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FLIDU (Floating Lidar Uncertainty) is a tool for the quantification of motion induced uncertainties of nacelle based lidar inflow measurments on floating offshore wind turbines (FOWT). The framework contains an analytical model of the wind field as well as the lidar measurements under consideration of FOWT dynamics. FOWT dynamics are modelled as harmonic oscillation and parameterized by their amplitude, frequency and mean value. Measurement uncertainty is derived by applying the GUM methodology on the equatuation of the analytical model.

Version of the model used for publications: "Climate change will cause non-analogue vegetation states in Africa and commit vegetation to long-term change" doi:10.5194/bg-2020-179 "Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies" doi:10.1111/gcb.15390 {"references": ["Scheiter and Higgins, Global Change Biology (2009) doi:10.1111/j.1365-2486.2008.01838.x"]}

Species records were compiled based on literature review and examination of specimens. Distribution modelling was performed using environmental layers from Bio-ORACLE for current conditions and Representative Concentration Pathway (RCP) scenarios 2.6, 4.5, 6.0, and 8.5 for the time periods 2040–2050 and 2090–2100 (Tyberghein et al. 2012; Assis et al., 2018). SDMs were computed using Maxent v. 3.4.4 ( Phillips et al. 2006; Phillips et al. 2017). Additionally, the R-packages raster (Hijmans 2016), dismo (Hijmans et al. 2017) and ENMeval (Muscarella et al. 2014) were used for further processing in R 4.0. Assis J., Tyberghein L., Bosch S., Verbruggen H., Serrão EA, De Clerck O (2017) Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27: 277-284. Hijmans R.J. (2016) raster: Geographic Data Analysis and Modeling. R package version 2.5-8. https://CRAN.R-project.org/package=raster. Hijmans R.J., Phillips S., Leathwick J., Elith J. (2017) dismo: Species Distribution Modeling. R package version 1.1-4. https://CRAN.R-project.org/package=dismo. Muscarella R., Galante P.J., Soley-Guardia M., Boria R.A., Kass J.M., Uriarte M., Anderson R.P. (2014) ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods in Ecology and Evolution 5: 1198-1205; https://doi.org/10.1111/2041-210x.12261 Phillips S.J., Anderson R.P., Dudik M., Schapire R.E., Blair M.E. (2017) Opening the black box: an open-source release of Maxent. Ecography 40: 887-893; https://doi.org/ 10.1111/ecog.03049 Phillips S.J., Anderson R.P., Schapire R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259. Tyberghein L., Verbruggen H., Pauly K., Troupin C., Mineur F., De Clerck O. (2012) Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography 21: 272-281; https://doi.org/10.1111/j.1466-8238.2011.00656.x Global warming threatens the viability of tropical coral reefs and associated marine calcifiers, including symbiont-bearing larger benthic foraminifera (LBF). The impacts of current climate change on LBF are debated because they were particularly diverse and abundant during past warm periods. Studies on the responses of selected LBF species to changing environmental conditions reveal varying results. Based on a comprehensive review of the scientific literature on LBF species occurrences, we applied species distribution modeling using Maxent to estimate present-day and future species richness patterns on a global scale for the time periods 2040–2050 and 2090–2100. For our future projections, we focus on Representative Concentration Pathway 6.0 from the Intergovernmental Panel on Climate Change, which projects mean surface temperature changes of +2.2°C by the year 2100. This data set comprises all raw data and results. Our results suggest that species richness in the Central Indo-Pacific is two to three times higher than in the Bahamian ecoregion, which we have identified as the present-day center of LBF diversity in the Atlantic. Our future predictions project a dramatic temperature-driven decline in low-latitude species richness and an increasing widening bimodal latitudinal pattern of species diversity. While the central Indo-Pacific, now the stronghold of LBF diversity, is expected to be most pushed outside of the currently realized niches of most species, refugia may be largely preserved in the Atlantic. LBF species will face large-scale non-analogous climatic conditions compared to currently realized climate space in the near future, as reflected in the extensive areas of extrapolation, particularly in the Indo-Pacific. Our study supports hypotheses that species richness and biogeographical patterns of LBF will fundamentally change under future climate conditions, possibly initiating a faunal turnover by the late 21st century. Species records are stored as *.csv files, which can be processed in Microsoft Excel, the environmental variables can be processed in any GIS software, e.g. QGIS (https://www.qgis.org/en/site/), while the associated R scripts can be edited in any text editor. The lambda files can be projected using Maxent (https://biodiversityinformatics.amnh.org/open_source/maxent/).Funding provided by: Deutsche ForschungsgemeinschaftCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001659Award Number: 426127743

