Written by renowned experts in the field, this first book to focus exclusively on energy balance climate models provides a concise overview of the topic. It covers all major aspects, from the simplest zero-dimensional models, proceeding to horizontally and vertically resolved models.
The text begins with global average models, which are explored in terms of their elementary forms yielding the global average temperature, right up to the incorporation of feedback mechanisms and some analytical properties of interest. The effect of stochastic forcing is then used to introduce natural variability in the models before turning to the concept of stability theory. Other one dimensional or zonally averaged models are subsequently presented, along with various applications, including chapters on paleoclimatology, the inception of continental glaciations, detection of signals in the climate system, and optimal estimation of large scale quantities from point scale data. Throughout the book, the authors work on two mathematical levels: qualitative physical expositions of the subject material plus optional mathematical sections that include derivations and treatments of the equations along with some proofs of stability theorems.
A must-have introduction for policy makers, environmental agencies, and NGOs, as well as climatologists, molecular physicists, and meteorologists.
Eine prägnante Einführung in alle wichtigen Aspekte von Energiegleichgewichtsmodellen, von ganz einfachen Modellen (Nulldimension) bis hin zu horizontalen und vertikalen Modellen.
Gerald R. North is University Distinguished Professor of Atmospheric Sciences Emeritus at Texas A&M University, having obtained his BS degree in physics from the University of Tennessee, PhD (1966) in theoretical physics from the University of Wisconsin, Madison. Among other positions he served eight years as research scientist at Goddard Space Flight Center before joining Texas A&M in 1986, where he served as department head 1995-2003. He is a fellow of AAAS, AGU, AMS, and recipient of several awards including the Jule G. Charney Award of the American Meteorology Society. He has served as Editor in Chief of the Reviews of Geophysics and Editor in Chief of the Encyclopedia of the Atmospheric Sciences, 2nd Edition. He has coauthored books on Paleoclimatology and Atmospheric Thermodynamics.
Kwang-Yul Kim is a professor in climatology and physical oceanography at Seoul National University. Upon graduation from Texas A&M with his Ph.D. degree in physical oceanography he was inducted into the Phi Kappa Phi Honor Society. He authored two books: Fundamentals of Fluid Dynamics and Cyclostationary EOF Analysis. He programmed several new energy balance models.
Climate and Climate Models
The global climate system consists of a large number of interacting parts. The material components and their sub-members include the following:
- 1. the atmosphere and its constituents such as free molecules and radicals of different chemical species, aerosol particles, and clouds;
- 2. the ocean waters and their members such as floating ice, dissolved species including electrolytes and gases as well as undissolved matter such as of biological origin and dust;
- 3. the land components with characteristics such as snow and ice cover, permafrost, moisture, topographical features and vegetation with all its ramifications.
The space-time configuration of abstract fields that are used to characterize properties of interest (such as temperature, density, and momentum) attributed to these components and their sub-members vary with time and position and each exhibits its own spectrum of time and length scales. Heat (or more formally, enthalpy) fluxes, moisture, and momentum fluxes pass from one of these material components to another, sometimes through subtle mechanisms. Determination of whether and how these constituent parts combine to establish a statistical equilibrium may seem challenge enough, but the climate dynamicist also seeks to understand how the system responds to time-dependent changes in certain control parameters such as the Sun's brightness, or the chemical composition of the atmosphere. Although we have been at it for many decades now, the grand problem is still far too complicated to solve at the desired level of accuracy (no bias) and precision (error variance) even though preliminary engineering-like calculations are being used routinely in scenario/impact studies because policymakers must (should!) make use of even tentative information in their deliberations (IPCC, 2007, 2013).
Serious attempts at quantitative climate theories can be said to have begun in the late 1960s, although some very clever attempts predate that by decades (see Weart, 2008). The theory of global climate is emerging from its infancy but it hardly constitutes a set of principles that can be converted into reliable numerical forecasts of climate decades ahead or that can be unequivocally used in explaining the paleoclimatic record. However, some valuable insights have been gained and many problems can be cast into the form of conceptual frameworks that can be understood. We now have an idea of which of the components are important for solving certain idealized problems, and indeed, in some cases, it appears that the problems can be made comprehensible (but not strictly quantitative) with models employing only a few variables.
