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Climate models suggest that anthropogenic emissions are likely to induce an important drying during summer over most of Europe in the late 21st century. However, the amplitude of the associated decrease in precipitation strongly varies among the different climate models. In order to reduce this spread, it is first necessary to identify its causes and the associated physical mechanisms. Consequently, the focus of this paper is to better estimate the role of large scale circulation (LSC) in precipitation changes over Europe using a multi-model framework and then to characterize the LSC changes using the weather regime paradigm. We show that LSC changes directly lead to a decrease of precipitation over northwestern Europe. This circulation-driven decrease in rainfall is mainly linked to an increase (decrease) of the occurrence of positive (negative) phase of the North Atlantic Oscillation regime. LSC is also responsible for a significant part of the models spread in precipitation changes over these regions. Over southern Europe, the role of LSC changes on multi-model mean precipitation changes is generally weak. We also show that the precipitation anomalies directly induced by LSC modifications seem to be further amplified through local feedbacks. [ABSTRACT FROM AUTHOR]

This paper introduces an original method for climate change detection, called temporal optimal detection method. The method consists in searching for a smooth temporal pattern in the observations. This pattern can be either the response of the climate system to a specific forcing or to a combination of forcings. Many characteristics of this new method are different from those of the classical “optimal fingerprint” method. It allows to infer the spatial distribution of the detected signal, without providing any spatial guess pattern. The spatial properties of the internal climate variability doesn’t need to be estimated either. The estimation of such quantities being very challenging at regional scale, the proposed method is particularly well-suited for such scale. The efficiency of the method is illustrated by applying it on real homogenized datasets of temperatures and precipitation over France. A multimodel detection is performed in both cases, using an ensemble of atmosphere-ocean general circulation models for estimating the temporal patterns. Regarding temperatures, new results are highlighted, especially by showing that a change is detected even after removing the uniform part of the warming. The sensitivity of the method is discussed in this case, relatively to the computation of the temporal patterns and to the choice of the model. The method also allows to detect a climate change signal in precipitation. This change impacts the spatial distribution of the precipitation more than the mean over the domain. The ability of the method to provide an estimate of the spatial distribution of the change following the prescribed temporal patterns is also illustrated. [ABSTRACT FROM AUTHOR]

We isolate the contribution of warming, other large-scale changes and soil moisture decline and feedbacks in driving future projected changes in daily precipitation across Europe. Our confidence in each of these mechanisms differs, so this analysis then allows us to determine an overall confidence (or reliability) in the projected changes. In winter, increases in extreme precipitation over Europe as a whole are judged to be reliable, dominated by increased atmospheric moisture with warming. At scales less than about 2,000 km changing circulation patterns could enhance or offset this increase. Additionally, over the Scandinavian mountains warming-induced circulation changes do offset the effect of increased moisture and the overall change is unreliable. In summer, increases in extreme precipitation over northern Scandinavia and decreases over the Mediterranean are reliable in the absence of considerable circulation change. Over central Europe, an increase in the proportion of summer rainfall falling as extreme events is reliable. [ABSTRACT FROM AUTHOR]

Global climate models predict that terrestrial northern high-latitude snow conditions will change substantially over the twenty-first century. Results from a Community Climate System Model simulation of twentieth and twenty-first (SRES A1B scenario) century climate show increased winter snowfall (+10–40%), altered maximum snow depth (−5 ± 6 cm), and a shortened snow-season (−14 ± 7 days in spring, +20 ± 9 days in autumn). By conducting a series of prescribed snow experiments with the Community Land Model, we isolate how trends in snowfall, snow depth, and snow-season length affect soil temperature trends. Increasing snowfall, by countering the snowpack-shallowing influence of warmer winters and shorter snow seasons, is effectively a soil warming agent, accounting for 10–30% of total soil warming at 1 m depth and ~16% of the simulated twenty-first century decline in near-surface permafrost extent. A shortening snow season enhances soil warming due to increased solar absorption whereas a shallowing snowpack mitigates soil warming due to weaker winter insulation from cold atmospheric air. Snowpack deepening has comparatively less impact due to saturation of snow insulative capacity at deeper snow depths. Snow depth and snow-season length trends tend to be positively related, but their effects on soil temperature are opposing. Consequently, on the century timescale the net change in snow state can either amplify or mitigate soil warming. Snow state changes explain less than 25% of total soil temperature change by 2100. However, for the latter half of twentieth century, snow state variations account for as much as 50–100% of total soil temperature variations. [ABSTRACT FROM AUTHOR]

We examine trends in climate variables and their interrelationships over the Tibetan Plateau using global climate model simulations to elucidate the mechanisms for the pattern of warming observed over the plateau during the latter half of the twentieth century and to investigate the warming trend during the twenty-first century under the SRES A1B scenario. Our analysis suggests a 4°C warming over the plateau between 1950 and 2100. The largest warming rates occur during winter and spring. For the 1961–2000 period, the simulated warming is similar to the observed trend over the plateau. Moreover, the largest warming occurs at the highest elevation sites between 1950 and 2100. We find that increases in (1) downward longwave radiation (DLR) influenced by increases in surface specific humidity ( q), and (2) absorbed solar radiation (ASR) influenced by decreases in snow cover extent are, in part, the reason for a large warming trend over the plateau, particularly during winter and spring. Furthermore, elevation-based increases in DLR (influenced by q) and ASR (influenced by snow cover and atmospheric aerosols) appear to affect the elevation dependent warming trend simulated in the model. [ABSTRACT FROM AUTHOR]

Changes in snow amount, as measured by the water equivalent of the snow pack (SWE), are studied using simulations of 21st century climate by 20 global climate models. Although the simulated warming makes snow season to shorten from its both ends in all of Eurasia and North America, SWE at the height of the winter generally increases in the coldest areas. Elsewhere, snow decreases throughout the winter. The average borderline between increasing and decreasing midwinter SWE coincides broadly with the −20°C isotherm in late 20th century November–March mean temperature, although with some variability between different areas. On the colder side of this isotherm, an increase in total precipitation generally dominates over reduced fraction of solid precipitation and more efficient melting, and SWE therefore increases. On the warmer side, where the phase of winter precipitation and snowmelt are more sensitive to the simulated warming, the reverse happens. The strong temperature dependence of the simulated SWE changes suggests that projections of SWE change could be potentially improved by taking into account biases in simulated present-day winter temperatures. A probabilistic cross verification exercise supports this suggestion. [ABSTRACT FROM AUTHOR]