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opencc-by-4.0Jul 2020View details →

Northwest Europe NEMO-ERSEM ocean model hindcast and climate projection under RCP8.5

Dataset of model hindcast and climate projection data from a NEMO-ERSEM simulation of the 7km-resolution Atlantic Margin Model (AMM7). Model description and data are presented in  Wakelin, S. L., Y. Artioli, J. T. Holt, M. Butenschön, and J. Blackford (2020), Controls on near-bed oxygen concentration on the Northwest European Continental Shelf under a potential future climate scenario, Progress in Oceanography, 102400. doi: https://doi.org/10.1016/j.pocean.2020.102400. Coupled NEMO-ERSEM model simulations are used to study temperature, salinity and near-bed oxygen concentrations on the northwest European Continental Shelf (NWES). Data are from a hindcast (1980 to 2007) and a climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario. The climate projection (1980 to 2099) under the RCP8.5 climate emissions scenario is described as experiment E1 in Holt, J., J. Polton, J. Huthnance, S. Wakelin, E. O'Dea, J. Harle, A. Yool, Y. Artioli, J. Blackford, J. Siddorn, and M. Inall (2018), Climate-Driven Change in the North Atlantic and Arctic Oceans Can Greatly Reduce the Circulation of the North Sea, Geophysical Research Letters, 45(21), 11,827-811,836. doi: 10.1029/2018gl078878. The dataset consists of   <ul> <li>Hindcast simulation data</li> </ul> <ol> <li>AMM7_hindcast_3D_S_1980_2007.nc - monthly mean salinity fields.</li> <li>AMM7_hindcast_3D_T_1980_2007.nc - monthly mean temperature fields.</li> <li>AMM7_hindcast_near_bed_O2o_1980_2007.nc - near-bed oxygen concentrations on the NWES.</li> </ol> <ul> <li>Climate projection data</li> </ul> <ol> <li>AMM7_RCP8_5_3D_S_1980_2099.nc - monthly mean salinity fields.</li> <li>AMM7_RCP8_5_3D_T_1980_2099.nc - monthly mean temperature fields.</li> <li>AMM7_RCP8_5_3D_U_1980_2099.nc - monthly mean eastwards currents.</li> <li>AMM7_RCP8_5_3D_V_1980_2099.nc - monthly mean northwards currents.</li> <li>AMM7_RCP8_5_near_bed_1980_2099.nc - monthly mean near-bed oxygen concentrations and near-bed bacterial respiration on the NWES.</li> <li>AMM7_RCP8_5_netPP_1980_2099.nc - monthly mean depth integrated net primary production.</li> </ol>

opencc-by-4.0Apr 2024View details →

ICARIA: climate projections from statistical downscaling outputs

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

opencc-by-4.0Jul 2024View details →

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

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

opencc-by-4.0Jul 2024View details →

ICARIA: spatially distributed climate projections from statistical downscaling

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

opencc-by-4.0Dec 2023View details →

List of capacity building resources for combating climate mis/disinformation created by EU-funded projects

This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.

opencc-by-4.0Dec 2023View details →

List of capacity building resources for climate change adaptation created by EU-funded projects

This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.

opencc-by-4.0Oct 2024View details →

Projection of temperature-related mortality in 854 European cities under climate change and adaptation scenarios

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (<a href="https://doi.org/10.1038/s41591-024-03452-2">https://doi.org/10.1038/s41591-024-03452-2</a>). It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.  The fully reproducible analysis code using the data and producing the results included in this repository is provided in <a href="https://github.com/PierreMasselot/EUcityProj" target="_blank" rel="noopener">GitHub</a>. The results can be visualised and explored in a dedicated <a href="https://ehm-lab.shinyapps.io/vistemphip/">Shiny app</a>. <h3>Content</h3> This repository contains three zip files, each with an internal codebook: <ul> <li>data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (<a href="https://doi.org/10.5281/zenodo.10288665" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10288665</a>). This file also include a script showing how each dataset was extracted for the purpose of this projection study.</li> <li>results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels). </li> <li>results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.</li> </ul> It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

opencc-by-4.0Oct 2021View details →

Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases

The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts  whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts  used for the analysis. Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a> Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a>

opencc-by-4.0Dec 2020View details →

Data Visualization - Final Project - Global Climate Change

This Project is part of the course work for Data visualization DATS 6401. In this project, I have created webpage to show data analysis on Global Climate Change. D3 & Google Visualization API is used for all visualization graphs in the webpage.

