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FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution
<h2>Overview</h2> <p>This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. </p> <p><strong>Resolution</strong>: 30x30 arcsec<br><strong>Projection</strong>: EPSG 4326<br><strong>Extent for historical data</strong>: 10.67°W – 47.67°E, 33.68°N – 71.33°N<br><strong>Extent for scenario data</strong>: 10.67°W – <em>39.33°E</em>, 33.68°N – 71.33°N<br><strong>Periods for historical data</strong>: 1961-1990 and 1991-2020<br><strong>Periods for scenario data</strong>: 2036-2065 and 2071-2100<br><strong>Format:</strong> GeoTIFF</p> <p><strong>List of climatologies (climate indicators) </strong> </p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y) or monthly (M)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation</p> </td> <td> <p> </p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> </tbody> </table> <h2>Data sources</h2> <p>The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. </p> <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li><em>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</em></li> </ul> <p><strong>Climate models from EURO-CORDEX </strong>(doi.org/10.1007/s10113-013-0499-2<strong>)</strong>:</p> <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
<p>Rainforest loss threatens terrestrial insectivorous birds throughout the world's tropics. Recent evidence suggests these birds are declining in undisturbed Amazonian rainforest, possibly due to climate change. Here, we first asked whether Amazonian terrestrial insectivorous birds were exposed to increasingly extreme ambient conditions using 38 years of climate data. We found long-term trends in temperature and precipitation at our study site, especially in the dry season, which was ~1.3 °C hotter and 21% drier in 2019 than in 1981. Second, to test whether birds actively avoided hot and dry conditions, we used field sensors to identify periodic intervals of ambient extremes and prospective microclimate refugia within undisturbed rainforest from 2017–2019. Simultaneously, we examined how tagged Black-faced Antthrushes (Formicarius analis) used this space. We collected >1.3 million field measurements quantifying ambient conditions in the forest understory, including along elevation gradients. For 11 birds, we obtained GPS data to test whether birds adjusted their cover usage using variation in GPS fix success (<em>n</em> = 2,724) as a proxy and elevation using successful locations (<em>n</em> = 640) across seasonal and daily cycles. For four additional birds, we collected >180,000 light and temperature readings to assess exposure. Field measurements in the modern landscape revealed that temperature was higher in the dry season and highest on plateaus. Thus, low-lying areas were relatively buffered, providing microclimate refugia during hot afternoons in the dry season. At those times, birds apparently entered cover and shifted downslope. Because climate change intensifies the hot, dry conditions that antthrushes seemingly avoid, our results are consistent with the hypothesis that climate change decreases habitat quality for this species. If other terrestrial insectivores are similarly sensitive, climate-induced changes to otherwise intact rainforest may be related to their recent declines.</p>
Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
<p><span>Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. H</span><span>ere</span><span> we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming. </span></p>
Mediterranean Sea Climatic Indices
<p>Climatic indices for the Mediterranean Sea (-6.25W–36.5E, 30N–46N) from 1950 to 2015. The file "Med_Climatic_Indices.tar.gz" contains : a) an ascii file named “inventory.lst” which lists all the available indices, and b) a directory named “Med_Climatic_Indices” with all the available indices under the following structure:</p> <ul> <li>Anomalies/Annual_Decadal/Variable_decade_allmonths_z1_z2.nc</li> <li> <p>Anomalies/Seasonal_Decadal/Variable_decade_season_z1_z2.nc</p> </li> <li> <p>ArealDensity/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</p> </li> <li> <p>ArealDensity/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</p> </li> <li> <p>Climashift_30yrs/Anomalies/Annual/Variable_decade2_decade1_allmonths_z1_z2.nc</p> </li> <li> <p>Climashift_30yrs/Anomalies/Seasonal/ Variable_decade2_decade1_season_z1_z2.nc</p> </li> <li> <p>Climashift_30yrs/Vertical_Averages/Annual/Variable_decade2_decade1_allmonths_z1z2.nc</p> </li> <li> <p>Climashift_30yrs/Vertical_Averages/Seasonal/Variable_decade2_decade1_season_z1z2.nc</p> </li> <li> <p>LinearTrends/Annual/Variable_period_allmonths_z1z2.nc</p> </li> <li> <p>LinearTrends/Seasonal/ Variable_period_season_z1z2.nc</p> </li> <li> <p>TimeSeries/Annual/Variable_period_allmonths_z1z2.dat</p> </li> <li> <p>TimeSeries/Seasonal/ Variable_period_season_z1z2.dat</p> </li> <li> <p>VerticalAverages/Annual_Decadal/Variable_decade_allmonths_z1z2.nc</p> </li> <li> <p>VerticalAverages/Seasonal_Decadal/ Variable_decade_season_z1z2.nc</p> </li> </ul> <p>The Variable naming is:</p> <ul> <li> <p>Tanom: Temperature anomaly</p> </li> <li> <p>Sanom: Salinity anomaly</p> </li> <li> <p>Tanomvavg: Vertically averaged temperature anomaly</p> </li> <li> <p>Sanomvavg: Vertically averaged salinity anomaly</p> </li> <li> <p>OHCad: areal density Ocean Heat Content anomaly</p> </li> <li> <p>OSCad: areal density Ocean Salt Content anomaly</p> </li> <li> <p>Tanomclimashift: Temperature anomaly difference between decade2 and decade1</p> </li> <li> <p>Sanomclimashift: Temperature anomaly difference between decade2 and decade1 at</p> </li> <li> <p>Tclimashift: Vertically averaged temperature anomaly difference between decade2 and decade1</p> </li> <li> <p>Sclimashift: Vertically averaged salinity anomaly difference between decade2 and decade1</p> </li> <li> <p>OHCclimashift: Ocean Heat Content anomaly difference between decade2 and decade1</p> </li> <li> <p>OSCclimashift: Ocean Salt Content anomaly difference between decade2 and decade1</p> </li> <li> <p>Tlineartrend: Vertically averaged temperature anomaly linear trend</p> </li> <li> <p>Slineartrend: Vertically averaged salinity anomaly linear trend</p> </li> <li> <p>OHClineartrends: Ocean Heat Content anomaly linear trend</p> </li> <li> <p>OSClineartrends: Ocean Salt Content anomaly linear trend</p> </li> </ul> <p>Other naming:</p> <ul> <li> <p>allmonths: 0112 (all months from January to December)</p> </li> <li> <p>seasons: 0103 for winter, 0406 for spring, 0709for summer, 1012 for autumn</p> </li> <li> <p>decade1: stands for 19501979</p> </li> <li> <p>decade2: stands for 19802015</p> </li> <li> <p>period: stands for 19502015</p> </li> <li> <p>z1z2: vertical layer between z1 and z2 depths</p> </li> <li> <p>z1, z2: standard depth levels</p> </li> </ul> <p>Examples:</p> <ol> <li> <p>OHC_19502015_0103_0150.dat, is the winter Ocean Heat Content anomaly for the period 1950 to 2015, at 0-150 m</p> </li> <li> <p>Tanomvavg_20062015_1012_6004000.nc, is the autumn vertically averaged temperature anomaly, for the decade 2006-2015, at 600 – 4000 m</p> </li> <li> <p>Sanomclimashift_19802015_19501979_0112_5_4000.nc, is the annual salinity anomaly difference between 1980-2015 and 1950-1970 from 5 to 4000 m.</p> </li> </ol> <p><br> </p>
Data from: Indices of Extremes: Geographic patterns of change in extreme temperature and precipitation under climate intervention
<p>This dataset comprises the python notebooks and associated data used to produce Figures 1-9, 12-14, and all supplemental figures in Tye et al. 2022 "Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention" Earth System Dynamic, 13, 1233-1257. https://doi.org/10.5194/esd-13-1233-2022</p> <p>Script is also included to process data from NCAR's HPC Campaign archive and produce figures 10 and 11.</p> <p>The full output from the GLENS simulation are available from from https://data.ucar.edu/dataset/stratospheric-aerosol-geoengineering-large-ensemble-project-glens</p> <p> </p> <p> </p>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
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Habitat use as an indicator of adaptive capacity to climate change
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Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change
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Data from: Experimentally induced low flows indicate climate change may shrink trophic niches of mountain-stream predators
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Data for: Positive feedback on climate warming by stream microbial decomposers indicated by a global space-for-time substitution study
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Data for: Parasitoids indicate major climate-induced shifts in Arctic communities
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Data from: Enriched East Asian oxygen isotope of precipitation indicates reduced summer seasonality in regional climate and westerlies
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Data from: Quantifying the climatic niche of symbiont partners in a lichen symbiosis indicates mutualist-mediated niche expansions
