Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming

Dataset used to analyize role of climate variability

opencc-zeroMar 2024View details →

Data from: Testing metabolic cold adaptation and the climatic variability hypotheses across the latitudinal range of a widespread, supratidal water beetle

Temperature significantly impacts ectotherm physiology, with thermal and metabolic traits varying with latitude but the drivers of this variation remain unclear, despite obvious consequences in the face of ongoing global change. This study explores metabolic cold adaptation (MCA) and the climatic variability hypothesis (CVH) to evaluate local adaptation and phenotypic plasticity of metabolic rates and thermal limits in two populations of the supratidal rockpool beetle Ochthebius lejolisii from localities experiencing contrasting thermal variability. Reciprocal acclimation was conducted under spring temperature regimes of both localities, incorporating local diurnal variation. Metabolic rates were measured by closed respirometry, and thermal tolerance limits estimated through thermography. In line with MCA, the northern population (colder climate) showed higher metabolic rates and Q10s at lower temperatures than the southern population. As predicted by the CVH, the southern population (more variable climate) showed higher upper thermal tolerance but only the northern population was able to acclimate upper thermal limits. This pattern suggests the existence of trade-offs in thermal adaptation in this species, likely increasing the vulnerability of populations on Mediterranean coasts to the projected increases in extreme temperatures under ongoing climate change.

opencc-by-4.0Apr 2024View details →

Datasets related to the study "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach"

This dataset contains the data of the manuscript "Spatial variability and future evolution of surface solar radiation over Northern France and Benelux: a regional climate model approach" under publication in Atmospheric Chemistry and Physics.  It includes CNRM-ALADIN64 simulations of surface solar radiation, cloud fraction, aerosol optical depth and water vapor content.  A directory is dedicated to HINDCAST simulations. It includes all datasets involved in the evaluation of CNRM-ALADIN64 simulations, as well as all datasets used for the analysis of the spatial variability of surface solar irradiance over the recent past.  Another directory is dedicated to future climate simulations. In this case, several sub directories can be found, representing either the simulations over the historical period (2005-2014, i.e. HIST directory), or simulations at mid (2045-2054, "mid" suffix) and long term (2091-2100, "end" suffix) horizons for SSP1-1.9 and SSP3-7.0. Each set of climate simulations is composed of three members (r1f, r2f, r3f), which were used collectively to increase the statistical significance of our analysis.

opencc-by-4.0Nov 2024View details →

Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s

Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s

opengpl-2.0-or-laterNov 2021View details →

Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.

This package contains data and codes related to the article: B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra.  A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research. R scripts The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh. The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps. <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in dataset_XXX/runs.</li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in dataset_XV and dataset_XV_1971-1990.</li> </ul> Data Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders dataset_XXX/data. <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul>

opencc-zeroJan 2022View details →

Variable vulnerability to climate change in New Zealand lizards

Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species' ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species' ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.

opencc-by-4.0Mar 2022View details →

Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis: Datasets

This dataset includes output for snowfall, near-surface air temperature and melt from the following regional climate models (RCMs): Met Office Unified Model version 11.1 (MetUMv11.1), the Modèle Atmosphérique Régional version 3.10 (MARv3.10) and the Regional Atmospheric Climate Model version 2.3p2 (RACMOv2.3p2). The data is aggregated to monthly timesteps from initial 3/6hourly data. The code for aggregation is available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability . Data goes from ~1971-2018 and includes two simulations from each RCM: 0.11° (12.25 km) and 0.44° (49 km) resolution simulations from the MetUM; ERA-Interim and ERA5 driven simulations from MAR and RACMO. The data used in the results for 'Variability in Antarctic Surface Climatology Across Regional Climate Models and Reanalysis Datasets' J.Carter et al, is included here and can be generated using the code available here: https://github.com/Jez-Carter/Antarctica_Climate_Variability .   Data usage notice: If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below. "We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations." In order to document MAR scientific impact and enable ongoing support of the model, users are encouraged to contact C. Kittel to add their works in the list of MAR-related publications. If you need other variables or output frequencies over Antarctica from: MAR, contact C.Kittel (c2kittel@gmail.com); RACMO, contact J.M. van Wessem; MetUM, contact A.Orr.

opencc-by-4.0Jun 2022View details →

Internal climate variability and spatial temperature correlations during the past 2000 years

Climate model output of the iLOVECLIM model. The repository contains: Data on 9 ensemble members corresponding to ensemble members 1-9 in the publication: 1: 0ka_trans_013000to015000.nc 2: 0ka_trans_precipcorr_AC_x0.4_AS_x1.6_013000to015000.nc 3: 0ka_trans_7params_6_013000to015000.nc 4: 0ka_trans_7params_12_013000to015000.nc 5: 0ka_trans_7params_30_013000to015000.nc 6: 0ka_trans_8params_6_013000to015000.nc 7: 0ka_trans_8params_26_013000to015000.nc 8: 0ka_trans_8params_35_013000to015000.nc 9: 0ka_trans_8params_40_013000to015000.nc   Three different data files for every ensemble member atmym: yearly mean atmospheric output (t2m is used for temperature in the manuscript) CLIO2: yearly mean ocean surface output (temp is used for SST in the manuscript) graevolu: collection of monthly mean 1 dimensional ocean outputs (ADPro is used for AMOC strength in the manuscript)

opencc-by-4.0Jun 2022View details →

Climate and Crop variables of tomato greenhouse simulation

Climate and growth variables of a simulated tomato greenhouse are shown. Description of columns: ---------------------------------- 'DateTime' : Time Stamp [dd-MM-yyyy hh:mm:ss] 'Cppm' : outdoor Concentration of CO2 [ppm] 'Wind' : wind velocity [m/s] 'HR' : exterior relative humidity [%] 'Rad' : exterior radiation [W/m^2] 'Temp' : outdoor temperature [K] 'Temp__Tcover' : temperature of cover [K] 'Temp__Tair' : temperature of air [K] 'Temp__Tfloor' : temperature of floor [K] 'Temp__Tsoil' : temperature of soil [K] 'QT__QT' : heat loss of crop by evapotranspiration [W] 'QS__R_int' : Indoor Radiation [W/m^2] 'Gas__C_w' : Absolute Humidity [kg/m^3] 'Gas__C_c' : CO2 Concentration [kg/m^3] 'Gas__rho_i' : Air density [kg/m^3] 'Gas__C_c_ppm' : CO2 Concentration [ppm] 'Gas__HRInt' : Indoor relative humidity [%] 'R' : Ratio of ventilation [1/s] 'Windows__value' : percent open window [%] 'Screen__value' : percent open screen [%] 'Carbon__Cbuff' : dry carbon in buffer by square meter of cultivation [kg/m^2] 'Carbon__Cfruit' : dry carbon in fruit by square meter of cultivation [kg/m^2] 'Carbon__Cleaf' : dry carbon in leaf by square meter of cultivation [kg/m^2] 'Carbon__Cstem' : dry carbon in stem by square meter of cultivation [kg/m^2] 'Tsum' : Acumulative temperature [ºC day] 'C_Total' : total dry carbon by square meter of cultivation [kg/m^2] 'WC' : water capacity of crop [kg/m^2] 'LAI' : leaf area index [-] 'CC' : CO2 flux from crop to air 'VPD' : Vapor pressure deficit [Pa] 'Water__WaterFlows__WaterUptake' : Water uptake of crop by square meter of cultivation [kg/(sm^2)]

opencc-zeroJun 2022View details →

Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level

The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for Bombus sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.

opencc-by-4.0Jul 2022View details →

What weather variables are important for wet and slab avalanches under a changing climate in low altitude mountain range in Czechia?

