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74 results for “2100”
Probabilistic projections of mean sea level change in Finland by 2100
<p><strong>Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023.</strong></p> <p>This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5).</p> <p>There are two data packages, <em>distributions.zip</em> and <em>timeseries.zip</em>. The data files included in these packages are tab- or space-delimited text files with the file extension .dat.</p> <p>All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example:</p> <p>v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6)<br> e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5)<br> t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5)</p> <p>The letter symbols and locations of the tide gauges are, from north to south along the coast:</p> <p>a - Kemi (65.67 N, 24.52 E)<br> o - Oulu (65.04 N, 25.42 E)<br> b - Raahe (64.67 N, 24.41 E)<br> p - Pietarsaari (63.71 N, 22.69 E)<br> v - Vaasa (63.08 N, 21.57 E)<br> s - Kaskinen (62.34 N, 21.21 E)<br> m - Mäntyluoto (61.59 N, 21.46 E)<br> r - Rauma (61.13 N, 21.44 E)<br> t - Turku (60.43 N, 22.1 E)<br> d - Degerby (60.03 N, 20.38 E)<br> h - Hanko (59.82 N, 22.98 E)<br> e - Helsinki (60.15 N, 24.96 E)<br> f - Hamina (60.56 N, 27.18 E)</p> <p>1) <em>distributions.zip > xxx_fitdistr_yyyy.dat</em><br> These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system.</p> <p>2)<em> timeseries.zip > xxx_timeseries.dat</em><br> These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system.</p> <p><strong>Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates.</strong></p> <p>Example figures <em>distributions.png</em> and <em>timeseries.png</em> are included to illustrate the data. The Matlab script <em>slrfinland_figures.m</em> used to produce these figures is also included.</p>
Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100
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Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p> <p>Scenario: Base scenario (situation LULC today)</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Daily SPEI dataset in China from 1980 to 2100
<p>The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs' simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO<sub>2</sub> concentration under high emission scenarios in the mid- and late- future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.</p>
Daily flood discharge dataset for 10 basins in the Third Pole during 1981‒2100
<p><span>This dataset describes the daily discharge during each river flood event in the 10 Third Pole basins (Indus, Yamuna, Upper Ganges, MahaKali, Karnali, Gandaki, Koshi, Brahmaputra, Salween, and Mekong) in the historical (1981</span><span>‒</span><span>2020) and future (2021</span><span>‒</span><span>2100). This was achieved using a hybrid model encompassing a validated physical model (Water and Energy Budget-based Distributed Hydrological Model, WEB-DHM) and deep-learning model (<a name="_Hlk153977673"></a>Long Short-Term Memory model, LSTM) with the latest climate projections. </span></p> <p><span>River flood events are defined using both the annual-maximum approach and peak-over-threshold (POT) approach. Details for identification of the annual-maximum and POT flood events are described in the file “readme.txt”. Future data was generated based on the climate projections </span><span>from five climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and </span><span>UKESM1-0-LL</span><span>) in phase 6 of the Coupled Model Intercomparison Project (CMIP6) under </span><span>two shared socio-economic pathway scenarios (SSP)</span><span> (a high-emission scenario of SSP585 and a low-emission scenario of SSP245). The unit of discharge is m<sup>3</sup>/s.</span></p>
Replication dataset for paper "A shift in transitional forests of the North American boreal will persist through 2100"
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China's natural forest restoration potential region until 2100
<p>This dataset includes areas of natural forest restoration in China, defined as natural forests that have recovered through natural growth since 1990 and remained undisturbed until 2020. Additionally, the dataset contains predictions of potential natural forest restoration areas in China under various future climate scenarios.</p>
Binary black-hole simulation SXS:BBH:2100
Simulation of a black-hole binary system evolved by the <a href="https://www.black-holes.org/code/SpEC.html">SpEC code</a>.