The large distributional areas and ecological niches of many lichenized fungi may in part be due to the plasticity in interactions between the fungus (mycobiont) and its algal or cyanobacterial partners (photobionts). On the one hand, broad-scale phylogenetic analyses show that partner compatibility in lichens is rather constrained and shaped by reciprocal selection pressures and codiversification independent of ecological drivers. On the other hand, sub-species-level associations among lichen symbionts appear to be environmentally structured rather than phylogenetically constrained. In particular, switching between photobiont ecotypes with distinct environmental preferences has been hypothesized as an adaptive strategy for lichen-forming fungi to broaden their ecological niche. The extent and direction of photobiont-mediated range expansions in lichens, however, have not been examined comprehensively at a broad geographic scale. Here we investigate the population genetic structure of Lasallia pustulata symbionts at sub-species-level resolution across the mycobiont's Europe-wide range, using fungal MCM7 and algal ITS rDNA sequence markers. We show that variance in occurrence probabilities in the geographic distribution of genetic diversity in mycobiont-photobiont interactions is closely related to changes in climatic niches. Quantification of niche extent and overlap based on species distribution modeling and construction of Hutchinsonian climatic hypervolumes revealed that combinations of fungal-algal interactions change at the sub-species level along latitudinal temperature gradients and in Mediterranean climate zones. Our study provides evidence for symbiont-mediated niche expansion in lichens. We discuss our results in the light of symbiont polymorphism and partner switching as potential mechanisms of environmental adaptation and niche evolution in mutualisms.
Weekly Carbon Monoxide Anomalies over Maritime Southeast Asia and Weekly Climate Indices
This repository contains weekly atmospheric carbon monoxide (CO) anomalies over the Maritime Southeast Asia (MSEA) region from 2001 to 2019, as well as weekly climate index data. Total column CO from the MOPITT satellite instrument were converted to column average volume mixing ratios (VMR) and were averaged within the MSEA region on a weekly time scale. A climatological seasonal cycle for the weekly time series was created using all 19 years of MOPITT data and was subtracted from the weekly VMRs to create the anomalies. Five climate indices are also provided on a weekly timescale (Nino3.4, AAO, DMI, TSA, OLR proxy for MJO). For details on the MOPITT CO retrieval parameters and the geometry of the MSEA region, see the provided README file. The geometry of the MSEA region and the spatial range of influence of the five climate indices are also plotted on the provided map. These data are associated with the JGR-Atmos. manuscript "Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models" by Daniels et al., (submitted October 2021).
IntelComp Climate Change Indicators
<p>The IntelComp Climate Change Indicators dataset provides essential indicators for STI policymakers in the energy and agrifood sectors of Europe and Greece. It covers a broad spectrum of societal sectors including Science, Technology, Industry, ESG (Environmental, Social, and Governance), Human Resources, and Policy. Each indicator set is derived from specific analytics: Science indicators are created from publication analytics, Technology from patent analytics, Industry from company/industry analytics, ESGs from ESG analytics, Human Resources from Green Skills analytics, and Policy from regulation analytics.</p> <p>This dataset, a product of multiple AI-driven pipelines, is derived by processes on raw datasets (Tier 1) and analytics datasets (Tier 2, as described in this DMP) to produce a comprehensive collection of Tier 3 indicators. These indicators provide a detailed view of the current state and trends within the energy and agrifood sectors, assisting policymakers in agenda setting and policy formulation. The data bridges various aspects of societal development and offers a framework for understanding the intersection of science, technology, industry, and human resource development in these critical domains."</p>
DMI Klimaatlas v2024b - Fremskrivninger af det danske klima (Projections of climate indicators in Denmark)
