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.
[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.
Advances in the field of seasonal forecasting have brought widespread socioeconomic benefits. However, seasonal forecast skill in the extratropics is relatively modest 1 , which has prompted the seasonal forecasting community to search for additional sources of predictability 2,3. For over a decade it has been suggested that the stratosphere can act as a source of enhanced seasonal predictability, as long-lived circulation anomalies in the lower stratosphere following Stratospheric Sudden Warmings are associated with same-signed circulation anomalies in the troposphere for up to two months 4,5. Here we show that such enhanced predictability can be realized in a dynamical seasonal forecast system, thus opening the door to prediction of a comprehensive suite of parameters of socioeconomic relevance. We employ a dynamical forecast system with a good representation of the stratosphere to perform ensemble model forecasts initialized at the onset date of Stratospheric Sudden Warmings. Our model forecasts faithfully reproduce the observed mean tropospheric response in the following months, with enhanced forecast skill of atmospheric circulation patterns, surface temperature over Northern Russia and Eastern Canada, and North Atlantic precipitation. Our results imply that seasonal forecast systems are likely to produce significantly higher forecast skill for certain regions when initialized during Stratospheric Sudden Warmings. Skillful seasonal forecasts rely on the predictability of slowly-varying components of the climate system, such as sea surface temperature (SST), sea ice, snow, and soil moisture. Most of the skill that is currently obtained by seasonal forecast systems stems from the predictability of El Niño Southern Oscillation (ENSO) and its remote influences 1. In general, ENSO's influence declines
Abstract. The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.