GCMs solve equations of motion to simulate the movement and properties of fluid (air and water) around the globe. The physics used to represent these processes can be divided into three categories. First, some of the physics can be calculated from first principles; well-known concepts such as the conservation of energy can be utilized. The second category includes physics that is well-known in theory, but that in practice must be approximated. For example, fluid flow is treated using the Navier-Stokes equations, which cannot be solved analytically. Finally, the third category contains empirically-known physics (derived from observations), such as formulas for evaporation as a function of humidity and wind speed.
Some important climate processes occur on scales smaller than a model grid box. These processes cannot be explicitly resolved in a climate model. One example is the process of cloud formation. Individual clouds are often smaller than the model grid boxes in which they form, so a climate model cannot distinguish between the cloudy and non-cloudy regions within a grid box. Instead, an average value representing the fraction of cloud in the grid box is predicted. This representation of small-scale processes to produce average grid box values is called parameterization. Developing parameterizations is a complex process; the goal is to identify the key effects of the sub-grid process and represent them in a simple manner. If a process is parameterized, it is not explicitly treated; instead, the impact of the process on the large-scale processes is estimated.
Sub-grid parameterization is not the only form of simplification used in a climate model. Simplifications are also used to treat processes that are not well understood and processes that are too complex to be included. For example, most vegetation models consider only a few general types of vegetation such as forest or grassland. It would be too computationally expensive to represent individual plant types or even to treat a large number of general types. This simplification of complex and sub-grid process is vital: without it, ESMs would be far too complex to run, but simplifying complex processes can introduce uncertainties in to the model, which must be assessed carefully. There are many parameterizations used in ESMs, some of which represent complex processes very well. Others (such as cloud formation) are more difficult to parameterize, and, thus, are the subject of ongoing research.
To run an ESM over a period of time (to do past and future simulations), scientists need to provide the model with information about changes in external factors, such as solar irradiance and also the variation of natural and anthropogenic emissions over the time period. For past time periods, much of this information is available, although uncertainties do exist. To simulate future periods, modelers typically consider a range of different emissions scenarios, which range from scenarios with lots of anthropogenic emissions reductions, to scenarios with large increases in future emissions.
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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.