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150 results for “climate extreme”
Data from: Extreme climate-induced life-history plasticity in an amphibian
Age specific survival and reproduction are closely linked to fitness and therefore subject to strong selection that typically limits their variability within species. Furthermore, adult survival rate in vertebrate populations is typically less variable over time than other life history traits, such as fecundity or recruitment. Hence, adult survival is often conserved within a population over time, compared to the variation in survival found across taxa. In stark contrast to this general pattern, we report evidence of extreme short-term variation of adult survival in Rose's Mountain Toadlet (Capensibufo rosei), which is apparently climate-induced. Over seven years, annual survival rate varied between 0.04 and 0.92, and 94% of this variation was explained by variation in breeding-season rainfall. Preliminary results suggest that this variation reflects adaptive life-history plasticity to a degree thus far unrecorded for any vertebrate, rather than direct rainfall induced mortality. In wet years, these toads appeared to achieve increased reproduction at the expense of their own survival whereas in dry years, their survival increased at the expense of reproduction. Such environmentally induced plasticity may reflect a diversity of life-history strategies not previously appreciated among vertebrates.
Data from: Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events
1. Extreme climatic events (ECEs) are predicted to become more frequent as the climate changes. A rapidly increasing number of studies - though few on animals - suggest that the biological consequences of ECEs can be severe. 2. However, ecological research on the impacts of extreme climatic events (ECEs) has been limited by a lack of cohesiveness and structure. ECEs are often poorly defined and have often been confusingly equated with climatic variability, making comparison between studies difficult. Additionally, a focus on short-term studies has provided us with little information on the long-term implications of ECEs, and the descriptive and anecdotal nature of many studies has meant it is still unclear what the key research questions are. 3. Synthesizing the current state of work is essential to identify ways to make progress. We conduct a synthesis of the literature and discuss conceptual and practical challenges faced by research on ECEs. 4. We consider three steps to advance research. First, we discuss the importance of choosing an ECE definition and identify the pros and cons of 'climatological' and 'biological' definitions of ECEs. Second, we advocate research beyond short-term descriptive studies to address questions concerning the long-term implications of ECEs, focussing on selective pressures and phenotypically plastic responses and how they might differ from responses to a changing climatic mean. Finally, we encourage a greater focus on multi-event studies that help us understand the implications of changing patterns of ECEs, through the combined use of modelling, experimental and observational field studies. 5. This paper aims to open a discussion on the definitions, questions and methods currently used to study ECEs, which will lead to a more cohesive approach to future ECE research.
Data from: Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines
Climate change and extreme events, such as drought, threaten ecosystems world-wide and in particular mountain ecosystems, where species often live at their environmental tolerance limits. In the European Alps, plant communities are also influenced by land-use abandonment leading to woody encroachment of subalpine and alpine grasslands. In this study, we explored how the forest–grassland ecotone of Alpine tree lines will respond to gradual climate warming, drought events and land-use change in terms of forest expansion rates, taxonomic diversity and functional composition. We used a previously validated dynamic vegetation model, FATE-HD, parameterized for plant communities in the Ecrins National Park in the French Alps. Our results showed that intense drought counteracted the forest expansion at higher elevations driven by land-use abandonment and climate change, especially when combined with high drought frequency (occurring every 2 or less than 2 years). Furthermore, intense and frequent drought accelerated the rates of taxonomic change and resulted in overall higher taxonomic spatial heterogeneity of the ecotone than would be expected under gradual climate and land-use changes only. Synthesis and applications. The results from our model show that intense and frequent drought counteracts forest expansion driven by climate and land-use changes in the forest–grassland ecotone of Alpine tree lines. We argue that land-use planning must consider the effects of extreme events, such as drought, as well as climate and land-use changes, since extreme events might interfere with trends predicted under gradual climate warming and agricultural abandonment.
