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DOE OSTI2012

The role of trace gas flux networks in biogeosciences

Vast networks of meteorological sensors ring the globe, providing continuous measurements of an array of atmospheric state variables such as temperature, humidity, rainfall, and the concentration of carbon dioxide [New etal., 1999; Tans etal., 1996]. These measurements provide input to weather and climate models and are key to detecting trends in climate, greenhouse gases, and air pollution. Yet to understand how and why these atmospheric state variables vary in time and space, biogeoscientists need to know where, when, and at what rates important gases are flowing between the land and the atmosphere. Tracking trace gas fluxes provides information on plant or microbial metabolism and climate-ecosystem interactions. The existence of trace gas flux networks is a relatively new phenomenon, dating back to research in 1984. The first gas flux measurement networks were regional in scope and were designed to track pollutant gases such as sulfur dioxide, ozone, nitric acid, and nitrogen dioxide. Atmospheric observations and model simulations were used to infer the depositional rates of these hazardous chemicals [Fowler etal., 2009; Meyers etal., 1991]. In the late 1990s, two additional trace gas flux measurement networks emerged. One, the United States Trace Gas Network (TRAGNET), was a short-lived effort that measured trace gas emissions from the soil and plants with chambers distributed throughout the country [Ojima etal., 2000]. The other, FLUXNET, was an international endeavor that brought many regional networks together to measure the fluxes of carbon dioxide, water vapor, and sensible heat exchange with the eddy covariance technique [Baldocchi etal., 2001]. FLUXNET, which remains active today, currently includes more than 400 tower sites, dispersed across most of the world's climatic zones and biomes, with sites in North and South America, Europe, Asia, Africa, and Australia. More recently, several specialized networks have emerged, including networks dedicated to urban areas (Urban Fluxnet), nitrogen compounds in Europe (NitroEurope), and methane (MethaneNet). Technical Aspects of Flux Networks Eddy covariance flux measurements are the preferred method by which biogeoscientists measure trace gas exchange between ecosystems and the atmosphere [Baldocchi, 2003].

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Saharan Dust Transport Predictability Utilizing a Subseasonal Experiment (SubX) Model

Prediction of Saharan dust customarily requires complex aerosols models and observations. A previous study of the Miami, Florida dust record in conjunction to reanalysis data discovered a possible source of subseasonal predictability using a dust-transport-efficiency (DTE) index. Development of the Subseasonal Forecast Experiment (SubX) has expanded global forecast products; producing multi-model ensemble forecasts out to 45 days. Retrospective forecast data from the Community Climate System Model version 4.0 (CCSM4) is used in direct comparison to National Centers for Environmental Prediction (NCEP) reanalysis to evaluate the CCSM4 subseasonal forecast and DTE index prediction skill of weekly dust variability. Successful prediction of weekly dust transport using the DTE index is variable year-to-year. The DTE most successfully predicts dust when there is high variability in the tropical winds, likely due to a fluctuating subtropical high, and is not dependent on the overall mean flow or total dust mass transported. The CCSM4 SubX retrospective forecast well represents North Atlantic meteorology out to week-3 in both mean flow and variability. Dust transport can be predicted using the DTE index and CCSM4 SubX retrospective forecasts at week-1 leads to the same success as contemporaneous NCEP reanalysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Annual sea level variations in the Red Sea observed using GNSS

SUMMARY Annual sea level variations in the Red Sea have amplitudes of 15–20 cm as observed using various techniques such as tide gauges, satellite altimetry and recently Gravity Recovery and Climate Experiment (GRACE) satellite data. In this study, we demonstrate that Global Navigation Satellite System (GNSS) observations can also be used to measure the effect of these sea level variations. The extra water mass presses on the seafloor, which causes horizontal and vertical deformations. Using time-series from 10 coastal GNSS stations, we observe annual horizontal and vertical loading displacements with amplitudes of 2–5 mm. When correcting for atmospheric, hydrological and surface water loading and a residual geocentre motion, significant annual signals of approximately 0.5 and 2 mm are still observed for the horizontal and vertical components, respectively. In the northern Red Sea, the observed annual signals and predicted annual sea level loading show good agreement. This confirms that the signal is mostly a result of the variations in water mass and thermal expansion. Furthermore, we conclude that the uncertainties in the hydrological model over Ethiopia and Eritrea influence the loading over the southern Red Sea, which was underestimated in previous studies using GRACE data.

