Super-parameterisation of ocean deep convection

Technical report on the use of emulators in the parameterisation of ocean convectionSub-grid scale processes play an important role in ocean and climate modelling. Typical examples include clouds in atmospheric models, flows over restricted topographies or the resolution of con- vective plumes in ocean models. Detailed numerical models of these sub-grid scale processes exist, but embedding them in a Global Circulation Model (GCM) for example, would be computationally prohibitive. In the present work we investigate the applicability of emulators for representing the sub-grid scale processes within a GCM simulation. Emulators can be thought as encapsulating our beliefs about the sub-grid dynamical model, derived from a designed computer experiment using a Bayesian framework. In particular, we propose to employ an emulator for parameterising the sub-grid scale process, and embed this within a GCM as a surrogate for the actual sub-grid scale model. The result of combining the GCM with the emulator will be a Super-parameterised model, which will also be computationally efficient, since the emulator incurs a very small computational overhead. The example we chose to illustrate the proposed methodology is deep ocean convection. The sub-grid scale dynamical model simulates deep convective plumes, while the large scale dy- namics simulate the geostrophic eddy scale. We present details on building the emulator of the convective plumes and its coupling with the large scale process model. We also discuss whether the emulator should be run as a deterministic or stochastic parameterisation

The implications of transporting architecture on human health

This is the author accepted manuscript.Where modern buildings are unable to maintain the internal environment to within comfort levels they often rely on mechanical systems to become habitable. This could be due to bad design or putting the building in an environment for which it is not suited. Due to climate change it is likely that all buildings will in effect and time be moved to an environment for which it is not suited. In this work the effects of changes in climate on the internal environment will be explored and an index to define how moveable a construction might be, will be developed.The authors would like to thank the EPSRC for their support [grant ref: EP/J002380/1

A comparison between Gaussian Process emulation and Genetic Algorithms for optimising energy use of buildings

Computing speed has increased greatly over recent years. Building designers can now simulate complex building models in a short time. However, even with short simulation times, building optimisation routines can still take too long for some applications. In this paper, we compare how well genetic algorithms (GAs) and Gaussian process emulation with sequential optimisation (GPESO) optimise a building to minimise the energy use. The GA approach performs a GA routine on an EnergyPlus model and the GPESO technique creates a Gaussian Process emulator (GPE) also based on the EnergyPlus model. The GPESO uses an expected improvement algorithm to sequentially improve the GPE. The results show that the GPESO technique outperforms the GA in terms of minimising the number of simulations required and the solution obtained.This work was supported by the Engineering and Physical Sciences Research Council [EPSRC grant number EP/J002380/1]

The potential of an observational data set for calibration of a computationally expensive computer model

PublishedJournal ArticleWe measure the potential of an observational data set to constrain a set of inputs to a complex and computationally expensive computer model. We use each member in turn of an ensemble of output from a computationally expensive model, corresponding to an observable part of a modelled system, as a proxy for an observational data set. We argue that, given some assumptions, our ability to constrain uncertain parameter inputs to a model using its own output as data, provides a maximum bound for our ability to constrain the model inputs using observations of the real system. The ensemble provides a set of known parameter input and model output pairs, which we use to build a computationally effic. © 2013 Author(s).This work was supported by funding from the ice2sea programme from the European Union 7th Framework Programme, grant number 226375. Ice2sea contribution number 154

Modeling Envisat RA-2 waveforms in the coastal zone: case-study of calm water contamination

Radar altimeters have so far had limited use in the coastal zone, the area with most societal impact. This is due to both lack of, or insufficient accuracy in the necessary corrections, and more complicated altimeter signals. This paper examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10 km2 island in the NW Mediterranean). Forty-six repeat passes were analysed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly one third of cases showed an anomalously bright hyperbolic feature. This feature may be due to extremely calm waters in the Golfo della Botte (northern side of the island), but the cause of its intermittency is not clear. The modelling of waveforms in such a complex land/sea environment demonstrates the potential for sea surface height retrievals much closer to the coast than is achieved by routine processing. The long-term development of altimetric records in the coastal zone will not only improve the calibration of altimetric data with coastal tide gauges, but also greatly enhance the study of storm surges and other coastal phenomena

Sensitivity of ferry services to the Western Isles of Scotland to changes in wave and wind climate

