11 documents found in 441ms
# 1
Krysanova, Valentina • Hattermann, Fred • Aich, Valentin • Alemayehu, Tadesse • Arheimer, Berit • (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. In the regional water sector, future simulations of climate-change impacts were also carried out, using climate data from five global climate models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M) for the four Representative Concentration Pathways (RCPs: RCP2.6, RCP4.5, RCP6.0 and RCP8.5). 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 regional hydrology models (river basins in brackets):HBV-CMA (Yangtze)HBV-IWW (Tagus)HBV-JLU (Rhine, Ganges, Mississippi)HBV-PIK (Rhine, Niger, Yellow, Blue Nile, Amazon)HYMOD-JLU (Rhine, Ganges, Mississippi)HYMOD-UFZ (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon)HYPE (Rhine, Tagus, Niger, Ganges, Lena, Mackenzie)mHM (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon)SWAP (Rhine, Tagus, Niger, Ganges, Yellow, Yangtze; Lena, Darling, MacKenzie, Mississippi, Amazon)SWAT (Yangtze; Darling; Blue Nile; Amazon; Mississippi; Niger)SWIM (Rhine, Yellow, Mississippi; Niger; Lena; Tagus; Blue Nile; Yangtze; Ganges, Amazon)VIC (Tagus, Blue Nile, Yellow, Lena, Darling, Amazon, MacKenzie; Rhine, Niger, Mississippi; Ganges; Yangtze)VIP (Yellow)WaterGAP3 (Rhine, Tagus, Niger, Blue Nile, Ganges, Yellow, Lena, Mississippi)ECOMAG (Lena, MacKenzie)
The ISIMIP2a water (regional) outputs are based on simulations from 15 regional 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/.
# 2
Arneth, Almut • Balkovic, Juraj • Ciais, Philippe • de Wit, Allard • Deryng, Delphine • (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 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 Agricultural Sector models: CGMS-WOFOST, CLM-Crop, EPIC-Boku, EPIC-IIASA, EPIC-TAMU, GEPIC, LPJ-GUESS, LPJmL, ORCHIDEE-CROP, pAPSIM, pDSSAT, PEGASUS, PEPIC, PRYSBI2.
The ISIMIP2a agriculture outputs are based on simulations from 14 agricultural sector models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate cop yields and irrigation water withdrawal (assuming unlimited water supply), based on planting dates, crop variety parameters, approximate maturity dates (to allow for spatially-explicit variety parameterization), as well as fertilizer use (N, P, K). A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 3
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/.
# 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
Geiger, Tobias • Frieler, Katja
Abstract: We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries: (1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv.(2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009.csv.(3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009.csv. These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv). We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (Data-Description-GDP_1850-2009.pdf). Version history: Please use the updated version of this dataset which contains correction of errors in the original dataset. For a detailed description of the changes please consult the CHANGELOG included in the data description document of the new version.
# 6
Geiger, Tobias • Frieler, Katja • Bresch, David N.
Abstract: Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information on the spatial extent of the storm and people or assets exposed. Here, we provide a collection of tropical cyclone exposure data (TCE-DAT) derived with the help of spatially-explicit data on population densities and Gross Domestic Product (GDP), also available at http://doi.org/10.5880/pik.2017.007. Up to now, this collection contains: 1) A global data set of tropical cyclone exposure accumulated to the country/event level http://doi.org/10.5880/pik.2017.0052) A global data set of spatially-explicit tropical cyclone exposure available for all TC events since 1950 http://doi.org/10.5880/pik.2017.008 TCE-DAT is considered key information to 1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders.
# 7
Geiger, Tobias • Frieler, Katja • Bresch, David N.
Abstract: Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate all areas (grid cells) exposed to wind speeds above 34 knots. Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the number of people and 2) the sum of assets exposed to above tropical storm force wind speeds for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated spatially-explicit exposure data (TCE-DAT) covers the period 1950 to 2015. It is considered key information to 1) assess the contribution of climatological versus socio-economic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We validate the adequateness of our methodology by comparing our exposure estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders. Files included in the zip folder: (1) TCE-DAT_single_events_historical.zip: Zipped archive containing 2707 files with exposed population and assets by grid cell using historical socio-economic exposure estimates.(2) TCE-DAT_single_events_2015.zip: Zipped archive containing 2713 files with exposed population and assets by grid cell using fixed socio-economic exposure at 2015 values.(3) Data-description_TCE-DAT_2017.008.pdf: full description of the data set including information on data sources and the description of variables/ data columns Additional information on each TC event in the zipped archive (e.g. TC name, NatCatSERVICE_ID, genesis_basin, aggregated exposure estimates by country) are available in the exposure data sets aggregated on country-event level (see Geiger et al., 2017; http://doi.org/10.5880/pik.2017.005 for details).
# 8
Geiger, Tobias • Frieler, Katja • Bresch, David N.
Abstract: Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots. Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the number of people and 2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated country-event level exposure data (TCE-DAT) covers the period 1950 to 2015. It is considered key information to 1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We validate the adequateness of our methodology by comparing our exposure estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders. Files included in the data set: (1) TCE-DAT_historic-exposure_1950-2015.csv: Exposed population and assets by event and country using historical socio-economic exposure estimates.(2) TCE-DAT_2015-exposure_1950-2015.csv: Exposed population and assets by event and country using fixed socio-economic exposure at 2015 values.(3) Data-description_TCE-DAT_2017.005.pdf: full description of the data set including information on data sources and the description of variables/ data columns
# 9
Geiger, Tobias • Daisuke, Murakami • Frieler, Katja • Yamagata, Yoshiki
Abstract: We here provide spatially-explicit economic time series for Gross Cell Product (GCP) with global coverage in 10-year increments between 1850 and 2100 with a spatial resolution of 5 arcmin. GCP is based on a statistcal downscaling procedure that among other predictors uses national Gross Domestic Product (GDP) time series and gridded population estimates as input. Historical estimates until 2000 are harmonized with future socio-economic projections from the Shared Socioeconomic Pathways (SSPs) according to SSP2 from 2010 onwards. We further provide a mapping file with identical spatial resolution to associate GCP values with specifc countries. Based on this mapping we provide nationally aggregated GDP estimates between 1850-2100 in a separate csv-file. Additionally, we provide a mapping file with identical spatial resolution providing national assets-GDP ratios, that can be used to transform GCP to asset values based on 2016 estimates from Credit Suisse's Global Wealth Databook 2016.
# 10
Gütschow, Johannes • Jeffery, Louise • Gieseke, Robert • Gebel, Ronja
Abstract: This is an updated version of Gütschow et al. (2016). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset. 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 2014 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_v1.1_06-Mar-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.1_06-Mar-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.1_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.1_updated_figures: updated figures of those published in Gütschow et al. (2016, http://doi.org/10.5194/essd-8-571-2016)(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) 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 (2017) UNFCCC Biennal Update Reports: UNFCCC (2016) UNFCCC Common Reporting Format (CRF): UNFCCC (2013), UNFCCC (2014) BP Statistical Review of World Energy: BP (2016) CDIAC:: Boden et al. (2016) 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.
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