The Relationships Between Climate And Weather Environmental Sciences Essay
Climate is usually described in terms of the mean and variability of temperature, precipitation and wind over a period of time, ranging from months to millions of years. The characteristic period is 30 years. Climate changes, along with our responses to it, are of great importance, and can exert profound impact on food production, water resources, and energy demand. Current research tells us that the Earth will become warmer over the time. Scientists have done a lot of work on constructing climate models, attempting to simulate the behavior of the complex climate system and predict the future changes. By using these models established by basic physical laws that govern the weather and climate, scientists are able to predict the weather for several days, but attempts to predict weather beyond several days are in vain. The main reason is the basic dynamical property of the weather system, which is called the chaotic nature. However, chaos property does not mean that we can do nothing in climate prediction. In fact, although small differences in initial conditions might change the exact day a hurricane would arrive, the climate in terms of the average temperature and precipitation would still be approximately the same for a specific region and a specific period of time.
II. Weather and Climate System
1. Climate and Weather
Climate can be contrasted to weather, which is the present condition of the same elements and their variations over periods up to two weeks. As a matter of fact, climate is usually defined as the average weather. The statistics changes in weather over time constitutes the climate changes. Although weather and climate are similar concepts, there are also important differences between them. A major misunderstanding between weather and climate is the opinion that since the weather forecast beyond several days is totally invalid, the climate predictions are considered as non-sense. The truth is that although long-term weather forecast is impossible due to the chaotic nature, predicting climate changes is quite different and can be manageable.
2. Climate system
The climate system is a complicated system, composed of 5 components: the atmosphere, oceans, the ice sheets (called cryosphere), living organisms(called biosphere), and the soils, sediments and rocks (called geosphere). The five components are not isolated but interact with each other. The atmosphere is the most important and dominating component in climate system.
Atmosphere has various functions ranging from sustaining life, to keeping the Earth warm. The composition of the atmosphere, especially the concentration of the so-called greenhouse gases, for example, the carbon dioxide, plays a key role in energy budget.
Oceans are another important component in climate system. The atmosphere does not function as an isolated system and interact with the oceans. Momentum is transferred through surface winds, causing the global surface ocean currents. Heat is also exchanged through water evaporating and condensing. When water evaporates from the surface of the oceans, the heat is stored and when it condenses to form clouds and rains, the heat is released.
III. Climate Modeling
1. Factors Determining the Earth��s Climate
The climate can be influenced both by its own internal dynamics and by changes in external factors. These external factors are called ��forcings��. External forcings include volcanic eruptions, solar variations and also human-induced changes in atmospheric composition.
Energy budget means that the total incoming energy must be equal to the total outgoing energy. Otherwise, the Earth will either become hotter and hotter or become cooler and cooler. The incoming energy comes from the solar radiation, consisting mainly of ultraviolet, visible light and short wavelength infrared. Some of them is directly reflected by the atmosphere back into the outer space, and some is absorbed by the Earth. Parts of the absorbed energy are re-emitted by the surface of the Earth at a longer wavelength in the form of heat. Most of the heat is re-absorbed by the greenhouse gases in the atmosphere and only a little escapes to the space. The energy absorbed by greenhouse gases are also re-emitted both to the Earth��s surface and to the space. The net result is that the radiation retained by the greenhouse gases is more than emitted out to the outer space.
The distribution of incoming solar energy and outgoing solar energy are different in different areas. In tropics, the incoming solar radiation is more than the outgoing radiation , so the tropics are net absorbers of energy. In contrast, poles are net emitters of energy, for the radiation emitted is greater than the radiation received. Hence, the energy must be transported from the tropics to the poles to keep energy budget. The circulations of atmosphere and oceans are the two most important energy transporting mechanisms.
Global Atmospheric Circulation
The atmospheric circulation assumes about half of the transport of energy from the tropics to the poles. The mechanism behind it is quite straightforward: warm air rises in the tropics, causing the pressure at the surface decreasing and pressure at high levels increasing. This forces the air at high levels to move polewards and air at the surface to move equatorwards. When the warm, polewards moving air from the tropics arrives at the poles, it cools down and sinks. When the cold, equatorwards moving air from the poles arrives at the tropics, it is heated and rises. A whole circulation is completed.
The Oceanic Circulation
The oceanic circulation accounts for the other half of the transport of energy from the tropics to the poles. The mechanism is similar to the atmosphere: cold waters moves equator wards, gets heated and rises and warm water moves pole wards, cools down and sinks.
The oceans and atmosphere are not isolated. On the contrary, they interact in various ways. An exchange of salt, heat, and momentum occurs between them. Feedback mechanisms between the oceans and the atmosphere also exist.
2. Constructing Climate Models
Climate models are numerical representations of the climate system. These models are generally expressed as a series of equations. These equations are based either on physical concepts or on credible observations of the real world. These equations are usually non-linear partial difference equations and cannot be solved directly. Finite difference methods running on powerful computers are introduced to solve these equations. Therefore, it is important to pay attention to the model resolution, in both time and space. The possible resolution are restricted due to the computational constraints. As a result, parameterization processes are required in order to simulate the small-scale and unresolved processes.
Space and Time Resolution
The climate system is so complex that simplifications must be made. This is in part due to our incomplete understanding of the climate system and in part due to the limitations in computing power. Simplifications can be achieved through dividing the space and time into boxes(that is space and time resolution), and through the parameterization process.
The surface of the Earth and the atmosphere and ocean in the models are divided into little boxes. The climate features in one box are considered as approximately the same, although it is not the case. Climate features are calculated at these discrete points. Besides dividing space into boxes, time is also divided into small intervals. The more space boxes and time intervals there are, the finer the resolution is. The finer the resolution is, the smaller scale climate features the model can represent. Obviously, the climate model with the finest resolution is the best. Nevertheless, the more boxes and intervals there are, the more calculations are needed, and the more time are taken. Scientists face tradeoff between the fine representation and computational time. A compromise must be made between resolution and time.
