Abstract
This paper describes the global climatic model that helps shed light on weather patterns. The paper studies three models; the global atmospheric model, ocean climate model, and land surface model. The main focus is to describe the uncertainties surrounding global climatic models that affect their reliability. The methods of reducing uncertainty are also discussed. If global climate models are reviewed regularly and more research and development done on the unpredictable issues, then they can be more reliable.
Introduction
The global weather pattern has been changing since time immemorial. Meteorologists have done studies intending to attempt to understand global weather patterns and the changes. They have come up with models to help understand changes in weather patterns. These models are called global climate models or global circulation models. These are mathematical models that are used in understanding the planetary atmosphere. The models are based on some equations that were developed by Navier-stokes to explain the changes and sources of energies that act on a rotating sphere (Houghton 2001, p. 29). The main sources of these energies are radiations and/or latent heat energies. The models use simulation techniques in analyzing the atmosphere. Computer programs are used in developing simulation problems. These models are used in weather forecasting to determine the expected changes in weather. They are also used in projecting climatic changes and understanding the climate and its pattern. The global climatic modeling is faced with a lot of uncertainties due to the complexity of the ecosystem and the unpredictable aspect of climatic change. In light of this, the objective of this essay is to discuss the scientific uncertainties of global climate modeling and how these could be reduced.
Background
Over the past decades, the science of climate change was not taken with much weight as is the case today. Models of global climate are now closely examined and their validity and reliability thoroughly scrutinized. There is much debate concerning climatic conditions. There is the question of whether the greenhouse effect does exist, whether it will continue or whether global warming is anticipated to continue soon (Cicerone 2001, p.123). The biggest issue facing weather forecasting is the sufficiency of data obtained through global climate models and whether the information is reliable or not. The other issue is whether the models provide sufficient information that can be used by policymakers in strategizing on how to mitigate the emissions of greenhouse gases and their effect (Houghton 2001, p.39). Can economists rely on the complex global climatic models in order to formulate economic models for cost analysis purposes? Future climate change science is presently focused on evaluating the effectiveness of climate models. Climate models represent information regarding the atmosphere and oceans. They also focus on continental vegetation and its changes, ice, and its pattern of changes. However, the models have several limitations: first, the models are accused of their incompleteness. There are many components not factored in the model that affect future climate patterns. For instance, how radiation is affected by aerosols is indirect and not factored in. The other limitation is that the models just represent how better we understand the climate system thus mirroring any gap in our knowledge f weather science. The models are just estimations since some processes in the atmosphere cannot be fully explained. It is argued that the components of the models may drift thus necessitating some adjustments in the model. Since the climate is computed on a grid of points, there are many factors not considered like tropical cyclones thus making the model unreliable to some extent.
Climate models
This is the application of quantitative methods in working out the interaction of the atmosphere and other features like ice, oceans, and earth’s surface to determine the degree to which these change the climate. They also evaluate the effects and level of interaction for purposes of weather forecasting. The models help study the dynamics of climate patterns and to explore the reasons behind the dynamics (McGuffey & Henderson 1997). The models are also used to predict future climatic changes and the effect of those changes. The models discussed in this essay are Global Atmospheric, Ocean Climate Models, and Land Surface models. All these models account for two different energies that maintain balance in the atmosphere. One of these energies is electromagnetic radiation. This is incoming short-wave radiation that takes part in maintaining atmospheric balance. A good example is an infrared radiation. The other energy is long-wave outgoing energy in the form of electromagnetic radiations also playing a big role in maintaining balance in the atmosphere. Where these two energies fail to balance, the result causes a change in the atmosphere which is only corrected if balance is restored.
Global atmospheric model
According to (Connolley 1992, p. 34), the atmospheric model focus on sea surface temperatures in order to model the temperature for future weather forecasting. It can be used to examine how the world’s largest rivers’ runoff takes place and how it affects the weather pattern. The model assumes that the water that in the river will eventually reach the mouth. It then compares the runoff in the river and that at the river mouth to see whether they consent. This should be done on monthly basis to ensure that there is consistency. In most river basins, there is too much runoff and evaporation is less. The main idea is to determine the water loss through evaporation and run-off in order to predict the changes in climate resulting from it (Ciret 1996, p.47). The measure is focused on evaporation, runoff, and precipitation thus determining the water loss through runoff and evaporation. This can determine how the weather will be in the future considering the runoff and precipitation. There are several uncertainties associated with the global climate models. First, there are many processes in different parts of the world that are not fully understood by e scientists. For instance, it is not easy to fully understand the effect of clouds on climate and climatic change (McGuffey & Henderson 1997, p.151). This makes it hard to include such factors in the model. The model, therefore, does not accurately describe the changes in climate as some factors are not fully incorporated. This makes the global economic models less reliable in forecasting future changes in the climate with certainty. The scientists are faced with the challenge of including such factors in the models to make them fully descriptive.
The other uncertainty is the inability to predict some things that affect climate. For instance, scientists cannot predict with certainty the amount of pollution likely to be emitted into the atmosphere. To predict the human activities that will add pollution that will be added into the atmosphere is not possible. The model will therefore include estimates and not the actual figures of climate changes. Using the model to predict future changes in climate is not possible. To incorporate such factors fully into the model becomes a big challenge. This will greatly affect the accuracy, validity, and reliability of the models. Scientists, therefore, work with estimates and not actual figures.
