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Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
ICARIA: climate projections from statistical downscaling outputs
<p><strong>ICARIA </strong>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.</p> <p>For further details, find here a brief of the <strong>methodology </strong>followed:<strong> </strong></p> <p><strong>----- </strong></p> <p><em>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.</em></p> <p><em>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.</em></p> <p><em>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”.</em></p> <p><em>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.</em></p> <p><em>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.</em></p> <p>-----</p> <p>The dataset shared here includes information for the three case studies tackled in ICARIA: <strong>Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR)</strong>. 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:</p> <p><strong>Table 1</strong>.<em> 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).</em></p> <div> <div> <table> <tbody> <tr> <td> <p><strong>CMIP6 MODELS</strong></p> </td> <td> <p><strong>Resolution</strong></p> </td> <td> <p><strong>Responsible Centre</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia</p> </td> <td> <p>Bi, D. et al (2020)</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China.</p> </td> <td> <p>Wu T. et al. (2019)</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>2,812º x 2,790º</p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá.</p> </td> <td> <p>Swart, N.C. et al. (2019)</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>1,000º x 1,000º</p> </td> <td> <p>Centro Mediterraneo sui Cambiamenti Climatici (CMCC).</p> </td> <td> <p>Cherchi et al, 2018</p> </td> </tr> <tr> <td> <p>CNRM-ESM2-1</p> </td> <td> <p>1,406º x 1,401º</p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia.</p> </td> <td> <p>Seferian, R. (2019)</p> </td> </tr> <tr> <td> <p>EC-EARTH3</p> </td> <td> <p>0,703º x 0,702º</p> </td> <td> <p>EC-EARTH Consortium</p> </td> <td> <p>EC-Earth Consortium. (2019)</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>0,938º x 0,935º</p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany.</p> </td> <td> <p>Müller et al., (2018)</p> </td> </tr> <tr> <td> <p>MRI-ESM2-0</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Meteorological Research Institute (MRI), Japan.</p> </td> <td> <p>Yukimoto, S. et al. (2019)</p> </td> </tr> <tr> <td> <p>NorESM2-MM</p> </td> <td> <p>1,250º x 0,942º</p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway.</p> </td> <td> <p>Bentsen, M. et al. (2019)</p> </td> </tr> <tr> <td> <p>UKESM1-0-LL</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>UK Met Office, Hadley Centre, United Kingdom</p> </td> <td> <p>Good, P. et al. (2019)</p> </td> </tr> </tbody> </table> <p> </p> <p>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.</p> <strong>Table 2.</strong> <em>Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Thermal indicators</strong></p> </td> </tr> <tr> <td> <p>TX90 / TX10</p> </td> <td> <p>Warm/cold days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>90 / 10%</p> </td> </tr> <tr> <td> <p>HD</p> </td> <td> <p>Heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>EHD</p> </td> <td> <p>Extreme heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 35 °C</p> </td> </tr> <tr> <td> <p>TR</p> </td> <td> <p>Tropical nights</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 20 °C</p> </td> </tr> <tr> <td> <p>EQ</p> </td> <td> <p>Equatorial nights</p> </td> <td> <p>AEMet 2020, ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 25 °C</p> </td> </tr> <tr> <td> <p>IN</p> </td> <td> <p>Infernal nights</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>FD</p> </td> <td> <p>Frost days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>< 0 °C</p> </td> </tr> <tr> <td> <p>Max consec</p> </td> <td> <p>Max spell length for above thermal indicators</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>nd</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Nº events</p> </td> <td> <p>Number of above thermal indicators