Parameterization
Parameterization refers to a technique used in climate modeling to represent processes that are too complex or not feasible to simulate directly within the model. Climate models rely on mathematical equations to simulate various chemical and physical processes, such as evaporation and cloud formation. However, certain phenomena, like local weather patterns and small-scale rainstorms, are often too intricate to be accurately modeled due to their scale or unpredictability. In these cases, parameterization involves selecting alternative variables or datasets, such as regional average rainfall, to approximate the effects of these complex processes.
This approach allows climate models to run more efficiently by simplifying the representation of intricate details, which is crucial given the limitations of current computing technology. Parameterization helps in predicting the potential impacts of climate change by allowing models to focus on larger-scale atmospheric dynamics while acknowledging uncertainties introduced by approximations. Consequently, understanding parameterization is vital for evaluating the accuracy and reliability of climate predictions and the potential effects of global warming.
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Subject Terms
Parameterization
Definition
In climate models, parameterization is the technique of representing processes that are too complex or otherwise cannot be effectively simulated in the model. Climate models are based on a series of mathematical equations that represent different chemical and physical processes—such as the evaporation of water from the ocean, the of water vapor in the atmosphere to form clouds, and the direction of cloud transport based on wind directions and strengths. While processes such as these can be exactly solved within the climate model, other processes are too complex to treat in this manner. An example of this is local weather patterns such as the development of small-scale rainstorms. In this case, some other variable or data set must be chosen to represent the effects of the rainstorms. Long-term data on the regional average rainfall could be chosen as the parameterization for rainstorms. Thus, rather than a model solving a series of equations to predict when or where future rainstorms will occur, the average regional data is used to predict variations in the total average amount of rain under different climate change models.
Significance for Climate Change
Global climate models are mathematical representations of a large series of complex chemical and physical phenomena. These mathematical models are the basis for future predictions of the potential effects of global warming. Global climate models are created by dividing the whole atmosphere into a horizontal and vertical series of grid boxes. These boxes can vary in size from 2°-10°, latitudinally and longitudally (approximately 200-1,000 kilometers), and tens of kilometers in height.
Certain processes—such as large-scale atmospheric circulation patterns—can be represented by relatively simple mathematical equations that can be solved for every time point and within every grid cell of the model. Other processes, such as small rainstorms or variations in local wind speed, are too complex, are not well understood, or occur on a geographic scale that is much smaller than the grid size of the model. Thus, these processes must be parameterized—or represented by some other type of data in order to let the model run. In effect, parameterization is a type of approximation, but is important in decreasing the complexity of the global climate models, so that climate models can be run using existing computing technology. However, an important part of developing and using a climate model is determining the amount of error that parameterizations introduce to predicted model results.
Batttey, Heather S. "On the Role of Parameterization in Models With a Misspecified Nuisance Component." PNAS, 30 Aug. 2024, doi.org/10.1073/pnas.2402736121. Accessed 21 Dec. 2024.
Schmiester, Leonard, et al. "Efficient Parameterization of Large-Scale Dynamic Models Based on Relative Measurements." Bioinformatics, vol. 36, no. 2, Jan. 2020, doi.org/10.1093/bioinformatics/btz581. Accessed 21 Dec. 2024.
Ucal, Meltem. "Parametic Equation." Britannica,www.britannica.com/science/parametric-equation. Accessed 21 Dec. 2024.