intensification of the Siberian high, along with the thermal impacts of enhanced snow cover and topographic forcing, corresponds to a positive wave activity flux anomaly in the late fall and early winter, leading to stratospheric warming and to a lagging tropospheric negative AO response in winter. As wintertime precipitation anomalies in Europe are well known to be associated with the North Atlantic Oscillation ( Hurrell 1995 ; Rodriguez-Puebla et al. 2001 ), which can be interpreted as the regional
intensification of the Siberian high, along with the thermal impacts of enhanced snow cover and topographic forcing, corresponds to a positive wave activity flux anomaly in the late fall and early winter, leading to stratospheric warming and to a lagging tropospheric negative AO response in winter. As wintertime precipitation anomalies in Europe are well known to be associated with the North Atlantic Oscillation ( Hurrell 1995 ; Rodriguez-Puebla et al. 2001 ), which can be interpreted as the regional
al. 2020 ). The European Climate Prediction system (EUCP) project aims to produce such improved projections of future European climate on a time horizon from the present to the middle of the century ( Hewitt and Lowe 2018 ). This work builds toward the EUCP goal by developing a common framework to compare different methods, by investigating underlying method properties, and by highlighting cases of high and low agreement across methods in terms of their output distributions. We analyze eight
al. 2020 ). The European Climate Prediction system (EUCP) project aims to produce such improved projections of future European climate on a time horizon from the present to the middle of the century ( Hewitt and Lowe 2018 ). This work builds toward the EUCP goal by developing a common framework to compare different methods, by investigating underlying method properties, and by highlighting cases of high and low agreement across methods in terms of their output distributions. We analyze eight
wind and evaporation undercatchment effects ( Sevruk 1982 , 1989 ; Hulme et al. 1995 ; Sevruk 1996 ). Notwithstanding its value, the CRU precipitation data is not completely free of such potential errors ( New et al. 1999 ). From Table 1 in New et al. (2002) , errors up to 32% in the square root of the generalized cross-validation (RTGCV) can be expected for Europe. Comparing those values with the same interpolation error test for temperature estimates [Table 4 in New et al. (2002) , with a
wind and evaporation undercatchment effects ( Sevruk 1982 , 1989 ; Hulme et al. 1995 ; Sevruk 1996 ). Notwithstanding its value, the CRU precipitation data is not completely free of such potential errors ( New et al. 1999 ). From Table 1 in New et al. (2002) , errors up to 32% in the square root of the generalized cross-validation (RTGCV) can be expected for Europe. Comparing those values with the same interpolation error test for temperature estimates [Table 4 in New et al. (2002) , with a
calculated over large (300 km) grid boxes. Second, use them to evaluate cloud properties simulated by a GCM at a regional scale. The method allows for the estimation of to what extent the defect of the global model affects a particular area—the southern European Mediterranean Sea area, which is poorly studied in terms of climate models. We consider the southern Europe Mediterranean area here (28°–50°N, 15°W–40°E): the particular land–sea distribution of the region and its rich topography are likely to
calculated over large (300 km) grid boxes. Second, use them to evaluate cloud properties simulated by a GCM at a regional scale. The method allows for the estimation of to what extent the defect of the global model affects a particular area—the southern European Mediterranean Sea area, which is poorly studied in terms of climate models. We consider the southern Europe Mediterranean area here (28°–50°N, 15°W–40°E): the particular land–sea distribution of the region and its rich topography are likely to
simulations ( Gómez-Navarro et al. 2012 , 2013 , 2014 , 2015 ; Schimanke et al. 2012 ; Gutiérrez et al. 2013 ), which should subsequently be considered in high-resolution paleoclimatology ( PAGES 2k Consortium 2014 ). The availability of extant instrumental, proxy, and model data from central Europe (CEU), in the first half of the nineteenth century, satisfies all these conditions. To assess the level of coherency between tree-ring deviations and climate swings following large (mainly tropical
simulations ( Gómez-Navarro et al. 2012 , 2013 , 2014 , 2015 ; Schimanke et al. 2012 ; Gutiérrez et al. 2013 ), which should subsequently be considered in high-resolution paleoclimatology ( PAGES 2k Consortium 2014 ). The availability of extant instrumental, proxy, and model data from central Europe (CEU), in the first half of the nineteenth century, satisfies all these conditions. To assess the level of coherency between tree-ring deviations and climate swings following large (mainly tropical