events</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>ne</p> </td> <td> <p>> 3 days</p> </td> </tr> <tr> <td> <p>TXm</p> </td> <td> <p>Mean maximum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TNm</p> </td> <td> <p>Mean minimum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TM</p> </td> <td> <p>Mean temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TA</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>HWle</p> </td> <td> <p>Heatwave length</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWim/HWix</p> </td> <td> <p>Mean and maximum heatwave intensity</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWf</p> </td> <td> <p>Heatwave frequency</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>ne</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWd</p> </td> <td> <p>Heatwave days</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HI - P90</p> </td> <td> <p>Heat Index (percentile 90)</p> </td> <td> <p>NWS (1994)</p> </td> <td> <p>TX, RH</p> </td> <td> <p>°C</p> </td> <td> <p>TX>27 °C, HR> 40%</p> </td> </tr> <tr> <td> <p>UTCI</p> </td> <td> <p>Universal Thermal Climate Index</p> </td> <td> <p>Bröde et al. (2012)</p> </td> <td> <p>TA<br>RH, W</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>UHI</p> </td> <td> <p>Isla de calor (BCN) anual y estacional</p> </td> <td> <p>AMB, Metrobs 2015</p> </td> <td> <p>T</p> </td> <td> <p>°C</p> </td> <td> <p>TM1-TM2 > 0 °C</p> </td> </tr> <tr> <td> <p><strong>Precipitation indicators</strong></p> </td> </tr> <tr> <td> <p>R20</p> </td> <td> <p>Number of heavy precipitation days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>20 mm</p> </td> </tr> <tr> <td> <p>R50, R100</p> </td> <td> <p>Days with extreme heavy rain</p> </td> <td> <p>AMB et al. (2017)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>50mm</p> <p>>100mm</p> </td> </tr> <tr> <td> <p>Ra</p> </td> <td> <p>Yearly and seasonal rainfall relative change</p> </td> <td> <p>ICARIA</p> </td> <td> <p>P</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>IDF - CCF</p> </td> <td> <p>IDF Curves - Climate Change Factor</p> </td> <td> <p>Arnbjerg-Nielsen (2012)</p> </td> <td> <p>P</p> </td> <td> <p>-</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Forest fire indicators</strong></p> </td> </tr> <tr> <td> <p>Mean FWI</p> </td> <td> <p>Mean Canadian FWI in fire season</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>.</p> </td> <td> <p>June-<br>September</p> </td> </tr> <tr> <td> <p>Very High FWI</p> </td> <td> <p>Very High Canadian FWI</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>nd</p> </td> <td> <p>FWI > 38</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>Table 3</strong>. <em>Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em></p> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Drought indicators</strong></p> </td> </tr> <tr> <td> <p>CDDx</p> </td> <td> <p>Maximum dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>CDDm</p> </td> <td> <p>Mean dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>SPI</p> </td> <td> <p>SPI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>McKee et al. (1993) </p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>SPEI</p> </td> <td> <p>SPEI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>Vicente-Serrano et al. (2010)</p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Oceanic indicators</strong></p> </td> </tr> <tr> <td> <p>SS</p> </td> <td> <p>Storm surge</p> </td> <td> <p>Bryant et al. (2016)</p> </td> <td> <p>MT</p> </td> <td> <p>cm</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>OW</p> </td> <td> <p>Significant/maximum wave height</p> </td> <td> <p>ICARIA</p> </td> <td> <p>WH</p> </td> <td> <p>m</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Wind indicators</p> </td> </tr> <tr> <td> <p>EWG</p> </td> <td> <p>Extreme wind gusts</p> </td> <td> <p>ICARIA</p> </td> <td> <p>W</p> </td> <td> <p>km/h</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> </div> <p> </p> </div> </div>
ICARIA: spatially distributed climate projections from statistical downscaling