Supplementary Data and Scripts for 'Extreme heat and drought typical of an end-of-century climate could occur soon and repeatedly over Europe', Suarez-Gutierrez et al., 2023
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Extreme rainfall events and cooling of sea turtle clutches: implications in the face of climate warming
<p class="m-4193862355675860270msoplaintext">Understanding how climate change impacts species and ecosystems is integral to conservation. When studying impacts of climate change, warming temperatures are a research focus, with much less attention given to extreme weather events and their impacts. Here we show how localized, extreme rainfall events can have a major impact on a species that is endangered in many parts of its range. We report incubation temperatures from the world's largest green sea turtle rookery, during a breeding season when two extreme rainfall events occurred. Rainfall caused nest temperatures to drop suddenly and the maximum drop in temperature for each rain-induced cooling averaged 3.6°C (n = 79 nests, min = 1.0°C, max = 7.4°C). Since green sea turtles have temperature-dependent sex determination, with low incubation temperatures producing males, such major rainfall events may have a masculinization effect on primary sex ratios. Therefore, in some cases, extreme rainfall events may provide a "get-out-of-jail-free card" to avoid complete feminization of turtle populations as climate warming continues.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kenny Park (50° 48' 2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
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>
Supplementary material 1 from: Hong Qu H, Wang C-J, Zhang Z-X (2018) Planning priority conservation areas under climate change for six plant species with extremely small populations in China. Nature Conservation 25: 89-106. https://doi.org/10.3897/natureconservation.25.20063
Table S1, S2; Figure S1, S2 : Explanation note:
Fig. 2. Plots A-B in Adaptations, life-history traits and ecological mechanisms of parasites to survive extremes and environmental unpredictability in the face of climate change
Fig. 2. Plots A-B. Hypothetical thermal curves of the free-living stages of two parasite populations with different thermal adaptation histories and similar thermal optimum (highest point in the curve). The blue curve represents a population adapted to a highly variable environment and the orange curve a population adapted to a less variable environment. The dashed black line is a hypothetical current mean temperature in the environment and the dashed grey line represents an increased mean temperature as a consequence of climate change. In plot A, the historical temperature sits close to the thermal optimum in both populations, and an increase in temperature results in a decrease in parasite performance, which is greater for the parasite adapted to the less variable environment. In plot B, the historical temperature is well below the thermal optimum of both parasites, and an increase in temperature results in improved performance for both parasites. In both scenarios, an increase in mean temperature causes a much higher relative change in performance in the population from the less variable environment as indicated in the difference in size among the shade areas. Plot C shows the hypothetical temperature and thermal development ranges for the free-living stages of parasites inhabiting three different latitudes. The temperature range increases with latitude but the development range of parasites does not because, although the thermal range in high latitudes is wider, a large portion of this range occurs <0 ◦C. While parasites from high latitudes might be highly resistant to freezing temperatures, they are also more vulnerable to high temperatures. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Adaptations, life-history traits and ecological mechanisms of parasites to survive extremes and environmental unpredictability in the face of climate change
Fig. 1. Schematic of two types of life cycles of parasitic nematodes highlighting stage-specific interactions with the environment and hosts, and adaptations to cope with extreme environmental conditions: A) direct life cycle and B) specific indirect life cycle of protostrongylid parasites. In red are indicated the developmental stages of the parasite. The performance (e.g., survival rate, development rate) of developmental stages in the orange area is directly influenced by changes in environmental conditions. Developmental stages in light blue area are indirectly influenced by environmental conditions experienced by the definitive or intermediate hosts. The effect of the environment on the L3 of protostrongylids can be direct or indirect depending if the L3 migrates out of the intermediate host (direct) or if the L3 remains in the intermediate host (indirect). In the inner triangles, examples of stage-specific adaptations to cope with extremes are indicated. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Data for "Hydrodynamic and Geomorphological Responses of Tidal Flats to Extreme Climate Events with Implications for Coastal Managements"
<p>Data used for the plots can be found:</p> <p>1) <strong>uav_z.mat </strong>is used for producing figure 2 and 3.</p> <p>2) <strong>timeseries.mat </strong>is used for producing figure 5 and 6.</p>
Environmental data from: Potential distributions of invasive vertebrates in the Iberian Peninsula under projected changes in climate extreme events
<p>This dataset includes climatic variables representing extreme events indices defined by the World Meteorological Organization (<a href="https://public.wmo.int/en">https://public.wmo.int/en</a>). The variables were calculated based on five Regional Climate Models or RCMs (UAHE-REM, UCAN-WRA, UCAN-WRB, UCLM-PRO and UMUR-MM5) for the periods 1971-2000 ('current climate') and 2021-2050 ('future climate') under the SRES A1B Emissions Scenario. We used RCMs instead of global climate models (GCMs) because the latter have an overly coarse resolution compared to the spatial resolution of our species distribution data. Since RCMs downscale climate fields from GCMs, they provide information at fine (meso or micro) scales that are more accurate for studies of regional phenomena and for application to climate impact assessments. Climatic variables were calculated in collaboration with the Numerical Modelling Group for the Environment and Climate (MOMAC, <a href="https://www.uclm.es/grupos/momac">https://www.uclm.es/grupos/momac</a>) of the University of Castilla-La Mancha (UCLM).</p>
Extreme rainfall events and cooling of sea turtle clutches: implications in the face of climate warming
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Raw data for: Extreme climate shifts pest dominance hierarchy through thermal evolution and transgenerational plasticity
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Data from: Extreme heat events and the vulnerability of endemic montane fishes to climate change
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Data from: Quantifying thermal extremes and biological variation to predict evolutionary responses to changing climate
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Data from: Patterns and drivers of biodiversity-stability relationships under climate extremes
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Data from: Human-dominated land uses favour species affiliated with more extreme climates, especially in the tropics
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Data from: Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events
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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.