• Energy Research

Las vidas y los medios de subsistencia de las personas en todo el mundo están cada vez más amenazados por los riesgos relacionados con el clima a medida que el cambio climático aumenta la frecuencia y la gravedad del clima de alto impacto. A su vez, el riesgo de que se produzcan múltiples peligros simultáneamente aumenta y los impactos compuestos se vuelven más probables. La Organización Meteorológica Mundial (OMM) propuso el uso de pronósticos basados en impactos de múltiples peligros (IbF) para anticipar y reducir mejor los impactos de los peligros concurrentes, pero hasta el momento, hay pocos ejemplos operativos en el sector humanitario. La sequía es particularmente susceptible a influencias de múltiples peligros. Sin embargo, los desafíos encontrados en el desarrollo de sistemas IbF de sequía, incluida la falta de comprensión de los impactos compuestos y los mandatos específicos centrados en los peligros, plantean preguntas importantes para la viabilidad de IbF de múltiples peligros según lo previsto por la OMM. Con estos desafíos en mente, proponemos un enfoque provisional en el que la evaluación en tiempo real de la vulnerabilidad dinámica proporciona un contexto para el IbF basado en la sequía. La incorporación de indicadores dinámicos de vulnerabilidad da cuenta de los efectos locales de los peligros no relacionados con la sequía, mientras que el uso de un sistema basado en la sequía facilita una intervención efectiva. El enfoque propuesto mejorará nuestra comprensión de los eventos compuestos, mejorará la adopción de IbF en el sector humanitario y mitigará mejor los impactos de los peligros concurrentes. La vie et les moyens de subsistance des populations du monde entier sont de plus en plus menacés par les risques liés au climat, car le changement climatique augmente la fréquence et la gravité des phénomènes météorologiques à fort impact. À son tour, le risque que plusieurs dangers se produisent simultanément augmente et que les impacts composés deviennent plus probables. L'Organisation météorologique mondiale (OMM) a proposé l'utilisation de prévisions basées sur les impacts multirisques (IbF) pour mieux anticiper et réduire les impacts des risques simultanés, mais il existe encore peu d'exemples opérationnels dans le secteur humanitaire. La sécheresse est particulièrement sensible aux influences multirisques. Cependant, les défis rencontrés dans le développement des systèmes IbF de sécheresse – y compris une mauvaise compréhension des impacts composés et des mandats spécifiques axés sur les risques – soulèvent des questions importantes pour la faisabilité des IbF multirisques tels qu'envisagés par l'OMM. Avec ces défis à l'esprit, nous proposons une approche intérimaire dans laquelle l'évaluation en temps réel de la vulnérabilité dynamique fournit un contexte pour l'IBC basée sur la sécheresse. L'incorporation d'indicateurs de vulnérabilité dynamiques tient compte des effets locaux des dangers autres que la sécheresse, tandis que l'utilisation d'un système basé sur la sécheresse facilite une intervention efficace. L'approche proposée améliorera notre compréhension des événements complexes, améliorera l'adoption d'IbF dans le secteur humanitaire et atténuera mieux les impacts des risques concomitants. The lives and livelihoods of people around the world are increasingly threatened by climate-related risks as climate change increases the frequency and severity of high-impact weather. In turn, the risk of multiple hazards occurring simultaneously grows and compound impacts become more likely. The World Meteorological Organization (WMO) proposed the use of multi-hazard impact-based forecasting (IbF) to better anticipate and reduce the impacts of concurrent hazards, but as yet, there are few operational examples in the humanitarian sector. Drought is particularly susceptible to multi-hazard influences. However, challenges encountered in the development of drought IbF systems – including poor understanding of compound impacts and specific hazard-focused mandates – raise important questions for the feasibility of multi-hazard IbF as envisioned by the WMO. With these challenges in mind, we propose an interim approach in which real-time assessment of dynamic vulnerability provides a context for drought-based IbF. The incorporation of dynamic vulnerability indicators account for the local effects of non-drought hazards, whilst the use of a drought-based system facilitates effective intervention. The proposed approach will improve our understanding of compound events, enhance adoption of IbF in the humanitarian sector, and better mitigate the impacts of concurrent hazards. تتعرض حياة الناس وسبل عيشهم في جميع أنحاء العالم لتهديد متزايد بسبب المخاطر المتعلقة بالمناخ حيث يزيد تغير المناخ من تواتر وشدة الطقس شديد التأثير. في المقابل، يزداد خطر حدوث مخاطر متعددة في وقت واحد وتصبح التأثيرات المركبة أكثر احتمالًا. اقترحت المنظمة العالمية للأرصاد الجوية (WMO) استخدام التنبؤ القائم على التأثير متعدد المخاطر (IbF) لتوقع آثار المخاطر المتزامنة والحد منها بشكل أفضل، ولكن حتى الآن، هناك عدد قليل من الأمثلة التشغيلية في القطاع الإنساني. الجفاف عرضة بشكل خاص للتأثيرات متعددة المخاطر. ومع ذلك، فإن التحديات التي ووجهت في تطوير أنظمة إيبف الجفاف – بما في ذلك سوء فهم الآثار المركبة والتفويضات المحددة التي تركز على المخاطر – تثير أسئلة مهمة حول جدوى إيبف متعددة المخاطر على النحو المتوخى من قبل المنظمة العالمية للأرصاد الجوية. مع وضع هذه التحديات في الاعتبار، نقترح نهجًا مؤقتًا يوفر فيه التقييم في الوقت الفعلي للضعف الديناميكي سياقًا لـ IbF القائم على الجفاف. إن دمج مؤشرات الضعف الديناميكية يفسر الآثار المحلية لمخاطر غير الجفاف، في حين أن استخدام نظام قائم على الجفاف يسهل التدخل الفعال. سيحسن النهج المقترح فهمنا للأحداث المركبة، ويعزز اعتماد IbF في القطاع الإنساني، ويخفف بشكل أفضل من آثار المخاطر المتزامنة.

