The game as a didactic resource for mathematics at the university. Experience in several Engineering Degrees

ABSTRACT: This work presents the results from a teaching innovation project focused on the inclusion of simple games, combined with Information and Communication Technologies and group dynamics, as a support tool in the learning of seven subjects in the field of Mathematics, taught in the first courses of different Degrees of Engineering. The participating teaching team has verified that the proposed methodology increases the motivation and active participation of the students, who have highlighted the usefulness of the implemented games in their learning process.RESUMEN: Este trabajo presenta los resultados de un proyecto de innovación docente centrado en la inclusión de juegos sencillos, combinados con Tecnologías de la Información y la Comunicación y dinámicas de grupo, como herramienta de apoyo en el aprendizaje de siete asignaturas de Matemáticas impartidas en los primeros cursos de Grado de distintas Ingenierías. El equipo docente participante ha comprobado que la metodología propuesta incrementa la motivación y la participación activa de los alumnos, quienes han resaltado la utilidad de los juegos implementados en su proceso de aprendizaje

Variability of extreme precipitation over Europe and its relationships with teleconnection patterns

A growing interest in extreme precipitation has spread through the scientific community due to the effects of global climate change on the hydrological cycle, and their threat to natural systems' higher than average climatic values. Understanding the variability of precipitation indices and their association to atmospheric processes could help to project the frequency and severity of extremes. This paper evaluates the trend of three precipitation extremes: the number of consecutive dry/wet days (CDD/CWD) and the quotient of the precipitation in days where daily precipitation exceeds the 95th percentile of the reference period and the total amount of precipitation (or contribution of very wet days, R95pTOT). The aim of this study is twofold. First, extreme indicators are compared against accumulated precipitation (RR) over Europe in terms of trends using non-parametric approaches. Second, we analyse the geographically opposite trends found over different parts of Europe by considering their relationships with large-scale processes, using different teleconnection patterns. The study is accomplished for the four seasons using the gridded E-OBS data set developed within the EU ENSEMBLES project. Different patterns of variability were found for CWD and CDD in winter and summer, with north–south and east–west configurations, respectively. We consider physical factors in order to understand the extremes' variability by linking large-scale processes and precipitation extremes. Opposite associations with the North Atlantic Oscillation in winter and summer, and the relationships with the Scandinavian and East Atlantic patterns as well as El Niño/Southern Oscillation events in spring and autumn gave insight into the trend differences. Significant relationships were found between the Atlantic Multidecadal Oscillation and R95pTOT during the whole year. The largest extreme anomalies were analysed by composite maps using atmospheric variables and sea surface temperature. The association of extreme precipitation indices and large-scale variables found in this work could pave the way for new possibilities regarding the projection of extremes in downscaling techniques

ENSO-Driven skill of ENSEMBLES STREAM 2 multimodel seasonal precipitation hindcasts over the globe

Trabajo presentado al European Geosciences Union General Assembly 2010 celebrado en Viena (Austria) del 2 al 7 de Mayo de 2010.Seasonal forecasting is a promising research field with enormous potential impact in different socio-economic sectors. The ability to forecast unusual climate conditions such as droughts or hot spells a few months in advance is particularly interesting in agriculture, energy requirements and other human affairs in order to avoid severe consequences. This study assesses the skill of state-of-the-art seasonal forecast considering five coupled atmosphere-ocean general circulation models from the Stream 2 multimodel experiment of the European ENSEMBLES Project. The methodology applied by Frías et al 2010 over Spain is here extended to the world to present a map of regions with a significant seasonal predictability related to ENSO. To this aim, seven month hindcast simulations produced four times per year (November, February, May and August initializations) in the period 1961-2000 are analyzed. In a first step, validations are carried out separately for each season; winter (DJF), spring (MAM), summer (JJA) and autumn (SON), considering both one and four-months lead time predictions (e.g. initializations of November and August are considered for winter). Then, since ENSO is considered to globally dominate interannual climate variability, the same process is repeated, but restricting validations exclusively to the years of the strongest El Niño and La Niña events.Peer reviewe

Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods

ABSTRACT. The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles). In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th) and low (5th) percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical). First, we analyse the performance of reanalysisdriven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies

Extremes of maximum temperatures over Iberia from ENSEMBLES regional projections

RESUMEN: El presente estudio se centra en la estimación de cambios en la temperatura máxima en el sur de Europa considerando dos modelos regionales de circulación del proyecto europeo ENSEMBLES. Los extremos son expresados en términos de los valores característicos obtenidos mediante la aplicación de un modelo basado en la distribución generalizada de extremos (GEV) dependiente del tiempo. El estudio se centra a finales del siglo XX (1961-2000), considerado como periodo de calibración/validación, y analiza los cambios proyectados en el periodo 2061-2100 considerando el escenario de emisiones A1B. El aumento de los valores característicos para un periodo de retorno de 40 años (que duplica el correspondiente a los valores medios en algunas zonas) indica un mayor impacto del cambio climático en los eventos extremos, como también se ha mostrado en otros estudios. Además los resultados obtenidos considerando diferentes periodos de retorno (40 o 100 años) son muy parecidos, siendo estas diferencias más pequeñas que las producidas por la señal de cambio climático mencionada antes.ABSTRACT: Two state-of-the-art regional circulation models from the EU ENSEMBLES project are used to estimate changes of maximum temperatures over Southern Europe. Extremes are expressed in terms of return values using a time-dependent generalized extreme value (GEV) model fitted to monthly maxima. The study focuses on the end of the 20th century (1961-2000), used as a calibration/validation period, and analyzes the changes projected for the period 2061-2100 considering the A1B emission scenario. The increments of the 40-year return values (up to 2 times higher than those corresponding to the mean in some areas) indicate a higher impact of global climate changes in extremes, in agreement with other studies. Moreover, the results for different return periods (40 or 100 years) are very similar (due to the bounded character of the corresponding extreme distributions) with differences smaller than those produced by the climate change driving signal above mentioned

Evaluation and projection of extreme temperature percentiles by means of statistical and dynamical downscaling methods

ABSTRACT: The study of extreme events has become of great interest in the recent years due to their direct impact on society. Extremes can be evaluated by using either extreme value statistics or extreme indicators, the latter being based in order statistics on the tail of the probability distribution (typically percentiles). In this study we analyze the highest (95p) and the lowest (5p) percentiles in maximum and minimum temperatures, respectively, derived from different downscaling methods (statistical and dynamical) in the Iberian Peninsula. In particular, we analyze the results of the esTcena and ESCENA projects, two strategic actions of Plan Nacional de I+D+i 2008-2011 funded by the Spanish government, which contributed to the new version of the regional climate change scenarios program Escenarios-PNACC 2012 within Plan Nacional de Adaptación al Cambio Climático. First, the skill of the downscaling methods to reproduce extreme percentiles is tested in present climate conditions, using reanalysis-driven simulations. The comparison among the different methods is performed in terms of the seasonal bias, considering the public gridded dataset Spain02, a new regular (approximately 20km) daily gridded precipitation and temperature dataset covering continental Spain and Balearic Islands. Secondly, we analyze future projections in different climate change scenarios to check the increments and the uncertainty of the results up to the mid of the century. We also study the effect of nesting the methods to different Global Circulation Models (GCMs), using the 20C3M historical scenario as reference. By analyzing these changes, we are able to extract differences due to the downscaling method and to the driving GCM.RESUMEN: El estudio de eventos extremos se ha convertido recientemente en un tema de gran interés debido a su impacto directo en la sociedad. Los extremos pueden ser evaluados por medio de la Teoría de Valores Extremos o mediante indicadores de extremos, estos últimos basados en los estadísticos de la cola de la distribución de probabilidad (típicamente percentiles). En este estudio analizamos uno de los percentiles más altos en temperatura máxima (95) y de los más bajos en la mínima (5) obtenidos a partir de diferentes métodos de regionalización (estadísticos y dinámicos) en la Península Ibérica. En particular, hemos analizado los resultados de los proyectos esTcena y ESCENA, dos acciones estratégicas del Plan Nacional de I+D+i 2008-2011 financiado por el Gobierno de España, que contribuyen a la nueva versión del programa de escenarios regionales de cambio climático Escenarios-PNACC 2012 dentro del Plan Nacional de Adaptación al Cambio Climático. En primer lugar, se ha probado la habilidad de los métodos de regionalización a la hora de reproducir los percentiles extremos en clima presente, usando simulaciones anidadas a datos de reanálisis. La comparación entre los distintos métodos se ha realizado en términos del bias estacional, considerando la nueva rejilla pública Spain02, una rejilla regular (de aproximadamente 20km) de precipitación y temperatura que cubre España continental y las Islas Baleares. A continuación, se han analizado proyecciones de futuro en distintos escenarios de cambio climático para conocer los incrementos e incertidumbre de los resultados a mediados del siglo XXI. También se ha estudiado el efecto de anidar los métodos a diferentes Modelos de Circulación General (GCMs), usando el escenario 20C3M como referencia. Analizando esos cambios, somos capaces de atribuir esas diferencias al método de regionalización o al GCM

