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214 results for “Climatic variables”

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edi60/100

Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022

This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

openCC0Feb 2025View details →

edi56/100

CBC02 Winter-spring survival and response of birds to variable climate using mist-net captures at Konza Prairie

This dataset includes captures of small-bodied landbirds captured via passive mist-netting efforts. The objectives are to (a) initiate a long-term survey of the non-breeding birds of the site, (b) understand the behavioral and physiological mechanisms that allow birds to cope with the unpredictable, variable, and often harsh conditions during winter months, and (c) provide a training platform for students. The collection of this dataset is fully integrated into the teaching of “Wild Bird Research” (an undergraduate hands-on research course in the Division of Biology) and less formal instruction in bird research methods for graduate students. Additionally, the banding efforts have benefited from the engagement of Konza Prairie docents and frequently hosts class visits and other visitors interested in witness bird banding operations.

openCC0Sep 2025View details →

zenodo52/100

Climate change velocity metrics calculated for three climate variables across Finland

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided:Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-81 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020).Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and *.tif.aux.xml) in the dataset: (a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26.*(b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.*(c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.*(d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.*(e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.*(f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.*(g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.*(h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.*(i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.*Note that velocity surfaces e and f include disappearing climate conditions.Summary of the study:Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates.The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century.Calculation of climate change velocity metrics for the three climate variablesThe overall process of calculation of climate velocities included three main steps. (1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et al. 2020). The resulting topoclimate data effectively captured the physiographic effects of solar radiation and cold-air pooling.To produce gridded precipitation data, we applied global kriging interpolation to the data from 343 rain gauges from the ECA&D dataset. The interpolation was carried out using information on geographical location, topography (elevation and eastness index) and proximity to the sea and R package gstat. The eastness index was obtained from a sine-transforming aspect raster surface calculated from a 50 m × 50 m digital elevation model to capture the effect of prevailing westerly winds on the accumulated precipitation on windward slopes. The gridding was first run at a resolution of 500 × 500 m, whereafter gridded precipitation values were bilinearly interpolated into the same 50 × 50 m resolution as the air temperature data. Next, the three bioclimatic variables ((i) growing degree days (GDD, °C days) indicating the accumulated warmth during the growing season; (ii) mean January air temperature -  TJan, °C; (iii) climatic water balance - WAB, mm) were calculated for each 50 x 50 grid cell from the high-resolution gridded 1981–2010 ('baseline') climate data. Earlier research has demonstrated the ecological relevance of these three complementary variables which provide estimations of winter cold, seasonal warmth and moisture availability (Sykes et al. 1996; Luoto et al. 2006; Huntley et al. 2007, 2008). Following Carter et al. (1991), GDD was calculated as the effective temperature sum above the base temperature of 5 °C as follows:GDD5 = ∑ni (Ti - Tb),  if Ti -Tb > 5where Ti denotes the mean temperature at day i, Tb represents the base temperature, and n is the length of the summation period. However, because the daily air temperature data was not available, here the GDD was estimated using monthly data as in Araújo & Luoto (2007). The WAB is the difference between the total annual precipitation sum and the potential evapotranspiration (PET), which was estimated from the monthly air temperatures following Skov and Svenning (2004): PET = 58.93 × Tabove 0°C / 12(2) In the second step we developed data on future climates by using the climate projections from the ensemble of 23 global climate models (GCMs), derived from the Coupled Model Intercomparison Project phase 5 archives (Taylor et al. 2012). From these archives, we processed to predicted averaged changes in mean temperature and precipitation with respect to the baseline 1981–2010 for the years 2070–2099, and the three RCP scenarios (cf. Moss et al. 2010). As the Coupled Model Intercomparison Project phase 5 climate scenario data represent coarse-scale resolution data, we converted it to match our fine-resolution baseline climate data by interpolation. For this, the climate model data depicting the predicted change in mean temperatures and precipitation with respect to the baseline climate were bilinearly interpolated to the 50 × 50 m grid system, and the change predicted by the GCMs was added to the spatially detailed baseline climate data. After this, the bioclimatic variables were recalculated for each RCP scenario to allow the calculation of climate change velocities across the whole country and the Natura 2000 protected areas.(3) In the third step we developed climate change velocities for the three bioclimatic variables using the climate-analog approach (Hamann et al. 2015) where velocity is calculated by measuring the distance between present-day locations with certain climatic conditions and their future climate analogues, divided by the number of years between the two points in time. Thus, we calculated climate-analog velocities for the 50-m resolution grid climate data by measuring the distance between climatically similar grid cells for the present and future climates under RCP2.6, RCP4.5 and RCP8.5. Prior the actual climate-analog velocity measurements, the climate variable surfaces were converted from continuous values into classified variable surfaces. For this, we defined the boundary values for the variable classes so that the climatically matching grid cells had their within-class ranges as small as possible but, at the same time, avoided artefactual extreme precision. After a set of pilot reclassifications, the following within-class ranges were applied: GDD, within-class range 50 °C with 51 categories; TJan, within-class range 0.5 °C with 60 categories; WAB, within-class range 50 mm with 55 categories. Next, using the reclassified present-day and future climate surfaces the search of the minimum distances between grid cells with similar present-day and future GDD/TJan/WAB climates were executed. The search was carried out using the ArcGIS software (Desktop 10.5.1.) by employing the Euclidean distance function. The minimum distances measured for each 50-m grid cell were divided by the difference between the mean points in the two time slices, 1981–2010 and 2070–2099. The resulting 50-m resolution climate velocity surfaces for the three climate variables are provided in the zipped files included this data repository. In Heikkinen et al. (2020), these climate velocity data were employed in a series of subsequent analyses. For example, high-velocity areas ('velocity hotspots') of the three climate variables were visually compared with each other based on maps showing their 50-m resolution velocities across mainland Finland and the degree of overlap between the present-day range and projected future range of the three climate variables were investigated in each of the 5,068 Natura 2000 polygons included in the study.ReferencesAalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M., 2017. Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamilton, H., Branciforte, R., Kraft, N.J.B., 2010. The geography of climate change: implications for conservation biogeography. Diversity and Distributions 16, 476-487.Ackerly, D.D., Kling, M.M., Clark, M.L., Papper, P., Oldfather, M.F., Flint, A.L., Flint, L.E., 2020. Topoclimates, refugia, and biotic responses to climate change. Frontiers in Ecology and the Environment 18, 288-297.Araujo, M.B., Luoto, M., 2007. The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography 16.Batllori, E., Parisien, M.-A., Parks, S.A., Moritz, M.A., Miller, C., 2017. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Global Change Biology 23, 3219-3230.Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J., 2018. Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.Carter, T.R., Porter, J.H., Parry, M.L., 1991. Climatic warming and crop potential in Europe: Prospects and uncertainties. Global Environmental Change 1, 291-312.Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J.A., Mynsberge, A.R., Holden, Z.A., Schwartz, M.K., 2013. The climate velocity of the contiguous United States during the 20th century. Global Change Biology 19, 241-251.Franklin, J., Davis, F.W., Ikegami, M., Syphard, A.D., Flint, L.E., Flint, A.L., Hannah, L., 2013. Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Global Change Biology 19, 473-483.Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E., 2015. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004. Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R., 2020. Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678.Huntley, B., Green, R.E., Collingham, Y.C., Willis, S.G., 2007. A climatic atlas of European breeding birds. Durham University, The RSPB and Lynx Edicions, Barcelona.Huntley, B., Collingham, Y.C., Willis, S.G., Green, R.E., 2008. Potential Impacts of Climatic Change on European Breeding Birds. Plos One 3.Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. International Journal of Climatology 29, 1182-1191.Lenoir, J., Hattab, T., Pierre, G., 2017. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253-266.Luoto, M., Heikkinen, R.K., Pöyry, J., Saarinen, K., 2006. Determinants of biogeographical distribution of butterflies in boreal regions. Journal of Biogeography 33, 1764-1778.Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747-756.R Development Core Team, 2011. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing).Skov, F., Svenning, J.-C., 2004. Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.Sykes, M.T., Prentice, I.C., Cramer, W., 1996. A bioclimatic model for the potential distributions of north European tree species under present and future climates. Journal of Biogeography 23, 203-233.Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society Series B 73, 3-36.

opencc-by-4.0Oct 2023View details →

zenodo48/100

Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"

Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate" Here you can find the input files needed to reproduce the results of the <a href="https://doi.org/10.2760/75312">report</a>: <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> The results in the report are generated with the Dispa-SET power system model, available and explained at <a href="https://www.dispaset.eu/">www.dispaset.eu</a>. A description of the data sources with the references can be found into the report. How to use this dataset This dataset can be used as input data for the Dispa-SET model. We refer to the <a href="https://doi.org/10.2760/75312">report</a> and the <a href="https://www.dispaset.eu">official model documentation</a> for information about the data and the model. Description of the dataset The file <code>EnVarClim.yml</code> is a template of the YAML configuration file used by Dispa-SET. To run a specific climate year the <code>XXXX</code> present in some input files must be replaced with the year. Availability factors In the folder <code>AvailabilityFactors</code> there are the availability factors (from 0 to 1) for the power plants and the renewable generation. There is a subfolder for each simulated zone and inside a file for each climate year: from <code>emh_and_cc_availability_1990.csv</code> to <code>emh_and_cc_availability_2015.csv</code>. Cross-border transmission In the folder <code>DayAheadNTC</code> there is the file <code>merged_constant_NTC.csv</code> containing the capacity (in MW). NOTE: due to an error in the pre-processing code there are some additional lines for the Western Balkans countries ending with a <code>1</code> (e.g. <code>GR -> MK1</code>). Those lines are ignored by the model because are not associated to any simulated zone. Cross-border historical flows In the file <code>CC_L_flows.csv</code> under the folder <code>Flows</code> are contained the hourly flows between the simulated zones and their neighbours (RU, TR, UA). Fuel prices In the folder <code>FuelPrices</code> are contained a set of files containing the hourly prices for the fuels (biomass, coal, lignite, gas, oil) and CO2 emissions. It is worth noting that in spite of their hourly resolution the time-series are constant through the year. Hourly load In the folder <code>Load_RealTime</code> there are hourly load time-series for each zone considering a different climate year. For the Western Balkans countries we use the same time-series for each climate year. Outage factors The files <code>CC_L_outages.csv</code> in the folder <code>OutageFactors</code> contain the outage factor (from 1, full outage, to 0) for the various generation units. Whenever a simulation zone is missing the model assumes the absence of outages. Power plants data In the folder <code>PowerPlants</code> there is a file named <code>CC_L_plants.mip.csv</code> for each simulated zone. The CSV files contain the data <a href="http://www.dispaset.eu/en/latest/data.html#power-plant-data">needed by Dispa-SET</a>. Water storage levels The folder <code>ReservoirLevel</code> contains the storage level (values from 0 to 1 relative to the size of the storage) for all the simulated zones. The levels have been computed for each climate year using a different inflow using the <a href="http://www.dispaset.eu/en/latest/mid_term.html">mid-term scheduler</a> recently implemented in Dispa-SET. For the Western Balkans countries we use the same time-series for each climate year. Hydro-power inflows In the folder <code>ScaledInflows</code> are contained the inflows used for the hydro-power generation. The values in the CSV files describes how much energy is available for hydro-power generation compared to the installed capacity. Linked resources <ul> <li>Model output files: https://zenodo.org/record/3778133</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul>

opencc-by-4.0Apr 2020View details →

zenodo48/100

Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.

The data files for figures in Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:Figure 1The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:<ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables.  </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables.  </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul>Figure 2<ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul>Figure 3<ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul>Figure 4The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.Figure 5The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.Figure 6The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.  Figure 7The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.Figure 8The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.Figure S1 and Figure S3The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.Figure S2<ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul>Figure S4<ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents.  </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents.  </li></ul>Figure S5There are no data associated with this figure. This figure was made using Microsoft Powerpoint.Figure S6The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.

