Predicting global surface temperature changes in the short-term is a challenge for climate models. Temperature changes over periods of a decade or two can be dominated by influences from ocean cycles like El Niño and La Niña. During El Niño phases, the oceans absorb less heat, leaving more to warm the atmosphere, and the opposite is true during a La Niña.
We can't yet predict ahead of time how these cycles will change. The good news is that it doesn't matter from a big picture climate perspective, because over the long-term, temperature influences from El Niño and La Niña events cancel each other out. However, when we examine how climate model projections have performed over the past 15 years or so, those natural cycles make a big difference.
A new paper led by James Risbey just out in Nature Climate Change takes a clever approach to evaluating how accurate climate model temperature predictions have been while getting around the noise caused by natural cycles. The authors used a large set of simulations from 18 different climate models (from CMIP5). They looked at each 15-year period since the 1950s, and compared how accurately each model simulation had represented El Niño and La Niña conditions during those 15 years, using the trends in what's known as the Niño3.4 index.
Each individual climate model run has a random representation of these natural ocean cycles, so for every 15-year period, some of those simulations will have accurately represented the actual El Niño conditions just by chance. The study authors compared the simulations that were correctly synchronized with the ocean cycles (blue data in the left frame below) and the most out-of-sync (grey data in the right frame) to the observed global surface temperature changes (red) for each 15-year period.
Red: 15-year observed trends for each period. Blue: 15-year average trends from CMIP5 runs where the model Niño3.4 trend is close to observations. Grey: average 15-year trends for only the models with the worst correspondence to the Niño3.4 trend. The sizes of the dots are proportional to the number of models selected. From Nature Climate Change
The authors conclude,
When the phase of natural variability is taken into account, the model 15-year warming trends in CMIP5 projections well estimate the observed trends for all 15-year periods over the past half-century.
It's also clear from the grey figure that models that are out-of-sync with the observed changes in these ocean cycles simulate dramatically higher warming trends over the past 30 years. In other words, the model simulations that happened not to accurately represent these ocean cycles were the ones that over-predicted global surface warming.
The claim that climate models are unreliable is the 6th-most popular contrarian myth. The argument is generally based on the claim that climate models didn't predict the slowdown in global surface warming over the past 15 years. That's in large part because during that time, we've predominantly experienced La Niña conditions. Climate modelers couldn't predict that ahead of time, but the models that happened to accurately simulate those conditions also accurately predicted the amount of global surface warming we've experienced.
Yu Kosaka and Shang-Ping Xie from the Scripps Institution of Oceanography published a paper in Nature last year taking a similar approach to that of Risbey and colleagues. In that study, the authors ran climate model simulations in which they incorporated the observed Pacific Ocean sea surface temperature changes, essentially forcing the models to accurately reflect the influence from these ocean cycles. When they did that, the authors found that the models simulated the observed global surface temperature changes remarkably well.
The results of these studies give us two important pieces of information:
Note: this post has been incorporated into the rebuttal to the myth 'Models are unreliable'.
Posted by dana1981 on Monday, 21 July, 2014
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