ABSTRACT

The impacts of winter weather on transport networks have been highlighted by various high-profile disruptions to road, rail, and air transport in the United Kingdom during recent winters. Recent advances in the predictability of the winter North Atlantic Oscillation (NAO) at seasonal time scales, using the Met Office Global Seasonal forecasting system, version 5 (GloSea5), present a timely opportunity for assessing the long-range predictability of a variety of winter-weather impacts on transport. This study examines the relationships between the observed and forecast NAO and a variety of U.K. winter impacts on transport in the road, rail, and aviation sectors. The results of this preliminary study show statistically significant relationships between both observed and forecast NAO index and quantities such as road-accident numbers in certain weather conditions, weather-related delays to flights leaving London Heathrow Airport, and weather-related incidents on the railway network. This supports the feasibility of the onward goal of this work, which is to investigate prototype seasonal forecasts of the relative risk of occurrence of particular impacts in a given winter for the United Kingdom, at lead times of 1–3 months. In addition, subject to the availability of relevant impacts data, there is scope for further work to make similar assessments for other parts of Europe and North America where the NAO has a strong effect on winter climate.

Abstract

The skill and reliability of forecasts of winter and summer temperature, wind speed, and irradiance over China are assessed using the Met Office Global Seasonal Forecast System, version 5 (GloSea5). Skill in such forecasts is important for the future development of seasonal climate services for the energy sector, allowing better estimates of forthcoming demand and renewable electricity supply. It was found that, although overall the skill from the direct model output is patchy, some high-skill regions of interest to the energy sector can be identified. In particular, winter mean wind speed is skillfully forecast around the coast of the South China Sea, related to skillful forecasts of the El Niño–Southern Oscillation. Such information could improve seasonal estimates of offshore wind-power generation. In a similar way, forecasts of winter irradiance have good skill in eastern central China, with possible use for solar-power estimation. Skill in predicting summer temperatures, which derives from an upward trend, is shown over much of China. The region around Beijing, however, retains this skill even when detrended. This temperature skill could be helpful in managing summer energy demand. While both the strengths and limitations of the results presented here will need to be considered when developing seasonal climate services in the future, the outlook for such service development in China is promising.

Abstract

The atmospheric response to Arctic and Antarctic sea ice changes typical of the present day and coming decades is investigated using the Hadley Centre global climate model (HadGEM3). The response is diagnosed from ensemble simulations of the period 1979 to 2009 with observed and perturbed sea ice concentrations. The experimental design allows the impacts of ocean–atmosphere coupling and the background atmospheric state to be assessed. The modeled response can be very different to that inferred from statistical relationships, showing that the response cannot be easily diagnosed from observations. Reduced Arctic sea ice drives a local low pressure response in boreal summer and autumn. Increased Antarctic sea ice drives a poleward shift of the Southern Hemisphere midlatitude jet, especially in the cold season. Coupling enables surface temperature responses to spread to the ocean, amplifying the atmospheric response and revealing additional impacts including warming of the North Atlantic in response to reduced Arctic sea ice, with a northward shift of the Atlantic intertropical convergence zone and increased Sahel rainfall. The background state controls the sign of the North Atlantic Oscillation (NAO) response via the refraction of planetary waves. This could help to resolve differences in previous studies, and potentially provides an “emergent constraint” to narrow the uncertainties in the NAO response, highlighting the need for future multimodel coordinated experiments.

Abstract

Significant predictive skill for the mean winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) has been recently reported for a number of different seasonal forecasting systems. These findings are important in exploring the predictability of the natural system, but they are also important from a socioeconomic point of view, since the ability to predict the wintertime atmospheric circulation anomalies over the North Atlantic well ahead in time will have significant benefits for North American and European countries.

In contrast to the tropics, for the mid latitudes the predictive skill of many forecasting systems at the seasonal time scale has been shown to be low to moderate. The recent findings are promising in this regard, suggesting that better forecasts are possible, provided that key components of the climate system are initialized realistically and the coupled models are able to simulate adequately the dominant processes and teleconnections associated with low-frequency variability. It is shown that a multisystem approach has unprecedented high predictive skill for the NAO and AO, probably largely due to increasing the ensemble size and partly due to increasing model diversity.

Predicting successfully the winter mean NAO does not ensure that the respective climate anomalies are also well predicted. The NAO has a strong impact on Europe and North America, yet it only explains part of the interannual and low-frequency variability over these areas. Here it is shown with a number of different diagnostics that the high predictive skill for the NAO/AO indeed translates to more accurate predictions of temperature, surface pressure, and precipitation in the areas of influence of this teleconnection.

