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269 results for “Regional climate”
Effect of Soil Moisture on Future Heatwaves over Eastern China: Convection-Permitting Regional Climate Simulations
<p>Data used in the manuscript "Effect of Soil Moisture on Future Heatwaves Over Eastern China: Convection-Permitting Regional Climate Simulations" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine
<p>This dataset contains results used to plot figure 1, 2, 3, 4 of the manucript "Regional irrigation expansion can support climate resilient crop production in post-invasion Ukraine" published in Nature Food. </p>
The evaluation of climate change competitiveness via DEA models and Shannon's entropy: EU regions case
<p>Supplementary materials for the article titled “The evaluation of climate change competitiveness via DEA models and Shannon’s entropy: EU regions case”.</p> <p><span>The data was collected under project provided by the National Science Centre, Poland; Grant No. 2019/35/B/HS5/01548.</span></p> <div> <div> <div> <p> </p> </div> </div> </div> <p> </p>
Climate perception of pastoralists in Kunene Region, Namibia
<p>The files include demographic data of all participants, as well as quantitative data used to create graphs and tables, provided in excel files. All qualitative data were analysed with NVIVO software and the files attached are nodes containing different information from participants. All the interview questions are also included as interview files.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Table 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
<p><b>Table 1:</b> Details of target genes (<i>hif-1α</i>, <i>hsp70</i>, <i>ras</i>, <i>mstn</i>, <i>acly</i>, <i>per-1</i>, <i>cry-1</i>, <i>ube3a</i> and <i>ogt</i>) and reference genes (<i>β- tubulin</i> and <i>β- actin</i>) primers.</p><table><tbody><tr><th><b>Gene</b></th><th><b>Length (bp)</b></th><th><b>R</b> <b>2</b></th><th><b>Efficiency (%)</b></th><th><b>Primers sequence (5ʹ-3ʹ) forward/reverse</b></th></tr></tbody><tbody><tr><th><i>tubulin</i> -F</th><td>20</td><td>0.99</td><td>109.5</td><td>GACGTGGTGCCCAAAGATGT</td></tr><tr><th><i>tubulin</i> -R</th><td>18</td><td>TGGATGGTGCGCTTGGT</td></tr><tr><th><i>β- actin</i> -F</th><td>21</td><td>0.99</td><td>100.5</td><td>GCTGTTTTCCCCTCCATTGTT</td></tr><tr><th><i>β- actin</i> -R</th><td>19</td><td>TCCCATGCCAACCATCACT</td></tr><tr><th><i>hif-1α</i> -F</th><td>20</td><td>0.99</td><td>105.2</td><td>CTTCTGAGCTCTGATGAGGC</td></tr><tr><th><i>hif-1α</i> -R</th><td>20</td><td>GAAAGCACCATCAGGAAGCC</td></tr><tr><th><i>hsp-70</i> -F</th><td>20</td><td>0.99</td><td>100.9</td><td>GCAAGGAGAACAAGATCACC</td></tr><tr><th><i>hsp-70</i> -R</th><td>19</td><td>CACTCCGTTGCACTTGTCC</td></tr><tr><th><i>mstn</i> -F</th><td>20</td><td>0.98</td><td>100.5</td><td>AATCCAAGCGAGGGAAAAGC</td></tr><tr><th><i>mstn</i> -R</th><td>22</td><td>CCTCCATCACCTGAAAGGTCTT</td></tr><tr><th><i>ras</i> -F</th><td>20</td><td>0.97</td><td>99.31</td><td>CCAGTACATGAGGACAGGAG</td></tr><tr><th><i>ras</i> -R</th><td>20</td><td>CAAGCACCATTGGCACATCG</td></tr><tr><th><i>acly</i> -F</th><td>19</td><td>0.99</td><td>100.7</td><td>ATCATCTCCCGCACTACAG</td></tr><tr><th><i>acly</i> -R</th><td>19</td><td>TACCTCCAATCTCTCCCAG</td></tr><tr><th><i>ube3a</i> -F</th><td>21</td><td>0.98</td><td>103.3</td><td>GCCATAAGCAAGCAGCACAAC</td></tr><tr><th><i>ube3a</i> -R</th><td>19</td><td>CCAGTCAGTCCGCACATCG</td></tr><tr><th><i>per-1</i> -F</th><td>20</td><td>0.98</td><td>104.1</td><td>TGTTGAAGTTTGTGCCCCAG</td></tr><tr><th><i>per-1</i> -R</th><td>18</td><td>CAGTCCAGATGCTCCTCC</td></tr><tr><th><i>cry-1</i> -F</th><td>19</td><td>0.99</td><td>103.6</td><td>GTCCAACAGCCCTCAAACT</td></tr><tr><th><i>cry-1</i> -R</th><td>18</td><td>TACGCCAAGCACTCCAGA</td></tr><tr><th><i>ogt</i> -F</th><td>19</td><td>0.99</td><td>104.1</td><td>CCTCCCTTTGCTGTGTTCC</td></tr><tr><th><i>ogt</i> -R</th><td>20</td><td>TGTCTGCTTTCCGCTTTCGC</td></tr></tbody></table>
Resilience Evaluation Table (Regional Climate Resilience Assessment)
<p>Assessment of regional resiliences of the five ClimEmpower regions: Costa del Sol in Andalusia, Spain, Trodos Mountains in Cyprus, Osjek-Baranja county in Croatia, Central Greece and Sicily, Italy. Result of initial ClimEmpower climate Resilience Asssessment (CLIM-RA) of these five regions, related to project deliverable D1.2.</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Data from: Genome-wide analysis reveals associations between climate and regional patterns of adaptive divergence and dispersal in American pikas
