Heat waves are large-scale atmospheric phenomena that may cause heat stress in ecosystems and socio-economic activities. In cities, morbidity and mortality may increase during a heat wave, overloading health and emergency services. In the face of climate change and associated warming, cities need to adapt and mitigate the effects of heat waves. This study suggests a new method to evaluate heat waves' impacts on cities by considering some aspects of heat waves that are not usually considered in other similar studies. The method devises heat wave quantities that are easy to calculate; it is relevant to assessing their impacts and permits the development of adaptation measures. This study applies the suggested method to quantify various aspects of heat waves in Lisbon for future climate projections considering future mid-term (2046-2065) and long-term (2081-2100) climates under the RCP8.5 greenhouse emission scenario. This is achieved through the analysis of various regional climate simulations performed with the WRF model and an ensemble of EURO-CORDEX models. This allows an estimation of uncertainty and confidence of the projections. To evaluate the climate change properties of heat waves, statistics for future climates are compared to those for a reference recent climate. Simulated temperatures are first bias corrected to minimize the model systematic errors relative to observations. The temperature for mid and long-term futures is expected to increase relative to the present by 1.6 • C and 3.6 • C, respectively, with late summer months registering the highest increases. The number of heat wave days per year will increase on average from 10, in the present climate, to 38 and 63 in mid and long-term climates, respectively. Heat wave duration, intensity, average maximum temperature, and accumulated temperature during a heat wave will also increase. Heat waves account for an annual average of accumulated temperature of 358 • C·day in the present climate, while in the mid and long-term, future climates account for 1270 • C·day and 2078 • C·day, respectively. The largest increases are expected to occur from July to October. Extreme intensity and long-duration heat waves with an average maximum temperature of more than 40 • C are expected to occur in the future climates.weather events such as storms and tornados. However, HW may negatively affect, for example, agriculture, forest fires, power supply for cooling, animal and human morbidity, and mortality, particularly of vulnerable people.HW are a synoptic scale weather phenomena that impact large regions, countries, and even great parts of a continent. Their effects may be locally amplified by various factors such as the type of land coverage, altitude, slope orientation of orography, and proximity to water bodies. In medium to large cities, HW may be strongly amplified by the nature of predominant materials in what is known as the urban heat island effect [2].Many HW studies have been published with a vast range of objectives, such as for example to evaluate the HW sp...

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This work aims to compare the performance of the single‑(SLUCM) and multilayer (BEP-Building effect parameterization) urban canopy models (UCMs) coupled with the Weather Research and Forecasting model (WRF), along with the application of two urban heat island (UHI) identification methods. The identification methods are: (1) the “classic method”, based on the temperature difference between urban and rural areas; (2) the “local method” based on the temperature difference at each urban location when the model land use is considered urban, and when it is replaced by the dominant rural land use category of the urban surroundings. The study is performed as a case study for the city of Lisbon, Portugal, during the record-breaking August 2003 heatwave event. Two main differences were found in the UHI intensity (UHII) and spatial distribution between the identification methods: a reduction by half in the UHII during nighttime when using the local method; and a dipole signal in the daytime and nighttime UHI spatial pattern when using the classic method, associated with the sheltering effect provided by the high topography in the northern part of the city, that reduces the advective cooling in the lower areas under prevalent northern wind conditions. These results highlight the importance of using the local method in UHI modeling studies to fully isolate urban canopy and regional geographic contributions to the UHII and distribution. Considerable improvements were obtained in the near‑surface temperature representation by coupling WRF with the UCMs but better with SLUCM. The nighttime UHII over the most densely urbanized areas is lower in BEP, which can be linked to its larger nocturnal turbulent kinetic energy (TKE) near the surface and negative sensible heat (SH) fluxes. The latter may be associated with the lower surface skin temperature found in BEP, possibly owing to larger turbulent SH fluxes near the surface. Due to its higher urban TKE, BEP significantly overestimates the planetary boundary layer height compared with SLUCM and observations from soundings. The comparison with a previous study for the city of Lisbon shows that BEP model simulation results heavily rely on the number and distribution of vertical levels within the urban canopy.

<p>This work investigates the physical interactions and feedback between wildfires and the atmosphere using the coupled atmosphere-fire spread modelling system, WRF-SFIRE. The Figueira da Foz forest fire, occurred in Portugal in October 2017, which ocurred in association with hurricane Ophelia, was simulated under two different scenarios of fuel moisture settings, one static and one dynamic. Results show an underestimation of burnt area in the dynamic case, while static fuel moisture has shown a very high agreement. Pyrocumulus formed during late afternoon with a very dry base and more humid top, creating conditions favourable for the occurrence of downbursts, with very high Convective Available Potential Energy values. Lifted Condensation Level increased above the fire front as moisture was transported upwards, increasing surface temperature. Official reports show an overestimation of fuel moisture near the surface, leading to high CAPE values, compared to near zero values reported by vertical soundings. Relative Humidity values were higher by 30% when compared to weather station observations, and temperatures approximately 4ºC lower. Further model testing is needed to provide more accurate surface temperature and moisture simulations, to allow a more accurate fire progression representation and energy exchange, and improve the modelling of potential convective events. </p>

<p>In an ever-growing demand for solar energy production and technologies, atmospheric aerosols pose a great challenge for solar power suppliers due to their high spatio-temporal variability, their direct influence on the scattering and absorption of the incoming solar radiation, and their role as cloud condensation nuclei, indirectly affecting cloud formation and precipitation. Hence, atmospheric aerosols are a preponderant element of an accurate weather and solar forecasting system. Located in Southwestern Europe, the Iberian Peninsula (IP) represents one of the regions with the highest solar power potential in Europe, but it is frequently affected by forest fires and high-concentration dust episodes with their origin in the major African deserts during the warmer months. In this study, a model setup using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is evaluated during an extreme dust episode that affected the IP in August 2010, aiming to perform aerosol-radiation-cloud interactions studies over the region. Model results, such as particle concentration and aerosol optical properties, are compared against different in-situ observations and remote sensing data from regional air quality stations and from the Aerosol Robotic Network (AERONET) to assess the model performance under these kinds of events.</p>