Global climate model simulations from the ‘ Half a degree Additional warming, Prognosis and Projected Impacts’ (HAPPI) project were used to assess how wind power generation over Europe would change in a future world where global temperatures reach 1.5 °C above pre-industrial levels. Comparing recent historical (2006–2015) and future 1.5 °C forcing experiments highlights that the climate models demonstrate a northward shift in the Atlantic jet, leading to a significant ( p < 0.01) increase in surface winds over the UK and Northern Europe and a significant ( p < 0.05) reduction over Southern Europe. We use a wind turbine power model to transform daily near-surface (10 m) wind speeds into daily wind power output, accounting for sub-daily variability, the height of the turbine, and power losses due to transmission and distribution of electricity. To reduce regional model biases we use bias-corrected 10 m wind speeds. We see an increase in power generation potential over much of Europe, with the greatest increase in load factor over the UK of around four percentage points. Increases in variability are seen over much of central and northern Europe with the largest seasonal change in summer. Focusing on the UK, we find that wind energy production during spring and autumn under 1.5 °C forcing would become as productive as it is currently during the peak winter season. Similarly, summer winds would increase driving up wind generation to resemble levels currently seen in spring and autumn. We conclude that the potential for wind energy in Northern Europe may be greater than has been previously assumed, with likely increases even in a 1.5 °C warmer world. While there is the potential for Southern Europe to see a reduction in their wind resource, these decreases are likely to be negligible.

AbstractThe ‘short rains’ in East Africa from October to December have significant year‐to‐year variability. Their abundance or deficiency is often associated with floods or droughts for which early warning is crucial, though even in normal seasons skillful forecasts facilitate planning and preparedness. Here we study the relationship between initial‐state sea surface temperatures and subseasonal rainfall forecast errors in the European Center for Medium‐Range Weather Forecasts model in the region. We demonstrate that the initial mode of the Indian Ocean Dipole (IOD) is a partial control on the rainfall error in weeks 3–4. This relationship is also clear on the seasonal scale, exemplified by too‐wet forecasts during the 2015 season when the IOD was positive, and too‐dry forecasts in 2010 when it was negative. Our results provide an entry point for model improvement, and we show that a priori forecast corrections based on the initial IOD index are feasible.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.

AbstractClimate 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.