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The energy demand in Europe is projected to be affected by climate change in the future. The heating needs are expected to decrease while the cooling needs are expected to increase.The study investigates the impact of climate change on the temporal fragmentation of heating and to a lesser extent cooling needs and its implication on the energy power system. Ten bias-corrected and downscaled simulations from CMIP6 at 25 × 25 km2 horizontal resolution over Europe have been used to estimate change in heating and cooling energy needs under four anthropogenic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), using heating degree days (HDD) and cooling degree days (CDD) as proxies. Consistent with already published literature, here updated using the last CMIP6 simulation ensemble, the results show a large decrease of HDD over Europe and an increase of CDD under all scenarios. However, the study goes one step further by showing a fragmentation of the periods of heating needs during winter in the future which can potentially lead to a fragmentation of heating energy demand. In the worst-case scenario, periods of heating needs could be separated by up to 7 days, 9 times per winter. The cooling needs in summer are expected to be more frequent and last longer compared to the present climate. The fragmentation of temperature-sensitive energy needs for heating and to a lesser extent for cooling are expected to have an operational and economical impact on the balancing of the energy system.

The residential sector is important for the energy transition to combat global warming. Due to the geographical variability of socio-economic factors, the highly dependent residential electricity consumption (REC) should be studied locally. This study aims to project future French REC considering climate change and air-conditioning (AC) scenarios and to quantify its spatial variability. For this purpose, a linear temperature sensitivity model fitted by annual observed electricity consumption data and historical temperature is applied at an intra-regional scale. Future temperature-sensitive REC is computed by applying the model to temperature projections under the climate change pathway RCP8.5. Three AC scenarios are considered: (1) A 100% AC rate scenario assuming that any region partially equipped with AC systems nowadays will have all its households equipped with AC, but local temperature sensitivity will no longer progress; (2) A gradual spreading scenario mimicking “do like my neighbor” behavior; (3) A combination of the two scenarios. Increasing temperatures lead to an overall REC decrease (−8 TWh by 2040 and down to −20 TWh by 2100) with significant spatial variability, which had never been quantified and mapped due to a lack of suited methodology and limited available data at the finest scale. The evolution of REC is modulated by the evolution of cooling needs and the deployment of AC systems to meet those needs. In the first 2 AC scenarios, the decrease of REC due to climate change could be totally offset in the South of France, which would then display an increase in REC. When the 2 AC scenarios are combined, an increase in REC could be seen over the whole country. The most extreme AC scenario shows a potential REC rise due to AC usage by 2% by 2040 and even 32% by 2100, which could be canceled by increasing the cooling setpoint up to 26–27 °C.

Abstract The Mediterranean basin and Northern Africa are projected to be among the most vulnerable areas to climate change. This research documents, analyzes, and synthesizes the projected changes in precipitation P, evapotranspiration E, net water supply from the atmosphere to the surface P–E, and surface soil moisture over these regions as simulated by 17 global climate models from the sixth exercise of the Coupled Model Intercomparison Project (CMIP6) under two Shared Socioeconomic Pathways, SSP2‐4.5, and SSP5‐8.5. It also explores the sensitivity of the results to the chosen climate scenario and model resolution and assesses how the projections have evolved from the fifth exercise (CMIP5). Models project a statistically robust drying over the entire Mediterranean and coastal North Africa. Over the Northern Mediterranean sector, a significant precipitation decrease reaching −0.4 ∓ 0.1 mm day−1 is projected during the 21st century under the SSP5‐8.5 scenario. Conversely, a significant increase in precipitation of +0.05 to 0.3 ∓ 0.1 mm day−1 is projected over South‐Eastern Sahara under the same scenario. Evapotranspiration and soil moisture exhibit decreasing trends over the Mediterranean basin and an increase over the Sahara for both SSPs, with a notable acceleration from the 2020s. As a result, P‐E is projected to decrease at a rate of about −0.3 mm day−1 under the high‐end scenario SSP5‐8.5 over the Mediterranean whilst no significant changes are expected over the Sahara due to evapotranspiration compensation effects. CMIP6 and CMIP5 models project qualitatively similar patterns of changes but CMIP6 models exhibit more intense changes over the Mediterranean basin and South‐Eastern Sahara, especially during winter.

Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections. We illustrate this by producing damage projections in representative concentration pathways (RCPs) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses. This model was primary developed to provide the economic and financial community with reliable signals allowing for a better quantification of physical risks in the long term, to estimate, for example, the impact on sovereign debt.

Deep convection in the Gulf of Lion is believed to be primarily driven by the mistral winds. However, our findings show that the seasonal atmospheric change provides roughly two-thirds of the buoyancy loss required for deep convection to occur for the year 2012 to 2013, with the mistral supplying the final third. Two NEMOMED12 ocean simulations of the Mediterranean Sea were run from 1 August 2012 to 31 July 2013, forced with two sets of atmospheric-forcing data from a RegIPSL coupled run within the Med-CORDEX framework. One set of atmospheric-forcing data was left unmodified, while the other was filtered to remove the signal of the mistral. The control simulation featured deep convection, while the seasonal simulation did not. A simple model was derived by relating the anomaly scale forcing (the difference between the control and seasonal runs) and the seasonal scale forcing to the ocean response through the stratification index. This simple model revealed that the mistral's effect on buoyancy loss depends more on its strength rather than its frequency or duration. The simple model also revealed that the seasonal cycle of the stratification index is equal to the net surface heat flux over the course of the year, with the stratification maximum and minimum occurring roughly at the fall and spring equinoxes.

The mistral is a northerly low-level jet blowing through the Rhône valley in southern France and down to the Gulf of Lion. It is co-located with the cold sector of a low-level lee cyclone in the Gulf of Genoa, behind an upper-level trough north of the Alps. The mistral wind has long been associated with extreme weather events in the Mediterranean, and while extensive research focused on the lower-tropospheric mistral and lee cyclogenesis, the different upper-tropospheric large- and synoptic-scale settings involved in producing the mistral wind are not generally known. Here, the isentropic potential vorticity (PV) structures governing the occurrence of the mistral wind are classified using a self-organizing map (SOM) clustering algorithm. Based upon a 36-year (1981–2016) mistral database and daily ERA-Interim isentropic PV data, 16 distinct mistral-associated PV structures emerge. Each classified flow pattern corresponds to a different type or stage of the Rossby wave life cycle, from broad troughs to thin PV streamers to distinguished cutoffs. Each of these PV patterns exhibits a distinct surface impact in terms of the surface cyclone, surface turbulent heat fluxes, wind, temperature and precipitation. A clear seasonal separation between the clusters is evident, and transitions between the clusters correspond to different Rossby-wave-breaking processes. This analysis provides a new perspective on the variability of the mistral and of the Genoa lee cyclogenesis in general, linking the upper-level PV structures to their surface impact over Europe, the Mediterranean and north Africa.

Introduction: Plasma volume (PV) changes in response to physical activity, possibly as a consequence of adrenergic activation. We estimated changes in PV in response to common exercise modalities; cycling and running as well as adrenaline infusion and control at rest.Methods: On separate days, forty circulatory healthy subjects [aged 60 years (range: 42–75)] with knee osteoarthritis underwent moderate-high intensity cycling, running, and intravenous adrenaline infusion to mimic the circulatory response to exercise. Blood samples were obtained from peripheral veins taken at several pre-defined time points before, during, and after the interventions. PV changes were estimated using venous hemoglobin and the derived hematocrit. The temporal associations between PV and selected biomarkers were explored.Results: Changes in PV were observed during all four interventions, and the response to cycling and running was similar. Compared to rest, PV decreased by -14.3% (95% CI: -10.0 to -18.7) after cycling, -13.9% (95% CI: -10.9 to -17.0) after running, and -7.8% (95% CI: -4.2 to -11.5) after adrenaline infusion.Conclusion: PV decreased in response to moderate-high intensity running and cycling. Adrenaline infusion mimicked the PV change observed during exercise, suggesting a separate influence of autonomic control on blood volume homeostasis. In perspective, a temporal association between PV and biomarker dynamics suggests that consideration of PV changes could be relevant when reporting plasma/serum constituents measured during exercise, but more research is needed to confirm this.

