Annette Osprey

A large ozone-circulation feedback and its implications for global warming assessments

State-of-the-art climate models now include more climate processes which are simulated at higher spatial resolution than ever1. Nevertheless, some processes, such as atmospheric chemical feedbacks, are still computationally expensive and are often ignored in climate simulations1,2. Here we present evidence that how stratospheric ozone is represented in climate models can have a first order impact on estimates of effective climate sensitivity. Using a comprehensive atmosphere-ocean chemistry-climate model, we find an increase in global mean surface warming of around 1°C (~20%) after 75 years when ozone is prescribed at pre-industrial levels compared with when it is allowed to evolve self-consistently in response to an abrupt 4×CO2 forcing. The difference is primarily attributed to changes in longwave radiative feedbacks associated with circulation-driven decreases in tropical lower stratospheric ozone and related stratospheric water vapour and cirrus cloud changes. This has important implications for global model intercomparison studies1,2 in which participating models often use simplified treatments of atmospheric composition changes that are neither consistent with the specified greenhouse gas forcing scenario nor with the associated atmospheric circulation feedbacks3-5.

Direct and ozone-mediated forcing of the Southern Annular Mode by greenhouse gases

We assess the roles of long-lived greenhouse gases and ozone depletion in driving meridional surface pressure gradients in the southern extratropics; these gradients are a defining feature of the Southern Annular Mode. Stratospheric ozone depletion is thought to have caused a strengthening of this mode during summer, with increasing long-lived greenhouse gases playing a secondary role. Using a coupled atmosphere-ocean chemistry-climate model, we show that there is cancelation between the direct, radiative effect of increasing greenhouse gases by the also substantial indirect—chemical and dynamical—feedbacks that greenhouse gases have via their impact on ozone. This sensitivity of the mode to greenhouse gas-induced ozone changes suggests that a consistent implementation of ozone changes due to long-lived greenhouse gases in climate models benefits the simulation of this important aspect of Southern Hemisphere climate.

Investigating Read Performance of Python and NetCDF When Using HPC Parallel Filesystems

New methods need to be developed to handle the increasing size of data sets in atmospheric science - traditional analysis scripts often inefficiently read and process the data. NetCDF4 is a common file format used in atmospheric and ocean sciences, and Python is widely used in atmospheric and ocean science data analysis. The aim of this work is to provide insight into which read patterns and sizes are most effective when using the netCDF4-python library. Quantitative information on these would be useful information for scientists, library developers, and data managers.

The development of a data-driven application benchmarking approach to performance modelling

Performance modelling is a useful tool in the lifeycle of high performance scientific software, such as weather and climate models, especially as a means of ensuring efficient use of available computing resources. In particular, sufficiently accurate performance prediction could reduce the effort and experimental computer time required when porting and optimising a climate model to a new machine. Yet as architectures become more complex, performance prediction is becoming more difficult. Traditional methods of performance prediction, based on source code analysis and supported by machine benchmarks, are proving inadequate to the task. In this paper, the reasons for this are explored by applying some traditional techniques to predict the computation time of a simple shallow water model which is illustrative of the computation (and communication) involved in climate models. These models are compared with real execution data gathered on AMD Opteron-based systems, including several phases of the U.K. academic community HPC resource, HECToR. Some success is had in relating source code to achieved performance for the K10 series of Opterons, but the method is found to be inadequate for the next-generation Interlagos processor. The experience leads to the investigation of a data-driven application benchmarking approach to performance modelling. Results for an early version of the approach are presented using the shallow model as an example. In addition, the data-driven approach is compared with a novel analytical model based on fitting logarithmic curves to benchmarked application data. The limitations of this analytical method provide further motivation for the development of the data-driven approach and results of this work have been published elsewhere.