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DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗

DOE OSTI2020

NMDQi Thermal-hydraulic Optimization, Analysis, and Scoping Tool (TOAST)

The Thermal-hydraulic Optimization, Analysis, and Scoping Tool (TOAST) is developed to support the thermal-hydraulic aspects for the design of optimal irradiation vehicles for the Nuclear Materials Discovery and Qualification initiative (NMDQi). TOAST is currently a MATLAB-based tool that utilizes a simplified steady-state analytical model where a radial thermal resistance circuit is utilized to account for conduction through up to 2 capsules, a gas gap, a thermal bond, a sample cladding, and a sample, as well as the convection from a water coolant outside the capsules. The geometry is discretized axially along the user-defined height of a basket with a user-defined number of points/nodes, essentially turning TOAST into a semi-2D heat transfer analysis utility for cylindrical irradiation vehicles with practically any configuration, solving for temperatures radially at multiple axial locations. TOAST utilizes a Graphical User Interface (GUI) in-which a user can define the geometrical layout, the materials utilized for each component, the coolant characteristics, and the required solution. The user can define the geometry by inputting the diameters, thicknesses, and heights for each component, whereas the materials are defined via the user-provided constant or variable thermal conductivities. The user can select the coolant's inlet temperature, pressure, and the pressure drop across the height of the problem (which is used to calculate the velocity of the flow). Finally, the user can choose to solve for a sample heat generation rate limit by inputting temperature limits for the sample and capsules or can choose to purely solve for the axial temperature distributions of each component by inputting a pre-selected heat generation rate. Either way, TOAST provides the user with axial temperature distributions of all the components including the coolant, and the heat generation rate limit as well as the maximum outlet coolant temperature. The user can also choose to do one of 6 sensitivity analyses in TOAST. The sensitivity analyses yield plots of the sensitivity of the heat generation rate limit, the maximum coolant temperature, and the pressure limit for the annular components due to perturbations in 1-2 unknown variables based on the selected sensitivity analysis. Benchmarks between computations in TOAST and equivalent 2D axis symmetric ABAQUS models are presented, showing that TOAST results are within less than 3% of ABAQUS results in most cases, and a maximum of 8% difference in some cases. The benchmarks also revealed that this uncertainty is tied to the selection of an appropriate Nusselt number correlation and appropriate thermal conductivities, which the user can do from the GUI. Regardless, TOAST is demonstrated as a computationally efficient, highly accessible, and accurate utility for optimization and scoping s of studies different irradiation vehicles.

42 ENGINEERING↗

DOE OSTI2019

Optimizing Long-term Monitoring of Radiation Air Dose Rates - 19169

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (NPP) have been steadily decreasing over the past seven years since the accident in March 2011. After seven years, the radiation monitoring program is expected to transition from short-term emergency responses to long-term monitoring. The objectives of long-term monitoring are often different from early emergency responses, since extensive data accumulation has led to an understanding of contaminant distributions and mobility. The main long-term monitoring objectives are (1) to confirm system stability and the continuing reduction of contaminant and hazard levels, (2) to provide assurance for the public, (3) to accumulate the basic datasets for scientific knowledge and future preparation, and (4) to detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the sampling locations of radiation air dose-rate monitoring. Three steps are taken in order to determine sampling locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations across the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air dose rates across the domain. In the second step, we use a Gaussian mixture model to identify the representative locations in multiple environmental variables, such as elevation and land-cover types. In the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air dose rates across the domain. We use the integrated dose-rate map from the Bayesian geostatistical method as the reference map and distribute the sampling in such a way as to capture the heterogeneity of the reference map. Although we assume hypothetical sets of measurement locations to evaluate the performance of the methodology and algorithms, we have demonstrated this approach using real, spatially extensive datasets, including the integrated air-dose-rate map of 2016. Our results have shown that this approach allows us to add sampling locations in a systematic manner. Increasing the number of sampling locations can better capture the heterogeneity of dose rates, although the estimation error does not decrease further after a certain number of samples. We have also found that when there are restrictions such as pre-existing monitoring locations or the ones along roads, the spatial estimation becomes poor even with the same number of monitoring locations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

The role of radio loudness in Eigenvector 1 and the Baldwin Effect of [O iii ] λ5007

ABSTRACT The radio emission is an important observable of quasars, but its relationship to Eigenvector 1 and the [O iii] λ5007 Baldwin Effect is not fully understood. We explore these issues based on a sample of 1800 quasars taken from the Sloan Digital Sky Survey Data Release 7. We employ a new approach of selecting subsamples in the plane of fundamental physical parameters of the black hole mass and Eddington ratio, so as to reduce variables and complexity in analyses. Based on these subsamples, we investigate the relationship between radio loudness R and Eigenvector 1 and find that radio loudness is correlated with [O iii] λ5007 emission, but has no clear relationship with optical Fe ii emission, which indicates that the radio power is probably not a driver of Eigenvector 1 but merely a secondary process. In addition, we also investigate the impact of radio loudness on the Baldwin Effect of [O iii] λ5007. We find that when the radio loudness is not strong (log R < 2), the Baldwin Effect of [O iii] λ5007 is clear, while in the samples of extreme radio loudness (log R ≥ 2), the Baldwin Effect of [O iii] λ5007 becomes weaker or even disappears. We suggest that both radio loudness and luminosity influence the relative strength of [O iii].