The level of technology in the world is advancing day by day. Innovation is also increasing at a high rate. The challenge to scientists is to quantify the extent to which innovation and technology will reduce the release of greenhouse gases (Holper 1994, p.45). Innovation might create new ways of garbage disposal, new methods of disposing of industrial wastes, and others. The extent to which this will change the number of greenhouse gases released into the atmosphere is not quantifiable. It is therefore not easy to incorporate them into the models. The laws and policies that are going to be enacted to mitigate the number of greenhouse gases released into the atmosphere also affect the accuracy of global climate models. It is not easy to determine with certainty the effect of these rules on the greenhouse gases released
To conduct the sensitivity analysis of climate to greenhouse gases is not possible. It is therefore impossible to determine how much climate will change as a result of greenhouse gases. To incorporate the sensitivity of climate to greenhouse gases is not easy. The models might be accused of not being all-inclusive.
These challenges need to be addressed to make the models more reliable.
Ocean climate models
70% of the earth’s surface is covered by water. Ocean, therefore, keeps a lot of heat and other components of the earth’s climate system because of the large coverage. There is anthropogenic carbon dioxide that is produced in the ocean. These models include both complex and realistic goal ocean models. The complex models are is represented by simple mathematics representation while the realistic model is represented by many mathematical equations that are complex and can only be solved using computer programs (Green, Ball & Schroeder 2004, P.65).
These models are used to simulate world ocean climate to obtain the changes and their courses. The ocean climate pattern is observed over a long time say decades or millennia. Various components of climate systems are considered in the upper and lower boundaries of the oceans and their interaction brings changes in climate. For instance, the interaction between rivers that drain into the oceans, sea ice, and the ocean interact to create a climate pattern that also affects the atmosphere. They may lead to the formation of deep-water masses that have effects on the atmosphere and other weather patterns. These changes are formulated into a simulation problem that is solved using a computer program in order to describe the weather pattern (Griffies 2004, p.23). The oceanic models evolve over time depending on the emerging ocean climate issues that necessitate the evolution of a model.
The main challenge associated with the ocean climate model is what the scientists should expect to change in the ocean climate. This can be a big challenge because it is hard to predict what the interaction of the key components of the ocean climate system will produce. For instance, the interaction of oceans and the rivers or the sea ice are expected to bring changes to the atmosphere but it is hard to predict what the change will be. It is therefore hard to incorporate these changes in the ocean climate models and therefore this affects the reliability of the models.
The other uncertainty facing the model is to determine how much of the change in climate is caused by human activities or unnatural factors. There are human factors that might also affect the ocean climate system like the industrial wastes that reach the ocean and may cause the interaction of the ocean climate system components to produce different results (Henderson &Robinson 1999, p.34). It is hard to determine this and factor it into the model. No sufficient answers can be given to these concerns. Scientists use probabilistic statements in order to describe these situations. A probabilistic statement is not a judgmental statement but gives the anticipated changes that may not be exact.
The other uncertainty in this model is that projections obtained by the models are varying and not consistent thus not very reliable (Marsland 2003, p.8). This is basically related to the uncertainties of greenhouse gases where it is expected that their effects will continue increasing more in higher latitudes than lower latitudes but it is not clear how other factors may affect these changes. For instance, the policies that may be put in place to reduce the greenhouse effects and will affect the model.
To mitigate these uncertainties, scientists should increase their research and development so that they can obtain some information regarding the effects of unnatural factors.
Land Surface Models
These are models that attempt to describe the ecological processes using atmospheric models to come up with the changes that are occurring or expected to change within the ecosystem and the land-atmosphere. The model considers the interaction between biochemistry and biogeophysics and their effects on the land-atmosphere that also affects the whole atmosphere. Three different land surface models work s together to describe the changes in the surface atmosphere. The three models simulated soil moisture in order to determine the changes in the land-atmosphere. The Noah model came up with higher soil moisture than others. The other models are the community land model and the simple biosphere model both of which showed very close results and they responded quickly to the atmospheric phenomenon (Knox & Schering 1991, P.23). They both got lower soil moisture compared to the Noah model. According to Sahoo and colleagues (2008, p. 56), the arithmetic model applied in the three models averaged soil moisture in-situ locations but they produced different results although they were put in similar conditions. This was the challenging model that will explain the three models. They yielded different results yet were exposed to the same conditions.
It is not easy to determine how the growing population of people will behave in relation to factors that affect climate. Maybe people will be more enlightened than today and they and their culture will seek to reduce pollution of the atmosphere. They can also live in a way that will increase pollution. The challenge to the scientists is to predict the way this growing population will live in the future.
It is also hard to determine the future change in temperature and how soon the change will take place. It is therefore hard to include actual figures in the model. The model, therefore, becomes less reliable.
Conclusion
Global climatic models attempt to use mathematical models to analyze the climate changes globally and also predict future changes. The challenge is that not all aspects of climate can be quantified. The non-natural factors of climate are very unpredictable and hence hard to include them correctly in the model. If models can be reviewed regularly and changes incorporated, then global climate models can be very reliable.
Reference List
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