Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.
Abbott, Benjamin W.; Brown, Michael J.; Carey, Joanna C.; Ernakovich, Jessica E.; Frederick, Jennifer M.; Guo, Laodong G.; Lee,
R.; Loranty, Michael M.; Macdonald, Robie M.; Mann, Paul J.; Natali, Susan M.; Olefeldt, David O.; Pearson,
P.; Rec, Abigail R.; Robards, Martin R.; Salmon, Verity G.; Sayedi, Sayedeh S.; Schädel, Christina S.; G. Schuur,
E.; Shakil, Sarah S.; Shogren, Arial J.; Strauss, Jens S.; Tank, Suzanne E.; Thornton, Brett F.; Treharne,
R.; Voigt, Carolina V.; Wright, Nancy W.; Yang, Yuanhe Y.; Zarnetske, Jay P.; Zhang,
Q.; Zolkos, Scott Z.
Climate change is an existential threat to the vast global permafrost domain. The diverse human cultures, ecological communities, and biogeochemical cycles of this tenth of the planet depend on the persistence of frozen conditions. The complexity, immensity, and remoteness of permafrost ecosystems make it difficult to grasp how quickly things are changing and what can be done about it. Here, we summarize terrestrial and marine changes in the permafrost domain with an eye toward global policy. While many questions remain, we know that continued fossil fuel burning is incompatible with the continued existence of the permafrost domain as we know it. If we fail to protect permafrost ecosystems, the consequences for human rights, biosphere integrity, and global climate will be severe. The policy implications are clear: the faster we reduce human emissions and draw down atmospheric CO2, the more of the permafrost domain we can save. Emissions reduction targets must be strengthened and accompanied by support for local peoples to protect intact ecological communities and natural carbon sinks within the permafrost domain. Some proposed geoengineering interventions such as solar shading, surface albedo modification, and vegetation manipulations are unproven and may exacerbate environmental injustice without providing lasting protection. Conversely, astounding advances in renewable energy have reopened viable pathways to halve human greenhouse gas emissions by 2030 and effectively stop them well before 2050. We call on leaders, corporations, researchers, and citizens everywhere to acknowledge the global importance of the permafrost domain and work towards climate restoration and empowerment of Indigenous and immigrant communities in these regions.
Using a combination of geospatial machine learning prediction and sediment thermodynamic/physical modeling, we have developed a novel software workflow to create probabilistic maps of geoacoustic and geomechanical sediment properties of the global seabed. This new technique for producing reliable estimates of seafloor properties can better support Naval operations relying on sonar performance and seabed strength, can constrain models of shallow tomographic structure important for nuclear treaty compliance monitoring/detection, and can provide constraints on the distribution and inventory of shallow methane gas and gas hydrate accumulations on the continental shelves.
Sayedi, Sayedeh S.; Abbott, Benjamin W.; Thornton, Brett F.; Frederick, Jennifer M.; Vonk, Jorien E.; Overduin, Paul; Schädel, Christina; Schuur, Edward A.G.; Bourbonnais, Annie; Demidov, Nikita; Gavrilov, Anatoly; He, Shengping; Hugelius, Gustaf; Jakobsson, Martin; Jones, Miriam C.; Joung, Dong J.; Kraev, Gleb; Macdonald, Robie W.; David McGuire, A.; Mu, Cuicui; O’Regan, Matt; Schreiner, Kathryn M.; Stranne, Christian; Pizhankova, Elena; Vasiliev, Alexander; Westermann, Sebastian; Zarnetske, Jay P.; Zhang, Tingjun; Ghandehari, Mehran; Baeumler, Sarah; Brown, Brian C.; Frei, Rebecca J.
The continental shelves of the Arctic Ocean and surrounding seas contain large stocks of organic matter (OM) and methane (CH4), representing a potential ecosystem feedback to climate change not included in international climate agreements. We performed a structured expert assessment with 25 permafrost researchers to combine quantitative estimates of the stocks and sensitivity of organic carbon in the subsea permafrost domain (i.e. unglaciated portions of the continental shelves exposed during the last glacial period). Experts estimated that the subsea permafrost domain contains ∼560 gigatons carbon (GtC; 170–740, 90% confidence interval) in OM and 45 GtC (10–110) in CH4. Current fluxes of CH4 and carbon dioxide (CO2) to the water column were estimated at 18 (2–34) and 38 (13–110) megatons C yr−1, respectively. Under Representative Concentration Pathway (RCP) RCP8.5, the subsea permafrost domain could release 43 Gt CO2-equivalent (CO2e) by 2100 (14–110) and 190 Gt CO2e by 2300 (45–590), with ∼30% fewer emissions under RCP2.6. The range of uncertainty demonstrates a serious knowledge gap but provides initial estimates of the magnitude and timing of the subsea permafrost climate feedback.
Two surrogate models are under development to rapidly emulate the effects of the Fuel Matrix Degradation (FMD) model in GDSA Framework. One is a polynomial regression surrogate with linear and quadratic fits, and the other is a k-Nearest Neighbors regressor (kNNr) method that operates on a lookup table. Direct coupling of the FMD model to GDSA Framework is too computationally expensive. Preliminary results indicate these surrogate models will enable GDSA Framework to rapidly simulate spent fuel dissolution for each individual breached spent fuel waste package in a probabilistic repository simulation. This capability will allow uncertainties in spent fuel dissolution to be propagated and sensitivities in FMD inputs to be quantified and ranked against other inputs.
PFLOTRAN is well-established in single-phase reactive transport problems, and current research is expanding its visibility and capability in two-phase subsurface problems. A critical part of the development of simulation software is quality assurance (QA). The purpose of the present work is QA testing to verify the correct implementation and accuracy of two-phase flow models in PFLOTRAN. An important early step in QA is to verify the code against exact solutions from the literature. In this work a series of QA tests on models that have known analytical solutions are conducted using PFLOTRAN. In each case the simulated saturation profile is rigorously shown to converge to the exact analytical solution. These results verify the accuracy of PFLOTRAN for use in a wide variety of two-phase modelling problems with a high degree of nonlinearity in the interaction between phase behavior and fluid flow.
The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Depat ment of Energy (DOE) Office of Nuclear Energy (NE), Office of Fuel Cycle Technology (OFCT) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high level nuclear waste (HLW). Two high priorities for SFWST disposal R&D are design concept development and disposal system modeling (DOE 2011, Table 6). These priorities are directly addressed in the SFWST Geologic Disposal Safety Assessment (GDSA) work package, which is charged with developing a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes specific GDSA activities in fiscal year 2018 (FY 2018) toward the development of GDSA Framework, an enhanced disposal system modeling and analysis capability for geologic disposal of nuclear waste. GDSA Framework employs the PFLOTRAN thermal-hydrologic-chemical multiphysics code (Hammond et al. 2011a; Lichtner and Hammond 2012) and the Dakota uncertainty sampling and propagation code (Adams et al. 2012; Adams et al. 2013). Each code is designed for massivelyparallel processing in a high-performance computing (HPC) environment. Multi-physics representations in PFLOTRAN are used to simulate various coupled processes including heat flow, fluid flow, waste dissolution, radionuclide release, radionuclide decay and ingrowth, precipitation and dissolution of secondary phases, and radionuclide transport through engineered barriers and natural geologic barriers to the biosphere. Dakota is used to generate sets of representative realizations and to analyze parameter sensitivity.