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openmit-licenseMay 2024View details →

Data underlying the article: "Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling"

This repository contains the data underlying the article: “Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling” available as a preprint on ChemRxiv. Main authors: Marina Gorostiola González & Olivier J.M. Béquignon (Leiden University) Senior author: Gerard J.P. van Westen (Leiden University) This analysis was performed using the code available at <a href="https://github.com/CDDLeiden/chembl_variants" target="_blank" rel="noopener">https://github.com/CDDLeiden/chembl_variants</a>

opencc-zeroMay 2024View details →

Data from: Microplastic exposure is associated with epigenomic effects in the model organism Pimephales promelas (fathead minnow)

Microplastics have evolutionary and ecological impacts across species, affecting organisms' development, reproduction, and behavior along with contributing to genotoxicity and stress. As plastic pollution is increasing and ubiquitous, gaining a better understanding of organismal responses to microplastics is necessary. Gene methylation is a heritable form of molecular regulation that is influenced by environmental conditions, including exposure to pollutants, therefore determining epigenetic responses to microplastics will reveal potential chronic consequences of this pollutant. We performed an experiment across two generations of fathead minnows (Pimephales promelas) to elucidate transgenerational effects of microplastic exposure. We exposed the first generation of fish to four different treatments of microplastics: two concentrations of each of pre-consumer polyethylene (PE) and PE collected from Lake Ontario. We then raised the second generation from these parents with no microplastic exposure. We used reduced-representation methylation sequencing on adult liver tissue and homogenized larvae to evaluate DNA methylation differences among treatments, sexes, and generations. Our findings show the origin of the plastic had a larger effect in female minnows whereas the effect of concentration was stronger in the males. We also observed transgenerational effects, highlighting a mechanism in which parents can pass on the effects of microplastic exposure to their offspring. Many of the differentially methylated genes found in our analyses are known to interact with estrogenic chemicals associated with plastic and are related to metabolism. This study highlights the persistent and potentially serious impacts of microplastic pollution on gene regulation in freshwater systems.

opencc-by-4.0May 2024View details →

Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".

This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets".  This supplementary data package includes the following files: -        Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production. -        Readme.txt: Includes a detailed description of the data packages   Sources of data: * Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark. * Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281                                                            * Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data                                        * Own data

Simulation scripts and data for the stochastic modelling of evolutionary rescue in resistance to pesticides

Evolutionary rescue occurs when the genetic evolution of adaptation saves a population from extinction after environmental change. The evolution of resistance to pesticides is a special scenario of abrupt environmental change, where rescue occurs under strong selection for one or a few de novo resistance mutations of large effect. Here, we develop continuous-time approximations that accurately predict classic discrete-time dynamics in population genetics and population ecology in an integrated eco-evolutionary model of adaptive rescue through pesticide resistance. We derive analytical approximations for the key distributions and statistics that characterise the results, including the probability density function for the time to resistance and the probability of population extinction. The time to resistance shows a lag period, a narrow peak and a long tail, which implies that it can be difficult to predict when resistance will arise. The probability of population extinction shows a sharp transition, in that when extinction is possible, it is also highly likely, which can make eradication a theoretically achievable goal. Alongside these results contributing to the theory of evolutionary rescue, the methods have produced powerful approximations that lay the foundations of a flexible modelling framework for the applied study of eco-evolutionary dynamics to improve scientific resistance management.

opencc-by-4.0May 2024View details →

Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"

Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"

opencc-by-4.0Dec 2020View details →

Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case

Open the record for dataset details and reuse information.

Extracted experimental data for research paper titled "Micro-thermomechanical Modeling of Rocks with Temperature-dependent Friction and Damage Laws"

This repository contains the experimental data on stress-strain curves extracted from the following original publications for constitutive model validation in our manuscript. [1] Jinping marble: Zhong, Y. Y. (2017). Research on mechanical properties of marble and the effects on rock burst under thermal-mechanical coupling (in Chinese) (Master’s thesis, Chengdu University of Technology). doi: 10.26986/d.cnki.gcdlc.2017.000109. [2] Beibei sandstone: Long, L. J. (2021). Study on mechanical and seepage properties of sandstone under the coupling of temperature-seepage-stress (in Chinese) (Doctoral dissertation, Chongqing University). doi: 10.27670/d.cnki.gcqdu.2021.001009. [3] Gongjue granite: Zhou, H. Y., Liu, Z. B., Shen, W. Q., Feng, T., & Zhang, G. Z. (2022). Mechanical property and thermal degradation mechanism of granite in thermal-mechanical coupled triaxial compression. International Journal of Rock Mechanics and Mining Sciences, 160, 105270. doi: 10.1016/j.ijrmms.2022.105270.

