Assimilating Data into Models

Authored by: Amarjit Budhiraja , Eric Friedlander , Colin Guider , Christopher KRT Jones , John Maclean

Handbook of Environmental and Ecological Statistics

Print publication date:  September  2017
Online publication date:  January  2019

Print ISBN: 9781498752022
eBook ISBN: 9781315152509
Adobe ISBN:

10.1201/9781315152509-30

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Abstract

Data abound in studies of the environment, and their abundance is increasing at an extraordinary rate. The instruments we use to observe the environment, be it the atmosphere, ocean, land or ice, are constantly improving in their accuracy and efficiency. Moreover, they follow the technological trend of becoming less expensive as time moves on. It is tempting then to think we will eventually enjoy enough observational data that a complete picture of the world around us will be available and constitute a virtual replica from which we can conclude both how the environment works, and how it will change. But more data does not necessarily mean more understanding. It is hard from raw data alone to conclude how different effects are related. Correlation may be detected in data, but to establish causation will usually involve more experimentation than passive data can provide. Needed is the ability to vary conditions and see what results, but these experiments may not be feasible in environmental applications. For instance: imagine wanting to see what would happen if the world’s oceans were to increase by 5°C in globally averaged temperature. Luckily, we have models that allow us to experiment in such ways. These vary from stripped down models that focus on a small number of key effects in a particular environmental application to the few very large, physically inclusive and computationally expensive models, residing at dedicated centers around the world, that amount to computational replicas of the entire Earth system.

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