Climate science is rooted in physics and in many of the methods used
by physicists. Although it’s a cliché to say that the practice of
science changed dramatically with the advent of the digital age,
computers have had an enormous impact on the growth and evolution of
climate science. Before computing, progress in explaining observations
or making predictions in the physical sciences, including climate
science [1], was made using pencil and paper calculations. Computers
changed this completely.
There are currently two main approaches to climate theory: numerical
simulations, which use large-scale general circulation models of the
atmosphere and/or oceans, and idealized models—a physicist’s bread and
butter—which are generally geared towards understanding the behavior of a
key physical phenomenon within the larger climate system [2].
Simulations of the climate operate like enormous coarse-grained weather
forecasts; the global climate is represented by the output from a
computational approximation of all of the known physics. In contrast,
idealized models focus on individual subsystems of the climate, such as
El Niño [3] or Arctic sea ice [4]. Breaking down the problem in this way
facilitates mathematical analysis of the processes involved and their
observational manifestations. There is a vast gulf, both conceptually
and in terms of space and time scales, between simulations and idealized
models. Attempts to reconcile them will have to focus on the problem of
scales, a task well suited to physicists: The challenge of scale
separation in both condensed matter and particle physics led to the
development of the renormalization group, unifying concepts in
previously disparate fields [5]. Renormalization group concepts and
methods have been successfully applied to fluid dynamics problems [6,7],
which are central to climate dynamics.
Climate science gave birth to one of the most far-reaching branches
of mathematics: chaos theory. Meteorologist Edward Lorenz uncovered
chaos theory when developing an idealized model of thermal convection,
similar to that which occurs when water is heated on a stove [8]. Some
50 years later, almost every physicist has heard of chaos, and ideas and
concepts based on the theory have lengthy tendrils that extend
throughout many branches of science [9]. In this sense, climate science
is indeed basic science. By considering idealized models motivated by
specific climate problems, could other discoveries akin to chaos be
made? We know that approaches from statistical mechanics normally used
to describe microscopic systems can be applied to large-scale
geophysical systems, such as planetary flows, rain, and sea ice
thickness [10–12]. What other concepts could shed light on idealized
models and inform our thinking about geophysical flows? Lorenz advocated
that examining the statistics of a flow could provide more insight into
the phenomena than calculating only the flow field itself [13]. His
idealizations continue to push our thinking in many new directions
[14–16].
Data analysis is another important area where mainstream physics and
climate science can connect. Experimental high-energy physicists, for
example, are experts in locating small signals in large quantities of
data so that they can correctly interpret particle collision events
[17]. Could climate scientists examining data from sediment or ice cores
learn from the theoretical and data analysis methodologies particle
physicists use? In turn, could physicists in general learn from the
methodologies employed in climate research [18–22]?
Physicists have successfully addressed a wide swath of science and
engineering problems using myriad methods. Many of these applications
have motivated the invention of entirely new approaches. Climate science
offers many exciting opportunities for physicists with broad interests.
The field is as interdisciplinary as, for example, soft matter [23],
with practitioners spanning nearly all science and engineering
departments. The problems are rich and vast; they range from figuring
out how to approach challenges like turbulence and multiscale phenomena
[24–26] to embracing the analysis of wide ranging climate proxy data
[27–29]. New ideas will emerge from perspectives that come from the
range of approaches used across all areas of physics. Not only will this
help scientists better understand the climate, but what they learn
will, as shown by the legacy of chaos theory, impact fields far beyond
climate science.
J.S. Wettlaufer
Yale University, New Haven, Connecticut 06520-8109, USA
Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
Nordita, Royal Institute of Technology and Stockholm University, SE-10691 Stockholm, Sweden
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Published 14 April 2016
DOI:
10.1103/PhysRevLett.116.150002
Source/Fonte:
http://journals.aps.org/prl/edannounce/10.1103/PhysRevLett.116.150002