Phytoplankton life strategies, phenological shifts and climate change in the North Atlantic Ocean from 1850‐2100
<p>Supporting data for the article entitled 'Phytoplankton life strategies, phenological shifts and climate change in the North Atlantic Ocean from 1850-2100'.</p> <p>Article abstract: Significant phenological shifts induced by climate change are projected within the phytoplankton community. However, projections from current Earth System Models (ESMs) understandably rely on simplified community responses that do not consider evolutionary strategies manifested as various phenotypes and trait groups. Here, we use a species-based modelling approach, combined with large-scale plankton observations, to investigate past, contemporary and future phenological shifts in diatoms (grouped by their morphological traits) and dinoflagellates in three key areas of the North Atlantic Ocean (North Sea, North-East Atlantic and Labrador Sea) from 1850 to 2100. Our study reveals that the three phytoplanktonic groups exhibit coherent and different shifts in phenology and abundance throughout the North Atlantic Ocean. The seasonal duration of large flattened (i.e., oblate) diatoms is predicted to shrink and their abundance to decline, whereas the phenology of slow-sinking elongated (i.e., prolate) diatoms and of dinoflagellates is expected to expand and their abundance to rise, which may alter carbon export in this important sink region. The increase in prolates and dinoflagellates, two groups currently not considered in ESMs, may alleviate the negative influence of global climate change on oblates, which are responsible of massive peaks of biomass and carbon export in spring. We suggest that including prolates and dinoflagellates in models may improve our understanding of the influence of global climate change on the biological carbon cycle in the oceans.</p> <p>The data provided here are the observed and modelled abundances (oblate and prolate diatoms and dinoflagellates), the observed and modelled environmental data (compiled data for sea surface temperature, Surface Downwelling Shortwave Radiation and disolved nitrates concentrations) in the North Sea, the North-East Atlantic and the Labrador Sea, and the phenological indices for the three different phytoplanktonic groups (i.e. oblate and prolate diatoms and dinoflagellates) and the three warming scenarios (the low, the medium and the high warming scenarios; SSP1-1.2.6 SSP2-4.5 and 5-8.5 respectively). The phenological indices are the maximum abundance, the day where the maximum abundance is reached, the day where the seasonal reproductive period is initiated and the day where it is terminated, the seasonal duration and the mean annual abundance. </p>
Daily SPEI dataset in China from 1980 to 2100
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Phytoplankton life strategies, phenological shifts and climate change in the North Atlantic Ocean from 1850‐2100
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Distribution range and richness of plant species are predicted to increase by 2100 due to a warmer and wetter climate in northern China
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Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v2.0)
<p><strong>Agricultural land resources – a global suitability evaluation</strong></p> <p><em>An inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. Within the context of the GLUES project, researchers at the Ludwig-Maximilians University (LMU) investigated the global agricultural suitability of land under changing climate conditions at high spatial resolution. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes land use/cover change and trade-offs between different uses of land and ecosystem services. In order to ensure food security, agricultural potentials need to be used more efficiently in the future. Therefore, the agricultural suitability of land are important information e.g. in order to identify todays suitable areas and possible future changes. The potential suitability of todays forested and protected areas can be used to identify possible hotspots of land use/cover change. Therefore, LMU is working on improving the knowledge of global agricultural potentials of land and better understanding the interdependencies between ecological and socio-economic systems which are driving land use/cover change.</em></p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Local climate, soil and topography determine the available energy, water and nutrient supply for agricultural crops and thus their natural suitability. In order to allow for computing the natural agricultural constraints on the globe at 30 arc seconds (1km) spatial resolution, the following high resolution data were applied:</p> <p>Daily data for temperature, precipitation and solar radiation from the global climate model ECHAM5. Soil data comes from the Harmonized World Soil Database (HWSD). Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Topography data was applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the crop’s suitability. It is considered on todays irrigated areas as given by the FAO Aquastat Global Maps of Irrigated Areas (GMIA) dataset. The determinant factors are contrasted with the crop-specific requirements, using a fuzzy-logic approach. The crop requirements are taken from literature.</p> <p><strong>Agricultural Suitability</strong></p> <p>General agricultural suitability at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The agricultural suitability represents for each pixel the maximum suitability value of the considered 16 plants. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Suitability Change due to Climate until 2100</strong></p> <p>Change in agricultural suitability and crop suitability due to climate change for SRES A1B scenario conditions for 16 crops between 1981-2010 and 2071-2100 at a spatial resolution of 30 arcsec.