Climate Modelling II: How Climate Models work?

Climate change is happening. Several studies applying forecasts models, with a certain level of uncertainty, suggest that global warming will continue increasing exponentially in the next decades if rigorous measures are not taken. Although climate has historically changed with significant intensity (Burroughs, 2007), the current climate phenomena and its clear relationship with anthropogenic activities is probably the main concern for humanity today; the climate change is a threat. In this context, whereas an intense social, economic and political debate is taking place, climate models appear as an essential and complex tool in order to measure and predict the climate change.

The complexity of climate models lies in trying to represent atmospheric processes occurring at planetary scale for long periods of time, such as decades, centuries or even more. This leads to the main challenge for climate modellers: developing computer models with the necessary accuracy to recreate the planet climate (Burroughs,2007).

Climate models are based on fluid dynamic and thermodynamics laws (Stute et al., 2001, Neelin, 2011 and Wainwright and Mulligan, 2013). Due to climate dynamics is governed by physics laws, these environmental processes can be expressed as a set of equations that normally have no general solution. However, numerical approximations can be carried out using computational fluid dynamics models. A common approximation to solve this numeric representation of the atmosphere is to divide the domain or study area into discrete control volumes or boxes (see Figure below). This method, known as finite volume method, allows to represent each box through a set of partial differential equations whose formulation and solution is related to the values in the surrounding control volumes (Neelin, 2011 and Wainwright and Mulligan, 2013). Due to this approach is based on small boxes representing together the whole area of study, the size of the control volumes is crucial for the model accuracy. In effect, the error of the representativeness of the equation solutions in a specific cell is related to the box size (Wainwright and Mulligan, 2013). 

Grid Section for a Typical Climate Model (Neelin, 2011)

As we can imagine at this point, the spatial scale plays a significant role in the development of climate models. Different variables, equations, assumptions and model’s definitions are considered according to the size of the area of interest which could be a specific region or the whole planet. According to this is possible to distinguish two main approaches: Global Climate Models or Global Circulation Models (GCMs) and Regional Climate Models (RCMs) (see Figure below). While the first ones simulate the climate system at planetary scale in order to understand and forecast global phenomena, the second ones work at regional scales in order to explain local climate processes being also useful for policymaking (World Meteorological Organization WMO, 2015).

In the next posts we will review different application and study cases of GCM and RCM highlighting their main features, some challenges associated to their development and the implication of the time scale.  

Grid Approach for GCMs and RCMs (WMO, 2015)