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Producción Científica, Mountain lee waves present significant hazards to aviation, often inducing turbulence and aircraft icing. The current study focuses on understanding the potential impact of global climate change on the precursor environments to mountain lee wave cloud episodes over central Iberia. We examine the suitability of several Global Climate Models (GCMs) from CMIP6 in predicting these environments using the ERA5 reanalysis as a benchmark for performance. The dataset is divided into two periods: historical data (2001–2014) and projections for the SSP5–8.5 future climate scenario (2015–2100). The variations and trends in precursor environments between historical data and future climate scenarios are exposed, with a particular focus on the expansion of the Azores High towards the Iberian Peninsula, resulting in increased zonal winds throughout the Iberian Peninsula in the future. However, the increase in zonal wind is insufficient to modify the wind pattern, so future mountain lee wave cloud events will not vary significantly. The relative humidity trends reveal no significant changes. Moreover, the risk of icing precursor environments connected with mountain lee wave clouds is expected to decrease in the future, due to rising temperatures. Our results highlight that the EC-EARTH3 GCM reveals the closest alignment with ERA5 data, and statistically significant differences between the historical and future climate scenario periods are presented, making EC-EARTH3 a robust candidate for conducting future studies on the precursor environments to mountain lee wave cloud events., Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 - (project PID2019-105306RB-I00)

Producción Científica, Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries., Ministerio de Ciencia e Innovación - (LANDSUSFIRE project PID2022-139156OB-C21), Junta de Castilla y León - (WUIFIRECYL project LE005P20), Fundación Portuguesa para la Ciencia y la Tecnología (FCT) - ( project UIDB/04033/2020), Ministerio de Educación, Formación Profesional y Deportes, Programa Salvador de Madariaga - (grants PRX22/00305 and PRX22/00307)

Producción Científica, In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a Bspline on the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand,model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution, The work of Ranalli has been carried out with the support of the project AIDMIX, Fondo di ricerca di Ateneo, edizione 2021, Universita degli Studi di Perugia. The work of Salvati has been carried out with the support of the project InGRID 2 (Grant Agreement N. 730998) and of the project LOCOMOTION (Grant Agreement N. 821105).