An R package to visualize and communicate uncertainty in seasonal climate prediction

Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-expert end users. visualizeR is aimed to fill this gap, implementing a set of advanced visualization tools for the communication of probabilistic forecasts together with different aspects of forecast quality, by means of perceptual multivariate graphical displays (geographical maps, time series and other graphs). These are illustrated in this work using the example of the strong El Niño 2015/16 event forecast. The package is part of the climate4R bundle providing transparent access to the ECOMS-UDG climate data service. This allows a flexible application of visualizeR to a wide variety of specific seasonal forecasting problems and datasets.This work has been funded by the European Union 7th Framework Program [FP7/20072013] under Grant Agreement 308291 (EUPORIAS Project). We are grateful to the EUPORIAS team on Communicating levels of con dence (Work Package 33)

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

ABSTRACT: Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).This is a contribution of the WATExR project (watexr.eu/), which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO-AEI (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by the European Union (Grant 690462 ). MINECO-AEI funded this research through projects PCIN- 2017-062 and PCIN-2017-092. We thank all water quality and quantity data providers: Ens d’Abastament d’Aigua Ter-Llobregat (ATL, https://www.atl.cat/es ), SA Water ( https://www.sawater.com. au/ ), Ruhrverband ( www.ruhrverband.de ), NIVA ( www.niva.no ) and NVE ( https://www.nve.no/english/ ). We acknowledge the contribution of the Copernicus Climate Change Service (C3S) in the production of SEAS5. C3S provided the computer time for the generation of the re-forecasts for SEAS5 and for the production of the ocean reanalysis (ORAS5), used as initial conditions for the SEAS5 re-forecasts

Dynamical and statistical downscaling of a global seasonal hindcast in eastern Africa

Within the FP7 EUPORIAS project we have assessed the utility of dynamical and statistical downscaling to provide seasonal forecast for impact modelling in eastern Africa. An ensemble of seasonal hindcasts was generated by the global climate model (GCM) EC-EARTH and then downscaled by four regional climate models and by two statistical methods over eastern Africa with focus on Ethiopia. The five-month hindcast includes 15 members, initialised on May 1?st covering 1991?2012. There are two sub-regions where the global hindcast has some skill in predicting June?September rainfall (northern Ethiopia ? northeast Sudan and southern Sudan - northern Uganda). The regional models are able to reproduce the predictive signal evident in the driving EC-EARTH hindcast over Ethiopia in June?September showing about the same performance as their driving GCM. Statistical downscaling, in general, loses a part of the EC-EARTH signal at grid box scale but shows some improvement after spatial aggregation. At the same time there are no clear evidences that the dynamical and statistical downscaling provide added value compared to the driving EC-EARTH if we define the added value as a higher forecast skill in the downscaled hindcast, although there is a tendency of improved reliability through the downscaling. The use of the global and downscaled hindcasts as input for the Livelihoods, Early Assessment and Protection (LEAP) platform of the World Food Programme in Ethiopia shows that the performance of the LEAP platform in predicting humanitarian needs at the national and sub-national levels is not improved by using downscaled seasonal forecasts.This work was done in the EUPORIAS project that received funding from the European Union Seventh Framework Programme (FP7) for Research, under grant agreement 308291. The authors thank the European Centre for Medium-Range Weather Forecasts (ECMWF), the Global Precipitation Climatology Centre (GPCC), the British Atmospheric Data Centre (BADC), the University of East Anglia (UEA), the University of Delaware, the University of Reading, the University of California, the Climate Prediction Center (CPC), the US Agency for International Development’s Famine Early Warning Network (FEWS NET) and the WATCH project for providing data. For the WRF simulations, the UCAN group acknowledges Santander Supercomputacion support group at the University of Cantabria, who provided access to the Altamira Supercomputer at the Institute of Physics of Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network. DWD wants to thank ECMWF for the support during the CCLM4 simulations which have been carried out at the ECMWF computing system. The SMHI RCA4 simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at National Supercomputer Centre (NSC) and the PDC Center for High Performance Computing (PDC-HPC)