Other language confidence: 0.9753438242178348
This dataset contains simulation data using the LPJmL-FIT model (Billing et al., 2019). The purpose of this dataset is to investigate the influence of functional diversity on European forest biomass dynamics under varying climate change scenarios (RCP2.6, RCP4.5, RCP8.5). The LPJmL-FIT ("Lund-Potsdam-Jena managed Land – Flexible Individual Traits") model is a dynamic flexible-trait vegetation model that simulates the establishment, growth, competition, and mortality of individual trees and grasses. Each tree individual is categorized into one of four main plant functional types (PFTs) and assigned a set of functional trait values, including specific leaf area (SLA), leaf longevity (LL), and wood density (WD). The model is driven by daily climate input data, atmospheric CO2 concentration, and soil texture. For this dataset, the model was applied to six different regions across central and eastern Europe, covering a range of environmental gradients. Those sites include: Alpine Mountains, Boreal flatland, Carpathian Mountains, central European flatland, central European low mountain range and eastern European flatland. Each region is represented by a set of 9 grid cells of 0.5° x 0.5° longitude and latitude in size. Four experimental set-ups were investigated, varying in the degree of functional diversity. These set-ups specify characteristics of newly establishing trees, including assignment to PFTs and the range of leaf traits drawn from the full spectrum. This dataset provides detailed model outputs from simulations exploring the effects of different levels of functional diversity on forest adaptation under changing climatic conditions.
This dataset contains simulated vegetation and fire variables using the LPJmLv5.6-SPITFIRE and LPJmLv5.6-SPITFIRE-BASE coupled vegetation-fire model. LPJmL is a Dynamic Global Vegetation Model (DGVM), which simulates impacts of climate change and vegetation including carbon, water and energy fluxes on land. SPITFIRE is a process-based fire model that is developed at the Potsdam Institute for Climate Impact Research (PIK) simulating ignitions, fire spread, fuel combustion and plant mortality. BASE is an empirical burned area model, developed at Senckenberg – Leibniz Institution for Biodiversity and Earth System Research (SGN), that is based on remotely sensed information using generalised linear model (GLM) techniques provided by data sources from within the HORIZON2020 project FirEUrisk and elsewhere. The dataset contains a set of future changes in vegetation and fire variables under future climate and land-use change at the European (ET) scale at 9 km covering 2000-2100 for both couple vegetation-fire models. The models were forced with 5 climate models from the SSP126 and SSP370 climate scenarios (its downscaling to ~9 km grid cell resolution) as well as the land-use projections corresponding to those climate scenarios (provided at ~9 km grid cell resolution). The variables provided in this dataset are at monthly and annual temporal resolution. The simulated changes in fire and vegetation spatio-temporal patterns are the result of changes in climate and land-use and subsequent fire-vegetation feedbacks. This data has been developed in the course of the HORIZON2020 project FirEUrisk (Deliverable 3.4; Grant Agreement no. 101003890).
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2018, http://doi.org/10.5194/gmd-2017-145). We here present an extended version of the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). Additional features of this version, including agricultural trees as a new cultivation type in LPJmL4, are described and used in Jans et al. (2020, HESS) The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. The model data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2018 part I). Additionally, these results include agricultural trees (olives, non-citrus orchards, and cotton) implemented as a new cultivation type into LPJmL4. Standard results are evaluated in Schaphoff et al. (2018 part II). Results of cotton as a newly implemented agricultural tree are evaluated in Jans et al. (2020), HESSD. The data collection includes some key output variables made with the model setup described by Jans et al. (2020, HESS). Overall, data sets are resulting from 40 different simulations, where we combined 5 different GCMs (GFDL, HadGEM, IPSL, MIROC, NorESM) with 4 different RCPs (2p6, 4p5, 6p0, 8p5) without and with CO2 fertilization, respectively. The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for crop yields, absorbed photosynthetically active radiation and the water fluxes (irrigation, transpiration, evaporation,interception, blue and green evapotranspiration). Crop yields, and water fluxes are given for each crop functional type (CFT), respectively. Monthly data are provided for one carbon flux (net primary production) and the water fluxes transpiration, evaporation, interception, and runoff. The data are provided in one binary file for each variable and simulation. An overview of all variables and information on how data are stored within the binary files are given in the file inventory.
The netCDF data stored here represent crop production simulations from the LPJmL biosphere model underlying the different steps of the U-turn portrayed in the main paper by Gerten et al. The LPJmL data cover the entire globe with a spatial resolution of 0.5° for the baseline period as well as for different scenarios reflecting the studied ways to restrict crop production through maintaining planetary boundaries on the one hand and the various opportunities to increase food supply within the boundaries on the other hand (see paper, specifically Figs. 1 & 2, Table 2). The stored variable is crop production (fresh matter) multiplied by the fractional coverage of different crop functional types, per 0.5° grid cell. The data are provided in one netCDF file for each scenario. An overview of the scenarios assigned to the folder names is given in the file inventory.The data support the study: Gerten, D., Heck, V., Jägermeyr, J., Bodirsky, B.L., Fetzer, I., Jalava, M., Kummu, M., Lucht, W., Rockström, J., Schaphoff, S., Schellnhuber, H.J.: Feeding ten billion people is possible within four terrestrial planetary boundaries. Nature Sustainability (2020).
Description of changes in the new version:- On October 18, 2018 we republished all simulation data for all impact models to get the data sets into the new search facet structure. There were no changes to the simulation data.- Files for JULES-B1 (formerly JULES_UoE) were not available since the date of issuing the DOI until March 13, 2019. Until that date, these files were only available in the ISIMIP DKRZ server.---------------------------------------------------------------------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.2018.006 before March 13, 2019 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de---------------------------------------------------------------------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.
LPJmL5 is a dynamical global vegetation model that simulates climate and land-use change impacts on the terrestrial biosphere, the water, carbon and nitrogen cycle and on agricultural production. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included into the model. A comprehensive description of the model is given by von Bloh et al. (2017,http://doi.org/10.5194/gmd-2017-228).We here present the LPJmL5 model code described and used by the publications in GMD: Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5) (von Bloh et al., 2017)The model code of LPJmL5 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL5 is given in the INSTALL file. Additionally to the publication a html documentation and manual pages are provided.The LPJmL5 version is based on LPJmL3.5 that is not publicly available. The LPJmL4 version without nitrogen cycle but with an updated phenology scheme can be found on github (https://github.com/PIK-LPJmL/LPJmL).
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145).We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146).The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
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.
| Organisation | Count |
|---|---|
| Wissenschaft | 8 |
| Type | Count |
|---|---|
| unbekannt | 8 |
| License | Count |
|---|---|
| Offen | 8 |
| Language | Count |
|---|---|
| Englisch | 8 |
| Resource type | Count |
|---|---|
| Keine | 8 |
| Topic | Count |
|---|---|
| Boden | 7 |
| Lebewesen und Lebensräume | 8 |
| Luft | 6 |
| Mensch und Umwelt | 8 |
| Wasser | 8 |
| Weitere | 8 |