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, Poor urban air quality is one of the main environmental concerns worldwide due to its implications for population exposure and health -related issues. However, the development of effective abatement strategies in cities requires a consistent and holistic assessment of air pollution processes, taking into account all the relevant scales within a city. This contribution presents the methodology and main results of an intensive experimental campaign carried out in a complex pollution hotspot in Madrid (Spain) under the TECNAIRE-CM research project, which aimed at understanding the microscale spatio-temporal variation of ambient concentration levels in areas where high pollution values are recorded. A variety of instruments were deployed during a three-week field campaign to provide detailed information on meteorological and micrometeorological parameters and spatio-temporal variations of the most relevant pollutants (NO2 and PM) along with relevant information needed to simulate pedestrian fluxes. The results show the strong dependence of ambient concentrations on local emissions and meteorology that turns out in strong spatial and temporal variations, with gradients up to 2 mu g m(-3) m(-1) for NO2 and 55 mu m(-3) min(-1) for PM10. Pedestrian exposure to these pollutants also presents strong variations temporally and spatially but it concentrates on pedestrian crossings and bus stops. The analysis of the results show that the high concentration levels found in urban hotspots depend on extremely complex dynamic processes that cannot be captured by routinely measurements made by air quality monitoring stations used for regulatory compliance assessment. The large influence from local traffic in the concentration fields highlights the need for a detailed description of specific variables that determine emissions and dispersion at microscale level. This also indicates that city-scale interventions may be complemented with local control measures and exposure management, to, Comunidad Autónoma de Madrid. Dirección General de Universidades e Investigación, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, Exceedances of NO2 hourly limit value (200 mu g.m(-3)) imply the need to implement short term action plans to avoid adverse effects on human health in urban areas. The Madrid City Council applied the stage 3 of the NO2 protocol during a high-pollution episode under stable meteorological conditions on December 2016 for the first time. This included road traffic access restrictions to the city centre (50% of conventional private vehicles based on plate numbers). In this contribution we analyse different meteorological and air quality observations, including non-standard parameters (such as number of ultrafine particles and remote sensing techniques MAXDOAS) for a better understanding of the effectivity of short-term emission abatement measures under real conditions and to identify options to improve the NO2 protocol in the future. According to our results, the inversion base height computed from vertical temperature soundings is a meaningful index to anticipate very unfavourable conditions and trigger the actions included in the protocol. The analysis of the concentration levels of the main pollutants from the Madrid air quality monitoring network indicate that only stage 3 of the protocol had a significant effect on NO2 maximum concentrations. The restrictions applied may have prevented NO2 concentrations to further increase in the city centre (up to 15%) although pollution levels in the city outskirts, outside the area directly affected by the traffic restrictions, remained unchanged or may have been slightly increased. Nonetheless, further studies are needed to estimate more precisely the effect of the measures taken and to assess potential trade-offs. Our results suggest that emissions play an important role also under very strong stability conditions although drastic measures are needed to achieve a significant impact. This highlights the importance of an appropriate timing for short-term actions and the need of permanent abatement measures related to air quality, Madrid City Council (Environment and Mobility Division of the General Directorate of Sustainability and Environmental Control), Madrid Greater Region under the TECNAIRE-CM research project, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, This work presents the main results of two experimental campaigns carried out in summer and winter seasons in a complex pollution hotspot near a large park, El Retiro, in Madrid (Spain). These campaigns were aimed at understanding the microscale spatio-temporal variation of ambient concentration levels in areas with high pollution values to obtain data to validate models on the effect of urban trees on particulate matter concentrations. Two different measuring approaches have been used. The first one was static, with instruments continuously characterizing the meteorological variables and the particulate matter concentration outside and inside the park. During the summer campaign, the particulate matter concentration was clearly influenced by a Saharan dust outbreak during the period 23 June to 10 July 2016, when most of the particulate matter was in the fraction PM2.5-10. During the winter campaign, the mass concentrations were related to the meteorological conditions and the high atmospheric stability. The second approach was a dynamic case with mobile measurements by portable instruments. During the summer campaign, a DustTrak instrument was used to measure PM10 and PM2.5 in different transects close to and inside the park at different distances from the traffic lane. It was observed a decrease in the concentrations up to 25% at 20 m and 50% at 200 m. High PK10 values were linked to dust resuspension caused by recreational activities and to a Saharan dust outbreak. The highest PM values were measured at the Independencia square, an area with many bus stops and high traffic density. During the winter campaign, three microaethalometers were used for Black Carbon measurement. Both pollutants also showed a reduction in their concentrations when moving towards inside the park. For PM10 and PM2.5, reductions up to 50% were observed, while for BC this reduction was smaller, about 20%. (C) 2019 Elsevier Ltd. All rights reserved., Madrid Regional Research Plan through TECNAIRE, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

Laser disdrometers measure the particle size distribution (PSD) of hydrometeors through a small cross-sectional (tens of square centimeters) surface. Such a limited area induces a sampling effect in the estimates of the PSD, which translates to error in the reflectivity-rain-rate (Z-R) relationship used for ground radar estimates of rainfall, estimates of kinetic energy of precipitation, and any other hydrometeorological application relying on particle size information. Here, the results of a dedicated experiment to estimate the extent of the effect of limited area sampling of rainfall are presented. Using 14 Parsivel, version 1 (Parsivel-1), disdrometers placed within 6 m(2), it was found that the combined area of at least seven disdrometers is required for the estimates to start converging to a stable value. The results can be used to quantify the degree of over-/underestimation of precipitation parameters for a single instrument due to the limited collecting area effect. It has been found that a single disdrometer may underestimate instantaneous rain rate by 70%., Ministerio de Economía y Competitividad (España), Instituto de Salud Carlos III, CYTEMA (UCLM), NASA Precipitation Science, GPM programs, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