opencc-by-4.0Dec 2024View details →

Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)

This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives.  The survey targeted European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens at the local and/or regional scale. The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a>

opencc-by-4.0Dec 2021View details →

Data provided for the Preenacting Climate Change Scenarios project 2021

CMIP6 model output data processed using the scripts provided here: https://github.com/lukasbrunner/preenact/

opencc-by-4.0Feb 2021View details →

Water risks to hydropower projects in the face of climate change

This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>. This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a> See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a>

opencc-by-4.0Feb 2022View details →

Soybean yield projections in Europe under historical (1981-2010) and future climate (2050-2059 and 2090-2099 for RCP4.5 and RCP8.5)

General information This dataset contains soybean yield projections in Europe under historical (1981-2010) and future climate with moderate (RCP 4.5) to intense (RCP 8.5) warming, up to the 2050s and 2090s time horizons. The data has been generated by Guilpart et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. All details can be found in this paper. A brief summary is provided below. Summary of soybean yield projections methodology Yield projections have been performed using data-driven relationships between climate and soybean yield derived from machine-learning (Random Forest). The Random Forest model was trained using (i) the the global dataset of historical yields updated version (Iizumi et al. 2014a), which includes grid-wise soybean yields worldwide with the grid size of 1.125 degree over 1981-2010, and (ii)  the global retrospective meteorological forcing dataset tailored for agricultural application (GRASP, Iizumi et al. 2014b), which covers the period 1961–2010 at the same spatial resolution as yield data, i.e. a grid size of 1.125 degree. Time-detrended soybean yield data was related (using Random Forest) to 35 climate variables defined at a monthly time step over the seven months of the soybean growing season, plus the fraction of irrigated area, i.e. a total of 36 variables. The 35 climate variables are monthly mean daily minimum and maximum temperatures (Tmin and Tmax, degree Celsius), monthly total precipitation (rain, mm month-1), monthly mean daily total solar radiation (solar, MJ m-2 day-1), monthly mean air vapor pressure (VP, hPa). The fitted model showed high R² (higher than 0.9) and low RMSE (0.35 t ha-1) between observed and predicted yields based on cross-validation. Then, soybean yield projections under historical over whole Europe have been performed using the GRASP climate data, and yield projections under future climate have been performed using 16 climate change scenarios consisting of bias-corrected data of eight Global Circulation Models (GCM; GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, and NorESM1-M, used in the Coupled Model Intercomparison phase 5 (CMIP5) and two Representative Concentration Pathways (RCPs; 4.5 and 8.5 W m-2). Soybean growing season used for projections is April to October. All projections assumed irrigated fraction equals to zero. Projections are shown only on agricultural area (cropland plus pasture), in the year 2000. Soybean yield is expressed in tons per hectare. Files description <ul> <li>RF_soybean_historical_GRASP_median_1981_2010.nc : random forest projections of soybean yield in Europe for the historical (1981-2010) period using GRASP climate data. This file contains the median yield (in tons per hectare) over 1981-2010.</li> <li>RF_soybean_rcp45_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li>RF_soybean_rcp45_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP4.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> <li>RF_soybean_rcp85_median_2050_2059.nc : random forest projections of soybean yield in Europe for the 2050-2059 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2050-2059 and the 8 GCMs.</li> <li>RF_soybean_rcp85_median_2090_2099.nc : random forest projections of soybean yield in Europe for the 2090-2099 time period under RCP8.5. This file contains the median yield (in tons per hectare) over 2090-2099 and the 8 GCMs.</li> </ul> References Guilpart N. et al. (2022) Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change, Nature Food. Iizumi T. et al. (2014a) Historical changes in global yields: Major cereal and legume crops from 1982 to 2006. Glob. Ecol. Biogeogr. 23, 346–357. Iizumi T. et al. (2014b). A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison. J. Geophys. Res. Atmos. Res. 119, 363–384.

