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This dataset and code are related to artificial light emissions in the arctic area. They are a supplement to the report "Capabilities and limitations of advanced optical satellite missions for snow, vegetation, and artificial light source applications in Arctic areas".Dataset:The Radiance Light Trends app was used to identify artificial light sources on the Yamal Peninsula in Russia. In order to determine whether a location was lit, a threshold of 5 nW/cm² sr (displayed in yellow in the Radiance Light Trends app) was defined. Visible band daytime imagery from Google Maps and Bing Maps was then used to identify what type of human activity was responsible for the light. The positions of the 78 lit areas and their light source classification are provided in a csv table and kmz file. The classes are defined as: industry, industry / flare, community, ship/ airport, road, water and unknown. This data publication includes the artificial light sources on the Yamal Penninsula (Western Siberia) in .csv and .kmz formats.Code:The data publication includes the python code "Arctic light pollution clustering script", which identifies areas with bright light emissions in the arctic. The script requires the monthly composite images from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite produced by the Earth Observation Group as an input. These data are currently available here: https://eogdata.mines.edu/download_dnb_composites.html
This publication contains the software and data used in the analysis presented in the paper "What’s in a Watt? Interpreting nightlights satellite data via citizen science observations". In the Nachtlichter project, citizen scientists went out at night and classified and counted light sources, reporting the data via an app. This publication contains a subset of that data, as well as software tools to compare the data to satellite data from the VIIRS DNB.
This is an updated version of Gütschow et al. (2019, http://doi.org/10.5880/pik.2019.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its previous updates. For a detailed description of the changes please consult the CHANGELOG included in the data description document.The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Agriculture is available. Version 2.1 of the PRIMAP-hist dataset does not include emissions from Land use, land use change and forestry (LULUCF).List of datasets included in this data publication:(1) PRIMAP-hist_v2.1_09-Nov-2019.csv: With numerical extrapolation of all time series to 2017. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.1_09-Nov-2019.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.1_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.1_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder)When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources.SOURCES:- Global CO2 emissions from cement production v4: Andrew (2019)- BP Statistical Review of World Energy: BP (2019)- CDIAC: Boden et al. (2017)- EDGAR version 4.3.2: JRC and PBL (2017), Janssens-Maenhout et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2019)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2019)- UNFCCC Biennal Update Reports: UNFCCC (2019)- UNFCCC Common Reporting Format (CRF): UNFCCC (2018), UNFCCC (2019), Jeffery et al. (2018)Full references are available in the data description document.
This dataset contains the results of a literature analysis on the potential future water demand of bioenergy plantations and for contextualization that of other water use sectors. For the bioenergy scenarios, it also contains the following parameters/assumptions of the studies included: type of study, modeling framework, bioenergy feedstock, land-type converted to biocrops, whether global maps for bioenergy locations are included, whether withdrawal or consumption is reported, type of water (blue/green/gray), simulation year for which data is extracted, carbon conversion efficiency (c_eff), plantation area, provided bioenergy and/or NEs (depending on study type) and the associated freshwater requirements
The need for the software is based on being able to make a statement as to whether the operation of a Pumped Hydropower Storage (PHS) facility in a former open-pit lignite mine can have a negative impact on the water quality in the lower reservoir and associated aquifers. The research question arises since flooded lignite mines are often associated with acidification and/or increased sulphate and metal concentrations. Thus, the software aims at modelling geochemical processes during the PHS operation in open-pit lignite mines. The reaction path modelling framework comprises a Python framework for data management and a solver for geochemical reactions (phreeqc/phreeqpy; Parkhurst and Appelo, 2013; Müller, 2011). The software is based on a conceptual geochemical model that includes the main geochemical processes that are expected to influence the hydrochemistry. It integrates different non-dimensional batch reactors, each representing the water composition of the reservoirs, and water sources or sinks in the PHS system (groundwater, rainwater, surface run-off, mine dump water). These waters are cyclically mixed with ratios deducted from flow rates and time-dependent influxes of a hypothetical PHS system. The water influxes have different chemical compositions based on the geochemical scenarios defined with the input data. An instant flooding of the mine with scenario-specific mixing ratios of rainwater, groundwater and mine dump water is simulated to provide an initial solution in the LR for the PHS operation. For the simulation of the PHS operation, the water volume of the UR is extracted from the LR and equilibrated with atmospheric partial pressures of oxygen and carbon dioxide to represent the water