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54 results for “winter climate”
Data from: Seasonality in spatial distribution: climate and land use have contrasting effects on the species richness of breeding and wintering birds
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Data from: Climate determinants of breeding and wintering ranges of lesser kestrels in Italy and predicted impacts of climate change
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Data from: Climate change surpasses land use change in the contracting range boundary of a winter-adapted mammal
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Where to spend the winter? The role of intraspecific competition and climate in determining the selection of wintering areas by migratory caribou
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Data from: Wintering bird communities are tracking climate change faster than breeding communities
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Data from: The role of bryophytes for tree seedling responses to winter climate change: implications for the stress gradient hypothesis
1.When tree seedlings establish beyond the current tree line due to climate warming, they encounter existing vegetation, such as bryophytes that often dominate in arctic and alpine tundra. The stress gradient hypothesis (SGH) predicts that plant interactions in tundra become increasingly negative as climate warms and conditions become less harsh. However, for seedlings climate warming might not result in lower winter stress, if insulating snow cover is reduced. 2.We aimed to understand to if bryophytes facilitate seedling survival in a changing winter climate and if these effects of bryophytes on tree seedlings comply with the SGH along elevational gradients under contrasting snow conditions. 3.In the Swedish subarctic, we transplanted intact bryophyte cores covered by each of three bryophyte species and bryophyte-free control soil from above the tree line to two field common-garden sites, representing current and future tree line air temperature conditions (i.e. current tree line elevation and a lower, warmer, elevation below the tree line). We planted seedlings of Betula pubescens and Pinus sylvestris into these cores and subjected them to experimental manipulation of snow cover during one winter. 4.In agreement with the SGH, milder conditions caused by increased snow cover enhanced the generally negative or neutral effects of bryophytes on seedlings immediately after winter. Further, survival of P. sylvestris seedlings after one full year was higher at lower elevation, especially when snow cover was thinner. However, in contrast with the SGH, impacts of bryophytes on over-winter survival of seedlings did not differ between elevations, and impacts on survival of B. pubescens seedlings after one year was more negative at lower elevation. Bryophyte species differed in their effect on seedling survival after winter, but these differences were not related to their insulating capacity. 5.Synthesis: Our study demonstrates that interactions from bryophytes can modify the impacts of winter climate change on tree seedlings, and vice versa. These responses do not always comply with SGH, but could ultimately have consequences for large-scale ecological processes such as tree-line shifts. These new insights need to be taken into account in predictions of plant species responses to climate change.
Data from: Winter wren populations show adaptation to local climate
Most studies of evolutionary responses to climate change have focused on phenological responses to warming, and provide only weak evidence for evolutionary adaptation. This could be because phenological changes are more weakly linked to fitness than more direct mechanisms of climate change impacts, such as selective mortality during extreme weather events which have immediate fitness consequences for the individuals involved. Studies examining these other mechanisms may be more likely to show evidence for evolutionary adaptation. To test this, we quantify regional population responses of a small resident passerine (winter wren Troglodytes troglodytes) to a measure of winter severity (number of frost days). Annual population growth rate was consistently negatively correlated with this measure, but the point at which different populations achieved stability (λ = 1) varied across regions and was closely correlated with the historic average number of frost days, providing strong evidence for local adaptation. Despite this, regional variation in abundance remained negatively related to the regional mean number of winter frost days, potentially as a result of a time-lag in the rate of evolutionary response to climate change. As expected from Bergmann's rule, individual wrens were heavier in colder regions, suggesting that local adaptation may be mediated through body size. However, there was no evidence for selective mortality of small individuals in cold years, with annual variation in mean body size uncorrelated with the number of winter frost days, so the extent to which local adaptation occurs through changes in body size, or another mechanism remains uncertain.
Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"
<h2>Accompanying data to "Could an extremely cold central European winter such as 1963 happen again despite climate change?"</h2> <div> </div> <div><strong>16.07.2024 This repository contains data that underlies the following publication:</strong></div> <div>Sippel, S., Barnes, C., Cadiou, C., Fischer, E., Kew, S., Kretschmer, M., Philip, S., Shepherd, T. G., Singh, J., Vautard, R., and Yiou, P.: Could an extremely cold central European winter such as 1963 happen again despite climate change? <em>Weather and Climate Dynamics</em> (accepted), 2024. Preprint: https://doi.org/10.5194/egusphere-2023-2523.</div> <div> </div> <div>This repository is a data collection, which contains simulated extremely cold Central European winter storylines. Climate model simulations use the technique of climate model boosting, and statistical generation using stochastic weather generators (SWG) empirical importance sampling. The repository contains the following data files:</div> <div> </div> <h3>(1) Climate model ensemble boosting for extremely cold winter storylines. </h3> <div> <ul> <li>Zip file BSSP370cmip6.0000013.zip: Contains all 750 files of the first-order boosting. First order boosting is based on ensemble member 21 in the CESM2-ETH ensemble, and with restart dates between 01.12 and 15.12.2022 (SSP3-70 scenario), with 50 members for each starting date. Example file: BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc</li> </ul> </div> <div>The boosting files follow a naming convention: </div> <div> <ul> <li> <ul> <li>BSSP370cmip6 all files based on CMIP6 SSP3-70 forcing.</li> <li><span>2022-12-06 starting date of the respective ensemble member.</span></li> <li><span>0000013 Ensemble member of CESM2-ETH that was used for boosting (i.e. member 13 of CESM2-ETH).</span></li> <li><span>ens023 Ensemble member of the boosted ensemble (i.e. member 23 with starting date 06.12.2022).</span></li> </ul> </li> </ul> </div> <div>The second-order boosting was branched off from first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc.</div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0230013.zip: Contains all 750 files of the first set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-06.ens023.cam.h1.2022-12-07-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 06.12.2022 and ensemble member 23 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0230013.2023-01-08.ens047.cam.h1.2023-01-09-00000.nc. That is, ensemble member 47 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> <ul> <li>Zip file BSSP370cmip6.0480013.zip: Contains all 750 files of the second set of second-order boosting simulations. All these simulations are based on first-order boosting file BSSP370cmip6.0000013.2022-12-15.ens048.cam.h1.2022-12-16-00000.nc. That is, the first-order boosting file started from ensemble member 13 of CESM2-ETH, starting date 15.12.2022 and ensemble member 48 of the first-order boosted ensemble. The second-order boosting file shown in Figs. 5-6 is the file BSSP370cmip6.0480013.2023-01-08.ens032.cam.h1.2023-01-09-00000.nc. That is, ensemble member 32 in second-order boosting ensemble from starting date 08.01.2023. </li> </ul> </div> <div> </div> <div> </div> <h3>(2) CESM2 maps of extremely cold winters (to generate Fig. 5)</h3> <div>* Zip file cesm2_maps.zip. Contains the following entries, all for DJF average anomalies (relative to the ensemble average climatology):</div> <div>- tas_ssp370_r2i1p1.2005-2035_anom.nc</div> <div>- tas_ssp370_r12i1p1.2005-2035_anom.nc</div> <div>Two members (r2i1p1 in 2008, r12i1p1 in 2007) from the CESM2-ETH ensemble, which produce very cold winters. Variables tas (surface air temperature), Z500 (geopotential height at 500 hPa), FSDS (surface downwelling shortwave radiation), and FSNS (surface net shortwave radiation) are available (FSDS and FSNS to calculate albedo). </div> <div>- tas_ssp370_0230013.2023-01-08.ens047_anom.nc</div> <div>- tas_ssp370_0480013.2023-01-08.ens032_anom.nc</div> <div>The two extremely cold boosted winters as described above, concatenated with their parent files from boosting. </div> <div> </div> <div> </div> <h3>(3) Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling</h3> <div>SWG-empirical-importance-sampling.zip Storylines of extremely cold winters generated via Stochastic weather generator (SWG) empirical importance sampling (Yiou and Jézéquel, 2020, https://doi.org/10.5194/gmd-13-763-2020). The available maps are seasonal average anomalies resampled from ERA5 (to generate Fig. 5):</div> <div> <ul> <li>Surface air temperature: t2m_WEGE_germany_1963_1972-2021_DJFmean.nc</li> <li><span>Albedo: fal_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> <li><span>z500: z500_WEGE_germany_1963_1972-2021_DJFmean.nc</span></li> </ul> </div> <div> </div>
Data from: The role of bryophytes for tree seedling responses to winter climate change: implications for the stress gradient hypothesis
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Data from: Winter wren populations show adaptation to local climate
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Large opportunities to expand winter wheat production on abandoned croplands for climate adaptation
<p>These are source datasets used to generate the figures in the paper "<strong>Large opportunities to expand winter wheat production on abandoned croplands for climate adaptation</strong>"</p> <p>(1) Fig.1.xlsx: Data for Fig. 1.<br>(2) Fig.2.xlsx: Data for Fig. 2.<br>(3) Fig.3.xlsx: Data for Fig. 3.<br>(4) Fig.4.xlsx: Data for Fig. 4.</p> <p> </p> <p>For any questions, please contact Liyin He (lhe@carnegiescience.edu) or Lorenzo Rosa (lrosa@carnegiescience.edu).</p>
Data from: Individual variation and seasonality drive bird feeder use during winter in a Mediterranean climate
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Agrivoltaics system impacts on winter wheat carbon fluxes and growth in a temperate climate
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Floral diversity enhances winter survival of honey bee colonies across climatic regions
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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)
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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.