Recent observational studies have shown that the centers of action of interannual variability of the North Atlantic Oscillation (NAO) were located farther eastward during winters of the period 1978-97 compared to previous decades of the twentieth century. In this study, which focuses on the winter season (December-March), new diagnostics characterizing this shift are presented. Further, the importance of this shift for NAO-related interannual climate variability in the North Atlantic region is discussed. It is shown that an NAO-related eastward shift in variability can be found for a wide range of different parameters like the number of deep cyclones, near-surface air temperature, and turbulent surface heat flux throughout the North Atlantic region. By using a near-surface air temperature dataset that is homogenous with respect to the kind of observations used, it is shown that the eastward shift is not an artifact of changes in observational practices that took place around the late 1970s. Finally, an EOF-based Monte Carlo test is developed to quantify the probability of changes in the spatial structure of interannual NAO variability for a relatively short (20 yr) time series given multivariate ''white noise.'' It is estimated that the likelihood for differences in the spatial structure of the NAO between two independent 20-yr periods, which are similar (as measured by the angle and pattern correlation between two NAO patterns) to the observed differences, to occur just by chance is about 18%. From the above results it is argued that care has to be taken when conclusions about long-term properties of NAO-related climate variability are being drawn from relatively short recent observational data (e.g., 1978-97).

A tracking method for tropical cyclones (TCs) is presented and their characteristics for data sets with a lower horizontal resolution, e.g., the ERA‐40 Reanalysis data set from 1958 to 2001 are explored. The tracking method uses sea level pressure, relative vorticity and wind speed at 850 hPa, and vertical wind shear. The method, assessed in the Atlantic basin, identifies a realistic number of TCs. However, the ERA‐40 TCs compared with best track data from the U.S. National Hurricane Center are too weak to reach hurricane character, i.e., the tracked TCs do not show hurricanes of category three to five. Another caveat is that the life cycle of central pressure values is often not realistically reproduced by ERA‐40 TCs. To correct the life cycle of the central pressure, a two‐step statistical downscaling approach is applied to the ERA‐40 TCs which strongly improves the finding of major hurricanes.

A method is presented to reconstruct decadal variations of the North Atlantic Oscillation (NAO). The spectral characteristics of the NAO on time scales of decades and longer are of particular interest for the understanding of North Atlantic ocean-atmosphere interactions. The reconstruction is based on a transfer model calibration that uses bandpass-filtered time series. The maximum overlap discrete wavelet transform (MODWT) is applied for decomposing the time series variance into different time scales. A total of 43 proxies, including Greenland ice cores and European tree-ring chronologies, are selected and regionally grouped providing four independent reconstructions for the period 1700-1978. The mean reconstruction agrees well with two recently published reconstructions during most of the time period. However, there are considerable differences in the earliest part before 1750. Running correlations between the reconstructions indicate that time-dependent relations exist among the different NAO reconstructions. The results suggest that the geographical distribution of proxies strongly affects the reconstruction and could explain some of the apparent discrepancies among the reconstructions recently published in literature. In the early eighteenth century, external forcing (solar, volcanic) seems to mask the NAO signature within the proxies.

A B S T R A C T Potential future changes in tropical cyclone (TC) characteristics are among the more serious regional threats of global climate change. Therefore, a better understanding of how anthropogenic climate change may affect TCs and how these changes translate in socio-economic impacts is required. Here, we apply a TC detection and tracking method that was developed for ERA-40 data to time-slice experiments of two atmospheric general circulation models, namely the fifth version of the European Centre model of Hamburg model (MPI, Hamburg, Germany, T213) and the Japan Meteorological Agency/ Meteorological research Institute model (MRI, Tsukuba city, Japan, T L 959). For each model, two climate simulations are available: a control simulation for present-day conditions to evaluate the model against observations, and a scenario simulation to assess future changes. The evaluation of the control simulations shows that the number of intense storms is underestimated due to the model resolution. To overcome this deficiency, simulated cyclone intensities are scaled to the best track data leading to a better representation of the TC intensities. Both models project an increased number of major hurricanes and modified trajectories in their scenario simulations. These changes have an effect on the projected loss potentials. However, these state-of-the-art models still yield contradicting results, and therefore they are not yet suitable to provide robust estimates of losses due to uncertainties in simulated hurricane intensity, location and frequency.