1.2 Climate prediction

Key concepts

• Climate prediction
• Climate modeling

Introduction

Over 100 years ago Svante Arrhenius, who would go on to win the Nobel Prize for Chemistry, postulated that changes in levels of carbon dioxide in the atmosphere could affect global temperatures. We now know that a number of natural and industrial chemicals, including water vapour and carbon dioxide, affect the properties of the atmosphere. Levels of carbon dioxide, methane and nitrous oxide have increased markedly as a result of fossil fuel use, agriculture and land use change since the start of the industrial revolution. This past and projected future rise in emissions, coupled with the observed rise in global mean temperatures over the past three decades, has led to considerable concern about future climate change. For geoscientists seeking to understand how the global climate system operates, the challenge has been how to represent a system that is not fully accessible, because of the time and space constraints of experiments conducted on, and observations of, environmental systems. The solution has involved the development of numerical models to represent physical processes.

Climate is the statistical average of the weather over long (30 year) periods of time. An old saying goes: “Climate is what you expect; weather is what you get.” Climate models have evolved over four decades from simple energy balance models to the massively complex global system models of today, which are largely extensions of models used for weather forecasting. This evolution has been enabled by the extraordinary development of computational power, largely in supercomputers but increasingly through dispersed applications, and the management of the correspondingly massive data sets. Not only have these developments provided far greater scope for more complex numerical models, the technology has fundamentally changed the scientific questions that can be posed.

At the heart of such climate models are the mathematical equations representing geophysical processes. Such relationships are non-linear, necessitating a range of numerical methods to provide approximate iterative solutions to the equations. There is no one “best” climate model. Model components and subcomponents are combined to answer specific questions and at a time and space scale of interest to the user. For example, a global system model might include ocean, atmosphere, ecosystem and ice sheet components at coarse resolutions. A regional model might use finer numerical grids to resolve small-scale meteorological phenomena, but will need to use the outputs of the global model as a boundary condition to its more detailed study.

The trade-off between model components and scale has typically reflected the computational efficiency of the model and its ability to include as much of the detailed physical processes as possible. However, even for models which incorporate detailed physical processes, the non-linear nature of the problem means that equations can only be solved approximately. There is inherent loss of information at scales below the averaging (grid) scales of models and through the process of parameterizing physical relationships within the model. As a result, confirmation that a complex climate model actually represents the underlying physical processes of the global climate is rather challenging; instead, the onus is on the modeller to establish a sufficient degree of confidence in the model through its ability to recreate observed data to a reasonable accuracy. This chapter first introduces the different approaches used by modellers for climate prediction, detailing the complexities of this endeavour. It concludes by considering the importance of collaborative working in development of predictive models and the future challenges facing climate science.