ALBERT

Description: Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs ( Lithobates sylvaticus ) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate. Demographic processes and climate interact and vary across a species’ range to determine how species’ distributions will respond to climate change. We predicted that populations at the extremes of a species’ climate envelope are most sensitive to climate shifts. We tested this using a dynamic species distribution model linking demographic rates to variation in climate for wood frogs ( Lithobates sylvaticus ) in North America. Sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope.

Description: Increases in atmospheric CO 2 levels and associated ocean changes are expected to have dramatic impacts on marine ecosystems. Although the Southern Ocean is experiencing some of the fastest rates of change, few studies have explored how Antarctic fishes may be affected by co-occurring ocean changes, and even fewer have examined early life stages. To date, no studies have characterized potential trade-offs in physiology and behavior in response to projected multiple climate change stressors (ocean acidification and warming) on Antarctic fishes. We exposed juvenile emerald rockcod Trematomus bernacchii to three P CO 2 treatments (~450, ~850 and ~1200 μatm P CO 2 ) at two temperatures (-1º or 2°C). After 2, 7, 14, and 28 days, metrics of physiological performance including cardiorespiratory function (heart rate [ f H ] and ventilation rate [ f V ]), metabolic rate (ṀO 2 ), and cellular enzyme activity were measured. Behavioral responses, including scototaxis, activity, exploration and escape response were assessed after 7 and 14 days. Elevated P CO 2 independently had little impact on either physiology or behavior in juvenile rockcod, whereas warming resulted in significant changes across acclimation time. After 14 days, f H , f V and ṀO 2 significantly increased with warming, but not with elevated P CO 2 . Increased physiological costs were accompanied by behavioral alterations including increased dark zone preference up to 14%, reduced activity by 12%, as well as reduced escape time suggesting potential trade-offs in energetics. After 28 days, juvenile rockcod demonstrated a degree of temperature compensation as f V , ṀO 2 , and cellular metabolism significantly decreased following the peak at 14 days; however, temperature compensation was only evident in the absence of elevated P CO 2 . Sustained increases in f V and ṀO 2 after 28 days exposure to elevated P CO 2 indicate additive ( f V ) and synergistic (ṀO 2 ) interactions occurred in combination with warming. Stressor-induced energetic trade-offs in physiology and behavior may be an important mechanism leading to vulnerability of Antarctic fishes to future ocean change. This article is protected by copyright. All rights reserved.

Description: Coal seam gas production involves generation and management of large amounts of co-produced water. One of the most suitable methods of management is injection into deep aquifers. Field injection trials may be used to support the predictions of anticipated hydrological and geochemical impacts of injection. The present work employs reactive transport modeling (RTM) for a comprehensive analysis of data collected from a trial where arsenic mobilization was observed. Arsenic sorption behavior was studied through laboratory experiments, accompanied by the development of a surface complexation model (SCM). A field-scale RTM that incorporated the laboratory-derived SCM was used to simulate the data collected during the field injection trial and then to predict the long-term fate of arsenic. We propose a new practical procedure which integrates laboratory and field-scale models using a Monte Carlo type uncertainty analysis and alleviates a significant proportion of the computational effort required for predictive uncertainty quantification. The results illustrate that both arsenic desorption under alkaline conditions and pyrite oxidation have likely contributed to the arsenic mobilization that was observed during the field trial. The predictive simulations show that arsenic concentrations would likely remain very low if the potential for pyrite oxidation is minimized through complete deoxygenation of the injectant. The proposed modeling and predictive uncertainty quantification method can be implemented for a wide range of groundwater studies that investigate the risks of metal(loid) or radionuclide contamination.

Description: The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Amongst other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific description of these components are in companion papers), and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present-day forcing. The suitability of the configuration for predictability on shorter timescales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2-AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2-AO, many aspects of the present-day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extra-tropical variability and top-of-atmosphere & surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

Description: Neutral-buoyancy vehicles demand high-density energy sources and lithium is light with high oxidation energy. PolyPlus Battery Company has developed a prototype lithium-seawater battery that is attractive for powering long-duration autonomous oceanographic vehicles (floats and underwater gliders). These batteries were tested in the laboratory and at sea. PolyPlus batteries use “Protected Lithium Electrodes” with proprietary “windows” protecting the volatile lithium anode from water while passing lithium ions. The cathode reduces oxygen dissolved in seawater, or hydrolyzes seawater to produce hydrogen. Not requiring additional electrolyte, fuel, or pressure cases, these cells have impressive weight advantages. Good electrode–seawater mass transfer is required but can increase drag and be impeded by biofouling. Tests assessing robustness of the PolyPlus batteries in oceanographic use, evaluating mass transfer issues, and observing biofouling impacts are reported. In sea trials, two cells were tested for 69 days mounted on a Spray glider. Findings are as follows: 1) the cells were robust over 900 dives, most to 400 m; 2) without antifouling measures, the cells became substantially biofouled, but their performance was undiminished; and 3) performance was complex, depending on current density, oxygen concentration, and flow conditions. For dissolved oxygen concentration above 1 mL L−1, the cells delivered 9 W m−2 of electrode surface at 3 V. For low oxygen, the cell shifted to hydrolysis near 2.3 V, but mass transfer was less critical so current density could be increased and observed power reached 5 W m−2. This could be increased using a lower resistance load.