This repository provides the supplementary data and Python code to the paper titled “Decarbonization scenarios for the iron and steel industry in context of a sectoral carbon budget: Germany as a case study”, published in the Journal of Cleaner Production. This repository comprises the following files: 2 Python scripts to create a biosphere database and a database for the steel industry (see 1-create_biosphere-database.py; 2-create_industry_database.py) 2 excel files which contain the input data for the biosphere and industry database (see 1_biosphere_steel_import.xlsx; 2_industry_steel_import.xlsx). The industry file contains the model for the iron and steel production processes. 1 excel file containing the scenario parameters, i.e. the modelled market shares of future steel production in Germany (see 3_scenario_parameters_for_import_into_AB.xlsx) 1 excel file which lists the assumed emission factors for different energy carriers in the study (4_emission_factors.xlsx) These files allow to reproduce the results of our study. These are the future energy demand and respective CO2 emissions for four steel production scenarios in Germany. The scenario results were calculated with the software of the activity browser. More details on the scenario data and the iron and steel production model is provided in the publication itself: Harpprecht, C., Naegler, T., Steubing, B., Tukker, A., Simon, S., 2022. Decarbonization scenarios for the iron and steel industry in context of a sectoral carbon budget: Germany as a case study. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2022.134846 Funding: This research was funded by the Helmholtz Initiative Climate Adaptation and Mitigation, Cluster I “Net-Zero-2050”. License: CC-BY 4.0 license for DLR (German Aerospace Center) {"references": ["Umweltbundesamt (2020). Carbon Dioxide Emissions for the German Atmospheric Emission Reporting 1990-2018 (in German). Retrieved 28.10.2020.", "IEAGHG (2013). IIron and Steel CCS Study (Techno-economics Integrated Steel Mill). Report 2013/04."]}

The model 4C (‘FORESEE’ - Forest Ecosystems in a Changing Environment) has been developed to describe long-term forest behaviour under changing environmental conditions. It describes processes on tree and stand level basing on findings from eco-physiological experiments, long term observations and physiological modelling. The model includes descriptions of tree species composition, forest structure, total ecosystem carbon content as well as leaf area index. The model shares a number of features with gap models, which have often been used for the simulation of long-term forest development. Establishment, growth and mortality of tree cohorts are explicitly modelled on a patch on which horizontal homogeneity is assumed.

This is the repository for the codes and input/output datasets used for the "Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach" paper published at Geoscientific Model Development Discussion. For a comprehensive description of the methods see (Harder et. al., 2022 and Fallah et. al., 2023 ). This repository contains: A Jupyter Notebook showing the workflow of the work used in the paper "Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach" [Climate_Model_Downscaling_GMD-main.zip]. List of analysed CMIP6 simulations [model_lists.pdf]. COSMO-CLM model ste-up files [cclm_setups.zip]. Snapshot of the code as used in the paper [constrained-downscaling.zip]. Input/output, as well as, trained CNN models, which could also be downloaded by the Jupyter notebook of Climate_Model_Downscaling_GMD-main.zip as following: - input_test.pt, target_test.pt, input_train.pt, target_train.pt, input_val.pt, target_val.pt , : test, train and val datasets for training the model. - my_own_test_generalization.zip: the required data fr generalization test. - my_own_test_twc_cnn_acadd_constraints_epochs_150_lr_0.00001_alpha_0.99_test.pt: output of the models. Please note that the Jupyter Notebook will download the original code of Physics-Constrained Deep Learning for Climate Downscaling which is has the following DOI at Zenodo: https://zenodo.org/uploads/8150694. Given that the complete COSMO-CLM model output is of order of ~100 TB, we could provide them upon individual requests. We aim to standardise and make the model output available according to the CORDEX standards. 

This QGIS plugin calculates solar irradiance based on arbitrary 3D geometry and EPW weather data. Built on the open-source QGIS platform, version 1.0 incorporates solar radiation adjustments for surface tilt and azimuth. Additionally, the plugin computes the true surface area of a given 3D plane.This application is characterized by the following key features: determination of real-time solar irradiation on a given plane using meteorological data (EPW) computationally scalable across an arbitrary number of planes when calculating solar radiation, corrections should be made for the three components: direct, diffuse, and reflected the results can be visualized in any way desired, and the calculations can be exported to any file format supported by QGIS

{"references": ["Letay and Bett, Eur. Photovoltaic Sol. Energy Conf., Proc. Int. Conf., 17th, 2001, 178\u2013181. Source of the solar spectrum.", "Amillo et al, Remote Sens., 2014, 6, 8165. PVGIS database. Source of the irradiance data.", "Qiao et al, Chem. Soc. Rev., 2014, 43, 631. Source of some Gibb's Free energy data.", "Hong et al, Anal. Methods, 2013, 5, 1086. Source of some Gibb's Free energy data.", "Jones E, Oliphant E, Peterson P, et al. SciPy: Open Source Scientific Tools for Python, 2001-, http://www.scipy.org/ [Online; accessed 2018-11-29].", "L. Kou, D. Labrie, and P. Chylek, Appl. Opt., 1993, 32, 3531-3540. Data on water absorption."]} YaSoFo was created in the search for a tool that extends detailed-balance calculations, which are common in photovoltaics to understand and improve solar cells, to solar fuel applications. The idea is that any parameter, from light absorption in the electrolyte over catalyst performance to electrochemical load can be varied in a scriptable loop. In doing so, one can determine the efficiency-limiting bottlenecks of a solar fuel device. The implementation in Python makes the tool platform-independent and easily extensible. The software is hosted at https://codeberg.org/photon/YaSoFo. v1.2.11 is mainly a maintenance release.

Version of the CARLIOS model for the paper: Krause, A.J., Sluijs, A., van der Ploeg, R. et al. Enhanced clay formation key in sustaining the Middle Eocene Climatic Optimum. Nat. Geosci. 16, 730–738 (2023). https://doi.org/10.1038/s41561-023-01234-y Model code last updated in 02-2023, uploaded 06-2023.