1. Introduction While it is no news that El Niño–Southern Oscillation (ENSO) is a primary source of global predictability, improving seasonal forecasts in the extratropics is constrained by the large internal variability and challenged by the limited understanding of the ENSO teleconnections. In this work, we clarify some aspects of the late-winter ENSO teleconnection to the North Atlantic–European (NAE) region by investigating its relationship with the North Atlantic Oscillation (NAO). In the
1. Introduction While it is no news that El Niño–Southern Oscillation (ENSO) is a primary source of global predictability, improving seasonal forecasts in the extratropics is constrained by the large internal variability and challenged by the limited understanding of the ENSO teleconnections. In this work, we clarify some aspects of the late-winter ENSO teleconnection to the North Atlantic–European (NAE) region by investigating its relationship with the North Atlantic Oscillation (NAO). In the
1. Introduction The location and intensity of the midlatitude storms are major influences on the climate of Europe. However, many potential variables for assessing storm climate (e.g., wind speed) are too short term and/or beset with severe inhomogeneities to be of great use ( von Storch and Weisse 2008 ). For the Northern Hemisphere, atmospheric pressure data throughout the troposphere on a subdaily basis currently exist in the form of National Centers for Environmental Prediction
1. Introduction The location and intensity of the midlatitude storms are major influences on the climate of Europe. However, many potential variables for assessing storm climate (e.g., wind speed) are too short term and/or beset with severe inhomogeneities to be of great use ( von Storch and Weisse 2008 ). For the Northern Hemisphere, atmospheric pressure data throughout the troposphere on a subdaily basis currently exist in the form of National Centers for Environmental Prediction
of the frequency of rare events is the number of observations of such events. Della-Marta et al. (2009) used the 40-yr European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA-40) to estimate the return period (RP) of extreme winds at continental and regional scales. They found that the uncertainties associated with long RPs (approximately 30 yr) are in the range between −60% and +200% of the RP estimate. This places limitations on the use of these data for reinsurance risk
of the frequency of rare events is the number of observations of such events. Della-Marta et al. (2009) used the 40-yr European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA-40) to estimate the return period (RP) of extreme winds at continental and regional scales. They found that the uncertainties associated with long RPs (approximately 30 yr) are in the range between −60% and +200% of the RP estimate. This places limitations on the use of these data for reinsurance risk
Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimesaerosol–cloud interactions, we performed numerical simulations with maritime, intermediate, continental, and continental polluted conditions for which the number density of condensation nuclei N CN , the mean radius of the larger aerosol mode R 2 , and the logarithm of the mode’s standard deviation [log( σ )] are prescribed ( Table 1 ). Typical conditions of central Europe are represented by the continental aerosol assumption ( Hande et al. 2016 ). Similar to other double-moment schemes (e
aerosol–cloud interactions, we performed numerical simulations with maritime, intermediate, continental, and continental polluted conditions for which the number density of condensation nuclei N CN , the mean radius of the larger aerosol mode R 2 , and the logarithm of the mode’s standard deviation [log( σ )] are prescribed ( Table 1 ). Typical conditions of central Europe are represented by the continental aerosol assumption ( Hande et al. 2016 ). Similar to other double-moment schemes (e
1. Introduction Warm-season thunderstorms over western Europe often develop along surface wind-shift lines characterized by horizontal convergence (hereafter referred to as convergence lines) within the warm sector east of the cold front of an extratropical cyclone. Typically, these lines first appear over France or the Bay of Biscay ahead of a cold front and then spread northeastward. The convergence lines are accompanied by a trough in the surface-pressure field and sometimes a rather
1. Introduction Warm-season thunderstorms over western Europe often develop along surface wind-shift lines characterized by horizontal convergence (hereafter referred to as convergence lines) within the warm sector east of the cold front of an extratropical cyclone. Typically, these lines first appear over France or the Bay of Biscay ahead of a cold front and then spread northeastward. The convergence lines are accompanied by a trough in the surface-pressure field and sometimes a rather
