A data logger is an electronic piece of equipment that is set to record information over a period of time. By recording information over a certain time period, people can compare data to check recurring patterns. They are usually based on a digital processor or computer that is set to record the data. A data logger is usually small, battery-powered, portable and has a microprocessor with an internal memory for storing information and sensors.
Data logging is used in many kind of places. One of the places where data logging is used a lot is in weather forecasts. Many weather people read the weather using electronic devices. They record the weather over a period of time and compare the results.
When trying to find information about the weather over a long period of time then a calibration service can be very helpful. Climate models do not perform well when describing the clouds, dust, chemistry and biology of farms, fields and forests. The models tend to agree more or less with the observed information. However, there is nothing that suggests that the same fudge factors would provide the correct behaviour in a world of different chemistry.
Climate models study the way the atmosphere, oceans, land, surface, ice and the sun communicate. This job is very complicated, so models are constructed to estimate the trends rather than the events. An example of this would be, the climatemodel tells you when it is cold in the winter months, but it cannot accurately tell you the temperature on a certain day.
If the climate model told you the temperature on a certain day then that would be weather forecasting. Therefore, the climate model does not have the ability to weather forecast. However, climate trends are weather averaged out over a specific amount of time commonly 30 years. Thus, trends are important because they eliminate or 'smooth out' individual events that may be extreme.
Climate models must be checked to see if they work. Most people cannot wait for 30 years to see if the model is any good. Therefore, climate models are tested against the past where we know what happened. If the model can predict accurately what happened in the past then it should be able to predict accurately what is going to happen in the future.
All climate models are tested first by a process known as Hind-casting. Therefore, the models used can predict any future global warming and precisely map previous climate changes. If they are accurate with the past then they should be able to predict correctly for the future. Testing models against current instrumental record suggested that CO2 must cause global warming, since the models could not simulate events that had already occurred unless the additional CO2 was added to the model.
When models have been running for a long time, they have proved to make precise predictions. An example of this would be, the Mt. Pinatubo eruption that allowed modellers to check the accuracy of models by feeding information about the eruption. Therefore, the model correctly predicted the climatic response after the eruption.
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