opencc-by-4.0Oct 2023View details →

zenodo48/100

Attributing decadal climate variability in coastal sea-level trends

The data produced from analysis to be published in Ocean Science Discussions, paper entitled "Attributing decadal climate variability in coastal sea-level trends". NetCDF contains the following sets of fields: 1. Indexing: An index and location (lat, lon) of the coastal grid cells, a locator index attributing each cell to Atlantic, Pacific and Indian Ocean basin, a time (decimal year) index. 2. NEMO model trends (nemo_<component>_trend): Rolling decadal trends at each coastal grid cell from the NEMO model run for steric, manometric (dynamic) and GRD. The sum of these components gives the equivalent to absolute sea level trend.  3. Climate and oceanographic mode indices: The rolling decadal trends in climate indices and the AMOC index calculated from the AMOC model (ci_trend) and their names (ci_index). 4. Empirical Orthogonal Function spatial pattern (eof_<basin>_<component>_D) and Principal Component time series (eof_<basin>_<component>_PC) of the NEMO model trends. 5. Coefficient of linear regression between PC and climate indices (recon_<basin>_<component>_beta) and the rolling trend time series at each grid cell from the reconstruction, sum{ci_trend*beta} (recon_<basin>_<component>_trend). In 4 and 5, the indices are given by basin. The total coastline is a concatenation of the Atlantic, Pacific and Indian basin data in that order. The absolute SSH is given by the sum of components. i.e. the SSH for all coastal cells in order index: recon_sum_trend([index(Atlantic_index); index(Pacific_index); index(Indian_index)] = ...     [recon_Atlantic_manometric_trend+recon_Atlantic_steric_trend+recon_Atlantic_grd_trend; ...      recon_Pacific_manometric_trend+recon_Pacific_steric_trend+recon_Pacific_grd_trend;  ...      recon_Indian_manometric_trend+recon_Indian_steric_trend+recon_Indian_grd_trend]

opencc-by-4.0Jan 2022View details →

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Relative Humidity from Copernicus Essential Climate Variables for July months from 1980 to 2018

This dataset can be used if you have issues with the Essential Climate Variables Galaxy Tool for the Training "Getting your hands-on climate data"  in the section "Essential Climate Variables".  You can then upload this dataset in your Galaxy history and skip the 1st step (Copernicus Essential Climate Variables) and directly start with 2. map plot gridded (lat/lon) netCDF data.

opencc-by-4.0Mar 2022View details →

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Monthly climate variables of isotope-enabled climate model simulations over the last millennium (850-1849CE) version 2

Here we provide climate fields in monthly resolution for five isotope-enabled model: ECHAM5-wiso (Sjolte et al. 2018, Werner et al. 2016), GISS-E2-R (Lewis and Legrande 2015, Colose et al 2016), iCESM (Brady et al. 2019, Stevenson et al 2019), iHadCM3 (Bühler et al. 2021, Tindall et al. 2009), and isoGSM (Yoshimura et al. 2008) in supplement to Buehler et al. (2021, submitted to Clim. Past. Discuss.). The model simulations were performed with different sets of boundary conditions as described in Bühler et al. (2021, submitted to Clim. Past. Discuss.) in line with the PMIP3 protocoll (Schmidt et al. 2012). We provide output for surface temperature (in K), total precipitation amount (in mm month^-1), evaporation (in mm month^-1), latent heat (in W m^-2), and oxygen isotope ratios of precipitation (in permil). Additionally, we provide simulation output extracted at cave locations within the SISAL v.2. database (https://researchdata.reading.ac.uk/256/, Comas-Bru et al. (2020)). We include output for sites that pass the resolution and dating screening, meaning that have at least 2 radiometric dates (or are lamina-counted) and provide 36 oxygen isotope ratio measurements within the last millennium. For version 2, we updated the damaged ECHAM5 precipitation file and the time axis to the iCESM precipitation and tsurf files.

opencc-by-4.0Jan 2023View details →

edi48/100

Rodent declines track regional climate variability in North American drylands

Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, which cover ~45% of Earth’s land surface and face increasingly drier and more variable climates. Here, we analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland habitats in the southwestern USA. We captured two time series: 1995-2006 and 2004-2013 that coincide with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of ~5% of animals captured. During the first time series (PDO wet phase), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (dry phase), climate best explained rodent abundance for 60% of taxa. Temporal dynamics in rodent diversity and abundance differed spatially among habitats and sites, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Both habitat type and phase of the PDO modulated which species were winners or losers under increasing drought and amplified interannual variability in drought. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among habitats for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995 - 2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major phase of the PDO.