Autoantibodies have the potential as cancer biomarkers as they may associate with the outcome and immune-related adverse events (irAEs) following immunotherapy. Cancer and other fibroinflammatory diseases, such as rheumatoid arthritis (RA), are associated with excessive collagen turnover leading to collagen triple helix unfolding and denaturation with exposure of immunodominant epitopes. In this study, we aimed to investigate the role of autoreactivity against denatured collagen in cancer. A technically robust assay to quantify autoantibodies against denatured type III collagen products (anti-dCol3) was developed and then measured in pretreatment serum from 223 cancer patients and 33 age-matched controls. Moreover, the association between anti-dCol3 levels and type III collagen degradation (C3M) and formation (PRO-C3) was investigated. Anti-dCol3 levels were significantly lower in patients with bladder (p = 0.0007), breast (p = 0.0002), colorectal (p < 0.0001), head and neck (p = 0.0005), kidney (p = 0.005), liver (p = 0.030), lung (p = 0.0004), melanoma (p < 0.0001), ovarian (p < 0.0001), pancreatic (p < 0.0001), prostate (p < 0.0001), and stomach cancers (p < 0.0001) compared to controls. High anti-dCol3 levels were associated with type III collagen degradation (C3M, p = 0.0002) but not type III collagen formation (PRO-C3, p = 0.26). Cancer patients with different solid tumor types have downregulated levels of circulating autoantibodies against denatured type III collagen compared to controls, suggesting that autoreactivity against unhealthy type III collagen may be important for tumor control and eradication. This autoimmunity biomarker may have the potential for studying the close relationship between autoimmunity and cancer.

Abstract Objective Associations between rheumatoid arthritis (RA) and effect of treatment at the tissue levels are poorly understood. We investigated the scope of released extracellular matrix (ECM) metabolites as a consequence of tissue remodelling in patients treated with methotrexate (MTX) and tocilizumab (TCZ) compared to placebo. Methods Tissue metabolites from 387 RA patients treated with either TCZ (8 mg/kg) or MTX monotherapy (7.5–20 mg/kg) were measured at baseline and 8 weeks sera by validated ELISA assays. The levels of collagen biomarkers (C1M, C2M, C3M and C4M) together with C-reactive protein (CRP) and CRP metabolite (CRPM) were investigated. Baseline levels of biomarkers have been compared with 72 age- and gender-matched healthy controls. Comparison between treatment and response groups were done by ANCOVA, Spearman’s correlation and logistic regression adjusted for age, gender, BMI and disease duration. Results C1M and C3M were significantly (P

Off-shore wind energy in Europe plays a key role in the transition to renewable energy, and its usage is expected to increase in the next few decades. According to the working regimes of a wind turbine, wind energy production can be disrupted by extreme atmospheric events related to low wind speed below the cut-in wind speed and high wind speed above the cut-out wind speed. The purpose of this work is to estimate the behavior of extreme winds on the European panorama, over the period 1950–2020, in order to investigate the large-scale weather regimes related to them and their impact on off-shore wind energy availability. We detected significant changes in the frequency of high and low extreme wind events, proving that climate change or long-term internal climate variability have already affected the off-shore wind power output. Moreover, the analysis of weather regimes showed that high and low extreme wind events can occur simultaneously over Europe. Our results suggest the necessity to implement efficient European energy management policies, to minimize the deficit in wind power supply.

Protein citrullination and degradation by matrix metalloproteinases (MMP) plays a central role in the pathology of rheumatoid arthritis (RA). Autoantibodies are known to target citrullinated vimentin. The aim of this study was to investigate the relationship between the blood levels of MMP-degraded and citrullinated vimentin (VICM), as compared with the levels of MMP-degraded and non-citrullinated vimentin (VIM), and the standard anti-CCP biomarker in RA patients undergoing treatment. Thus, VIM, VICM and anti-CCP were quantified by ELISA in serum samples from baseline and week 8 of patients (n = 257) with RA, treated with either tocilizumab (8 mg/kg), methotrexate (7.5–15 mg/kg) or a placebo and compared with a reference cohort (n = 64). The three biomarkers were elevated in RA serum compared with the reference cohort: medians were 1.7 vs. 0.8 ng/mL (p < 0.05) for VIM; 7.5 vs. 0.7 ng/mL (p < 0.0001) for VICM; 57 vs. 4 RU/mL (p < 0.001) for anti-CCP. VICM was decreased in response to tocilizumab (2.9-fold, p < 0.0001) and to methotrexate (1.5-fold, p < 0.05) compared with the placebo, while anti-CCP was not. Serum VIM was also modulated by both drugs, although to a lesser degree. A high baseline level of VICM was predictive of a low disease activity response at week 8. In conclusion, VICM can differentiate between RA and healthy donors in a similar manner to anti-CCP; furthermore, VICM is also a pharmacodynamic marker.

