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Modelling Climate ​Change

The Earth’s climate results from interactions between many processes in the atmosphere, ocean, land surface and cryosphere (snow, ice and permafrost) which operate at a variety of spatial and temporal scales.  The interactions are complex and extensive (see figure below), so that quantitative predictions of the impact on the climate of greenhouse gas increases cannot be made just through simple intuitive reasoning. 

Therefore, computer models have been developed which try to mathematically simulate the climate.  Ideally, climate models would simulate all of the physical, chemical and biological mechanisms shown in the figure below, on a computational grid in which the points were close enough together to resolve the development of clouds and the influence of hills and mountains, but which also covered Earth entirely.  However this is computationally impossible, even with today’s fastest computers, as detailed calculations of climate would take impractically long (e.g. a simulation in detail for the climate in 30 years-time would take longer than 30 years to calculate).




A schematic view of many of the processes and interactions in the global climate system (from IPCC AR4 WGI FAQ 1.2)

As a result, today’s Global Climate Models (GCMs) divide the globe into grid cells, to enable computers to perform manageable climate forecasts  per grid cell.  Parameterisations are also included, (a parameterisation is a way of representing processes which occur on smaller spatial scales than the model grid).  GCMs are a trade-off between obtaining the highest-possible spatial resolution of climate forecasts, versus obtaining the results of such calculations within a practical period of time.  As computers become increasingly powerful, GCMs can perform at greater spatial resolution (see figure below). enable computers to perform manageable climate forecasts for each grid cell.  In addition, GCMs include parameterisations (a parameterisation is a way of representing processes which occur on smaller spatial scales than the model grid).  As such, GCMs are in effect a trade-off between obtaining the highest-possible spatial resolution of climate forecasts, versus obtaining the results of such calculations within a practical period of time.  As computers become increasingly powerful, GCMs can perform at greater spatial resolution (see figure below).

The focus in climate modelling is not on individual weather events, which are unpredictable on long time scales, but on the statistics of these events and on the slow evolution of oceans and ice sheets.  There will always be natural variability within a changing climate.  For example, the climate of New Zealand is expected to warm over the 21st century.  However, this doesn’t mean there will no longer be frost events throughout New Zealand in winter.  What it does mean, is frost events are likely to become more infrequent in future.

 

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