<p>See below for English version</p> <p> ---</p> <p><strong>DMI Klimaatlas v2024b - Fremskrivninger af det danske klima</strong></p> <p>Klimaatlas leverer ét samlet datagrundlag for det fremtidige danske klima. Klimaatlas er udarbejdet på baggrund af DMI's egne data, internationale samarbejder og viden fra rapporter fra FN’s Klimapanel (IPCC). Finansieringen kommer fra Finansloven 2018 og 2022. Klimaindikatorer er udregnet og samlet for hele Danmark, kommuner, alle vandoplande (afvandingsområder) og kyststrækninger, i et højtopløsningsgitter (1x1 km).</p> <p>Denne opdatering indebærer:</p> <ul> <li>Alle havniveau og stormflodsindikatorer er opdateret på baggrund af Kystdirektoratets nyeste opdatering af ”Højvandsstatistikkerne” fra Juli 2024 og rediveret 5 november 2024<strong>. </strong></li> <li>Tilføjelse af to nye SSP-udledningsscenarier SSP1-1.9 og SSP3-7.0 for havniveau og stormflodsindikatorer.</li> <li>Ændring i indikatorerne: <ul> <li><strong>Tilføjelse 1 indikator</strong> <ul> <li>Hyppighed af nuværende 100 årshændelse</li> </ul> </li> <li><strong>Fjernelse 3 indikatorer</strong> <ul> <li>Samlet varighed af vandstandsvarslinger</li> <li>Hyppighed af vandstandsvarslinger</li> <li>10.000 årshændelse stormflod</li> </ul> </li> <li>Alle andre indikatorer fra tidligere udgaver findes også i dette datasæt.</li> </ul> </li> <li>Referenceåret for vandstand er flyttet fra 1990 til 1995, der giver en mindre afvigelse på tværs af alle hav målestationer på cirka 2 cm.</li> <li>Tilføjelse af to nye klimamodel datasæt for maksimum og minimum dagstemperatur. Alle relevante indikatorer er blevet genberegnet med den udvidet ensemble.</li> </ul> <p>Datasættet indeholder de følgende elementer:</p> <ul> <li><strong>DMI_Klimaatlas_v2024b_Danmark_rapport.pdf</strong> – Klimaatlas rapport som giver et overblik over klimaforandring i Danmark.</li> <li><strong>DMI_Klimaatlas_v2024b_Alle_indikator.xlsx</strong> – Microsoft Excel regneark med alle Klimaatlas indikatorer.</li> <li><strong>DMI_Klimaatlas_v2024b_Excel_regnearker.zip</strong> - Microsoft Excel-regneark med indikatorer opdelt efter kommuner, vandopleande eller kystrækninger.</li> <li><strong>DMI_Klimaatlas_v2024b_Kommune_rapporter.zip</strong> - PDF rapporter om klimaforandringer i alle 98 kommuner i Danmark.</li> <li><strong>DMI_Klimaatlas_v2024b_NetCDF_indicators.zip</strong> – Indikatorer på en 1km gitter over Danmark i NetCDF format.</li> <li><strong>DMI_Klimaatlas_v2024b_Udvidet_havniveau.xlsx</strong> – Udvidet havniveau datasæt til ekspertbrugere som har behov for en højere tidsopløsning, længere tidsdækning og eller andre scenarier for havniveaustigning.</li> <li><strong>DMI_Report_24_12.pdf</strong> - " Methods used in Klimaatlas, the Danish Climate Atlas (v2024b) ", tekniske rapport som beskriver hvordan Klimaatlas data beregnes (på engelsk).</li> </ul> <p>----</p> <p><strong>DMI Klimaatlas v2024b - Projections of climate indicators in Denmark</strong></p> <p>DMI’s <em>Klimaatlas</em> provides data for the future Danish climate. Klimaatlas is based on DMI's own data, international collaborations and knowledge from reports by the Intergovernmental Panel on Climate Change (IPCC). Funding comes from the Danish Finance Act 2018 and 2022. Climate indicators are calculated and compiled for all of Denmark, municipalities, all water basins (catchment areas) and coastlines, on a high-resolution grid (1x1 km).</p> <p>This update includes:</p> <ul> <li>All sea level and storm-surge indicators have been updated in line with the latest version of the Danish Coastal Authority’s “<em>Højvandsstatistikkerne” </em>from July 2024, revised 5<sup>th</sup> November 2024.</li> <li>Addition of two new SSP emissions scenarios, SSP1-1.9 and SSP3-7.0, for sea level and storm surge indicators.</li> <li>Changes in indicators: <ul> <li><strong>Addition of 1 indicator</strong> <ul> <li>Frequency of current 100-year storm surge event</li> </ul> </li> <li><strong>Removal of 3 indicators</strong> <ul> <li>Total duration of high-water warnings</li> <li>Frequency of high-water warnings</li> <li>Height of a 10 000 year storm surge event</li> </ul> </li> <li>All other indicators from previous versions can also be found in this dataset.</li> </ul> </li> <li>The reference year for sea level is moved from 1990 to 1995, giving a minor shift across all stations of around 2cm.</li> <li>Addition of two new climate models to the dataset for the maximum and minimum daily temperatures. All relevant indicators have been recalculated with the expanded ensemble.