<p><strong>ICARIA </strong>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 <strong>100x100m resolution grid, </strong>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.</p> <p>For further details, find here a brief of the <strong>methodology </strong>followed:<strong> </strong></p> <p><strong>----- </strong></p> <p><em>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.</em></p> <p><em>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.</em></p> <p><em>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”.</em></p> <p><em>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.</em></p> <p><em>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.</em></p> <p>-----</p> <p>The dataset shared here includes information for the three case studies tackled in ICARIA: <strong>Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR)</strong>. 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:</p> <p><strong>Table 1</strong>.<em> 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).</em></p> <div> <div> <table> <tbody> <tr> <td> <p><strong>CMIP6 MODELS</strong></p> </td> <td> <p><strong>Resolution</strong></p> </td> <td> <p><strong>Responsible Centre</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia</p> </td> <td> <p>Bi, D. et al (2020)</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China.</p> </td> <td> <p>Wu T. et al. (2019)</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>2,812º x 2,790º</p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá.</p> </td> <td> <p>Swart, N.C. et al. (2019)</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>1,000º x 1,000º</p> </td> <td> <p>Centro Mediterraneo sui Cambiamenti Climatici (CMCC).</p> </td> <td> <p>Cherchi et al, 2018</p> </td> </tr> <tr> <td> <p>CNRM-ESM2-1</p> </td> <td> <p>1,406º x 1,401º</p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia.</p> </td> <td> <p>Seferian, R. (2019)</p> </td> </tr> <tr> <td> <p>EC-EARTH3</p> </td> <td> <p>0,703º x 0,702º</p> </td> <td> <p>EC-EARTH Consortium</p> </td> <td> <p>EC-Earth Consortium. (2019)</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>0,938º x 0,935º</p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany.</p> </td> <td> <p>Müller et al., (2018)</p> </td> </tr> <tr> <td> <p>MRI-ESM2-0</p> </td> <td> <p>1,125º x 1,121º</p> </td> <td> <p>Meteorological Research Institute (MRI), Japan.</p> </td> <td> <p>Yukimoto, S. et al. (2019)</p> </td> </tr> <tr> <td> <p>NorESM2-MM</p> </td> <td> <p>1,250º x 0,942º</p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway.</p> </td> <td> <p>Bentsen, M. et al. (2019)</p> </td> </tr> <tr> <td> <p>UKESM1-0-LL</p> </td> <td> <p>1,875º x 1,250º</p> </td> <td> <p>UK Met Office, Hadley Centre, United Kingdom</p> </td> <td> <p>Good, P. et al. (2019)</p> </td> </tr> </tbody> </table> <p>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 <strong>spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model</strong> considering diverse adjustments and topographic corrections. The results presented here are the<strong> median of the 10 models used, obtained for each of the 4 SSP</strong>s 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.</p> <strong>Table 2.</strong> <em>Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Thermal indicators</strong></p> </td> </tr> <tr> <td> <p>TX90 / TX10</p> </td> <td> <p>Warm/cold days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>90 / 10%</p> </td> </tr> <tr> <td> <p>HD</p> </td> <td> <p>Heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>EHD</p> </td> <td> <p>Extreme heat day</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>> 35 °C</p> </td> </tr> <tr> <td> <p>TR</p> </td> <td> <p>Tropical nights</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 20 °C</p> </td> </tr> <tr> <td> <p>EQ</p> </td> <td> <p>Equatorial nights</p> </td> <td> <p>AEMet 2020, ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 25 °C</p> </td> </tr> <tr> <td> <p>IN</p> </td> <td> <p>Infernal nights</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>> 30 °C</p> </td> </tr> <tr> <td> <p>FD</p> </td> <td> <p>Frost days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>TN</p> </td> <td> <p>nd</p> </td> <td> <p>< 0 °C</p> </td> </tr> <tr> <td> <p>Max consec</p> </td> <td> <p>Max spell length for above thermal