Two Examples One Model One Vote and a Formal Bayesian Model

Here we explore two examples of analyses based on multi-model ensembles. We refer to the related papers for details on the actual results, aiming simply at juxtaposing two approaches that could be seen as spanning the methodology range of multi-model analysis.

We present first the approach from Lobell et al. (2008), which sought to rank 12 food-insecure regions in the developing world according to metrics of vulnerability in order to inform the prioritization of adaptation measures. The analysis uses projections of temperature and precipitation change by 2030 from 20 GCMs, part of CMIP3, and is performed by giving equal weight to each model simulation, computing summary statistics (medians and percentiles) of the ensemble simulations without applying any statistical synthesis of the climate projections first.

Our second example is the study by Tebaldi and Lobell (2008). Here the aim is to propose a formal probabilistic analysis of the impacts of climate change on the global yield of three important crops. In this case a statistical model combining the ensemble of simulations is used to derive joint probability distribution functions of temperature and precipitation changes, which are then sampled as input to the statistical crop model.

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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