Aerosols may impact precipitation in a complex way involving their direct and indirect effects. In a previous numerical study, the overall microphysical effect of aerosols was found to weaken precipitation through reduced precipitable water and convective instability. The present study aims to quantify the relative importance of these two processes in the reduction of summer precipitation using temperature–precipitation scaling. Based on a numerical sensitivity experiment conducted in central Europe aiming to isolate indirect effects, the results show that, all others effects being equal, the scaling of hourly convective precipitation with temperature follows the Clausius–Clapeyron (CC) relationship, whereas the decrease in convective precipitation does not scale with the CC law since it is mostly attributable to increased stability with increased aerosol concentration rather than to decreased precipitable water content. This effect is larger at low surface temperatures at which clouds are statistically more frequent and optically thicker. At these temperatures, the increase in stability is mostly linked to the stronger reduction in temperature in the lower troposphere compared to the upper troposphere, which results in lower lapse rates.

A methanol island, powered by solar or wind energy, indirectly captures atmospheric CO2 through the ocean and combines it with hydrogen gas to produce a synthetic fuel. The island components include a carbon dioxide extractor, a desalinator, an electrolyzer, and a carbon dioxide-hydrogen reactor to complete this process. In this study, the optimal locations to place such a device in the Mediterranean Sea were determined, based on three main constraints: power availability, environmental risk, and methanol production capability. The island was numerically simulated with a purpose built python package pyseafuel. Data from 20 years of ocean and atmospheric simulation data were used to “force” the simulated methanol island. The optimal locations were found to strongly depend on the power availability constraint, with most optimal locations providing the most solar and/or wind power, due to the limited effect the ocean surface variability had on the power requirements of methanol island. Within this context, optimal locations were found to be the Alboran, Cretan, and Levantine Sea due to the availability of insolation for the Alboran and Levantine Sea and availability of wind power for the Cretan Sea. These locations were also not co-located with areas with larger maximum significant wave heights, thereby avoiding areas with higher environmental risk. When we simulate the production at these locations, a 10 L s−1 seawater inflow rate produced 494.21, 495.84, and 484.70 mL m−2 of methanol over the course of a year, respectively. Island communities in these regions could benefit from the energy resource diversification and independence these systems could provide. However, the environmental impact of such systems is poorly understood and requires further investigation.

Investments into wind power generation may be hampered by the uncertainty of future revenues caused by the natural variability of the wind resource, the impact of climate change on wind potential and future electricity prices, and the regulatory risks. We quantify the uncertainty of the economic value of wind farms in France, Germany, and Denmark, and evaluate the cost of support mechanisms needed to ensure the profitability of wind farms under present and future climates. To this end, we built a localised model for wind power output and a country-level model for electricity demand and prices. Our study reveals that support mechanisms are needed for current market conditions and the current climate, as well as under future climate conditions according to several scenarios for climate change and energy transition. The cost of support mechanisms during a 15-year period is evaluated to EUR 3.8 to EUR 11.5 billion per year in France, from EUR 15.5 to EUR 26.5 billion per year in Germany, and from EUR 1.2 to EUR 3.3 billion per year in Denmark, depending on the scenario considered and the level of penetration of wind energy.