indicators</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>nd</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Nº events</p> </td> <td> <p>Number of above thermal indicators events</p> </td> <td> <p>ICARIA</p> </td> <td> <p>-</p> </td> <td> <p>ne</p> </td> <td> <p>> 3 days</p> </td> </tr> <tr> <td> <p>TXm</p> </td> <td> <p>Mean maximum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TNm</p> </td> <td> <p>Mean minimum temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TN</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>TM</p> </td> <td> <p>Mean temperatures</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TA</p> </td> <td> <p>°C</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>HWle</p> </td> <td> <p>Heatwave length</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWim/HWix</p> </td> <td> <p>Mean and maximum heatwave intensity</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>°C</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWf</p> </td> <td> <p>Heatwave frequency</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>ne</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HWd</p> </td> <td> <p>Heatwave days</p> </td> <td> <p>ICARIA</p> </td> <td> <p>TX</p> </td> <td> <p>nd</p> </td> <td> <p>3d > 95% TX</p> </td> </tr> <tr> <td> <p>HI - P90</p> </td> <td> <p>Heat Index (percentile 90)</p> </td> <td> <p>NWS (1994)</p> </td> <td> <p>TX, RH</p> </td> <td> <p>°C</p> </td> <td> <p>TX>27 °C, HR> 40%</p> </td> </tr> <tr> <td> <p>UTCI</p> </td> <td> <p>Universal Thermal Climate Index</p> </td> <td> <p>Bröde et al. (2012)</p> </td> <td> <p>TA<br>RH, W</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>UHI</p> </td> <td> <p>Isla de calor (BCN) anual y estacional</p> </td> <td> <p>AMB, Metrobs 2015</p> </td> <td> <p>T</p> </td> <td> <p>°C</p> </td> <td> <p>TM1-TM2 > 0 °C</p> </td> </tr> <tr> <td> <p><strong>Precipitation indicators</strong></p> </td> </tr> <tr> <td> <p>R20</p> </td> <td> <p>Number of heavy precipitation days</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>20 mm</p> </td> </tr> <tr> <td> <p>R50, R100</p> </td> <td> <p>Days with extreme heavy rain</p> </td> <td> <p>AMB et al. (2017)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>>50mm</p> <p>>100mm</p> </td> </tr> <tr> <td> <p>Ra</p> </td> <td> <p>Yearly and seasonal rainfall relative change</p> </td> <td> <p>ICARIA</p> </td> <td> <p>P</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>IDF - CCF</p> </td> <td> <p>IDF Curves - Climate Change Factor</p> </td> <td> <p>Arnbjerg-Nielsen (2012)</p> </td> <td> <p>P</p> </td> <td> <p>-</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Forest fire indicators</strong></p> </td> </tr> <tr> <td> <p>Mean FWI</p> </td> <td> <p>Mean Canadian FWI in fire season</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>.</p> </td> <td> <p>June-<br>September</p> </td> </tr> <tr> <td> <p>Very High FWI</p> </td> <td> <p>Very High Canadian FWI</p> </td> <td> <p>Stock, B.J. et al. (1989)</p> </td> <td> <p>RHn, TX, P, W</p> </td> <td> <p>nd</p> </td> <td> <p>FWI > 38</p> </td> </tr> </tbody> </table> </div> <p> </p> <p><strong>Table 3</strong>. <em>Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.</em></p> <div> <table> <tbody> <tr> <td> <p><strong>Index/name</strong></p> </td> <td> <p><strong>Short description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Variable</strong></p> </td> <td> <p><strong>Units</strong></p> </td> <td> <p><strong>Threshold</strong></p> </td> </tr> <tr> <td> <p><strong>Drought indicators</strong></p> </td> </tr> <tr> <td> <p>CDDx</p> </td> <td> <p>Maximum dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>CDDm</p> </td> <td> <p>Mean dry spell duration</p> </td> <td> <p>Zhang et al. (2011)</p> </td> <td> <p>P</p> </td> <td> <p>nd</p> </td> <td> <p>< 1 mm</p> </td> </tr> <tr> <td> <p>SPI</p> </td> <td> <p>SPI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>McKee et al. (1993) </p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p>SPEI</p> </td> <td> <p>SPEI </p> <p>of 1, 3, 6, 12, 24 & 36 months</p> </td> <td> <p>Vicente-Serrano et al. (2010)</p> </td> <td> <p>P, TA</p> </td> <td> <p>mm</p> </td> <td> <p>≥ 0.1mm</p> </td> </tr> <tr> <td> <p><strong>Oceanic indicators</strong></p> </td> </tr> <tr> <td> <p>SS</p> </td> <td> <p>Storm surge</p> </td> <td> <p>Bryant et al. (2016)</p> </td> <td> <p>MT</p> </td> <td> <p>cm</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>OW</p> </td> <td> <p>Significant/maximum wave height</p> </td> <td> <p>ICARIA</p> </td> <td> <p>WH</p> </td> <td> <p>m</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Wind indicators</p> </td> </tr> <tr> <td> <p>EWG</p> </td> <td> <p>Extreme wind gusts</p> </td> <td> <p>ICARIA</p> </td> <td> <p>W</p> </td> <td> <p>km/h</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> </div> <p> </p> </div> </div>
