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

REFERENCES

[1] Geophysical fluid dynamics summer program: Woods Hole Oceanographic Institution, (c.f., Program History) (2016) .

[2] I. M. Held, The gap between simulation and understanding in climate modeling, Bull. Am. Meteorol. Soc. 86, 1609 (2005).

[3] E. Tziperman, H. Scher, S. E. Zebiak, and M. A. Cane, Controlling
Spatiotemporal Chaos in a Realistic El Nino Prediction Model, Phys. Rev. Lett. 79, 1034 (1997).

[4] W. Moon and J. S. Wettlaufer, A stochastic perturbation theory for non-autonomous systems, J. Math. Phys. (N.Y.) 54, 123303 (2013).

[5] L. P. Kadanoff, Innovations in statistical physics, Annu. Rev. Condens. Matter Phys. 6, 1 (2015).

[6] N. Goldenfeld, Lectures on Phase Transitions and the Renormalization Group (Addison-Wesley, Reading, MA, 1992).

[7] G. I. Barenblatt, Scaling, Self-Similarity, and Intermediate
Asymptotics: Dimensional Analysis and Intermediate Asymptotics
(Cambridge University Press, Cambridge, England, 1996).

[8] E. N. Lorenz, Deterministic nonperiodic flow, J. Atmos. Sci. 20, 130 (1963).

[9] J. Gleick, Chaos: Making a New Science (Viking, New York, NY, 1987).

[10] A. Venaille and F. Bouchet, Statistical Ensemble Inequivalence and
Bicritical Points for Two-Dimensional Flows and Geophysical Flows, Phys. Rev. Lett. 102, 104501 (2009).

[11] M. Wilkinson, Large Deviation Analysis of Rapid Onset of Rain Showers, Phys. Rev. Lett. 116, 018501 (2016).

[12] S. Toppaladoddi and J. S. Wettlaufer, Theory of the Sea Ice Thickness Distribution, Phys. Rev. Lett. 115, 148501 (2015).

[13] J. B. Marston, Looking for new problems to solve? Consider the climate, Physics 4, 20 (2011).

[14] H. M. Arnold, I. M. Moroz, and T. N. Palmer, Stochastic parametrizations and model uncertainty in the Lorenz 96 system, Phil. Trans. R. Soc. A 371, 20110479 (2013).

[15] A. N. Souza and C. R. Doering, Maximal transport in the Lorenz equations, Phys. Lett. A 379, 518 (2015).

[16] S. Agarwal and J. S. Wettlaufer, Maximal stochastic transport in the Lorenz equations, Phys. Lett. A 380, 142 (2016).

[17] G. J. Feldman and R. D. Cousins, Unified approach to the classical statistical analysis of small signals, Phys. Rev. D 57, 3873 (1998).

[18] J. Pedlosky, Geophysical Fluid Dynamics (Springer, New York, NY, 1992).

[19] A. J. Majda and X. Wang, Nonlinear Dynamics and Statistical
Theories for Basic Geophysical Flows (Cambridge University Press,
Cambridge, England, 2006).

[20] R. T. Pierrhumbert, Principles of Planetary Climate (Cambridge University Press, Cambridge, England, 2010).

[21] H. Dijkstra, Nonlinear Climate Dynamics (Cambridge University Press, Cambridge, England, 2013).

[22] C. Wunsch, Modern Observational Physical Oceanography:
Understanding the Global Ocean (Princeton University Press, Princeton,
NJ, 2015).

[23] S. C. Glotzer, Editorial: Soft Matters, Phys. Rev. Lett. 114, 050001 (2015).

[24] E. N. Lorenz, The predictability of a flow which possesses many scales of motion, Tellus 21, 289 (1969).

[25] T. N. Palmer, More reliable forecasts with less precise
computations: A fast-track route to cloud-resolved weather and climate
simulators?, Phil. Trans. R. Soc. A 372, 20130391 (2014).

[26] F. Bouchet, T. Grafke, T. Tangarife, and E. Vanden-Eijnden, Large deviations in fast-slow systems, J. Stat. Phys. 162, 793 (2016).

[27] B. Saltzman, Dynamical Paleoclimatology: Generalized Theory of
Global Climate Change, International Geo-physics Series (Academic, San
Diego, CA, 2002), Vol. 80.

[28] A. Bunde, J. F. Eichner, J. W. Kantelhardt, and S. Havlin,
Long-Term Memory: A Natural Mechanism for the Clustering of Extreme
Events and Anomalous Residual Times in Climate Records, Phys. Rev. Lett. 94, 048701 (2005).

[29] D. H. Rothman, Earth’s carbon cycle: A mathematical perspective, Bull. Am. Math. Soc. 52, 47 (2015).

Published 14 April 2016

DOI: 10.1103/PhysRevLett.116.150002

Source/Fonte: http://journals.aps.org/prl/edannounce/10.1103/PhysRevLett.116.150002

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