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zenodo52/100

JasonAlongTrack: A reformatted version of the Integrated Multi-Mission Ocean Altimeter Data for Climate Research Version 5.1

JasonAlongTrack contains geo-registered along-track sea surface height anomalies with respect to the DTU15 mean sea surface at 1-second intervals from Jason-class altimeters, reformatted for convenience into a 3D array with dimensions of along-track direction by geographically sorted track number by cycle.This is a reformatted version of Beckley et al.'s Integrated Multi-Mission Ocean Altimeter Data for Climate Research complete time series Version 5.1 dataset, available from <a href="https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_ALL_V51">https://podaac.jpl.nasa.gov/dataset/MERGED_TP_J1_OSTM_OST_ALL_V51</a>.   The changes are as follows. Altimeter passes are sorted according to their initial longitude, then split into descending and ascending potions with all descending tracks preceding all ascending tracks. Descending tracks are then flipped so that latitude increases in the alongtrack direction for all tracks. This leads to a 3373 x 254 matrix of observational locations, with the first dimension being the along-track location and the second dimension being the track index. Sea surface height anomaly, time, and flag values are then placed into their correct locations within this matrix, such that these three variables are all of size 3373 x 254 x K where K is the number of cycles, currently 1087. A very good approximation to the time at each of the 3373 x 254 x K observation points is constructed with a length K array of cycles times together with a 3373 x 254 array of time offsets. A median-based editing criterion in introduced to identify a small number of suspect data points.  These are set to a value of NaN in sla, but their positions and values are recorded in rejected_index and rejected_values, respectively.  The DTU15 mean dynamic topography (mdt) is included, in addition to the mean sea surface field already provided, interpolated onto the track locations using bicubic interpolation.  Finally, an estimate of the small-scale noise level, sigma, is produced using a wavelet transform filter.

opencc-by-4.0Nov 2023View details →

zenodo52/100

ICARIA: climate projections from statistical downscaling outputs

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table>   The results shared here are developed over each of the observational locations that were retrieved to run the statistical downscaling. Both the observational datasets and the future climate change projections can be found here in a TXT format for each of the locations where they were developed. Observations include the main variables retrieved after a quality and homogeneity control, and climate projections together with extreme indicators include each of the 10 models, the 4 Tier 1 SSPs and data until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

opencc-by-4.0Apr 2024View details →

zenodo52/100

Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks"  https://doi.org/10.5194/nhess-2021-181 This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE For questions about the data ask: tim.leijnse@deltares.nl For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a>

opencc-by-4.0Apr 2022View details →

zenodo52/100

Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)

These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are: <ul> <li>Temperatures: Gilfordetal2024_BAMS-EEE_T2022.nc</li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): Gilfordetal2024_BAMS-EEE_ChIP2022.nc</li> </ul> And an accompanying land-sea mask from ERA5 (Gilfordetal2024_BAMS-EEE_LandSeaMask.nc). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis. For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>). Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.

opengpl-3.0-or-laterJul 2024View details →

zenodo52/100

Survey data on climate policy in three countries (Peru, Ghana, Philippines) within the project "Sustainable Middle Classes in Middle Income Countries: Transforming Carbon Consumption Patterns (SMMICC)"

The unprecedented growth of the new middle classes in middle income developing countries implies a strong growth in both consumption and carbon emissions. The research project Sustainable Middle Classes in Middle Income Countries (SMMICC) investigates the drivers of carbon consumption choices of the new middle classes and policy options to decrease their carbon footprints, including the implementation of carbon taxes The research of the authors generated quantitative data on the acceptability of carbon taxes in three countries (Peru, Ghana, Philippines).   The data is provided in the following formats: - 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_peru.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.dta - 2024-07-26_malerba_10.5281/zenodo.12662722_peru.dta - 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.dta Additionally, the codebooks on variables of questionnaire and political parties in each country are attached in a csv format.

