[1] Boreal winter 2009-2010 made headlines for cold anomalies in many countries of the northern mid-latitudes. Northern Europe was severely hit by this harsh winter in line with a record persistence of the negative phase of the North Atlantic Oscillation (NAO). In the present study, we first provide a wider perspective on how unusual this winter was by using the recent 20th Century Reanalysis. A weather regime analysis shows that the frequency of the negative NAO was unprecedented since winter 1939-1940, which is then used as a dynamical analog of winter 2009-2010 to demonstrate that the latter might have been much colder without the background global warming observed during the twentieth century. We then use an original nudging technique in ensembles of global atmospheric simulations driven by observed sea surface temperature (SST) and radiative forcings to highlight the relevance of the stratosphere for understanding if not predicting such anomalous winter seasons. Our results demonstrate that an improved representation of the lower stratosphere is necessary to reproduce not only the seasonal mean negative NAO signal, but also its intraseasonal distribution and the corresponding increased probability of cold waves over northern Europe.
Abstract. Due to the evolution of the observation network, hourly 2 m temperature analysis performed by reanalysis systems shows temporal inhomogeneities. The observation network gap is less present for extreme daily temperature observations. In order to reduce inhomogeneities and enable a climatological use of temperature analysis, information from extreme temperatures could be useful. In this study, the diurnal temperature cycle has been reconstructed for stations which only record extreme temperatures. These new "pseudo" hourly temperature observations are then provided to the analysis system. Two methods have been used to deduce hourly temperatures from extremes and compared to real observations. The results have shown that using those new pseudo-observations as an input for two different reanalysis systems enables reducing the bias in temperature analysis.
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