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Abstract This work introduces an assessment of the projected climatic changes in wind characteristics in Greece through the WRF model 5 × 5 km2, forced by the EC-EARTH Global Climate Model (GCM). Firstly, the model is validated against historic observations at 10 m, before being applied to the Representative Concentration Pathways (RCPs) 4.5 and 8.5 that represent an average expected future and a worst-case scenario. Projected changes in the mean annual wind speed at 100 m, between the historic (1980–2004) and future (2020–2044) scenarios are found to vary locally between −5% and +20%, whereas for the Wind Energy Density (WED) this variation lies between −15% and +60%. Overall, robust and significant increases regarding the mean wind speeds were found mainly over the north and central-western Aegean region, the Island of Crete, as well over Greek mainland and the Ionian Sea. Both scenarios predicted higher statistically significant increases in the Weibull shape parameter values (of about 0.2) in the north–central Aegean, while the summer seasonal analysis, yielded significant decreases over the western Ionian Sea and south and south-western parts of Crete, which might be indicative of more gusty events. Finally, extreme wind speeds analysis indicated increases, which might affect wind turbines structural integrity.

Time series has been a popular tool for the analysis and forecasting of a large number of data. Very often, the applied approaches forecasts had limited success and the main reason was the lack of statistically significant historical information. We focus our attention on three common series, which are formed from the averaging of data collected over a shorter time interval. These include weekly and biweekly foreign exchange rates, mean hourly wind speed and electric load data. The proposed scheme, which takes advantage of the dominant characteristics of the shorter interval data, produced superior forecasts to those based on conventional approaches based only on historical observations of the target data. In the first two series, the proposed approach generated forecasts that significantly lower to those of the trivial random walk, a benchmark in series dominated by short-term correlation. On the load series, this approach made possible that a simple auto-regressive model returned lower forecasting error compared to a neural network that included special indicators to account for the periodic nature of the data.

The impacts of climate change are anticipated to become stronger in the future, leading to higher costs and more severe accidents in the oil industry’s facilities and surrounding communities. Motivated by this, the main objective of this paper is to develop, for the oil industry, a risk assessment methodology that considers future climate projections. In the context of an action research effort, carried out in a refinery in Greece, we adapted the organization’s extant risk management approach based on the Risk Assessment Matrix (RAM) and suggested a risk quantification process that incorporates future climate projections. The Climate Risk Assessment Matrix (CRAM) was developed to be used to assess the exposure of the facility’s assets, including human resources, to future climate risks. To evaluate CRAM, a comparison with RAM for the specific organization for the period 1980–2004 was made. Next, the application of CRAM for the period 2025–2049 indicated that, even though the resilience of the operations of the company to extreme conditions seems adequate at present, increased attention should be paid in the future to the resilience of refinery processes, the cooling system, and human resources. Beyond the specific case, the paper provides lessons for similar organizations and infrastructures located elsewhere.

Abstract The present work attempts to provide more accurate estimate of HDD and CDD and investigates the suitability of high resolution downscaled seasonal climatic forecasting models for assessing and accurately estimating the energy demands of buildings. The analysis has been established through a series of indices for estimating heating (HDD) and cooling degree days (CDD) using interpolated hourly data which were produced from the model output. The work has considerable potential to provide refined inputs for assessing building sector-specific vulnerability to climate change: energy supply and demand. In this work the application of the above mentioned methodological approach in the assessment of the energy performance and requirements of buildings on Greece are presented, for a period and with a forecast horizon of 6 months. The ARW-WRF model has been set up and validated to produce downscaled climatological fields for Greece, forced by the output of the CFSv2 model, with a horizontal spatial resolution of 5 km × 5 km. The data, that covered all Greek regions and climatology zones according to the existing building regulations code and the region elevation present a very reasonable correlation with data published in previous studies.

The present work introduces a case study on the climate resilience of interconnected critical infrastructures to forest fires, that was performed within the framework on H2020 EU-CIRCLE project (GA 653824). It was conducted in South France, one of the most touristic European regions, and also one of the regions at the highest forest fire risk that is projected to be amplified under future climate conditions. The case study has been implemented through a co-creation framework with local stakeholders, which is critical in moving beyond physical damages to the infrastructures, introducing the elements of infrastructure business continuity and societal resilience. Future forest fires extremes are anticipated to impact the interconnections of electricity and transportation networks that could further cascade to communities throughout South France. The work highlighted the benefits of enhancing co-operation between academia, emergency responders, and infrastructure operators as a critical element in enhancing resilience through increased awareness of climate impacts, new generated knowledge on fire extremes and better cooperation between involved agencies.

Abstract Under its Kyoto and EU obligations, Greece has committed to a greenhouse gas (GHG) emissions increase of at most 25% compared to 1990 levels, to be achieved during the period 2008–2012. Although this restriction was initially regarded as being realistic, information derived from GHG emissions inventories shows that an increase of approximately 28% has already taken place between 1990 and 2005, highlighting the need for immediate action. This paper explores the reallocation of production in Greece, on a sector-by-sector basis, in order to meet overall demand constraints and GHG emissions targets. We pose a constrained optimization problem, taking into account the Greek environmental input–output matrix for 2005, the amount of utilized energy and pollution reduction options. We examine two scenarios, limiting fluctuations in sectoral production to at most 10% and 15%, respectively, compared to baseline (2005) values. Our results indicate that (i) GHG emissions can be reduced significantly with relatively limited effects on GVP growth rates, and that (ii) greater cutbacks in GHG emissions can be achieved as more flexible production scenarios are allowed.

Weather conditions affect the microclimate of architectural monuments. The alteration of microclimate conditions may create risks for monuments, accelerating their weathering process. For Greece, hosting numerous monuments, the identification of the risks that climate change possess is essential for planning mitigation actions. The main soluble salts that affect archaeological materials are halite and the system of thenardite/mirabilite. The thermodynamics of the salts’ equilibrium are affected by atmospheric conditions. We study the climatology of these conditions, adopting modeled data produced by high-resolution simulations. Possible climate change impacts are investigated, aiming at mapping monuments’ vulnerability in Greece.

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or meteorological models, gives significantly lower prediction error than the elementary persistent approach. This was combined with the characteristics of the wind speed data, which are determined by the power spectrum values, distinguished by the spectral gap in intervals between 20 minutes and 2 hours. The finally proposed methodology is based on the multi-step forecasting of 10 minutes averaged data and the subsequent averaging to generate mean hourly predictions. When applied to two independent data sets, this approach outperformed by a factor of four, the conventional one which utilizes past mean hourly wind speed values as inputs to the forecasting models.

Many modern frameworks for community resilience and emergency management in the face of extreme hydrometeorological and climate events rely on scenario building. These scenarios typically cover multiple hazards and assess the likelihood of their occurrence. They are quantified by their main characteristics, including likelihood of occurrence, intensity, duration, and spatial extent. However, most studies in the literature focus only on the first two characteristics, neglecting to incorporate the internal hazard dynamics and their persistence over time. In this study, we propose a multidimensional approach to construct extreme event scenarios for multiple hazards, such as heat waves, cold spells, extreme precipitation and snowfall, and wind speed. We consider the intensity, duration, and return period (IDRP) triptych for a specific location. We demonstrate the effectiveness of this approach by developing pertinent scenarios for eight locations in Greece with diverse geographical characteristics and dominant extreme hazards. We also address how climate change impacts the scenario characteristics.