Climate Modelling III: Global Climate Models (GCMs), Challenges of Modelling the Whole Planet

In the previous post we reviewed general concepts of climate modelling. In this post the GCMs approach will be analysed, which are mainly characterized by considering the largest possible scale in the environmental science: the entire Earth. As we already analysed in this blog, the discrete control volume or box is the minimum spatial unit to represent numerically the atmosphere over a specific portion of the land. In addition to other sources of error, the size of the box is directly related to the model accuracy (Wainwright and Mulligan, 2013).

Although the uncertainty of GCMs has been measured and defined as substantial (Palmer,2014), the identification of sources of uncertainty along the modelling process represents clear opportunities to improving GMCs. In this context, it is fundamental a suitable understanding of the limitations and challenges facing GMCs modellers. This viewpoint was emphasized by Burroughs (2007) who identified different potential areas to improve GMCs and minimize the uncertainty:

• Clouds: this area involves clouds formation processes, their properties absorbing and reflecting sunlight and better understanding of precipitation dynamics (see Figure below). In addition, the anthropogenic emission of particles, such as sulphur dioxide by fossil fuel consumption, leads to a disturbance in the natural balance of sunlight absorption and reflection processes. Moreover, there is still no clarity about the precise effect of this pollution phenomena on cloudiness (increase or decrease clouds cover). Thus, the main challenge is related to the definition of more accurate parameters in models to represent clouds dynamics at suitable space and time scales (Burroughs, 2007).
• Tropical cyclones: it is necessary an adequate representation of tropical cyclones intensity and their effects in energy fluxes processes. According to Burroughs (2007) the resolution of GMCs is unable to represent suitably this local phenomenon highly relevant to predict global warming.
• Land-surface dynamics: it is necessary a better understanding of the effect of some land variables on climate processes. For instance, weather models with considerable higher resolution have shown that appropriate handling of soil moisture data can lead to more accurate rainfall predictions. Therefore, more accurate description of physical features of the land, such as run off, stored precipitation and its interaction with the atmosphere will lead to a better representation between these and their effect on climate forecast (Burroughs, 2007).
• Winds, waves and water cycle: the representation of water cycle and the interaction between oceans and the atmosphere represent probably the most relevant source of uncertainties in GCMs (Palmer2014). Furthermore, the energy exchange between sea and the atmosphere is also essential for climate dynamics. Therefore, including a more detailed representation of this processes as well as an accurate analysis of physical interaction between winds and waves could represent a substantial improvement for climate models (Burroughs,2007).
• Greenhouse gas emission: the representation of multiple emissions of greenhouse gases (e.g. Carbon Dioxide (CO2), Methane (CH4) and Fluorinated gases (F-gases)) represents a relevant source of uncertainty in climate models, due to the dynamics of human activities and behaviour of these pollutant in the atmosphere.

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Simplified Scheme of Absorption and Reflection of Sunlight (Tour of the Electromagnetic Spectrum, NASA, 2014)

As can we see, the challenges to minimize the uncertainty of this powerful modelling tool could be summarized in two main ideas. Firstly, it is necessary to improve the knowledge in some relevant climate processes and define suitable parameterizations of these in GCMs. This effort implies not only a better understanding of complex chemical and physical processes through monitoring data but also requires an improvement in how monitoring activities should carried out for higher resolution scales. Secondly, as Neelin (2011) argues, a factor that must not be disregarded is the spatial and time scale and its close relationship with computational requirement, which are not necessarily available with current technology (Burroughs, 2007 and Palmer, 2014). 

Finally, it is relevant to highlight that some local climate processes may generate large scale effects, such as convective clouds dynamics (Palmer, 2014) and turbulence (WMO, 2015) which are not able to be represented in a typical GCMs horizontal resolution (around 100 km). In this context, Regional Climate Models (RCMs) are playing a fundamental role to understand local phenomena which GCMs are not able to represent (see Figure below). Indeed, both approaches working together represent a current solution. This and other solutions must be urgently explored due to climate models play an overriding role in the understanding and prediction of global environmental change.  

High Resolution Simulation of Convective Clouds System (Palmer,2014)