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This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

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.CMIP.MOHC.HadGEM3-GC31-MM.historical' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

L'empreinte carbone des technologies numériques est une préoccupation depuis plusieurs années. Cela concerne principalement la consommation électrique des datacenters; beaucoup de fournisseurs dans le domaine du cloud s'engagent à n'utiliser que des sources d'énergie renouvelables. Cependant, cette approche néglige la phase de fabrication des composants des infrastructures numériques. Nous considérons dans ce travail de recherche la question du dimensionnement des énergies renouvelables pour une infrastructure de type cloud géographiquement distribuée autour de la planète, considérant l'impact carbone à la fois de l'électricité issue du réseau électrique local en fonction de la location de sa production, et de la fabrication des panneaux photovoltaïques et des batteries pour la part renouvelable de l'alimentation des ressources. Nous avons modélisé ce problème de minimisation de l'impact carbone d'une telle infrastructure cloud sous la forme d'un programme linéaire. La solution est le dimensionnement optimal d'une fédération de cloud sur une année complète en fonction des localisations des datacenters, des traces réelles des travaux à exécuter et valeurs d'irradiation solaire heure par heure. Nos résultats montrent une réduction de l'impact carbone de 30% comparés à la même architecture cloud totalement alimentée par des énergies renouvelables et 85% comparés à un modèle qui n'utiliserait qu'une alimentation via le réseau local d'électricité. The carbon footprint of IT technologies has been a significant concern in recent years. This concern mainly focuses on the electricity consumption of data centers; many cloud suppliers commit to using 100% of renewable energy sources. However, this approach neglects the impact of device manufacturing. We consider in this work the question of dimensioning the renewable energy sources of a geographically distributed cloud with considering the carbon impact of both the grid electricity consumption in the considered locations and the manufacturing of solar panels and batteries. We design a linear program to optimize cloud dimensioning over one year, considering worldwide locations for data centers, real-life workload traces, and solar irradiation values. Our results show a carbon footprint reduction of about 30% compared to a cloud fully supplied by solar energy and of 85% compared to the 100% grid electricity model. Données computationnelles ou de simulation: En tenant compte des données en entrée (description de la fédération de centres de données, fichiers de configuration appropriés, conditions météorologiques, etc.), le logiciel est capable de proposer un dimensionnement optimal pour la fédération des datacenters à faible émission de carbone distribuée à l'échelle mondiale : surface des panneaux photovoltaïques et capacité des batteries pour chaque datacenter de la fédération. Des scripts sont disponibles pour mettre en forme les solutions proposées. Simulation or computational data: Considering given inputs (datacenter federation, appropriate configuration files, weather conditions, etc.), the software is able to propose an optimal sizing for the globally distributed low carbon cloud federation: surface area of solar panels, battery capacity for each data center location. . Scripts are available to shape the optimal configuration. Audience: Research, Policy maker UpdatePeriodicity: as needed

Dataset for the journal paper "Towards Active Buildings: rating grid-servicing buildings", which describes the simulations for the 20 case study buildings. The simulation inputs describe the intended characteristics as part of the early design stage process, and the outputs the performance metrics under the rating system introduced in the journal paper, called the ABCode1. Such outputs rate the relative merits of each case study in terms of embodied carbon, energy requirements, energy generation and energy flexibility. The simulation outputs have been generated using the inputs included in the dataset, which were then simulated in David Coley’s ZEBRA and then evaluated with the rating system proposed in the journal publication as part of ABCode1. The files are in the original Excel xlsx file (Microsoft Office 365), but it may be viewed by any other spread sheet tools such as LibreOffice's Calc.

