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We present a methodology for estimating solar radiation climatologies from a sparse network of global radiation and/or sunshine duration records: it allows to obtain high-resolution grids of monthly normal values for global radiation (and for the direct and diffuse components), atmospheric turbidity, and surface absorbed radiation. We discuss the application of the methodology to a preliminary version of an Italian global radiation and sunshine duration data set, which completion is still in progress and present the resulting 1961–1990 monthly radiation climatologies.

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

We discuss the evolution of summer temperature extremes over Western Europe during 1961–2004 in the context of current climate warming. Using a parametric approach, we investigate the role of properties and changes in probability density functions of daily temperatures in modifying the frequency of severe, isolated events. In this perspective, the recent intensification of extremely warm events over Europe turns out to be well consistent with a pure, nonuniform shift of mean values, with no room for conjectures about increasing temperature variability.

The low-lying Netherlands is at risk from multiple threats of sea level rise, storm surges and extreme river discharges. Should these occur simultaneously, a catastrophe will be at hand. Knowledge about the likelihood of simultaneous occurrence or the so-called "compound effect" of such threats is essential to provide guidance on legislation for dike heights, flood barrier design and water management in general. In this study, we explore the simultaneous threats of North Sea storm surges and extreme Rhine river discharge for the current and future climate in a large 17-member global climate model ensemble. We use a simple approach, taking proxies of north-northwesterly winds over the North Sea and multiple~day precipitation averaged over the Rhine basin for storm surge and discharge respectively, so that a sensitivity analysis is straightforward to apply. By investigating soft extremes, we circumvent the need to extrapolate the data and thereby permit the model's synoptic development of the extreme events to be inspected. Our principle finding based on the climate model data is that, for the current climate, the probability of extreme surge conditions following extreme 20-day precipitation sums is around 3 times higher than that estimated from treating extreme surge and discharge probabilities as independent, as previously assumed. For the future climate (2070–2100), the assumption of independence cannot be rejected, at least not for precipitation sums exceeding 7 days.

The impact of the upper level (500 hPa) teleconnection between the North-Sea and the Caspian (NCP) on the temperature and precipitation regimes in the Eastern Mediterranean (EM) have been studied and reported and an index (NCPI) that measures the normalized geopotential heights' differences between the two poles of this teleconnection has been defined. In the present study, the impact of the NCP on the temperature regime over the entire European continent is presented. In particular, the correlation between temperature and the NCPI has been evaluated, on a monthly basis, over the entire Euro-Mediterranean domain for the 1948–2007 period. The results highlight a significant positive correlation in the north-western area of the domain and a significant negative correlation in the south-eastern one. These two poles were also highlighted by comparing the temperature anomalies associated with both phases of NCP. The importance of this sort of NCP-induced temperature bi-pole in the context of temperature variability over Europe and the Mediterranean has been evaluated by applying a Principal Component Analysis to the temperature dataset. The results showed that the temperature bi-pole is associated with the second most important mode of temperature variability over the domain, but if the analysis is restricted to the months associated to NCP (+) and NCP (−), it becomes the first mode with 29.2 % of associated variance.