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212 results for “Climate Simulation”
Additional data simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>The data presented here are the additional data used for the performance of the COSMO-CLM Mid-Holocene and Pre-Industrial simulations, used for the analysis of the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The data available here are:</p> <p>-ext_data_044.nc: The external parameters used for the simulations, including information on soil type, albedo, land mask, etc. <br> -W_SO_ref_half_rel_soil_moisture_18650401.nc: Soil Moisture at 50% saturation level on the first of April of the 15th year of the simulations. The data are used as reference for the simulation with different soil moisture.</p> <p> </p>
Data PI simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the PRE-Industrial (PI) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> <br> For more specific Information on the presented data, please refer to the corresponding paper:</p>
Data MH simulations COSMO-CLM Russo et al. 2021, Climate of the Past
<p>Postprocessed data of the Mid-Holocene (MH) simulations performed for the manuscript of Russo et al. 2021, submitted to the journal Climate of the Past and entitled: "The long-standing dilemma of European summer temperatures at the Mid-Holocene and other considerations on learning from the past for the future using a regional climate model".</p> <p>The structure of the directories of the data is as follow:</p> <p>--PPE_exp: data of the Physically Perturbed Ensembles<br> Inside this directory there are 3 subfolders:<br> -day_anm: daily mean anomalies<br> -mon_bias: decadal monthly means<br> -mon_fld: spatial averages of monthly means<br> In each of these subfolders, there are data for each of the considered variables:<br> -T_2M; CLCT; TOT_PREC<br> --soil_pert: sensitivity tests with different initial soil moisture on 1st of April at MH<br> soil_pert only has data for MH for T_2M<br> <br> For more specific Information on the presented data, [please refer to the corresponding paper.</p> <p> </p> <p> </p> <p> </p>
A Gaussian process emulator for simulating ice sheet-climate interactions on a multi-million year timescale: CLISEMv1.0 (Video supplement)
<p>This video illustrates the ice sheet evolution during a 3 Myr period for three different emulators as described in the manuscript "A Gaussian process emulator for simulating ice sheet - climate interactions on a multi-million year timescale", submitted to Geoscientific Model Development. The ice sheet is forced by declining carbon dioxide concentrations from 980 to 720 ppmv and orbital parameter variations during the late Eocene (between 38 Ma and 35 Ma).</p>
Physics‐Based Narrowband Optical Parameters for Snow Albedo Simulation in Climate Models
<p>This is a supplementary file for a submitted paper"Physics-based effective broadband optical parameters for snow albedo simulation in climate models".</p> <p>The authors derived a set of snow optical properties that effective in broadband snow radiative transfer simulation. These parameters are physically-based. </p>
CP_OdU (Climate Projections for Odesa, Ukraine): Climate indices and daily meteorological variables for Odesa (Ukraine) in 2021-2050 by different RCM simulations from Euro-CORDEX
<ol> <li>ODS-UA_RCM_outputs_day_20210101-20501231.zip file contains outputs from Euro-CORDEX RCM’s simulation for a land-located point closest to the Odesa meteorological site (46.44N, 30.77E).</li> <li>The RCM grids define the coordinates for this point (the gridpoint is mostly located in the city center (Kateryninska^Troitska) or near the 7-km market.</li> <li>The nomenclature of files and variables in these files are defined in http://is-enes-data.github.io/cordex_archive_specifications.pdf.</li> <li>Other files contain the so-called climate indices as described in <a href="https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf">https://knmi-ecad-assets-prd.s3.amazonaws.com/documents/atbd.pdf</a> and table in 0readme.pdf.</li> </ol>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Simulation outputs for the manuscript "Snowball Earth transitions from Last Glacial Maximum conditions provide an independent upper limit on Earth's climate sensitivity"
<p>This dataset contains outputs for the simulations shown in the paper "Snowball Earth transitions from Last Glacial Maximum conditions provide an independent upper limit on Earth’s climate sensitivity" by Renoult et al. (submitted). </p> <p>The outputs contain different 2D and 3D variables described in the paper and the file "README.txt".</p>
Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum
<p>Simulation data (TS, FSNT, and FLNT) and apap cloud feedback analysis for CESM2 LGM simulation</p> <p><strong>Simulation boundary condition files in 1-degree resolution: boundary_condition_files.zip</strong></p> <p><strong>Please cite: </strong></p> <p>Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., & DiNezio, P. (2021). Assessment of equilibrium climate sensitivity of the Community Earth System Model version 2 through simulation of the Last Glacial Maximum. <em>Geophysical Research Letters</em>, <em>n/a</em>(n/a), e2020GL091220. https://doi.org/10.1029/2020GL091220</p>
