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We study summer heat waves and winter cold spells and their links to atmospheric circulation in an ensemble of EURO-CORDEX RCMs in Central Europe. Results of 19 simulations were compared against observations over 1980–2005. Atmospheric circulation was represented by circulation types and supertypes derived from daily gridded mean sea level pressure. We examined observed and simulated characteristics of hot and cold days (defined using percentiles of temperature anomalies from the mean annual cycle) and heat waves and cold spells (periods of at least three hot/cold days in summer/winter). Although the ensemble of RCMs reproduces on average the frequency and the mean length of heat waves and cold spells relatively well, individual simulations suffer from biases. Most model runs have an enhanced tendency to group hot/cold days into sequences, with several simulations leading to extremely long heat waves or cold spells (the maximum length overestimated by up to 2-3 times). All simulations also produce an extreme winter season with (often considerably) higher number of cold days than in any observed winter. The RCMs reproduce in general the observed circulation significantly conducive to heat waves and cold spells. Zonal flow reduces the probability of temperature extremes in both seasons, while advection of warm/cold air from the south-easterly/north-easterly quadrant plays a dominant role in developing heat waves/cold spells. Because of these links, the simulation of temperature extremes in RCMs is strongly affected by biases in atmospheric circulation. For almost all simulations and all circulation supertypes, the persistence of supertypes is significantly overestimated (even if the frequency of a given supertype is underestimated), which may contribute to development of too-long heat waves/cold spells. We did not identify any substantial improvement in the EURO-CORDEX RCMs in comparison to previous ENSEMBLES RCMs, but the patterns of the biases are generally less conclusive as to general RCMs' drawbacks. [ABSTRACT FROM AUTHOR]

The study deals with changes in large-scale atmospheric circulation (represented by circulation types) and associated surface air temperatures as projected in an ensemble of regional climate models (RCMs) from the ENSEMBLES project. We examine changes of circulation type frequencies and means of daily maximum and minimum temperatures within circulation types in individual seasons for two time slices of transient runs under the SRES A1B scenario (2021–2050 and 2071–2100) with respect to the control period (1961–1990). To study the influence of driving data, simulations of the driving general circulation models (GCMs) also are evaluated. We find that all models project changes of atmospheric circulation that are statistically significant for both future time slices. The models tend to project strengthening of the westerly circulation in winter and its weakening in summer. We show that increases of daily maximum and minimum temperatures in all seasons differ for individual circulation types. There are, however, only few features of the projected changes in the future circulation–temperature links that are common among the models, in particular relatively smaller warming for westerly types. Only in winter, projected changes in circulation types tend to contribute to the projected overall warming. This effect is negligible and mostly opposite in the other seasons. We also detect a strong influence of driving data on RCMs’ simulation of atmospheric circulation and temperature changes. [ABSTRACT FROM AUTHOR]

The study examines simulation of atmospheric circulation, represented by circulation indices (flow direction, strength and vorticity), and links between circulation and daily surface air temperatures in regional climate models (RCMs) over Central Europe. We explore control simulations of five high-resolution RCMs from the ENSEMBLES project driven by re-analysis (ERA-40) and the same global climate model (ECHAM5 GCM) plus of one RCM (RCA) driven by different GCMs. The aims are to (1) identify errors in RCM-simulated distributions of circulation indices in individual seasons, (2) identify errors in simulated temperatures under particular circulation indices, and (3) compare performance of individual RCMs with respect to the driving data. Although most of the RCMs qualitatively reflect observed distributions of the airflow indices, each produces distributions significantly different from the observations. General biases include overestimation of the frequency of strong flow days and of strong cyclonic vorticity. Some circulation biases obviously propagate from the driving data. ECHAM5 and all simulations driven by ECHAM5 underestimate frequency of easterly flow, mainly in summer. Except for HIRHAM, however, all RCMs driven by ECHAM5 improve on the driving GCM in simulating atmospheric circulation. The influence on circulation characteristics in the nested RCM differs between GCMs, as demonstrated in a set of RCA simulations with different driving data. The driving data control on circulation in RCA is particularly weak for the BCM GCM, in which case RCA substantially modifies (but does not improve) the circulation from the driving data in both winter and summer. Those RCMs with the most distorted atmospheric circulation are HIRHAM driven by ECHAM5 and RCA driven by BCM. Relatively strong relationships between circulation indices and surface air temperatures were found in the observed data for Central Europe. The links differ by season and are usually stronger for daily maxima than minima. RCMs qualitatively reproduce these relationships. Effects of the driving model biases were found on RCMs' performance in reproducing not only atmospheric circulation but also the links to surface temperature. However, the RCM formulation appears to be more important than the driving data in representing the latter. Differences of the circulation-to-temperature links among the RCA simulations are smaller and the links tend to be more realistic compared to the driving GCMs. [ABSTRACT FROM AUTHOR]

Temperature trends across Antarctica over the last few decades reveal strong and statistically significant warming in West Antarctica and the Antarctic Peninsula (AP) contrasting with no significant change overall in East Antarctica. However, recent studies have documented cooling in the AP since the late 1990s. This study aims to place temperature changes in the AP and West Antarctica into a larger spatial and temporal perspective by analyzing monthly station-based surface temperature observations since 1957 across the extratropical Southern Hemisphere, along with sea surface temperature (SST) data and mean sea level pressure reanalysis data. The results confirm statistically significant cooling in station observations and SST trends throughout the AP region since 1999. However, the full 60-yr period shows statistically significant, widespread warming across most of the Southern Hemisphere middle and high latitudes. Positive SST trends broadly reflect these warming trends, especially in the midlatitudes. After confirming the importance of the southern annular mode (SAM) on southern high-latitude climate variability, the influence is removed from the station temperature records, revealing statistically significant background warming across all of the extratropical Southern Hemisphere. Antarctic temperature trends in a suite of climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are then investigated. Consistent with previous work the CMIP5 models warm Antarctica at the background temperature rate that is 2 times faster than that observed. However, removing the SAM influence from both CMIP5 and observed temperatures results in Antarctic trends that differ only modestly, perhaps due to natural multidecadal variability remaining in the observations. [ABSTRACT FROM AUTHOR]

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a 'region' (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070-2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available. [ABSTRACT FROM AUTHOR]