Monthly, seasonal and annual trends in mean temperatures have been analysed in this study using data from 37 weather stations from the Pakistan Meteorological Department with records from 1952 to 2009. Statistical tests including Sen's slope and Mann‐Kendall were applied to each of the 37 stations in order to determine the sign and slopes of trends and their statistical significance. The study reveals that the temperature has generally increased in Pakistan at all time scales analysed over the past few decades. March and the pre‐monsoon season were the periods with the highest number of weather stations showing statistical significance, and also with the highest magnitudes of trends. Mean annual temperature increased around 0.36 °C/decade. This rise in temperature is slightly higher than other results found for Pakistan. The association between temperatures and certain teleconnection patterns, as well as the influence of the urban effect, might be among the causes of the trends found in this study. The largest number of correlations between mean temperatures and teleconnection patterns was found in March, April and May with NAO, ENSO and NCP, respectively. The North Atlantic Oscillation (NAO) may also have an influence on the temperatures of certain months in the monsoon season and particularly in August. At a seasonal resolution, NAO and NCP may control temperatures in the pre‐monsoon season. The lowest number of sites exhibiting correlations was found in winter and in the post‐monsoon seasons., Depto. de Didáctica de las Ciencias Experimentales , Sociales y Matemáticas, Fac. de Educación, TRUE, pub

Near-monochromatic gravity waves (GWs) associated with a mesoscale convective system (MCS) were detected during the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign in Lannemezan (France) on 21 June 2011. These GWs are analyzed using available instrumental data (e.g. an array of microbarometers, a microwave system Humidity And Temperature PROfiler (HATPRO) and an ultra-high-frequency (UHF) wind profiler). Pressure oscillations of up to 0.5 hPa were recorded after a pronounced pressure drop of 1.4 hPa, identified as the MCS weak low. Wavelet analysis and evaluated wave parameters confirm the occurrence of such GWs (period approximate to 9 min, horizontal wavelength approximate to 7 km), which propagated from southwest to northeast, i.e. in the same direction of propagation as the MCS. Observational evidence suggests the downdraughts associated with the rear-inflow jet at the weak low zone of the MCS as the most likely generator mechanism of the GWs. However, the complex orography and proximity of the Pyrenees to the field campaign could also play an important role. Wave propagation was possible through the ducting mechanism, favoured by the existence of a critical level in a wind-sheared environment around 2000 m above ground level. Wave-like motions related to the passage of the GWs were also observed in other atmospheric parameters close to the surface and within the lower troposphere. The effects of GWs on the surface fluxes have also been analyzed through Multi-Resolution Flux Decomposition (MRFD) methods., Unión Europea, Gobierno de España, Centre National de la Recherche Scientifique (CNRS), Meteo-France, German Research Foundation (DFG), UK Research & Innovation (UKRI), Natural Environment Research Council (NERC), Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, This paper tackles a problem of surface wind speed reconstruction based on synoptic-scale meteorological fields. Specifically, two different approaches are discussed and compared: a pure Machine Learning method, formed by a Support Vector Regression and a genetic algorithm that only considers synoptic pressure as input variable, and a Weather Regimes Classification Technique, based on a k-means clustering of the main three principal components of the geopotential height field and a simple, but efficient, linear regression between the surface pressure gradient and the observed surface wind. Both algorithms are shown to be accurate enough for wind speed reconstruction at medium latitude regions, even when there are only a few years of observations. These methodologies can also be used for filling gaps in wind speed series and, with some modifications and further research, they could be used for wind speed forecasting. The algorithms proposed are fully described and compared in this paper, and their performance has been comparatively evaluated in several real problems of wind speed reconstruction at three sites (Cabauw (The Netherlands), Capel (Wales, UK) and Kaegnes (Denmark)), obtaining excellent results in terms of wind speed reconstruction with moderate complexity in data processing and algorithms. Copyright (c) 2014 John Wiley & Sons, Ltd., Ministerio de Ciencia e Innovación (España), Solute Ingenieros, through the Project "Desarrollo de nuevos sistemas de caracterizacon del recurso colico, evaluacion de emplazamientos especificos y prediccion de viento: ETSWIND", Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

