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.

International audienceIn light of growing interest in data-driven methods for oceanic, atmospheric and climate sciences, this work focuses on the field of data assimilation and presents the Analog Data Assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the non-parametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems, namely Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings

The ability of California's water supply system to adapt to long-term climatic and demographic changes is examined. Two climate warming and a historical climate scenario are examined with population and land use estimates for the year 2100 using a statewide economic-engineering optimization model of water supply management. Methodologically, the results of this analysis indicate that for long-term climate change studies of complex systems, there is considerable value in including other major changes expected during a long-term time-frame (such as population changes), allowing the system to adapt to changes in conditions (a common feature of human societies), and representing the system in sufficient hydrologic and operational detail and breadth to allow significant adaptation. While the policy results of this study are preliminary, they point to a considerable engineering and economic ability of complex, diverse, and inter-tied systems to adapt to significant changes in climate and population. More specifically, California's water supply system appears physically capable of adapting to significant changes in climate and population, albeit at a significant cost. Such adaptation would entail large changes in the operation of California's large groundwater storage capacity, significant transfers of water among water users, and some adoption of new technologies.

Flow in a stably stratified environment is characterized by anisotropic and intermittent turbulence and wavelike motions of varying amplitudes and periods. Understanding turbulence intermittency and wave-turbulence interactions in a stably stratified flow remains a challenging issue in geosciences including planetary atmospheres and oceans. The stable atmospheric boundary layer (SABL) commonly occurs when the ground surface is cooled by longwave radiation emission such as at night over land surfaces, or even daytime over snow and ice surfaces, and when warm air is advected over cold surfaces. Intermittent turbulence intensification in the SABL impacts human activities and weather variability, yet it cannot be generated in state-of-the-art numerical forecast models. This failure is mainly due to a lack of understanding of the physical mechanisms for seemingly random turbulence generation in a stably stratified flow, in which wave-turbulence interaction is a potential mechanism for turbulence intermittency. A workshop on wave-turbulence interactions in the SABL addressed the current understanding and challenges of wave-turbulence interactions and the role of wavelike motions in contributing to anisotropic and intermittent turbulence from the perspectives of theory, observations, and numerical parameterization. There have been a number of reviews on waves, and a few on turbulence in stably stratified flows, but not much on wave-turbulence interactions. This review focuses on the nocturnal SABL; however, the discussions here on intermittent turbulence and wave-turbulence interactions in stably stratified flows underscore important issues in stably stratified geophysical dynamics in general.