opencc-by-4.0Nov 2017View details →

Climate based seed zones for Mexico: spatial grids to guide reforestation under observed and projected climate change

This database entry provides climate-based seed zone system for Mexico to address climate change observed over the last 30 years and projected climate change for the 2050s. The database corresponds to a journal publication by Castellanos-Acuña et al. (2018), available at https://doi.org/10.1007/s11056-017-9620-6. This seed zone classification is based on bands of two climate variables that have often been shown to drive genetic adaptation of tree species: mean coldest month temperature (MCMT), and an aridity index (AHM). MCMT was divided into ten bands of 3°C intervals, with the limits of these bands being, temperatures below <2°C, 2-5°, 5-8°, 8-11°, 11-14°, 14-17°, 17-20°, 20-23°, 23-26°, >26°C. AHM was divided into seven bands with intervals that are approximately equal width under a log-transformation: <20, 20-30, 30-45, 45-65, 65-95, 95-140, and >140 °C/mm. The gridded files provided in this database entry, the classes are coded as integer numbers, with the last digit representing the AHM class (1-7) and the first or first and second digit representing the MCMT class (1-10).

opencc-by-4.0May 2024View details →

Scripts and datas for "Climate-driven projections of future global wetlands extent"

Computations scripts (1, 2), associated input dataset (3), and output datasets for wetland fractions (4, 5) used and presented in the study: L. Hardouin, B. Decharme, J. Colin, C. Delire: Climate-driven projections of future global wetlands extent. The calculation and input scripts include: 1_var_comput : Calculation of the main variables used to diagnose wetlands: depth of the "active" layer d_wtl, liquid water content w_l, ice content and maximum content in the layer d_wtl. 2_TOPMODEL : The scripts used to diagnose the wetland fraction and to calibrate the models using the TOPMODEL approach. In this folder, the mean, maximum, minimum, standard deviation and skewness datasets of the topographic indices at the grid-cell level are also included. 3_alpha_and_beta : Calibrated alpha and beta parameters used to obtain the historical and projected wetland fractions with the calibrated version. The outputs datasets contain: 4_fwtl_model_period : The fraction of wetlands computed from each model in the calibrated version, for the historical period and the 4 SSPs scenarios presented in the submitted work. 5_not_calibrated_fwtl_model_period : The fraction of wetlands computed from each model in the uncalibrated version with alpha=0.65, for the historical period and the 4 SSPs scenarios, where only the historical period is used in the submitted work.   Additional data not created by the authors are needed to reproduce the study (see the Open research section in the submitted article). Feel free to contact the authors (lucas.hardouin@meteo.fr) for any help or questions.

opencc-by-4.0Jul 2024View details →

Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing

The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing". OGGM_area_projections.nc contains data for Figure 3. OGGM_volume_projections contains data for Figure 4. OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5. OGGM_solid_ice_discharge_regions.nc contains data for Figure 6. OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7. OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.

opencc-by-4.0Aug 2019View details →

Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence

This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication: Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. GeoHealth.  The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario.  Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3.

opencc-by-4.0Apr 2024View details →

Projected distribution of invasive plant species in the tropical Andes under climate change

Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission. Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS.  For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change List of modeled invasive plant species and their known impacts in the tropics. <table> <tbody> <tr> <td> Species </td> <td> Biogeographic origin </td> <td> Impacts </td> <td> References </td> <td> GBIF data (DOIs) </td> </tr> <tr> <td> Acacia decurrens </td> <td> Australian </td> <td> Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires. </td> <td>  (Cárdenas López et al., 2017; Le Maitre et al., 2011) </td> <td> https://doi.org/10.15468/dl.mjyxhw </td> </tr> <tr> <td> Acacia melanoxylon </td> <td> Australian </td> <td> Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture. </td> <td> (Kumschick and Jansen, 2023; Le Maitre et al., 2011)   </td> <td> https://doi.org/10.15468/dl.4cugnk </td> </tr> <tr> <td> Arundo donax   </td> <td> Holarctic </td> <td> Alter the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock. </td> <td> (Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010) </td> <td> https://doi.org/10.15468/dl.bfep4t </td> </tr> <tr> <td> Genista monspessulana </td> <td> Holarctic </td> <td> Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans. </td> <td> (Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008) </td> <td> https://doi.org/10.15468/dl.gyhnxh </td> </tr> <tr> <td> Hedychium coronarium </td> <td> Indo-Malesian </td> <td> Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops. </td> <td> (Cárdenas López et al., 2017; Costa et al., 2019) </td> <td> https://doi.org/10.15468/dl.6z2jgb </td> </tr> <tr> <td> Melinis minutiflora </td> <td> African </td> <td> Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species. </td> <td> (Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022) </td> <td> https://doi.org/10.15468/dl.fsqwsv </td> </tr> <tr> <td> Pteridium aquilinum </td> <td> Holarctic </td> <td> Alter vegetation success processes affect crops and cause livestock poisoning.  It also produces acids that inhibit root growth in native and cultivated species. </td> <td>  (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)   </td> <td> https://doi.org/10.15468/dl.sp4uuv </td> </tr> <tr> <td> Ricinus communis </td> <td> African </td> <td> Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species. </td> <td> (Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022) </td> <td> https://doi.org/10.15468/dl.dhbphb </td> </tr> <tr> <td> Senecio madagascariensis </td> <td> African </td> <td> Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans.  </td> <td> (Wijayabandara et al., 2021) </td> <td> https://doi.org/10.15468/dl.7e8eyx </td> </tr> <tr> <td> Thunbergia alata </td> <td> African </td> <td> Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems. </td> <td> (Cárdenas López et al., 2017; Quijano-Abril et al., 2021) </td> <td> https://doi.org/10.15468/dl.g9zybc </td> </tr> <tr> <td> Ulex europeaus </td> <td> Holarctic </td> <td> Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands. </td> <td> (Anderson and Anderson, 2009; Cárdenas López et al., 2017) </td> <td> https://doi.org/10.15468/dl.6642q9 </td> </tr> </tbody> </table>

opencc-by-sa-4.0Nov 2024View details →

Song capturing lived-experiences of flooding and climate resilience with St. Eugenes Choir Newtownstewart (BluePrint project)

This audio piece represents one of the creative risk communication outputs co-created within the BluePrint project. Between March and October 2024, socially engaged artist Sara Walmsley worked creatively with flood-affected community representatives in Newtownstewart, Co. Tyrone and Eglinton, Co. Derry-Londonderry exploring their lived-experiences of flooding and need for climate adaptation and resilience.  In the audio piece, you will hear the melodic, polyphonic harmonies of St. Eugene’s Church choir (Newtownstewart) as they give music to the words of members of their community whose homes were destroyed and lives endangered by flood water. The piece captures the voices of those striving to adapt to our changing climate, those who are responding to the urgency by finding solace, hope, strength and courage in the unending and unsurprising resilience and creativity of our communities.  The BluePrint project is led by the MaREI Centre, University College Cork, with partners the Playhouse, Derry City and Strabane District Council, and Mayo County Council. The BluePrint project is a recipient of the Creative Climate Action fund, an initiative from the Creative Ireland Programme. It is funded by the Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media in collaboration with the Department of the Environment, Climate and Communications.  Find out more: <a href="https://www.marei.ie/project/blueprint/">https://www.marei.ie/project/blueprint/</a>