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climate change

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  • The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30′ E-119°45′ E, 39°10′ N-42°40′ N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012; (3) the flood inundation prediction under different development strategies and climate scenarios to 2030; (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168

  • The QUEST-GSI WPd1 "Climate scenarios". The aim was to construct climate scenarios representing the effects of uncertainty and different rates of climate forcing. This dataset contains model data which construct climate scenarios. The project requires climate scenarios which (a) characterise the uncertainty in the climate change associated with a given forcing, including changes in climate variability and extreme events, and (b) allow the construction of generalised relationships between climate forcing and impact.

  • This data contains the time series flow discharge results of hydrological simulation of the River Trent at Colwick using UKCP09 Weather Generator inputs for a variety of time slices and emissions scenarios. The Weather Generator (WG) inputs were run on a hydrological model (Leathard et al., unpublished), calibrated using the observed record 1961-2002. Each simulation is derived from 100 30-year time series of weather at the WG location 4400355 for Control, Low, Medium and High emissions scenarios for the 2020s, 2030s, 2040s, 2050s and 2080s time slices. The datasets include the relevant accompanying input WG data. Full details about this dataset can be found at https://doi.org/10.5285/986d3df3-d9bf-42eb-8e18-850b8d54f37b

  • This dataset is derived from modelled changes to the distributions of >12,700 terrestrial mammal and bird species under four different climate scenarios, projected to 2070. It contains national-level projections of species richness change under each climate scenario, based on species' modelled climatic niches, as well as projected range shifts in relation to political borders globally. Full details about this dataset can be found at https://doi.org/10.5285/5bf972a8-c9a3-4721-8089-552dfe3ff124

  • This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate. Full details about this dataset can be found at https://doi.org/10.5285/da5d7028-2aec-4da2-96ff-f347a0dfa77e

  • The QUEST-GSI WP-I5 "Aquatic Ecosystems" project provided an analysis of global fisheries vulnerability across a range of global climate models, emissions scenarios, fixed degree scenarios and alternative impact metrics. This dataset contains model output data from the emission, fixed degree, Cheung potential analysis, Allison socio-economic comparison and freshwater run-off analysis scenarios. -Emission Scenarios- These results are from the analysis using the SRES emissions scenarios from the IPCC AR4 - A1b, A2, B1 and B2. -Fixed Degree- This analysis was driven by the fixed degree rise scenarios, corresponding to a fixed increase in global temperature by 2050. These are 1 to 4 degrees C, in half degree increments, with each fishery impact equally weighted across freshwater, EEZ and High Seas (see report). They are also carried out for a variety of GCMs and socio-economic scenarios. -Cheung Potential Catch Analysis- These results were generated for marine fisheries using an alternative metric to temperature change in calculating potential impact- that of predicted change in potential catch from the study carried out by W.W.L. Cheung et al. (2009 Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology 16, 24-35). This was carried out for the A1b SRES scenario using the GFDL CM2.1 global climate model. -Allison Socio-economic comparison- A comparison study using the adaptive capacity metric developed in Allison et al. (2009 Vulnerability of national economies to the impacts of climate change on fisheries. Fish and Fisheries 10, 173–196). This was undertaken for the A1b Emission Scenario using HadCM3. -Freshwater Runoff Analysis- Using predicted changes in freshwater availability from the outputs of QUEST-GSI WP-I1 global water resources project, an alternative analysis for freshwater fisheries vulnerability was carried out. This was under the 2 degrees fixed increase scenario using HadCM3.

  • Quaternary QUEST was led by Dr Tim Lenton at UEA, with a team of 10 co-investigators at the Universities of Cambridge, Oxford, Reading, Leeds, Bristol, Southampton and at UEA. This dataset contains FAMOUS (FAst Met Office/UK Universities Simulator) glacial cycle model data from 150,000 years ago to present. The project team aimed to compile a synthesis of palaeodata from sediments and ice cores, improve the synchronization of these records with each other, and use this greater understanding of the Earth’s ancient atmosphere to improve Earth system models simulating climate over very long timescales. A combined long-term data synthesis and modelling approach has helped to constrain some key mechanisms responsible for glacial-interglacial CO2 change, and Quaternary QUEST have narrowed the field of ocean processes that could have caused glacial CO2 drawdown.

  • This dataset represent hydrological statistics calculated over a 30‐year period, at a spatial resolution (over land) of 0.5x0.5o across the global domain. The simulations were made using the global hydrological model Mac‐PDM.09. The data files represent runoff simulated with the baseline (1961‐1990) climate, together with runoff simulated by climate change scenarios derived from CMIP3 global climate model output (i) based on specific IPCC SRES emissions scenarios (“SRES”) and (ii) scaled to represent prescribed changes in global mean temperature (“PRESC”), and from CMIP5 global climate model output based on RCP scenarios. The simulations were run at the University of Reading between 2009 and 2013. See Gosling & Arnell (2011)mfor a description and validation of Mac‐PDM.09, and Arnell & Gosling (2013) for details of the CMIP3 climate change scenarios and their application to the simulation of river runoff. Arnell & Lloyd‐Hughes (2013) describe the application of the model with CMIP5 scenarios.

  • World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Norwegian Climate Centre (NCC) NorESM1-F model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

  • The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Eireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the the EC-Earth-Consortium team EC-Earth3-Veg model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.