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  • Data  (3)
  • bic Book Industry Communication::J Society & social sciences::JF Society & culture: general
  • climate change
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  • 1
    Publication Date: 2023-02-23
    Description: Abstract
    Description: 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.
    Description: Methods
    Description: Country resolved data are combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). They are supplemented with growth rates from regionally resolved sources and numerical extrapolations.
    Keywords: greenhouse gas emissions ; historical emissions ; emissions time series ; composite source ; national emissions ; climate change ; EARTH SCIENCE SERVICES 〉 MODELS 〉 SOCIAL AND ECONOMIC MODELS ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 ENVIRONMENTAL IMPACTS ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 ENVIRONMENTAL IMPACTS 〉 INDUSTRIAL EMISSIONS ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 ENVIRONMENTAL IMPACTS 〉 FOSSIL FUEL BURNING ; EARTH SCIENCE 〉 ATMOSPHERE 〉 ATMOSPHERIC CHEMISTRY 〉 TRACE GASES/TRACE SPECIES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS ; environmental policy
    Language: English
    Type: Dataset
    Format: 3 Files
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  • 2
    Publication Date: 2023-12-06
    Description: Abstract
    Description: The new climate dataset BASD-CMIP6-PE for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 projections of 10 GCMs addresses the need for reliable high-resolution (1d, 10km) climate data covering Peru and Ecuador. This dataset includes both historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method (Lange, 2019, 2021) and data from regional observational datasets such as RAIN4PE (Fernandez-Palomino et al., 2021a, b) for precipitation and PISCO-temperature (Huerta et al., 2018) for temperatures as reference data. The Reliability of the BASD-CMIP6-PE was evaluated through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. The evaluation showed that the BASD-CMIP6-PE is reliable for describing the spatial patterns of atmospheric variables and streamflow simulation, including low and high flows. This suggests the usefulness of the new dataset for climate change impact assessment studies in Peru and Ecuador. The BASD-CMIP6-PE data are available for the domain covering Peru and Ecuador, located between 19°S and 2°N and 82-67°W, at 0.1° spatial and daily temporal resolution. The precipitation unit is mm, and the temperature is in °C. The data are in the NetCDF format and arranged by model, model member, experiment, variable, temporal resolution, and subset period (e.g., canesm5_r1i1p1f1_ssp126_pr_daily_2015_2020.nc).
    Keywords: CMIP6 ; projections ; climate models ; climate change ; high-resolution ; precipitation ; temperature ; Peru ; Ecuador ; Andes ; Amazon ; EARTH SCIENCE 〉 ATMOSPHERE 〉 ATMOSPHERIC TEMPERATURE ; EARTH SCIENCE 〉 ATMOSPHERE 〉 PRECIPITATION
    Type: Dataset , Dataset
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  • 3
    Publication Date: 2023-12-14
    Description: Abstract
    Description: The data comprise Climber3alpha+C simulations created by Matthias Hofmann (PIK) as part of the Work Package 2.1 of the COMFORT project as well as the PyFerret scripts (written by Ralf Liebermann and Matthias Hofmann) used for their evaluation. The simulation data consist of snap_*.nc files and history.nc files for ocean, atmosphere and mixed layer depth (hmxl) performed for different idealized scenarios: CONTROL, double and fourfold atmospheric CO2 (CO2X2 and CO2X4), also with additional Greenland freshwater influx (CO2X2_HOSING and CO2X4_HOSING). Furthermore, tracer simulations (CONTROL, CO2X4, CO2X4_HOSING) and simulations with constant scavenging (CO2X4) are also included. The aim was to analyse the simulations regarding climate change-induced changes in marine biogeochemistry and primary production, which will be published under the title "Shutdown of Atlantic overturning circulation could cause persistent increase of primary production in the Pacific" (see Related Work). Simulation data were generated with Climber3alpha+C (Earth system model of intermediate complexity) and evaluated with PyFerret v7.41. CDO was used to aggregate monthly simulation data into annual means.
    Keywords: Dissolved Iron ; climate change ; Climber3alpha+C ; EARTH SCIENCE 〉 BIOSPHERE 〉 ECOLOGICAL DYNAMICS 〉 ECOSYSTEM FUNCTIONS 〉 PRIMARY PRODUCTION ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 ALKALINITY ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 BIOGEOCHEMICAL CYCLES ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 CARBON ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 NUTRIENTS ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 OCEAN TRACERS ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 ORGANIC CARBON ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CHEMISTRY 〉 PHOSPHATE ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CIRCULATION 〉 FRESH WATER FLUX ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN CIRCULATION 〉 OCEAN MIXED LAYER ; EARTH SCIENCE 〉 OCEANS 〉 OCEAN TEMPERATURE ; EARTH SCIENCE 〉 OCEANS 〉 SALINITY/DENSITY 〉 SALINITY ; effect 〉 climatic effect 〉 man-made climate change
    Type: Dataset , Dataset
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