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Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy ≈60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

Meiobenthic community structure was investigated at different spatial scales (from 100 metres to centimetres) on and adjacent to a group of coral-topped sandy mounds in the bathyal north-east Atlantic (Darwin Mounds, Rockall Trough) and related to the environmental conditions in the area, mainly differences in sediment organic carbon content and presence of biogenic structures. Meiobenthos abundances were similar to those observed in other deep-sea sites, with nematodes representing at least 94% of the total community. The dominant nematode genera were Microlaimus, followed by Sabatieria, Richtersia, Rhynchonema and Trefusia, together with typical deep-sea genera (e.g. Halalaimus and Acantholaimus). Multivariate analysis of nematode generic relative abundances at the different stations indicated that there was no significant influence on distribution resulting from large scale topographic and biogeochemical conditions around the mounds. The same genera were associated with dead tests of the xenophyophore Syringammina fragilissima and in the surrounding sediments. The vertical distribution of nematodes on and adjacent to the mound showed some unusual features, as the deeper layers of the sediments were inhabited by stilbonematids. These genera harbour ectosymbiotic, chemoautotrophic bacteria and have not previously been recorded from the deep sea. The occurrence of stilbonematids in notable numbers in the subsurface layers of the sediments in the vicinity of the Darwin Mounds provides evidence for the occurrence of anoxic microenvironments.

Census of Marine Life (CoML) and the Sloan Foundation to G. T. Rowe and E. Escobar-Briones.

In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events.