The European Multidisciplinary Seafloor and water column Observatory (EMSO-ERIC, https://emso.eu/) is a research infrastructure distributed throughout Europe for seabed and water column observatories. It aims to further explore the oceans, better understand the phenomena that occur on the seabed, and elucidate the critical role that these phenomena play in global Earth systems. This observatory is based on observation sites (or nodes) that have been deployed in strategic locations in European seas, from the Arctic to the Atlantic, from the Mediterranean to the Black Sea. There are currently eleven deepwater nodes plus four shallow water test nodes. EMSO-Western Ligurian Sea Node (https://www.emso-fr.org/fr) is a second generation permanent submarine observatory deployed offshore of Toulon, France, as a follow up of the pioneering ANTARES neutrino telescope. This submarine network, deployed at a depth of 2450m, is part of KM3NeT (https://www.km3net.org/) which has a modular topology designed to connect up to 120 neutrino detection units, i.e. ten times more than ANTARES. The Earth and Sea Science (ESS) instrumentation connected to KM3NeT is based on two complementary components: an Instrumented Interface Module (MII) and an autonomous mooring line (ALBATROSS). The ALBATROSS line is an inductive instrumented mooring line (2000 m) composed of an acoustic communication system, two inductive cables equipped with CTD-O2 sensors, current meters and two instrumented buoys. The MII and the ALMBATROSS mooring line communicate through an acoustic link. The MII is connected to an electro-optical cable via the KM3NeT node allowing the data transfer from and to the land based controlled room.

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

Article Abstract To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.

# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Reference | Source for the data. | ## Sharing/Access information Data was derived from the following sources: **Appendix 1. Full list of references providing the data used in impact and recovery analyses supporting Table S1** Arceo, H. O., Quibilan, M. C., Aliño, P. M., Lim, G., & Licuanan, W. Y. (2001). Coral bleaching in Philippine reefs: Coincident evidences with mesoscale thermal anomalies. Bulletin of Marine Science, 69(2), 579-593. Aronson, R. B., Precht, W. F., Toscano, M. A., & Koltes, K. H. (2002). The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology, 141(3), 435-447. Aronson, R. B., Sebens, K. P., & Ebersole, J. P. (1994). Hurricane Hugo's impact on Salt River submarine canyon, St. Croix, US Virgin Islands. Proceedings of the colloquium on global aspects of coral reefs, Miami, 1993, 189-195. Bahr, K. D., Rodgers, K. S., & Jokiel, P. L. (2017). Impact of three bleaching events on the reef resiliency of Kāne'ohe Bay, Hawai'i. Frontiers in Marine Science, 4(DEC). Baird, A. H., Álvarez-Noriega, M., Cumbo, V. R., Connolly, S. R., Dornelas, M., & Madin, J. S. (2018). Effects of tropical storms on the demography of reef corals. Marine Ecology Progress Series, 606, 29-38. Barranco, L. M., Carriquiry, J. D., Rodríguez-Zaragoza, F. A., Cupul-Magaña, A. L., Villaescusa, J. A., & Calderón-Aguilera, L. E. (2016). Spatiotemporal variations of live coral cover in the Northern Mesoamerican reef system, Yucatan Peninsula, Mexico. Scientia Marina, 80(2), 143-150. Bastidas, C., Bone, D., Croquer, A., Debrot, D., Garcia, E., Humanes, A., . . . Rodríguez, S. (2012). Massive hard coral loss after a severe bleaching event in 2010 at Los Roques, Venezuela. Revista de Biologia Tropical, 60(SUPPL. 