Atmospheric processes

Abstract:

<p><strong>Abstract.</strong> We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.</p>

Abstract:

In recent years, numerical weather prediction models have begun to show notable levels of skill at predicting the average winter North Atlantic Oscillation (NAO) when initialised one month ahead. At the same time, these model predictions exhibit unusually low signal-to-noise ratios, in what has been dubbed a `signal-to-noise paradox'. We analyse both the skill and signal-to-noise ratio of the Integrated Forecast System (IFS), the European Center for Medium-range Weather Forecasts (ECMWF) model, in an ensemble hindcast experiment. Specifically, we examine the contribution to both from the regime dynamics of the North Atlantic eddy-driven jet. This is done by constructing a statistical model which captures the predictability inherent to to the trimodal jet latitude system, and fitting its parameters to reanalysis and IFS data. Predictability in this regime system is driven by interannual variations in the persistence of the jet latitude regimes, which determine the preferred state of the jet. We show that the IFS has skill at predicting such variations in persistence: because the position of the jet strongly influences the NAO, this automatically generates skill at predicting the NAO. We show that all of the skill the IFS has at predicting the winter NAO over the period 1980-2010 can be attributed to its skill at predicting regime persistence in this way. Similarly, the tendency of the IFS to underestimate regime persistence can account for the low signal-to-noise ratio, giving a possible explanation for the signal-to-noise paradox. Finally, we examine how external forcing drives variability in jet persistence, as well as highlight the role played by transient baroclinic eddy feedbacks to modulate regime persistence.

Abstract:

This thesis is dedicated to the kinetic energy power spectrum of the Earth's atmosphere. This spectrum refers to the distribution of kinetic energy at different length scales of motion, from the energy in the largest swirling weather systems, to the smallest convective clouds. The spectrum is a multifaceted and deep topic, and it has earned academic attention for several reasons.

One reason is the inherent beauty the spectrum holds in many turbulent systems. Turbulence is a highly complex phenomenon that is composed of motion at a great range of scales; however, when viewed through the lens of a power spectrum, a simple power law often neatly describes the magnitude of the kinetic energy over a range of scales. This simplification of turbulence, which is an incredibly ubiquitous and challenging phenomenon in physics, invites us to investigate the spectral perspective further. In Part One of this thesis, we do this by applying novel coarse-graining techniques to extract spectra from the atmosphere. This technique allows us to bring a spectral perspective to spaces that were previously difficult to reach. For example, we are able to investigate how the distribution of energy changes at different locations on Earth, creating the first consistently generated map of mesoscale spectral slopes in the atmosphere. We also investigate how the spectrum changes under different conditions using a new method of ``conditioned spectra''; some of these analyses are completely novel, such as the distribution of energy when mean sea level pressure is high or low or mesoscale spectral energy transfers are from large to small or small to large scales. Others are comparable to existing simulation or observational work, such as the spectrum corresponding to different levels of precipitation or different orographic heights. Spectra are often used to assess the smallest scale to which a simulation can represent the real world, and so a more nuanced understanding of local spectra could open the door to deeper and more meaningful comparisons between different models and between models and observations. In addition to spectra, our methodology can resolve local spectral fluxes. Spectral fluxes are central to parametrisations of unresolved scales in weather models, raising the possibility of evaluating and improving these parametrisations in the future through our methodology.

Another reason for investigating the spectrum is that it contains clues to a system's underlying dynamics. In Part Three, we present two-dimensional turbulence theory and review how details of the spectrum, such as the spectral slope or the direction of energy transfer, can indicate the underlying turbulent dynamics active within a particular range of scales. Mesoscale weather dynamics are much less understood than synoptic weather. It is still unclear how waves, turbulence and external forcings interact to produce the motion at these scales. Much of the progress towards understanding this topic has come from investigations into the spectrum. In Part One, we investigate how the direction of energy flux differs at different locations and under different atmospheric conditions. We also investigate the local partition of energy into rotational and divergent motion. From this, we are able to conclude that purely turbulent models of the atmosphere, which have become unpopular over the past 20 years, may be valid under certain conditions or in certain locations. We also support the prevailing notion that gravity waves are centrally important in energising the upper troposphere and lower stratospheric spectrum in the mesoscales. This work lays the foundations for a more detailed local breakdown of the mesoscale fluxes that could lead to a far deeper understanding of mesoscale processes than at present.

The last reason we will give for the academic attention the atmosphere's spectrum has attracted is the most intriguing. The spectrum has a link with predictability. \cite{lorenz1969predictability} showed that the power spectrum can be used in simple turbulent systems to predict the rate of error growth. In Part One, we explore local power spectra, a logical possibility is that if a link between the global spectrum and predictability exists, a link between local predictability and the local spectrum may exist. In Part Two, we provide background on the link between spectra and predictability. After giving this background, we extend the global theory of error growth in atmospheric turbulence by including the effects of the condensate, the shallowing of the spectrum that occurs at large scales. We show that including a realistic condensate significantly extends the predictability estimates returned by turbulence models (with the caveat that we ignore the anisotropy of the real atmosphere in our model). We then take the first steps towards a local model of error growth based on a cascade of error to larger scales by creating a toy model. This toy model illustrates how error would behave if it was related in a simple way to the local eddy turnover time. We point out this approach's limitations but find some broad consistencies with known spatial patterns in error growth, such as larger absolute errors in the midlatitudes and larger relative errors over orography. This work represents the start of investigations into the local relationships between error growth and the power spectrum, research which could be incredibly important for weather prediction and turbulence modelling generally. If a diagnostic or prognostic relationship between local spectra and error growth could be found, it may be of incredible utility, since representing the inevitable growth of error is the most challenging and important aspect of weather prediction.