El texto completo de este trabajo no se encuentra disponible por no haber sido facilitado aún por su autor, por restricciones de copyright, o por no existir una versión digital, Mountain wave and icing episodes are adverse meteorological conditions that can affect aviation safety and air traffic management in the vicinity of airports. This study presents a mountain wave characterization performed for the winter months from 2001 to 2010 in the center of the Iberian Peninsula, close to an important orographic barrier and next to the busiest Spanish airport. Sixty-eight episodes were simulated using the Weather Research and Forecasting model (WRF). Five simulated atmospheric variables (wind direction, wind speed, atmospheric stability, liquid water content and temperature) were evaluated in several grid points at 2800 meters above sea level in leeward, windward and over the summit of the mountains. Based on the percentiles and thresholds obtained from the characterization, a decision tree was developed with the aim to forecast and warn the occurrence of mountain waves, wave clouds and icing events. The decision tree and the three warning methods were validated against satellite images. The results show satisfactory scores, with a percentage correct of detection above 70% for the three warnings., Ministerio de Economía y Competitividad (España), Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

Hurricane Leslie (2018) was a non-tropical system that lasted for a long time undergoing several transitions between tropical and extratropical states. Its trajectory was highly uncertain and difficult to predict. Here the extratropical transition of Leslie is simulated using the Model for Prediction Across Scales (MPAS) with two different sets of initial conditions (IC): the operational analysis of the Integrate Forecast System (IFS) and the Global Forecast System (GFS). Discrepancies in Leslie position are found in the IC patterns, and in the intensity and amplitude of the dorsaltrough system in which Leslie is found. Differences are identified both in the geopotential height at 300 hPa and the geopotential thickness. Potential temperature in the dynamic tropopause shows a broader, more intense trough displaced western when using the IC-IFS. The IC-IFS simulation shows lesser trajectory errors but wind speed overestimation than the IC-GFS one. The complex situation of the extratropical transition, where Leslie interacts with a trough, increases the uncertainty associated with the intensification process. The disparities observed in the simulations are attributed to inaccuracies in generating the ICs. Both ICs generate different atmospheric configurations when propagated in time. Results suggest that during an extratropical transition in a highly baroclinic atmosphere, the IFS model’s data assimilation method produced a more precise analysis than GFS due to the greater number of observations assimilated by the IFS, the greater spatial resolution of the model and the continuous adjustment of the simulations with the field of observations., ECMWF, Instituto Frontera, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

Producción Científica, Temperature, as one of the most important factors in meteorological data analysis, is a variable parameter with severe changes in different periods. The trend of temperature changes over time is also particularly important to investigating climate change. In this research, using the data from the TRY Project, which includes meteorological data with an accuracy of 1 km grid and a time accuracy of 1 hour, the temperature parameter of the city of Berlin is selected and the average temperature of the urban area of Berlin was calculated at different temporal scales. In addition to finding the linear regression trend of average annual temperature increase, Fourier transforms analysis and the least squared error fitting method was used to investigate harmonic temperature fluctuations to find the main sinusoidal period. Further, with the statistical analysis of data in daily averages and 1 h intervals by considering medians of data as the benchmark for classification, months from April to October were determined as the hot months of the year, and hours from 9 to 19 were determined as daytime. Based on the mentioned classification, it was found that while the median difference between hot and cold months is more than 12 °C, the median difference between days and nights for the hot and cold months’ data is 5.2 °C and 2.1 °C, respectively. With this classification, the probability distribution of temperature was studied for each group, and the degree of similarity of this distribution with probability distribution functions such as normal, beta, gamma, and cosine, were investigated. The separate analysis of the data categorized by this method had the highest degree of similarity with beta and normal functions.

