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The data comprise Climber3alpha+C simulations created by Matthias Hofmann (PIK) as part of the Work Package 2.1 of the COMFORT project as well as the PyFerret scripts (written by Ralf Liebermann and Matthias Hofmann) used for their evaluation. The simulation data consist of snap_*.nc files and history.nc files for ocean, atmosphere and mixed layer depth (hmxl) performed for different idealized scenarios: CONTROL, double and fourfold atmospheric CO2 (CO2X2 and CO2X4), also with additional Greenland freshwater influx (CO2X2_HOSING and CO2X4_HOSING). Furthermore, tracer simulations (CONTROL, CO2X4, CO2X4_HOSING) and simulations with constant scavenging (CO2X4) are also included. The aim was to analyse the simulations regarding climate change-induced changes in marine biogeochemistry and primary production, which will be published under the title "Shutdown of Atlantic overturning circulation could cause persistent increase of primary production in the Pacific" (see Related Work). Simulation data were generated with Climber3alpha+C (Earth system model of intermediate complexity) and evaluated with PyFerret v7.41. CDO was used to aggregate monthly simulation data into annual means.

The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.

In order to identify a potential surrogate of planktonic ciliate communities for marine bioassessments and evaluating biological conservations, the different taxonomic/numerical resolutions and taxa as surrogates were studied in Jiaozhou Bay, northern China during a 1-year cycle (June 2007-May 2008).Samples were collected biweekly from a depth of 1 m at each of five sites. A range of physicochemical parameters were also measured in order to determine water quality.The genus- and family-level resolutions maintained sufficient information to evaluate the ecological patterns of the ciliate communities in response to environmental status. The non-loricate oligotrichous ciliate assemblages in both abundance and occurrence may be used as a surrogate of planktonic ciliate communities. Heavy data transformations were an optimal strategy for the species level of planktonic ciliates, while mild data transformations were for the higher. The ordination patterns based on species biomass, occurrence, and biomass/abundance ratio matrices were significantly consistent with that of species abundance data.The results suggest that the use of simplifications at both taxonomic and numerical resolutions are time-efficient and would allow improving sampling strategies of large spatial/temporal scale monitoring programs and biological conservation researches in the marine ecosystem with a relative paucity of scientists.

Integrated multitrophic aquaculture (IMTA) is a versatile technology emerging as an ecological and sustainable solution for traditional monoculture aquacultures in terms of effluent treatment. Nevertheless, IMTA is still poorly applied in aquaculture industry due to, among other reasons, the lack of effective, low-investment and low-maintenance solutions. In this study, one has developed a practical and low maintenance IMTA-pilot system, settled in a semi-intensive coastal aquaculture. The optimisation and performance of the system was validated using Ulva spp., a macroalgae that naturally grows in the fishponds of the local aquaculture. Several cultivation experiments were performed at lab-scale and in the IMTA-pilot system, in static mode. The specific growth rate (SGR), yield, nutrient removal, N and C enrichment, protein and pigment content were monitored. Ulva spp. successfully thrived in effluent from the fish species sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) production tanks and significantly reduced inorganic nutrient load in the effluent, particularly, NH4+, PO43− and NO3−. The enrichment of nitrogen in Ulva spp.’s tissues indicated nitrogen assimilation by the algae, though, the cultivated Ulva spp. showed lower amounts of protein and pigments in comparison to the wild type. This study indicates that the designed IMTA-pilot system is an efficient solution for fish effluent treatment and Ulva spp., a suitable effluent remediator.

This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction.Time Range:1993.01-2018.12Region:GlobalLongitude:180°W~180°ELatitude:70°S~70°NParameters:SalinityHorizontal Resolution:0.25° × 0.25°Vertical Resolution:41 levels (1-2000 m)Temporal Resolution:monthlyStorage Format:netcdf This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction.Time Range:1993.01-2018.12Region:GlobalLongitude:180°W~180°ELatitude:70°S~70°NParameters:SalinityHorizontal Resolution:0.25° × 0.25°Vertical Resolution:41 levels (1-2000 m)Temporal Resolution:monthlyStorage Format:netcdf

Total oxygen uptake rates were assessed by conducting sediment core incubations. After MUC retrieval and sediment core preparation on deck, three cores were taken to a dark, temperature controlled laboratory on board Polarstern that was refrigerated to 2 °C-4 °C. Incubation procedure generally followed the approach described by Link et al. (2013, https://doi.org/10.5194/bg-10-5911-2013).

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters. Data of environmental characteristics and brown trout populations in 102 Norwegian hydropower reservoirsThe data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.Data-in_doi.org-10.1016-j.scitotenv.2017.10.268.xlsx