Explore the vast range of titles available.

Recognizing the threat from climate change that is facing and will face agroecosystems is the first step in determining adaptation to climate change. One way is through Global Climate Models (GCMs), but their spatial resolution is not best suited for making decisions locally, further reducing scale, seen as a way to resolve the resolution problem, has not yielded the expected results. This study puts forth an exercise in which we study the climatic time series of precipitation and temperature to determine if there are effects of climate change on one of the most important national agricultural areas, using the Mann-Kendall analysis to determine the existence of statistically significant trends, i.e. signs of change in the variables analyzed. It was found that the variable that presents the most significant trends is the average maximum temperature, while precipitation and average minimum temperature do not. Key words: mathematical models, climate observations, temperature, mountain farming. Reconocer la amenaza climática a la que se enfrentan y se enfrentaran los agroecosistemas es el primer paso para determinar las medidas de adaptación frente al cambio climático. Una forma de hacerlo es a través de los Modelos Climáticos Globales (MCG), sin embargo la resolución espacial de éstos no es la más indicada para tomar decisiones a escala local; además, la reducción de escala, vista como una forma de mejorar el problema de resolución, no ha dado los resultados esperados. Se plantea un ejercicio en el que se estudian las series de tiempo climáticas de precipitación y temperatura para determinar si hay efectos del cambio climático en una de las zonas agropecuarias de mayor importancia a nivel nacional. Se plantea el análisis de Mann-Kendall para determinar la existencia de tendencias estadísticamente significativas, es decir señales de cambio en las variables analizadas. Se encontró que la variable que presenta tendencias más significativas es la temperatura máxima media, mientras que la precipitación y la temperatura mínima media no. Palabras clave: modelos matemáticos, observaciones del clima, temperatura, agricultura de montaña.

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate-fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

Recent studies have indicated the importance of fall climate forcings and teleconnections in influencing the climate of the northern mid- to high latitudes. Here, we present some exploratory analyses using observational data and seasonal hindcasts, with the aim of highlighting the potential of the El Niño–Southern Oscillation (ENSO) as a driver of climate variability during boreal late fall and early winter (November and December) in the North Atlantic–European sector, and motivating further research on this relatively unexplored topic. The atmospheric ENSO teleconnection in November and December is reminiscent of the east Atlantic pattern and distinct from the well-known arching extratropical Rossby wave train found from January to March. Temperature and precipitation over Europe in November are positively correlated with the Niño-3.4 index, which suggests a potentially important ENSO climate impact during late fall. In particular, the ENSO-related temperature anomaly extends over a much larger area than during the subsequent winter months. We discuss the implications of these results and pose some research questions.

Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (∼1° and ∼60 km in the ocean and atmosphere models, respectively), intermediate resolution (∼0.25° and ∼60 km), and high resolution (∼0.25° and ∼39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-mtemperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements aremore difficult to detect atmiddle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.

Climate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter estimation—essential features for the small sample sizes of current climate forecast systems. This paper is the first to explore the impact of the necessary bias adjustment on the forecast quality of an operational seasonal forecast system, using the European Centre for Medium-Range Weather Forecasts seasonal predictions of near-surface wind speed to produce useful information for wind energy users. The results reveal to what extent the bias adjustment techniques, in particular the calibration method, are indispensable to produce statistically consistent and reliable predictions. The forecast-quality assessment shows that calibration is a fundamental requirement for high-quality climate service.

The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.

The recent emergence of near-term climate prediction, wherein climate models are initialized with the contemporaneous state of the Earth system and integrated up to 10 years into the future, has prompted the development of three different multiannual forecasting techniques of North Atlantic hurricane frequency. Descriptions of these three different approaches, as well as their respective skill, are available in the peer-reviewed literature, but because these various studies are sufficiently different in their details (e.g., period covered, metric used to compute the skill, measure of hurricane activity), it is nearly impossible to compare them. Using the latest decadal reforecasts currently available, we present a direct comparison of these three multiannual forecasting techniques with a combination of simple statistical models, with the hope of offering a perspective on the current state-of-the-art research in this field and the skill level currently reached by these forecasts. Using both deterministic and probabilistic approaches, we show that these forecast systems have a significant level of skill and can improve on simple alternatives, such as climatological and persistence forecasts.

O presente estudo teve como principal objetivo desenvolver e validar um Sistema de Observaçâo do Clima de Aula em Aulas de Grupo de Fitness (SOCA-AGF) bem como realizar uma aplicaçâo piloto do mesmo. Participaram no estudo 6 instrutoras de 2 atividades (hidroginástica e localizada), em populaçâo idosa, para testar a funcionalidade do instrumento. Verificou-se que o SOCA-AGF, constituido por 2 dimensöes, 36 categorias e 8 subcategorias, apresentou fiabilidade e validade. Os resultados obtidos indicam que o comportamente observável dos instrutores de fitness, em aulas de grupo, poderá ser codificado recorrendo a este instrumento. Concluiu-se que o SOCA-AGF regista aspetos fundamentais do comportamente dos instrutores de fitness que influencia o clima em aulas de grupo e, como tal, poderá ser utilizado para estudar aspetos relevantes no ámbito do comportamente que influencia o clima de aula em diferentes atividades de grupo, no contexto do fitness, e em diferentes tipos de populaçâo.

The simultaneous analysis of aerosol microphysical properties profiles at different European stations is made in the framework of the ChArMEx/EMEP 2012 field campaign (9–11 July 2012). During and in support of this campaign, five lidar ground-based stations (Athens, Barcelona, Bucharest, Évora, and Granada) performed 72 h of continuous lidar measurements and collocated and coincident sun-photometer measurements. Therefore it was possible to retrieve volume concentration profiles with the Lidar Radiometer Inversion Code (LIRIC). Results indicated the presence of a mineral dust plume affecting the western Mediterranean region (mainly the Granada station), whereas a different aerosol plume was observed over the Balkans area. LIRIC profiles showed a predominance of coarse spheroid particles above Granada, as expected for mineral dust, and an aerosol plume composed mainly of fine and coarse spherical particles above Athens and Bucharest. Due to the exceptional characteristics of the ChArMEx database, the analysis of the microphysical properties profiles' temporal evolution was also possible. An in-depth analysis was performed mainly at the Granada station because of the availability of continuous lidar measurements and frequent AERONET inversion retrievals. The analysis at Granada was of special interest since the station was affected by mineral dust during the complete analyzed period. LIRIC was found to be a very useful tool for performing continuous monitoring of mineral dust, allowing for the analysis of the dynamics of the dust event in the vertical and temporal coordinates. Results obtained here illustrate the importance of having collocated and simultaneous advanced lidar and sun-photometer measurements in order to characterize the aerosol microphysical properties in both the vertical and temporal coordinates at a regional scale. In addition, this study revealed that the use of the depolarization information as input in LIRIC in the stations of Bucharest, Évora, and Granada was crucial for the characterization of the aerosol types and their distribution in the vertical column, whereas in stations lacking depolarization lidar channels, ancillary information was needed. Results obtained were also used for the validation of different mineral dust models. In general, the models better forecast the vertical distribution of the mineral dust than the column-integrated mass concentration, which was underestimated in most of the cases.