Producción Científica, This study focuses on the analysis of the distribution, both spatial and temporal, of the PM10 (particulate matter with a diameter of 10 µm or less) concentrations recorded in nine EMEP (European Monitoring and Evaluation Programme) background stations distributed throughout mainland Spain between 2001 and 2019. A study of hierarchical clusters was used to classify the stations into three main groups with similarities in yearly concentrations: GC (coastal location), GNC (north–central location), and GSE (southeastern location). The highest PM10 concentrations were registered in summer. Annual evolution showed statistically significant decreasing trends in PM10 concentration in all the stations covering a range from −0.21 to −0.50 µg m−3/year for Barcarrota and Víznar, respectively. Through the Lamb classification, the weather types were defined during the study period, and those associated with high levels of pollution were identified. Finally, the values exceeding the limits established by the legislation were analyzed for every station assessed in the study., Junta de Castilla y Leon y Fondo Europeo de Desarrollo Regional (FEDER) - (grant LE025P20), Ministerio de Ciencia, Innovación y Universidades - (Grants PID2019-106164RBI00 and TED2021-132292B-I00)

Producción Científica, Hurricane Leslie (2018) was a non-tropical system that lasted for a long time undergoing several transitions between tropical and extratropical states. Its trajectory was highly uncertain and difficult to predict. Here the extratropical transition of Leslie is simulated using the Model for Prediction Across Scales (MPAS) with two different sets of initial conditions (IC): the operational analysis of the Integrate Forecast System (IFS) and the Global Forecast System (GFS). Discrepancies in Leslie position are found in the IC patterns, and in the intensity and amplitude of the dorsal-trough system in which Leslie is found. Differences are identified both in the geopotential height at 300 hPa and the geopotential thickness. Potential temperature in the dynamic tropopause shows a broader, more intense trough displaced western when using the IC-IFS. The IC-IFS simulation shows lesser trajectory errors but wind speed overestimation than the IC-GFS one. The complex situation of the extratropical transition, where Leslie interacts with a trough, increases the uncertainty associated with the intensification process. The disparities observed in the simulations are attributed to inaccuracies in generating the ICs. Both ICs generate different atmospheric configurations when propagated in time. Results suggest that during an extratropical transition in a highly baroclinic atmosphere, the IFS model's data assimilation method produced a more precise analysis than GFS due to the greater number of observations assimilated by the IFS, the greater spatial resolution of the model and the continuous adjustment of the simulations with the field of observations., Ministerio de Ciencia e Innovación- AEI (PID2019-105306RB-I00)

Producción Científica, Subtropical transition events (STT) are a challenge for forecasting and research due to the hybrid characteristics they give to the cyclones. The ability and skillfulness of the HARMONIE-AROME model to reproduce the cloud structure and convection associated to the October 2014 STT is here evaluated. Brightness temperature, cloud top height and accumulated precipitation are assessed against satellite data using traditional skill scores and object-based techniques specific to forecasting spatial evaluation. The results present differences in the simulation of the cyclone between the periods before and after the transition. They also show a very good performance of the model in the location of the events and a good simulation of the intensity of the variables. The performance is sub-optimal for the estimation of the sizes of the convective objects. Brightness temperature and cloud top heights yield very good results in general, with a slight overestimation in both cases. However, the model struggles to capture the accumulated precipitation. There is scarce work evaluating the HARMONIE-AROME model in this type of events; nevertheless, the results are in line with those produced by the simulations with other numerical models. The overall performance of the model is very adequate, although it might be hindered by the internal stability of the model produced by the deep-convection computation., Agencia Estatal de Investigación (PID2019-105306RB-I00/AEI/10.13039/501100011033), Ministerio de Ciencia e Innovación de España - FPI program (PRE2020-092343)

Producción Científica, A methodology is proposed for the study of snowfall, applied to the region and autonomous community of Castilla y León (Spain). It is based on two major pillars: the elaboration of a Snowfall Catalogue from the consultation of documentary sources (press) between 1979 and 2021, which allows cataloging all the episodes that occurred from October to May, and the synoptic study of the atmospheric dynamics in those 43 years, identifying the different types of weather as a means of validating the cataloged episodes. The database created provides information on its intensity, duration, and frequency, as well as its genesis, distribution, typology, and climatic hazard. All this has been analyzed and has allowed to recognize different stages in its way of occurring and to estimate trends and behaviors in both the number of episodes and the number of snow days and their assessment in the current situation of climate change., Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE

