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Supercomputing Extreme Weather Events and Climate Change

climateA recent study conducted by the Barcelona Supercomputer Center suggests that calibrated model ensembles improve the trustworthiness of climate event attribution to extreme weather events. The study also found that current climate model limitations tend to overestimate climate change attribution.

Extreme weather events (such as the European heatwave in 2015) have been partly attributed to climate change by comparing the probability that the event would occur in the world as we observe it with the probability that it would occur in a hypothetical world where climate change does not exist. Climate models are the best tools we have to perform an event attribution study. However, the models have known imperfections with respect to reliably simulating the probability that an event might occur.

These probabilities are typically estimated using climate model simulations with known limitations to simulate extreme events. A study entitled: Attribution of extreme weather and climate events overestimated by unreliable climate simulations” published recently in Geophysical Research Letters suggests that there is a tendency to overestimate the attribution as a result of the shortcomings of these models.

The authors of the study point out that model reliability is not always ensured and that past studies have paid too little attention to this requirement. The paper states: “Current generation of climate models imperfectly simulate extreme events due to limitations of model resolution and erroneous representation relevant physical mechanisms.”

For this study the team used fraction attributable risk (FAR) to ascertain model reliability. FAR measures how much of the event can be attributed to human influence from reasonable probability, although there will always be some uncertainty based on the accuracy and precision of models.

The paper states: “Current generation of climate models imperfectly simulate extreme events due to limitations of model resolution and erroneous representation relevant physical mechanisms.”

The study suggests that “event attribution approaches using single climate model would benefit from ensemble calibration and other bias correction approaches in order to avoid systematic overestimation of FAR.” Calibration helps to ensure that the model ensemble variability at different temporal scales follows what has been previously observed, including the variability arising from a long-term trend.

The paper states: “These trends are known to be deficient in current models on regional scales and although the implication of incorrect trends has not been explored in this study, we illustrate that ensemble calibration is also an elegant solution to that particular problem.”

With the increased precision derived from these improved models it is expected that weather and climate researchers will be able to more effectively appraise the cause of extreme weather events. This could help to inform the public on the seriousness of climate change – in addition to improving early warning systems for extreme weather events.

This story appears here as part of a cross-publishing agreement with Scientific Computing World.

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