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212 results for “Climate Simulation”
Alpine range by species input to simulations that reveal climate and legacy effects
<p>Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe (SACEU) are compared to those of the western US and Canada (WUSAC) because they differ in their geographies and histories. Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America. In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation. The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western US and Canada, less isolation and more mixing of species from refugia have caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria.</p>
CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"
<p>Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).</p>
Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period
<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project: </strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>
Challenges simulating the AMOC in climate models
<p>This is a data set for the article:</p> <p>Jackson, Laura C, Helene T. Hewitt, Diego Bruciaferri, Daley Calvert, Tim Graham, Catherine Guiavarc’h, Matthew B. Menary, Adrian L. New, Malcolm Roberts and David Storkey, 2023: Challenges simulating the AMOC in climate models, submitted to Philosophical Transactions of the Royal Society A.</p> <p>Fig 1 calculated correlations between AMOC strength or trend in 1% CO2 experiment (values given in CMIP6_AMOC.txt) and sea surface salinity fields in the controls (fields for each model given in CMIP6_SSS.nc)</p> <p>We also include data of the AMOC streamfunction (*amoc.nc), March mixed layer depth (MarchMLD.nc) and temperature and salinity fields (TS.nc) for the mean of years 20-30 of models HadGEM3-GC3-1ML, MM and MH. These models are documented here <a href="https://doi.org/10.5194%2fgmd-2019-148">doi:10.5194/gmd-2019-148</a>. This data was used for plotting figures 2-4. </p> <p>Data for Fig 5 is in HadGEM3-GC3-1MM-noGM-av-20-40_MarchMLD.nc and HadGEM3-GC3-1MM-withGM-av-20-40_MarchMLD.nc</p> <p>Data for Fig 6 is in HadGEM3-GC1_control_20390301_20780330_MLD.nc and HadGEM3-GC1_relax_20390301_20780330_MLD.nc</p> <p>Data for Fig 7 is included in <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F7764695&data=05%7C01%7Claura.jackson%40metoffice.gov.uk%7Cceeee8cf4c9d4401060e08db2bcc93f2%7C17f1816120d7474687fd50fe3e3b6619%7C0%7C0%7C638151929867577211%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nkL88P4lDCDB5E4qPxvL6%2B6pu6nU5kDtCofZg5%2FAoDs%3D&reserved=0">https://zenodo.org/record/7764695</a> </p> <p> </p>
Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
<p>Climate change may alter phenology within populations with cascading consequences for community interactions and ongoing evolutionary processes. Here, we measured the response to climate change in two sympatric, recently diverged (~170 years) populations of <em>Rhagoletis</em> <em>pomonella</em> flies specialized on different host fruits (hawthorn and apple) and their parasitoid wasp communities. We tested whether warmer temperatures affect dormancy regulation and its consequences for synchrony across trophic levels and temporal isolation between divergent populations. Under warmer temperatures, both fly populations developed earlier. However, warming significantly increased the proportion of maladaptive pre-winter development in apple, but not hawthorn, flies. Parasitoid phenology was less affected, potentially generating ecological asynchrony. Observed shifts in fly phenology under warming may decrease temporal isolation, potentially limiting ongoing divergence. Our findings of complex sensitivity of life-history timing to changing temperatures predict that coming decades may see multifaceted ecological and evolutionary changes in temporal specialist communities.</p>
Holocene climate change in southern Oman deciphered by speleothem records and climate model simulations
<p>8 ka BP (thousand year, before present, 8K) and pre-industrial period (PI) paleoclimate equilibrium experiment, including precipitation (pr), near-surface relative humidity (hurs), evaporation (evspsbl) and 850hPa wind (u850 & v850).</p>
Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Earth Syst. Dynam.</i>, 2023). For details see the README.md file.</p>
NEMO-FABM-ERSEM-MIZER climate change simulation data
<p>This dataset corresponds to data produced from running NEMO-FABM-ERSEM MIZER on the AMM7 domain as part of the COMFORT project and used in the Deliverable 3.3 (see related identifiers). A scientific manuscript is in preparation. The climate simulation was run from 1981-2100 forced by atmospheric and lateral boundary condition from the IPSL-CMR5a-MR (RCP 8.5) climate simulation (Dufresne et al., 2013; WCRP, 2016) . Data reported here are from 1990-2100, unless otherwise stated. </p> <p>Data has been post-processed to convert it to depth independent (2D), monthly averaged variables. Consequently all data are either depth-integrated, mean across depth (temperature, salinity) or surface data (nutrients).</p> <p>For access to the raw data or more details, please contact Helen Powley (hpo@pml.ac.uk).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data for the publication "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization"
<p>This dataset includes a set of 11yr simulations using the MIROC6 global aerosol-climate model under the pre-industrial (PI, aerosol emission at the year 1850), present-day (PD, aerosol emission at the year 2000), and future warming (SST+4K, a uniform 4 K increase in sea surface temperature) conditions.</p> <p>The data are used in the manuscript entitled "Significant Increase in Graupel and Lightning Occurrence in a Warmer Climate Simulated by Prognostic Graupel Parameterization".</p>
Simulated data and code from: Socio-economic predictors of Inuit hunting choices and their implications for climate change adaptation