This work uses a network of GPS stations over Europe from which a homogenized integrated water vapor (IWV) dataset has been retrieved, completed with colocated temperature and precipitation measurements over specific stations to (i) estimate the biases of six regional climate models over Europe in terms of humidity; (ii) understand their origins; and (iii) finally assess the impact of these biases on the frequency of occurrence of precipitation. The evaluated simulations have been performed in the framework of HYMEX/Med-CORDEX programs and cover the Mediterranean area and part of Europe at horizontal resolutions of 50 to 12 km. The analysis shows that models tend to overestimate the low values of IWV and the use of the nudging technique reduces the differences between GPS and simulated IWV. Results suggest that physics of models mostly explain the mean biases, while dynamics affects the variability. The land surface–atmosphere exchanges affect the estimation of IWV over most part of Europe, especially in summer. The limitations of the models to represent these processes explain part of their biases in IWV. However, models correctly simulate the dependance between IWV and temperature, and specifically the deviation that this relationship experiences regarding the Clausius–Clapeyron law after a critical value of temperature (Tbreak). The high spatial variability of Tbreak indicates that it has a strong dependence on local processes which drive the local humidity sources. This explains why the maximum values of IWV are not necessarily observed over warmer areas, which are often dry areas. Finally, it is shown over the SIRTA observatory (near Paris) that the frequency of occurrence of light precipitation is strongly conditioned by the biases in IWV and by the precision of the models to reproduce the distribution of IWV as a function of the temperature. The results of the models indicate that a similar dependence occurs in other areas of Europe, especially where precipitation has a predominantly convective character. According to the observations, for each range of temperature, there is a critical value of IWV from which precipitation starts to increase. The critical values and the probability of exceeding them are simulated with a bias that depends on the model. Those models, which generally present light precipitation too often, show lower critical values and higher probability of exceeding them.

Aerosols affect atmospheric dynamics through their direct and semi-direct effects as well as through their effects on cloud microphysics (indirect effects). The present study investigates the indirect effects of aerosols on summer precipitation in the Euro-Mediterranean region, which is located at the crossroads of air masses carrying both natural and anthropogenic aerosols. While it is difficult to disentangle the indirect effects of aerosols from the direct and semi-direct effects in reality, a numerical sensitivity experiment is carried out using the Weather Research and Forecasting (WRF) model, which allows us to isolate indirect effects, all other effects being equal. The Mediterranean hydrological cycle has often been studied using regional climate model (RCM) simulations with parameterized convection, which is the approach we adopt in the present study. For this purpose, the Thompson aerosol-aware microphysics scheme is used in a pair of simulations run at 50 km resolution with extremely high and low aerosol concentrations. An additional pair of simulations has been performed at a convection-permitting resolution (3.3 km) to examine these effects without the use of parameterized convection. While the reduced radiative flux due to the direct effects of the aerosols is already known to reduce precipitation amounts, there is still no general agreement on the sign and magnitude of the aerosol indirect forcing effect on precipitation, with various processes competing with each other. Although some processes tend to enhance precipitation amounts, some others tend to reduce them. In these simulations, increased aerosol loads lead to weaker precipitation in the parameterized (low-resolution) configuration. The fact that a similar result is obtained for a selected area in the convection-permitting (high-resolution) configuration allows for physical interpretations. By examining the key variables in the model outputs, we propose a causal chain that links the aerosol effects on microphysics to their simulated effect on precipitation, essentially through reduction of the radiative heating of the surface and corresponding reductions of surface temperature, resulting in increased atmospheric stability in the presence of high aerosol loads.

The viability of Variable Renewable Energy (VRE)-investment strategies depends on the response of dispatchable producers to satisfy the net load. We lack a simple research tool with sufficient complexity to represent major phenomena associated with the response of dispatchable producers to the integration of high shares of VRE and their impact on system costs. We develop a minimization of the system cost allowing one to quantify and decompose the system value of VRE depending on an aggregate dispatchable production. Defining the variable cost of the dispatchable generation as quadratic with a coefficient depending on macroeconomic factors such as the cost of greenhouse gas emissions leads to the simplest version of the model. In the absence of curtailment, and for particular parameter values, this version is equivalent to a mean-variance problem. We apply this model to France with solar and wind capacities distributed over the administrative regions of metropolitan France. In this case, ignoring the wholesale price effect and variability has a relatively small impact on optimal investments, but leads to largely underestimating the system total cost and overestimating the system marginal cost.

