Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases

The occurrence of wind storms in Central Europe is investigated with respect to large-scale atmospheric flow and local wind speeds in the investigation area. Two different methods of storm identification are applied for Central Europe as the target region: one based on characteristics of large-scale flow (circulation weather types, CWT) and the other on the occurrence of extreme wind speeds. The identified events are examined with respect to the NAO phases and CWTs under which they occur. Pressure patterns, wind speeds and cyclone tracks are investigated for storms assigned to different CWTs. Investigations are based on ERA40 reanalysis data. It is shown that about 80% of the storm days in Central Europe are connected with westerly flow and that Central European storm events primarily occur during a moderately positive NAO phase, while strongly positive NAO phases (6.4% of all days) account for more than 20% of the storms. A storm occurs over Central Europe during about 10% of the days with a strong positive NAO index. The most frequent pathway of cyclone systems associated with storms over Central Europe leads from the North Atlantic over the British Isles, North Sea and southern Scandinavia into the Baltic Sea. The mean intensity of the systems typically reaches its maximum near the British Isles. Differences between the characteristics for storms identified from the CWT identification procedure (gale days, based on MSLP fields) and those from extreme winds at Central European grid points are small, even though only 70% of the storm days agree. While most storms occur during westerly flow situations, specific characteristics of storms during the other CWTs are also considered. Copyright © 2009 Royal Meteorological Society

European storminess and associated circulation weather types: future changes deduced from a multi-model ensemble of GCM simulations

A range of possible changes in the frequency and characteristics of European wind storms under future climate conditions was investigated on the basis of a multi-model ensemble of 9 coupled global climate model (GCM) simulations for the 20th and 21st centuries following the IPCC SRES A1B scenario. A multi-model approach allowed an estimation of the (un)certainties of the climate change signals. General changes in large-scale atmospheric flow were analysed, the occurrence of wind storms was quantified, and atmospheric features associated with wind storm events were considered. Identified storm days were investigated according to atmospheric circulation, associated pressure patterns, cyclone tracks and wind speed patterns. Validation against reanalysis data revealed that the GCMs are in general capable of realistically reproducing characteristics of European circulation weather types (CWTs) and wind storms. Results are given with respect to frequency of occurrence, storm-associated flow conditions, cyclone tracks and specific wind speed patterns. Under anthropogenic climate change conditions (SRES A1B scenario), increased frequency of westerly flow during winter is detected over the central European investigation area. In the ensemble mean, the number of detected wind storm days increases between 19 and 33% for 2 different measures of storminess, only 1 GCM revealed less storm days. The increased number of storm days detected in most models is disproportionately high compared to the related CWT changes. The mean intensity of cyclones associated with storm days in the ensemble mean increases by about 10 (±10)% in the Eastern Atlantic, near the British Isles and in the North Sea. Accordingly, wind speeds associated with storm events increase significantly by about 5 (±5)% over large parts of central Europe, mainly on days with westerly flow. The basic conclusions of this work remain valid if different ensemble contructions are considered, leaving out an outlier model or including multiple runs of one particular model.

Characterising North Atlantic weather patterns for climate-optimal aircraft routing

Daily weather patterns over the North Atlantic are classified into relevant types: typical weather patterns that may characterize the range of climate impacts from aviation in this region, for both summer and winter. The motivation is to provide a set of weather types to facilitate an investigation of climate-optimal aircraft routing of trans-Atlantic flights (minimizing the climate impact on a flight-by-flight basis). Using the New York to London route as an example, the time-optimal route times are shown to vary by over 60 min, to take advantage of strong tailwinds or avoid headwinds, and for eastbound routes latitude correlates well with the latitude of the jet stream. The weather patterns are classified by their similarity to the North Atlantic Oscillation and East Atlantic teleconnection patterns. For winter, five types are defined; in summer, when there is less variation in jet latitude, only three types are defined. The types can be characterized by the jet strength and position, and therefore the location of the time-optimal routes varies by type. Simple proxies for the climate impact of carbon dioxide, ozone, water vapour and contrails are defined, which depend on parameters such as the route time, latitude and season, the time spent flying in the stratosphere, and the distance over which the air is supersaturated with respect to ice. These proxies are then shown to vary between weather types and between eastbound and westbound routes.

Is there a Rhythm Of The Rain? An analysis of weather in popular music

Weather is frequently used in music to frame events and emotions, yet quantitative analyses are rare. From a collated base set of 759 weather-related songs, 419 were analysed based on listings from a karaoke database. This article analyses the 20 weather types described, frequency of occurrence, genre, keys, mimicry, lyrics and songwriters. Vocals were the principal means of communicating weather: sunshine was the most common, followed by rain, with weather depictions linked to the emotions of the song. Bob Dylan, John Lennon and Paul McCartney wrote the most weather-related songs, partly following their experiences at the time of writing.