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Megadrought: A Series of Unfortunate La Niña Events?

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving-blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño-Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land-surface processes. We indeed find that megadroughts in this simulation have an amplitude-duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Shifts in Carbon Emissions Versus Sequestration From Hydropower Reservoirs in the Southeastern United States

Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. Here we compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (CO 2 ) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (CH 4 ) diffusion or CH 4 ebullition dominated. CH 4 diffusion rates were positively associated with water temperature. However, we found no clear predictors of CH 4 ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m -2 day -1 . For CO 2 diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted CO 2 in summer 2012, but every station sequestered CO 2 in summer 2022. Here, indicators of greater algal production were associated with CO 2 sequestration, including surface chlorophyll- a concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of CO 2 diffusive fluxes, which shifted to positive CO 2 fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing CO 2 sequestration in field and modeling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability are vital to reducing uncertainties in global upscaling efforts.

13 HYDRO ENERGY↗

DOE OSTI2019

Signatures of Biologically Driven Hemicellulose Modification Quantified by Analytical Pyrolysis Coupled with Multidimensional Gas Chromatography Mass Spectrometry

Biomass storage operation is one major source of feedstock quality variability that impacts on downstream preprocessing, feeding, handling and conversion into biofuels, chemicals and products. In field Ccorn stover biomass bales undergoes degradation and cell wall components modification resulting from microbial biological heating occurring during storage. A molecular characterization of the modification was achieved to unveil and understand the changes of structural cell wall modification in corn stover samples by conducting low temperature (400°C) pyrolysis, comparing samples that were mildly biologicallyun heated to those that had been subjected to microbial severe biological heating (~60°C). Pyrolysis of the samples originating from biomass that had been heated during storage generated small oxygenates such as furfural, 5-methylfurfural and 2-(5H)-furanone with efficiencies that were as much as ten times greater than those measured for the mildly unheated samples. Most of the compounds having enhanced efficiencies were C5 oxygenates, suggesting that the pyrolysis products may be formed from hemicellulosic precursor polymers in the corn stover. The findings suggest that microbial heating may be disrupting the cell wall structure, fragmenting the hemicellulose or cellulose chains, generating more polymer termini that have higher efficiency for generating the oxygenates at lower temperatures. Pyrolysis variability with temperature may be a beneficial strategy for improved biomass cell wall characterization, and for guiding control strategies for enhanced control over product distributions , providing insights to understand and manage the feedstock variability as well as to inform harvest and storage best management practices.

09 BIOMASS FUELS↗

DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Background Concentrations of Argon-39 in Shallow Soil Gas

While radioisotopes of noble gases are known to be indicators of underground nuclear explosions (UNE), McIntyre et al. (2017) was the first to report the presence of 39Ar in shallow soil gas in association with a decades old UNE. While this finding hinted at the potential application of 39Ar to be used as an indicator of an UNE, doing so would also require an understanding of the natural concentrations of 39Ar present in soil gas. Without knowing the expected range and variability of naturally occurring concentrations of 39Ar, it is difficult to determine what measured concentrations would be indicative of an elevated concentration. This paper presents results from 16 soil gas samples and three atmospheric air samples collected from various locations across the western United States. Shallow soil gas samples were collected into self-contained underwater breathing apparatus (SCUBA) tanks using a custom-built soil gas sampling system and then processed and analyzed for 39Ar. The measured concentrations of 39Ar varied from atmospheric air concentrations to about 3.5 times atmospheric air concentrations. The variation in concentration was primarily attributed to the latitude the sample was collected at, which was consistent with previous work (Johnson et al. 2015). The results presented here represent the first measurements of natural background 39Ar concentrations in shallow soil gas. This data will be necessary if 39Ar is to be used as an indicator of UNE.

