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This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

This dataset contains the underlying data for the following publication: Song, L., Lieu, J., Nikas, A., Arsenopoulos, A., Vasileiou, G., & Doukas, H. (2020). Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment. Energy Research & Social Science, 59, 101306., https://doi.org/10.1016/j.erss.2019.101306. Full details of methods used to create the dataset and provided within this publication.

pre-built Euro-Calliope Ready to use models of the European electricity system built using Calliope. Models are available on three different spatial resolutions: continental, national, and regional. In addition, euro-calliope models can be built manually which adds more configuration options. To build euro-calliope manually, head over to GitHub. At a glance euro-calliope models the European electricity system with each location representing an administrative unit. It is built on three spatial resolutions: on the continental level as a single location, on the national level with 34 locations, and on the regional level with 497 locations. On each node, renewable generation capacities (wind, solar, bioenergy) and balancing capacities (battery, hydrogen) can be built. In addition, hydro electricity and pumped hydro storage capacities can be built up to the extent to which they exist today. All capacities are used to satisfy electricity demand on all locations which is based on historic data. Locations are connected through transmission lines of unrestricted capacity. Using Calliope, the model is formulated as a linear optimisation problem with total monetary cost of all capacities as the minimisation objective. The pre-built models can be manipulated by updating any of the files. In addition to the pre-built models, models can be built manually. Manual builds provide more flexibility in adapting and configuring the model. To build euro-calliope manually, head over to GitHub. Get ready to run the models You need a Gurobi license installed on your computer. You may as well choose another solver than Gurobi. See Calliope���s documentation to understand how to switch to another solver. You need to have Calliope and Gurobi installed in your environment. The easiest way to do so is using conda. Using conda, you can create a conda environment from within you can build the model: conda env create -f environment.yaml conda activate euro-calliope Run the models There are three models in this directory ��� one for each of the three spatial resolutions continental, national, and regional. You can run all three models out-of-the-box, but you may want to modify the model. By default, the model runs for the first day of January only. To run the example model on the continental resolution type: $ calliope run ./continental/example-model.yaml For more information on how to use and modify Calliope models, see Calliope���s documentation. Manipulating the model using overrides Calliope overrides allow to easily manipulate models. An override named freeze-hydro-capacities can be used for example in this way: calliope run build/model/continental/example-model.yaml --scenario=freeze-hydro-capacities You can define your own overrides to manipulate any model component. The following overrides are built into euro-calliope: directional-rooftop-pv By default, euro-calliope contains a single technology for rooftop PV. This technology comprises the total rooftop PV potential in each location, in particular including east-, west-, and north-facing rooftops. While this allows to fully exploit the potential of rooftop PV, it leads to less than optimal capacity factors as long as the potential is not fully exploited. That is because, one would likely first exploit all south-facing rooftop, then east- and west-facing rooftops, and only then ��� if at all ��� north-facing rooftops. By default, euro-calliope cannot model that. When using the directional-rooftop-pv override, there are three instead of just one technologies for rooftop PV. The three technologies comprise (1) south-facing and flat rooftops, (2) east- and west-facing rooftops, and (3) north-facing rooftops. This leads to higher capacity factors of rooftop PV as long as the potential of rooftop PV is not fully exploited. However, this also increases the complexity of the model. freeze-hydro-capacities By default, euro-calliope allows capacities of run-of-river hydro, reservoir hydro, and pumped storage hydro capacities up to today���s levels. Alternatively, it���s possible to freeze these capacities to today���s levels using the freeze-hydro-capacities override. Model components The models contain the following files. All files in the root directory are independent of the spatial resolution. All files that depend on the spatial resolution are within subfolders named by the resolution. ��������� {resolution} <- For each spatial resolution an individual folder. ��� ��������� capacityfactors-{technology}.csv <- Timeseries of capacityfactors of all renewables. ��� ��������� directional-rooftop.yaml <- Override discriminating rooftop PV by orientation. ��� ��������� electricity-demand.csv <- Timeseries of electricity demand on each node. ��� ��������� example-model.yaml <- Calliope model definition. ��� ��������� link-all-neighbours.yaml <- Connects neighbouring locations with transmission. ��� ��������� locations.csv <- Map from Calliope location id to name of location. ��� ��������� locations.yaml <- Defines all locations and their max capacities. ��������� build-metadata.yaml <- Metadata of the build process. ��������� demand-techs.yaml <- Definition of demand technologies. ��������� environment.yaml <- Conda file defining an environment to run the model in. ��������� interest-rate.yaml <- Interest rates of all capacities. ��������� link-techs.yaml <- Definition of link technologies. ��������� README.md <- The file you are currently looking at. ��������� renewable-techs.yaml <- Definition of supply technologies. ��������� storage-techs.yaml <- Definition of storage technologies. Units of quantities The units of quantities within the models are the following: power: 100,000 MW energy: 100,000 MWh area: 10,000 km2 monetary cost: 1e+09 EUR These units were chosen in order to minimise numerical issues within the optimisation algorithm. License and attribution euro-calliope has been developed and is maintained by Tim Tr��ndle, IASS Potsdam. If you use euro-calliope in an academic publication, please cite the following article: Tr��ndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Contains modified Copernicus Atmosphere Monitoring Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. Contains modified data from Renewables.ninja. Contains modified data from Open Power System Data.

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

AbstractThe European Union’s 2030 climate and energy package introduced fundamental changes compared to its 2020 predecessor. These changes included a stronger focus on the internal market and an increased emphasis on technology-neutral decarbonization while simultaneously de-emphasizing the renewables target. This article investigates whether changes in domestic policy strategies of leading member states in European climate policy preceded the observed changes in EU policy. Disaggregating strategic change into changes in different elements (goals, objectives, instrumental logic), allows us to go beyond analyzing the relative prioritization of different goals, and to analyze how policy requirements for reaching those goals were dynamically redefined over time. To this end, we introduce a new method, which based on insights from social network analysis, enables us to systematically trace those strategic chances. We find that shifts in national strategies of the investigated member states preceded the shift in EU policy. In particular, countries reframed their understanding of supply security, and pushed for the internal electricity market also as a security measure to balance fluctuating renewables. Hence, the increasing focus on markets and market integration in the European 2030 package echoed the increasingly central role of the internal market for electricity supply security in national strategies. These findings also highlight that countries dynamically redefined their goals relative to the different phases of the energy transition.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.