</li> </ul> <p>The dataset consists of the following elements:</p> <ul> <li><strong>DMI_Klimaatlas_v2024b_Danmark_rapport.pdf</strong> – <em>Klimaatlas</em> report describing the effects of climate change in Denmark (in Danish)</li> <li><strong>DMI_Klimaatlas_v2024b_Alle_indikator.xlsx</strong> – Microsoft Excel spreadsheet with all <em>Klimaatlas</em> indicators (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024b_ Excel_regnearker.zip</strong> - Microsoft Excel spreadsheet with indicators divided by municipalities ("<em>Kommune</em>"), coastal stretches ("<em>Kyststrækninger</em>") and drainages ("<em>Vandoplande</em>") (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024b_Kommune_rapporter.zip</strong> - PDF report summarising the findings of <em>Klimaatlas</em> for each of the 98 municipalities in Denmark (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024b_NetCDF_indicators.zip</strong> – Indicators on a 1km grid over Denmark in the NetCDF format (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024b_Udvidet_havniveau.xlsx</strong> – Extended sea level rise dataset for expert uses that have a need for a higher time resolution, longer time coverage or other climate scenarios for sea level rise (in Danish).</li> <li><strong>DMI_Report_24_12.pdf</strong> - " Methods used in Klimaatlas, the Danish Climate Atlas (v2024b)", technical report describing the methods used in generating <em>Klimaatlas</em> data</li> </ul>
Source data of the lithologic indicators of climate for NC
<p>Source data are the ~290 Ma (Figure 4a) and ~280 Ma (Figure 4b) lithologic indicators of climate from Boucot et al. (2013).</p>
Data from: Finding common ground: Toward comparable indicators of adaptive capacity of tree species to a changing climate
<p>Adaptive capacity, one of the three determinants of vulnerability to climate change, is defined as the capacity of species to persist in their current location by coping with novel environmental conditions through acclimation and/or evolution. Although studies have identified indicators of adaptive capacity, few have assessed this capacity in a quantitative way that is comparable across tree species. Yet, such multi-species assessments are needed by forest management and conservation programs to refine vulnerability assessments and to <span>guide the choice of adaptation measures</span>. In this paper, we propose a framework to quantitatively evaluate five key components of tree adaptive capacity to climate change: individual adaptation through phenotypic plasticity, population phenotypic diversity as influenced by genetic diversity, genetic exchange within populations, genetic exchange between populations and genetic exchange between species. For each component, we define the main mechanisms that underlie adaptive capacity and present associated metrics that can be used as indices. To illustrate the use of this framework, we evaluate the relative adaptive capacity of 26 northeastern North American tree species using values reported in the literature. Our results show adaptive capacity to be highly variable among species and between components of adaptive capacity, such that no one species ranks consistently across all components. On average, the conifer <i>Picea glauca</i> and the broadleaf <i>Betula papyrifera </i>show the greatest adaptive capacity among the 26 species we documented, whereas the conifers <i>Picea rubens </i>and <i>Thuja occidentalis</i>,<i> </i>and the broadleaf <i>Ostrya virginiana</i> possess the lowest. We discuss limitations that arise when comparing adaptive capacity among species, including poor data availability and comparability issues in metrics derived from different methods or studies. The breadth of data required for such an assessment exemplifies the multidisciplinary nature of adaptive capacity and the necessity of continued cross-collaboration to better anticipate the impacts of a changing climate.</p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
DMI Klimaatlas v2024a - Fremskrivninger af det danske klima (Projections of climate indicators in Denmark)
