• Energy Research

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project). J.F. acknowledges nancial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project). The ESCENA project (200800050084265) of the Spanish \Strategic action on energy and climate change" provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.

AbstractThe Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made available an enormous amount of regional climate projections in different domains worldwide. This information is crucial for the development of adaptation strategies and policy‐making. A relevant open issue in this context is assessing the potential multidomain conflicts that may result in overlapping regions and developing appropriate ensemble methods trying to make the most of all available information. This work addresses this timely topic by focusing on precipitation over the Mediterranean region, a first illustrative case study that is encompassed by both the Euro‐ and Africa‐CORDEX domains. We focus on several mean, extreme, and temporal indices and use variance decomposition to assess the separate contribution of the domain and models to the climate change signal, concluding that the contribution of the domain alone is nearly negligible (below in all cases). Nevertheless, for some cases, the combined model/domain effect triggers up to of the total variance.

AbstractAssessing the regional responses to different Global Warming Levels (GWLs; e.g. + 1.5, 2, 3 and 4 ºC) is one of the most important challenges in climate change sciences since the Paris Agreement goal of keeping global temperature increase well below 2 °C with respect to the pre-industrial period. Regional responses to global warming were typically analyzed using global projections from Global Climate Models (GCMs) and, more recently, using higher resolution Regional Climate Models (RCMs) over limited regions. For instance, the IPCC AR6 WGI Atlas provides results of the regional response to different GWLs for several climate variables from both GCMs and RCMs. These results are calculated under the assumption that the regional signal to global warming is consistent between the GCMs and the nested RCMs. In the present study we investigate the above assumption by evaluating the consistency of regional responses to global warming from global (CMIP5) and regional (CORDEX) projections. The dataset aggregated over the new IPCC reference regions, available from the IPCC AR6 WGI Atlas repository, is analyzed here for temperature and precipitation. The existing relationships between the regional climate change signals and global warming are compared for both CMIP5 and CORDEX. Our results show significant linear scaling relationships between regional changes and global warming for most of the regions. CORDEX and CMIP5 show remarkably similar scaling relationships and similar robustness in the emergence of the climate change signal for most of the regions. These results support the use of regional climate models in the context of global warming level studies.

AbstractSea surface temperature (SST) and sea surface air temperature (SSAT) are commonly used as proxies for investigating the impact of climate change on oceans. These variables have been warming since pre-industrial times and are expected to continue to warm in the future under all Shared Socioeconomic Pathways (SSPs). However, they are warming in a spatially heterogeneous way, even with some cooling spots. In this work, we provide a general overview on the regional scaling of SST and SSAT with global warming, based on a 26-member CMIP6 ensemble. We utilize the global warming level (GWL) as a climate change dimension to analyze scaling patterns between sea temperature anomalies and the corresponding GWLs during the 21st century. This analysis is conducted globally, regionally, and on grid-point basis. The results show that SST and SSAT scale linearly with GWL at global scale, with scaling factors $$\beta $$ β = 0.71 ± 0.001 K/K and $$\beta $$ β = 0.86 ± 0.001 K/K, respectively. These results are robust, showing only minor differences between seasons, SSPs, and horizontal model resolutions. However, large differences emerge at regional scale, and the scaling of the two temperatures are strongly influenced by sea-ice. The lowest values are obtained for the Southern Ocean region, $$\beta $$ β = 0.54 ± 0.005 K/K, projecting that the mean SST will increase only half as fast as the global mean temperature. These results provide valuable insight for refining the ocean IPCC reference regions, considering spatial homogeneity in terms of the regional response to global warming. A refinement of six ocean reference regions has been proposed.

In Press. The goal of this study is to develop a high-resolution atmospheric hindcast over the Mediterranean area using the WRF-ARW model, focusing on offshore surface wind fields. In order to choose the most adequate model configuration, the study provides details on the calibration of the experimental saet-up through a sensitivity test considering the October–December 2001 period (the 2001 super-storm event in the West Mediterranean). A daily forecast outperforms the spectral technique of previous products and the boundary data from ERA-Interim reanalysis produces the most accurate estimates in terms of wind variability and hour-to-hour correspondence. According to the sensitivity test, two data sets of wind hindcast are produced: the SeaWind I (30-km horizontal resolution for a period of 60 years) and the SeaWind II (15-km horizontal resolution for 20 years). The validation of the resulting surface winds is undertaken considering two offshore observational datasets. On the one hand, hourly surface buoy stations are used to validate wind time series at specific locations; on the other hand, wind altimeter satellite observations are considered for spatial validation in the whole Mediterranean Sea. The results obtained from this validation process show a very good agreement with observations for the southern Europe region. Finally, SeaWind I and II are used to characterize offshore wind fields in the Mediterranean Sea. The statistical structure of sea surface wind is analyzed and the agreement with Weibull probability distribution is discussed. In addition, wind persistence and extreme wind speed (50 year return period) are characterized and relevant areas of wind power generation are described by estimating wind energy quantities. The work was partly funded by the projects iMar21 (CTM2010-15009) and CORWES (GL2010-22158-C02-01) from the Spanish government, and the FP7 European project CoCoNet (287844). The large amount of WRF simulations performed in this study were managed by WRF4G, which is an open-source tool funded by the Spanish government and co-funded by the European Regional Development Fund under grant CGL2011-28864. Peer reviewed

AbstractWind energy is susceptible to global climate change because it could alter the wind patterns. Then, improvement of our knowledge of wind field variability is crucial to optimize the use of wind resources in a given region.Here, we quantify the effects of climate change on the surface wind speed field over the Iberian Peninsula and Balearic Islands using an ensemble of four regional climate models driven by a global climate model.Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analysed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. The models forced by the European Reanalysis Interim (ERA‐Interim) reanalysis are validated against the European Climate Assessment and Dataset wind. We find that regional models are able to simulate with reasonable skill the spatial distribution of wind speed at 10 m in the Iberian Peninsula, identifying areas with common wind variability.Under the Special Report on Emissions Scenarios (SRES) A1B climate change scenario, the wind speed in the identified regions for 2031–2050 is up to 5% less than during the 1980–1999 control period for all models. The models also agree on the time evolution of spatially averaged wind speed in each region, showing a negative trend for all of them. These tendencies depend on the region and are significant at p = 5% or slightly more for annual trends, while seasonal trends are not significant in most of the regions and seasons. Copyright © 2015 John Wiley & Sons, Ltd.

Climate-driven sectoral applications commonly require different types of climate data (e.g. observations, reanalysis, climate change projections) from different providers. Data access, harmonization and post-processing (e.g. bias correction) are time-consuming error-prone tasks requiring different specialized software tools at each stage of the data workflow, thus hindering reproducibility. Here we introduce climate4R, an R-based climate services oriented framework tailored to the needs of the vulnerability and impact assessment community that integrates in the same computing environment harmonized data access, post-processing, visualization and a provenance metadata model for traceability and reproducibility of results. climate4R allows accessing local and remote (OPeNDAP) data sources, such as the Santander User Data Gateway (UDG), a THREDDS-based service including a wide catalogue of popular datasets (e.g. ERA-Interim, CORDEX, etc.). This provides a unique comprehensive open framework for end-to-end sectoral reproducible applications. All the packages, data and documentation for reproducing the experiments in this paper are available from http://www.meteo.unican.es/climate4R. This work has been funded by the Spanish R+D Program of the Ministry of Economy and Competitiveness, through grants MULTI-SDM (CGL2015-66583-R) and INSIGNIA (CGL2016-79210-R), co-funded by ERDF/FEDER.