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DOE OSTI2013

Does enhanced regulation improve EIA report quality? Lessons from South Africa

Recently, various EIA systems have been subjected to system review processes with a view to improve performance. Many of these reviews resulted in some form of legislative reform. The South African Environmental Impact Assessment (EIA) regulations were modified in 2006 with the express intent to improve EIA effectiveness. In order to evaluate to what extent the desired outcome was achieved, the quality of EIA reports produced under the 2006 regulations was investigated for comparative analysis with the preceding regime. A sample of EIA reports from the two legislative regimes was reviewed using an adapted version of a well established method known colloquially as the 'Lee and Colley' review package. Despite some improvements in certain aspects, overall report quality has decreased slightly from the 1997 EIA regime. It therefore appears that the modifications to the regulations, often heralded as the solution to improvements in performance have not resulted in improved quality of EIA reports. - Highlights: Black-Right-Pointing-Pointer EIA regulations in South Africa were revised and became more comprehensive in 2006. Black-Right-Pointing-Pointer The report quality of a sample of EIAs was reviewed using the Lee and Colley review package. Black-Right-Pointing-Pointer Report quality showed a slight decline from the previous regulatory regime. Black-Right-Pointing-Pointer EIA good practice needs flexibility rather than over-detailed regulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A COMPARATIVE STUDY OF DENSITY FIELD ESTIMATION FOR GALAXIES: NEW INSIGHTS INTO THE EVOLUTION OF GALAXIES WITH ENVIRONMENT IN COSMOS OUT TO z ∼ 3

It is well-known that a galaxy’s environment has a fundamental influence in shaping its properties. We study the environmental effects on galaxy evolution, with an emphasis on the environment defined as the local number density of galaxies. The density field is estimated with different estimators (weighted adaptive kernel smoothing, 10th and 5th nearest neighbors, Voronoi and Delaunay tessellation) for a K{sub s} < 24 sample of ∼190,000 galaxies in the COSMOS field at 0.1 < z < 3.1. The performance of each estimator is evaluated with extensive simulations. We show that overall there is a good agreement between the estimated density fields using different methods over ∼2 dex in overdensity values. However, our simulations show that adaptive kernel and Voronoi tessellation outperform other methods. Using the Voronoi tessellation method, we assign surface densities to a mass complete sample of quiescent and star-forming galaxies out to z ∼ 3. We show that at a fixed stellar mass, the median color of quiescent galaxies does not depend on their host environment out to z ∼ 3. We find that the number and stellar mass density of massive (>10{sup 11} M{sub ⊙}) star-forming galaxies have not significantly changed since z ∼ 3, regardless of their environment. However, for massive quiescent systems at lower redshifts (z ≲ 1.3), we find a significant evolution in the number and stellar mass densities in denser environments compared to lower density regions. Our results suggest that the relation between stellar mass and local density is more fundamental than the color–density relation and that environment plays a significant role in quenching star-formation activity in galaxies at z ≲ 1.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗

DOE OSTI2025

PPI DataHub Project Data Package: S. elongatus PCC 7942 Limited Proteolysis and Thermal Proteome Profiling Structural Proteomics (JM-PB-DP3)

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques using limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and redox enrichment (Redox-MS) in evaluating alterations solvent accessibility and structural stability caused by light perturbation at the molecular level. Experimentally processed sample data for LiP and TPP proteomic datasets were derived from the same cell culture stock, prepared simultaneously in parallel, and acquired by mass spectrometry. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental samples processed for LiP-MS label-free quantification (LFQ) or TPP-MS tandem mass tag (TMT) 10-plex were acquired using a Q-Exactive HF-X mass spectrometer and processed/compiled using either MSGF+ (v2024.03.26) or PlexedPiper for proteome evaluation. Additional software supporting downstream proteomic analysis include FragPipe (v.4.0), MSFragger (v.22.1), and an adapted Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and processed protein annotated abundance files.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

An automated multiplexed turbidometric and data collection system for measuring growth kinetics of anaerobes dependent on gaseous substrates

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, here we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. Finally, the results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