Abstract

There is growing use of machine learning algorithms to replicate subgrid parameterization schemes in global climate models. Parameterizations rely on approximations; thus, there is potential for machine learning to aid improvements. In this study, a neural network is used to mimic the behavior of the nonorographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability. The neural network is found to require only two of the six inputs used by the parameterization scheme, suggesting the potential for greater efficiency in this scheme. Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be chosen based on emergent features of the coupled system with minimal computational cost, and providing a testbed prior to coupling to a climate model. A climate model simulation, using the neural network in place of the existing parameterization scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the nonorographic gravity wave variability associated with El Niño–Southern Oscillation and stratospheric polar vortex variability. These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

Abstract

For the first time, a formal comparison is made between gravity wave momentum fluxes in models and those derived from observations. Although gravity waves occur over a wide range of spatial and temporal scales, the focus of this paper is on scales that are being parameterized in present climate models, sub-1000-km scales. Only observational methods that permit derivation of gravity wave momentum fluxes over large geographical areas are discussed, and these are from satellite temperature measurements, constant-density long-duration balloons, and high-vertical-resolution radiosonde data. The models discussed include two high-resolution models in which gravity waves are explicitly modeled, Kanto and the Community Atmosphere Model, version 5 (CAM5), and three climate models containing gravity wave parameterizations, MAECHAM5, Hadley Centre Global Environmental Model 3 (HadGEM3), and the Goddard Institute for Space Studies (GISS) model. Measurements generally show similar flux magnitudes as in models, except that the fluxes derived from satellite measurements fall off more rapidly with height. This is likely due to limitations on the observable range of wavelengths, although other factors may contribute. When one accounts for this more rapid fall off, the geographical distribution of the fluxes from observations and models compare reasonably well, except for certain features that depend on the specification of the nonorographic gravity wave source functions in the climate models. For instance, both the observed fluxes and those in the high-resolution models are very small at summer high latitudes, but this is not the case for some of the climate models. This comparison between gravity wave fluxes from climate models, high-resolution models, and fluxes derived from observations indicates that such efforts offer a promising path toward improving specifications of gravity wave sources in climate models.

Advances in weather and climate research have demonstrated the role of the stratosphere in the Earth system across a wide range of temporal and spatial scales. Stratospheric ozone loss has been identified as a key driver of Southern Hemisphere tropospheric circulation trends, affecting ocean currents and carbon uptake, sea ice, and possibly even the Antarctic ice sheets. Stratospheric variability has also been shown to affect short-term and seasonal forecasts, connecting the tropics and midlatitudes and guiding storm-track dynamics. The two-way interactions between the stratosphere and the Earth system have motivated the World Climate Research Programme's (WCRP) Stratospheric Processes and their Role in Climate's (SPARC) activity on Modelling the Dynamics and Variability of the Stratosphere-Troposphere System (DynVar) to investigate the impact of stratospheric dynamics and variability on climate. This assessment will be made possible by two new multimodel datasets. First, roughly 10 models with a well-resolved stratosphere are participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), providing the first multimodel ensemble of climate simulations coupled from the stratopause to the sea floor. Second, the Stratosphere Resolving Historical Forecast Project (Strat-HFP) of WCRP's Climate Variability and Predictability (CLIVAR) program is forming a multimodel set of seasonal hind-casts with stratosphere-resolving models, revealing the impact of both stratospheric initial conditions and dynamics on intraseasonal prediction. The CMIP5 and Strat-HFP model datasets will offer an unprecedented opportunity to understand the role of the stratosphere in the natural and forced variability of the Earth system and to determine whether incorporating knowledge of the middle atmosphere improves seasonal forecasts and climate projections.

Abstract

Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.

Abstract

This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ∼50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July.

Abstract

The stratosphere contains ~17% of Earth’s atmospheric mass, but its existence was unknown until 1902. In the following decades our knowledge grew gradually as more observations of the stratosphere were made. In 1913 the ozone layer, which protects life from harmful ultraviolet radiation, was discovered. From ozone and water vapor observations, a first basic idea of a stratospheric general circulation was put forward. Since the 1950s our knowledge of the stratosphere and mesosphere has expanded rapidly, and the importance of this region in the climate system has become clear. With more observations, several new stratospheric phenomena have been discovered: the quasi-biennial oscillation, sudden stratospheric warmings, the Southern Hemisphere ozone hole, and surface weather impacts of stratospheric variability. None of these phenomena were anticipated by theory. Advances in theory have more often than not been prompted by unexplained phenomena seen in new stratospheric observations. From the 1960s onward, the importance of dynamical processes and the coupled stratosphere–troposphere circulation was realized. Since approximately 2000, better representations of the stratosphere—and even the mesosphere—have been included in climate and weather forecasting models. We now know that in order to produce accurate seasonal weather forecasts, and to predict long-term changes in climate and the future evolution of the ozone layer, models with a well-resolved stratosphere with realistic dynamics and chemistry are necessary.