datasets and scripts for Avalanche paper figures and avalanche path characteristics: Avalanche_paths_souckova.xlsx

opencc-zeroAug 2022View details →

Data for Contrasting life-history responses to climate variability in eastern and western North Pacific sardine populations

Massive populations of sardines inhabit both the western and eastern boundaries of the world's subtropical ocean basins, supporting both commercial fisheries and populations of marine predators. Sardine populations in western and eastern boundary current systems have responded oppositely to decadal scale anomalies in ocean temperature, but the mechanism for differing variability has remained unclear. Here, based on otolith microstructure and high-resolution stable isotope analyses, we show that habitat temperature, early life growth rates, energy expenditure, metabolically optimal temperature and, most importantly, the relationship between growth rate and temperature were remarkably different between the two subpopulations in the western and eastern North Pacific. Varying metabolic response to environmental changes partly explain the contrasting growth responses. Consistent differences in the life-history traits are observed between subpopulations in the western and eastern boundary current systems around South Africa. These growth and survival characteristics can facilitate the contrasting responses of sardine populations to climate change.

opencc-by-4.0Sep 2022View details →

New insights into the decadal variability in glacier volume of a tropical ice-cap explained by the morpho-topographic and climatic context, Antisana, (0°29' S, 78°09' W)

The dataset contains five periods of surface elevation change observed on the Antisana icecap in the inner tropical region. Data were obtained by geodetic observations of aerial photographs and high-resolution satellite images for the study periods: 1956-1965, 1965-1979, 1979-1997, 1997-2009, and 2009-2016.

opencc-zeroSep 2022View details →

Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results

The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O2) concentrations increased from ~10−5 times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O2 mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O2 and H2O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O2 concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O2 concentrations of <1% PAL.

FIGURE 4 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 4. Site-specific data for lamina length and lamina width plotted for each taxon. 4A: Lamina length for Platanus neptuni. 4B: Lamina width for P. neptuni. 4C: Lamina length for Eotrigonobalanus furcinervis. 4D: Lamina width for E. furcinervis. 4E: Lamina length for Daphnogene cinnamomifolia. 4F: Lamina width for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.

FIGURE 1 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 1. Map showing the locations of the considered sites, which are numbered according to Table 1.

FIGURE 3 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 3. Site-specific data for lamina area and lamina perimeter plotted for each taxon. 3A: Lamina area for Platanus neptuni. 3B: Lamina perimeter for P. neptuni. 3C: Lamina area for Eotrigonobalanus furcinervis. 3D: Lamina perimeter for E. furcinervis. 3E: Lamina area for Daphnogene cinnamomifolia. 3F: Lamina perimeter for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.

FIGURE 6 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 6. Site-specific data for lamina centroid and leaf length-to-width ratio (LWR) plotted for each taxon. 6A: Lamina centroid for Platanus neptuni. 6B: LWR for P. neptuni. 6C: Lamina centroid for Eotrigonobalanus furcinervis. 6D: LWR for E. furcinervis. 6E: Lamina centroid for Daphnogene cinnamomifolia. 6F: LWR for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.

FIGURE 2 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 2. Plasticity index (PI) of various leaf traits, for the considered sites and taxa. 2A: PI for lamina area. 2B: PI for lamina length. 2C: PI for lamina perimeter. 2D: PI for lamina width. 2E: PI for lamina circularity. 2F: PI for lamina centroid. Squares: Platanus neptuni. Circles: Daphnogene cinnamomifolia. Triangles: Eotrigonobalanus furcinervis. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.

FIGURE 5 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?

FIGURE 5. Site-specific data for lamina circularity and lamina roundness plotted for each taxon. 5A: Lamina circularity for Platanus neptuni. 5B: Lamina roundness for P. neptuni. 5C: Lamina circularity for Eotrigonobalanus furcinervis. 5D: Lamina roundness for E. furcinervis. 5E: Lamina circularity for Daphnogene cinnamomifolia. 5F: Lamina roundness for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.