1), 29-37. Booth, D. J., & Beretta, G. A. (2002). Changes in a fish assemblage after a coral bleaching event. Marine Ecology Progress Series, 245, 205-212. Brandl, S. J., Emslie, M. J., & Ceccarelli, D. M. (2016). Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. Ecosphere, 7(11). Brown, D., & Edmunds, P. J. (2013). Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. Journal of Experimental Marine Biology and Ecology, 441, 62-70. Brown, V. B., Davies, S. A., & Synnot, R. N. (1990). Long-term Monitoring of the Effects of Treated Sewage Effluent on the Intertidal Macroalgal Community Near Cape Schanck, Victoria, Australia. Botanica Marina, 33(1), 85-98. Bruckner, A. W., Coward, G., Bimson, K., & Rattanawongwan, T. (2017). Predation by feeding aggregations of Drupella spp. inhibits the recovery of reefs damaged by a mass bleaching event. Coral Reefs, 36(4), 1181-1187. Burt, J. A., Paparella, F., Al-Mansoori, N., Al-Mansoori, A., & Al-Jailani, H. (2019). Causes and consequences of the 2017 coral bleaching event in the southern Persian/Arabian Gulf. Coral Reefs. Bythell, J. (1997). Assessment of the impacts of hurricanes Marilyn and Luis and post-hurricane community dynamics at Buck Island Reef National Monument as part of the long-term coral reef monitoring program in the north-eastern Caribbean. Retrieved from Newcastle, United Kingdom: Coles, S. L., & Brown, E. K. (2007). Twenty-five years of change in coral coverage on a hurricane impacted reef in Hawai'i: The importance of recruitment. Coral Reefs, 26(3), 705-717. Connell, J. H., Hughes, T. P., Wallace, C. C., Tanner, J. E., Harms, K. E., & Kerr, A. M. (2004). A long‐term study of competition and diversity of corals. Ecological Monographs, 74(2), 179-210. Couch, C. S., Burns, J. H. R., Liu, G., Steward, K., Gutlay, T. N., Kenyon, J., . . . Kosaki, R. K. (2017). Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE, 12(9). Crabbe, M. J. C. (2014). Evidence of initial coral community recovery at Discovery Bay on Jamaica’s north coast. Revista de Biologia Tropical, 62, 137-140. Crosbie, A. J., Bridge, T. C., Jones, G., & Baird, A. H. (2019). Response of reef corals and fish at Osprey Reef to a thermal anomaly across a 30 m depth gradient. Marine Ecology Progress Series, 622, 93-102. Darling, E. S., McClanahan, T. R., & Côté, I. M. (2010). Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conservation Letters, 3(2), 122-130. De Bakker, D. M., Meesters, E. H., Bak, R. P. M., Nieuwland, G., & Van Duyl, F. C. (2016). Long-term Shifts in Coral Communities On Shallow to Deep Reef Slopes of Curaçao and Bonaire: Are There Any Winners? Frontiers in Marine Science, 3(247). Depczynski, M., Gilmour, J. P., Ridgway, T., Barnes, H., Heyward, A. J., Holmes, T. H., . . . Wilson, S. K. (2013). Bleaching, coral mortality and subsequent survivorship on a West Australian fringing reef. Coral Reefs, 32(1), 233-238. Diaz-Pulido, G., McCook, L. J., Dove, S., Berkelmans, R., Roff, G., Kline, D. I., . . . Hoegh-Guldberg, O. (2009). Doom and Boom on a Resilient Reef: Climate Change, Algal Overgrowth and Coral Recovery. PLoS ONE, 4(4). Dollar, S. J., & Tribble, G. W. (1993). Recurrent storm disturbance and recovery: a long-term study of coral communities in Hawaii. Coral Reefs, 12(3-4), 223-233. Donner, S. D., Kirata, T., & Vieux, C. (2010). Recovery from the 2004 coral bleaching event in the Gilbert Islands, Kiribati. Atoll Research Bulletin(587), 1-25. Edmunds, P. J. (2013). Decadal-scale changes in the community structure of coral reefs of St. John, US Virgin Islands. Marine Ecology Progress Series, 489, 107-123. Edmunds, P. J. (2018). Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Scientific Reports, 8(1). Edmunds, P. J. (2019). Three decades of degradation lead to diminished impacts of severe hurricanes on Caribbean reefs. Ecology, 100(3). Edward, J. K. P., Mathews, G., Diraviya Raj, K., Laju, R. L., Selva Bharath, M., Arasamuthu, A., . . . Malleshappa, H. (2018). Coral mortality in the Gulf of Mannar, southeastern India, due to bleaching caused by elevated sea temperature in 2016. Current Science, 114(9), 1967-1972. Edwards, A. J., Clark, S., Zahir, H., Rajasuriya, A., Naseer, A., & Rubens, J. (2001). Coral bleaching and mortality on artificial and natural reefs in Maldives in 1998, sea surface temperature anomalies and initial recovery. Marine Pollution Bulletin, 42(1), 7-15. Emslie, M. J., Bray, P., Cheal, A. J., Johns, K. A., Osborne, K., Sinclair-Taylor, T., & Thompson, C. A. (2020). Decades of monitoring have informed the stewardship and ecological understanding of Australia's Great Barrier Reef. Biological Conservation, 252, 108854. Fenner, D. P. (1991). Effects of Hurricane Gilbert on coral reefs, fishes and sponges at Cozumel, Mexico. Bulletin of Marine Science, 48(3), 719-730. Fox, M. D., Carter, A. L., Edwards, C. B., Takeshita, Y., Johnson, M. D., Petrovic, V., . . . Smith, J. E. (2019). Limited coral mortality following acute thermal stress and widespread bleaching on Palmyra Atoll, central Pacific. Coral Reefs. García-Sais, J. R., Williams, S. M., & Amirrezvani, A. (2017). Mortality, recovery, and community shifts of scleractinian corals in Puerto Rico one decade after the 2005 regional bleaching event. PeerJ, 2017(7). Garpe, K. C., Yahya, S. A. S., Lindahl, U., & Öhman, M. C. (2006). Long-term effects of the 1998 coral bleaching event on reef fish assemblages. Marine Ecology Progress Series, 315, 237-247. Gilmour, J. P., Cook, K. L., Ryan, N. M., Puotinen, M. L., Green, R. H., Shedrawi, G., . . . Oades, D. (2019). The state of Western Australia’s coral reefs. Coral Reefs. Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H., & Pratchett, M. S. (2013). Recovery of an isolated coral reef system following severe disturbance. Science, 340(6128), 69-71. Glynn, P. W. (1984). Widespread coral mortality and the 1982-1983 El Niño warming event. Environmental Conservation, 11(2), 133-146. Glynn, P. W., Enochs, I. C., Afflerbach, J. A., Brandtneris, V. W., & Serafy, J. E. (2014). Eastern Pacific reef fish responses to coral recovery following El Niño disturbances. Marine Ecology Progress Series, 495, 233-247. Gouezo, M., Golbuu, Y., Van Woesik, R., Rehm, L., Koshiba, S., & Doropoulos, C. (2015). Impact of two sequential super typhoons on coral reef communities in Palau. Marine Ecology Progress Series, 540, 73-85. Guest, J. R., Tun, K., Low, J., Vergés, A., Marzinelli, E. M., Campbell, A. H., . . . Steinberg, P. D. (2016). 27 years of benthic and coral community dynamics on turbid, highly urbanised reefs off Singapore. Scientific Reports, 6. Guillemot, N., Chabanet, P., & Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). Coral Reefs, 29(2), 445-453. Guzmán, H. M., & Cortés, J. (2001). Changes in reef community structure after fifteen years of natural disturbances in the Eastern Pacific (Costa Rica). Bulletin of Marine Science, 69(1), 133-149. Guzman, H. M., Cortes, J., Richmond, R. H., & Glynn, P. W. (1987). Effects of "El Nino - Southern oscillation' 1982/83 in the coral reefs at Isla del Cano, Costa Rica. Revista de Biologia Tropical, 35(2), 325-332. Haapkylä, J., Melbourne-Thomas, J., Flavell, M., & Willis, B. L. (2013). Disease outbreaks, bleaching and a