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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>
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>
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>
Sample of high-resolution climate dataset based on ML downscaling.
<p>The ClimateByte project created a high-resolution (downscaled) climate dataset for specific regions based on the CINECA MISTRAL observational dataset. AMIGO selected one region from the larger dataset and made that portion openly available to the other members of the EUH4D project for research and non-commercial purposes. Resolution of this dataset is 300m and covers the Adige Valley of Trentino Alto-Adige (Italy)</p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</p>
Selected near-bottom and other variables from NW European shelf physics-biogeochemistry downscaled ocean climate projections, 3-member ensemble.
<p>Selected fields of physical and biogeochemical ocean variables from a 3-member ensemble of coupled physics-biogeochemistry downscaled climate runs on the North Western European Continental Shelf. All ensemble members use the NEMO-ERSEM model suite and cover the 1990-2099 period. Easch member is foced with a different set of atmospheric and oceanic boundary conditions from one of three CMIP5 ESMs that are: HADGEM2-ES, IPSL-CM5A-MR and GFDL-ESM2G. This dataset contains monthly average values saved as 2D fields either near-bottom, at the surface or depth integrated. The variables here saved are near-bottom oxygen, oxygen solubility, oxygen saturation state, temperature and bacterial respiration, surface salinity, depth integrated net primary production, and potential energy anomaly. Additionally the Western Norwegian Trench Current flux is provided (its values come smoothed with a gaussian filter). reference publication: https://doi.org/10.5194/egusphere-2023-1049. The complete set of variables is available from the authors upon request.</p>
Downscaled climate grids at 30m for a variety of bioclimatic variables over the San Joaquin Experimental Range, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Teakettle Experimental Forest, 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
Downscaled climate grids at 30m for a variety of bioclimatic variables over the Tejon Ranch, CA: 2001-2099
Statistically-downscaled grids of bioclimatic variables were produced to study how fine-scale spatio-temporal variation in climate might influence the exposure of tree species to projected climate change in southern California.
A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1> <p>This dataset accompanies the research paper titled <strong>"A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</strong>, currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>.</p> <h2>Aims</h2> <p>Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes</p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2014</li> <li>Validation Data: 1986-2005</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM</li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li> <li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:</strong></p> <ul> <li><strong>NorESM2-MM:</strong> <ul> <li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li> <li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li> </ul> </li> <li><strong>EC-Earth3:</strong> <ul> <li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li> <li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li> </ul> </li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul>
CRISI-ADAPT II: free downscaled climate projection layers
<p>CRISI-ADAPT II 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. 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.</p> <p>For further details, find here a brief of the methodology followed:</p> <p> </p> <p><strong> Methodology</strong></p> <p>Information provided by 10 models belonging to CMIP6 have been included. 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><em>Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. </em></p> <table> <tbody> <tr> <td> <p><strong>CMPI6 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><strong>BCC-CSM2-MR</strong> </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><strong>CanESM5</strong> </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><strong>CNRM-ESM2-1</strong> </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><strong>EC-EARTH3</strong> </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><strong>GFDL-ESM4</strong> </p> </td> <td> <p>1,250º x 1,000º </p> </td> <td> <p>National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. </p> </td> <td> <p>Krasting, J.P. et al. (2018) </p> </td> </tr> <tr> <td> <p><strong>MPI-ESM1-2-HR</strong> </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>Von Storch, J. et al. (2017) </p> </td> </tr> <tr> <td> <p><strong>MRI-ESM2-0</strong> </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><strong>UKESM1-0-LL</strong> </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> <tr> <td> <p><strong>NorESM2-MM</strong> </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><strong>ACCESS-ESM1-5</strong> </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>Ziehn, T. et al. (2019)</p> </td> </tr> </tbody> </table> <p>Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo <em>et al.</em> 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. </p> <p>In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: </p> <ol> <li> <p>Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. </p> </li> <li>For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. </li> <li> <p>The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. </p> </li> <li>For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. </li> <li> <p>Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. </p> </li> <li>Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. </li> <li> <p>Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013). </p> </li> </ol>
Downscaled ERA-Interim gridded historical climate data over China (1980-2010)
<p><strong>Gridded historical climate data over China, spanning 1981 to 2010. Dynamically downscaled to 25km resolution using the PRECIS2.0 (HadRM3P) Met Office regional climate model, driven by ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF).</strong></p> <p>This data has been un-rotated to true latitude longitude coordinates from its original rotate pole frame of reference. For more information on the PRECIS regional climate model, visit <a href="http://www.metoffice.gov.uk/precis">www.metoffice.gov.uk/precis</a>. Data near the boundaries should be used with caution due to model configuration aspects of regional climate modelling, and the interpolation method applied. Data created as part of the Met Office Climate Science for Service Partnership China (<a href="https://www.metoffice.gov.uk/research/collaboration/cssp-china">CSSP China</a>), work package 1 output, supported by the Newton Fund and the Department for Business, Energy & Industrial Strategy (BEIS) <a href="https://www.gov.uk/government/publications/newton-fund-building-science-and-innovation-capacity-in-developing-countries/newton-fund-building-science-and-innovation-capacity-in-developing-countries">UK-China Research Innovation Partnership Fund</a>.</p> <p><strong>Domain</strong>: 17N to 58.84N, 73E to 135.7E</p> <p><strong>Countries covered</strong>: China, Nepal, Bhutan, Bangladesh, Taiwan, Mongolia, North Korea, South Korea, Kyrgzstan, and northern parts of India, Myanmar, Lao PDR & Vietnam.</p> <p><strong>Variables</strong>: pr (mean precipitation flux), tm (mean surface temperature), tn (minimum surface temperature) & tx (maximum surface temperature)</p> <p><strong>Time averaging</strong>: monthly</p> <p> </p> <p><em>This data set supplements the equivalent downscaled 20CRv2c data set: <a href="https://zenodo.org/record/2558135#.XJj2uaD7RWE">Downscaled 20CRv2c (#37) gridded historical climate data over China (1851-2010)</a> doi: 1</em>0.5281/zenodo.2558135</p>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Downscaled climate grids of California at 90m for a variety of bioclimatic variables from 1971-2000, derived from historical climate grids
This dataset is comprised of 90 Geotiff images of selected bioclimatic variables for the state of California (extended past state lines to river basin boundaries). Originally created to model plant species distributions in California (Franklin et al. 2013. Modeling plant species distributions under future climates: how fine-scale do climate projections need to be? Global Change Biology 19: 473-483).
Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Jupyter Notebook and comprising data for GRL2023GL106264R: Understanding the Cascade: Removing GCM biases improves dynamically downscaled climate projections
<p>This notebook and attendant files allows users to interface with a small subset of the data used to create the data in GRL2023GL106264R. Also feel free to check out the overall description of the non-bias corrected dynamically downscaled GCMs in WUS-D3 here: https://zenodo.org/records/10635867. This DOI also contains version of WRF 4.1.3 allowing for yearly CH4, CO2, and N2O updates, as well as a 360-day calendar version.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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