Data from: Thermal plasticity in protective wing pigmentation is modulated by genotype and food availability in an insect model of seasonal polyphenism

Phenotypic variation in natural populations results from complex interactions between organisms and their changing environments. The environment shapes both phenotypic frequencies (during adaptation) and organismal phenotypes (through phenotypic plasticity). Developmental plasticity, in particular, refers to the phenomenon whereby an organism's phenotype depends on the environmental conditions during development. It can match phenotype to ecological conditions and help organisms to cope with environmental heterogeneity, including differences between alternating seasons. Experimental studies of developmental plasticity often focus on the impact of individual environmental cues and do not take explicit account of genetic variation. In contrast, natural environments are complex, comprising multiple variables with combined effects that are poorly understood and may vary among genotypes. We investigated the effects of multifactorial environments on the development of the seasonally plastic eyespots of Bicyclus anynana butterflies. Eyespot size depends on developmental temperature and is involved in alternative seasonal strategies for predator avoidance. In nature, both temperature and food availability undergo seasonal fluctuations. However, our understanding of how thermal plasticity in eyespot size varies in response to food availability and across genotypes remains limited. To address this, we investigated the combined effects of temperature (T; two levels: 20°C and 27°C) and food availability (N; two levels: control and limited) during development. We examined their impact on wing and eyespot size in adult males and females from multiple genotypes (G; 28 families). We found evidence of thermal and nutritional plasticity and temperature-by-nutrition interactions (significant TxN) on the size of eyespots in both sexes. Food limitation resulted in relatively smaller eyespots and tempered the effects of temperature. Additionally, we found differences among families for thermal plasticity (significant GxT effects), but not for nutritional plasticity (non-significant GxN effects) nor for the combined effects of temperature and food limitation (non-significant GxTxN effects). Our results reveal the context dependence of thermal plasticity, with the slope of thermal reaction norms varying across genotypes and across nutritional environments. We discuss these results in light of the ecological significance of pigmentation and the value of considering thermal plasticity in studies of the biological impact of climate change.

opencc-by-4.0Mar 2024View details →

Data, models, and outputs for an agent-based hydro-economic modeling study in an intensively irrigated region of the U.S. High Plains

In this dataset, we include all the models developed for the study "An integrated modeling approach to simulate human-crop-groundwater interactions in intensively irrigated regions", which is published in Environmental Modelling & Software (<a href="https://doi.org/10.1016/j.envsoft.2024.106120">https://doi.org/10.1016/j.envsoft.2024.106120</a>). Additionally, we provide all the data used in this study. Below, you will find a description of the contents of each file: <ul> <li>abm_modflow.zip: This file includes the agent-based hydro-economic model (ABM-MODFLOW), including model inputs and outputs, Python post-processing scripts, and the Windows batch script for the integration process.</li> <li>modflow.zip: This file contains the standalone MODFLOW model files. Each folder includes files for individual simulation periods, starting with a steady-state model for the predevelopment period, followed by seven transient models.</li> <li>modflow_rs.zip: This file contains the MODFLOW-RS model files. Given that remote sensing data is provided for years from 1984 onwards, only models for the post-1980 simulation periods are included. For model files corresponding to years prior to 1980, refer to modflow.zip.</li> <li>Figurers.zip: This file includes all data and Python scripts used to produce the figures in the main text.</li> <li>Tables.zip: This file contains all tables and the associated data included in the main text.</li> <li>Supporting_Info_Figures: This file includes all data and Python scripts used to produce the figures in the Supporting Information.</li> <li>Supporting_Info_Tables: This file contains all tables and the associated data included in the Supporting Information.</li> <li>Supporting_Info_Videos: This file stores Videos S1 and S2 of the Supporting Information, displaying the historical development of irrigation wells and groundwater-fed irrigated lands in the study area from 1946 to 2018.</li> </ul>

Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models

Ecological niche models (ENMs) are a powerful tool in ecological research and conservation planning. Since ENMs provide probability maps of suitable areas under environmental change, they may assist in designing conservation actions and addressing conservation priorities. However, ENMs are usually implemented by learning the species climatic preferences from their current geographic distribution, which leaves them vulnerable to the issue of niche truncation issues, as if comes with non-climatic limits to the current species distribution posed by e.g. anthropic activities and settlements, and is bound to assume that species are at equilibrium with their environments. These problems might be alleviated by the inclusion of fossil occurrences, which refer to moments during species evolution when such limits were absent, and a larger fraction of the species fundamental niche was probably explored. Here, we combined current and fossil occurrence data for 38 medium-large mammal species of conservation concern to assess the influence of the fossil record on ENM predictions under future climate change scenarios. We found that ignoring or including fossil data yields consistent trends in terms of predicted range increase/decrease. Yet, although adding fossil data invariably results in increased niche width, estimates of range change magnitude improved for just one half only of the species. These results suggest that most species might be in non-equilibrium with their environment, and that the inclusion of fossil data may be crucial to the better understanding of species climatic requirements, hence for designing effective conservation strategies.

opencc-by-4.0Jan 2024View details →

Data for publication "A unified surface tension model for multi-component salt, organic and surfactant solutions"

This repository contains the data of the publication: Title: "A unified surface tension model for multi-component salt, organic and surfactant solutions" Authors: Judith Kleinheins, Claudia Marcolli, Cari Dutcher, Nadia Shardt Date: 2024

EGFs, gravity and crustal models data around the Solonker suture zone in NE China

The observed EGFs, complete Bouguer gravity anomalies data and the 3-D crustal Vs and density models from our joint inversion around the Solonker suture zone in NE China.

Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 3. Location map of the study area.

opencc-by-4.0Jun 2019View details →

Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2

The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO2 fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here. Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the load function or can be read into R using the R.matlab package. <ul> <li>H.tar.gz: This tar file contains the H matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The H matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the H matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li>Z.mat: This file contains the synthetic OCO-2 observations used in the case study. </li> <li>areas_us.mat: This file lists the area of each model grid box used in the case study in units of meters2. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO2 fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li>distmat.mat: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li>land_mask.mat: We only estimate CO2 fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li>H_all_OCO2.mat: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li>Xvar.tar.gz: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat                     Evaporation</li> <li>Xvar_msdwswrf.mat      Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat                     Specific humidity</li> <li>Xvar_stl1.mat                 Soil temperature level 1</li> <li>Xvar_stl3.mat                Soil temperature level 3</li> <li>Xvar_swvl1.mat             Volumetric soil water layer 1</li> <li>Xvar_t2m.mat               2 metre temperature</li> <li>Xvar_tp.mat                  Total precipitation</li> <li>Xvar_mer.mat               Mean evaporation rate</li> <li>Xvar_pev.mat               Potential evaporation</li> <li>Xvar_r.mat                   Relative humidity</li> <li>Xvar_swvl3.mat          Volumetric soil water layer 1</li> <li>Xvar_tcc.mat              Total cloud cover</li> </ul> </li> </ul>

Modeling polar bear (Ursus maritimus) snowdrift den habitat on Alaska's Beaufort Sea coast using SnowDens-3D and ArcticDEM data

