Validating a mathematical model. Climate Science Glossary.



Validating a mathematical model

Validating a mathematical model

Models successfully reproduce temperatures since globally, by land, in the air and the ocean. Models are unreliable "[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.

This is clearly a very complex task, so models are built to estimate trends rather than events. Climate trends are weather, averaged out over time - usually 30 years. Trends are important because they eliminate - or "smooth out" - single events that may be extreme, but quite rare.

Climate models have to be tested to find out if they work. If a model can correctly predict trends from a starting point somewhere in the past, we could expect it to predict with reasonable certainty what might happen in the future. So all models are first tested in a process called Hindcasting. The models used to predict future global warming can accurately map past climate changes.

If they get the past right, there is no reason to think their predictions would be wrong. Testing models against the existing instrumental record suggested CO2 must cause global warming, because the models could not simulate what had already happened unless the extra CO2 was added to the model.

All other known forcings are adequate in explaining temperature variations prior to the rise in temperature over the last thirty years, while none of them are capable of explaining the rise in the past thirty years.

CO2 does explain that rise, and explains it completely without any need for additional, as yet unknown forcings. Where models have been running for sufficient time, they have also been proved to make accurate predictions. For example, the eruption of Mt. Pinatubo allowed modellers to test the accuracy of models by feeding in the data about the eruption.

The models successfully predicted the climatic response after the eruption. Models also correctly predicted other effects subsequently confirmed by observation, including greater warming in the Arctic and over land, greater warming at night, and stratospheric cooling. The climate models, far from being melodramatic, may be conservative in the predictions they produce. Observed sea level rise since from tide gauge data red and satellite measurements blue compared to model projections for from the IPCC Third Assessment Report grey band.

The Copenhagen Diagnosis, Here, the models have understated the problem. In reality, observed sea level is tracking at the upper range of the model projections. There are other examples of models being too conservative , rather than alarmist as some portray them. All models have limits - uncertainties - for they are modelling complex systems. However, all models improve over time, and with increasing sources of real-world information such as satellites, the output of climate models can be constantly refined to increase their power and usefulness.

Climate models have already predicted many of the phenomena for which we now have empirical evidence. Climate models form a reliable guide to potential climate change. Mainstream climate models have also accurately projected global surface temperature changes. Climate contrarians have not. Created by Dana Nuccitelli. There's one chart often used to argue to the contrary, but it's got some serious problems, and ignores most of the data.

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The Modeling Process - Model Building and Validation



Validating a mathematical model

Models successfully reproduce temperatures since globally, by land, in the air and the ocean. Models are unreliable "[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data.

But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere. This is clearly a very complex task, so models are built to estimate trends rather than events. Climate trends are weather, averaged out over time - usually 30 years.

Trends are important because they eliminate - or "smooth out" - single events that may be extreme, but quite rare. Climate models have to be tested to find out if they work. If a model can correctly predict trends from a starting point somewhere in the past, we could expect it to predict with reasonable certainty what might happen in the future.

So all models are first tested in a process called Hindcasting. The models used to predict future global warming can accurately map past climate changes. If they get the past right, there is no reason to think their predictions would be wrong. Testing models against the existing instrumental record suggested CO2 must cause global warming, because the models could not simulate what had already happened unless the extra CO2 was added to the model.

All other known forcings are adequate in explaining temperature variations prior to the rise in temperature over the last thirty years, while none of them are capable of explaining the rise in the past thirty years. CO2 does explain that rise, and explains it completely without any need for additional, as yet unknown forcings. Where models have been running for sufficient time, they have also been proved to make accurate predictions. For example, the eruption of Mt. Pinatubo allowed modellers to test the accuracy of models by feeding in the data about the eruption.

The models successfully predicted the climatic response after the eruption. Models also correctly predicted other effects subsequently confirmed by observation, including greater warming in the Arctic and over land, greater warming at night, and stratospheric cooling. The climate models, far from being melodramatic, may be conservative in the predictions they produce. Observed sea level rise since from tide gauge data red and satellite measurements blue compared to model projections for from the IPCC Third Assessment Report grey band.

The Copenhagen Diagnosis, Here, the models have understated the problem. In reality, observed sea level is tracking at the upper range of the model projections.

There are other examples of models being too conservative , rather than alarmist as some portray them. All models have limits - uncertainties - for they are modelling complex systems. However, all models improve over time, and with increasing sources of real-world information such as satellites, the output of climate models can be constantly refined to increase their power and usefulness.

Climate models have already predicted many of the phenomena for which we now have empirical evidence. Climate models form a reliable guide to potential climate change. Mainstream climate models have also accurately projected global surface temperature changes. Climate contrarians have not. Created by Dana Nuccitelli. There's one chart often used to argue to the contrary, but it's got some serious problems, and ignores most of the data.

Validating a mathematical model

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3 Comments

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