Downscaled 8km March Snow Water Equivalent Estimates for the Western US, 1901-2010
<p>Downscaled estimates of March mean snow water equivalent at approximately 8km resolution across the western United States for the years 1901-2010. Data downscaled from the CERA-20c reanalysis using UA-SWE daily observations. Downscaled data using both the CERA-20c ensemble mean as well as each individual ensemble member as predictors are included. Units are in millimeters of snow water equivalent.</p>
Climate Solutions Explorer - downscaled country-level IAM scenarios
<p><strong>This is a pre-release dataset and is subject to change.</strong></p> <p>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <strong><a href="https://www.climate-solutions-explorer.eu">www.climate-solutions-explorer.eu</a></strong></p> <p>The Mitigation (and Summary) Dashboards present mitigation information, i.e. emissions, energy and carbon sequestration, for over 200 countries and 10 regions. To present data for all countries, Integrated Assessment Model runs from the MESSAGEix-GLOBIOM model have been downscaled by using a methodology described in Sferra et al. 2021 <a href="#_ftn1">[1]</a>. The algorithm produces a range of pathways consistent with the underlying IAM-results, based on criteria such as historical data, planned capacities, country-available resource in the form of supply cost-curves, quality of governance as well as regional benchmarks based on IAM results. The data is provided from 2020 to 2070, for a limited set of variables used on the website.</p> <p>The scenarios included are:</p> <ul> <li><strong>Current Policies:</strong> Current Policies scenarios here are based on the implementation of national mitigation targets implemented by country without any further strengthening of action. Expected to lead to 2.7 °C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_CurPol_T45 scenario.</li> <li><strong>NDCs Delayed Action to 2030:</strong> Assumes trajectory based on the implemented NDCs until 2030, and then reduces emissions typically in line with a globally 2°C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_NDC2030_T45 scenario.</li> <li><strong>Glasgow Pledges:</strong> "Glasgow Pledges" scenarios here are based on the pledges made by countries at the 2022 COP26 Glasgow Summit, and represent increased ambition, likely taking the world closer to below 2°C in 2100, but still some distance away from the aspirations of 1.5°C of the Paris Agreement. The data is from the MESSAGEix-GLOBIOM_1.1 GP_Glasgow scenario.</li> <li><strong>Glasgow Pledges+:</strong> "Glasgow Pledges+" scenarios drops the NDC pledges and expands mid-century strategy pledges to net-zero for all countries and regions. The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowP scenario.</li> <li><strong>Glasgow Pledges++:</strong> "Glasgow Pledges++" scenarios here aims at filling the gap between national mid-century strategies and the 1.5/2 °C global scenarios. This scenario builds upon the Glasgow+ scenario and anticipates the action (net-zero target year defined for each region) in 5 or 10 years (depending on the model’s time steps). The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowPP scenario.</li> </ul> <p><a href="#_ftnref1">[1]</a> Sferra, F. et al. 2021. Downscaling IAMs results to the country level – a new algorithm. IIASA Report. IIASA, Laxenburg, Austria. <a href="https://pure.iiasa.ac.at/17501">https://pure.iiasa.ac.at/17501</a>.</p> <p> </p> <p> </p> <p><strong>Release notes (v0.2)</strong></p> <p>This version brings improvements in:</p> <ul> <li>harmonization data source, now done for 2018 using PRIMAP</li> <li>calculation of Kyoto Gases for R10, and Kyoto Gases (incl. indirect AFOLU) for countries</li> <li>addition of R10 and EU27 region data</li> <li>Corrections to variable aggregation</li> </ul> <p> </p>