Pregnant polar bears (Ursus maritimus) excavate maternal dens in seasonal snowdrifts during fall along Alaska's Beaufort Sea coast to shelter their altricial young during birth and development. With recent sea ice decreases, bears are denning more frequently on land. Each year, the weather and blowing-snow conditions control the creation of snowdrifts across the landscape, and the available snowdrift den habitat can vary widely from one year to the next, depending on the late fall and early winter air temperature, snowfall, and wind speed and direction. We implemented a physics-based, spatiotemporal, polar bear snowdrift den habitat model (SnowDens-3D) across the eastern Alaska Beaufort Sea coast (an area of approximately 17,000 km^2^). High-resolution (2.0 m) topography data were provided by the ArcticDEM, and daily meteorological forcings were provided by NASA's MERRA-2 reanalysis. A 21-year (2000–2020) SnowDens-3D simulation was performed, and model outputs were compared with 91 historical polar bear den locations. The year-specific simulations produced viable den habitat for 98% of the observed den locations. The interannual variation in den habitat area over the 21-year period increased by approximately a factor of three from the minimum year (2001; 554 km^2^) to the maximum year (2017; 1,566 km^2^). This data archive provides the key den and den-habitat datasets produced, used, and analyzed by this project.

openbsd-3-clauseJan 2024View details →

Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON"

Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON". The code used to generate, analyze and plot these data is provided separately on Zenodo. The zip files named 2.zip_file_name are used in the 2.make_comparison_plots notebooks in the companion Zenodo software repository. The zip files named 3.zip_file_name are generated using the code contained in 1.process_data in the software repository and used for the analyses in 3.calculate_causal_effects in the software repository.  References - Code ______________________________________________ J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. <a href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a> J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34. <a href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a> References - Data ______________________________________________ SEVIRI  Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: The Third Edition of the CM SAF Cloud Data Record Based on SEVIRI Observations, Earth System , Science Data Discussions, pp. 1-38, <a href="https://doi.org/10.5194/essd-2023-79">https://doi.org/10.5194/essd-2023-79</a>,  2023. GOES Walther, A. and Straka, W.: Algorithm Theoretical Basis Document For Daytime Cloud Optical and Microphysical Properties (DCOMP), 2020 ERA5 Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Single Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, 2018a. Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Pressure Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>, 2018b. GPM Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 Degree x 0.1 Degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">https://doi.org/10.5067/GPM/IMERG/3B-</a><a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">HH/07</a>, 2023 MODIS Platnick, S. et al. MODIS atmosphere L3 daily product. NASA<a href="https://doi.org/10.5067/MODIS/MOD08_D3.006"> https://doi.org/10.5067/MODIS/MOD08_D3.006 </a>, 2015. MIDAS Eastman, R., McCoy, I. L., Schulz, H., and Wood, R.: A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies, EGUsphere, pp. 1–33, <a href="https://doi.org/10.5194/egusphere-2023-2118">https://doi.org/10.5194/egusphere-2023-2118</a>, 2023.  McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophysical Research Letters, 50, e2022GL101 042, <a href="https://doi.org/10.1029/2022GL101042">https://doi.org/10.1029/2022GL101042</a>, 2023. ICON Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) Modelling Framework of DWD and MPI-M: Description of the Non-Hydrostatic Dynamical Core, Quarterly Journal of the Royal Meteorological Society, 141, 563–579, <a href="https://doi.org/10.1002/qj.2378">https://doi.org/10.1002/qj.2378</a>, 2015 <a href="https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license">https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license </a>

