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

The Long-Rains wet season of March-May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden-Julian Oscillation (MJO) events in MJO phases 2-4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October-December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed Atmosphere 2018, 9, 472 2 of 30 and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM.

Seasonal hindcast experiments, using prescribed sea surface temperatures (SSTs), are analyzed for Northern Hemisphere winters from 1900 to 2010. Ensemble mean Pacific/North American index (PNA) skill varies dramatically, dropping toward zero during the mid‐twentieth century, with similar variability in North Atlantic Oscillation (NAO) hindcast skill. The PNA skill closely follows the correlation between the observed PNA index and tropical Pacific SST anomalies. During the mid‐century period the PNA and NAO hindcast errors are closely related. The drop in PNA predictability is due to mid‐century negative PNA events, which were not forced in a predictable manner by tropical Pacific SST anomalies. Overall, negative PNA events are less predictable and seem likely to arise more from internal atmospheric variability than positive PNA events. Our results suggest that seasonal forecasting systems assessed over the recent 30 year period may be less skillful in periods, such as the mid‐twentieth century, with relatively weak forcing from tropical Pacific SST anomalies.

Four large outbreaks of Rift Valley Fever (RVF) occurred in Mauritania in 1998, 2003, 2010 and 2012 which caused lots of animal and several human deaths. We investigated rainfall and vegetation conditions that might have impacted on RVF transmission over the affected regions. Our results corroborate that RVF transmission generally occurs during the months of September and October in Mauritania, similarly to Senegal. The four outbreaks were preceded by a rainless period lasting at least a week followed by heavy precipitation that took place during the second half of the rainy season. First human infections were generally reported three to five weeks later. By bridging the gap between meteorological forecasting centers and veterinary services, an early warning system might be developed in Senegal and Mauritania to warn decision makers and health services about the upcoming RVF risk.

This study investigates the influence of atmospheric initial conditions on winter seasonal forecasts of the North Atlantic Oscillation (NAO). Hindcast (or reforecast) experiments -which differ only in their initial conditions -are performed over the period 1960-2009, using prescribed sea surface temperature (SST) and sea-ice boundary conditions. The first experiment ("ERA-40/Int IC") is initialized using the ERA-40 and ERA-Interim reanalysis datasets, which assimilate upper-air, satellite and surface observations; the second experiment ("ERA-20C IC") is initialized using the ERA-20C reanalysis dataset, which assimilates only surface observations. The ensemble mean NAO skill is largest in ERA-40/Int IC (r = 0.54), which is initialized with the superior reanalysis data. Moreover, ERA-20C IC did not exhibit significantly more NAO hindcast skill (r = 0.38) than in a third experiment, which was initialized with incorrect (shuffled) initial conditions. The ERA-40/Interim and ERA-20C initial conditions differ substantially in the tropical stratosphere, where the quasi-biennial oscillation (QBO) of zonal winds is not present in ERA-20C. The QBO hindcasts are highly skilful in ERA-40/Int IC -albeit with a somewhat weaker equatorial zonal wind amplitude in the lower stratosphere -but are incorrect in ERA-20C IC, indicating that the QBO is responsible for the additional NAO hindcast skill; this is despite the model exhibiting a relatively weak teleconnection between the QBO and NAO. The influence of the QBO is further demonstrated by regressing out the QBO influence from each of the hindcast experiments, after which the difference in NAO hindcast skill between the experiments is negligible. Whilst ERA-40/Int IC demonstrates a more skilful NAO hindcast, it appears to have a relatively weak predictable signal; this is the so-called "signal-to-noise paradox" identified in previous studies. Diagnostically amplifying the (weak) QBO-NAO teleconnection increases the ensemble-mean NAO signal with negligible impact on the NAO hindcast skill, after which the signal-to-noise problem seemingly disappears.