The Strength and Limits of Models

In order to provide quantitative measures of climate change impacts, we will rely heavily on numerical models of various pieces of the puzzle, including climate, agricultural, and economic systems (Fig. 1.1). Models are needed because it is rarely possible to perform controlled experiments where one or two factors are changed while others are held constant, particularly for the time scales and spatial scales of interest. One cannot measure, for example, global crop production with climate change and compare it to a world without. Instead, one must perform the controlled experiments in the simplified world of computer models, which can be run at any scale.

However, it is important to remember that models are only simplified representations of reality, tools that can be used to estimate things that often cannot be directly measured. When their output is compared to things that can be measured, they almost always contain some error. In the case of predicting the future, this error arises both from not knowing perfectly how the climate and agricultural systems currently behave, and not knowing the future decisions that humans will make (both on the mitigation and adaptation side) that will influence the result.

The goal of modeling must therefore be to estimate not only a "best-guess", but also a probability distribution function (pdf), which describes the probability that the true value will take on each possible value. Often of interest is the chance that a particular threshold will be exceeded, such as 500 ppm atmospheric CO2, 2°C global average annual temperature, or 1 billion food insecure people. For these purposes, a single best guess of impacts is essentially useless. While nearly everyone acknowledges that treating a single output of a model as a firm "prediction" can be foolish, there appears a strong and persistent desire in humans to ignore uncertainties and overstate confidence in predictions.

Of course, the alternative of throwing up our hands and claiming no knowledge about the future is equally unattractive. Instead, we seek to clearly distinguish between those aspects of the future we know well and those that we do not - a task that can only be achieved by tracking uncertainties. The job is made somewhat easier by the fact that the goal is often not to actually predict the future, but instead to predict the difference between two outcomes. For example, impacts on wheat in China versus India; impacts on corn versus rice; impacts for low versus high CO2 emissions; or impacts for low versus high investments in a certain adaptation technology. In these cases, errors that are similar for each individual projection will tend to cancel out when looking at differences. It is thus often helpful to remember that while we would love to be able to predict everything about the future, our actual goals (and certainly our abilities!) are often much more modest.

Fig. 1.1 The cascade of models needed to evaluate the impacts of climate change on food security

Fig. 1.1 The cascade of models needed to evaluate the impacts of climate change on food security

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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