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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.

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

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).

In recent years, in order to promote the independent development of the new energy vehicle industry, Chinese government has decided to reduce the consumption subsidies for new energy vehicles until the subsidies are completely withdrawn. The reduction of consumption subsidy has a great impact on the production and sales of new energy vehicles in the whole vehicle market. However, does the reduction of this subsidy also have an impact on other enterprises in the new energy vehicles industry chain? This paper tests this problem using data from 2016 to 2018, and finds, through empirical analysis, that during the period of subsidy decline, the profitability of component enterprises is significantly positively correlated with this subsidies, while the r&d investment of enterprises is significantly negatively correlated with this subsidies. The results show that in terms of profitability, the reduction of consumer subsidies not only has an impact on the whole vehicle industry of new energy vehicles, but also has an adverse impact on the core component companies in the industrial chain. However, in terms of r&d, the reduction of subsidies has more negatively strengthened the input and attention of R&D in component companies.

Suggested citation: Pörtner, H.O., Scholes, R.J., Agard, J., Archer, E., Arneth, A., Bai, X., Barnes, D., Burrows, M., Chan, L., Cheung, W.L., Diamond, S., Donatti, C., Duarte, C., Eisenhauer, N., Foden, W., Gasalla, M. A., Handa, C., Hickler, T., Hoegh-Guldberg, O., Ichii, K., Jacob, U., Insarov, G., Kiessling, W., Leadley, P., Leemans, R., Levin, L., Lim, M., Maharaj, S., Managi, S., Marquet, P. A., McElwee, P., Midgley, G., Oberdorff, T., Obura, D., Osman, E., Pandit, R., Pascual, U., Pires, A. P. F., Popp, A., Reyes-García, V., Sankaran, M., Settele, J., Shin, Y. J., Sintayehu, D. W., Smith, P., Steiner, N., Strassburg, B., Sukumar, R., Trisos, C., Val, A.L., Wu, J., Aldrian, E., Parmesan, C., Pichs-Madruga, R., Roberts, D.C., Rogers, A.D., Díaz, S., Fischer, M., Hashimoto, S., Lavorel, S., Wu, N., Ngo, H.T. 2021. IPBES-IPCC co-sponsored workshop report on biodiversity and climate change; IPBES and IPCC, DOI:10.5281/zenodo.4782538 This report presents the main conclusions of the first-ever IPCC-IPBES co-sponsored workshop which took place in December 2020. The workshop explored diverse facets of the interaction between climate and biodiversity, from current trends to the role and implementation of nature-based solutions and the sustainable development of human society. This report is underpinned by the Scientific Outcome, which includes seven sections, the complete references and the report glossary. You can find the Scientific Outcome here https://doi.org/10.5281/zenodo.4659158

{"references": ["Allison EH, Ellis F. 2001. The livelihoods approach and management of small-scale fisheries. Marine Policy 25, 377-388.", "Aphunu A, Nwabeze G. 2012. Fish Farmers' Perception of Climate change impact on fish production in Delta State, Nigeria. Journal of Agricultural Extension 16(2), 1-13.", "Brander K. 2010. Impacts of climate change on fisheries. Journal of Marine Systems 79(3-4), 389-402.", "Brooks N, Adger N. 2005. The determinants vulnerability and adaptive capacity at the national level and implications for adaptation. Global Environmental Change 15, 151-163.", "Cheung WWL, Lam VWY, Sarmiento JL, Kearney K, Watson R, Zeller D, Pauly D. 2010. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology 16(1), 24-35.", "Cinner JE, Huchery C, Hicks CC, Daw TM, Marshall N, Wamukota A, Allison EH. 2015. Changes in adaptive capacity of Kenyan fishing communities. Nature Climate Change 5(9), 872-876.", "Hoegh-Guldberg O, Bruno JF. 2010. The impact of climate change on the world's marine ecosystems. Science 328(5985), 1523-1528.", "Hollowed AB, Kim S, Barange M, Loeng H. 2013. Report of the Pices/Ices Working Group on Forecasting Climate Change Impacts on Fish and Shellfish. Report of the PICES/ICES Working Group on Forecasting Climate Change Impacts on Fish and Shellfish. North Pacific Marine Science Organization (PICES), Sidney, British Columbia, 1-197.", "IPCC. 2007. Summary for policymakers. In M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, C.E. Hanson, editors. Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, 23.", "Mcclanahan T, Cinner J, Maina J, Graham N, Daw T, Stead S, Polunin, N. 2008. Conservation action in a changing climate. Conservation Letters 1(2), 53-59.", "Pana MCF. 2012. Perceptions and adaptation capacities of fishermen on climate change: the case of Palawan, Philippines. Journal of Applied Sciences in Environmental Sanitation 7(3), 153-160.", "Pollnac RB, Crawford B. 2000. Assessing behavioral aspects of coastal resource use. Proyek Pesisir Publications Special Report. Coastal Resources Center Coastal Management Report 2226. Coastal Resorces Center, University of Rhode Island, Narragansett, Rhode Island, 1-139.", "Poloczanska ES, Brown CJ, Sydeman WJ, Kiessling W, Schoeman DS, Moore PJ, Richardson AJ. 2013. Global imprint of climate change on marine life. Nature Climate Change 3(10), 919-925.", "Pretty J, Ward H. 2001. Social capital and the environment. World Development 29, 209-277.", "Roy TN. 2012. Economic Analysis of Producers' Perceptions about Impact of Climate Change on Fisheries in West Bengal. Agricultural Economics Research Review 25(1), 161-166.", "Tompkins EL. 2005. Planning for climate change in small islands: Insights from national hurricane preparedness in the Cayman Islands. Global Environmental Change 15, 139-149.", "Climate Change Commission. 2011. National Climate Change Action Plan 2011-2028, 1-128."]} Climate change has been affecting many coastal communities around the world. With the location of the Philippines in the tropics, the country is vulnerable to the impacts brought by this phenomenon affecting the safety, livelihood, and income distribution of the fishing communities in particular. Quantifying adaptive capacity to climate change is critical in reducing the vulnerability of these affected communities. This study was conducted to assess the adaptive capacity in the household level of the different social groups in the fishing communities of the Municipality of Sindangan, Zamboanga del Norte, Philippines. Using the interval-level scale generated from the eight indicators of adaptive capacity comprising human agency, capacity to change, occupational mobility, material assets, occupational multiplicity, social capital and infrastructure, and the Analytical Hierarchy Process (AHP), the data revealed differentiation between social groups. The result showed that fishers who are land tenants, members in fishing boats, large household sizes, fishers belonging in the age group of 29 to 36 years old, and fishers who finished High School are least prepared for the changes in climate. Findings of this study support previous findings of the adaptive capacity conducted around the world. These results highlight the most vulnerable sectors of society, which will help guide local policymakers to formulate environmental adaptation plans appropriate for the social groups in a fishing community.

