Halocarbon emissions from marine phytoplankton and climate change

Long-lived and short-lived halocarbons have long been known for their adverse effects on atmospheric chemistry, especially ozone depletion that may be directly or indirectly influenced by global climate change. Marine organisms including phytoplankton contribute shorter-lived halocarbon compounds to the atmosphere. Oceans cover more than 70% of the Earth’s surface making the marine phytoplankton a significant presence. Changes in the environment will inevitably affect this widely distributed group of organisms. Various predictions have been made about how phytoplankton will respond to climate change, but as yet little is known about the interactions between phytoplankton, climate change and halocarbon emissions. We provide a summary of studies on halocarbon emissions by marine phytoplankton isolated from different climatic zones that includes data from our recent studies on tropical marine phytoplankton. It is important to determine and characterize the contribution of the phytoplankton to the halocarbon load in the atmosphere to allow their interaction with the changing global climate to be understood. Using these data, we compare the range of halocarbons emitted by phytoplankton with halocarbon emission data for seaweeds, a well-known biogenic contributor of short-lived halocarbons. Sørensen’s coefficient of similarity of 0.50 was calculated, which suggests that half of the detected halocarbon species present in seaweeds are also present in phytoplankton.     

Palaeoenvironmental record of glacial lake evolution during the early Holocene at Sokli,NE Finland

The development of a glacial lake impounded along the retreating, northeastern ice margin of the Fennoscandian Ice Sheet during the last deglaciation and environmental conditions directly following the early Holocene deglaciation have been studied in NE Finland. This so‐called Sokli Ice Lake has been reconstructed previously using topographic and geomorphologic evidence. In this paper a multiproxy approach is employed to study a 3‐m‐thick sediment succession consisting of laminated silts grading into gyttja cored in Lake Loitsana, a remnant of the Sokli Ice Lake. Variations in the sediment and siliceous microfossil records indicate distinct changes in water depth and lake size in the Loitsana basin as the Sokli Ice Lake was drained through various spillways opening up along the retreating ice front. Geochemical data (XRF core‐scanning) show changes in the influence of regional catchment geochemistry (Precambrian crystalline rocks) in the glacial lake drainage area versus local catchment geochemistry (Sokli Carbonatite Massif) within the Lake Loitsana drainage area during the lake evolution. Principal component analysis on the geochemical data further suggests that grain‐size is an additional factor responsible for the variability of the sediment geochemistry record. The trophic state of the lake changed drastically as a result of morphometric eutrophication once the glacial lake developed into Lake Loitsana. The AMS radiocarbon dating on tree birch seeds found in the glaciolacustrine sediment indicates that Lake Loitsana was deglaciated sometime prior to 10 700 cal. a BP showing that tree Betula was present on the deglaciated land surrounding the glacial lake. Although glacial lakes covered large areas of northern Finland during the last deglaciation, only few glaciolacustrine sediment successions have been studied in any detail. Our study shows the potential of these sediments for multiproxy analysis and contributes to the reconstruction of environmental conditions in NE Finland directly following deglaciation in the early Holocene.     

Some challenges of monitoring a potentially active volcanic field in a large urban area: Auckland volcanic field,New Zealand

Natural Hazards - The city of Auckland (population 1.3 million) is built on and around a potentially active basaltic intraplate volcanic system, the Auckland volcanic field. This...     

Multiple ruptures for Long Valley microearthquakes: a link to volcanic tremor?

Despite several episodes of ground deformation and intense seismic activity starting in 1978, the Long Valley, California, volcanic area has not produced clearly recognized volcanic tremor. Instead, a variety of atypical microearthquakes have been recorded during these episodes, including events dominated by low-frequency (long-period) or mixed high and low-frequency (hybrid) signals. During a 1997 episode, a number of unusual microearthquakes occurred within a temporary 40-station seismic network surrounding the Casa Diablo area, allowing the events to be precisely located and analyzed as a function of azimuth, offset, and source characteristics. Eight prime examples lie within two, 7 km-deep clusters of seismicity separated by about 1 km, with four events in each cluster. Empirical Green's function deconvolution shows that these events are composed of two to three sub-events, the sub-events consisting of ordinary (single rupture, double-couple) microearthquakes. The delay times between the sub-events are constant within each cluster, equaling 0.092 s in one and 0.06 s in the other. Events from other clusters show similar delays. The signal interference produced by the closely spaced sub-events gives rise to modulated, delay-dependent source spectra. The regularity of the delays suggests that the sub-events are being triggered by a fixed length and/or time scale process, an example being the length/inflation rate of a magmatic or hydrothermal flow structure. With continued action of the triggering process, the sub-events could proliferate and evolve into observable volcanic tremors at Mammoth.     

Rectangular polynomial analysis of the regional geomagnetic field

The method of rectangular polynomial analysis (RPA) is developed and refined to represent a curl-free potential field of internal origin. It is applied to annual mean values of the geomagnetic field from 42 European observatories. RPA is found to be an efficient means of representing the regional field, though less suitable for modelling the anomaly field.