In this study, we examine how Battery Storage (BES) and Thermal Storage (TES) combined with solar Photovoltaic (PV) and Concentrated Solar Power (CSP) technologies with an increased storage duration and rental cost together with diversification would influence the Moroccan mix and to what extent the variability (i.e., adequacy risk) can be reduced; this is done using recent (2013) cost data and under various penetration scenarios. To do this, we use MERRA-2 climate reanalysis to simulate hourly demand and capacity factors (CFs) of wind, solar PV and CSP without and with increasing storage capabilities—as defined by the CSP Solar Multiple (SM) and PV Inverter Loading Ratio (ILR). We adjust these time series to observations for the four Moroccan electrical zones over the year 2018. Our objective is to maximize the renewable (RE) penetration and minimize the imbalances between RE production and consumption considering three optimization strategies. We analyze mixes along Pareto fronts using the Mean-Variance Portfolio approach—implemented in the E4CLIM model—in which we add a maximum-cost constraint to take into account the different rental costs of wind, PV and CSP. We propose a method to calculate the rental cost of storage and production technologies taking into account the constraints on storage associated with the increase of SM and ILR in the added PV-BES and CSP-TES modules, keeping the mean solar CFs fixed. We perform some load bands-reduction diagnostics to assess the reliability benefits provided by each RE technology. We find that, at low penetrations, the maximum-cost budget is not reached because a small capacity is needed. The higher the ILR for PV, the larger the share of PV in the mix compared to wind and CSP without storage is removed completely. Between PV-BES and CSP-TES, the latter is preferred as it has larger storage capacity and thus stronger impact in reducing the adequacy risk. As additional BES are installed, more than TES, PV-BES is favored. At high penetrations, optimal mixes are impacted by cost, the more so as CSP (resp., PV) with high SM (resp., ILR) are installed. Wind is preferably installed due to its high mean CF compared to cost, followed by either PV-BES or CSP/CSP-TES. Scenarios without or with medium storage capacity favor CSP/CSP-TES, while high storage duration scenarios are dominated by low-cost PV-BES. However, scenarios ignoring the storage cost and constraints provide more weight to PV-BES whatever the penetration level. We also show that significant reduction of RE variability can only be achieved through geographical diversification. Technological complementarity may only help to reduce the variance when PV and CSP are both installed without or with a small amount of storage. However, the diversification effect is slightly smaller when the SM and ILR are increased and the covariances are reduced as well since mixes become less diversified.

The methodology used in wind resource assessments often relies on modeling the wind-speed statistics using a Weibull distribution. In spite of its common use, this distribution has been shown to not always accurately model real wind-speed distributions. Very few studies have examined the arising errors in power outputs, using either observed power productions or theoretical power curves. This article focuses on France, using surface wind measurements at 89 locations covering all regions of the country. It investigates how statistical modeling using a Weibull distribution impacts the prediction of the wind energy content and of the power output in the context of an annual energy production assessment. For this purpose it uses a plausible power curve adapted to each location. Three common methods for fitting the Weibull distribution are tested (maximum likelihood, first and third moments, and the Wind Atlas Analysis and Application Program (WAsP) method). The first two methods generate large errors in the production (mean absolute error around 5 %), especially in the southern areas where the goodness of fit of the Weibull distribution is poorer. The production is mainly overestimated except at some locations with bimodal wind distributions. With the third method, the errors are much lower at most locations (mean absolute error around 2 %). Another distribution, a mixed Rayleigh–Rice distribution, is also tested and shows better skill at assessing the wind energy yield.

The HYdrological cycle in the Mediterranean Experiment (HyMeX) Special Observing Period 2 (SOP2, January 27–March 15, 2013) was dedicated to the study of dense water formation in the Gulf of Lion in the northwestern Mediterranean. This paper outlines the deep convection of winter 2012–2013 and the meteorological conditions that produced it. Alternating phases of mixing and restratification are related to periods of high and low heat losses, respectively. High-resolution, realistic, three-dimensional models are essential for assessing the intricacy of buoyancy fluxes, horizontal advection, and convective processes. At the submesoscale, vertical velocities resulting from symmetric instabilities of the density front bounding the convection zone are crucial for ventilating the deep ocean. Finally, concomitant atmospheric and oceanic data extracted from the comprehensive SOP2 data set highlight the rapid, coupled evolution of oceanic and atmospheric boundary layer characteristics during a strong wind event.

We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.