opencc-by-4.0Jul 2024View details →

zenodo52/100

ICARIA: spatially distributed climate projections from statistical downscaling

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes. For further details, find here a brief of the methodology followed:          -----                                      The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required. For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach. For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”. For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs. This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future. ----- The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI). <div> <div> <table> <tbody> <tr> <td> CMIP6 MODELS </td> <td> Resolution </td> <td> Responsible Centre </td> <td> References </td> </tr> <tr> <td> ACCESS-CM2 </td> <td> 1,875º x 1,250º </td> <td> Australian Community Climate and Earth System Simulator (ACCESS), Australia </td> <td> Bi, D. et al (2020) </td> </tr> <tr> <td> BCC-CSM2-MR </td> <td> 1,125º x 1,121º </td> <td> Beijing Climate Center (BCC), China Meteorological Administration, China. </td> <td> Wu T. et al. (2019) </td> </tr> <tr> <td> CanESM5 </td> <td> 2,812º x 2,790º </td> <td> Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </td> <td> Swart, N.C. et al. (2019) </td> </tr> <tr> <td> CMCC-ESM2 </td> <td> 1,000º x 1,000º </td> <td> Centro Mediterraneo sui Cambiamenti Climatici (CMCC). </td> <td> Cherchi et al, 2018 </td> </tr> <tr> <td> CNRM-ESM2-1 </td> <td> 1,406º x 1,401º </td> <td> CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </td> <td> Seferian, R. (2019) </td> </tr> <tr> <td> EC-EARTH3 </td> <td> 0,703º x 0,702º </td> <td> EC-EARTH Consortium </td> <td> EC-Earth Consortium. (2019) </td> </tr> <tr> <td> MPI-ESM1-2-HR </td> <td> 0,938º x 0,935º </td> <td> Max-Planck Institute for Meteorology (MPI-M), Germany. </td> <td> Müller et al., (2018) </td> </tr> <tr> <td> MRI-ESM2-0 </td> <td> 1,125º x 1,121º </td> <td> Meteorological Research Institute (MRI), Japan. </td> <td> Yukimoto, S. et al. (2019) </td> </tr> <tr> <td> NorESM2-MM </td> <td> 1,250º x 0,942º </td> <td> Norwegian Climate Centre (NCC), Norway. </td> <td> Bentsen, M. et al. (2019) </td> </tr> <tr> <td> UKESM1-0-LL </td> <td> 1,875º x 1,250º </td> <td> UK Met Office, Hadley Centre, United Kingdom </td> <td> Good, P. et al. (2019) </td> </tr> </tbody> </table> The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections. Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Thermal indicators </td> </tr> <tr> <td> TX90 / TX10 </td> <td> Warm/cold days </td> <td> Zhang et al. (2011) </td> <td> TX </td> <td> nd </td> <td> 90 / 10% </td> </tr> <tr> <td> HD </td> <td> Heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> EHD </td> <td> Extreme heat day </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> > 35 °C </td> </tr> <tr> <td> TR </td> <td> Tropical nights </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> > 20 °C </td> </tr> <tr> <td> EQ </td> <td> Equatorial nights </td> <td> AEMet 2020, ICARIA </td> <td> TN </td> <td> nd </td> <td> > 25 °C </td> </tr> <tr> <td> IN </td> <td> Infernal nights </td> <td> ICARIA </td> <td> TN </td> <td> nd </td> <td> > 30 °C </td> </tr> <tr> <td> FD </td> <td> Frost days </td> <td> Zhang et al. (2011) </td> <td> TN </td> <td> nd </td> <td> < 0 °C </td> </tr> <tr> <td> Max consec </td> <td> Max spell length for above thermal indicators </td> <td> ICARIA </td> <td> - </td> <td> nd </td> <td> - </td> </tr> <tr> <td> Nº events </td> <td> Number of above thermal indicators events </td> <td> ICARIA </td> <td> - </td> <td> ne </td> <td> > 3 days </td> </tr> <tr> <td> TXm </td> <td> Mean maximum temperatures </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TNm </td> <td> Mean minimum temperatures </td> <td> ICARIA </td> <td> TN </td> <td> °C </td> <td> - </td> </tr> <tr> <td> TM </td> <td> Mean temperatures </td> <td> ICARIA </td> <td> TA </td> <td> °C </td> <td> - </td> </tr> <tr> <td> HWle </td> <td> Heatwave length </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWim/HWix </td> <td> Mean and maximum heatwave intensity </td> <td> ICARIA </td> <td> TX </td> <td> °C </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWf </td> <td> Heatwave frequency </td> <td> ICARIA </td> <td> TX </td> <td> ne </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HWd </td> <td> Heatwave days </td> <td> ICARIA </td> <td> TX </td> <td> nd </td> <td> 3d > 95% TX </td> </tr> <tr> <td> HI - P90 </td> <td> Heat Index (percentile 90) </td> <td> NWS (1994) </td> <td> TX, RH </td> <td> °C </td> <td> TX>27 °C, HR> 40% </td> </tr> <tr> <td> UTCI </td> <td> Universal Thermal Climate Index </td> <td> Bröde et al. (2012) </td> <td> TA RH, W </td> <td> - </td> <td> - </td> </tr> <tr> <td> UHI </td> <td> Isla de calor (BCN) anual y estacional </td> <td> AMB, Metrobs 2015 </td> <td> T </td> <td> °C </td> <td> TM1-TM2 > 0 °C </td> </tr> <tr> <td> Precipitation indicators </td> </tr> <tr> <td> R20 </td> <td> Number of heavy precipitation days </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> >20 mm </td> </tr> <tr> <td> R50, R100 </td> <td> Days with extreme heavy rain </td> <td> AMB et al. (2017) </td> <td> P </td> <td> nd </td> <td> >50mm >100mm </td> </tr> <tr> <td> Ra </td> <td> Yearly and seasonal rainfall relative change </td> <td> ICARIA </td> <td> P </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> IDF - CCF </td> <td> IDF Curves - Climate Change Factor </td> <td> Arnbjerg-Nielsen (2012) </td> <td> P </td> <td> - </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Forest fire indicators </td> </tr> <tr> <td> Mean FWI </td> <td> Mean Canadian FWI in fire season </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> . </td> <td> June- September </td> </tr> <tr> <td> Very High FWI </td> <td> Very High Canadian FWI </td> <td> Stock, B.J. et al. (1989) </td> <td> RHn, TX, P, W </td> <td> nd </td> <td> FWI > 38 </td> </tr> </tbody> </table> </div>   Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events. <div> <table> <tbody> <tr> <td> Index/name </td> <td> Short description </td> <td> Source </td> <td> Variable </td> <td> Units </td> <td> Threshold </td> </tr> <tr> <td> Drought indicators </td> </tr> <tr> <td> CDDx </td> <td> Maximum dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> CDDm </td> <td> Mean dry spell duration </td> <td> Zhang et al. (2011) </td> <td> P </td> <td> nd </td> <td> < 1 mm </td> </tr> <tr> <td> SPI </td> <td> SPI  of 1, 3, 6, 12, 24 & 36 months </td> <td> McKee et al. (1993)  </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> SPEI </td> <td> SPEI  of 1, 3, 6, 12, 24 & 36 months </td> <td> Vicente-Serrano et al.  (2010) </td> <td> P, TA </td> <td> mm </td> <td> ≥ 0.1mm </td> </tr> <tr> <td> Oceanic indicators </td> </tr> <tr> <td> SS </td> <td> Storm surge </td> <td> Bryant et al. (2016) </td> <td> MT </td> <td> cm </td> <td> - </td> </tr> <tr> <td> OW </td> <td> Significant/maximum wave height </td> <td> ICARIA </td> <td> WH </td> <td> m </td> <td> - </td> </tr> <tr> <td> Wind indicators </td> </tr> <tr> <td> EWG </td> <td> Extreme wind gusts </td> <td> ICARIA </td> <td> W </td> <td> km/h </td> <td> - </td> </tr> </tbody> </table> </div>   </div> </div>