cyclone drive changes in coral assemblages on an inshore reef of the Great Barrier Reef. Coral Reefs, 32(3), 815-824. Hagan, A., & Spencer, T. (2008). Reef resilience and change 1998–2007, Alphonse Atoll, Seychelles. Paper presented at the Proc 11th Int Coral Reef Symp. Harii, S., Hongo, C., Ishihara, M., Ide, Y., & Kayanne, H. (2014). Impacts of multiple disturbances on coral communities at Ishigaki Island, Okinawa, Japan, during a 15 year survey. Marine Ecology Progress Series, 509, 171-180. Harrison, H. B., Álvarez-Noriega, M., Baird, A. H., Heron, S. F., MacDonald, C., & Hughes, T. P. (2018). Back-to-back coral bleaching events on isolated atolls in the Coral Sea. Coral Reefs. Holbrook, S. J., Adam, T. C., Edmunds, P. J., Schmitt, R. J., Carpenter, R. C., Brooks, A. J., . . . Briggs, C. J. (2018). Recruitment Drives Spatial Variation in Recovery Rates of Resilient Coral Reefs. Scientific Reports, 8(1). Hongo, C., & Yamano, H. (2013). Species-Specific Responses of Corals to Bleaching Events on Anthropogenically Turbid Reefs on Okinawa Island, Japan, over a 15-year Period (1995-2009). PLoS ONE, 8(4). Huang, H., Yang, Y., Li, X., Yang, J., Lian, J., Lei, X., . . . Zhang, J. (2014). Benthic community changes following the 2010 Hainan flood: Implications for reef resilience. Marine Biology Research, 10(6), 601-611. Hughes, T. P. (1994). Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science, 265(5178), 1547-1551. Jokiel, P. L., Hunter, C. L., Taguchi, S., & Watarai, L. (1993). Ecological impact of a fresh-water "reef kill" in Kaneohe Bay, Oahu, Hawaii. Coral Reefs, 12(3-4), 177-184. Jones, A. M., & Berkelmans, R. (2014). Flood impacts in Keppel Bay, Southern Great Barrier Reef in the aftermath of cyclonic rainfall. PLoS ONE, 9(1). Jonker, M., Johns, K., & Osborne, K. (2008). Surveys of benthic reef communities using underwater digital photography and counts of juveniles. Long-term monitoring of the Great Barrier Reef Standard Operation Procedure Number 10. Retrieved from Townsville: Kuo, C. Y., Yuen, Y. S., Meng, P. J., Ho, P. H., Wang, J. T., Liu, P. J., . . . Chen, C. A. (2012). Recurrent Disturbances and the Degradation of Hard Coral Communities in Taiwan. PLoS ONE, 7(8). Lam, V. Y. Y., Chaloupka, M., Thompson, A., Doropoulos, C., & Mumby, P. J. (2018). Acute drivers influence recent inshore Great Barrier Reef dynamics. Proceedings of the Royal Society B: Biological Sciences, 285(1890). Lambo, A. L., & Ormond, R. F. G. (2006). Continued post-bleaching decline and changed benthic community of a Kenyan coral reef. Marine Pollution Bulletin, 52(12), 1617-1624. Lamy, T., Galzin, R., Kulbicki, M., Lison de Loma, T., & Claudet, J. (2016). Three decades of recurrent declines and recoveries in corals belie ongoing change in fish assemblages. Coral Reefs, 35(1), 293-302. Lamy, T., Legendre, P., Chancerelle, Y., Siu, G., & Claudet, J. (2015). Understanding the spatio-temporal response of coral reef fish communities to natural disturbances: Insights from beta-diversity decomposition. PLoS ONE, 10(9). Liddell, W. D., & Ohlhorst, S. L. (1992). Ten years of disturbance and change on a Jamaican fringing reef. Paper presented at the 7th Int. Coral Reef Symp. Lirman, D., Glynn, P. W., Baker, A. C., & Morales, G. E. L. (2001). Combined effects of three sequential storms on the huatulco coral reef tract, mexico. Bulletin of Marine Science, 69(1), 267-278. Lovell, E., & Sykes, H. Rapid recovery from bleaching events-Fiji Coral Reef Monitoring Network Assessment of hard coral cover from. Loya, Y., Sakai, K., Yamazato, K., Nakano, Y., Sambali, H., & Van Woesik, R. (2001). Coral