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014)
<p><strong>MASS2ANT Snowfall Dataset: Information for users</strong></p> <p>November 2020</p> <p> </p> <p>N. Ghilain<sup>1</sup>, S. Vannitsem<sup>1</sup>, Q. Dalaiden<sup>2</sup>, H. Goosse<sup>2</sup>, L. De Cruz<sup>1</sup></p> <p><sup>1</sup>Royal Meteorological Institute, Uccle, Belgium</p> <p><sup>2</sup>UCLouvain, Earth and Life Institute, Louvain-la-Neuve, Belgium</p> <p> </p> <p>contacts RMI: <a href="mailto:stephane.vannitsem@meteo.be">stephane.vannitsem@meteo.be</a>, <a href="mailto:nicolas.ghilain@meteo.be">nicolas.ghilain@meteo.be</a>, <a href="mailto:lesley.decruz@meteo.be">lesley.decruz@meteo.be</a></p> <p>contacts UCLouvain: <a href="mailto:hugues.goosse@uclouvain.be">hugues.goosse@uclouvain.be</a>, <a href="mailto:quentin.dalaiden@uclouvain.be">quentin.dalaiden@uclouvain.be</a></p> <p> </p> <p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em>, submitted to Earth System Science Data (ESSD).</p> <p> </p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain (2 files are given in example, the all set is available on zenodo: 10.5281/zenodo.6355455, 10.5281/zenodo.6359385, 10.5281/zenodo.6362299), the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs</p> <p> </p> <p>The method could be easily expanded to other sources (other GCM, other reference RCM or other reanalysis), and potentially for other parts of Antarctica. Samples of the programs used to generate this database are therefore also provided here.</p> <p>Refer to <a href="https://zenodo.org/api/files/d8e358fa-f3d7-457e-949b-94f323ff04f9/MASS2ANT_Snowfall_Dataset_InfoUsers.pdf">MASS2ANT_Snowfall_Dataset_InfoUsers.pdf</a></p> <p> </p>
Evaluation of dynamically downscaled CMIP6-CCAM models over Australia
<p>Downscaled CCAM-CMIP6 model data used in the evaluation of CCAM-CMIP6 models against AGCD observations:</p><ol><li>Data required for daily evaluation of precipitation and temperature variables, and calculation of Perkins skill score</li><li>Data required for evaluation of bias for precipitation and temperature variables</li><li>Data required for KGE skill score</li></ol>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast
<p>The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay
<p>The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p> <p> </p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
Intensified atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial from a dynamical downscaling perspective
<p>We provide the datasets run for investigating the atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial (127 ka), based on the mesoscale Weather Research and Forecasting (WRF) model driven by the Community Earth System Model (CESM). We upload summer mean of the model outputs from the WRF over the Tibetan Plateau used in estimating the atmospheric branch of the hydrological cycle.</p>
GFDL hurricane model track data associated with "Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity"
<p>These data include North Atlantic tropical cyclone track and intensity for control and projected late 21st century simulation from the GFDL hurricane model used in a <em>Climatic</em> <em>Change</em> manuscript: </p> <p>Knutson, T., J. Sirutis, M. Bender, R. Tuleya, and B. Schenkel, 2022: Dynamical downscaling projections of late 21st century U.S. landfalling hurricane activity. <em>Clim. Change</em>, <strong>171</strong>, 1–23.<br> <br> A readme file included below describes the variables and format of the tropical cyclone track data. Questions about the dataset may be directed to Ben Schenkel (<a href="mailto:benschenkel@gmail.com">benschenkel@gmail.com</a>) and Tom Knutson (<a href="mailto:tom.knutson@noaa.gov">tom.knutson@noaa.gov</a>). </p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Bottom water acidification and warming on the western Eurasian Arctic shelves: Dynamical downscaling projections. Data archive.