<p>Understanding the role of adaptation in species responses to climate change is important for evaluating the evolutionary potential of populations and informing conservation efforts. Population genomics provides a useful approach for identifying putative signatures of selection and the underlying environmental factors or biological processes that may be involved. Here, we employed a population genomic approach within a space-for-time study design to investigate the genetic basis of local adaptation and reconstruct patterns of movement across rapidly changing environments in a thermally-sensitive mammal, the American pika (<i>Ochotona princeps</i>). Using genotypic data at 49,074 single nucleotide polymorphisms (SNPs), we analyzed patterns of genome-wide diversity, structure, and migration along three independent elevational transects located at the northern extent (Tweedsmuir South Provincial Park, British Columbia, Canada) and core (North Cascades National Park, Washington, USA) of the Cascades lineage. We identified 899 robust outlier SNPs within- and among-transects. Of those annotated to genes with known function, many were linked with cellular processes related to climate stress including ATP-binding, ATP citrate synthase activity, ATPase activity, hormone activity, metal ion-binding, and protein-binding. Moreover, we detected evidence for contrasting patterns of directional migration along transects across geographic regions that suggest an increased propensity for American pikas to disperse among lower elevation populations at higher latitudes where environments are generally cooler. Ultimately, our data indicate that fine-scale demographic patterns and adaptive processes may vary among populations of American pikas, providing an important context for evaluating biotic responses to climate change in this species and other alpine-adapted mammals.</p>
Biasadjusted Regional Climate Model Data for Europe - Temperature
<p>This repository contains the bias-adjusted temperature data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Biasadjusted Regional Climate Model Data for Europe - Precipitation
<p>This repository contains the bias-adjusted precipitation data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Orchid-mycorrhizal fungi interactions reveal a duality in their network structure in two European regions differing in climate
<p><span>Network analysis is an effective tool to describe and quantify the ecological interactions between plants and root-associated fungi.</span><span> Mycoheterotrophic plants, such as orchids, critically rely on mycorrhizal fungi for nutrients to survive, therefore, investigating the structure of those intimate interactions brings new insights into the plant community assembly and coexistence. So far, there is little consensus on the structure of those interactions, described either as nested (generalist interactions), modular (highly specific interactions) or of both topologies. Biotic factors (e.g., mycorrhizal specificity) were shown to influence the network structure, while there is less evidence of abiotic factor effects. By</span><span> using next-generation sequencing of the orchid mycorrhizal fungal (OMF) community associated with 238 plant individuals belonging to 17 orchid species, we assessed the structure of four orchid-OMF networks in two European regions under contrasting climatic conditions (Mediterranean vs Continental).</span> <span>Each network contained four to 12 co-occurring orchid species, including up to eight species shared among the sites</span><span>. All four networks were both nested and modular, and fungal communities were different between co-occurring orchid species, despite multiple sharing of fungi across some orchids. Co-occurring orchid species growing in Mediterranean climates were associated with more dissimilar fungal communities, consistent with a greater modular structure compared to the Continental ones. The OMF diversity was comparable among orchid species since most orchids were associated with multiple rarer fungi and with only a few highly dominant ones in the roots. Our results provide useful highlights on potential factors involved in structuring plant-mycorrhizal fungi interactions in different climatic conditions.</span></p>
Regional and global climate risks for reef corals: incorporating species-specific vulnerability and exposure to climate hazards
