Properties of unstable waves in the lower St Lawrence Estuary

Abstract

The lower St Lawrence Estuary is an interesting case amongst estuaries in that it is wide enough to accommodate the development of mesoscale unstable waves and eddies. These features are generated by the runoff‐driven jet along this body's south shore. We present data yielding estimates of the length, time and velocity scales of these unstable disturbances. To relate these quantities to the dynamics we employ a 2‐layer quasigeos‐trophic instability model featuring realistic lateral shear. All model runs show short time and length scales, e‐folding periods of less than 10 days and wavelengths less than 50 km.     

Winter‐time near‐surface currents in the strait of Juan de Fuca

Abstract

During November 1976 to February 1977 near‐surface wind, current and temperature measurements were made at three sites along the Strait of Juan de Fuca. Strong tidal currents and major intrusions of warmer, fresher offshore coastal water were superimposed upon the estuarine circulation of near‐surface seaward flow. The r.m.s. amplitudes of the diurnal and semidiurnal tidal currents were ～30 cms^‐1 and 30–47 cm s^‐1, respectively. The vector‐mean flow at 4 m‐depth was seaward and decreased in speed from 28 cm s^‐1 at 74 km from the entrance to 9 cm s^‐1 at 11 km from the entrance. On five occasions intrusions of 1–3 C warmer northeast Pacific coastal water occurred for durations of 1–10 days. The 25 cm s^‐1 up‐strait speed of the intrusive lens agreed to within 20% of the gravity current speed computed from Benjamin's (1968) hydraulic model. The near‐surface currents associated with the intrusions and the southerly coastal winds were significantly correlated, indicating that the intrusions were initiated when shoreward Ekman currents advected Pacific coastal water into the Strait. The reversals were not significantly coherent with the along‐strait sea surface slope measured along the north side of the Strait nor were they strongly related to local wind forcing.     

Does temperature contain a stochastic trend: linking statistical results to physical mechanisms

By construction, the time series for radiative forcing that are used to run the 20c3m experiments, which are implemented by climate models, impart non-stationary movements (either stochastic or deterministic) to the simulated time series for global surface temperature. Here, we determine whether stochastic or deterministic trends are present in the simulated time series for global surface temperature by examining the time series for radiative forcing. Statistical tests indicate that the forcings contain a stochastic trend against the alternative hypothesis that the series are trend stationary with a one-time structural change. This result is consistent with the economic processes that impart a stochastic trend to anthropogenic emissions and the physical processes that integrate emissions in the atmosphere. Furthermore, the stochastic trend in the aggregate measure of radiative forcing also is present in the simulated time series for global surface temperature, which is consistent with the relation between these two variables that is represented by a zero dimensional energy balance model. Finally, we propose that internal weather variability imposed on the stochastic trend in radiative forcings is responsible for statistical results, which gives the impression that global surface temperature is trend stationary with a one-time structural change. We conclude that using the ideas of stochastic trends, cointegration, and error correction can generate reliable conclusions regarding the causes of changes in global surface temperature during the instrumental temperature record.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Impacts of increased variability in precipitation and air temperature on net primary productivity of the Tibetan Plateau: a modeling analysis

We analyzed interannual variability (IAV) of precipitation and air temperature over a 40-year period (1969–2008) for 11 sites along a precipitation gradient on the Tibetan Plateau. The observed IAV for both precipitation and air temperature decreases with increasing mean annual precipitation. Using Biome-BGC, a process-based ecosystem model, we simulated net primary production (NPP) along this gradient and find that the IAV of NPP is positively correlated to the IAV of precipitation and temperature. Following projected climate change scenarios for the Tibetan Plateau, our simulations suggest that with increasing IAV of precipitation and temperature, the IAV of NPP will also increase and that climate thresholds exist that, if surpassed, lead to ecosystem die-off. The impacts of these changes on ecosystem processes and climate-vegetation feedbacks on the rapidly warming Tibetan Plateau are potentially quite significant.     

