions. The Applied Science Program has restarted the Wildland Fire Applications Program with a focus on engaging wildland fire management and the fire science community in transitioning EO science efforts into routine use by land management entities at the local, state, national and international level. The NASA Aeronautics Research Mission Directorate will focus on arenas where their aeronautics science and engineering outcomes can benefit the fire management community as well, specifically in the innovative development of Uncrewed Aircraft systems, congested mixed-use platform airspace management issues, new platform configurations supporting wildland fire missions, and other aeronautics-related science / engineering capabilities which may benefit the fire management community. In total, these developments represent a major thrust forward, supporting the goals of utilizing NASA science to benefit humankind. This presentation will highlight the various wildland fire science focus areas identified through collaborations with the wildland fire science and management community and highlight the plans of this new NASA focus area.

NASA↗

Porting the WAVEWATCH III (v6.07) wave action source terms to GPU

Abstract. Surface gravity waves play a critical role in several processes, including mixing, coastal inundation, and surface fluxes. Despite the growing literature on the importance of ocean surface waves, wind–wave processes have traditionally been excluded from Earth system models (ESMs) due to the high computational costs of running spectral wave models. The development of the Next Generation Ocean Model for the DOE’s (Department of Energy) E3SM (Energy Exascale Earth System Model) Project partly focuses on the inclusion of a wave model, WAVEWATCH III (WW3), into E3SM. WW3, which was originally developed for operational wave forecasting, needs to be computationally less expensive before it can be integrated into ESMs. To accomplish this, we take advantage of heterogeneous architectures at DOE leadership computing facilities and the increasing computing power of general-purpose graphics processing units (GPUs). This paper identifies the wave action source terms, W3SRCEMD, as the most computationally intensive module in WW3 and then accelerates them via GPU. Our experiments on two computing platforms, Kodiak (P100 GPU and Intel(R) Xeon(R) central processing unit, CPU, E5-2695 v4) and Summit (V100 GPU and IBM POWER9 CPU) show respective average speedups of 2× and 4× when mapping one Message Passing Interface (MPI) per GPU. An average speedup of 1.4× was achieved using all 42 CPU cores and 6 GPUs on a Summit node (with 7 MPI ranks per GPU). However, the GPU speedup over the 42 CPU cores remains relatively unchanged (∼ 1.3×) even when using 4 MPI ranks per GPU (24 ranks in total) and 3 MPI ranks per GPU (18 ranks in total). This corresponds to a 35 %–40 % decrease in both simulation time and usage of resources. Due to too many local scalars and arrays in the W3SRCEMD subroutine and the huge WW3 memory requirement, GPU performance is currently limited by the data transfer bandwidth between the CPU and the GPU. Ideally, OpenACC routine directives could be used to further improve performance. However, W3SRCEMD would require significant code refactoring to make this possible. We also discuss how the trade-off between the occupancy, register, and latency affects the GPU performance of WW3.

58 GEOSCIENCES↗

Development of the earth-moon system with implications for the geology of the early earth

Established facts regarding the basic features of the earth and the moon are reviewed, and some important problems involving the moon are discussed (extent of melting, time of crustal differentiation and nature of bombardment, bulk chemical composition, and nature and source of mare basins), with attention given to the various existing theories concerning these problems. Models of the development of the earth-moon system from the solar nebula are examined, with particular attention focused on those that use the concept of capture with disintegration. Impact processes in the early crust of the earth are briefly considered, with attention paid to Green's (1972) suggestion that Archaean greenstone belts may be the terrestrial equivalent of lunar maria.

Smith, J. V.↗