opencc-by-4.0Jul 2024View details →

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Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark

This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_X_(T)", where X denotes the case label and T refers to the temperature value. Each file "Case_X_(T).zip" contains two folders "Case_X_(T)_1" and "Case_X_(T)_2", wherein the test results from the two sensor layouts are stored.

opencc-by-4.0May 2019View details →

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Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent

This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>. The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology. For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated: bioclim01 = Annual Mean Temperature bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) bioclim03 = Isothermality (BIO2/BIO7) (×100) bioclim04 = Temperature Seasonality (standard deviation ×100) bioclim05 = Max Temperature of Warmest Month bioclim06 = Min Temperature of Coldest Month bioclim07 = Temperature Annual Range (BIO5-BIO6) bioclim08 = Mean Temperature of Wettest Quarter bioclim09 = Mean Temperature of Driest Quarter bioclim10 = Mean Temperature of Warmest Quarter bioclim11 = Mean Temperature of Coldest Quarter bioclim12 = Annual Precipitation bioclim13 = Precipitation of Wettest Month bioclim14 = Precipitation of Driest Month bioclim15 = Precipitation Seasonality (Coefficient of Variation) bioclim16 = Precipitation of Wettest Quarter bioclim17 = Precipitation of Driest Quarter bioclim18 = Precipitation of Warmest Quarter bioclim19 = Precipitation of Coldest Quarter huss = Average (arithmetric mean) specific humidity rsds = Average (arithmetric mean) Surface downwelling shortwave radiation sfcwind = Average near-surface wind speed (arithmetric mean) --- Data properties: <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse.

opencc-by-4.0Aug 2024View details →

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German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid

<h1>Overview</h1> These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model. The base rasters from DWD are: <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model. Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus. The following image illustrates the generation of the refined rasters on a small example window:

opencc-by-4.0Nov 2023View details →

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A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.

Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from  GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090.  At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were  based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.

opencc-by-4.0Nov 2024View details →

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CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.

Climate change impact assessments require local-scale climate scenarios. The climate change projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of realisations to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A dataset of future climate change scenarios was therefore generated at 26 representative sites across the UK based on the latest CMIP6 multi-model ensemble downscaled to local-scale by using a stochastic weather generator LARS-WG 7.0. The data set provides 1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (Shared Socioeconomic Pathways - SSPs viz. SSP2-4.5 and SSP5-8.5). A total of 15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, viz.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs. The 1000 years of realisations of daily weather for the baseline as well as future climate change scenarios are helpful for estimating seasonality and inter-annual variation, and for detecting short, low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset can be used as an input to climate change impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.

opencc-by-4.0Jan 2024View details →

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Macroeconomic assessment of Climate Change Impacts

Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts

opencc-by-4.0Oct 2021View details →

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Rainfall data from WRF simulations for the Atacama Desert for present and mid-Pliocene climate

We provide model output for rainfall from WRF experiments for the present-day and mid-Pliocene climate. These are netCDF files that contain processed data shown in figures of Reyers et al. (accepted). Details on the files and content are listed in the primary data information Reyers_et_al_primary_data_information.pdf Refer to Reyers et al. (2022) for the full information on the data production and interpretation. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1198. The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 268236062 – SFB1211 "Earth-evolution at the dry limit" (https://sfb1211.uni-koeln.de/). Reference Reyers, M., Fiedler, S., Ludwig, P., Böhm, C., Wennrich, V., and Shao, Y.: On the importance of moisture conveyor belts from the tropical East Pacific for wetter conditions in the Atacama Desert during the Mid-Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-72, 2022, accepted.

opencc-by-4.0Feb 2023View details →

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Supplemental Information to Climate-driven habitat shifts of high-ranked prey species structure Late Upper Paleolithic hunting