bleaching: The winners and the losers. Ecology Letters, 4(2), 122-131. Lozano-Montes, H. M., Keesing, J. K., Grol, M. G., Haywood, M. D. E., Vanderklift, M. A., Babcock, R. C., & Bancroft, K. (2017). Limited effects of an extreme flood event on corals at Ningaloo Reef. Estuarine, Coastal and Shelf Science, 191, 234-238. Madin, J. S., Baird, A. H., Bridge, T. C. L., Connolly, S. R., Zawada, K. J. A., & Dornelas, M. (2018). Cumulative effects of cyclones and bleaching on coral cover and species richness at Lizard Island. Marine Ecology Progress Series, 604, 263-268. Magdaong, E. T., Fujii, M., Yamano, H., Licuanan, W. Y., Maypa, A., Campos, W. L., . . . Martinez, R. (2014). Long-term change in coral cover and the effectiveness of marine protected areas in the Philippines: A meta-analysis. Hydrobiologia, 733(1), 5-17. McField, M. (2000). Influence of disturbance on coral reef community structure in Belize. Paper presented at the Proc 9th Int Coral Reef Symp. Monaco, M. E., Friedlander, A. M., Caldow, C., Hile, S. D., Menza, C., & Boulon, R. H. (2009). Long-term monitoring of habitats and reef fish found inside and outside the U.S. Virgin Islands Coral Reef National Monument: A comparative assessment. Caribbean Journal of Science, 45(2-3), 338-347. Montefalcone, M., Morri, C., & Bianchi, C. N. (2018). Long-term change in bioconstruction potential of Maldivian coral reefs following extreme climate anomalies. Global Change Biology, 24(12), 5629-5641. Morgan, K. M., Perry, C. T., Johnson, J. A., & Smithers, S. G. (2017). Nearshore turbid-zone corals exhibit high bleaching tolerance on the Great Barrier Reef following the 2016 ocean warming event. Frontiers in Marine Science, 4. Obura, D., Gudka, M., Rabi, F. A., Gian, S. B., Bijoux, J., Freed, S., . . . Sola, E. (2017). Coral Reef Status Report for the Western Indian Ocean (2017). Paper presented at the Nairobi Convention. Obura, D., & Mangubhai, S. (2011). Coral mortality associated with thermal fluctuations in the Phoenix Islands, 2002-2005. Coral Reefs, 30(3), 607-619. Ostrander, G. K., Armstrong, K. M., Knobbe, E. T., Gerace, D., & Scully, E. P. (2000). Rapid transition the structure of a coral reef community: The effects of coral bleaching and physical disturbance. Proceedings of the National Academy of Sciences of the United States of America, 97(10), 5297-5302. Pereira, M. A. M., & Gonçalves, P. M. B. (2004). Effects of the 2000 southern Mozambique floods on a marginal coral community: The case at Xai-Xai. African Journal of Aquatic Science, 29(1), 113-116. Perry, C. T. (2003). Reef development at Inhaca Island, Mozambique: Coral communities and impacts of the 1999/2000 southern African floods. Ambio, 32(2), 134-139. Phongsuwan, N., Chankong, A., Yamarunpatthana, C., Chansang, H., Boonprakob, R., Petchkumnerd, P., . . . Bundit, O. A. (2013). Status and changing patterns on coral reefs in Thailand during the last two decades. Deep-Sea Research Part II: Topical Studies in Oceanography, 96, 19-24. Reyes-Bonilla, H., Carriquiry, J. D., Leyte-Morales, G. E., & Cupul-Magaña, A. L. (2002). Effects of the El Niño-Southern Oscillation and the anti-El Niño event (1997-1999) on coral reefs of the western coast of México. Coral Reefs, 21(4), 368-372. Ridgway, T., Inostroza, K., Synnot, L., Trapon, M., Twomey, L., & Westera, M. (2016). Temporal patterns of coral cover in the offshore Pilbara, Western Australia. Marine Biology, 163(9). Riegl, B. (2002). Effects of the 1996 and 1998 positive sea-surface temperature anomalies on corals, coral diseases and fish in the Arabian Gulf (Dubai, UAE). Marine Biology, 