Scientific Uncertainties of Global Climate Modeling - Report Example

Scientific Uncertainties of Global Climate Modeling Synopsis This paper refers purposely to uncertainties involving global climate models that couple ocean dynamics and atmosphere to predict changes in precipitation and temperature. Although global climate models have improved in recent time serious problems have that challenge climate models have continued to exist. Some of the major sources of uncertainties highlighted in this paper include resolution and scale, natural variability, pollution particles and clouds. Various processes function on dissimilar scales in space and time and thus produce scale problems affect the number of processes models can be able to capture. Another major source of uncertainty are clouds that projection and analysis of climate quite difficult. They do this by obstructing sunlight from getting to the earth’s surface while their position of the cloud in the atmosphere influences their effectiveness at trapping heat or blocking sunlight. Pollution particles are also major sources of unpredictability and affect both precipitation and temperature in ways that are tricky to predict and model. Lastly but not least, climate change models are developed using current data on climate, which does not capture the entire range of unpredictability to be found in the near future. Introduction Global climate models have improved significantly since the 1980s. In spite of this assessable improvement, some issues have remained that tend to challenge the knowledge of climate models. Some of the factors complicating model projections involve scale and resolution, clouds, pollution particles and natural variability. Models that try to reflect on other factors including the changing climate’s effect on vegetation have even more uncertainties. Uncertainties in climate model predictions have received increased interest in recent times because such a broad range of future predictions have surfaced from mixture of different social-economic cases and various climate models. Scientists and decision-makers alike have shown a craving to have probabilities given to every scenario so that there is a superior sense of whether specific cases are more probable than others. Difficulty in giving probabilities to different scenarios can originate from stochastic or epistemic uncertainties sources. Epistemic uncertainties sources are those that can be lessened by further analysis of the system enhancing the state of knowledge. On the other hand, stochastic uncertainty sources are those considered unknowable and include items like unpredictability in the system, the disordered nature of the climate model and the unpredictability of the human system (Reichler 316). In addition, probabilities of different scenarios happening will even change shortly after prediction is executed, because community starts to respond and thus alter the results in ways the projection did not incorporate. The major factors that serve as sources of uncertainties in climate modeling include resolution and scale, natural variability, pollution particles and clouds. How Climate System and Model Works A global climate model employs many mathematical equations to characterize processes that occur on the planet such as ocean currents and wind. Mathematical equations are also employed to describe how natural processes are interrelated. For example, how wind sequences impact affect the transfer of storms from one place to another. Huge climate models are complex and have large number of equations and have to be run on computers. Using computers to keep up with the computations, models run through simulated years, months, and weeks. Normally this is accomplished to make climate projections for one or even more than one century. Sometimes climate models are run reverse to find out how climate might have changed in the history. Climate models show how natural processes of the planet work using an unrealistic three-dimensional grid. The grid is part of the climate model and covers the modeled Earth’s surface and extends in an upward direction through the replicated atmosphere. At all intersections in the grid, the model does calculations. These models sprint faster and are helpful when less detail is required. Some type of models have very intimately spaced grid spots. These are much more comprehensive models and it can take a considerable time for a computer, even a quick one, to run the replica. Resolution and Scale Generally, climate models have diverse resolutions that have a significant effect on how confined patterns are represented. Various processes function on dissimilar scales in space and time. Spatial levels are frequently referred to as resolution. Loosely analogous to the total number of pixels found in a digital photo, resolution relates to the figure of grid cells subdividing the planet for modeling purposes (Baliunas 1). These scale problems affect the types of processes models can be able to capture. With their comparatively crude resolution, climate models tend to replicate larger scale occurrence, which include fluctuations in temperature, superior than smaller-scale occurrence, such as tremendous precipitation events. For example, while heat waves have been known to affect territories across more than a few grid cells of a climate mock-up, the extreme rainfall that results to floods over and over again occurs at levels smaller than a grid cell. Certainly, this is one of the rationales rainfall and clouds remains challenging to global climate model. Researchers characteristically must resort to employing equations to estimate the activity happening at levels smaller than the grid cells (Bader 16). In some instances, improving spatial resolution can assist. For example, climate models with superior resolution came closer to reproducing weighty precipitation occurrences in the continental America. In the same way, the climate able to simulate El Niño events more practically tend to have additional grid cells spanning tropical seas than those that do not. Nevertheless, higher resolution does not constantly translate into enhanced performance. In a review of fourteen diverse climatic factors, it was found that only one of the three most excellent performing climate models had resolutions better than a degree longitude by a degree latitude (Bader 16). Clouds Cirrus clouds are high-altitude type of clouds