Studies have shown that food chain length is governed by interactions between species richness, ecosystem size, and resource availability. While redundant trophic links may buffer impacts of species loss on food chain length, higher extinction risks associated with predators may result in bottom-heavy food webs with shorter food chains. The lack of consensus in earlier empirical studies relating species richness and food chain length reflects the need to account robustly for the factors described above. In response to this, we conducted an empirical study to elucidate impacts of land-use change on food chain length in tropical forest streams of Southeast Asia. Despite species losses associated with forest loss at our study areas, results from amino acid isotope analyses showed that food chain length was not linked to land use, ecosystem size or resource availability. Correspondingly, species losses did not have a significant effect on occurrence likelihoods of all trophic guilds except herbivores. Impacts of species losses were likely buffered by high levels of initial trophic redundancy, which declined with canopy cover. Declines in trophic redundancy were most drastic amongst invertivorous fishes. Declines in redundancy across trophic guilds were also more pronounced in wider and more resource-rich streams. While our study found limited evidence for immediate land-use impacts on stream food chains, the potential loss of trophic redundancy in the longer term implies increasing vulnerability of streams to future perturbations, as long as land conversion continues unabated.

Aim: Environmental drivers and host richness play key roles in affecting herbivore diversity. However, the relative effects of these factors and their effects on lineages characterized by high host specificity are not well known. In this study, we explored the extent to which contemporary climate, Quaternary climate change, habitat heterogeneity, and host plants determine the species richness and endemism patterns of herbivorous eriophyoid mites. Location: Global. Taxon: Eriophyoid mites (Acari: Eriophyoidea). Methods: We compiled a dataset comprising 4,278 eriophyoid mite species from 22,973 occurrence sites based on a comprehensive search of the published literature and the Global Biodiversity Information Facility (GBIF) as a basis for predicting their global distribution patterns. We measured the association of environmental variables and host plant richness with species richness and endemism of eriophyoid mites through multiple regression analyses using a simultaneous autoregressive (SAR) model, an ordinary least squares (OLS) model, and a random forest model. We examined the direct and indirect effects of these environmental variables and the host plant richness on eriophyoid mite diversity using structural equation models (SEMs). Results: The species richness and endemism patterns of eriophyoid mites are concentrated in temperate regions. Contemporary climate, Quaternary climate change, habitat heterogeneity, and host plants all significantly affected eriophyoid mite richness, while Quaternary climate change, habitat heterogeneity, and host plants contributed to the eriophyoid mite endemism. Abiotic factors indirectly influenced the species richness and endemism of eriophyoid mites, via biotic factors—host plants. Main conclusions: The species richness and endemism of eriophyoid mites peak in temperate regions, opposite to the patterns of plants and some other organisms. Complex interactions among biotic and abiotic factors shape the current eriophyoid mite species diversity.