Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980–2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.The authors acknowledge funding support from the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2), the New European Wind Atlas (NEWA) project funded by ERA-NET Plus, Topic FP7 ENERGY.2013.10.1.2, the RESILIENCE (CGL2013–41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO), and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Thanks to Daniel Cabezón and Sergio Lozano for their valuable feedback. We acknowledge the s2dverification R-based package (http://cran.rproject. org/web/packages/s2dverification) developers. Finally, we would like to thank Pierre-Antoine Bretonniere, Júlia Giner, Nicolau Manubens and Javier Vegas for their technical support at different stages of this project.Peer ReviewedPostprint (published version

Multi-model seasonal forecasts for the wind energy sector

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.Peer ReviewedPostprint (author's final draft

Challenges in the selection of atmospheric circulation patterns for the wind energy sector

Abstract Atmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user-oriented classification of atmospheric circulation patterns in the Euro-Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K-means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year-to-year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro-Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.This research has been funded by the S2S4E (GA 776787) Horizon 2020 project, the Ministerio de Ciencia, Innovación y Universidades as part of the CLINSA project (CGL2017‐85791‐R) and the Juan de la Cierva – Incorporación Grant (IJCI‐2016‐29776). The analyses and plots of this work have been performed with the s2dverification (Manubens et al., 2018), CSTools (https://CRAN.R-project.org/package=CSTools) and startR (https://CRAN.R-project.org/package=startR) R‐language‐based software packages. Finally, we would like to thank Pierre‐Antoine Bretonnière, Margarida Samsó, Nicolau Manubens and Núria Pérez‐Zanón for their technical support at different stages of this project. We also acknowledge the two anonymous reviewers for their useful comments.Peer ReviewedPostprint (published version

Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980–2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.The authors acknowledge funding support from the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2), the New European Wind Atlas (NEWA) project funded by ERA-NET Plus, Topic FP7 ENERGY.2013.10.1.2, the RESILIENCE (CGL2013–41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO), and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Thanks to Daniel Cabezón and Sergio Lozano for their valuable feedback. We acknowledge the s2dverification R-based package (http://cran.rproject. org/web/packages/s2dverification) developers. Finally, we would like to thank Pierre-Antoine Bretonniere, Júlia Giner, Nicolau Manubens and Javier Vegas for their technical support at different stages of this project.Peer Reviewe

Multi-model seasonal forecasts for the wind energy sector

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.Peer Reviewe

Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

It is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.The research leading to these results was funded by the Sub-seasonal to Seasonal climate forecasting for Energy (S2S4E) Project (GA776787), by the New European Wind Atlas (NEWA-II) project (PCIN-2016-029) and by the Ministerio de Economía y Competitividad (MINECO) as part of the CLINSA project (CGL2017-85791-R) and of the Juan de la Cierva-Incorporación Grant (IJCI-2016-29776). We also acknowledge the developers of the s2dverification software package (Manubens et al 2018), used for the data analysis and the visualization of the results presented in this work. The authors would also like to express their gratitude to Pierre-Antoine Bretonniére, Margarida Samsó and Núria Pérez-Zanón for their fundamental contribute in downloading and pre-processing data, and to Dr. Stefano Materia, Dr. Markus Donat, Dr. Simon Wild for their insightful comments and suggestions.Peer ReviewedPostprint (published version

Adaptation of Arabidopsis halleri to extreme metal pollution through limited metal accumulation involves changes in cell wall composition and metal homeostasis

info:eu-repo/semantics/publishe

What have we learnt from EUPORIAS climate service prototypes?

The international effort toward climate services, epitomised by the development of the Global Framework for Climate Services and, more recently the launch of Copernicus Climate Change Service has renewed interest in the users and the role they can play in shaping the services they will eventually use. Here we critically analyse the results of the five climate service prototypes that were developed as part of the EU funded project EUPORIAS. Starting from the experience acquired in each of the projects we attempt to distil a few key lessons which, we believe, will be relevant to the wider community of climate service developers.The authors wish to acknowledge all of those who contributed indirectly to the development of the EUPORIAS prototypes, through scientific discussion, review, data provision, stakeholder engagement and facilitation: for SPRINT, Adam Scaife, Anca Brookshaw, Alberto Arribas, Emily Wallace, Jeff Knight, Margaret Gordon, Kate Brown, Brent Walker, Mathew Richardson, Jodie Wild, and the DfT-led stakeholder group; for RESILIENCE, Melanie Davis for conceiving the prototype and for her vision on Climate Services. In the LMTool, Clinton Devon Estates, the National Farmers Union, and all the farmers involved in the development of the tool. The UK Government Department for Transport is acknowledged for providing financial support, in parallel to that received from EUPORIAS, for the SPRINT prototype. The visualisation, project UKKO, within RESILIENCE prototype was done by Moritz Stefaner. EUPORIAS was funded by the European Commission through the 7th Framework Programme for Research, grant agreement 308291.Peer Reviewe