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51 results for “Historical climate data”
C3-EURO4M-MEDARE Mediterranean historical climate data - v.2
<p>Historical surface climate data files and meta-data for stations in Mediterranean North Africa and Middle East areas (1852-2008).</p>
C3-EURO4M-MEDARE Mediterranean historical climate data
<p>Historical surface climate data files and meta-data for stations in Mediterranean North Africa and Middle East areas (1852-2008)</p>
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
Climate Watch Historical Country Greenhouse Gas Emissions Data (1990-2018)
<p>Climate Watch Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 194 countries and the European Union (EU) for the period 1990-2018, including emissions of the six major GHGs from most major sources and sinks. Non-CO2 emissions are expressed in CO2 equivalents using 100-year global warming potential values from IPCC Fourth Assessment Report. See <a href="http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf">http://cait.wri.org/docs/CAIT2.0_CountryGHG_Methods.pdf</a> for details regarding data source and methodology.</p> <p> </p> <p>Climate Watch Historical GHG Emissions. 2021. Washington, DC: World Resources Institute. Available online at: <a href="https://www.climatewatchdata.org/ghg-emissions">https://www.climatewatchdata.org/ghg-emissions</a></p>
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Data for: Phylogenomics and historical biogeography of Hydrangeeae (Hydrangeaceae) elucidate the effects of geologic and climatic dynamics on diversification
<p>Demonstrating the process of transregional biogeography and mechanisms underlying evolutionary radiations is crucial to understanding biological evolution. Here, we use Hydrangeeae (Hydrangeaceae), a tribe with a unique disjunct distribution and complex trait variations, using a solid phylogenetic framework, to investigate how geographical and climatic factors interact with functional traits to trigger plant evolutionary radiations. We constructed the first highly supported and dated phylogenetic framework using 79 protein-coding genes obtained from 81 plastomes, representing 63 species and all major clades, and found that most extant species originated from asynchronous diversification of two lineages undergoing repeated expansion and retraction, at middle and high latitudes of the Northern Hemisphere between East Asia and North America, during the Eocene to Pleistocene (driven by geologic and climatic dynamics). In accordance with these drivers, interactions of flora between central-eastern China and Japan occurred frequently after the Late Tertiary. We found that resource limitation and range fragmentation likely accelerated the diversification of Hydrangeeae, which supports the resource-use hypothesis. Our study sheds light on the evolutionary radiation and assembly of flora within East Asia, and the East Asian-North American disjunction, through integration of phylogenomic and biogeographic data with functional trait and ecological data.</p>
Data and code from: Decades of historical outbreak cycles in a multivoltine insect reveal a plastic phenological response to climate change
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Data from: Historic land use modifies impacts of climate and isolation in rear edge European beech (Fagus sylvatica L.) populations
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Data for: Phylogenomics and historical biogeography of Hydrangeeae (Hydrangeaceae) elucidate the effects of geologic and climatic dynamics on diversification
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Data from: Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance
The Neotropical region exhibits the greatest worldwide diversity and the diversification history of several clades is related to the puzzling geomorphologic and climatic history of this region. The freshwater Amazon ecoregion contains the main hydrographic basins of the Neotropical region that are highly dendritic and ecologically diverse. It contains a rich and endemic fish fauna, including one of its most iconic and economically important representatives, the bony-tongue Arapaima gigas (Teleostei, Osteoglossiformes). Here, we evaluated the projected distribution of the genus in different historical periods (Present, Last Glacial Maximum, Last Interglacial Maximum and Near Future) and interpreted these results in light of the genomic diversity and modeled historical demography. For that, we combined species distribution models, population genetic analysis using SNPs and deep learning model selection. We analyzed a representative sample of the genus from the two basins where it naturally occurs, four localities in the Amazon (Am) and three in the Tocantins-Araguaia (To-Ar) basin, as well as individuals from three fish farms. We inferred a potentially smaller distribution in the glacial period, with a possible refuge in central Am. Our genetic data agrees with this result, suggesting a higher level of genetic diversity in the Am basin, compared to that observed in To-Ar. Our deep learning model comparison indicated that the To-Ar basin was colonized by the population from the Am basin. Considering a global warming scenario in the near future, A. gigas could reach an even larger range, especially if anthropogenic related dispersal occurs, potentially invading new areas and impacting their communities.