Fritz, Brad G.↗

DOE OSTI2015

Low temperature magneto-dielectric measurements on BiFeO{sub 3} lightly substituted by cobalt

Dielectric and magnetodielectric measurements are done on BiFe{sub 1−x}Co{sub x}O{sub 3}: x = 0, 0.01, and 0.02 in the temperature range 70–300 K and up to magnetic field 1.3 T. The dielectric data are well described by Haverliak–Negami expression plus an additional term for the Maxwell Wagner (MW) type relaxations, whose contribution is dominant near room temperature. The parameters obtained from the fitting of data using the above mentioned expression, suggest slowing down of relaxation and approach towards ideal Debye type relaxations, as the temperature is lowered. The dielectric relaxations obey polaronic variable range hopping model with distinct activation energies (E{sub a}) in the extrinsic (6.67T{sup 3/4 }meV) and intrinsic (2.88T{sup 3/4 }meV) regions for the parent sample (x = 0), and thus a distinct transition from extrinsic to intrinsic behavior is seen at 215 K while lowering the temperature. This distinct transition is missing for Co substituted samples probably due to the extrinsic region values of E{sub a} (3.42T{sup 3/4 }meV and 2.42T{sup 3/4 }meV for x = 0.01 and 0.02, respectively) comparable to that of the intrinsic region (see x = 0). The magnetodielectric measurement shows positive magnetodielectricity (MD) in the intrinsic region (T < 215 K for x = 0) and negative MD in the extrinsic region (T > 215 K for x = 0). The extrinsic region is found to be dominated by MW and magnetoresistance effects, whereas MD in intrinsic regions is due to the spin reorientation transitions. The Co substitution is found to increase the extrinsic and non-Debye contributions to dielectricity, which becomes so large that no spin reorientation transitions are seen in x = 0.02 sample. The pyroelectric active region in x = 0 is found to be dominated by the diffusive behavior having contribution of the form ω{sup −0.5}.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Eulerian finite element simulations of the drop weight impact test with a dislocation Density-based continuum model

During a drop weight impact test, the kinetic energy of the falling weight is transferred into the sample resting on the anvil. The plastic deformation in the sample is an important mechanism for the dissipation of the input kinetic energy. We use Eulerian finite element analysis to simulate the deformation and temperature evolution in a 1,3,5-trinitro-1,3,5-triazine (RDX) sample consisting of multiple crystals. In Eulerian finite element simulations, the mesh moves relative to the material. After every change of position between the mesh and the material, the state variables are interpolated to the new mesh position, i.e., advection. In an effort to reduce the advection errors, we use a rate form of a dislocation density-based continuum model by Luscher et al. Here, the simulations predict localization of plastic deformation, and plastic dissipation as a significant source of heat generation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Slow slip source characterized by lithological and geometric heterogeneity

Slow slip events (SSEs) accommodate a significant proportion of tectonic plate motion at subduction zones, yet little is known about the faults that actually host them. The shallow depth (<2 km) of well-documented SSEs at the Hikurangi subduction zone offshore New Zealand offers a unique opportunity to link geophysical imaging of the subduction zone with direct access to incoming material that represents the megathrust fault rocks hosting slow slip. Two recent International Ocean Discovery Program Expeditions sampled this incoming material before it is entrained immediately down-dip along the shallow plate interface. Drilling results, tied to regional seismic reflection images, reveal heterogeneous lithologies with highly variable physical properties entering the SSE source region. These observations suggest that SSEs and associated slow earthquake phenomena are promoted by lithological, mechanical, and frictional heterogeneity within the fault zone, enhanced by geometric complexity associated with subduction of rough crust.

58 GEOSCIENCES↗

DOE OSTI2020

Multidecadal shifts in forest plant diversity and community composition across glacial landforms in northern lower Michigan, USA

Understanding how plant community assemblage is affected by spatial and temporal patterns is crucial to understanding forest ecosystem responses to disturbance, including future climate change. In this article, we tracked how diversity and composition are distributed through space and time in a midsuccessional mixed hardwood forest in northern lower Michigan, United States. This region’s geographically and abiotically distinct glacial landforms influence both the spatial and temporal dynamics of its forest communities. Vegetation sampling plots (n = 87) were established at the University of Michigan Biological Station in 1990 and resampled in 2015. Vegetation in the overstory, sapling, and groundcover layers was censused. Abiotic variables, including elevation, pH, and soil nutrients, were measured in a subset of plots (n = 40). There were strong differences in diversity and community composition among glacial landforms, with the moraine having a 31% greater species richness in the groundcover layer compared with those of the other glacial landforms. Surprisingly, plant communities across all three vegetation layers showed little change over the 25-year period, and we found no evidence of differences in successional rates among glacial landforms. Our findings indicate that glacial landforms have a large influence on the production and maintenance of local plant diversity and community composition in this area and suggest that successional dynamics may manifest themselves over much longer time periods in these northern biomes.