Parameterizations and Tuning
The complex physical processes involved in the climate system interact with each other on various time and space scales. Due to the limitations of resolution, many small scale processes cannot be resolved, and a number of approximations must be made. This process is called parameterizations. Numerical parameters that must be specified as input beforehand also need to be parameterized. Parameterization connects the large scale processes to the small scale processes in the models. While a number of parameters can be measured or well restricted by observation, there are still many parameters that cannot be measured or even cannot be understood. Therefore, it is necessary to adjust these parameters in order to obtain an optimum model simulation. This process is often known as ��tuning��. Some combinations of parameters work quite well, producing reliable results. But some combinations will not work at all, producing impossible or unrealistic results.
By forcing we mean that when it changes, it forces the climate to change. Forcings are the causes of climate change. Forcing mechanisms can be classified into two categories: the internal forcing and the external forcing. External forcing refers to the mechanisms acting from outside the climate system. Internal forcing refers to the mechanisms acting within the climate system.
Three components of orbital variations (obliquity, eccentricity and precession), along with the solar variation are the major external forcing mechanisms. The volcanic activity, atmospheric composition(especially the concentration of greenhouse gases) as well as the ocean circulation are the most significant internal forcing mechanisms.
The climate system is in an dynamic equilibrium state, which means that the state is not stationery but ever-changing. Feedbacks referred to those processes in which outputs from the process have an effect on the inputs to the same process. The feedback mechanisms that add the effects of a change in climate forcing are referred to as positive feedback. In contrast, feedback mechanisms that diminish the effects of a change in climate forcing is called negative feedback. There exist a lot of examples of feedbacks in climate system. A rising concentrations of carbon dioxide makes the Earth warm, causing the snow and ice melting, more land revealed. The consequence is that less incoming solar radiation is reflected into space and more radiation is absorbed by the Earth. This will make the Earth become even warmer. This is a positive feedback. On the other hand, nevertheless, plants grow faster due to the increasing amount of carbon dioxide, absorbing more carbon dioxide and the result is decreasing amount of carbon dioxide. This a negative feedback.
Everyone has heard of the famous saying that 'The flap of a butterfly's wings in Amazon rainforest can cause a tornado in Texas'. This is known as ��Butterfly Effect��, induced by the chaotic nature of the weather and climate system. It illustrates the fact quite well that very slight differences early in the history can have incredible influences on what will happen in the future. This means that, when we forecast the future, we must know everything that is happening now as detailedly and precisely as possible. Otherwise, the forecast is unreliable.
3. Confidence and Validation
A frequently asked question is whether we can trust these climate models. The answer is the climate models are trustable, producing reliable estimate of future climate changes. There are mainly two sources of confidence. One source comes from the foundation of the models. These models are established by well-accepted physical laws, such as conservation of mass, momentum and energy, as well as reliable observations. These physical principles have been examined by numerous experiments or observations and are regarded as true. The other source of confidence comes from empirical studies. Models are used to duplicate past climate changes. Then the results of simulations are compared with practical observations. These results produced by models agree with the observations well. In spite of this, models still have significant limitations and errors. An important source of errors comes from the parameterization process. Small-scale processes cannot be resolved in models, and therefore must be parameterized. This is in part due to the computational restraints, and in part as a result of limitations in understanding the complicated physical processes.
IV. Evolution and Classification of Models
The complexity of climate models has been increasing over the last few decades, with the additional physics incorporated in the models. In the mid-1970s, only the solar radiation, greenhouse gases and precipitation were involved in the climate models. By the end of the last century, the land surface, the volcanic activity and ocean circulation had been added into the models. At the beginning of the 21st century, the carbon cycle, rivers and aerosols were also included into the models, along with the further treated ocean circulation. Nowadays, the models even deal with the interactive vegetation and a chemistry perspective has been introduced to analyze the atmospheric system.
For convenience, climate models can be classified into four main categories: energy balance models (EBMs), one dimensional radiative-convective models (RCMs), two-dimensional statistical-dynamical models (SDMs), and three-dimensional general circulation models (GCMs). From the first to the last, the complexity of these models increases. The simplest models assume little interaction between major processes, whereas the most complex models are fully interactive and coupled.
Uncertainty exists at all stages of the climate prediction. There are two sources of uncertainty in climate models. One is from errors in the representation of real climate system. These errors are introduced by limited resolution of equations, known as structural error; and by parameterization of small-scale or unresolved processes, known as parameter error. The other comes from the internal climate variability, caused by the chaotic nature. Uncertainty arising from the effects of internal variability can be reduced by creating ensembles of simulations of a single model, running the model many times using enormous different initial conditions. The impact of uncertainty from parameter errors can be quantified by perturbed physics ensemble. The source of structural error can be addressed through multi-model ensemble.
Multi-Model Ensemble is generated by collecting results from a range of models from different modeling centers. Assuming that simulation errors in different models are independent, the errors can be reduced by multi-model ensemble. Therefore, although ensemble members give different predictions, the mean of the ensemble should perform better than individual ensemble members, giving an improved prediction which is close to the true state.
Perturbed Physics Ensemble is obtained by generating multiple model versions within a particular model structure, by varying internal model parameters within plausible ranges. Although climate models work well on large scale climate features, there are still a lot of parameters in the model that are not well understood and thus provide considerable uncertainty in climate predictions. In order to explore and quantify the impact of different choices of parameters, massive ensembles are created.
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