<p>This archive includes one .mat file (MATLAB format) containing all the data and interpolated SINMOD model used for skill assessment and bias correction, and several NetCDF files containing the SINMOD SRES A1B projections (bias corrected where possible) for the bottom water in the pan-Arctic model domain for years 2001-2099 inclusive. Temporal resolution is biweekly and spatial resolution is 20km (see grid info in NetCDF files).</p>
Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)
<p><strong>Gridded historical climate </strong><strong>data over China, spanning 1851 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by 20th century reanalysis (20CRv2c, NOAA/ESRL PSD 20th Century Reanalysis version 2c, ensemble member 37).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled ERA-Interim data set: <a href="https://zenodo.org/record/2600192#.XJj3uKD7RWE">Downscaled ERA-Interim gridded historical climate data over China (1980-2010)</a> doi: 10.5281/zenodo.2600192</em></p>
Downscaled 1 km RACMO2 data used in CESM2 Greenland SMB evaluation paper
<p>Post-processed RACMO2.3p2 data used to carry out part of the analysis in the paper titled “Present day Greenland ice sheet climate and surface mass balance in CESM2”, in review with JGR Earth Surface.</p> <p>This data stems from a RACMO2.3p2 regional climate simulation over Greenland at 11 km which was averaged over the period 1961-1990 and statistically downscaled to 1 km. The original resolution of this dataset is 11 km and further described in Noël et al., 2018, <a href="https://doi.org/10.5194/tc-12-811-2018">https://doi.org/10.5194/tc-12-811-2018</a></p> <p>This data is published for archiving purposes only. Data requests for the latest version of RACMO2 output can be made free of charge to Brice Noël (B.P.Y.Noel@uu.nl) and Michiel van den Broeke (M.R.vandenBroeke@uu.nl). In your request, please specify the variables of interest, time period and time frequency, and area of interest.</p> <p> </p>
CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.
<h1>CRCM-CMIP</h1> <h2>Data reference</h2> <p>Paquin, D., C. McCray, C. B. Gauthier, M. Giguère, O. Asselin, P .Bourgault, M.-P. Labonté and D. Matte. The CRCM5-CMIP6 Ouranos’ ensemble : A dynamically-downscaled ensemble of CMIP6 simulations over North America. Accepted in Scientific Data.</p> <p><a href="https://www.ouranos.ca/en">Ouranos</a> : Canadian Regional Climate Model – version 5</p> <p><strong>Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>Based on GEM 3.3.3.1</p> <h3>Configuration</h3> <p>NAM-11 CORDEX North American domain at 0.11° 695x668 grid points including a 20-point sponge (and halo) zone surrounding the domain, 5-minute time steps, xlat1=28.525 xlon2=145.955. 56 vertical levels and a top at 10 hPa. 17 surface levels and a bottom at 15 m.</p> <h3>Spectral Nudging</h3> <p>A spectral nudging is applied to the horizontal wind component with a half-response wavelength of 1177km and a relaxation time of 13.34 h. The nudging strength is set to zero from the surface to a height of 500 hPa and increases linearly onward to the top of the model’s simulated atmosphere (10 hPa).</p> <h2>Parameterization</h2> <h3>Atmosphere</h3> <p>Precipitation: modified Sundqvist (1998); precipitation partition Bourgouin (2000) ; Implicit vertical diffusion. <br>Shallow convection: Kuo (1965) transient shallow, Non‐cloudy boundary layer formulation. <br>Deep convection: Kain-Fritsch (1990); <br>Radiation: Li & Barker (2005)</p> <h3>Surface</h3> <p>CLASS3.5c (Verseghy, 1993)</p> <p>Lake model: FLake</p> <h3>Ocean</h3> <p>Prescribed SST & sea ice fraction</p> <h3>Aerosol</h3> <p>Prescribed</p> <h2>Data Access</h2> <p>Due to its large size, the full dataset can't yet be shared publicly.</p> <p>A subset of the variables are stored on Ouranos' THREDDS server.</p> <p>- Annual files : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html</a><br>- Aggregated datasets : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html</a></p> <p>Other variables can be provided upon request by writing to simulations_ouranos@ouranos.ca.