Data from: Isolation by instability: historical climate change shapes population structure and genomic divergence of treefrogs in the Neotropical Cerrado savanna
Although the impact of Pleistocene glacial cycles on the diversification of the tropical biota was once dismissed, increasing evidence suggests that Pleistocene climatic fluctuations greatly affected the distribution and population divergence of tropical organisms. Landscape genomic analyses coupled with paleoclimatic distribution models provide a powerful way to understand the consequences of past climate changes on the present-day tropical biota. Using genome-wide SNP data and mitochondrial DNA, combined with projections of the species distribution across the late Quaternary until the present, we evaluate the effect of paleoclimatic shifts on the genetic structure and population differentiation of Hypsiboas lundii, a treefrog endemic to the South American Cerrado savanna. Our results show a recent and strong genetic divergence in H. lundii across the Cerrado landscape, yielding four genetic clusters that do not seem congruent with any current physical barrier to gene flow. Isolation by distance (IBD) explains some of the population differentiation, but we also find strong support for past climate changes promoting range shifts and structuring populations even in the presence of IBD. Post Pleistocene population persistence in four main areas of historical stable climate in the Cerrado seems to have played a major role establishing the present genetic structure of this treefrog. This pattern is consistent with a model of reduced gene-flow in areas with high climatic instability promoting isolation of populations, defined here as "isolation by instability", highlighting the effects of Pleistocene climatic fluctuations structuring populations in tropical savannas.
Data from: A framework for simultaneous tests of abiotic, biotic, and historical drivers of species distributions: empirical tests for North American wood warblers based on climate and pollen
Understanding how abiotic, biotic and historical factors shape species distributions remains a central question in ecology, but studies linking biotic factors to continental-scale patterns remain scarce. Here, we present a novel framework for simultaneously testing patterns expected when abiotic, biotic or historical factors drive species range limits. We use ecological niche models to produce empirical estimates of the "Biotic, Abiotic, and Movement" paradigm (BAM diagrams), which previously had only been used theoretically. Based on climatic and pollen data, as well as explicit consideration of dispersal limitations, we implement the framework for a group of North American birds (Oreothlypis warblers) with clear habitat associations. Because the pollen-based predictor variables characterize vegetation, they represent biotic factors needed by each bird species. Although continental-scale patterns of distribution traditionally are attributed to abiotic factors, only one species matched the hypothesis of solely abiotic drivers. In contrast, pollen-based models indicate biotic drivers for two species, correctly predicting their absence in climatically suitable areas. These results highlight the feasibility of considering and quantifying potential effects of biotic interactions on species ranges, especial when interactions can be decoupled from abiotic factors. Furthermore, the availability of pollen data now and in the Holocene highlights the potential of these data to be used to predict range shifts of other organisms tightly dependent on particular vegetation types.