<p>Climate change is driving rapid and widespread erosion of the environmental conditions that formerly supported species persistence. Existing projections of climate change typically focus on forecasts of acute environmental anomalies and global extinction risks. The current projections also frequently consider all species within a broad taxonomic group together without differentiating species-specific patterns. Consequently, we still know little about the explicit dimensions of climate risk (i.e., species-specific vulnerability, exposure and hazard) that are vital for predicting future biodiversity responses (e.g., adaptation, migration) and developing management and conservation strategies. Here, we use reef corals as model organisms (n = 741 species) to project the extent of regional and global climate risks of marine organisms into the future. We characterise species-specific vulnerability based on the global geographic range and historical environmental conditions (1900–1994) of each coral species within their ranges and quantify the projected exposure to climate hazard beyond the historical conditions as climate risk. We show that many coral species will experience a complete loss of pre-modern climate analogs at the regional scale and across their entire distributional ranges, and such exposure to hazardous conditions is predicted to pose substantial regional and global climate risks to reef corals. Although high-latitude regions may provide climate refugia for some tropical corals until the mid-21st century, they will not become a universal haven for all corals. Notably, high-latitude specialists and species with small geographic ranges remain particularly vulnerable as they tend to possess limited capacities to avoid climate risks (e.g., via adaptive and migratory responses). Predicted climate risks are amplified substantially under the SSP5-8.5 compared with the SSP1-2.6 scenario, highlighting the need for stringent emission controls. Our projections of both regional and global climate risks offer unique opportunities to facilitate climate action at spatial scales relevant to conservation and management.</p>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
Dataset for terrestrial climate and vegetation change in the western Tasmanian region from the late Eocene to late Oligocene
<p>Datasets accompanying Terrestrial climate and vegetation change in the western Tasmanian region from the late Eocene to late Oligocene by Amoo et al.</p> <p>Supplementary table S1: Raw palynomorph assemblage data, total counts, ODP Site 1168 </p> <p>Supplementary table S2: Sporomorph-based climate estimates including MAT, WMMT, CMMT and MAP </p> <p>Supplementary table S3: Sporomorph diversity indices</p> <p>Supplementary table S4: Nearest living relatives, botanical affinity, and climate range of individual taxa.</p>
Data for: The start of frozen dates over northern permafrost regions with the changing climate
<p>The soil freeze-thaw cycle in the permafrost regions has a significant impact on regional surface energy and water balance. Although increasing efforts have been made to understand the responses of spring thawing to climate change, the mechanisms controlling the global interannual variability of the start date of permafrost frozen (SOF) remain unclear. Using long-term SOF from the combinations of multiple satellite microwave sensors between 1979–2020, and analytical techniques, including partial correlation, ridge regression, path analysis, and machine learning, we explored the responses of SOF to multiple climate change factors, including warming (surface and air temperature), start date of permafrost thawing (SOT), soil properties (soil temperature and volume of water), and the snow depth water equivalent (SDWE). Overall, climate warming exhibited the maximum control on SOF, but SOT in spring was also an important driver of SOF variability; among the 65.9% significant SOT and SOF correlations, 79.3% were positive, indicating an overall earlier thawing would contribute to an earlier frozen in winter. The machine learning analysis also suggested that apart from warming, SOT ranked as the second most important determinant of SOF. Therefore, we identified the mechanism responsible for the SOT-SOF relationship using the SEM analysis, which revealed that soil temperature change exhibited the maximum effect on this relationship, irrespective of the permafrost type. Finally, we analyzed the temporal changes in these responses using the moving window approach and found an increased effect of soil warming on SOF. Therefore, these results provide important insights into understanding and predicting SOF variations with future climate change.</p>
Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: A global synthesis