The data provided here are the supplemental information accompanying Yaworsky et al, 2023 in the journal Scientific Reports. These data represent the following, which are referenced in the published work at DOI: 10.1038/s41598-023-31085-x. Below is the legend for the Supplementary Information, including how it is referenced within the text of the publication, the file name, and a brief description. More thorough descriptions of the data can be found within the publication in Scientific Reports. Supplementary 1 – UpperPaleoDietV4.html – HTML document of the analyses performed and presented in the paper. This is a Markdown document compiled in R with R code chunks and descriptions. Supplementary 2 – Support Information 2.docx – Word document containing supplementary tables 2 and 3. Supplementary 3 – ArchaeoloigcalDataset_v8.csv – Archaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 4 – EuroUpperPaleoFaunas_v6.csv – Zooarchaeological data referenced in the Material and Methods. These data are necessary for running the code presented in SI 1. Supplementary 5 – Lupo2016.csv – Data of Arficant fauna weight derived from table in Lupo and Schmitt 2016 (Table 2). These data are necessary for running the code presented in SI 1. Supplementary 6 – PushkinaRaia_FaunaWeights.csv – Data of Pleistocene fauna weights derived from table in Pushkina and Raia 2008 (Table 1). These data are necessary for running the code presented in SI 1. Supplementary 7 – environmental_BG.csv – Data representing background environmental conditions derived from the CHELSA TRaCE21k data. These data are necessary for running the code in SI 1. For more information on the data, methods, and results, please see the main paper.

opencc-by-4.0Feb 2023View details →

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Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic

This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).

opencc-by-4.0May 2023View details →

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Climate change velocity metrics calculated for three climate variables across Finland

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

opencc-by-4.0Oct 2023View details →

edi52/100

Daily rainfall series and rainfall erosivity in Mexico for three climatic normals (1968-1997, 1978-2007, and 1988-2017)

As in many countries around the world, there are some issues in the Mexican rainfall series, such as missing values, short measurement periods, and series homogeneity (breaks due to station relocation and measurement mistakes), which further compound the challenge of using climate data. Furthermore, it is necessary to develop a complete and helpful rainfall series database by following an imputing and homogenization process of the rainfall series. This research has compiled and systematized a national dataset with daily rainfall and rainfall erosivity for three climatic normals CN (1968-1997, 1978-2007, and 1988-2017). We have used the "climatol" package to fill the data. After, we calculated daily rainfall erosivity using a power law model. As a result, we obtained 1370, 1679, and 1683 rainfall series for the CNs 1968-1997, 1978-2007, and 1988-2017, respectively. The median values of the rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm/ ha h yr, respectively. We are making this database available for public consultation for researchers and students, technical assistants, decision-makers, and others interested in environmental studies in Mexico.

openCC (other)May 2025View details →

edi52/100

Population persistence, phenotypic divergence and metabolic adaptation in yarrow (Achillea millefolium L.) along a climate gradient, CA, 1920 to 2023

This dataset provides insights into the persistence and adaptation of yarrow (Achillea millefolium L.) populations over a 100-year period of climate change. The data include plant height measurements and climatic variables (temperature and precipitation) from historical and resurveyed sites spanning a broad environmental gradient (1–3,200 m a.s.l.), alongside metabolic profiles obtained from a common-garden experiment. The dataset captures phenotypic changes in plant growth, metabolic diversity, and site-specific climatic shifts between 1920 and 2020. These data support analyses of how temperature and precipitation interact to shape plant responses over time and allow for exploring patterns of local adaptation in phenotypic and metabolic traits. This comprehensive dataset is valuable for understanding the ecological and evolutionary mechanisms underlying population persistence and can inform conservation strategies under future climate scenarios.

openCC (other)Dec 2024View details →

edi52/100

Ecosystem responses to changes in climate and carbon dioxide in twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics

We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. This dataset consists of the MEL model Windows executable, the driver and parameter file for each site, and the output files for each of the six simulations listed above.

openCC (other)Mar 2022View details →

edi52/100

Climate data from the former Arctic LTER Toolik Inlet Wet Sedge Flux site, Toolik Field Station, Alaska 2017 to 2025.

One-minute averages of air temperature and relative humidity, soil temperature and moisture, and solar radiation from 2017 to 2025 for the former Arctic LTER Toolik wet sedge eddy covariance flux site. This site was originally set up for seasonal eddy covariance flux measurements during the summers of 2010–2015 and partially during winters starting in 2014, until 15 June 2016. Flux data are reported in Eugster et al.(2020)(doi:10.5194/amt-2019-402). IMPORTANT NOTE: The data has only been checked for obvious outliers. Use with caution since most of the data has not been carefully checked. It was decided to release this version instead of waiting until there were resources for a complete check.

openCC (other)Jan 2026View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

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

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

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

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

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

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

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

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record