Understanding climate change projections for precipitation over western Europe with a weather typing approach

Precipitation over western Europe (WE) is projected to increase (decrease) roughly northward (equatorward) of 50°N during the 21st century. These changes are generally attributed to alterations in the regional large-scale circulation, e.g., jet stream, cyclone activity, and blocking frequencies. A novel weather typing within the sector (30°W–10°E, 25–70°N) is used for a more comprehensive dynamical interpretation of precipitation changes. A k-means clustering on daily mean sea level pressure was undertaken for ERA-Interim reanalysis (1979–2014). Eight weather types are identified: S1, S2, S3 (summertime types), W1, W2, W3 (wintertime types), B1, and B2 (blocking-like types). Their distinctive dynamical characteristics allow identifying the main large-scale precipitation-driving mechanisms. Simulations with 22 Coupled Model Intercomparison Project 5 models for recent climate conditions show biases in reproducing the observed seasonality of weather types. In particular, an overestimation of weather type frequencies associated with zonal airflow is identified. Considering projections following the (Representative Concentration Pathways) RCP8.5 scenario over 2071–2100, the frequencies of the three driest types (S1, B2, and W3) are projected to increase (mainly S1, +4%) in detriment of the rainiest types, particularly W1 (−3%). These changes explain most of the precipitation projections over WE. However, a weather type-independent background signal is identified (increase/decrease in precipitation over northern/southern WE), suggesting modifications in precipitation-generating processes and/or model inability to accurately simulate these processes. Despite these caveats in the precipitation scenarios for WE, which must be duly taken into account, our approach permits a better understanding of the projected trends for precipitation over WE.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland

A first step in interpreting the wide variation in trace gas concentrations measured over time at a given site is to classify the data according to the prevailing weather conditions. In order to classify measurements made during two intensive field campaigns at Mace Head, on the west coast of Ireland, an objective method of assigning data to different weather types has been developed. Air-mass back trajectories calculated using winds from ECMWF analyses, arriving at the site in 1995–1997, were allocated to clusters based on a statistical analysis of the latitude, longitude and pressure of the trajectory at 12 h intervals over 5 days. The robustness of the analysis was assessed by using an ensemble of back trajectories calculated for four points around Mace Head. Separate analyses were made for each of the 3 years, and for four 3-month periods. The use of these clusters in classifying ground-based ozone measurements at Mace Head is described, including the need to exclude data which have been influenced by local perturbations to the regional flow pattern, for example, by sea breezes. Even with a limited data set, based on 2 months of intensive field measurements in 1996 and 1997, there are statistically significant differences in ozone concentrations in air from the different clusters. The limitations of this type of analysis for classification and interpretation of ground-based chemistry measurements are discussed.

Conditioning model output statistics of regional climate model precipitation on circulation patterns

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.

Large-scale atmospheric dynamics of the wet winter 2009–2010 and its impact on hydrology in Portugal

The anomalously wet winter of 2010 had a very important impact on the Portuguese hydrological system. Owing to the detrimental effects of reduced precipitation in Portugal on the environmental and socio-economic systems, the 2010 winter was predominantly beneficial by reversing the accumulated precipitation deficits during the previous hydrological years. The recorded anomalously high precipitation amounts have contributed to an overall increase in river runoffs and dam recharges in the 4 major river basins. In synoptic terms, the winter 2010 was characterised by an anomalously strong westerly flow component over the North Atlantic that triggered high precipitation amounts. A dynamically coherent enhancement in the frequencies of mid-latitude cyclones close to Portugal, also accompanied by significant increases in the occurrence of cyclonic, south and south-westerly circulation weather types, are noteworthy. Furthermore, the prevalence of the strong negative phase of the North Atlantic Oscillation (NAO) also emphasises the main dynamical features of the 2010 winter. A comparison of the hydrological and atmospheric conditions between the 2010 winter and the previous 2 anomalously wet winters (1996 and 2001) was also carried out to isolate not only their similarities, but also their contrasting conditions, highlighting the limitations of estimating winter precipitation amounts in Portugal using solely the NAO phase as a predictor.

Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble

A statistical-dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021–2060 and 2061–2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061–2100 compared to 2021–2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model ensembles are needed to provide a better quantification and understanding of the future changes.

Regional atmospheric circulation over Europe during the Last Glacial Maximum and its links to precipitation

The Last Glacial Maximum (LGM) exhibits different large-scale atmospheric conditions compared to present-day climate due to altered boundary conditions. The regional atmospheric circulation and associated precipitation patterns over Europe are characterized for the first time with a weather typing approach (circulation weather types, CWT) for LGM paleoclimate simulations. The CWT approach is applied to four representative regions across Europe. While the CWTs over Western Europe are prevailing westerly for both present-day and LGM conditions, considerable differences are identified elsewhere: Southern Europe experienced more frequent westerly and cyclonic CWTs under LGM conditions, while Central and Eastern Europe was predominantly affected by southerly and easterly flow patterns. Under LGM conditions, rainfall is enhanced over Western Europe but is reduced over most of Central and Eastern Europe. These differences are explained by changing CWT frequencies and evaporation patterns over the North Atlantic Ocean. The regional differences of the CWTs and precipitation patterns are linked to the North Atlantic storm track, which was stronger over Europe in all considered models during the LGM, explaining the overall increase of the cyclonic CWT. Enhanced evaporation over the North Atlantic leads to higher moisture availability over the ocean. Despite the overall cooling during the LGM, this explains the enhanced precipitation over southwestern Europe, particularly Iberia. This study links large-scale atmospheric dynamics to the regional circulation and associated precipitation patterns and provides an improved regional assessment of the European climate under LGM conditions.

O clima do oeste do Paraná: análises da presença do lago de Itaipu

Pós-graduação em Geografia - IGCE

Alterações climáticas e a percepção dos munícipes de Rio Claro - SP

Pós-graduação em Geografia - IGCE

Tipos de tempo e incidência de doenças respiratórias: um estudo geográfico aplicado ao Distrito Federal

Pós-graduação em Geografia - IGCE