INTERACT arctic research stations: Historic weather data fetched and plotted from Copernicus Climate Data Store
<p>Plots with historic weather data for arctic research stations covered by International Network for Terrestrial Research and Monitoring in the Arctic (INTERACT). Comparison with WMO climate normal periods 1961-1990 and 1991-2020, averages and stations deviations from the normal periods are calculated and plotted.</p><p>The data source for historic data of temperature and precipitation is: </p><p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.f17050d7 </p><p>See the linked github repository https://zenodo.org/doi/10.5281/zenodo.10214962 for how data was fetched from Copernicus Climate Data Store, prepared and plotted. The github repository also contains CSVs with source data for the plots.</p>
Data from: Evaluating the impact of historical climate and early human groups in the Araucaria Forest of Eastern South America
<p>It has been hypothesized that the Araucaria Forest in Southern Brazil underwent expansions in the past, driven either by human groups or by climate fluctuations of the Holocene and Pleistocene. Fossil pollen records of the Paraná Pine (<em>Araucaria angustifolia</em>), a dominant tree in that forest, provide some insights into when those may have occurred. Still, the timing of those expansions has never been estimated. To infer past range shifts and shed light on their main drivers, we employed next-generation DNA sequencing (ddRADseq), machine learning, and a comprehensive database of fossil pollen records in a study of historical demographic inference and paleo-distribution modeling of the Paraná Pine. We found that <em>A. angustifolia</em> comprises two populations expanding at different times: one in the Mantiqueira mountain chain, and the other in the southern Brazilian plateau. The Southern population began to expand during the Last Glacial Period ~70kya, long before human arrival in South America. Still, genetic analyses support that humans later impacted this population, resulting in lower genetic diversity, higher inbreeding, and high levels of gene flow over large distances with a weak pattern of isolation by distance. It is possible this resulted from human influence on seed dispersal and germination on the Southern Brazilian plateau. The Mantiqueira population, in contrast, expanded only recently (~3kya). This timing coincides with Holocene climatic changes and human settlements established further south, although, to date, there is little archeological evidence of human impact in the Mantiqueira. In addition, multitemporal species distribution models built from a combination of present-day and pollen records infer range expansion of the Araucaria Forest during glacial times until the cold humid HS1 event (~16kya), when the forest was most widespread, with no evidence of glacial refugia. The combination of genomic and spatial analyses suggests that both human and climatic controls played a role in the dynamics of the Araucaria Forest.</p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</p>
Data from: Vrba was right: Historical climatic fragmentation, and not current climate, explains mammal biogeography
<p>Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba's Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth.</p>
Data from: Estimating potential global sources and secondary spread of freshwater invasions under historical and future climates
<p>Aim: We employ a climate-matching method to evaluate potential source regions of freshwater invasive species to an introduced region and their potential secondary spread under historical and future climates.</p> <p>Location: Global source regions, with primary introductions to the Laurentian Great Lakes and secondary introductions throughout North America</p> <p>Methods: We conducted a climate-match analysis using the CLIMATE algorithm to estimate global source freshwater ecoregions under historical and future climates with an ensemble of general circulation models for climate change scenario SSP5-8.5. Given existing research, we use a climate match of ≥ 71.7% between ecoregions to indicate climatic conditions that will not inhibit the survival of introduced freshwater organisms. Further, we estimate the secondary spread of freshwater invaders to the ecoregions of North America under historical and future climates.</p> <p>Results: We identified 54 global freshwater ecoregions with a climate match ≥ 71.7% to the recipient Laurentian Great Lakes under historical climatic conditions and 11 additional ecoregions were predicted to exceed the threshold under climate change. Three of the 11 ecoregions were located in South America, a continent where no matches existed under historical climates and eight were located in the southern United States, southern Europe, Japan, and New Zealand. Further, we identify 34 North American ecoregions of potential secondary spread of freshwater invasions from the Great Lakes under historical climatic conditions, and five ecoregions were predicted to exceed the threshold under climate change.</p> <p>Main conclusion: We provide a climate-match method that can be employed to assess the sources and spread of freshwater invasions under historical and future climate scenarios. Our climate-match method predicted increases in climate match between the recipient region and several potential source regions, and changes in areas of potential spread under climate change. The identified ecoregions are candidates for detailed biosecurity risk assessments and related management actions. The identified ecoregions are candidates for detailed biosecurity risk assessments and related management actions.</p>
Data from: Isolation by instability: historical climate change shapes population structure and genomic divergence of treefrogs in the Neotropical Cerrado savanna
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Data from: Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance
Open the record for dataset details and reuse information.
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