1.2 Climate prediction  (Page 2/3)

Predicting the future? model ensembles

Attempting to predict the future has profound implications for model development and application. Until very recently, information from climate models about possible future climates has been presented as a scenario or projection, without specified probabilities. This has reflected the difficulty of managing the core uncertainties associated with climate modelling:

• Changing boundary conditions: these are the factors affecting the climate system that are treated as separate from, or outside, the climate system. These include changes in solar output, volcanic eruptions and human factors, such as levels of emissions of greenhouse gases
• The natural internal variability of the global climate system: the global climate system is chaotic, which means that very small changes in one location and at one point in time can lead to large differences in other locations at a future point in time
• The extent to which the models accurately represent (parameterize) the physical processes of the climate system; in other words our understanding of the component parts of the global climate system and how they respond to change

With increasing demands from the public and private sectors for information to manage future changes in climate, and with enhanced computational power, climate modellers can now begin to explore this range of uncertainty. Different approaches exist for developing probabilistic climate predictions. One relies on brute force, based on large ensembles of simulations from computationally efficient models. This approach carries out large numbers of model runs in which model parameters are varied within their current range of uncertainty. Model parameterizations which fail to replicate existing climate observations are rejected, with the remainder used to explore future climate scenarios. This approach is complemented by continuous improvement in model representations of physical processes and higher resolution data, which improves the parameterizations – the model representation of physical processes. The second approach for developing probabilistic predictions relies on “expert judgement”, drawn from small ensembles of state-of-the-art models.

An ensemble consists of many simulations run with a specific climate model, each one slightly different from the rest. The uncertainty associated with natural climate variability is studied using “initial condition” ensembles , which vary the distribution of temperature, wind, humidity and other factors at the beginning of the simulation. The uncertainty associated with the model boundary conditions is studied using ensembles with different scenarios for human-induced or natural greenhouse gas emissions. These seek to examine the full range of possible boundary conditions of, for example, future global greenhouse gas emissions from society under different economic futures. The final source of uncertainty reflects the quality of the model representation of the climate; this is studied by using ensembles of different climate models. This approach assumes that the available models from climate modelling centres capture the full range of plausible behaviour, though this is unlikely to be the case. This source of uncertainty remains least studied, and potentially most important.