Estimating environmentally relevant fixed nitrogen demand in the 21st century

Human activities affect the impact of the nitrogen cycle on both the environment and climate. The rate of anthropogenic nitrogen fixation from atmospheric N₂ may serve as an indicator to the magnitude of this impact, acknowledging that relationship to be effect-dependent and non-linear. Building on the set of Representative Concentration Pathway (RCP) scenarios developed for climate change research, we estimate anthropogenic industrial nitrogen fixation throughout the 21st century. Assigning characteristic key drivers to the four underlying scenarios we arrive at nitrogen fixation rates for agricultural use of 80 to 172 Tg N/yr by 2100, which is slightly less to almost twice as much compared with the fixation rate for the year 2000. We use the following key drivers of change, varying between scenarios: population growth, consumption of animal protein, agricultural efficiency improvement and additional biofuel production. Further anthropogenic nitrogen fixation for production of materials such as explosives or plastics and from combustion are projected to remain considerably smaller than that related to agriculture. While variation among the four scenarios is considerable, our interpretation of scenarios constrains the option space: several of the factors enhancing the anthropogenic impact on the nitrogen cycle may occur concurrently, but never all of them. A scenario that is specifically targeted towards limiting greenhouse gas emissions ends up as the potentially largest contributor to nitrogen fixation, as a result of large amounts of biofuels required and the fertilizer used to produce it. Other published data on nitrogen fixation towards 2100 indicate that our high estimates based on the RCP approach are rather conservative. Even the most optimistic scenario estimates that nitrogen fixation rate will remain substantially in excess of an estimate of sustainable boundaries by 2100.     

Climate change and mental health: an exploratory case study from Rigolet, Nunatsiavut, Canada

As the impacts from anthropogenic climate change are increasing globally, people are experiencing dramatic shifts in weather, temperature, wildlife and vegetation patterns, and water and food quality and availability. These changes impact human health and well-being, and resultantly, climate change has been identified as the biggest global health threat of the 21st Century. Recently, research is beginning to indicate that changes in climate, and the subsequent disruption to the social, economic, and environmental determinants of health, may cause increased incidences and prevalence of mental health issues, emotional responses, and large-scale sociopsychological changes. Through a multi-year, community-led, exploratory case study conducted in Rigolet, Nunatsiavut, Labrador, Canada, this research qualitatively explores the impacts of climate change on mental health and well-being in an Inuit context. Drawing from 67 in-depth interviews conducted between January 2010 and October 2010 with community members and local and regional health professionals, participants reported that changes in weather, snow and ice stability and extent, and wildlife and vegetation patterns attributed to climate change were negatively impacting mental health and well-being due to disruptions in land-based activities and a loss of place-based solace and cultural identity. Participants reported that changes in climate and environment increased family stress, enhanced the possibility of increased drug and alcohol usage, amplified previous traumas and mental health stressors, and were implicated in increased potential for suicide ideation. While a preliminary case study, these exploratory findings indicate that climate change is becoming an additional mental health stressor for resource-dependent communities and provide a baseline for further research.     

What can ecological science tell us about opportunities for carbon sequestration on arid rangelands in the United States?

Scientific interest in carbon sequestration on rangelands is largely driven by their extent, while the interest of ranchers in the United States centers on opportunities to enhance revenue streams. Rangelands cover approximately 30% of the earth's ice-free land surface and hold an equivalent amount of the world's terrestrial carbon. Rangelands are grasslands, shrublands, and savannas and cover 312 million hectares in the United States. On the arid and semi-arid sites typical of rangelands annual fluxes are small and unpredictable over time and space, varying primarily with precipitation, but also with soils and vegetation. There is broad scientific consensus that non-equilibrium ecological models better explain the dynamics of such rangelands than equilibrium models, yet current and proposed carbon sequestration policies and associated grazing management recommendations in the United States often do not incorporate this developing scientific understanding of rangeland dynamics. Carbon uptake on arid and semi-arid rangelands is most often controlled by abiotic factors not easily changed by management of grazing or vegetation. Additionality may be impossible to achieve consistently through management on rangelands near the more xeric end of a rangeland climatic gradient. This point is illustrated by a preliminary examination of efforts to develop voluntary cap and trade markets for carbon credits in the United States, and options including payment for ecosystem services or avoided conversion, and carbon taxation. A preliminary analysis focusing on cap and trade and payment for avoided conversion or ecosystem services illustrates the misalignment between policies targeting vegetation management for enhanced carbon uptake and non-equilibrium carbon dynamics on arid United States rangelands. It is possible that current proposed carbon policy as exemplified by carbon credit exchange or offsets will result in a net increase in emissions, as well as investment in failed management. Rather than focusing on annual fluxes, policy and management initiatives should seek long-term protection of rangelands and rangeland soils to conserve carbon, and a broader range of environmental and social benefits.     

Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization

Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system.     

Current approaches to MRV in South Africa: a scoping study

It is shown that there are MRV-related activities underway in South Africa, particularly focusing on measuring electricity consumption and monitoring GHG emissions. Yet currently many of these activities happen in parallel systems within multi-polar governance structures. A bottom-up perspective of MRV in South Africa, informed by interviews, workshops, desktop research, and stakeholder consultations, is provided and the systems, data, methodologies, and the institutional environment relevant to a South African MRV system are examined. The development of the local monitoring and evaluation system, and its relevance within the international MRV context, is also discussed. Some recommendations are made: most importantly, there is a need for a coherent approach to be developed, one that is coordinated by government and built on existing MRV systems.