composition after pumping. The water composition evolving at the reservoir-mine dump interface layer is simulated by a kinetically controlled reaction of pyrite (Williamson and Rimstid, 1994) and calcite (Plummer, 1978) with the LR water. During the PHS discharge cycle, water flows into the adjacent mine dump sediments due to the increasing hydraulic head gradient in the LR compared to the surrounding groundwater aquifers. Water from the LR is mixed with rainwater, groundwater, surface run-off, and water from the reservoir-mine dump interface layer according to the water volumes that enter the reservoir during the respective cycle. Finally, the new water composition in the LR is mixed with the water from the UR to simulate the PHS discharge into the LR. Apart from gas exchange, evaporation and precipitation, no reactions are simulated for the water in the UR, as the reservoir is assumed to be artificially sealed. Pump and discharge cycles are simulated until the pH and sulfate concentrations in the LR do not change by more than 1 x 10-4 and 1 x 10-5 mol kgw-1 within two consecutive PHS cycles, respectively. Otherwise, the simulation is terminated after 7,300 PHS cycles, representing 20 years of operation with a duration of one day per cycle. Input parameter ranges can cover a wide range of potential hydrogeochemical scenarios. In the software provided with this manual, a small range of generic data is defined as input to limit the simulation time and data output. However, the input can be modified to simulate a broader range of geochemical scenarios as described in the associated data description file.
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes. ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017). The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction). This entry refers to the ISIMIP2b simulation data from three agricultural models: GEPIC, LPJmL and PEPIC. ---------------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications). ----------------------------------------------------------------------------
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors.ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes.ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017).The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction).This entry refers to the ISIMIP2b simulation data from eight global vegetation (biomes) models:CARAIBCLM4.5,DLEM,LPJmL,ORCHIDEE,VEGAS,VISIT,LPJ-GUESS----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications).----------------------------------------------------------------------------
The datasets contain low resolution image data [1] that can be used to approximately remove natural light from the monthly composite nighttime images produced by the Earth Observation Group (EOG) using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) [2]. Natural light includes airglow and reflected light from stars, as well as polar light at high latitudes.The low-resolution images can be expanded and then subtracted from the EOG monthly composites, using python scripts included in this data publication [1]. More details are provided in the data description and in Coesfeld et al. (2020).The background correction is based on the VIIRS-DNB monthly composites data from April 2012 to December 2019 without stray light correction produced by the EOG, and the associated file containing the number of cloud-free observations (Earth Observation Group, 2015). Locations away from human settlements and artificial lights were selected using the Global Human Settlement Layer (GHSL) population density map from 2015 [3] and the VIIRS-DNB 2015 vcm-orm annual composite by the EOG (Earth Observation Group, 2015).The python script we used in Coesfeld et al. (2020) to select locations on an equally spaced grid over the global DNB extent (from 75N to 65S) is included. The six images of the EOG composite are processed separately. This code identifies locations on a 72x28 grid (2016 locations total). In order to optimize the location of the specified grid points, the Global Human Settlement Layer (GHSL) population density and VIIRS-DNB annual composite of 2015 are consulted.
VERSION HISTORY:- On April 10, 2018 we renamed some simulation files of impact models LPJ-GUESS, ORCHIDEE, JULES-UoE and VISIT, due to the correction of social scenario label “nosoc” for “varsoc”. For impact model VISIT, “nosoc” was relabeled to “pressoc”. These data caveats were documented in the ISIMIP website (ISIMIP2a Biomes: correction of scenario names in file names).- On October 17, 2018, we republished all simulation data for all biomes sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/.For accessing the data set as in the previous version (http://doi.org/10.5880/PIK.2017.002) before October 17, 2018 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.deDATA DESCRIPTION:The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors.ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.The focus topic for ISIMIP2a is model validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to all these data is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction).The ISIMIP2a biome outputs are based on simulations from 8 global vegetation (biomes) models (CARAIB, DLEM, JULES-B1, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a).
The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors.ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.The focus topic for ISIMIP2a is model validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to all these data is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction).The ISIMIP2a biome outputs are based on simulations from 8 global vegetation (biomes) models (CARAIB, DLEM, JULES-B1, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a).
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