</p> <p><strong>Multiple Cropping</strong></p> <p>Potential number of suitable crop cycles for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Growing Cycle</strong></p> <p>Start of the growing cycle for 16 crops at a spatial resolution of 30 arcsec, considering rainfed conditions and irrigation on currently irrigated areas. In case of multiple cropping, the start of the first growing cycle is shown. The dataset contains four time periods (1961-1990, 1981-2010, 2011-2040, 2071-2100).</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publication:<br> Zabel F., Putzenlechner B., Mauser W. (2014): <strong>Global agricultural land resources – a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. </strong> Online available: <a href="http://dx.plos.org/10.1371/journal.pone.0107522">PLOS ONE</a>. DOI: 10.1371/journal.pone.0107522</p> <p><strong>Improvements in v2.0</strong></p> <p>Compared to previous versions, v2.0 uses updated input data for soil and minor improvements of the statistical downscaling and the bias correction of the climate model data.</p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department für Geographie, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Data from: Predicted 2100 climate scenarios affects growth and skeletal development of tambaqui (Colossoma macropomum) larvae
Climate changes driven by greenhouse gas emissions have been occurring in an accelerated degree, affecting environmental dynamics and living beings. Among all affected biomes, the Amazon is particularly subjected to adverse impacts, such as temperature rises and water acidification. This study aimed to evaluate the impacts of predicted climate change on initial growth and development of an important Amazonian food fish, the tambaqui. We analyzed growth performance, and monitored the initial osteogenic process and the emergence of skeletal anomalies, when larvae were exposed to three climate change scenarios: mild (B1, increase of 1.8 °C, 200 ppm of CO2); moderate (A1B, 2.8 °C, 400 ppm of CO2); and drastic (A2, 3.4 °C, 850 ppm of CO2 ), in addition to a control room that simulated the current climatic conditions of a pristine tropical forest . The exposure to climate change scenarios (B1, A1B and A2) resulted in low survival, especially for the animals exposed to A2, (24.7 ± 1.0 %). Zootechnical performance under the B1 and A1B scenarios was higher when compared to current and A2, except for condition factor, which was higher in current (2.64 ± 0.09) and A1B (2.41 ± 0.14) scenarios. However, skeletal analysis revealed higher incidences of abnormalities in larvae exposed to A1B (34.82 %) and A2 (39.91 %) scenarios when compared to current (15.38 %). Furthermore, the bone-staining process revealed that after 16 days post-hatch (7.8 ± 0.01 mm total length), skeletal structures were still cartilaginous, showing no mineralization in all scenarios. We concluded that tambaqui larvae are well-adapted to high temperatures and may survive mild climate change . However, facing more severe climate conditions, its initial development may be compromised, resulting in high mortality rates and increased incidence of skeletal anomalies, giving evidence that global climate change will hamper tambaqui larvae growth and skeletal ontogeny.
Data from: The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976–2100
<p>Recent climatic trends have increased forest fire activity in Canada. This study aimed to evaluate how forest fire conditions might evolve across the Canadian borael forests in the future and to inform discussion about the impact of climate change on fire danger and severity.</p>
Future electricity demand time series for European Countries from 2023 to 2100
<p>This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy. To cite this dataset, please cite the published paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.123387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apenergy.2024.123387</a></p> <p>This dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file. </p> <p>The column explanation is as below:</p> <ul> <li>'country_code': the country code in 2 digits</li> <li>'year': the projection year</li> <li>'month': month of the year</li> <li>'day': day of the month</li> <li>'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> </ul>
High-resolution global gridded population between 1870 and 2100
<p>history_pop.zip contains historical gridded population data from 1870 to 2010 at a 10-year interval</p> <p>SSP1.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP1</p> <p>SSP2.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP2</p> <p>SSP3.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP3</p> <p>SSP4.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP4</p> <p>SSP5.zip contains future population projection data unter from 2020 to 2100 at a 10-year interval under SSP5</p>
Supplementary code for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Code to perform the analysis in:</p> <p>Geyman, E.C., van Pelt, W., Maloof, A.C., Faste Aas, H., and Kohler, J., 2021. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one third of current sea level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Leveraging an archive of historical aerial images from 1936 and 1938, we use structure-from-motion (SfM) photogrammetry to reconstruct the 3D geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a >70-year timespan, allowing us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.27 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution to make first-order predictions of 21st century glacier change across Svalbard. Even in the most modest scenario (a ~1.4°C rise in mean summer temperature by 2100), we predict average glacier thinning rates in 2010-2100 of -0.67 m yr<sup>-1</sup>, approximately twice the 1936-2010 rates.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.