Has global warming “stopped”? Do models “over-predict” warming? There has been much recent talk in the media about those two questions. The answer to the first question is a fairly clear “no.” Global warming continues unabated.
To illustrate, for the coverage-bias-corrected data published by Cowtan and Way last year, the temperature trend for the last 15 years up to and including 2013 is significant—in the same way that the trend was significant for the last 15 years in 2000 and in 1990. So from any of those vantage points, the Earth has warmed significantly during the preceding 15 years.
“The rate of heat building up on Earth over the past decade is equivalent to detonating about 4 Hiroshima atomic bombs per second. Take a moment to visualize 4 atomic bomb detonations happening every single second. That’s the global warming that we’re frequently told isn’t happening.”
Let’s turn to the second question: Have models over-estimated the rate of warming? This question has a more nuanced but quite fascinating answer.
We begin by noting that the observed global temperature increase remains comfortably within the 95% envelop of model runs, as shown in the figure below, which is taken from a recent Nature Climate Change paper by Doug Smith.
Now, arguably, the observed temperatures for the last decade or so are tending towards the lower end of the model envelope (note, though, that this figure does not plot the coverage-bias-corrected data from Cowtan and Way, which would raise the final observed temperatures and trends slightly).
Does this then mean that the models “over-predict” warming?
To understand why the answer is no, we need to consider three issues.
First, it will be noted that occasional brief excursions of observed temperatures outside the 95% model envelope are not unusual new—and indeed the most recent excursion occurred when the Earth warmed faster than the models. This is the result of natural variability and represents short-term disturbances that do not affect the underlying long-term trend.
Second, we need to consider the expected relationship between the models’ output and the observed data. This is a profound issue that is routinely overlooked by media commentators, and it pertains to the common confusion between climate projections and climate forecasts. Climate forecasts seek to predict the climate over a certain range, taking into account natural variability and—similar to a weather forecast—modeling the evolution of the climate from a known starting point and taking future internal variability into account. For example, the UK Met Office publishes decadal forecasts, which are explained very nicely here.
Climate projections, by contrast, seek to describe the evolution of the climate in the long run, irrespective of its current state and without seeking to predict internal variability. The figure above, like all figures that show model output to the end of the century, plots projections rather than predictions. Because projections have no information about the phase (sequence and timing) of internal climate variability, there is no expectation that any particular projection would align with what the Earth is actually doing. In fact, it would be highly surprising if global temperatures always tracked the center of the model projections—we expect temperatures to jiggle up and down within the envelope. To buttress this point, recent work by Mike Mann and colleagues has shown that warming during the most recent decade is well within the spread of a model ensemble.
Finally, we need to consider the reasons underlying natural variability, both in the models and in the planet’s warming trend. One of the major drivers of this variability involves the El Niño – La Niña oscillation in the Pacific, which determines how much heat is taken up by the oceans rather than the atmosphere. La Niña conditions favour cooler temperatures whereas El Niño leads to warmer temperatures. The animated figure below from Skepticalscience illustrates this nicely:
The figure clarifies that internal climate variability over a short decadal or 15-year time scale is at least as important as the forced climate changes arising from greenhouse gas emissions.
Those three issues converge on the conclusion that in order to meaningfully compare model projections against observed trends, the models must be brought into phase with the oceans. In particular, the models must be synchronized with El Niño – La Niña.
The evidence has been mounting during the last few years that when this synchronization is achieved, the models capture recent temperature trends very well.
At least four different approaches have been pursued to achieve synchronization.
One approach relied on specifying some observed fields in the climate models while they are “free” to evolve on their own everywhere else. For example, Kosaka and Xie showed than when the El Niño-related changes in Pacific ocean temperature are entered into a model, it not only reproduced the global surface warming over the past 15 years but it also accurately reproduced regional and seasonal changes in surface temperatures. Similarly, Matthew England and colleagues reproduced observed temperature trends by providing the model with the pronounced and unprecedented strengthening in Pacific trade winds over the past two decades—and the winds in turn lead to increased heat uptake by the oceans.
A second approach involved initialization of the model to the observed state of the planet at the beginning of a period of interest. Meehl and Teng recently showed that when this is done, thereby turning a model projection into a hindcast, the models reproduced the observed trends—accelerated warming in the 1970s and reduced rate of surface warming during the last 15 years—quite well.
The third approach, by Gavin Schmidt and colleagues, statistically controlled for variables that are known to affect model output. This was found to largely reconcile model projections with global temperature observations.
The fourth approach was used in a paper by James Risbey, myself, and colleagues from CSIRO in Australia and at Harvard which appeared in Nature Climate Change today.
This new approach did not specify any of the observed outcomes and left the existing model projections from the CMIP5 ensemble untouched. Instead, we select only those climate models (or model runs) that happened to be synchronized with the observed El Niño – La Niña preference in any given 15-year period. In other words, we selected those models whose projected internal natural variability happened to coincide with the state of the Earth’s oceans at any given point since the 1950’s. We then looked at the models’ predicted global mean surface temperature for the same time period.
For comparison, we also looked at output from those models that were furthest from the observed El Niño – La Niña trends.
The results are shown in the figure below, showing the Cowtan and Way data (in red) against model output (they don’t differ qualitatively for the other temperature data sets):
The data represent decadal trends within overlapping 15-year windows that are centered on the plotted year. The left panel shows the models (in blue) whose internal natural variability was maximally synchronized with the Earth’s oceans at any point, whereas the right panel shows the models (in gray) that were maximally out of phase with the Earth.
The conclusion is fairly obvious: When the models are synchronized with the oceans, they do a great job. Not only do they reproduce global warming trends during the last 50 years, as shown in the figure, but they also handle the spatial pattern of sea surface temperatures (the figure for that is available in the article).
In sum, we now have four converging lines of evidence that highlight the predictive power of climate models.
From a scientific perspective, this is a gratifying result, especially because the community has learned a lot about the models from those parallel efforts.
From another perspective, however, the models’ power is quite distressing. To understand why, just have a look at where the projections are heading.
Udpate 21/7/14, 9am: The date in the post was initially misspelled and should have read July rather than June.