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1. Tidal stream energy converters (turbines) are currently being installed in tidally energetic coastal sites. However, there is currently a high level of uncertainty surrounding the potential environmental impacts on marine mammals. This is a key consenting risk to commercial introduction of tidal energy technology. Concerns derive primarily from the potential for injury to marine mammals through collisions with moving components of turbines. To understand the nature of this risk, information on how animals respond to tidal turbines is urgently required. 2. We measured the behaviour of harbour seals in response to acoustic playbacks of simulated tidal turbine sound within a narrow coastal channel subject to strong, tidally induced currents. This was carried out using data from animal-borne GPS tags and shore-based observations, which were analysed to quantify behavioural responses to the turbine sound. 3. Results showed that the playback state (silent control or turbine signal) was not a significant predictor of the overall number of seals sighted within the channel. 4. However, there was a localised impact of the turbine signal; tagged harbour seals exhibited significant spatial avoidance of the sound which resulted in a reduction in the usage by seals of between 11 and 41% at the playback location. The significant decline in usage extended to 500 m from the playback location at which usage decreased by between 1 and 9% during playback. 5. Synthesis and applications: This study provides important information for policy makers looking to assess the potential impacts of tidal turbines and advise on development of the tidal energy industry. Results showing that seals avoid tidal turbine sound suggest that a proportion of seals encountering tidal turbines will exhibit behavioural responses resulting in avoidance of physical injury; in practice, the empirical changes in usage can be used directly as avoidance rates when using collision risk models to predict the effects of tidal turbines on seals. There is now a clear need to measure how marine mammals behave in response to actual operating tidal turbines in the long term to learn whether marine mammals and tidal turbines can co-exist safely at the scales currently envisaged for the industry. JApEcol_Hastie_etal_observation_data_DryadLand based observer data (.xlsx) used in the analysis of seal responses to tidal turbine sounds. This is effectively counts of seals observed in the water during acoustic playbacks of tidal turbine sound and silent controls. Data were collected by a series of observers located on a clifftop overlooking the study area (Kyle Rhea, Isle of Skye, Scotland) README file is provided as a tab in the file.JApEcol_Hastie_etal_seal_telemetry_data_DryadHarbour seal telemetry data (.xlsx) used in the analysis of changes in usage with distance from the location of playbacks of tidal turbine sound. The data are regularised lat-lon locations from 10 individual harbour seals tagged with GPS telemetry devices. README is provided as a tab in the file.STIMweighted_J11_1hour_withRampSound file (.wav) used during playbacks of simulated tidal turbine sound to harbour seals to investigate avoidance responses. The file has a 10 second ramp at the start and end of the file, and is frequency weighted for use with a J11 underwater speaker.

Associated data and R code for the paper Epstein & Roberts 2022 - Identifying priority areas to manage mobile bottom fishing on seabed carbon in the UK. This repository contains the primary output data from a desk-based investigation of seabed sediment organic carbon (OC) and mobile demeresal fishing in the UKEEZ. Best available published datasets were combined to produce unified maps of predicted seabed OC stocks, mean annual mobile bottom fishing disturbance, mean value of fish landed by mobile bottom fishing, and mean annual cummulative disturbance to seabed carbon from mobile bottom fishing. This data was combined with modeling of estimated fishing displacement to idenitfy priority areas for managmement and/or future research. For further methodological information please refer to the full paper published at PLOS Climate.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

In the future, the Baltic Sea ecosystem will be impacted both by climate change and by riverine and atmospheric nutrient inputs. Multi-model ensemble simulations comprising one IPCC scenario (A1B), two global climate models, two regional climate models, and three Baltic Sea ecosystem models were performed to elucidate the combined effect of climate change and changes in nutrient inputs. This study focuses on the occurrence of extreme events in the projected future climate. Results suggest that the number of days favoring cyanobacteria blooms could increase, anoxic events may become more frequent and last longer, and salinity may tend to decrease. Nutrient load reductions following the Baltic Sea Action Plan can reduce the deterioration of oxygen conditions.

AbstractThe Paris Conference of Parties (COP21) agreement renewed momentum for action against climate change, creating the space for solutions for conservation of the ocean addressing two of its largest threats: climate change and ocean acidification (CCOA). Recent arguments that ocean policies disregard a mature conservation research field and that protected areas cannot address climate change may be oversimplistic at this time when dynamic solutions for the management of changing oceans are needed. We propose a novel approach, based on spatial meta‐analysis of climate impact models, to improve the positioning of marine protected areas to limit CCOA impacts. We do this by estimating the vulnerability of ocean ecosystems to CCOA in a spatially explicit manner and then co‐mapping human activities such as the placement of renewable energy developments and the distribution of marine protected areas. We test this approach in the NE Atlantic considering also how CCOA impacts the base of the food web which supports protected species, an aspect often neglected in conservation studies. We found that, in this case, current regional conservation plans protect areas with low ecosystem‐level vulnerability to CCOA, but disregard how species may redistribute to new, suitable and productive habitats. Under current plans, these areas remain open to commercial extraction and other uses. Here, and worldwide, ocean conservation strategies under CCOA must recognize the long‐term importance of these habitat refuges, and studies such as this one are needed to identify them. Protecting these areas creates adaptive, climate‐ready and ecosystem‐level policy options for conservation, suitable for changing oceans.