<p>These data are part of a data portal that accompanies the special issue 'Climate change adaptation needs a science of culture,' published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit <strong><a href="https://doi.org/10.5061/dryad.bnzs7h4h4">10.5061/dryad.bnzs7h4h4</a>.</strong></p> Summary <p>Repository of R and Stan code to simulate and analyse foraging trip data (patch choice and harvest success).</p> <a class="anchor" href="https://github.com/fhillemann/MSrepo_harvest_patch_choice#accompanying-manuscript"></a>Accompanying manuscript <p>F. Hillemann, B. A. Beheim, E. Ready. 2023. Socio-economic predictors of Inuit hunting strategies and their implications for climate change adaptation. Phil. Trans. R. Soc. B 378: 20220395. <a href="https://doi.org/10.1098/rstb.2022.0395" rel="nofollow">https://doi.org/10.1098/rstb.2022.0395</a></p> <p><strong>Manuscript Abstract:</strong><br>In the Arctic, seasonal variation in the accessibility of the land, sea ice, and open waters influences which resources can be harvested safely and efficiently. Climate stressors are also increasingly affecting access to subsistence resources. Within Inuit communities, people differ in their involvement with subsistence activities, but little is known about how engagement in the cash economy (time and money available) and other socio-economic factors shape the food production choices of Inuit harvesters, and their ability to adapt to rapid ecological change. We analyse 281 foraging trips involving 23 Inuit harvesters from Kangiqsujuaq, Nunavik, using a Bayesian approach modelling both patch choice and within-patch success. Gender and income predict Inuit harvest strategies: while men, especially men from low-income households, often visit patches with a relatively low success probability, women and high-income hunters generally have a higher propensity to choose low-risk patches. Inland hunting, marine hunting, and fishing differ in the required equipment and effort, and hunters may have to shift their subsistence activities if certain patches become less profitable or less safe due to high costs of transportation or climate change (e.g., navigate larger areas inland instead of targeting seals on the sea ice). Our finding that household income predicts patch choice suggests that the capacity to maintain access to country foods depends on engagement with the cash economy.</p>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
MITgcm simulations to study ENSO in warmer climates
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Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results
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Data from: A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks
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Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
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Simulated data and code from: Socio-economic predictors of Inuit hunting choices and their implications for climate change adaptation
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Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
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Alpine range by species input to simulations that reveal climate and legacy effects
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Model data created by MANDIFORE using ED2 simulating structure and biomass under harvest legacies and climate change as the Morton Arboretum, IL, USA, 2007-2099"
The data in this archive was used to investigate the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model and an ensemble of climate change drivers in a midwestern USA hardwood forest. The model used was a modified version of the Ecosystem Demography model version 2.1 (ED2) where trees could be grouped based on plant functional type and size allowing for different management regimes to be simulated. The simulation site was an actively managed mid-continental North American temperate deciduous forest at The Morton Arboretum (TMA) near Chicago, USA. The initial conditions (composition and structure) of the forest simulated were initialized using field survey data from 487, 8.92-m radius plots collected in 2018. Different levels of tree removal based on traditional forestry management techniques for temperate deciduous forests were simulated and evaluated for their effects on forest structure and resistance to drought induced mortality for the period of 2006 to 2100. Model initialization and spin up occurred from 2006-2020 at which point management was simulated for the period of 2020-2024 before running without management for 2024 to 2100. Fourteen CMIP5 GCMs were used for meteorological drivers for 2006 to 2100 for both RCP4.5 and RCP 8.5 warming scenarios. CO2 concentration was held constant at 380 ppm over the simulation period. Four management regimes were simulated for each of these GCM-RCP pairings leading to a total of 112 distinct model runs. The four management regimes were group selection, shelterwood, understory thinning and an additional no management scenario served as a control. Data in this archived is separated into the 112 distinct model runs.
A 30-year high resolution simulation of the future climate over the Interior Western United States
<p>A high-resolution (4 km) convection-permitting regional climate simulation is conducted in the Interior Western United States (IWUS) using the Weather Research and Forecasting (WRF) model. The model integration is conducted over a 30-year period from October 1981 through September 2011, and over a 30-year future period centered at 2050 using the Pseudo–Global Warming (PGW) technique. The IWUS model output for the retrospective climate is available from https://doi.org/10.5281/zenodo.1157112.</p> <p>This repository contains a 30-year gridded dataset of precipitation, surface (2 m) temperature, surface pressure, water vapor mixing ratio at 2 m, and 10 m wind speed at a daily frequency from the IWUS simulation of future climate centered at 2050. Anyone interested in the full dataset of the IWUS simulation is encouraged to contact the lead author at yongganga.wang@gmail.com.</p>
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OpenNeuro
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