140(1), 29-40. Rioja-Nieto, R., Chiappa-Carrara, X., & Sheppard, C. (2012). Effects of hurricanes on the stability of reef-associated landscapes. Ciencias Marinas, 38(1), 47-55. Rogers, C. S., Gilnack, M., & Fitz Iii, H. C. (1983). Monitoring of coral reefs with linear transects: A study of storm damage. Journal of Experimental Marine Biology and Ecology, 66(3), 285-300. Rousseau, Y., Galzin, R., & Maréchal, J. P. (2010). Impact of hurricane Dean on coral reef benthic and fish structure of Martinique, French West Indies. Cybium, 34(3), 243-256. Russ, G. R., & Leahy, S. M. (2017). Rapid decline and decadal-scale recovery of corals and Chaetodon butterflyfish on Philippine coral reefs. Marine Biology, 164(1). Ruzicka, R. R., Colella, M. A., Porter, J. W., Morrison, J. M., Kidney, J. A., Brinkhuis, V., . . . Colee, J. (2013). Temporal changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño. Marine Ecology Progress Series, 489, 125-141. Sheppard, C. R. C. (1999). Coral decline and weather patterns over 20 years in the Chagos Archipelago, central Indian Ocean. Ambio, 28(6), 472-478. Shulman, M. J., & Robertson, D. R. (1996). Changes in the coral reefs of San Bias, Caribbean Panama: 1983 to 1990. Coral Reefs, 15(4), 231-236. Smith, T. B., Brandt, M. E., Calnan, J. M., Nemeth, R. S., Blondeau, J., Kadison, E., . . . Rothenberger, P. (2013). Convergent mortality responses of Caribbean coral species to seawater warming. Ecosphere, 4(7). Steneck, R. S., Arnold, S. N., Boenish, R., de León, R., Mumby, P. J., Rasher, D. B., & Wilson, M. W. (2019). Managing Recovery Resilience in Coral Reefs Against Climate-Induced Bleaching and Hurricanes: A 15 Year Case Study From Bonaire, Dutch Caribbean. Frontiers in Marine Science, 6(265). Stobart, B., Teleki, K., Buckley, R., Downing, N., & Callow, M. (2005). Coral recovery at Aldabra Atoll, Seychelles: Five years after the 1998 bleaching event. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 363(1826), 251-255. Torda, G., Sambrook, K., Cross, P., Sato, Y., Bourne, D. G., Lukoschek, V., . . . Willis, B. L. (2018). Decadal erosion of coral assemblages by multiple disturbances in the Palm Islands, central Great Barrier Reef. Scientific Reports, 8(1). Trapon, M. L., Pratchett, M. S., & Penin, L. (2011). Comparative effects of different disturbances in coral reef habitats in Moorea, French Polynesia. Journal of Marine Biology, 2011. Tsounis, G., & Edmunds, P. J. (2017). Three decades of coral reef community dynamics in St. John, USVI: A contrast of scleractinians and octocorals. Ecosphere, 8(1). Van Woesik, R., De Vantier, L. M., & Glazebrook, J. S. (1995). Effects of Cyclone "Joy' on nearshore coral communities of the Great Barrier Reef. Marine Ecology Progress Series, 128(1-3), 261-270. Van Woesik, R., Sakai, K., Ganase, A., & Loya, Y. (2011). Revisiting the winners and the losers a decade after coral bleaching. Marine Ecology Progress Series, 434, 67-76. Vercelloni, J., Kayal, M., Chancerelle, Y., & Planes, S. (2019). Exposure, vulnerability, and resiliency of French Polynesian coral reefs to environmental disturbances. Scientific Reports, 9(1). Walsh, W. J. (1983). Stability of a coral reef fish community following a catastrophic storm. Coral Reefs, 2(1), 49-63. Wilkinson, C. (2004). Status of coral reefs of the world: 2004 (Vol. 2). Queensland, Australia: Global Coral Reef Monitoring Network. Wilkinson, C. R., & Souter, D. (2008). Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.

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.MOHC.HadGEM3-GC31-MM.ssp126' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.