opencc-by-4.0May 2024View details →

Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions

Model data for probabilistic projections of the Amery Ice Shelf catchment,  Antarctica, under high ice-shelf basal melt conditions ====================================================================== This archive contains ice-sheet model output and statistical models  used in the manuscript: "Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions" by: Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro Perego, Stephen Price, John D. Jakeman OVERVIEW -------- Statistical models and corresponding datasets are archived in the file Amery_UQ_Study_Statistical_Models.zip.  That file contains a README that describes its contents. Contact for statistical models: Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov The remaining files in this archive contain output from ice-sheet model  simulations using the MPAS-Albany Land Ice (MALI) model  (Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM) applied to a regional domain of the Amery Ice Shelf catchment of Antarctica with mesh resolution varying from 4 to 20 km. The contents of these files are described below. Contact for MALI simulations: Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov ENSEMBLES --------- The ensembles consist of all or a subset of 200 model runs with values for 6 parameters sampled from a Sobol' sequence over a specified range. Parameters and Sampled Ranges: 1. Ice stiffness scaling factor, Cφ: (0.8, 1.2) 2. Basal friction scaling factor, Cμ: (0.8, 1.2) 3. Basal slip exponent, q: (0.1, 0.333) 4. Calving yield stress, σmax: (80, 180) kPa 5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1 6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1 SCENARIOS --------- The archive contains output from 4 scenarios described in the manuscript: Historical relaxation (RELX): For each ensemble member, we conducted a 50 year relaxation from the initial condition using historical climate forcing to integrate out fast transient behavior. For surface mass balance, we applied a 1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van den Broeke, 2019). The ocean thermal forcing was the observation-based climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018 (Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration was chosen as the most rapid adjustments occur in the first few decades of integration, while the long term adjustment to a fully steady state takes thousands of years. Relaxation to full steady state would require substantially more computing resources than our entire set of ensembles and also leads to the complication of different runs having potentially very different initial states. Future improvements to model initialization that account for surface elevation change (Perego et al., 2014) may reduce model drift and adjust this requirement. The final model state in each run at the end of RELX was given the nominal date of January 1, 2015, and all three projection ensembles were branched from these states. Control projection (CTRL): The CTRL projection ensemble were an extension of the RELX configurations, continuing the same surface mass balance and ocean thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was used to assess model drift relative to the forced response of the climate scenarios.  SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean thermal forcing were applied as anomalies relative to the climatological mean forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt changes in forcing. This ensemble was also run from January 1, 2015, to January 1, 2300. SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5 projection forcings (expAE05 from Seroussi et al., 2024), again applied as anomalies and from 2015 to 2300. OUTPUT ------ Each ensemble directory contains at the base level: * branch_ensemble.cfg - configuration file for the ensemble_generator test   case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html)    of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)   used to set up the ensemble. * mesh_vars.nc - netCDF file containing the mesh variables.  This file is the   same for all ensembles.  These fields need to be appended to an output file to   visualize in, e.g., Paraview.  This can be done with the command:   ncks -A mesh_vars.nc output.nc Each ensemble contains subdirectories for each run numbered 000-199.  Each run directory contains the files: * globalStats.nc - scalar metrics at every time step * output.nc - spatial fields at 10 year intervals * run_info.cfg - summary of parameter values Notes: * The SSP1 and SSP5 ensembles contain a subset of the full 200 runs   because some runs were filtered out as being unnecessary during model   calibration (see manuscript). * The RELX ensemble is run for 50 years with an arbitrary start year of 2000.   The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state   of the corresponding RELX run (nominally 2050).  Because spatial data has   been saved in this archive at 10 year intervals, the first output for the   three projection ensembles is 2020.  To get the initial condition for the   projection ensembles, use the 2050 state from RELX for the corresponding   model run. * Spatial field output at 1 year intervals, as well as run input, log, and   restart files are available from the corresponding contact.  The complete   run data is about 2 TB. CITATION -------- If you find these data useful, please cite the DOI for this  archive (10.5281/zenodo.11166628), as well as our manuscript submitted to The Cryosphere. This archive is approved by Los Alamos National Laboratory for public release under LA-UR-24-24969; distribution is unlimited.   REFERENCES ---------- Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.: MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids, Geoscientific Model Development, 11, 3747–3780, 2018. Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020. Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020. Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181, 2014. Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol. https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica.  van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica (1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science, https://doi.org/10.1594/PANGAEA.896940, 2019. van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley, B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate Model, Journal of Glaciology, 60, 761–770, https://doi.org/10.3189/2014JoG14J051, 2014.

Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California

We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future. Location: California, United States Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state. Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.

opencc-by-nc-4.0Jun 2024View details →

Inputs, results data and analysis script for the evaluation of the PDG-Arena forest growth model on beech-fir stands

Supplementary files for simulations in Rouet et al. (2024): PDG-Arena: An eco-physiological model for characterizing tree-tree interactions in heterogeneous and mixed stands (doi: <a href="https://doi.org/10.1101/2024.02.09.579667" target="_blank" rel="noopener">10.1101/2024.02.09.579667</a>). This repository is an archive of the github repository PDG-Arena-extra (release v1.0.3), accessible at <a href="https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3" target="_blank" rel="noopener">https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3</a>.

Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models

This contains: <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> For the paper Using feature importance as exploratory data analysis tool on earth system models.