Purpose – The current “Anthropocene” epoch has witnessed an imbalanced global change, but it is an opportunity to design a better and sustainable future. Sustainability criteria need to be fully implemented in political institutions, companies and universities. Moreover, sustainable pedagogies must accompany the process in terms of students’ professional competences to overcome crisis situations. This paper aims to evaluate geography degrees in Spain to corroborate whether students and teachers consider that students are prepared to address the challenges of global change. Design/methodology/approach – The research is focused on the sustainable development goals (SDGs), a clear and easily identifiable framework for society that reflects the principles of the UN 2030 Agenda. A statistically validated questionnaire answered by 319 respondents has been used. Data analysis with descriptive and inferential techniques was performed. Findings – The results show that geography degrees do not meet the needs of students, for whom career opportunities will be related to the implementation of the 2030 Agenda; there are significant gaps between the perception of teachers and students; some geographical topics are undervalued; SDGs are more present in optional subjects; and there is a need to “sustain” the curriculum, following this highly valued framework. Originality/value – Geography educative programs had scarcely been empirically evaluated to check a sustainability framework implementation. This research provides innovative and unprecedented self-assessment results from higher education agents, in an empirical and statistically validated way., Agencia Estatal de Investigación, Universidad Complutense de Madrid, Depto. de Didáctica de las Ciencias Experimentales , Sociales y Matemáticas, Depto. de Geografía, Fac. de Educación, Fac. de Geografía e Historia, TRUE, pub

Following a copious and famous snowfall in Madrid city (Spain) and its surrounding area, the tree masses were analysed to assess the impact of this snowfall. In this way, this paper proposes an approach towards urban forest impact inventory mapping as a consequence of the snowfall of January 2021, and the subsequent atmospheric stability, which kept surface temperatures in Madrid close to −10 °C. The study has been carried out using snowfall data from 1920 to 2020 and images from the Sentinel, Landsat and MODIS satellites. The results obtained by means of image changes in the NDVI reveal a clear impact on the trees, with 11% of the winter vegetation cover of the municipality being affected. Especially significant has been the damage detected both in the forest areas in the city and in the parks, gardens or urban roads., Universidad Complutense de Madrid, Banco de Santander, Depto. de Geografía, Fac. de Geografía e Historia, TRUE, pub

Climate patterns are natural processes that drive climate variability in the short, medium, and long term. Characterizing the patterns behind climate variability is essential to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate patterns in specific regions, the magnitude of that influence in meteorological records usually remains unclear. The central Peruvian Andes are affected by most of the common climate patterns of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature (SST), solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as the South American Low-Level Jet (SALLJ), South American Monsoon System (SAMS), Bolivian High (BH), and Humboldt Current. The aim of this research is to study the climate variability of precipitation, maximum and minimum temperatura records over Cordillera Blanca (Peru), and its relationship with the intensity and periodicity of the common climate patterns that affect this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram was performed over meteorological records (1986–2019) and over different climate pattern indexes. Results show a coincidence in periodicity between MJO and SALLJ, with monthly cycles for precipitation and temperature (27-day, 56-day, and 90-day cycles). Moreover, the most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of the Humboldt Current and SALLJ. Additionally, interannual periodicities (3-year, 4.5-year, 5.6–7-year and 11-year cycles) would have coincidence with the ENSO–solar combination, while the longest cycles (16 years) could match PDO variabilit, Depto. de Geografía, Fac. de Geografía e Historia, FALSE, pub