openCC0Mar 2021View details →

zenodo44/100

Centennial clonal stability of asexual Daphnia in Greenland lakes despite climate variability

Daphnia_microsatellite_data_Dane_etal.2020.csv: Microsatellite genotypes from three study lakes (SS4, SS1381, and SS1590) in the Kangerlussuaq area, West Greenland. Microsatellite loci were amplified in single, 12.5 µl multiplex reactions (Type-it PCR kit, Qiagen Inc, Valencia, CA, USA), using an Eppendorf Nexus Thermal Cycler with thermal cycle conditions recommended in the Type-it PCR kit manual. Ten microsatellite primers representing genome-wide loci were used for genotyping; details in (Colbourne et al. 2004; Frisch et al., 2014). Two primers (Dp90, Dp377) failed to amplify in a consistent manner and were therefore excluded from further analysis. Amplified microsatellites were genotyped on an Applied Biosystems 3730 genetic analyser. We used the microsatellite plugin for Geneious 7.0.6 (https://www.geneious.com) for peak calling and binning. Called peaks were visually inspected and manually adjusted when necessary.  SS4_sediment.core_data_Fig2_Dane_et_al2020.xlsx:   Information on various parameters of sediment cores collected in Lake SS4, Kangerlussuq area, West Greenland. Data used in Dane et al. 2020, Figure 2 (panels B and C) are derived from two sediment cores: one for fluorescence (section at 0.5 cm intervals, Depth) and one for Daphnia ephippia analyses (1-cm intervals). Percentage organic matter content (loss-on-ignition at 550 °C,OM%) was used to correlate the two cores to each other and to a previously-dated sediment core (see Dane et al. 2020, Methods). The fluorescence derived parameter Parafac component C2 was used as an indicator of the abundance of purple sulphur bacteria. The organic carbon burial rate (OC AR, g C m–2 yr–1) was also calculated for this core (see Anderson et al. 2019). The Daphnia core was used for the microsatellite analyses and the accumulation rate of ephippia (ephippia AR) at the core site was estimated. For further details please see associated publication in Ecology and Evolution.

opencc-by-4.0Oct 2020View details →

zenodo44/100

Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability

The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EIR) between consecutive years (e.g. HSi= |∆ EIR /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016.  A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years.  For each map, where HSi >1, regions are classified as Sensitive and for HSi ≤1, Resilient. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive.  This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against  latitudinal change. We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees. Contents: <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names. <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>

opencc-by-4.0Jan 2021View details →

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Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"

Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository. In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook boxmodel.ipynb.

opencc-by-4.0Jul 2022View details →

zenodo44/100

Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)

This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"

opencc-by-4.0Apr 2024View details →

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Waves Hindcast on the Senegalese Coast over the Last Four Decades (from 1980 to 2021). [A Dataset use in : SAMOU, M.S.; BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave Climate Variability along the Coastlines of Senegal over the Last Four Decades. Atmosphere 2023]

Computed from the WW3 Model, the last Four Decades Wave Hindcast is available on the Senegalese Coast through this present Dataset. Covering the period 1980 to 2021, this high resolution hindcast, both spatial (0.05x0.05) and temporal (1 h) provided all the wave parameters such as: the significant wave heights, the mean wave periods, the wave directions and the peak wave periods (to compute from wave frequencies) with an hourly interval. More details on this data (e.g., model implementation and validation) can be obtained in: SAMOU, M.S.;  BERTIN, X.; SAKHO, I.; LAZAR, A.; SADIO, M.; DIOUF, M.B. Wave  Climate Variability along the  Coastlines of Senegal over the Last Four Decades. Journal Atmosphere 2023].

opencc-by-4.0Jul 2023View details →

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Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.

Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.

opencc-by-4.0Aug 2023View details →

edi44/100

Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099

Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.

openCC (other)Mar 2018View details →

edi44/100

Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099

openCC (other)Apr 2018View details →

edi44/100

Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099

openCC (other)Mar 2018View details →

zenodo40/100

EStreams: An Integrated Dataset and Catalogue of Streamflow, Hydro-Climatic Variables and Landscape Descriptors for Europe

Check out the paper at: https://doi.org/10.1038/s41597-024-03706-1 (published at Nature Scientific Data). EStreams is an extensive catalog of openly available stream records, along with a dataset of hydro-climatic variables and landscape descriptors for +17,000 European catchments. It spans up to 120 years of records. The catalog provides detailed guidance, enabling users to directly access the sources of streamflow used. Additionally, the dataset comprises catchment-aggregated hydro-climatic indices and signatures, as well as landscape attributes and meteorological records. Updates <ul> <li>30 June 2025: version 1.3 <ul> <li>General: <ul> <li>Added the file: "estreams_catchments_hierarchy.csv" file, which defines pairwise hierarchical relationships between sub-catchments and their containing catchments using the "sub_catchment" and "catchment" columns. It can be usefull when making distributed hydrological modelling, and one needs to define the topology of the network. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page was also updated with the new code for computing and saving such information <a href="https://github.com/thiagovmdon/EStreams/blob/main/code/python/E_complementary_extra_codes/estreams_extras_nested_catchments.ipynb" target="_blank" rel="noopener">here</a>.  "EStreams/code/python/E_complementary_extra_codes". </li> </ul> </li> <li>In the "streamflow_indices/" folder we:  <ul> <li>Updated all indices for all 270 catchments from the Nordrhein-Westfalen region in Germany (basin_id starting with "DENW"). </li> </ul> </li> <li>In the "hydroclimatic_signatures.csv" file we:  <ul> <li>Updated all streamflow signatures (not climatic signatures) for all 270 catchments from the Nordrhein-Westfalen region in Germany (basin_id starting with "DENW").  </li> </ul> </li> </ul> </li> </ul> <ul> <li>11 February 2025: version 1.2 <ul> <li>General: <ul> <li>Added the file: "estreams_geologycontinental_attributes.csv", which encompass geological attributes from a European based source: International Hydrogeological Map of Europe (IHME), version 11, downloaded at www.bgr.bund.de, scale: 1:1,500,000. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page was also updated with the new code. </li> </ul> </li> <li>In the "estreams_meteorology_density.csv" file we:  <ul> <li>Replaced "NaN" to "0" in the row of catchment CZ000043.</li> </ul> </li> </ul> </li> </ul> <ul> <li>22 October 2024: version 1.1  <ul> <li>In the "estreams_hydrometeo_signatures.csv" file we: <ul> <li>Corrected the field "p_seasonality". The function previously provided in the hydroanalysis python package had a mistake, and it is now corrected. Additionally, the <a href="https://github.com/thiagovmdon/EStreams" target="_blank" rel="noopener">EStreams GitHub</a> page and the <a href="https://github.com/dalmo1991/HydroAnalysis" target="_blank" rel="noopener">hydroanalysis GitHub</a> page were now updated with the correct formulation. </li> </ul> </li> </ul> </li> </ul> <ul> <li>07 August 2024: version 1.0  <ul> <li>General:  <ul> <li>Version of the official paper release.</li> </ul> </li> <li>In the "estreams_gauging_stations.csv" file we: <ul> <li>Updated the field "nested_catchments" to include also the basin itself, for some cases where the basin was not yet included.</li> </ul> </li> </ul> </li> </ul> <ul> <li>15 June 2024: version 0.2 <ul> <li>General:  <ul> <li>Added 2,114 new basins: 1,813 in France, 111 in Iceland, 13 in Estonia and 18 in Serbia; exclusion of a total of 31 stations from the previous release due to overlap with the new stations (Serbia and Iceland GRDC). </li> <li>Fix the name of the meteorological variable "mean air pressure at sea level" from "sp_min" to "sp_mean" in all the meteorological time-series files. </li> <li>Inclusion of an appendix folder with licenses information and a further description of the classes names included for land cover and lithology.</li> <li>Fix the name of the monthly streamflow indices from "mothnly" to "monthly".</li> <li>Correction of the name of the static topographical attributes from "estreams_terrain_attributes.csv" to "estreams_topography_attributes.csv".</li> </ul> </li> <li>In the "estreams_gauging_stations.csv" file we: <ul> <li>Renamed the fields "area", "area_calc" and "area_perc" to respectivelly "area_official", "area_estreams" and "area_rel" for consistency. </li> <li>Added the fields "nested_catchments", "num_days_reliable", "num_days_noflag", "num_days_suspect" and "gauge_flag" were added.</li> <li>Updated the field "gauges_upstream" to include also the basin itself.</li> </ul> </li> <li>In the "estreams_streamflow_catalogue.csv" file we: <ul> <li>Added the field  "download_method".</li> <li>Updated the fields "observations" and "references".</li> </ul> </li> <li>In the "estreams_meteorology_coverage.csv" file we: <ul> <li>Updated the names of the fields to: stations_num_{p_mean, t_mean, t_min, t_max, sp_mean, rh_mean, ws_mean, swr_mean}  and stations_dens_{p_mean, t_mean, t_min, t_max, sp_mean, rh_mean, ws_mean, swr_mean}, as it is presented in the manuscript. </li> </ul> </li> </ul> </li> </ul>