Alothman, A. O.↗

DOE OSTI2017

Hydrologic and water quality impacts of biofuel feedstock production in the Ohio River Basin

Abstract This study addresses the uncertainties related to potential changes in land use and management and associated impacts on hydrology and water quality resulting from increased production of biofuel from the conventional and cellulosic feedstock. The Soil Water Assessment Tool ( SWAT ) was used to assess the impacts on regional and field scale evapotranspiration, soil moisture content, stream flow, sediment, and nutrient loadings in the Ohio River Basin. The model incorporates spatially and temporally detailed hydrologic, climate and agricultural practice data that are pertinent to simulate biofuel feedstock production, watershed hydrology and water quality. Three future biofuel production scenarios in the region were considered, including a feedstock projection from the DOE Billion‐Ton (BT2) Study, a change in corn rotations to continuous corn, and harvest of 50% corn stover. The impacts were evaluated on the basis of relative changes in hydrology and water quality from historical baseline and future business‐as‐usual conditions of the basin. The overall impact on water quality is an order of magnitude higher than the impact on hydrology. For all the three future scenarios, the sub‐basin results indicated an overall increase in annual evapotranspiration of up to 6%, a decrease in runoff up to 10% and minimal change in soil moisture. The sediment and phosphorous loading at both regional and field levels increased considerably (up to 40–90%) for all the biofuel feedstock scenario considered, while the nitrogen loading increased up to 45% in some regions under the BT2 Study scenario, decreased up to 10% when corn are grown continuously instead of in rotations, and changed minimally when 50% of the stover are harvested. Field level analyses revealed significant variability in hydrology and water quality impacts that can further be used to identify suitable locations for the feedstock productions without causing major impacts on water quantity and quality.

09 BIOMASS FUELS↗

DOE OSTI2021

Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects

Decadal climate variability and change affects nearly every aspect of our world, including weather, agriculture, ecosystems, and the economy. Predicting its expression is thus of critical importance on multiple fronts. Power et al. review what is known about tropical Pacific decadal climate variability and change, the degree to which it can be simulated and predicted, and how we might improve our understanding of it. Here, more accurate projections will require longer and more detailed instrumental and paleoclimate records, improved climate models, and better data assimilation methods. —HJS.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Interannual variation of the surface temperature of tropical forests from satellite observations

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability of cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗

DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

DOE OSTI2014

Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. And for a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. An adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Unveiling Current Guanaco Distribution in Chile Based upon Niche Structure of Phylogeographic Lineages: Andean Puna to Subpolar Forests

Niche description and differentiation at broad geographic scales have been recent major topics in ecology and evolution. Describing the environmental niche structure of sister taxa with known evolutionary trajectories stands out as a useful exercise in understanding niche requirements. Here we model the environmental niche structure and distribution of the recently resolved phylogeography of guanaco (Lama guanicoe) lineages on the western slope of the southern Andes. Using a maximum entropy framework, field data, and information on climate, topography, human density, and vegetation cover, we identify differences between the two subspecies (L.g.cacsilensis, L.g.guanicoe) and their intermediate-hybrid lineage, that most likely determine the distribution of this species. While aridity seems to be a major factor influencing the distribution at the species-level (annual precipitation ,900 mm), we also document important differences in niche specificity for each subspecies, where distribution of Northern lineage is explained mainly by elevation (mean = 3,413 m) and precipitation seasonality (mean = 161 mm), hybrid lineage by annual precipitation (mean = 139 mm), and Southern subspecies by annual precipitation (mean = 553 mm), precipitation seasonality (mean = 21 mm) and grass cover (mean = 8.2%). Among lineages, we detected low levels of niche overlap: I (Similarity Index) = 0.06 and D (Schoener’s Similarity Index) = 0.01; and higher levels when comparing Northern and Southern subspecies with hybrids lineage (I = 0.32-0.10 and D = 0.12-0.03, respectively). This suggests that important ecological and/or evolutionary processes are shaping the niche of guanacos in Chile, producing discrepancies when comparing range distribution at the species-level (81,756 km 2 ) with lineages-level (65,321 km 2 ). The subspecies-specific description of niche structure is provided here based upon detailed spatial distribution of the lineages of guanacos in Chile. Such description provides a scientific tool to further develop large scale plans for habitat conservation and preservation of intraspecific genetic variability for this far ranging South American camelid, which inhabits a diversity of ecoregion types from Andean puna to subpolar forests.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Y-12 Site Sustainability Plan

The accomplishments to date and the long-range planning of the Y-12 Energy Management and Sustainability and Stewardship programs support the U.S. Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) vision for a commitment to energy effi ciency and sustainability and to achievement of the Guiding Principles. Specifi cally, the Y-12 vision is to support the Environment, Safety and Health Policy and the DOE Strategic Sustainability Performance Plan (SSPP), while promoting overall sustainability and reduction of greenhouse gas (GHG) emissions. The mission of the Y-12 Energy Management program is to incorporate energy-efficient technologies site-wide and to position Y-12 to meet NNSA energy requirement needs through 2025 and beyond. This plan addresses: Greenhouse Gas Reduction and Comprehensive Greenhouse Gas Inventory; Buildings, ESPC Initiative Schedule, and Regional and Local Planning; Fleet Management; Water Use Efficiency and Management; Pollution Prevention and Waste Reduction; Sustainable Acquisition; Electronic Stewardship and Data Centers; Renewable Energy; Climate Change; and Budget and Funding.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2013