Data for "Dynamical Downscaling of Climate Simulations in the Tropics"
<p>Precipitation, radiation and vertical mass flux data. 'MPI' indicates conventional downscaling results. 'biascor' indicates bias-corrected downscaling results. 'sstcor' indicates SST-corrected downscaling results.</p>
Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD
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CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
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Data from: Simulating local adaptation to climate of forest trees with a Physio-Demo-Genetics model
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Data from: Blue mussel (Genus Mytilus) transcriptome response to simulated climate change in the Gulf of Maine
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Water isotope data for "Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction"
<p><strong>iCESM1.2 simulated seawater oxygen isotopes for the Early Eocene</strong></p> <p><strong>Citation: </strong>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP) and sea-surface oxygen isotope ratio (R18O) from four Eocene simulations with 1×, 3×, 6×, and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>Seawater d18O = (R18O - 1.0) * 1000.0</li> <li>TEMP and R18O are on the POP ocean grid (~1°; see here: <a href="http://www.cesm.ucar.edu/models/cesm1.2/pop2/">http://www.cesm.ucar.edu/models/cesm1.2/pop2/</a>).</li> </ul>
Model simulation data used in "Coupling aerosols to (cirrus) clouds in the global aerosol-climate model EMAC-MADE3" (Righi et al., Geosci. Model Dev., 2020)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Geosci. Model Dev.</i>, 2020). An overview of the numerical experiments performed for this study is given in the file "experiments.dat".</p>
Reference Climate (1971-2005) and RCP 8.5 scenario (2010-2100) of Destra sele for SWAP simulation
<p>The database refers to the weather files for the simulation run of SWAP model (Kroes et al., 2017) in the Destra Sele area (Regione Campania, southern Italy) under Reference Climate (RC, 1971-2005) and future climate scenario (RCP 8.5, 2010-2100).</p> <p>The future climate scenarios were obtained by using the high resolution regional climate model (RCM) COSMO-CLM (Rockel et al., 2008), with a configuration employing a spatial resolution of 0.0715°(about 8 km), which was optimised over the Italian area. The validations performed showed that these model data agree closely with different regional high-resolution observational datasets, in terms of both average temperature and precipitation in Bucchignani et al. (2015) and in terms of extreme events in Zollo et al. (2015). In particular, the Representative Concentration Pathway (RCP) 8.5 scenario was applied, based on the IPCC (Intergovernmental Panel on Climate Change) modelling approach to generate greenhouse gas (GHG) concentrations (Meinshausen et al., 2011). Initial and boundary conditions for running RCM simulations with COSMO-CLM were provided by the general circulation model CMCC-CM (Scoccimarro et al., 2011), whose atmospheric component (ECHAM5) has a horizontal resolution of about 85 km. The simulations covered the period from 1971 to 2100; more specifically, the CMIP5 historical experiment (based on historical greenhouse gas concentrations) was used for the period 1976–2005 (Reference Climate scenario - RC), while for the period 2006–2100, a simulation was performed using the IPCC scenario mentioned. The analysis of results was made on RC (1971–2005) and RCP 8.5 divided into three different time periods (2010–2040, 2040–2070 and 2070–2100). Daily reference evapotranspiration (ET<sub>0</sub>) was evaluated according to Hargreaves and Samani, (1985) equation (HS). The reliability of this equation in the study area was perrformed by Fagnano et al., (2001) comparing the HS equation with the Penman–Monteith (PM) equation (Allen et al., 1998).</p> <p>Under the RCP 8.5 scenario the temperature in Destra Sele is expected to increase approximately two degrees celsius respectively every 30 years to 2100 starting from the RC. The differences in temperature between RC and the period 2070–2100 showed an average increase of minimum and maximum temperatures of about 6.2°C (for both min and max). The projected increase of temperatures produces an increase of the expected ET<sub>0</sub>. In particular, during the maize growing season, an average increase of ET<sub>0</sub> of about 18% is expected until 2100.</p> <p>The climate data were provided by the “Regional Models and Geo-Hydrogeological Impacts Division” of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Capua (CE) – Italy, through the support of Dr. Paola Mercogliano and Dr. Edoardo Bucchignani.</p> <p><strong>References</strong></p> <p>Allen, R. G., Pereira, L. S., Raes, D., Smith, M. and W, a B.: Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56, Irrig. Drain., 1–15, doi:10.1016/j.eja.2010.12.001, 1998.</p> <p>Bucchignani, E., Montesarchio, M., Zollo, A. L. and Mercogliano, P.: High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections for the 21st century, Int. J. Climatol., 36(2), 735–756, 2015.