32 documents found in 520ms
# 1
Arzhanov, Maxim • Betts, Richard • Eliseev, Alexey • Morfopoulos, Catherine • Schaphoff, Sibyll • (et. al.)
Abstract: 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 may 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 evaluation and 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 the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 2
Gosling, Simon • Müller Schmied, Hannes • Betts, Richard • Chang, Jinfeng • Ciais, Philippe • (et. al.)
Abstract: VERSION HISTORY:-On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.- On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit). ----------------------------------------------------------------------------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 http://doi.org/10.5880/PIK.2017.010 before November 27, 2018 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de.---------------------------------------------------------------------------- DATA 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 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may 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 evaluation and 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 the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2
The ISIMIP2a water (global) outputs are based on simulations from 13 global hydrology models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 3
Reyer, Christopher • Asrar, Gassem • Betts, Richard • Chang, Jinfeng • Chen, Min • (et. al.)
Abstract: 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.de DATA 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 ISIMIP2a biome outputs are based on simulations by different global vegetation models (CARAIB, DLEM, JULES-UoE, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) following the ISIMIP2a protocol. The biome models simulate biogeochemical processes, biogeography and ecosystem dynamics of natural vegetation and managed lands based on soil, climate and land-use information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 4
Reyer, Christopher • Asrar, Gassem • Betts, Richard • Chang, Jinfeng • Chen, Min • (et. al.)
Abstract: 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 ISIMIP2a biome outputs are based on simulations by different global vegetation models (CARAIB, DLEM, JULES-UoE, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) following the ISIMIP2a protocol. The biome models simulate biogeochemical processes, biogeography and ecosystem dynamics of natural vegetation and managed lands based on soil, climate and land-use information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 5
Gosling, Simon • Müller Schmied, Hannes • Betts, Richard • Chang, Jinfeng • Ciais, Philippe • (et. al.)
Abstract: 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 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may 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 evaluation and 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 the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2.
The ISIMIP2a water (global) outputs are based on simulations from 13 global hydrology models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 6
Lange, Stefan
Abstract: VERSION HISTORY:- On June 26, 2018 all files were republished due to the incorporation of additional observational data covering years 2014 to 2016. Prior to that date, the dataset only covered years 1979 to 2013. Data for all years prior to 2014 are identical in this and the original version of the dataset. DATA DESCRIPTION:The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways. The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018). Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).
# 7
Lange, Stefan
Abstract: The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways. The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018). Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).
# 8
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert
Abstract: This is an updated version of Gütschow et al. (2018, http://doi.org/10.5880/pik.2018.003). 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 2016, 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 2.0 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_v2.0_11-Dec-2018.csv: With numerical extrapolation of all time series to 2016. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v2.0_11-Dec-2018.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v2.0_data-format-description: including CHANGELOG(4) PRIMAP-hist_v2.0_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 v2: Andrew (2018)- BP Statistical Review of World Energy: BP (2018)- 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 (2018)- RCP historical data: Meinshausen et al. (2011)- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2018)- UNFCCC Biennal Update Reports: UNFCCC (2018)- UNFCCC Common Reporting Format (CRF): UNFCCC (2017), UNFCCC (2018), Jeffery et al. (2018) Full references are available in the data description document.
Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.
# 9
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert • Gebel, Ronja
Abstract: This is an updated version of Gütschow et al. (2017, http://doi.org/10.5880/pik.2017.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its first update. For a detailed description of the changes please consult the CHANGELOG included in the data description document. This 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 2015 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) 1996 categories. For CO2‚‚ from energy and industry time series for subsectors are available. List of datasets included in this data publication:(1) PRIMAP-hist_v1.2_14-Dec-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.2_14-Dec-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.2_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.2_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:- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2017B)- UNFCCC Biennal Update Reports: UNFCCC (2016)- UNFCCC Common Reporting Format (CRF): UNFCCC (2016), UNFCCC (2017)- BP Statistical Review of World Energy: BP (2017)- CDIAC: Boden et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2016)- Houghton land use CO2: Houghton (2008)- RCP historical data: Meinshausen et al. (2011)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)- SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999)- FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015)
Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and calculations for the needed agricultural land.
# 10
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
spinning wheel Loading next page