Since global reanalysis datasets first appeared in the 1990s, they have become an essential tool to understand the climate of the past. The wind power industry uses those products extensively for wind resource assessment, while several climate services for energy rely on them as well. Nowadays various datasets coexist, which complicates the selection of the most suitable source for each purpose. In an effort to identify the products that best represent the wind speed features at turbine hub heights, five state‐of‐the‐art global reanalyses have been analysed: ERA5, ERA‐Interim, the Japanese 55‐year Reanalysis (JRA55), the Modern Era Retrospective Analysis for Research and Applications‐2 (MERRA2), and the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 (R1). A multi‐reanalysis ensemble approach is used to explore the main differences amongst these datasets in terms of surface wind characteristics. Then, the quality of the surface and near‐surface winds is evaluated with a set of 77 instrumented tall towers. Results reveal that important discrepancies exist in terms of boreal winter seasonal means, interannual variability (IAV), and decadal linear trends. The differences in the computation of these parameters, which are mainly concentrated inland, reach up to the order of magnitude of the parameters themselves. Comparison with in situ observations shows that the ERA5 surface winds offer the best agreement, correlating and reproducing the observed variability better than a multi‐reanalysis mean in 35.1% of the tall tower sites on a daily time‐scale. However, none of the reanalyses stands out from the others when comparing seasonal mean winds. Regarding the IAV, near‐surface winds from ERA5 offer the values closest to the observed IAV.

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.

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.The authors acknowledge funding support from 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. Special thanks to Nube Gonzalez-Reviriego and Albert Soret for helpful comments and discussion.\ud We also acknowledge the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2) and the New European Wind Atlas (NEWA) project funded from ERA-NET Plus, topic FP7-ENERGY.2013.10.1.2. We acknowledge the s2dverification and SpecsVerification R-based packages. Finally we would like to thank Pierre-Antoine Bretonnière, Oriol Mula and Nicolau\ud Manubens for their technical support at different stages of this project.Peer ReviewedPostprint (author's final draft

Abstract. The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top–down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.

This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)-e.g. quantile mapping-to more sophisticated ensemble recalibration (RC) methods-e.g. non-homogeneous Gaussian regression,-which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service (C3S) dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to feed their impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.