ALBERT

Description: The simulations of the end‐Cretaceous climate and the effects of the impact are carried out with a coupled climate model consisting of a modified version of the ocean general circulation model MOM3, a dynamic/thermodynamic sea ice model, and a fast statistical‐dynamical atmosphere model. Our impact simulations are based on a climate simulation of the end‐Cretaceous climate state using a Maastrichtian (70 Ma) continental configuration. The solar constant is scaled to 1354 W/m2, based on the present‐day solar constant of 1361 W/m2 and a standard solar model. A baseline simulation with 500 ppm of atmospheric CO2 and a sensitivity experiment at 1000 ppm CO2 concentration. The impact is assumed to release 100 Gt sulfur and 1400 Gt CO2. We simulate stratospheric residence times of 2.1 y, 4.3 y and 10.6 y. More information about the model can be found in the manuscript (https://doi.org/10.1002/2016GL072241).

Description: RAIN4PE is a novel daily gridded precipitation dataset obtained by merging multi-source precipitation data (satellite-based Climate Hazards Group InfraRed Precipitation, CHIRP (Funk et al. 2015), reanalysis ERA5 (Hersbach et al. 2020), and ground-based precipitation) with terrain elevation using the random forest regression method. Furthermore, RAIN4PE is hydrologically corrected using streamflow data in catchments with precipitation underestimation through reverse hydrology. Hence, RAIN4PE is the only gridded precipitation product for Peru and Ecuador, which benefits from maximum available in-situ observations, multiple precipitation sources, elevation data, and is supplemented by streamflow data to correct the precipitation underestimation over páramos and montane catchments. The RAIN4PE data are available for the terrestrial land surface between 19°S-2°N and 82-67°W, at 0.1° spatial and daily temporal resolution from 1981 to 2015. The precipitation dataset is provided in netCDF format. For a detailed description of the RAIN4PE development and evaluation of RAIN4PE applicability for hydrological modeling of Peruvian and Ecuadorian watersheds, readers are advised to read Fernandez-Palomino et al. (2021).

Description: In “Investigating Mesozoic Climate Trends and Sensitivities with a Large Ensemble of Climate Model Simulations” we study global trends in the climatic evolution through the Mesozoic era (252-66 Ma). The data presented here is the model output on which the results of this manuscript are based. Also included are different boundary condition model input files and scripts to generate the included figures (using the Python programming language in a Jupyter Notebook). The model output is provided in different netcdf files. The data is generated using the coupled ocean-atmosphere model CLIMBER3alpha (Montoya et al. 2005) which models climate globally on a 3.75° x 3.75° (ocean, lon.x lat.) and 22.5° x 7.5° (atmosphere) grid. Please note that data from other research that is shown in the figures in Landwehrs et al. (2021a) is not included in this data publication to avoid copyright issues. Methods The data is model input and output for/from the coupled ocean-atmosphere model CLIMBER3alpha which models climate globally on a 3.75° x 3.75° (ocean, lon.x lat.) and 22.5° x 7.5° (atmosphere) grid.

Description: In "A pronounced spike in ocean productivity triggered by the Chicxulub impact" we study the combined effect of sulfate aerosols, carbon dioxide and dust on the oceans and the marine biosphere after the Chicxulub impact using simulations with a climate model including ocean biogeochemistry. The data presented here is the model output the results of this manuscript are based on. Additionally, the figures of the publication and scripts (Python) to analyse the model output and generate the figures are contained. The model output is provided in different netcdf files. The structure of the model output is explained in a readme file. The data is generated using the coupled ocean-atmosphere model CLIMBER-3α+C which models climate globally on a 3.75° x 3.75° (ocean) and 22.5° (longitude) x 7.5° (latitude) (atmosphere) grid. More information about the model can be found in the manuscript and the README of this data publication.

Description: For the article “How to Tailor my Process-based Model? Dynamic Identifiability Analysis of Flexible Model Structures” (Pilz et al., submitted) a flexible simulation environment is coupled with an algorithm for dynamic identifiability analysis to form a diagnostic tool for process-based model building. This software publication provides the simulation environment ECHSE along with the new WASA engine. The latter was developed and employed for the associated research article. The original ECHSE software was introduced by Kneis (2015) and can be obtained from http://echse.github.io/

Description: 29 April 2020: Release of Version 0.3 This is an updated version of Reyer et al., (2019, V. 0.1.12, http://doi.org/10.5880/PIK.2019.008). All changes and updates are documented in the changelog available via the data download section. Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives: - Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales - Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite download as ProfoundData.zip, ProfoundData_ASCII.zip) accompanied by a change-log to the previous published version (changelog_Profound-DB_v03.pdf), auxiliary data of reconstructed single tree data at the site Sorø (Soroe_DBH_H_AGE_20200428.zip) and documented by the three explanatory documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB. (2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots. (3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.

Description: For the article “How to Tailor my Process-based Model? Dynamic Identifiability Analysis of Flexible Model Structures” (Pilz et al., submitted) a flexible simulation environment is coupled with an algorithm for dynamic identifiability analysis to form a diagnostic tool for process-based model building. This associated data description describes first which software is needed (R and the ECHSE model) and how to configure your computer in order to run the model and the analysis. Second, the input data provided within this data set are described. Eventually, the R scripts are described, which were used to initialize and run the model and conduct the subsequent identifiability analysis. This publication comprises the ECHSE model, all R packages in their employed versions, the model setup and results, figures and data presented in the associated research paper, and the R scripts used to initialize and run the model and conduct all analyses.