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Applying math to cancer, climate, crime and cameras

35(6), B1321B1340

Evaluating climate models

The simulation of elements affecting the Earth's climate is usually carried out by coupled atmosphere-ocean circulation models, output from which is used to provide insights into future climate states. Climate models have traditionally been assessed by comparing summary statistics (such as global annual mean temperatures) or point estimates from simulated models to the corresponding observed quantities.

A paper published last December in the SIAM/ASA Journal on Uncertainty Quantification argues that it is more appropriate to compare the distribution of climate model output data (over time and space) to the corresponding distribution of observed data. Distance measures between probability distributions, also called divergence functions, can be used to make this comparison.

The authors evaluate 15 different climate models by comparing simulations of past climate to corresponding reanalysis data. Reanalysis datasets are created by inputting climate observations using a given climate model throughout the entire reanalysis period in order to reduce the effects of modeling changes on climate statistics. Historical weather observations are used to reconstruct atmospheric states on a global grid, thereby allowing direct comparison to climate model output.

View the paper:

Using Proper Divergence Functions to Evaluate Climate Models

SIAM/ASA Journal on Uncertainty Quantification, 1, 522-534

Math models analyze long-term criminal activity patterns in a population

In a paper published last November in Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, authors Henri Berestycki, Nancy Rodrguez, and Lenya Ryzhik show that the assumption of a population's natural tenden

Contact: Karthika Muthukumaraswamy
Society for Industrial and Applied Mathematics

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