In recent years, environmental concerns resulted in an increase in the use of renewable resources such as wind energy. However, high penetration of the wind power is a challenge due to the intermittency of this resource. In this context, the wind energy forecasting has become a major issue. In particular, for the end users of wind energy forecasts, a critical but often neglected issue is the economic value of the forecast. In this work, we investigate the economic value of forecasting from 30 min to 3 h ahead, also known as nowcasting. Nowcasting is mainly used to inform trading decisions in the intraday market. Two sources of uncertainty affecting wind farm revenues are investigated, namely forecasting errors and price variations. The impact of these uncertainties is assessed for six wind farms and several balancing strategies using market data. Results are compared with the baseline case of no nowcasting and with the idealized case of perfect nowcast. The three settings show significant differences while the impact of the choice of a specific balancing strategy appears minor.

Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principal components of large-scale atmospheric predictors. By integrating the joint probability of consumption and production over different areas, we define two kind of risk measures: one quantifies the probablity of deviating from the climatological means, while the other, which is the value at risk at 95% confidence level (VaR95) of the difference between consumption and production, quantifies extreme risks of imbalance. In the first case, the reconstructed risk accurately reproduces the actual risk with over 0.80 correlation in time, and a hit rate around 70–80%. In the second case, we find a mean absolute error (MAE) between the reconstructed and real extreme risk of 2.5 to 2.8 GW, a coefficient of variation of the root mean square error (CV-RMSE) of 3.8% to 4.2% of the mean actual VaR95 and a correlation of 0.69 and 0.66 for winter and fall, respectively. By applying our model to ensemble forecasts performed with a numerical weather prediction model, we show that forecasted risk measures up to 1 month horizon can outperform the climatology often used as the reference forecast (time correlation with actual risk ranging between 0.54 and 0.82). At seasonal time horizon (3 months), our forecasts seem to tend to the climatology.

In this paper, we analyze the sensitivity of the optimal mixes to cost and variability associated with solar technologies and examine the role of Thermal Energy Storage (TES) combined to Concentrated Solar Power (CSP) together with time-space complementarity in reducing the adequacy risk—imposed by variable Renewable Energies (RE)—on the Moroccan electricity system. To do that, we model the optimal recommissioning of RE mixes including Photovoltaic (PV), wind energy and CSP without or with increasing levels of TES. Our objective is to maximize the RE production at a given cost, but also to limit the variance of the RE production stemming from meteorological fluctuations. This mean-variance analysis is a bi-objective optimization problem that is implemented in the E4CLIM modeling platform—which allows us to use climate data to simulate hourly Capacity Factors (CFs) and demand profiles adjusted to observations. We adapt this software to Morocco and its four electrical zones for the year 2018, add new CSP and TES simulation modules, perform some load reduction diagnostics, and account for the different rental costs of the three RE technologies by adding a maximum-cost constraint. We find that the risk decreases with the addition of TES to CSP, the more so as storage is increased keeping the mean capacity factor fixed. On the other hand, due to the higher cost of CSP compared to PV and wind, the maximum-cost constraint prevents the increase of the RE penetration without reducing the share of CSP compared to PV and wind and letting the risk increase in return. Thus, if small level of risk and higher penetrations are targeted, investment must be increased to install more CSP with TES. We also show that regional diversification is key to reduce the risk and that technological diversification is relevant when installing both PV and CSP without storage, but less so as the surplus of energy available for TES is increased and the CSP profiles flatten. Finally, we find that, thanks to TES, CSP is more suited than PV and wind to meet peak loads. This can be measured by the capacity credit, but not by the variance-based risk, suggesting that the latter is only a crude representation of the adequacy risk.

In this article we investigate the use of statistical methods for wildfire risk assessment in the Mediterranean Basin using three meteorological covariates, the 2 m temperature anomaly, the 10 m wind speed and the January–June rainfall occurrence anomaly. We focus on two remotely sensed characteristic fire variables, the burnt area (BA) and the fire radiative power (FRP), which are good proxies for fire size and intensity respectively. Using the fire data we determine an adequate parametric distribution function which fits best the logarithm of BA and FRP. We reconstruct the conditional density function of both variables with respect to the chosen meteorological covariates. These conditional density functions for the size and intensity of a single event give information on fire risk and can be used for the estimation of conditional probabilities of exceeding certain thresholds. By analysing these probabilities we find two fire risk regimes different from each other at the 90 % confidence level: a "background" summer fire risk regime and an "extreme" additional fire risk regime, which corresponds to higher probability of occurrence of larger fire size or intensity associated with specific weather conditions. Such a statistical approach may be the ground for a future fire risk alert system.