CTP synthase forms the cytoophidium in human hepatocellular carcinoma

Highlights: • CTPS forms the cytoophidium in various human cancers and some non-cancerous tissues. • CTPS cytoophidia are observed in HCC tissues in 28% of patient samples, but not in adjacent non-cancerous hepatocytes. • HCC tissues with many CTPS cytoophidia exhibit significantly higher HSP90 level than samples expressing no CTPS cytoophidium. CTP synthase (CTPS) can aggregate into an intracellular macrostructure, the cytoophidium, in various organisms including human cells. Previous studies have shown that assembly of human CTPS cytoophidia may be correlated with the cellular metabolic status, and is able to promote the activity of CTPS. A correlation between the cytoophidium and cancer metabolism has been proposed but not yet been revealed. In the current study we provide clear evidence of the presence of CTPS cytoophidia in various human cancers and some non-cancerous tissues. Moreover, among 203 tissue samples of hepatocellular carcinoma, 56 (28%) samples exhibited many cytoophidia, whereas no cytoophidia were detected in adjacent non-cancerous hepatocytes for all samples. Our findings suggest that the CTPS cytoophidium may participate in the adaptive metabolism of human hepatocellular carcinoma.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Development of a scanning time of flight microscope and its application to the study of charge transport in phase separated structured organic semiconductors

We describe a tool for studying the two-dimensional spatial variation in electronic properties of organic semiconductors: the scanning time-of-flight microscope (STOFm). The STOFm simultaneously measures the transmittance of polarized light and time-of-flight current transients with a pixel size <30 μm, making it especially valuable for studies of the correlations of structure with charge generation and transport in liquid crystalline organic semiconductors (LC OSCs). Adapting a previously developed photopolymerization technique, we characterize the instrument using patterned samples of a LC OSC bounded by a non-semiconducting polymer matrix.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

THE STAR FORMATION HISTORY OF BCGs TO z = 1.8 FROM THE SpARCS/SWIRE SURVEY: EVIDENCE FOR SIGNIFICANT IN SITU STAR FORMATION AT HIGH REDSHIFT

We present the results of an MIPS-24 μm study of the brightest cluster galaxies (BCGs) of 535 high-redshift galaxy clusters. The clusters are drawn from the Spitzer Adaptation of the Red-Sequence Cluster Survey, which effectively provides a sample selected on total stellar mass, over 0.2 < z < 1.8 within the Spitzer Wide-Area Infrared Extragalactic (SWIRE) Survey fields. Twenty percent, or 106 clusters, have spectroscopically confirmed redshifts, and the rest have redshifts estimated from the color of their red sequence. A comparison with the public SWIRE images detects 125 individual BCGs at 24 μm ≳ 100 μJy, or 23%. The luminosity-limited detection rate of BCGs in similar richness clusters (N{sub gal} > 12) increases rapidly with redshift. Above z ∼ 1, an average of ∼20% of the sample have 24 μm inferred infrared luminosities of L{sub IR} > 10{sup 12} L{sub ⊙}, while the fraction below z ∼ 1 exhibiting such luminosities is <1%. The Spitzer-IRAC colors indicate the bulk of the 24 μm detected population is predominantly powered by star formation, with only 7/125 galaxies lying within the color region inhabited by active galactic nuclei (AGNs). Simple arguments limit the star formation activity to several hundred million years and this may therefore be indicative of the timescale for AGN feedback to halt the star formation. Below redshift z ∼ 1, there is not enough star formation to significantly contribute to the overall stellar mass of the BCG population, and therefore BCG growth is likely dominated by dry mergers. Above z ∼ 1, however, the inferred star formation would double the stellar mass of the BCGs and is comparable to the mass assembly predicted by simulations through dry mergers. We cannot yet constrain the process driving the star formation for the overall sample, though a single object studied in detail is consistent with a gas-rich merger.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Determination of the components of a radioactive source/shield system using the system’s radiation signature is of great importance in homeland security, material safeguards, and waste management. Although significant progress has been made toward solving this inverse transport problem in recent years, work remains to be done to quantify the uncertainty in reconstructed results. In this article we apply two Markov chain Monte Carlo (MCMC) approaches, the delayed rejection adaptive metropolis (DRAM) and differential evolution adaptive metropolis (DREAM) methods, to solve inverse problems and quantify uncertainty. The DRAM method uses delayed rejection combined with global adaptation of the proposal covariance matrix. Furthermore, the DREAM method hybridizes MCMC sampling with the differential evolution (DE) algorithm. In numerical test cases, the DRAM and DREAM methods are shown to be superior to a first-order inverse Hessian approach for problems with noisy data and multiple unknown quantities, with DREAM converging to the posterior distribution more quickly than DRAM. The DREAM and DRAM results indicate that a full posterior distribution is required to quantify uncertainty in many inverse transport problems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Carbon Data Assimilation with a Coupled Ensemble Kalman Filter (Final Technical Report)

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"

58 GEOSCIENCES↗

DOE OSTI2014

Sensing the wavefront of x-ray free-electron lasers using aerosol spheres

Characterizing intense, focused x-ray free electron laser (FEL) pulses is crucial for their use in diffractive imaging. We describe how the distribution of average phase tilts and intensities on hard x-ray pulses with peak intensities of 10 21 W/m 2 can be retrieved from an ensemble of diffraction patterns produced by 70 nm-radius polystyrene spheres, in a manner that mimics wave-front sensors. Besides showing that an adaptive geometric correction may be necessary for diffraction data from randomly injected sample sources, the paper demonstrates the possibility of collecting statistics on structured pulses using only the diffraction patterns they generate and highlights the imperative to study its impact on single-particle diffractive imaging.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

Microbial Diversity in the Deep Sandstones of a Carbon Sequestration Well

A lack of data on microbes in the deep subsurface hinders our ability to predict how these microbial communities will respond to carbon sequestration. Previous studies predicted how microbial communities may shift due to an influx of carbon dioxide (CO2) by 1) examining waters with naturally-occurring elevated CO2 concentration; and/or 2) via experiments involving exposure of laboratory microbial strains to elevated concentrations of CO2 relative to their native subsurface environment. To identify microbes naturally found in carbon sequestration target reservoirs, water samples were collected from three potential target reservoirs in the Powder River Basin, Wyoming which include the Hulett, Lakota, and Minnelusa sandstones. Water samples were collected at depths of 8,060 ft (Lakota), 8,330 ft (Hulett), 9,380 ft (Minnelusa), 9,463 ft (Minnelusa), and 9,544 ft (Minnelusa) for microbial DNA analysis as well as water chemistry and stable isotope analyses to look for any microbial trends that could be related to water chemistry alone. Identification of microbial communities present in the sampled water was determined via 16S rRNA sequencing; these data were compared to 16S rRNA isolated from drilling mud to distinguish native from any microbes introduced via drilling processes. Enrichment experiments reveal that the collected water samples contain active (e.g., growing) microbes and how the microbial community may adapt upon the introduction of CO2.

Wawrousek, K↗

DOE OSTI2022

Towards a neutron multiplicity measurement with the Accelerator Neutrino Neutron Interaction Experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26 ton Gadolinium (Gd)-loaded water Cherenkov detector located on the Booster Neutrino Beam line at Fermilab. Its main goals are the measurement of the neutron multiplicity in neutrino-nucleus interactions as well as the cross-section of Charged Current Quasi-Elastic (CCQE) neutrino interactions on water. Besides the physics goals, the experiment also aims to be a testbed for new technologies such as Large Area Picosecond Photodetectors (LAPPDs) and Water-based Liquid Scintillators (WbLS). This thesis presents a preliminary measurement of the neutron multiplicity with {ANNIE}, using an analysis conducted on a fraction of the 2021 beam year. As preparatory measures, the efficiency of ANNIE's Front Muon Veto (FMV) was determined to be {$\bar{\varepsilon}_{\mathrm{FMV}} = (95.6 \pm 1.6)\%$} while the average efficiency for active scintillator paddles in the Muon Range Detector (MRD) was found to be {$\bar{\varepsilon}_ {\mathrm{MRD}} = (92.1 \pm 7.9)\%$}. Furthermore, the simulation framework used for ANNIE was validated and adapted to reproduce the experimental data by comparing the detector response for samples of Michel electrons, Americium Beryllium neutrons, and through-going muons. The analysis finds average neutron yields of {$\bar{n}_{\mathrm{data}} (\mathrm{beam}) = (0.272 \pm 0.010_{\mathrm{stat}})$} for an inclusive set of all identified muon neutrino candidates and {$\bar{n}_{\mathrm{data}} (\mathrm{beam,FV}) = (0.287 \pm 0.044_{\mathrm{stat}})$ for interactions which happened inside of the Fiducial Volume of ANNIE, which was optimized to increase the neutron detection acceptance. The presented neutron multiplicity values represent the number of detected neutrons after all event selection cuts and are not yet corrected for the neutron detection efficiency. An equivalent analysis on a simulated beam sample predicts neutron yields of $\bar{n}_{\mathrm{MC}}(\mathrm{beam}) = (0.515 \pm 0.0 07_{\mathrm{stat}})$ and $\bar{n}_{\mathrm{MC}}(\mathrm{beam,FV}) = (0.627 \pm 0.031_{\mathrm{stat}})$, indicating that the models tend to overpredict the number of neutrons produced in such interactions. Systematic errors have been briefly considered to contribute {$\sigma_{\mathrm{sys,FMV}} \sim 0.01\,$neutrons/$\nu$-interaction} due to the slight FMV inefficiency and {$\sigma_{\mathrm{sys,n}} \sim 0.05\,$neutrons/$\nu$-interaction} due to the neutron detection efficiency. Simulation studies further highlighted the importance of neutron detection in Diffuse Supernova Background (DSNB) searches. A combination of neutron tagging and Convolutional Neural Networks was found to reduce the most relevant Neutral Current Quasi-Elastic (NCQE) interaction background below the signal level, achieving a Signal-to-Background ratio of 4:1. In a further study, we investigated the positive impact of a deployment of a WbLS target on the energy reconstruction in ANNIE. WbLS provides a scintillation signal from hadronic recoils in addition to the charged lepton that can be included in neutrino energy reconstruction. It was found that a deployed WbLS volume in ANNIE improves the neutrino energy reconstruction from 14\% to 12\%, with the potential of going beyond this if more sophisticated reconstruction algorithms are developed in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

SU-E-T-144: Bayesian Inference of Local Relapse Data Using a Poisson-Based Tumour Control Probability Model

Purpose: The purpose of this project is to develop a robust method of parameter estimation for a Poisson-based TCP model using Bayesian inference. Methods: Bayesian inference was performed using the PyMC3 probabilistic programming framework written in Python. A Poisson-based TCP regression model that accounts for clonogen proliferation was fit to observed rates of local relapse as a function of equivalent dose in 2 Gy fractions for a population of 623 stage-I non-small-cell lung cancer patients. The Slice Markov Chain Monte Carlo sampling algorithm was used to sample the posterior distributions, and was initiated using the maximum of the posterior distributions found by optimization. The calculation of TCP with each sample step required integration over the free parameter α, which was performed using an adaptive 24-point Gauss-Legendre quadrature. Convergence was verified via inspection of the trace plot and posterior distribution for each of the fit parameters, as well as with comparisons of the most probable parameter values with their respective maximum likelihood estimates. Results: Posterior distributions for α, the standard deviation of α (σ), the average tumour cell-doubling time (Td), and the repopulation delay time (Tk), were generated assuming α/β = 10 Gy, and a fixed clonogen density of 10{sup 7} cm−{sup 3}. Posterior predictive plots generated from samples from these posterior distributions are in excellent agreement with the observed rates of local relapse used in the Bayesian inference. The most probable values of the model parameters also agree well with maximum likelihood estimates. Conclusion: A robust method of performing Bayesian inference of TCP data using a complex TCP model has been established.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Learning to Correct Climate Projection Biases

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.

58 GEOSCIENCES↗