Forestry↗

DOE OSTI2022

Nutrient and carbon concentrations in dated soil cores at US-OWC Ameriflux wetland site (OWC NERR)

Soil cores were sampled at the Old Woman Creek (OWC) National Estuarine Research Reserve at the south shore of Lake Erie, near Huron, Ohio, USA. OWC is a temperate mineral soil marsh. We dated the soil cores using lead isotope analysis and measured the concentrations of carbon and nutrients (nitrogen, phosphorus) throughout the core depths. We analyzed 36 cores, sampled along three transects at areas of the wetland with different hydrological regimes. Each transect included three coring sites at different water depth categories (shallow, intermediate deep) with four core samples per coring site. The data can be used to determine the carbon sequestration rates and nutrient accumulation rates, at multiple locations throughout OWC wetland.Dataset, in csv format, with data variables in columns, and different cores and core depths slices in rows, includes results from 36 sediment cores (0-30 cm depth) taken at 9 locations (4 replicates at each location) along 3 gradients of water depth (shallow, intermediate, deep), each at a different hydrologic location (outflow, backflow, middle). At each depth slice within a core, we provide depth, date (using 210Pb), bulk density, and the concentrations of carbon, nitrogen, phosphorus, d13C, and d15N.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

LOW-MASS ACTIVE GALACTIC NUCLEI WITH RAPID X-RAY VARIABILITY

We present a detailed study of the optical spectroscopic properties of 12 active galactic nuclei (AGNs) with candidate low-mass black holes (BHs) selected by Kamizasa et al. through rapid X-ray variability. The high-quality, echellette Magellan spectra reveal broad Hα emission in all the sources, allowing us to estimate robust virial BH masses and Eddington ratios for this unique sample. We confirm that the sample contains low-mass BHs accreting at high rates: the median M{sub BH} = 1.2 × 10{sup 6} M{sub ⊙} and median L{sub bol}/L{sub Edd} = 0.44. The sample follows the M{sub BH}–σ{sub *} relation, within the considerable scatter typical of pseudobulges, the probable hosts of these low-mass AGNs. Various lines of evidence suggest that ongoing star formation is prevalent in these systems. We propose a new strategy to estimate star formation rates in AGNs hosted by low-mass, low-metallicity galaxies, based on modification of an existing method using the strength of [O ii] λ3727, [O iii] λ5007, and X-rays.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Enhanced climate reproducibility testing with false discovery rate correction

Simulating the Earth's climate is an important and complex problem, thus climate models are similarly complex, comprised of millions of lines of code. In order to appropriately utilize the latest computational and software infrastructure advancements in Earth system models running on modern hybrid computing architectures to improve their performance, precision, accuracy, or all three; it is important to ensure that model simulations are repeatable and robust. This introduces the need for establishing statistical or non-bit-for-bit reproducibility, since bit-for-bit reproducibility may not always be achievable. Here, we propose a short-simulation ensemble-based test for an atmosphere model to evaluate the null hypothesis that modified model results are statistically equivalent to that of the original model. We implement this test in version 2 of the US Department of Energy's Energy Exascale Earth System Model (E3SM). The test evaluates a standard set of output variables across the two simulation ensembles and uses a false discovery rate correction to account for multiple testing. The false positive rates of the test are examined using re-sampling techniques on large simulation ensembles and are found to be lower than the currently implemented bootstrapping-based testing approach in E3SM. We also evaluate the statistical power of the test using perturbed simulation ensemble suites, each with a progressively larger magnitude of change to a tuning parameter. The new test is generally found to exhibit more statistical power than the current approach, being able to detect smaller changes in parameter values with higher confidence.

Kelleher, Michael E. [Oak Ridge National Laborator↗

DOE OSTI2020

CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica

Abstract. The ice sheet model intercomparison project for CMIP6 (ISMIP6) effort brings together the ice sheet and climate modeling communities to gain understanding of the ice sheet contribution to sea level rise. ISMIP6 conducts stand-alone ice sheet experiments that use space- and time-varying forcing derived from atmosphere–ocean coupled global climate models (AOGCMs) to reflect plausible trajectories for climate projections. The goal of this study is to recommend a subset of CMIP5 AOGCMs (three core and three targeted) to produce forcing for ISMIP6 stand-alone ice sheet simulations, based on (i) their representation of current climate near Antarctica and Greenland relative to observations and (ii) their ability to sample a diversity of projected atmosphere and ocean changes over the 21st century. The selection is performed separately for Greenland and Antarctica. Model evaluation over the historical period focuses on variables used to generate ice sheet forcing. For stage (i), we combine metrics of atmosphere and surface ocean state (annual- and seasonal-mean variables over large spatial domains) with metrics of time-mean subsurface ocean temperature biases averaged over sectors of the continental shelf. For stage (ii), we maximize the diversity of climate projections among the best-performing models. Model selection is also constrained by technical limitations, such as availability of required data from RCP2.6 and RCP8.5 projections. The selected top three CMIP5 climate models are CCSM4, MIROC-ESM-CHEM, and NorESM1-M for Antarctica and HadGEM2-ES, MIROC5, and NorESM1-M for Greenland. This model selection was designed specifically for ISMIP6 but can be adapted for other applications.

54 ENVIRONMENTAL SCIENCES↗