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 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.

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.

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 collection contains glacial and isotope model data. Over the last million years, the Earth has experienced a sequence of temperature oscillations between glacial and interglacial states, linked to variations in the Earth’s orbit around the sun. These climate oscillations were accompanied by changes in atmospheric CO2, but the fundamental reasons for this relationship are still unresolved. This 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 narrowed the field of ocean processes that could have caused glacial CO2 drawdown.

QUEST GSI was led by Nigel Arnell (University of Reading) with co-investigators from the Universities of Aberdeen, Leeds, UEA, Edinburgh, Southampton, UCL, London School of Hygiene and Tropical Medicine, CEH and CEFAS. This dataset collection contains model data simulations under various climate, run-off and aquatic scenarios. A central aim of this project was to assess the global-scale impacts of climate change under a range of scenarios, across a number of sectors. A methodology was developed to construct scenarios from a range of climate models, representing changes under different emissions scenarios and fixed amounts of change in global mean temperature. Impacts were estimated across a range of sectors, including water resources, fluvial and coastal flooding, crop productivity and food security, ecosystem productivity and human health, at regional and global scales. The project has provided quantitative information on these impacts and their distribution across the world. The general conclusions are that impacts may be significant at relatively low levels of climate change, that estimates of impact in some sectors are very uncertain due largely to uncertainty in projected changes in rainfall (particularly in south Asia), that there are no obvious thresholds for step changes in impact that are consistent across region and sector, and that socio-economic conditions may amplify or reduce impacts, depending on context. A second project aim was to develop the methodology in such a way that it could be readily applied to estimate impacts under other climate scenarios representing for example specific policy objectives. With additional funding from other sources, the project methodology has been applied successfully to estimate the impacts avoided by a set of feasible emissions policies.

The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the the CNRM-CERFACS team CNRM-CM6-1 model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Meteorological Research Institute of the Japan Meteorological Agency (MRI) MRI-ESM2-0 model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Institut Pierre-Simon Laplace (IPSL) IPSL-CM6A-LR model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.