CRUE-CSIC (Acuerdos Transformativos 2022), Weather and climate conditions drive the evolution of tropical glaciers which play an important role as water reservoirs for Peruvian inhabitants in the arid coast and semi-arid Andean region. The scarcity of long-term high-quality observations over Peruvian glaciers has motivated the extensive use of reanalysis data to describe the climatic evolution of these glaciers. However, the representativeness and uncertainties of these reanalysis products over these glaciers are still poorly constrained. This study evaluates the ability of the ERA5-Land reanalysis (ERA5L) to reproduce hourly and monthly 2 m air temperature and relative humidity (T2m and Rh2m, respectively) over several Peruvian glaciers. We compared the ERA5L with data from four on-glacier automatic weather stations (AWS), whose hourly time series were completed with nearby stations, for the period January 2017 to December 2019. Results indicates a better performance of the reanalysis for T2m (r >0.80) than for Rh2m (∼0.4< r <∼0.6) in all four glaciers. Concerning the observations, both parameters show a daily cycle influenced by the presence of the glacier. This influence is more prominent during the dry months when the so-called glacier damping and cooling effects are stronger. On a monthly time scale, the ERA5L validation for both parameters are better in wet outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m) rather than in dry outer tropical sites (RMSE between ±0.2°C for T2m and between 3%–7% for Rh2m). Among all sites considered in the study, the Rh2m bias is the highest in the Cavalca glacier (correlation of 0.81; RMSE 13%, MAE 11% and bias 8.3%) and the lowest in Artesonraju glacier (correlation of 0.96; RMSE 3%; MAE 2.3% and bias — 0.8%). Based on certain considerations outlined in this paper, it is appropriate to use ERA5L to characterize T2m and Rh2m conditions on Peruvian glaciers, particularly in the wet outer tropics., Ministerio de Ciencia e Innovación (MICINN), Depto. de Geografía, Fac. de Geografía e Historia, TRUE, pub

Producción Científica, In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

Producción Científica, This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications., Unión Europea - (project H2020-FNR-2020-1/CE-FNR-07-2020)

CRUE-CSIC (Acuerdos Transformativos 2021), The objective of this work is to chronologically establish the origin of the different glacial and rock glacier complex landforms deposited by Héðinsdalsjökull glacier (65°39′ N, 18°55′ W), in the Héðinsdalur valley (Skagafjörður fjord, Tröllaskagi peninsula, central northern Iceland). Multiple methods were applied: geomorphological analysis and mapping, glacier reconstruction and equilibrium-line altitude calculation, Cosmic-Ray Exposure dating (in situ cosmogenic 36Cl), and lichenometric dating. The results reveal that a debris-free glacier receded around 6.6 ± 0.6 ka, during the Holocene Thermal Maximum. The retreat of the glacier exposed its headwall and accelerated paraglacial dynamics. As a result, the glacier terminus evolved into a debris-covered glacier and a rock glacier at a slightly higher elevation. The front of this rock glacier stabilized shortly after it formed, although nuclide inheritance is possible, but its sector close the valley head stabilized between 1.5 and 0.6 ka. The lowest part of the debris-covered glacier (between 600 and 820 m altitude) collapsed at ca. 2.4 ka. Since then, periods of glacial advance and retreat have alternated, particularly during the Little Ice Age. The maximum advance during this phase occurred in the 15th to 17th centuries with subsequent re-advances, namely at the beginning of the 19th and 20th centuries. After a significant retreat during the first decades of the 20th century, the glacier advanced in the 1960s to 1990s, and then retreated again, in accordance with the local climatic evolution. The internal ice of both the debris-covered and the rock glacier have survived until the present day, although enhanced subsidence provides evidence of their gradual degradation. A new rock glacier developed from an ice-cored moraine from around 1940–1950 CE. Thus, the Holocene coupling between paraglacial and climatic shifts has resulted in a complex evolution of Héðinsdalsjökull, which is conflicting with previously propos, Ministerio de Ciencia e Innovación (MICINN), Universidad Complutense de Madrid/Banco de Santander, Fundação para a Ciência e a Tecnologia, Portugal, Generalitat de Catalunya, Depto. de Geografía, Fac. de Geografía e Historia, TRUE, pub

