C oupled climate models are sophisticated tools designed to simulate the Earth climate system and the complex interactions between its components. Currently, more than a dozen centers around the world develop climate models to enhance our understanding of climate and climate change and to support the activities of the Intergovernmental Panel on Climate Change (IPCC). However, climate models are not perfect. Our theoretical understanding of climate is still incomplete, and certain simplifying assumptions are unavoidable when building these models. This introduces biases into their simulations, which sometimes are surprisingly difficult to correct. Model imperfections have attracted criticism, with some arguing that model-based projections of climate are too unreliable to serve as a basis for public policy. In particular, early attempts at coupled modeling in the 1980s resulted in relatively crude representations of climate. Since then, however, we have refined our theoretical understanding of climate, improved the physical basis for climate modeling, increased the number and quality of observations, and multiplied our computational capabilities. Against the background of these developments, one may ask how much climate models have improved and how much we can trust the latest coupled model generation.The goal of this study is to objectively quantify the agreement between model and observations using a single quantity derived from a broad group of variables, which is then applied to gauge several Several important issues complicate the model validation process. First, identifying model errors is difficult because of the complex and sometimes poorly understood nature of climate itself, making it difficult to decide which of the many aspects of climate are important for a good simulation. Second, climate models must be compared against present (e.g., 1979-99) or past climate, since verifying observations for future climate are unavailable. Present climate, however, is not an independent dataset since it has already been used for the model development. On the other hand, information about past climate carries large inherent uncertainties, complicating the validation process of past climate simulations. Third, there is a lack of reliable and consistent observations for present climate, and some climate processes occur at temporal or spatial scales that are either unobservable or unresolvable. Finally, good model performance evaluated from the present climate does not necessarily guarantee reliable predictions of future climate. Despite these difficulties and limitations, model agreement with observations of today's climate is the only way to assign model confidence, with the underlying assumption that a model that accurately describes present climate will make a better projection of the future.Considering the above complications, it is clear that there is no single "ideal way" to characterize and compare model performances. Most previous model validation studies used conventional statistics to measure the similarity b...

[1] We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the stratopause and relatively fine stratospheric vertical resolution (high-top), and those that have a model top below the stratopause (low-top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low-top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low-top models with less than half the frequency of events observed in the reanalysis data and high-top models. The lack of stratospheric variability in the low-top models affects their stratosphere-troposphere coupling, resulting in short-lived anomalies in the Northern Annular Mode, which do not produce long-lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low-top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high-top models compared to the low-top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions.All supporting information may be found in the online version of this article.

We propose a new relaying scheme referred to as space full-duplex max-max relay selection (SFD-MMRS), which uses relay selection and half-duplex (HD) relays with buffers to mimic full-duplex (FD) relaying. SFD-MMRS allows the selection of different relays for reception and transmission, which, in turn, enables the relays selected for reception and transmission to simultaneously receive and transmit. With SFD-MMRS, the prelog factor 1/2 is removed from the capacity expression, and better performance in terms of both throughput and outage probability is achieved. We provide a comprehensive analysis of the capacity and outage probability of the proposed scheme for a decode-andforward (DF) protocol in Rayleigh fading. This analysis reveals that the proposed scheme provides better performance, compared with HD MMRS and HD best relay selection (BRS). Moreover, our simulation results show that the capacity of the proposed scheme with HD relays exceeds twice the capacity of BRS with HD relays for any number of relays. Furthermore, the proposed scheme provides full diversity and large signal-to-noise ratio (SNR) gains, compared with competing schemes in the literature.Index Terms-Capacity analysis, cooperative relaying, fixed relays with buffers, outage analysis, relay selection, space full-duplex (SFD).

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[1] Climate research relies on realistic atmospheric data over long periods of time. Global reanalyses or observations are commonly used for this type of work. However, the many problems associated with both the reanalyses and observations cast doubts on the reliability of such data for climate applications, and users often need to know how large the errors and uncertainties associated with the different data sets are. This paper is a systematic assessment of the errors and uncertainties contained in the time mean of many different climate quantities taken from a variety of global data sets, including four popular reanalyses, the output of the climate model developed at the Geophysical Fluid Dynamics Laboratory (GFDL), and a wide range of observations. We find that the ability of reanalyses to reproduce the observed climate mean state varies widely, with radiative quantities exhibiting the largest discrepancies. The different reanalysis products share many common errors, but overall the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA-40) matches best the observations. Interestingly, the climate model reproduces the observed climate mean state of certain quantities more faithfully than the reanalyses. This indicates that modern models have reached a high level of realism in their mean state and that care must be taken when reanalyses are used to validate models. A particular concern of this paper is the time mean uncertainty associated with specific observation-based atmospheric quantities. Observational uncertainties are estimated from the difference amongst alternative data sets for the same quantity. We show that for most quantities the observational uncertainty is smaller than the error of the reanalyses or the model. However, there are some notable exceptions. In particular, for the surface fluxes of heat, momentum, and radiation the observational uncertainties can be as large as the errors seen in the reanalyses or the model. The investigation of uncertainties in upper atmospheric quantities is restricted to reanalysis and model data, since no appropriate observations are available. In this case, the reanalyses uncertainties are generally smaller than the model errors, except for quantities which describe the meridional component of the atmospheric circulation.Citation: Reichler, T., and J. Kim (2008), Uncertainties in the climate mean state of global observations, reanalyses, and the GFDL climate model,