A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.

In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm.The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a ''nature run'' were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.

Although a theory of the climatology of tropical cyclone formation remains elusive, high-resolution climate models can now simulate many aspects of tropical cyclone climate. T he effect of climate change on tropical cyclones has been a controversial scientific issue for a number of years. Advances in our theoretical understanding of the relationship between climate and tropical cyclones have been made, enabling us to understand better the links between the mean climate and the potential intensity (PI; the theoretical maximum intensity of a tropical cyclone for a given climate condition) of tropical cyclones. Improvements in the capabilities of climate models, the main tool used to predict future climate, have enabled them to achieve a considerably improved and more credible simulation of the present-day climatology of tropical cyclones. Finally, the increasing ability of such models to predict the interannual variability of tropical cyclone formation in various regions of the globe indicates that they are capturing some of the essential physical relationships governing the links between climate and tropical cyclones. HURRICANES AND CLIMATEPrevious climate model simulations, however, have suggested some ambiguity in projections of future numbers of tropical cyclones in a warmer world. While many models have projected fewer tropical cyclones globally (Sugi et al. 2002;Bengtsson et al. 2007b; Gualdi et al. 2008; Knutson et al. 2010), other climate models and related downscaling methods have suggested some increase in future numbers (e.g., Broccoli and Manabe 1990;Haarsma et al. 1993; Emanuel 2013a). When future projections for individual basins are made, the issue becomes more serious: for example, for the Atlantic basin there appears to be little consensus on the future number of tropical cyclones or on the relative importance of forcing factors such as aerosols or increases in carbon dioxide (CO 2 ) concentration. One reason could be statistical: annual numbers of tropical cyclones in the Atlantic are relatively small, making the identification of such storms sensitive to the detection method used.Further, there is substantial spread in projected responses of regional tropical cyclone (TC) frequency and intensity over the twenty-first century from downscaling studies (Knutson et al. 2007; Emanuel 2013a). Interpreting the sources of those differences is complicated by different projections of large-scale climate and by differences in the present-day reference period and sea surface temperature (SST) datasets used. A natural question is whether the diversity in responses to projected twenty-firstcentury climate of each of the studies is primarily | a reflection of uncertainty arising from different large-scale forcing (as has been suggested by, e.g., Villarini et al. 2011;Villarini and Vecchi 2012;Knutson et al. 2013) or whether this spread reflects principally different inherent sensitivities across the various downscaling techniques, even including different sensitivity of responses within the same model due to...

The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models.

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (18-0.258), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. For both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks' seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors' results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Finally, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.

An ensemble of seasonal Atlantic hurricane simulations is conducted using The Florida State University/Center for Ocean–Atmospheric Prediction Studies (FSU–COAPS) global spectral model (Cocke and LaRow) at a resolution of T126L27 (a Gaussian grid spacing of 0.94°). Four integrations comprising the ensembles were generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) time-lagged initial atmospheric conditions centered on 1 June for the 20 yr from 1986 to 2005. The sea surface temperatures (SSTs) were updated weekly using the Reynolds et al. observed data. An objective-tracking algorithm obtained from the ECMWF and modified for this model’s resolution was used to detect and track the storms. It was found that the model’s composite storm structure and track lengths are realistic. In addition, the 20-yr interannual variability was well simulated by the ensembles with a 0.78 ensemble mean rank correlation. The ensembles tend to overestimate (underestimate) the numbers of storms during July (September) and produced only one CAT4–level storm on the Saffir–Simpson scale. Similar problems are noted in other global model simulations. All ensembles did well in simulating the large number of storms forming in the Atlantic basin during 1995 and showed an increase in the number of storms during La Niña and a decrease during El Niño events. The results are found to be sensitive to the choices of convection schemes and diffusion coefficients. The overall conclusion is that models such as the one used here are needed to better hindcast the interannual variability; however, going to an even higher resolution does not guarantee better interannual variability, tracks, or intensity. Improved physical parameterizations, such as using an explicit convection scheme and better representation of surface roughness at high wind speeds, are likely to more accurately represent hurricane intensity.

Western North Pacific tropical cyclone (TC) model tracks are analyzed in two large multimodel ensembles, spanning a large variety of models and multiple future climate scenarios. Two methodologies are used to synthesize the properties of TC tracks in this large data set: cluster analysis and mass moment ellipses. First, the models' TC tracks are compared to observed TC tracks' characteristics, and a subset of the models is chosen for analysis, based on the tracks' similarity to observations and sample size. Potential changes in track types in a warming climate are identified by comparing the kernel smoothed probability distributions of various track variables in historical and future scenarios using a Kolmogorov‐Smirnov significance test. Two track changes are identified. The first is a statistically significant increase in the north‐south expansion, which can also be viewed as a poleward shift, as TC tracks are prevented from expanding equatorward due to the weak Coriolis force near the equator. The second change is an eastward shift in the storm tracks that occur near the central Pacific in one of the multimodel ensembles, indicating a possible increase in the occurrence of storms near Hawaii in a warming climate. The dependence of the results on which model and future scenario are considered emphasizes the necessity of including multiple models and scenarios when considering future changes in TC characteristics.

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