<p>See below for English version</p> <p> ---</p> <p><strong>DMI Klimaatlas v2024a - Fremskrivninger af det danske klima</strong></p> <p>Klimaatlas leverer ét samlet datagrundlag for det fremtidige danske klima. Klimaatlas er udarbejdet på baggrund af DMI's egne data, internationale samarbejder og viden fra rapporter fra FN’s Klimapanel (IPCC). Finansieringen kommer fra Finansloven 2018 og 2022. Klimaindikatorer er udregnet og samlet for hele Danmark, kommuner, alle vandoplande (afvandingsområder) og kyststrækninger, i et højtopløsningsgitter (1x1 km). </p> <p>Denne opdatering tilføjer 6 nye indikatorer for brandfare og tørre perioder:</p> <ul> <li>Brandfare <ul> <li>Gennemsnit af brandfareindekset.</li> <li>Dage med ’meget høj’ brandfare.</li> <li>Dage med ’ekstrem’ brandfare.</li> </ul> </li> <li>Tørre perioder <ul> <li>Andel af år/årstider som er ’tørre’.</li> <li>Tørre perioder på minimum 5 døgn.</li> <li>Tørre perioder på minimum 10 døgn.</li> </ul> </li> </ul> <p> Alle indikatorer fra tidligere udgaver findes også i dette datasæt.</p> <p>Ud over dette er 7 nye klimamodeller blevet tilføjet til ensemblet og antallet af modeller dermed steget til 72. Alle indikatorer er blevet genberegnet med den udvidet ensemble.</p> <p>Formatet af Excel-regnearkerne er blevet opdateret med fokus på maskinlæsbarhed.</p> <p>Datasættet indeholder de følgende elementer:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – Klimaatlas rapport som giver et overblik over klimaforandring i Danmark.</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", tekniske rapport som beskriver hvordan Klimaatlas data beregnes (på engelsk).</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel regneark med alle Klimaatlas indikatorer.</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF rapporter om klimaforandringer i alle 98 kommuner i Danmark.</li> <li><strong>DMI_Klimaatlas_v2024a_Excel_regnearker.zip</strong> - Microsoft Excel-regneark med indikatorer opdelt efter kommuner, vandopleande eller kystrækninger.</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Udvidet havniveau datasæt til ekspertbrugere som har behov for en højere tidsopløsning, længere tidsdækning og eller andre scenarier for havniveaustigning.</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indikatorer på en 1km gitter over Danmark i NetCDF format.</li> </ul> <p>----</p> <p><strong>DMI Klimaatlas v2024a - Projections of climate indicators in Denmark</strong></p> <p>DMI’s <em>Klimaatlas</em> provides data for the future Danish climate. Klimaatlas is based on DMI's own data, international collaborations and knowledge from reports by the Intergovernmental Panel on Climate Change (IPCC). Funding comes from the Danish Finance Act 2018 and 2022. Climate indicators are calculated and compiled for all of Denmark, municipalities, all water basins (catchment areas) and coastlines, on a high-resolution grid (1x1 km). </p> <p>This update adds 6 new indicators for fire danger and dry periods:</p> <ul> <li>Fire danger <ul> <li>Average of the fire danger index.</li> <li>Days with ‘very high’ fire danger.</li> <li>Days with ‘extreme’ fire danger.</li> </ul> </li> <li>Dry periods <ul> <li>Dry periods of minimum 5 days.</li> <li>Dry periods of minimum 10 days. </li> <li>Proportion of years/seasons that are ‘dry’. </li> </ul> </li> </ul> <p>All indicators from previous versions are also available in this dataset.</p> <p>In addition, 7 new climate models have been added to the ensemble, increasing the number of models to 72. All indicators have been recalculated with the extended ensemble.</p> <p>The format of the Excel spreadsheets has been updated with a focus on machine readability.</p> <p>The dataset consists of the following elements:</p> <ul> <li><strong>DMI_Klimaatlas_v2024a_Danmark_rapport.pdf</strong> – <em>Klimaatlas</em> report describing the effects of climate change in Denmark (in Danish).</li> <li><strong>DMIrapport_24_11.pdf</strong> - "Methods used in the Danish Climate Atlas", technical report describing the methods used in generating <em>Klimaatlas</em> data.</li> <li><strong>DMI_Klimaatlas_v2024a_Alle_indikator.xlsx</strong> – Microsoft Excel spreadsheet with all <em>Klimaatlas</em> indicators (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Kommune_rapporter.zip</strong> - PDF report summarising the findings of <em>Klimaatlas</em> for each of the 98 municipalities in Denmark (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_ Excel_regnearker.zip</strong> - Microsoft Excel spreadsheet with indicators divided by municipalities ("<em>Kommune</em>"), coastal stretches ("<em>Kyststrækninger</em>") and drainages ("<em>Vandoplande</em>") (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_Udvidet_havniveau.xlsx</strong> – Extended sea level rise dataset for expert uses that have a need for a higher time resolution, longer time coverage or other climate scenarios for sea level rise (in Danish).</li> <li><strong>DMI_Klimaatlas_v2024a_NetCDF_indicators.zip</strong> – Indicators on a 1km grid over Denmark in the NetCDF format (in Danish).</li> </ul>
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