Description: Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

Description: The arid subtropics are situated at the edges of the tropical belt, where subsidence in the Hadley cells suppresses precipitation. Any meridional shift in these edge latitudes could have significant impacts on surface climate. Recent studies have investigated past and future changes in the tropical belt width and have found discrepancies in the rates of expansion estimated with different metrics and between climate models and reanalyses. Here, CMIP5 simulations and four modern reanalyses are analyzed using an ensemble of objective tropical belt width metrics to reexamine if such inconsistencies exist. The authors do not find sufficient evidence to demonstrate this discrepancy between models and reanalyses, as reanalysis trends in the tropical belt width fall within the range of model trends for any given metric. Furthermore, only metrics based on the Hadley cells are found to exhibit robust historical and future expansion. Metrics based on the subtropical jet and the tropopause show no robust response. This differentiation may be due to the strong correlation, on all time scales, between the Hadley cell edge latitudes and the latitudes of the eddy-driven jets, which consistently shift poleward in response to radiative forcings. In contrast, the subtropical jet and tropopause metrics appear to be decoupled from the Hadley cells and the eddy-driven jets and essentially measure a different tropical belt. The tropical belt width metrics are inconsistently correlated with surface climate indices based on precipitation and surface evaporation. This may make assessing the surface impacts of observed and future tropical expansion challenging.

Description: ABSTRACT Organic compounds are removed from the atmosphere and deposited to the earth's surface via precipitation. In this study, we quantified variations of dissolved organic carbon (DOC) in precipitation during storm events at the Shale Hills Critical Zone Observatory, a forested watershed in central Pennsylvania (USA). Precipitation samples were collected consecutively throughout the storm during 13 events, which spanned a range of seasons and synoptic meteorological conditions, including a hurricane. Further, we explored factors that affect the temporal variability by considering relationships of DOC in precipitation with atmospheric and storm characteristics. Concentrations and chemical composition of DOC changed considerably during storms, with the magnitude of change within individual events being comparable or higher than the range of variation in average event composition among events. While some previous studies observed that concentrations of other elements in precipitation typically decrease over the course of individual storm events, results of this study show that DOC concentrations in precipitation are highly variable. During most storm events concentrations decreased over time, possibly as a result of washing out of the below-cloud atmosphere. However, increasing concentrations that were observed in the later stages of some storm events highlight that DOC removal with precipitation is not merely a dilution response. Increases in DOC during events could result from advection of air masses, local emissions during breaks in precipitation, or chemical transformations in the atmosphere that enhance solubility of organic carbon compounds. This work advances understanding of processes occurring during storms that are relevant to studies of atmospheric chemistry, carbon cycling, and ecosystem responses.

Description: The potential for storm surge to cause extensive property damage and loss of life has increased urgency to more accurately predict coastal flooding associated with landfalling tropical cyclones. This work investigates the sensitivity of coastal inundation from storm tide (surge + tide) to four hurricane parameters—track, intensity, size, and translation speed—and the sensitivity of inundation forecasts to errors in forecasts of those parameters. An ensemble of storm tide simulations is generated for three storms in the Gulf of Mexico, by driving a storm surge model with best track data and systematically generated perturbations of storm parameters from the best track. The spread of the storm perturbations is compared to average errors in recent operational hurricane forecasts, allowing sensitivity results to be interpreted in terms of practical predictability of coastal inundation at different lead times. Two types of inundation metrics are evaluated: point-based statistics and spatially integrated volumes. The practical predictability of surge inundation is found to be limited foremost by current errors in hurricane track forecasts, followed by intensity errors, then speed errors. Errors in storm size can also play an important role in limiting surge predictability at short lead times, due to observational uncertainty. Results show that given current mean errors in hurricane forecasts, location-specific surge inundation is predictable for as little as 12–24 h prior to landfall, less for small-sized storms. The results also indicate potential for increased surge predictability beyond 24 h for large storms by considering a storm-following, volume-integrated metric of inundation.