<p><span>Global warming intensifies the hydrological cycle and may result in changes in the frequency and intensity of precipitation events. Although the effects of changes in precipitation amount and inter-annual precipitation variability on terrestrial plant productivity and carbon sequestration have been well studied, how intra-annual precipitation variability affects terrestrial ecosystem function remains unclear. </span><span>Here, we synthesized field manipulative experiments from 71 publications to quantify the effects of intra-annual precipitation variability increases (IPVI) on community biomass and plant diversity in grasslands worldwide. </span><span>At the global scale, we found that IPVI generally increased grassland community aboveground biomass (AGB) by 6%, and decreased grass biomass and soil ammonium nitrogen by 12% and 31%, respectively. IPVI stimulated AGB, belowground biomass, and plant species richness in arid regions, but not changed them in humid regions. Changes in AGB under IPVI were related to changes in the biomass of plant functional groups, species richness, and soil moisture. Structural equation modelling demonstrated that that climate conditions (mean annual temperature and mean annual precipitation) and background soil properties (soil sand content and soil organic carbon content) jointly regulated grassland AGB responses to IPVI across climate types.</span></p> <p><span>Synthesis: Overall, our study shows that grassland productivity and diversity may increase under IPVI in arid climates, and that humid grasslands may be highly resistant to the effects of IPVI. These findings have important implications for understanding ecosystem carbon cycling under global precipitation change scenarios.</span></p>
WRF model configuration and data used for the NHESS manuscript "Heat wave characteristics: evaluation of regional climate model performances for Germany"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: daily values of maximum temperature for the time period 1980-2009 from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul>
Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region
<p>This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.</p>
Gray wolf range in the western Great Lakes region under forecasted land use and climate change
<p>Land use and climate alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous USA, gray wolves (<em>Canis</em> <em>lupus</em>) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9–35% increases in wolf habitat, while a solely climate-based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation, and identify areas where recolonization is plausible, thus where promoting human-wolf co-existence is most pertinent.</p>
Data from: Regional variation in climate change alters the range-wide distribution of colour polymorphism in a wild bird
<p><span>According to Gloger's rule animal colouration is expected to be darker in wetter and warmer climates. Such environmental clines are predicted to occur in colour polymorphic species and to be shaped by selection if colour morphs represent adaptations to different environments. We studied if the distribution of the colour polymorphic tawny owl (<em>Strix aluco</em>) morphs (a pheomelanic brown and a pale grey) across Europe follow the predictions of Gloger's rule and if there is a temporal change in the geographical patterns corresponding to regional variations in climate change. We used data on tawny owl museum skin specimen collections. First, we investigated long-term spatiotemporal variation in the probability of observing the colour morphs in different climate zones. Second, we studied if the probability of observing the colour morphs was associated with general climatic conditions. Third, we studied if weather fluctuations prior the finding year of an owl explains colour morph in each climate zone. The brown tawny owl morph was historically more common than the grey morph in every studied climate zone. Over time the brown morph has become rarer in the temperate and Mediterranean zone, whereas it has first become rarer but then again more common in the boreal zone. Based on general climatic conditions winter and summer temperature were positively and negatively associated with proportion of brown morph, respectively. Winter precipitation was negatively associated with proportion of brown morph. The effects of five-year means of weather on the probability to observe a brown morph differed between climate zones, indicating region dependent effect of climate change and weather on tawny owl colouration. To conclude, tawny owl colouration does not explicitly follow Gloger's rule, implying a time and space dependent complex system shaped by many factors. We provide novel insights in how the geographic distribution of pheomelanin-based colour polymorphism is changing.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.