</p> <p>All data are available through a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY 4.0</a> license.</p> <h2>Acknowlegments</h2> <p>Developed by the <a href="https://escer.uqam.ca/">ESCER Centre</a> at UQAM (Université du Québec à Montréal) with the collaboration of Environment and Climate Change Canada (ECCC). <strong>CRCM5; Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>The CRCM5 data has been generated and supplied by Ouranos.</p> <p>CRCM5 computations were made on the supercomputers beluga and narval managed by Calcul Québec and the <a href="https://alliancecan.ca/en">Digital Research Alliance of Canada</a>. The operation of this supercomputer received financial support from Innovation, Science and Economic Development Canada and the Ministère de l’Économie et de l’Innovation du Québec.</p> <h2>Some references for CRCM5</h2> <p>Asselin, M. Leduc, D. Paquin, K. Winger, A. Di Luca, M. Bukovsky, B. Music, and M. Giguère (2022). On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation. MDPI's Climate <br><a href="https://doi.org/10.3390/cli10100138">https://doi.org/10.3390/cli10100138</a> </p> <p>Bresson, E., R. Laprise, D. Paquin, J. M. Thériault, R. de Elia, 2017: Evaluating CRCM5 ability to simulate mixed precipitation. Atmosphere-Ocean. 55(2); 79-93. <a href="http://dx.doi.org/10.1080/07055900.2017.1310084">http://dx.doi.org/10.1080/07055900.2017.1310084</a> </p> <p>Leduc, M., A. Mailhot, A. Frigon, J.-L. Martel, R. Ludwig, G.B. Brietzke, M. Giguère, F. Brissette, R. Turcotte, M. Braun, (2019) ClimEx project: a 50-member ensemble of climate change projections at 12-km resolution over Europe and northeastern North America with the Canadian Regional Climate Model (CRCM5). Journal of Applied Meteorology and Climatology. <a href="https://doi.org/10.1175/JAMC-D-18-0021.1" target="_blank" rel="noopener">https://doi.org/10.1175/JAMC-D-18-0021.1</a></p> <p>Martynov A, R Laprise, L Sushama, K Winger, L Separovic, B Dugas. 2013. Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation. Clim Dyn 41:2973-3005. <a href="https://doi.org/10.1007/s00382-013-1778-9">https://doi.org/10.1007/s00382-013-1778-9</a></p> <p>Martynov A, L Sushama, R Laprise, K Winger, B Dugas. 2012. Interactive lakes in the Canadian regional climate model version 5: the role of lakes in the regional climate of North America. Tellus A 64, 016226. <a href="https://doi.org/10.3402/tellusa.v64i0.16226">https://doi.org/10.3402/tellusa.v64i0.16226</a>.</p> <p>Martynov A, L Sushama, R Laprise. 2010. Simulation of temperate freezing lakes by one-dimensional lake models: performance assessment for interactive coupling with regional climate models. Boreal Env Res 15:143-164.</p> <p>Matte, D., Thériault, J. M., & Laprise, R. (2019). Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model. Climate Dynamics, 53(1), 1125–1141. <a href="https://doi.org/10.1007/s00382-018-4231-2">https://doi.org/10.1007/s00382-018-4231-2</a></p> <p>McCray, C. D., D. Paquin, J. M. Thériault, É. Bresson (2022). A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations. Journal of Geophysical Research -Atmospheres <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935</a></p> <p>McCray, D. C., J. M. Thériault, D. Paquin, É. Bresson, 2022. Quantifying the impact of precipitation-type algorithm selection on the representation of freezing rain in an ensemble of regional climate model simulations. Journal of Applied Meteorology and Climatology. <a href="https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml">https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml</a> </p> <p>McCray, C.D., G. Schmidt, D. Paquin, M. Leduc, Z. Bi, M. Radiyat, C. Silverman, M. Spitz, B. Brettschneider (2023). Changing Nature of High-Impact Snowfall Events in Eastern North America. 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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.