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identify these multidecadal climate oscillations.
My collaborator Jeffrey Park, a seismologist, and I used an entirely different statistical approach
than that employed by Schlesinger and Ramankutty, one borrowed from the field of seismology.
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Earthquakes and other seismological disturbances yield waves—oscillations—that travel through
solid earth. These waves have a complex spatial pattern as they reach Earth's surface. The surface
disturbances are detected by a seismograph, which responds to the subtle vibrations of the ground and
registers these vibrations in the form of a trace called a seismogram. Much like medical technicians
use electromagnetic oscillations (X-rays, to be precise) in the form of CAT scans to examine the
interior of the human body, seismologists study seismograms to infer the structure of Earth's interior.
Jeff and I realized that we could apply the same techniques to search for oscillatory signals in the
climate record. Just as with seismograms, we had the benefit of a large array of climate measurements
around the globe. Much as seismological signals consist of oscillations obscured by random local
effects (noise), the climate signals we were looking for were climate oscillations buried in their own
type of noise: instrumental biases, episodic climate events like volcanic eruptions, and other
competing impacts, including anthropogenic climate change. Our method attempted to identify global-
scale climate oscillations with a particular periodicity, buried in the noise, and it tested whether the
putative oscillations were statistically significant (i.e., that the apparent signal was sufficiently
unlikely to arise from the chance random fluctuations of the noise). Just as in my childhood tic-tac-toe
discovery, we had found what scientists and mathematicians call a “trick,” a shortcut in
systematically attempting to solve a problem, in this case adapting a method originally designed for
seismological signals to search instead for climate oscillations.
To me, this kind of work is what made science so exciting. We could now apply our method to
search for oscillatory signals in a dataset of global surface temperature records spanning the past
century. Our findings, published around the same time as Schlesinger and Ramankutty's study,
periodicities in the three- to seven-year range and were related to El Niño, while others had
periodicities in the interdecadal (ten- to twenty-year) range and appeared to be related to the
interaction of the atmosphere with the sluggish subtropical ocean gyres—ocean current systems that
include the well-known Gulf Stream in the Atlantic and the Kurushio current of the Pacific.
The longest oscillation detected in our study, however, was centered in the North Atlantic region
and had a periodicity of sixty to eighty years, consistent with the multidecadal oscillation Schlesinger
and Ramankutty had found independently. What's more, other scientists were finding evidence that
such oscillations were being produced in theoretical climate models by the interactions between the
ocean and atmosphere in the North Atlantic, with the long timescales set by the very sluggish subpolar
North Atlantic ocean current systems.
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None of these studies suggested that such multidecadal oscillations could explain away modern
global warming, though. In fact, those oscillations were only of secondary importance in
characterizing long-term variations in the global surface temperature record. The primary pattern was
instead one of long-term warming of the globe, and that long-term warming had already been
attributed to human influences.
The multidecadal oscillation I'd helped discover would nonetheless become a cause célèbre
among climate change contrarians. It would even get a name: the “Atlantic multidecadal oscillation”