comprising ice crystals. Cirrus clouds have been known to play an integral role in cooling the Earth by reflecting external radiation received from the sun. However, they have also been known to warm the Earth by absorbing departing radiation. Improved climate change predictions must account for alterations in the large-scale presence of clouds together with their net warming and cooling effects. Clouds are a most important source of uncertainty in predictions for expected temperature rise in climate models. Even variations in the pollutants and particle they contain impact results (Andrea 1340). For example, one study found that doubling of carbon dioxide amounts could increase temperatures by 10 degree, with the array depending on modeled variations in cloud particle sizes. Clouds, together with their particle sizes, render projection and analysis of climate more multifaceted for a number of rationales: Daytime clouds tend to obstruct some of the sunlight from getting to the earth’s surface, while clouds all through the day obstruct some of the warmth from escaping from the earth’s plane (Andrea 1340). The latter impact is most obvious at night. The position of the cloud in the environment influences their effectiveness at trapping heat or blocking sunlight. So do the size of water droplets and particles they contain and their color. Smaller droplets found in the clouds are less probable to rain out compared to bigger ones. Together with rates of precipitation, this can impact temperature because clouds having smaller particles are more probable to persist, and as a result continue to block sunlight. Pollution particles Pollution particles have been known to affect both precipitation and temperature in ways that are tricky to predict and model, escalating the uncertainty in climate predictions. Particles, also known as aerosols created from incineration mask the continuing global warming to an unidentified measure because of their impact on clouds and temperature. Sulfate particles and other pollutants more often than not reduce the bulk of droplets found in clouds. Smaller drops tend to stay airborne longer compare to bigger droplets (Senior 7). In a review of how toxic waste from forest fires impacted rain clouds on top of the Amazon, it was discovered that clouds transferring smoke particles persist longer. Nevertheless, if these clouds did attain heights that permitted them to create rainfall, the outcomes tended to be more severe, involving powerful thunderstorms. Also, most aerosols have a cooling effect on levels of temperatures. The less important droplets they endorse help clouds persevere while at the same time making them superior reflectors of sunlight (Senior 7). Further aerosols themselves also tend to reflect sunlight. As a consequence, the topical warming has been less harsh than it would have been with no the shielding incidence of toxic waste particles. The resultant dimming of the sun from the continuing influx of toxic waste particles is comparable to the kind that happens when volcanic aerosols get to the atmosphere’s weather sheet, where they can stay airborne for a considerable period of time (Reichler 316).. Analyzing how responsive climate has been to the addition of toxic waste particles is challenging. Reducing Uncertainties Given the extreme need of decision-makers to analyze scientific projections on climate change, uncertainties can be addressed by researchers becoming transparent and explicit concerning the assumptions used to characterize uncertainties. Experts concur about how climate might considerably change in the future. They also agree that most decisions made concerning climate whether to lessen emission of green house gases or not also involve some level of uncertainty intrinsic in the climate system. Nonetheless, those decisions can be enhanced with an exact description of the uncertainty intrinsic in the climate model. A thorough scientific initiative will undeniably enhance the understanding of the climate model and may lessen uncertainties following years of measurement and observation. According to Halber (1) MIT 2D Climate Model is another way to quantify the environment of worldwide climate change forecast and lessen some of the uncertainties. The MIT 2D can tell where all major climate change replicas falls in terms of prospect with respect to observations. Also, the MIT model produces an objective measure of how well every model matched up to what actually happened based on the most topical one hundred years of recorded information. Conclusion It is clear from the continuing discussion that various factors uncertainties affect global climate modeling. Various processes function on dissimilar scales in space and time producing scale problems that affect the types of processes models can be able to capture. With their crude resolution, climate models tend to replicate larger scale occurrence, which include fluctuations in temperature, greater than smaller-scale occurrence, such as tremendous precipitation events. Clouds, together with their particle sizes, render projection and analysis of climate more multifaceted for a number of rationales. Climate model decisions can be enhanced with an exact description of the uncertainty intrinsic in the climate model and use of MIT 2D Climate Model. Lastly, a methodical scientific initiative will incontestably augment the understanding of the climate model and may diminish uncertainties following measurement and observation. Bottom of Form Works Cited Andrea, Morgan et al. Smoking rain clouds over the Amazon. Science, 303 (2004): 1337-1342. Baliunas, Sallie. Uncertainties in Climate Modeling: Solar Variability and Other Factors. September 17, 1996. April 18, 2010 < http://www.marshall.org/article.php?id=12> Bader, Duncan et al. Climate models: An assessment of strengths and limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. Department of Energy, Office of Biological and Environmental Research, Washington, D.C, 2008 Halber, Deborah. Method helps reduce uncertainty of global climate prediction . January 10, 2001. April 18, 2010 < http://web.mit.edu/newsoffice/2001/climate-0110.html> Reichler, Kim. How well do coupled models simulate today’s climate? Bulletin for the American Meteorological Society, 89.3 (2008): 303-311. Senior, Mitchell. Carbon dioxide and climate: The impact of cloud parameterization. Journal of Climate, 6 (2009): 5-12. Read More