Response of the North Atlantic wave climate to atmospheric modes of variability

This study investigates the relationship between the wind wave climate and the main climate modes of atmospheric variability in the North Atlantic Ocean. The modes considered are the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, the East Atlantic Western Russian (EA/WR) pattern and the Scandinavian (SCAN) pattern. The wave dataset consists of buoys records, remote sensing altimetry observations and a numerical hindcast providing significant wave height (SWH), mean wave period (MWP) and mean wave direction (MWD) for the period 1989–2009. After evaluating the reliability of the hindcast, we focus on the impact of each mode on seasonal wave parameters and on the relative importance of wind-sea and swell components. Results demonstrate that the NAO and EA patterns are the most relevant, whereas EA/WR and SCAN patterns have a weaker impact on the North Atlantic wave climate variability. During their positive phases, both NAO and EA patterns are related to winter SWH at a rate that reaches 1 m per unit index along the Scottish coast (NAO) and Iberian coast (EA) patterns. In terms of winter MWD, the two modes induce a counterclockwise shift of up to 65° per negative NAO (positive EA) unit over west European coasts. They also increase the winter MWP in the North Sea and in the Bay of Biscay (up to 1 s per unit NAO) and along the western coasts of Europe and North Africa (1 s per unit EA). The impact of winter EA pattern on all wave parameters is mostly caused through the swell wave component.

Variability and trends in England and Wales precipitation

The England and Wales precipitation (EWP) dataset is a homogeneous time series of daily accumulations from 1931 to 2014, composed from rain gauge observations spanning the region. The daily regional-average precipitation statistics are shown to be well described by a Weibull distribution, which is used to define extremes in terms of percentiles. Computed trends in annual and seasonal precipitation are sensitive to the period chosen, due to large variability on interannual and decadal timescales. Atmospheric circulation patterns associated with seasonal precipitation variability are identified. These patterns project onto known leading modes of variability, all of which involve displacements of the jet stream and storm-track over the eastern Atlantic. The intensity of daily precipitation for each calendar season is investigated by partitioning all observations into eight intensity categories contributing equally to the total precipitation in the dataset. Contrary to previous results based on shorter periods, no significant trends of the most intense categories are found between 1931 and 2014. The regional-average precipitation is found to share statistical properties common to the majority of individual stations across England and Wales used in previous studies. Statistics of the EWP data are examined for multi-day accumulations up to 10 days, which are more relevant for river flooding. Four recent years (2000, 2007, 2008 and 2012) have a greater number of extreme events in the 3-and 5-day accumulations than any previous year in the record. It is the duration of precipitation events in these years that is remarkable, rather than the magnitude of the daily accumulations.

Revisiting Swiss temperature trends 1959-2008

Temperature is a key variable for monitoring global climate change. Here we perform a trend analysis of Swiss temperatures from 1959 to 2008, using a new 2 × 2 km gridded data-set based on carefully homogenised ground observations from MeteoSwiss. The aim of this study is twofold: first, to discuss the spatial and altitudinal temperature trend characteristics in detail, and second, to quantify the contribution of changes in atmospheric circulation and local effects to these trends. The seasonal trends are all positive and mostly significant with an annual average warming rate of 0.35 °C/decade (∼1.6 times the northern hemispheric warming rate), ranging from 0.17 in autumn to 0.48 °C/decade in summer. Altitude-dependent trends are found in autumn and early winter where the trends are stronger at low altitudes (<800 m asl), and in spring where slightly stronger trends are found at altitudes close to the snow line. Part of the trends can be explained by changes in atmospheric circulation, but with substantial differences from season to season. In winter, circulation effects account for more than half the trends, while this contribution is much smaller in other seasons. After removing the effect of circulation, the trends still show seasonal variations with higher values in spring and summer. The circulation-corrected trends are closer to the values simulated by a set of ENSEMBLES regional climate models, with the models still tending towards a trend underestimation in spring and summer. Our results suggest that both circulation changes and more local effects are important to explain part of recent warming in spring, summer, and autumn. Snow-albedo feedback effects could be responsible for the stronger spring trends at altitudes close to the snow line, but the overall effect is small. In autumn, the observed decrease in fog frequency might be a key process in explaining the stronger temperature trends at low altitudes.

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.

Diagnosing links between atmospheric moisture and extreme daily precipitation over the UK

Atmospheric moisture characteristics associated with the heaviest 1% of daily rainfall events affecting regions of the British Isles are analysed over the period 1997–2008. A blended satellite/rain-gauge data set (GPCP-1DD) and regionally averaged daily rain-gauge observations (HadUKP) are combined with the ERA Interim reanalysis. These are compared with simulations from the HadGEM2-A climate model which applied observed sea surface temperature and realistic radiative forcings. Median extreme daily rainfall across the identified events and locations is larger for GPCP (32 mm day−1) than HadUKP and the simulations (∼25 mm day−1). The heaviest observed and simulated daily rainfall events are associated with increased specific humidity and horizontal transport of moisture (median 850 hPa specific humidity of ∼6 g kg−1 and vapour transport of ∼150 g kg−1 m s−1 for both observed and simulated events). Extreme daily rainfall events are less common during spring and summer across much of the British Isles, but in the south east region, they contribute up to 60% of the total number of distinct extreme daily rainfall events during these months. Compared to winter events, the summer events over south east Britain are associated with a greater magnitude and more southerly location of moisture maxima and less spatially extensive regions of enhanced moisture transport. This contrasting dependence of extreme daily rainfall on moisture characteristics implies a range of driving mechanisms that depend upon location and season. Higher spatial and temporal resolution data are required to explore these processes further, which is vital in assessing future projected changes in rainfall and associated flooding.

Global climatology of surface water temperatures of large lakes by remote sensing

Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake-mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 (inline image = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 (inline image = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 (inline image) of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake-mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake-mean open water phase and the spring and fall 0 °C air temperature transition days, (inline image = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter-lake variation in the start and end of the open water phase.