Better seasonal forecasts for the renewable energy industry

Anomalous seasons such as extremely cold winters or low-wind summers can seriously disrupt renewable energy productivity and reliability. Better seasonal forecasts providing more accurate information tailored to stakeholder needs can help the renewable energy industry prepare for such extremes.The authors acknowledge funding from the EU Horizon 2020 project “Sub-seasonal to seasonal climate forecasting for energy (S2S4E)” (GA776787).Peer ReviewedPostprint (author's final draft

Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes

A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid (0.25 deg). Our results show that a) weather regimes must be considered when modeling cold and weak-wind events, b) it is essential to account for the correlations between these events when modeling their joint distribution, c) we need to analyze each month separately, and d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian Blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events

The EU needs a demand-driven innovation policy for climate services

Climate services have climbed high on the agenda of EU research policy, yet few contributions have reflected on the actual usability of climate services from the perspectives of the intended users, let alone the implications for future EU research and innovation policy. This commentary reflects on four key lessons learnt from engagement in climate services research projects and discusses implications for future EU research policy: i) all end-users have pre-established decision-making processes and tools for their purposes, hence all new information needs to be adapted ii) one size fits none – and tailoring takes time iii) building trust between different actors, processes and confidence in new information is key in the tailoring process – and resource-demanding iv) purveyors and intermediaries can facilitate tailoring processes but need to finance their activities until end-users demonstrate willingness to pay and/or the climate service is readily implemented. The main argument is that more attention needs to be paid to the demand-side of climate services to help viable climate services make it through the innovation “valley of death” – that is, the twilight zone between technical invention and (commercially) successful innovation. EU Research and Innovation (R&I) funding streams and policies for establishing truly transdisciplinary learning loops driven by (actual) user needs can function as vehicles through the valley of death.This research was funded by the EU Horizon 2020 program under grant agreement number 776787.Peer ReviewedPostprint (published version

Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities

Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction

Asking the right questions in adaptation research and practice : Seeing beyond climate impacts in rural Nepal

Adaptation research and practice too often overlooks the wider social context within which climate change is experienced. Mainstream approaches frame adaptation problems in terms of the consequences that flow from biophysical impacts and as a result, we argue, ask the wrong questions. A complementary approach gaining ground in the field, foregrounding the social, economic and political context, reveals differentiation in adaptation need, and how climate impacts interconnect with wider processes of change. In this paper, we illustrate how this kind of approach frames a different set of questions about adaptation using the case of Nepal. Drawing on fieldwork and a review of literature, we contrast the questions that emerge from adaptation research and practice that take climate risk as a starting point with the questions that emerge from examination of contemporary rural livelihoods. We find that while adaptation efforts are often centred around securing agricultural production and are predicated on climate risk management, rural livelihoods are caught in a wider process of transformation. The numbers of people involved in farming are declining, and households are experiencing the effects of rising education, abandonment of rural land, increasing wages, burgeoning mechanisation, and high levels of migration into the global labour market. We find the epistemological framing of adaptation too narrow to account for these changes, as it understands the experiences of rural communities through the lens of climate risk. We propose that rather than seeking to integrate local understandings into a fixed, impacts-orientated epistemology, it is necessary to premise adaptation on an epistemology capable of exploring how change occurs. Asking the right questions thus means opening up adaptation by asking: ‘what are the most significant changes taking place in people's lives?’ along with the more standard: ‘what are the impacts of climate change?’ Viewing adaptation as occurring between and within these two perspectives has the potential to reveal new vulnerabilities and opportunities for adaptation practice to act upon

A multi-model comparison of meteorological drivers of surface ozone over Europe

The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty

Estudo do Pré-Tratamento Ácido e Hidrólise Enzimática da Borra de Café Visando a Produção de Bioetanol