opencc-by-nc-4.0Feb 2024View details →

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Data and scripts for "Simulating AMOC tipping driven by internal climate variability with a rare event algorithm."

This dataset contains supplementary material for the paper Simulating AMOC tipping driven by internal climate variability with a rare event algorithm." (M.Cini, G. Zappa, F. Ragone, S. Corti, 2023) submitted to npj Climate and Atmospheric Science.  Preprint is available at https://www.researchsquare.com/article/rs-3215995/latest. Here we uploaded most relevant data and scripts concerning this study. Feel free to contact us for other resources. This datasets contains:1) The output of one 125-years ensemble simulation performed with the Algorithm.2) Time series of the AMOC index for all the simulations performed. 3) All data-analysis related scripts. These scripts have been used to plot all the figures in the paper.The script for the rare event algorithm is already available in the Supplementary material for "Rare event algorithm study of extreme warm summers and heatwaves over Europe" zenodo repository, available at https://zenodo.org/records/4763283. Simulation Architecture and model setupAll the simulations have been performed with an intermediate complexity coupled climate model, composed by the Planet Simulator (PlaSim) and the Large Scale Geostrophic Ocean (LSG). All simulations are performed at stationary greenhouse gases forcing. More information about model setup and scope of the simulations can be found in the paper. We performed 10 100-member ensemble simulations. First 125 years of the simulation are performed with the algorithm on (k=3). Then, simulations have been restarted at year 120 with the algorithm off (k=0) up to year 400. This last 380 years of simulation have been performed with only 20 members. y2480_k3_ntraj100y2480_k3_ntraj100.tar.gz contains the output of one ensemble simulation (y2480, i.e. the one that starts at year 2480 of the control run simulation) performed with the algorithm, i.e. contains data of 125 years simulation of 100 members. Data is organized in blocks for each years. Every block contains the 4 NetCDF light file for each member and 4 files with full-size output that represent the mean state as the average of the 100 members. The 4 different file name accounts for the 4 different module output of the model: "data" for the atmosphere, "ice" for sea ice, "ocean" for the slab ocean layer in PlaSIM, lsg for LSG dynamical ocean. Time SeriesTime series of the AMOC index are contained in 10 files representing the 10 different simulations performed. In each file are present 125 .txt files, one for each year of the simulation with the algorithm on, with the annual average AMOC indices of the 100 members, and 380 .txt files, one for each year of the simulation with the algorithm off, with the annual average AMOC indices of the 20 members. Response Analysis REAResponse Analysis REA contains the script that generates Fig.2, Fig. S3, Fig. S5, Fig. S6 and Fig. S7 of the paper. In general it provides tools for data analysis of the climate response to an AMOC slowdown.  Be aware that data of the lsg module needs different processing.  AMOC Evolution REAAMOC Evolution REA contains the script that generates Fig.1, Fig. 4, Fig. 5, Fig. 6 and Fig. S2 of the paper. In general it provides tools for analysis of time series and scatter plots of the AMOC evolution.   Causes REACauses REA  contains the script that generates Fig.3, Fig. S4 of the paper. . In general it provides tools for analysis of driving elements of the AMOC decline. More information about these methods can be found in the "Triggering mechanisms" section of the paper.  Be aware that data of the lsg module needs different processing.

opencc-by-4.0Dec 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record