For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step towards quantifying parametric uncertainty in Arctic climate, we performed a variance-based global sensitivity analysis (GSA) using a fully-coupled, ultra-low resolution (ULR) configuration of version 1 of the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interest (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed pre-industrial forcing. Uncertainties in the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher-resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.
The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications and their role in the overall ECP ecosystem. Our FY22 March/April milestone (ADCD- 504-28) proposed to: Assess the fidelity of proxy applications compared to their respective parents in terms of kernel and I/O behavior, and predictability. Similarity techniques will be applied for quantitative comparison of proxy/parent kernel behavior. MACSio evaluation will continue and support for OpenPMD backends will be explored. The execution time predictability of proxy apps with respect to their parents will be explored through a carefully designed scaling study and code comparisons. Note that in this FY, we also have quantitative assessment milestones that are due in September and are, therefore, not included in the description above or in this report. Another report on these deliverables will be generated and submitted upon completion of these milestones. To satisfy this milestone, the following specific tasks were completed: Study the ability of MACSio to represent I/O workloads of adaptive mesh codes. Re-define the performance counter groups for contemporary Intel and IBM platforms to better match specific hardware components and to better align across platforms (make cross-platform comparison more accurate). Perform cosine similarity study based on the new performance counter groups on the Intel and IBM P9 platforms. Perform detailed analysis of performance counter data to accurately average and align the data to maintain phases across all executions and develop methods to reduce the set of collected performance counters used in cosine similarity analysis. Apply a quantitative similarity comparison between proxy and parent CPU kernels. Perform scaling studies to understand the accuracy of predictability of the parent performance using its respective proxy application. This report presents highlights of these efforts.
As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.
Lignin is often overlooked in the valorization of lignocellulosic biomass, but lignin-based materials and chemicals represent potential value-added products for biorefineries that could significantly improve the economics of a biorefinery. Fluctuating crude oil prices and changing fuel specifications are some of the driving factors to develop new technologies that could be used to convert polymeric lignin into low molecular weight lignin and or monomeric aromatic feedstocks to assist in the displacement of the current products associated with the conversion of a whole barrel of oil. Our project of understanding microbial lignolysis for renewable platform chemicals aimed to understand microbial and enzymatic lignolysis processes to break down lignin for conversion into commercially viable drop-in fuels. We developed novel lignin analytics to interrogate enzymatic and microbial lignolysis of native polymeric lignin and established a detailed understanding of lignolysis as a function of fungal enzyme, microbes and endophytes. Bioinformatics pipeline was developed for metatranscryptomic analysis of aridland ecosystem for investigating the potential discovery of new lignolysis gene and gene products.
Increasing energy costs, the dependence on foreign oil supplies, and environmental concerns have emphasized the need to produce sustainable renewable fuels and chemicals. The strategy for producing next-generation biofuels must include efficient processes for biomass conversion to liquid fuels and the fuels must be compatible with current and future engines. Unfortunately, biofuel development generally takes place without any consideration of combustion characteristics, and combustion scientists typically measure biofuels properties without any feedback to the production design. We seek to optimize the fuel/engine system by bringing combustion performance, specifically for advanced next-generation engines, into the development of novel biosynthetic fuel pathways. Here we report an innovative coupling of combustion chemistry, from fundamentals to engine measurements, to the optimization of fuel production using metabolic engineering. We have established the necessary connections among the fundamental chemistry, engine science, and synthetic biology for fuel production, building a powerful framework for co-development of engines and biofuels.
The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.
Progress in algal biofuels has been limited by significant knowledge gaps in algal biology, particularly as they relate to scale-up. To address this we are investigating how culture composition dynamics (light as well as biotic and abiotic stressors) describe key biochemical indicators of algal health: growth rate, photosynthetic electron transport, and lipid production. Our approach combines traditional algal physiology with genomics, bioanalytical spectroscopy, chemical imaging, remote sensing, and computational modeling to provide an improved fundamental understanding of algal cell biology across multiple cultures scales. This work spans investigations from the single-cell level to ensemble measurements of algal cell cultures at the laboratory benchtop to large greenhouse scale (175 gal). We will discuss the advantages of this novel, multidisciplinary strategy and emphasize the importance of developing an integrated toolkit to provide sensitive, selective methods for detecting early fluctuations in algal health, productivity, and population diversity. Progress in several areas will be summarized including identification of spectroscopic signatures for algal culture composition, stress level, and lipid production enabled by non-invasive spectroscopic monitoring of the photosynthetic and photoprotective pigments at the single-cell and bulk-culture scales. Early experiments compare and contrast the well-studied green algae chlamydomonas with two potential production strains of microalgae, nannochloropsis and dunnaliella, under optimal and stressed conditions. This integrated approach has the potential for broad impact on algal biofuels and bioenergy and several of these opportunities will be discussed.
This report documents progress in discovering new catalytic technologies that will support the development of advanced biofuels. The global shift from petroleum-based fuels to advanced biofuels will require transformational breakthroughs in biomass deconstruction technologies, because current methods are neither cost effective nor sufficiently efficient or robust for scaleable production. Discovery and characterization of lignocellulolytic enzyme systems adapted to extreme environments will accelerate progress. Obvious extreme environments to mine for novel lignocellulolytic deconstruction technologies include aridland ecosystems (ALEs), such as those of the Sevilleta Long Term Ecological Research (LTER) site in central New Mexico (NM). ALEs represent at least 40% of the terrestrial biosphere and are classic extreme environments, with low nutrient availability, high ultraviolet radiation flux, limited and erratic precipitation, and extreme variation in temperatures. ALEs are functionally distinct from temperate environments in many respects; one salient distinction is that ALEs do not accumulate soil organic carbon (SOC), in marked contrast to temperate settings, which typically have large pools of SOC. Low productivity ALEs do not accumulate carbon (C) primarily because of extraordinarily efficient extracellular enzyme activities (EEAs) that are derived from underlying communities of diverse, largely uncharacterized microbes. Such efficient enzyme activities presumably reflect adaptation to this low productivity ecosystem, with the result that all available organic nutrients are assimilated rapidly. These communities are dominated by ascomycetous fungi, both in terms of abundance and contribution to ecosystem-scale metabolic processes, such as nitrogen and C cycling. To deliver novel, robust, efficient lignocellulolytic enzyme systems that will drive transformational advances in biomass deconstruction, we have: (1) secured an award through the Department of Energy (DoE) Joint Genome Institute (JGI) to perform metatranscriptomic functional profiling of eukaryotic microbial communities of blue grama grass (Bouteloua gracilis) rhizosphere (RHZ) soils and (2) isolated and provided initial genotypic and phenotypic characterization data for thermophilic fungi. Our preliminary results show that many strains in our collection of thermophilic fungi frequently outperform industry standards in key assays; we also demonstrated that this collection is taxonomically diverse and phenotypically compelling. The studies summarized here are being performed in collaboration with University of New Mexico and are based at the Sevilleta LTER research site.
This short-term, late-start LDRD examined the effects of nutritional deprivation on the energy harvesting complex in microalgae. While the original experimental plan involved a much more detailed study of temperature and nutrition on the antenna system of a variety of TAG producing algae and their concomitant effects on oil production, time and fiscal constraints limited the scope of the study. This work was a joint effort between research teams at Sandia National Laboratories, New Mexico and California. Preliminary results indicate there is a photosystem response to silica starvation in diatoms that could impact the mechanisms for lipid accumulation.