</p> <p>Fagnano, M., Acutis, M. and Postiglione, L.: Valutazione di un metodo semplificato per il calcolo dell'ET<sub>0</sub> in Campania, Model. di Agric. sostenibile per la pianura meridionale Gest. delle risorse idriche nelle pianure irrigue. Gutenberg, Salerno, ISBN, 88–900475, 2001.</p> <p>Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1(2), 96–99, 1985.</p> <p>Kroes, J. G., Van Dam, J. C., Bartholomeus, R. P., Groenendijk, P., Heinen, M., Hendriks, R. F. A., Mulder, H. M., Supit, I. and Van Walsum, P. E. V: Theory description and user manual SWAP version 4, http://www.swap.alterra.nl, Wageningen [online] Available from: www.wur.eu/environmental-research (Accessed 24 July 2019), 2017.</p> <p>Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K. and others: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Clim. Change, 109(1–2), 213, 2011.</p> <p>Rockel, B., Will, A. and Hense, A.: The regional climate model COSMO-CLM (CCLM), Meteorol. Zeitschrift, 17(4), 347–348, 2008.</p> <p>Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Fogli, P. G., Manzini, E., Vichi, M., Oddo, P. and Navarra, A.: Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model, J. Clim., 24(16), 4368–4384, doi:Doi 10.1175/2011jcli4104.1, 2011.</p> <p>Zollo, A. L., Turco, M. and Mercogliano, P.: Assessment of hybrid downscaling techniques for precipitation over the Po river basin, in Engineering Geology for Society and Territory-Volume 1, pp. 193–197, Springer., 2015.</p> <p> </p> <p> </p>
Mating under climate change: impact of simulated heatwaves on the reproduction of model pollinators (Dataset)
<ol> <li>Climate change is related to an increase in frequency and intensity of extreme events such as heatwaves. It is well established that such events may worsen the current worldwide biodiversity decline. In many organisms, heat stress is associated with direct physiological perturbations and could lead to a decrease of fitness. In contrast to endotherms, heat stress resistance has been poorly investigated in heterotherms; especially in insects, in which the internal physiological mechanisms available to regulate body temperature are almost negligible making them sensitive to extreme temperature variations.</li> <li>Wild bees are crucial pollinators for wild plants and crops. Among them, bumblebees are experiencing a strong decline across the world. Therefore, the ongoing global decline of these insect pollinators partly due to climate change could cause major economic issues.</li> <li>Here, we assess how simulated heatwaves impact fertility and attractiveness (key parameters of sustainability) of bumblebee males. We used three model species: <i>Bombus terrestris</i>, a widespread and warm-adapted species, <i>B. magnus</i> and <i>B. jonellus</i>, two declining and cold-adapted species.</li> <li>We highlight that heat shock (40°C) negatively affects sperm viability and sperm DNA integrity only in the two cold-adapted species. Heat shock can also impact the structure of cephalic labial glands and the production of pheromones only in the declining species.</li> <li>The specific disruption in key reproductive traits we identify following simulated heatwave conditions could provide one important mechanistic explanation for why some pollinators are in decline through climate change.</li> </ol>
Data from: Introduced garden plants are strong competitors of native and alien residents under simulated climate change
1) Most invasive plants have been originally introduced for horticultural purposes. Still, most alien garden plants have not naturalized yet, probably due in part to inadequate climatic conditions. Climate change may alter this, but few experimental studies have addressed this for non-naturalized alien garden plants, and those that have, addressed only singular aspects of climate change. 2) In a greenhouse experiment, we examined the performance of nine non-naturalized alien herbaceous garden plants of varying climatic origins in response to simulated climate warming and reduced water availability, in a factorial design, as projected for southern Germany. To assess their invasion potential, we grew the species in competition with resident native and already-naturalized alien species. 3) Reduced watering negatively affected non-naturalized garden plants, as well as the native and naturalized competitors, particularly at higher temperatures. However, non-naturalized aliens performed better relative to competitors when temperatures increased. Naturalized and native resident competitor responses to climate change were both negative, but across climate treatments, non-naturalized aliens, irrespective of their climatic origins, performed better against native than against naturalized competitors. 4) Synthesis. We conclude that relative performance compared to resident species may increase for non-naturalized alien garden plants under climate change, as resident species become less competitive. Ongoing climate change is therefore likely to promote naturalization of commonly-planted alien herbaceous species.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.