IDEA Clean Energy Application Center

The DOE Clean Energy Application Centers were launched with a goal of focusing on important aspects of our nation’s energy supply including Efficiency, Reliability and Resiliency. Clean Energy solutions based on Combined Heat & Power (CHP), District Energy and Waste Heat Recovery are at the core of ensuring a reliable and efficient energy infrastructure for campuses, communities, and industry and public enterprises across the country. IDEA members which include colleges and universities, hospitals, airports, downtown utilities as well as manufacturers, suppliers and service providers have long-standing expertise in the planning, design, construction and operations of Clean Energy systems. They represent an established base of successful projects and systems at scale and serve important and critical energy loads. They also offer experience, lessons learned and best practices which are of immense value to the sustained growth of the Clean Energy sector. IDEA has been able to leverage the funds from the project award to raise the visibility, improve the understanding and increase deployment CHP, District Energy and Waste Heat Recovery solutions across the regions of our nation, in collaboration with the regional CEAC’s. On August 30, 2012, President Obama signed an Executive Order to accelerate investments in industrial energy efficiency (EE), including CHP and set a national goal of 40 GW of new CHP installation over the next decade IDEA is pleased to have been able to support this Executive Order in a variety of ways including raising awareness of the goal through educational workshops and Conferences and recognizing the installation of large scale CHP and district energy systems. A supporting key area of collaboration has involved IDEA providing technical assistance on District Energy/CHP project screenings and feasibility to the CEAC’s for multi building, multi-use projects. The award was instrumental in the development of a first-order screening/feasibility tool for these types of community energy projects. The Excel based tool incorporates hourly climate based building loads data to arrive at the composite energy demand for the district and compares the Net Present Value (NPV) of the costs of CHP/DE alternatives. This tool has been used to provide assistance to several projects in the Northeast, Mid-Atlantic, Intermountain and Pacific Regions. The tool was disseminated to the CEACs and supplemented by a Training Webinar and a How to Guide IDEA produced a US Community Energy Development Guide to support mayors, planners, community leaders, real estate developers and economic development officials who are interested in planning more sustainable urban energy infrastructure, creating community energy master plans and implementing CHP/ District Energy systems in cities, communities and towns. IDEA has collected industry data and provided a comprehensive data set containing information on District Energy installations in the US. District energy systems are present in 49 states and theDistrict of Columbia. Of the 597 systems 55% were DE alone while the remainder was some combination of CHP, district heating, and district cooling. District energy systems that do not currently involve electric generation are strong near-term candidates for the adoption of CHP due to the magnitude of their aggregated thermal load. This data has helped inform specific and targeted initiatives including technical assistance provided by the CEAC’s for EPA’s Boiler MACT Compliance by large District Heating System boilers. These outcomes have been greatly enabled by the close coordination and collaboration with DOE CEAC leadership and with the eight regional US DOE Clean Energy Application Centers and the award’s incremental funding has allowed IDEA to leverage our resources to be an effective champion for Clean Energy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Standard Diagnostics for the Diurnal Cycle

The purpose of this document is to suggest a strategy for constructing standard diagnostics of the diurnal cycle from both climate model and observational data products. The guiding philosophy is “keep it simple,” in the hope that a diagnostic software package can be readily constructed and widely used. Of course this means that the output of the package will form only the beginning of necessary examination of the diurnal cycle in models and in the real world. The latest version of the Coupled Model Intercomparison Project, CMIP5, provides several fields at 3- hourly time resolution near the surface: air and surface temperatures, pressure, humidity, soil moisture, horizontal wind, energy flux components, overhead cloudiness, evaporation, precipitation, convective precipitation, and snowfall. (See the “3hr” tab in the Standard Output spreadsheet at http://cmip-pcmdi.llnl.gov/cmip5/data_description.html.) Meanwhile satellite observations provide at least equally fine time resolution and global coverage for some of these fields. This data makes possible an extensive study of the diurnal cycle near the surface.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Standard Diagnostics for the Diurnal Cycle of Precipitation

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Los Alamos National Laboratory Fall Avian Migration Monitoring Report 2010-2018

During the fall of 2018, biologists at Los Alamos National Laboratory completed the ninth year of monitoring fall migration passerines (songbirds). Songbirds were captured at a mist-netting station located in a wetland/riparian complex in Technical Area 36 on the north side of Pajarito Road in Los Alamos County. Captured birds were identified, measured, and banded with a United States Fish and Wildlife Service (USFWS) migratory bird band. Banding operations took place between August and October, with the completion of a total of 10 mist-netting sessions annually. This project was conducted as part of the implementation of the Biological Resources Management Plan and is in compliance with the 2013 Memorandum of Understanding between the USFWS and the United States Department of Energy/National Nuclear Security Administration and Executive Order 13186. Between 2010 and 2018 the overall number of birds and species captured was variable. We saw significant differences in the bird community composition when we compared earlier years to later years. Although environmental variables including a drought severity index, were not shown to influence the community composition, insectivorous birds and the percentage of hatch year birds were influenced by drought severity. Additionally, Audubon’s Warbler and Virginia’s Warbler numbers decreased from 2010 and showed declining trends in their abundances. The variability in bird populations is likely driven by regional climatic factors, but more data are needed for robust assessments.

54 ENVIRONMENTAL SCIENCES↗