The field of climate dynamics is vast, embracing virtually every subfield of the geosciences (even "pure" physics, chemistry, and biology) from the quantum mechanics of photons being scattered, absorbed, and emitted by/from atmospheric molecules in radiative transfer processes to the study of proxies such as tree-ring widths and isotopic evidence based on fossilized species deposited and buried long ago in sediments deep below the ocean's floor. The in-depth coverage of these subfields is generally presented in the traditionally separate course offerings of curricula in the geosciences. This book is concerned with the integration of this array of material into a composite picture of the global climate system through simplified phenomenological models. The approach will be to pose and examine some problems that can be solved or analyzed with the classical techniques of mathematical physics. Throughout we attempt to use these analytical methods, but will introduce and use numerical methods and simulations when necessary. However, our main strategy will be to idealize the physical problem in such a way as to render it solvable or at least approachable, then compare or draw analogies either to the real world or to the results of solving a more believable model - hardly a foolproof procedure but likely to be instructive. In short, we hope to get at the heart of some climate problems in such a way that the reader's intuition for the composite system can be developed and more informed approaches can be taken toward the solution of specific problems.
The energy balance climate models (EBCMs) generally deal with an equation or a set of coupled equations whose solution yields a space-time average of the surface temperature field. Unfortunately, the solutions cannot usually describe the temperature field above the boundary layer of the atmosphere except in rare circumstances. This is a severe limitation, leaving us with only partial answers to many questions we would like to pose. On the other hand, we are blessed with many reasons supporting the importance of the surface temperature field:
- 1. Space-time averages of surface temperature are easily estimated and many instrumental records provide good data, not just contemporarily, but over the last century.
- 2. Space-time averages of surface temperature data are close to being normally distributed, making them easy to understand and treat. This is not so for precipitation and some other variables. Moreover, the larger the space-time scale, the more information from point sources can be combined into the average, resulting in a reduction in the random measurement errors on the mean estimates.
- 3. The time series of space-time averages of surface temperature is particularly simple, resulting in applicability of autoregressive behavior of order unity in many cases.
- 4. Nearly all paleoclimate indicators provide information about the surface temperature, extending the data base that can be used in testing. There are never enough data to check and adjust models, especially complex numerical models. Paleoclimatology can potentially provide more data that can be used to understand climate models.
- 5. As we will show, the surface temperature is also the easiest variable to model, especially for large area and time averages. It becomes more difficult as the space-time scales in the problem decrease. In this book, we will start with the largest space and time scales and find that there is a natural progression of estimates from the largest to the smallest space-time scales. Moreover, averaging over large scales reduces some errors in models as well as in measurements.
- 6. Most of the externally applied perturbations to the climate system that are of interest are directed at large spatial and temporal scales. This happens to be the case for the four best known perturbations: greenhouse gases, volcanic dust veils, anthropogenic aerosols, and solar brightness. It is intuitively appealing (as well as motivated by physics, as we shall see) that the large space-time scale perturbations result primarily in the same large space-time scales of thermal response patterns in the climate system.
- 7. The study of energy balance models is cheap. This can be a factor when questions are posed from paleoclimatology, for example. Big models are simply too expensive to experiment with in the first trials. With the speed up of modern computers, many paleoclimate problems can be examined with general circulation models (GCMs), but not every one of them.
- 8. The study of exoplanets has become important in recent years. The habitable zone of a planet's orbital and atmospheric/oceanic dynamical/chemical parameters may fall into the purview of energy balance models.
- 9. Finally, the surface temperature is important for societal well-being and it is easily grasped, although the idea of large space-time scales is less easily identifiable and appreciated by the average person.
Unfortunately, as soon as we go above the near-surface environment, the mathematical difficulties of solving the climate problem even for the temperature becomes orders of magnitude more difficult. Also, for all its importance, precipitation cannot be solved by simple models because it depends too sensitively on the circulation of the atmosphere (and the ocean).
1.1 Defining Climate
Before proceeding, we must define what we mean by climate. As an illustrative example, we restrict ourselves at first to the global average surface temperature. Our definition is abstract and not strictly an operational one unless certain (reasonable but, unfortunately, unverifiable) conditions are fulfilled. When we examine records of globally averaged temperature at the Earth's surface we find that it fluctuates in time. Figure 1.1 shows a century-long record of both annual and global averages (estimates of these, to be more precise) and, except for a possible upward slope, we find departures from the mean linear trend that persist over a few years or even decades.
Figure 1.1 Time series of thermometer-based global average temperatures from the website of Goddard Institute for Space Studies: www.giss.nasa.gov. The units are in Kelvin and the temperature values are "anomalies" or deviations from a long-term mean (1951-1980).
(Goddard Institute for Space Studies (NASA) (2017).)
Consider an abstraction of the real system. We borrow from the discipline of time series analysis (which may have originated in the subdiscipline of theoretical physics called statistical mechanics) the concept of an ensemble.1 By this, we mean to consider a segment of a record of some quantity versus time (e.g., the record of estimates of annual-mean and global-average temperatures...