Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

Oxygen isotope biogeochemistry of pore water sulfate in the deep biosphere: Dominance of isotope exchange reactions with ambient water during microbial sulfate reduction (ODP Site 1130)

Microbially mediated sulfate reduction affects the isotopic composition of dissolved and solid sulfur species in marine sediments. Experiments and field data show that the composition is also modified in the presence of sulfate-reducing microorganisms. This has been attributed either to a kinetic isotope effect during the reduction of sulfate to sulfite, cell-internal exchange reactions between enzymatically-activated sulfate (APS), and/or sulfite with cytoplasmic water. The isotopic fingerprint of these processes may be further modified by the cell-external reoxidation of sulfide to elemental sulfur, and the subsequent disproportionation to sulfide and sulfate or by the oxidation of sulfite to sulfate. Here we report values from interstitial water samples of ODP Leg 182 (Site 1130) and provide the mathematical framework to describe the oxygen isotope fractionation of sulfate during microbial sulfate reduction. We show that a purely kinetic model is unable to explain our data, and that the data are well explained by a model using oxygen isotope exchange reactions. We propose that the oxygen isotope exchange occurs between APS and cytoplasmic water, and/or between sulfite and adenosine monophosphate (AMP) during APS formation. Model calculations show that cell external reoxidation of reduced sulfur species would require up to 3000 mol/m³ of an oxidant at ODP Site 1130, which is incompatible with the sediment geochemical data. In addition, we show that the volumetric fluxes required to explain the observed data are on average 14 times higher than the volumetric sulfate reduction rates (SRR) obtained from inverse modeling of the porewater data. The ratio between the gross sulfate flux into the microbes and the net sulfate flux through the microbes is depth invariant, and independent of sulfide concentrations. This suggests that both fluxes are controlled by cell density and that cell-specific sulfate reduction rates remain constant with depth.     

A Framework for Quantitative Assessment of Impacts Related to Energy and Mineral Resource Development

Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.     

Habitat modification in tidepools by bioeroding sea urchins and implications for fine‐scale community structure

By creating novel habitats, habitat‐modifying species can alter patterns of diversity and abundance in marine communities. Many sea urchins are important habitat modifiers in tropical and temperate systems. By eroding rocky substrata, urchins can create a mosaic of urchin‐sized cavities or pits separated by exposed, often flat surfaces. These microhabitats appear to harbor distinct assemblages of species. We investigated how a temperate rocky intertidal community uses three small‐scale (<100 cm²) microhabitats created by or adjacent to populations of the purple sea urchin (Strongylocentrotus purpuratus): pits occupied by urchins, unoccupied pits, and adjacent flat spaces. In tidepools, flat spaces harbored the highest percent cover of algae and sessile fauna, followed by empty pits and then occupied pits. The Shannon diversity and richness of these sessile taxa were significantly higher in flat spaces and empty pits than in occupied pits. The composition of these primary space holders in the microhabitats also varied. Unlike primary space holders, mobile fauna exhibited higher diversity and richness in empty pits than in flat spaces and occupied pits, although results were not significant. The protective empty pit microhabitat harbored the highest densities of most trophic functional groups. Herbivores, however, were densest in flat spaces, concordant with high algal coverage. These results suggest the habitats created by S. purpuratus in addition to its biological activities alter community structure at spatial scales finer than those typically considered for sea urchins.     

On the estimation of systematic error in regression-based predictions of climate sensitivity

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.     

A modeling study of coastal inundation induced by storm surge,sea-level rise,and subsidence in the Gulf of Mexico

The northern coasts of the Gulf of Mexico (GoM) are highly vulnerable to the direct threats of climate change, such as hurricane-induced storm surge, and such risks are exacerbated by land subsidence and global sea-level rise. This paper presents an application of a coastal storm surge model to study the coastal inundation process induced by tide and storm surge, and its response to the effects of land subsidence and sea-level rise in the northern Gulf coast. The unstructured-grid finite-volume coastal ocean model was used to simulate tides and hurricane-induced storm surges in the GoM. Simulated distributions of co-amplitude and co-phase lines for semi-diurnal and diurnal tides are in good agreement with previous modeling studies. The storm surges induced by four historical hurricanes (Rita, Katrina, Ivan, and Dolly) were simulated and compared to observed water levels at National Oceanic and Atmospheric Administration tide stations. Effects of coastal subsidence and future global sea-level rise on coastal inundation in the Louisiana coast were evaluated using a “change of inundation depth” parameter through sensitivity simulations that were based on a projected future subsidence scenario and 1-m global sea-level rise by the end of the century. Model results suggested that hurricane-induced storm surge height and coastal inundation could be exacerbated by future global sea-level rise and subsidence, and that responses of storm surge and coastal inundation to the effects of sea-level rise and subsidence are highly nonlinear and vary on temporal and spatial scales.     

Modeling fluid injection in fractures with a reservoir simulator coupled to a boundary element method

We describe an algorithm for modeling saturated fractures in a poroelastic domain in which the reservoir simulator is coupled with a boundary element method. A fixed stress splitting is used on the underlying fractured Biot system to iteratively couple fluid and solid mechanics systems. The fluid system consists of Darcy’s law in the reservoir and is computed with a multipoint flux mixed finite element method, and a Reynolds’ lubrication equation in the fracture solved with a mimetic finite difference method. The mechanics system consists of linear elasticity in the reservoir and is computed with a continuous Galerkin method, and linear elasticity in the fracture is solved with a weakly singular symmetric Galerkin boundary element method. This algorithm is able to compute both unknown fracture width and unknown fluid leakage rate. An interesting numerical example is presented with an injection well inside of a circular fracture.     

The Chemical Nature of Phosphorus in Subtropical Lake Sediments

The phosphorus (P) composition of sediment profiles in three subtropical lakes of contrasting trophic state in Florida, USA, was determined by sequential fractionation and solution ³¹P NMR spectroscopy. Sediment from Lake Annie, an oligo-mesotrophic sinkhole with moderately acidic sediment (pH 5.4; loss on ignition 58 %), contained higher total P concentrations than sediment from eutrophic Lake Okeechobee (pH 7.7, loss on ignition 36 %) and hyper-eutrophic Lake Apopka (pH 7.5, loss on ignition 69 %). The chemical nature of sediment P varied markedly among the three lakes, suggesting the predominance of different diagenetic processes. Lake Okeechobee sediment was dominated by inorganic P, indicating the dominance of abiotic reactions; Lake Annie sediment contained abundant organic P throughout the sediment profile, indicating the importance of organic P stabilization at acidic pH; Lake Apopka contained almost half of its sediment P in microbial biomass, indicating the importance of biotic processes in regulating P dynamics. Solution ³¹P NMR spectroscopy of NaOH–EDTA extracts revealed that organic P occurred mainly as phosphomonoesters in all lakes. However, sediment from Lake Apopka also contained abundant phosphodiesters and was the only lake to contain detectable concentrations of polyphosphate, perhaps due to a combination of alternating redox conditions and high concentrations of inorganic phosphate and organic carbon. Organic P concentrations determined by sequential fractionation and solution ³¹P NMR spectroscopy were similar for all lakes when microbial P was included in values for sequential fractionation. We conclude that the chemical nature of sediment P varies markedly depending on trophic state and can provide important information on the dominant processes controlling P cycling in subtropical lakes.