Recent observational and theoretical studies of the global properties of small-scale atmospheric gravity waves have highlighted the global effects of these waves on the circulation from the surface to the middle atmosphere. The effects of gravity waves on the large-scale circulation have long been treated via parametrizations in both climate and weather-forecasting applications. In these parametrizations, key parameters describe the global distributions of gravity-wave momentum flux, wavelengths and frequencies. Until recently, global observations could not define the required parameters because the waves are small in scale and intermittent in occurrence. Recent satellite and other global datasets with improved resolution, along with innovative analysis methods, are now providing constraints for the parametrizations that can improve the treatment of these waves in climate-prediction models. Research using very-highresolution global models has also recently demonstrated the capability to resolve gravity waves and their circulation effects, and when tested against observations these models show some very realistic properties. Here we review recent studies on gravitywave effects in stratosphere-resolving climate models, recent observations and analysis methods that reveal global patterns in gravity-wave momentum fluxes and results of very-high-resolution model studies, and we outline some future research requirements to improve the treatment of these waves in climate simulations.

The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue “The SPARC Reanalysis Intercomparison Project (S-RIP)” in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports

Nearly all chemistry-climate models (CCMs) have a systematic bias of a delayed springtime breakdown of the Southern Hemisphere (SH) stratospheric polar vortex, implying insufficient stratospheric wave drag. In this study the Canadian Middle Atmosphere Model (CMAM) and the CMAM Data Assimilation System (CMAM-DAS) are used to investigate the cause of this bias. Zonal wind analysis increments from CMAM-DAS reveal systematic negative values in the stratosphere near 608S in winter and early spring. These are interpreted as indicating a bias in the model physics, namely, missing gravity wave drag (GWD). The negative analysis increments remain at a nearly constant height during winter and descend as the vortex weakens, much like orographic GWD. This region is also where current orographic GWD parameterizations have a gap in wave drag, which is suggested to be unrealistic because of missing effects in those parameterizations. These findings motivate a pair of free-running CMAM simulations to assess the impact of extra orographic GWD at 608S. The control simulation exhibits the cold-pole bias and delayed vortex breakdown seen in the CCMs. In the simulation with extra GWD, the cold-pole bias is significantly reduced and the vortex breaks down earlier.Changes in resolved wave drag in the stratosphere also occur in response to the extra GWD, which reduce stratospheric SH polar-cap temperature biases in late spring and early summer. Reducing the dynamical biases, however, results in degraded Antarctic column ozone. This suggests that CCMs that obtain realistic column ozone in the presence of an overly strong and persistent vortex may be doing so through compensating errors.

The Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canada's Canadian Meteorological Centre (CMC) in December 2011, replacing CMC's previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses by means of a procedure that resembles the incremental analysis update technique, but introduces only a fraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimilative meteorology, whereas sea ice concentration and sea surface temperature are relaxed toward gridded observational values. The subsurface ocean is initialized through surface forcing provided by the assimilation run, together with an offline variational assimilation of gridded observational temperatures followed by an adjustment of the salinity field to preserve static stability. The performance of CanSIPS historical forecasts initialized every month over the period 1981-2010 is documented in a companion paper. The CanCM4 model and the initialization procedures developed for CanSIPS have been employed as well for decadal forecasts, including those contributing to phase 5 of the Coupled Model Intercomparison Project.

<p><strong>Abstract.</strong> The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This overview paper for the S-RIP special issue summarizes the motivation and goals of the S-RIP activity, and reviews key technical aspects of the reanalysis data sets that are the focus of the S-RIP report.</p>

| iv) Utilization of subseasonal and seasonal predictions for social and economic benefits.These are particularly promising areas of research that will greatly accelerate realizing the common goals of WWRP and WCRP and in turn any Earthsystem prediction initiative that would embrace our research (Nobre 2010;Shapiro et al. 2010;Shukla et al. 2010). The advance of predictive skill of weather/ climate EPSs, promoted by the first of the four areas of collaboration, will depend crucially on progress in the other three areas. These are the most pressing issues to solve before achieving optimal utilization of EPSs and their applications. Because they lie at the intersection of weather and climate, these research priorities require the multidisciplinary, collaborative approach promoted by an Earth-system prediction initiative. SEAMLESS WEATHER/CLIMATE EPSS.A fundamental principle of seamless prediction is that the Earth system 1 exhibits a wide range of dynamical, physical, biological, and chemical interactions involving spatial and temporal variability continuously spanning all weather/climate scales. The traditional boundaries between weather and climate are artificial (Shapiro et al. 2010).As explained in Hurrell et al. (2009), for example, the slowly varying planetary-scale circulation preconditions the environment for the "fast acting" microscale and mesoscale processes of daily highimpact weather and regional climate. As an example, there is evidence that natural climate variations, such as ENSO and the North Atlantic Oscillation (NAO)/ northern annular mode, significantly alter the intensity, track, and frequency of extratropical and tropical cyclones and also affect decadal variability in tropical cyclones and the multidecadal drought in the Sahel region. Conversely, small-scale processes have significant upscale effects on large-scale circulation and on the interactions among the components of the global climate system.The challenge facing our scientific community is to improve the prediction of the spatial-temporal continuum of the interactions among weather, climate, and the Earth system. The most important aspect of the challenge is the chaotic nature of weather and climate predictability that needs to be characterized with probabilistic information.EPSs are widely used for weather and environmental (e.g., hydrological) prediction by operational services. Ensemble forecasts offer not only an estimate of the most probable future state of a system, but also a range of possible outcomes. Assessing how climate subseasonal-to-seasonal variations may alter the frequencies, intensities, and locations of highimpact events is a high priority for decision making. Many users are risk averse-more concerned with the probability of high-impact events than with the most probable future mean state. This makes the AFFILIATIONS: brunet-meteorological research division, environment Canada, dorval, Quebec, Canada; Shapiro-national Center for atmospheric research, boulder, Colorado, and Geophysical institute, university of berge...

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A new system that resolves the stratosphere was implemented for operational medium-range weather forecasts at the Canadian Meteorological Centre. The model lid was raised from 10 to 0.1 hPa, parameterization schemes for nonorographic gravity wave tendencies and methane oxidation were introduced, and a new radiation scheme was implemented. Because of the higher lid height of 0.1 hPa, new measurements between 10 and 0.1 hPa were also added. This new high-top system resulted not only in dramatically improved forecasts of the stratosphere, but also in large improvements in medium-range tropospheric forecast skill. Pairs of assimilation experiments reveal that most of the stratospheric and tropospheric forecast improvement is obtained without the extra observations in the upper stratosphere. However, these observations further improve forecasts in the winter hemisphere but not in the summer hemisphere. Pairs of forecast experiments were run in which initial conditions were the same for each experiment but the forecast model differed. The large improvements in stratospheric forecast skill are found to be due to the higher lid height of the new model. The new radiation scheme helps to improve tropospheric forecasts. However, the degree of improvement seen in tropospheric forecast skill could not be entirely explained with these purely forecast experiments. It is hypothesized that the cycling of a better model and assimilation provide improved initial conditions, which result in improved forecasts.