Climate change projections point to an increase in the intensity and frequency of extreme drought events with important negative impacts on forest functioning. Predicting these impacts constitutes a crucial challenge for forest managers and for the maintenance of ecosystem services supply. Promoting mixed stands seems a promising strategy for adapting forest ecosystems to ongoing climate change. However, some uncertainty exists regarding whether admixture can improve growth resilience to extreme drought events. Here, we aim to assess tree growth response to drought in mixed and monospecific stands of Pinus sylvestris L. and Quercus pyrenaica Willd. in central Spain. We built tree-ring chronologies and evaluated tree growth sensitivity to water availability and growth resilience components to extreme droughts using linear mixed models. We found contrasting speciesand climate-specific responses to admixture. Q. pyrenaica growth was significantly higher in mixed than in monospecific stands, especially in years without water limitations, while P. sylvestris showed higher growth in mixed stands under dry conditions. However, our results showed a species-specific trade-off between resistance and recovery. While P. sylvestris showed higher resistance but lower recovery to drought events in mixed than monospecific stands, Q. pyrenaica showed higher recovery but lower resistance. This trade-off might explain the absence of admixture effects on species resilience. Our results highlight the importance of considering speciesspecific responses to water availability and associated trade-offs when evaluating admixture effects on drought vulnerability. Overall, we show a positive effect of admixture on the long-term growth stability in response to average climate conditions, but no effects in short-term resilience capacity to increasingly common extreme dry conditions. Consequently, admixture can promote forest productivity stability but should be carefully considered as a manageme, Ministerio de Ciencia e Innovación (MICINN)/Fondo Europeo de Desarrollo Regional (FEDER), Comunidad de Madrid, Universidad Complutense de Madrid (UCM)/Banco de Santander, Universidad de Alcalá de Henares, Gobierno Vasco, Depto. de Biodiversidad, Ecología y Evolución, Fac. de Ciencias Biológicas, TRUE, pub

Producción Científica, Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land–sea and mountain–valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on., Junta de Castilla y León (Projecto VA027G19)

Humans and fire form a coupled and co-evolving natural-human system in Mediterranean-climate ecosystems. In this context, recent trends in landscape change, such as urban sprawl or the abandoning of agricultural and forest land management in line with new models of economic development and lifestyles, are leading to new fire scenarios. A fire scenario refers to the contextual factors of a fire regime, i.e., the environmental, socio-economic and policy drivers of wildfire initiation and propagation on different spatial and temporal scales. This is basically a landscape concept linking territorial dynamics (related to ecosystem evolution and settlement patterns) with a fire regime (ignition causes; spread patterns; fire frequency, severity, extent and seasonality). The aim of this article is to identify and characterize these land-based fire scenarios in Spain on a national and regional scale, using a GIS-based methodology to perform a spatial analysis of the area attributes of homogenous fire spread patterns. To do this, the main variables considered are: land use/land cover, fuel load and recent fire history. The final objective is to reduce territorial vulnerability to forest wildfires and facilitate the adaptation of fire policies and land management systems to current challenges of preparedness and uncertainty management., Unión Europea. FP7, Unión Europea. FP6, Ministerio de Economía y Competitividad (MINECO)/FEDER, Depto. de Geografía, Fac. de Geografía e Historia, TRUE, pub

A six-year climatology of radiation fog has been compiled at two sites: the Research Centre for the Lower Atmosphere (CIBA, Spain) and the Cabauw Experimental Site for Atmospheric Research (CESAR, The Netherlands). These sites are contrasted in terms of geographical situation, climate zone, altitude, humidity and soil water availability. Therefore, several climatological differences in fog abundance, onset, dissipation and duration have been quantified between the two sites. The more humid site (CESAR) is characterised by relatively short radiation fog events distributed throughout the year. However, radiation fog at the drier site (CIBA) is more persistent and appears during late autumn/winter months. In general, its formation requires more time after sunset (approximate to 2 h more), since further cooling is required to reach saturation. The forecast of these fog events has been evaluated through two different approaches. First, we extend the statistical method presented by Menut et al. (2014) (M14). This method uses statistics to define threshold values on key variables for fog formation (pre-fog) and verifies its predictability using observations and numerical model output. We present some of the most appropriate threshold values for the forecasting of pre-fog periods at both sites, which differ from those presented in M14 and depend on the optimisation of the hit rate or the false-alarm rate. Additionally, we also extend M14 by suggesting other variables as potential predictors for fog formation (friction velocity and visibility tendency). Finally, we focus on fog simulation by the Weather Research and Forecasting (WRF) model in terms of liquid water content. The WRF model was able to simulate radiation fog when configured with sophisticated physical options and high resolution. However it failed in simulating the onset, dissipation and the vertical extent of fog (which was overestimated). The model results were extremely sensitive to the spin-up time., Ministerio de Economía y Competitividad (España), Netherlands Organization for Scientific Research (NWO), Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub