Relationships between North Atlantic salmon, plankton and hydroclimatic change in the Northeast Atlantic

The abundance of wild salmon (Salmo salar) in the North Atlantic has declined markedly since the late 1980s as a result of increased marine mortality that coincided with a marked rise in sea temperature in oceanic foraging areas. There is substantial evidence to show that temperature governs the growth, survival, and maturation of salmon during their marine migrations through either direct or indirect effects. In an earlier study (2003), long-term changes in three trophic levels (salmon, zooplankton, and phytoplankton) were shown to be correlated significantly with sea surface temperature (SST) and northern hemisphere temperature (NHT). A sequence of trophic changes ending with a stepwise decline in the total nominal catch of North Atlantic salmon (regime shift in ∼1986/1987) was superimposed on a trend to a warmer dynamic regime. Here, the earlier study is updated with catch and abundance data to 2010, confirming earlier results and detecting a new abrupt shift in ∼1996/1997. Although correlations between changes in salmon, plankton, and temperature are reinforced, the significance of the correlations is reduced because the temporal autocorrelation of time-series substantially increased due to a monotonic trend in the time-series, probably related to global warming. This effect may complicate future detection of effects of climate change on natural systems.

Temporal change in UK marine communities: trends or regime shifts?

A regime shift is a large, sudden, and long-lasting change in the dynamics of an ecosystem, affecting multiple trophic levels. There are a growing number of papers that report regime shifts in marine ecosystems. However, the evidence for regime shifts is equivocal, because the methods used to detect them are not yet well developed. We have collated over 300 biological time series from seven marine regions around the UK, covering the ecosystem from phytoplankton to marine mammals. Each time series consists of annual measures of abundance for a single group of organisms over several decades. We summarised the data for each region using the first principal component, weighting either each time series or each biological component (e.g. plankton, fish, benthos) equally. We then searched for regime shifts using Rodionov’s regime shift detection (RSD) method, which found regime shifts in the first principal component for all seven marine regions. However, there are consistent temporal trends in the data for six of the seven regions. Such trends violate the assumptions of RSD. Thus, the regime shifts detected by RSD in six of the seven regions are likely to be artefacts caused by temporal trends. We are therefore developing more appropriate time series models for both single populations and whole communities that will explicitly model temporal trends and should increase our ability to detect true regime shift events.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI's omission rate can be attributed to a coupling between SEVIRI's low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI's commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

Detection and impacts of leakage from sub-seafloor deep geological carbon dioxide storage

Fossil fuel power generation and other industrial emissions of carbon dioxide are a threat to global climate1, yet many economies will remain reliant on these technologies for several decades2. Carbon dioxide capture and storage (CCS) in deep geological formations provides an effective option to remove these emissions from the climate system3. In many regions storage reservoirs are located offshore4, 5, over a kilometre or more below societally important shelf seas6. Therefore, concerns about the possibility of leakage7, 8 and potential environmental impacts, along with economics, have contributed to delaying development of operational CCS. Here we investigate the detectability and environmental impact of leakage from a controlled sub-seabed release of CO2. We show that the biological impact and footprint of this small leak analogue (<1 tonne CO2 d−1) is confined to a few tens of metres. Migration of CO2 through the shallow seabed is influenced by near-surface sediment structure, and by dissolution and re-precipitation of calcium carbonate naturally present in sediments. Results reported here advance the understanding of environmental sensitivity to leakage and identify appropriate monitoring strategies for full-scale carbon storage operations.

Detection and impacts of leakage from sub-seafloor deep geological carbon dioxide storage

Fossil fuel power generation and other industrial emissions of carbon dioxide are a threat to global climate1, yet many economies will remain reliant on these technologies for several decades2. Carbon dioxide capture and storage (CCS) in deep geological formations provides an effective option to remove these emissions from the climate system3. In many regions storage reservoirs are located offshore4, 5, over a kilometre or more below societally important shelf seas6. Therefore, concerns about the possibility of leakage7, 8 and potential environmental impacts, along with economics, have contributed to delaying development of operational CCS. Here we investigate the detectability and environmental impact of leakage from a controlled sub-seabed release of CO2. We show that the biological impact and footprint of this small leak analogue (<1 tonne CO2 d−1) is confined to a few tens of metres. Migration of CO2 through the shallow seabed is influenced by near-surface sediment structure, and by dissolution and re-precipitation of calcium carbonate naturally present in sediments. Results reported here advance the understanding of environmental sensitivity to leakage and identify appropriate monitoring strategies for full-scale carbon storage operations.