O presente trabalho tem como objetivo avaliar a possibilidade de produzir um biocombustível, o bioetanol, valorizando resíduos de baixo valor, nomeadamente as borras de café. Temos cada vez mais assistido a uma evolução tecnológica em larga escala e a um aumento populacional, que se traduz numa vertente positiva no primeiro caso, mas devido ao crescente consumo de energia pela sociedade atual e alteração de padrões de consumo, origina uma grande dependência de utilização de combustíveis fósseis, o que levará a um previsível esgotamento das suas reservas naturais. No que respeita ao aumento populacional, este provoca um aumento da produção de resíduos. Assim, cada vez mais se torna necessário procurar recursos energéticos alternativos, de natureza renovável, designadamente através da produção de biocombustíveis a partir de resíduos de baixo valor comercial. Esta utilização de resíduos como matéria-prima para a produção de biocombustíveis traduz-se em duas vantagens: i) por um lado diminui-se a acumulação dos resíduos gerados, e ii) por outro, podem estes ser aproveitados de uma forma eficiente. O trabalho foi realizado em duas etapas fundamentais: a caraterização físico-química da borra de café e a quantificação dos açúcares formados, após aplicação do pré-tratamento ácido e hidrólise enzimática, por três métodos: DNS (ácido 3,5-dinitrosalicílico), refratometria e cromatografia líquida de alta eficiência (HPLC). Para a caraterização físico-química da borra de café analisaram-se vários parâmetros, dos quais se salientam, o poder calorífico (5082,82 kcal/kg), o teor de cinzas (1,83%), o teor de proteínas (13,74 g / 100gcafé), o teor de celulose (16,17%) e o teor de lenhina (29,04%). No pré-tratamento ácido utilizou-se concentrações (v/v) de 1% a 5% dos ácidos H2SO4, HCI, HNO3, CH3COOH e de 7% para o CH3COOH, a uma temperatura de 85 ºC, com tempo de reação de 270 min e agitação de 50 rpm. A hidrólise enzimática foi concretizada num banho a 50 ºC, durante 120 minutos, mantendo a mesma velocidade de agitação do pré-tratamento e recorrendo à enzima Viscozyme L, na proporção de 100 L enzima/g amostra. A eficiência do pré-tratamento e hidrólise enzimática foi avaliada através da quantificação dos açúcares formados. Na quantificação pelo método DNS, o ácido sulfúrico e nítrico a 3% foram os que apresentaram rendimentos superiores de açúcares redutores totais, com 43,1% e 43,2%, respetivamente, seguido do ácido nítrico a 5%, cujo rendimento igualou os 41,4%. Na determinação dos açúcares totais, realizada por refratometria, confirmou-se que o ácido sulfúrico a 5% foi aquele que apresentou um rendimento mais alto - 78,9% - seguido da concentração de 3% com 65,3%. Finalmente, no método de HPLC, o ácido sulfúrico a 5% proporcionou rendimentos mais elevados de xilose, arabinose e glucose, que tomaram os valores de 6,1%, 9,3% e 11,0%, respetivamente.The present work aims to evaluate the possibility of producing a biofuel, bioethanol, by valuing low value residues, namely coffee grounds. We have been increasingly witnessing a large-scale technological evolution and a population increase, which translates into a positive aspect in the first case, but, due to the increasing consumption of energy by the current society and changes in consumption patterns, causes a great dependence on the use of fossil fuels, which will lead to a predictable depletion of their natural reserves. As regards population growth, this causes an increase in waste production. Thus, it is increasingly necessary to look for alternative energy resources of a renewable nature, namely through the production of biofuels from low commercial value waste. This use of waste as a raw material for the production of biofuels has two advantages: (i) on the one hand, the accumulation of generated waste is reduced, and (ii) on the other, it can be used efficiently. This work was carried out in two fundamental stages: the physical-chemical characterization of coffee grounds and the quantification of the sugars formed, after application of acid pretreatment and enzymatic hydrolysis, by three methods: DNS (3,5-Dinitrosalicylic acid), refractometry and high performance liquid chromatography (HPLC). In the chemical characterization of coffee grounds, several parameters were used, such as calorific value (5082,82 cal/g), ash content (1,83%), protein (13,74 g/100gcoffee), cellulose (16,17%) and lignin (29,04%). In acid pre-treatment, 1% to 5% concentrations of H2SO4, HCl, HNO3, CH3COOH acids and 7% of CH3COOH were used (v/v) at a temperature of 85°C with a reaction time of 270 min and stirring at 50 rpm. The enzymatic hydrolysis was carried out in a bath at 50°C for 120 minutes, maintaining the same stirring speed of the pretreatment and using the enzyme Viscozyme L in the proportion of 100 μl enzyme/g sample. The efficiency of pretreatment and enzymatic hydrolysis was evaluated by quantifying the sugars formed. In the DNS method, 3% sulfuric and nitric acid were those that presented higher yields of total reducing sugars, with 43,1% and 43,2%, respectively, followed by 5% nitric acid, whose yield was equal to 41,4%. In the determination of total sugars, performed by refractometry, 5% sulfuric acid was the one that presented a higher yield – 78,9% - followed by the concentration of 3% with 65,3%. Finally, in the HPLC method, 5% sulfuric acid provided higher yields of xylose, arabinose and glucose, which took the values of 6,1%, 9,3% and 11%, respectively

Predictive skill of teleconnection patterns in twentieth century seasonal hindcasts and their relationship to extreme winter temperatures in Europe

European winter weather is dominated by several low-frequency teleconnection patterns, the main ones being the North Atlantic Oscillation, East Atlantic, East Atlantic/Western Russia and Scandinavian patterns. We analyze the century-long ERA-20C reanalysis and ASF-20C seasonal hindcast datasets and find that these patterns are subject to decadal variability and fluctuations in predictive skill. Using indices for determining periods of extreme cold or warm temperatures, we establish that the teleconnection patterns are, for some regions, significantly correlated or anti-correlated to cold or heat waves. The seasonal hindcasts are however only partly able to capture these relationships. There do not seem to be significant changes to the observed links between large-scale circulation patterns and extreme temperatures between periods of higher and lower predictive skill

Event-based storylines to address climate risk

The climate science community is challenged to adopt an actionable risk perspective, which is difficult to align with the traditional focus on model-based probabilistic climate change projections. Event-based storylines can provide a way out of this conundrum by putting emphasis on plausibility rather than probability. This links directly to common practices in disaster risk management using “stress-testing” for emergency preparedness based on events that are conditional on specific and plausible assumptions. Event-based storylines allow for conditional explanations, without full attribution of every causal factor, which is crucial when some aspects of the latter are complex and highly uncertain