PublishedJournal ArticleThis is the final version of the article. Available from AMS via the DOI in this record.The roughness of the seas is rarely mentioned as a major factor in the economic or social welfare of a region. In this study, the relationship between the ocean wave climate and the economy of the Western Isles of Scotland is examined. This sparsely populated region has a high dependency on marine activities, and ferry services provide vital links between communities. The seas in the region are among the roughest in the world during autumn and winter, however, making maintenance of a reliable ferry service both difficult and expensive. A deterioration in wave and wind climate either in response to natural variability or as a regional response to anthropogenic climate change is possible. Satellite altimetry and gale-frequency data are used to analyze the contemporary response of wave and wind climate to the North Atlantic Oscillation (NAO). The sensitivity of wave climate to the NAO extends to ferry routes that are only partially sheltered and are exposed to ocean waves; thus, the reliability of ferry services is sensitive to NAO. Any deterioration of the wave climate will result in a disproportionately large increase in ferry-service disruption. The impacts associated with an unusually large storm event that affected the region in January 2005 are briefly explored to provide an insight into vulnerability to future storm events. © 2013 American Meteorological Society.This research was largely supported by the Tyndall Centre for Climate Change Research project “Toward a vulnerability assessment for the UK coastline” (IT 1.15)

The effect of the nugget on Gaussian process emulators of computer models

The effect of a Gaussian process parameter known as the nugget, on the development of computer model emulators is investigated. The presence of the nugget results in an emulator that does not interpolate the data and attaches a non-zero uncertainty bound around them. The limits of this approximation are investigated theoretically, and it is shown that they can be as large as those of a least squares model with the same regression functions as the emulator, regardless of the nugget’s value. The likelihood of the correlation function parameters is also studied and two mode types are identified. Type I modes are characterised by an approximation error that is a function of the nugget and can therefore become arbitrarily small, effectively yielding an interpolating emulator. Type II modes result in emulators with a constant approximation error. Apart from a theoretical investigation of the limits of the approximation error, a practical method for automatically imposing restrictions on its extent is introduced. This is achieved by means of a penalty term that is added to the likelihood function, and controls the amount of unexplainable variability in the computer model. The main findings are illustrated on data from an Energy Balance climate model

On the use of discrete seasonal and directional models for the estimation of extreme wave conditions

Extreme value theory is commonly used in offshore engineering to estimate extreme significant wave height. To justify the use of extreme value models it is of critical importance either to verify that the assumptions made by the models are satisfied by the data or to examine the effect violating model assumptions. An important assumption made in the derivation of extreme value models is that the data come from a stationary distribution. The distribution of significant wave height varies with both the direction of origin of a storm and the season it occurs in, violating the assumption of a stationary distribution. Extreme value models can be applied to analyse the data in discrete seasons or directional sectors over which the distribution can be considered approximately stationary. Previous studies have suggested that models which ignore seasonality or directionality are less accurate and will underestimate extremes. This study shows that in fact the opposite is true. Using realistic case studies, it is shown that estimates of extremes from non-seasonal models have a lower bias and variance than estimates from discrete seasonal models and that estimates from discrete seasonal models tend to be biased high. The results are also applicable to discrete directional models

Uncertainty in wave energy resource assessment part 1: historic data

The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. This first article deals with the accuracy of the historic data and the second article considers the uncertainty which arises from variability in the wave climate. Estimates of the historic resource for a specific site are usually calculated from wave model data calibrated against in-situ measurements. Both the calibration of model data and estimation of confidence bounds are made difficult by the complex structure of errors in model data. Errors in parameters from wave models exhibit non-linear dependence on multiple factors, seasonal and interannual changes in bias and short-term temporal correlation. An example is given using two hindcasts for the European Marine Energy Centre in Orkney. Before calibration, estimates of the long-term mean WEC power from the two hindcasts differ by around 20%. The difference is reduced to 5% after calibration. The short-term temporal evolution of errors in WEC power is represented using ARMA models. It is shown that this is sufficient to model the long-term uncertainty in estimated WEC yield from one hindcast. However, seasonal and interannual changes in model biases in the other hindcast cause the uncertainty in estimated long-term WEC yield to exceed that predicted by the ARMA model

Uncertainty in wave energy resource assessment part 2: variability and predictability

The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit high levels of interannual variability. Moreover, many previous studies have demonstrated longer-term decadal changes in wave climate. The effect of interannual and climatic changes in wave climate on the predictability of long-term mean WEC power is examined for an area off the north coast of Scotland. In this location anomalies in mean WEC power are strongly correlated with the North Atlantic Oscillation (NAO) index. This link enables the results of many previous studies on the variability of the NAO and its sensitivity to climate change to be applied to WEC power levels. It is shown that the variability in 5, 10 and 20 year mean power levels is greater than if annual power anomalies were uncorrelated noise. It is also shown that the change in wave climate from anthropogenic climate change over the life time of a wave farm is likely to be small in comparison to the natural level of variability. Finally, it is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield