Theorem

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Theorem

The Pythagorean theorem has at least 370 known proofs.

In mathematics and formal logic, a theorem is a statement that has been proven, or can be proven. The proof of a theorem is a logical argument that uses the inference rules of a deductive system to establish that the theorem is a logical consequence of the axioms and previously proved theorems.

In mainstream mathematics, the axioms and the inference rules are commonly left implicit, and, in this case, they are almost always those of Zermelo–Fraenkel set theory with the axiom of choice (ZFC), or of a less powerful theory, such as Peano arithmetic. Generally, an assertion that is explicitly called a theorem is a proved result that is not an immediate consequence of other known theorems. Moreover, many authors qualify as theorems only the most important results, and use the terms lemma, proposition and corollary for less important theorems.

In mathematical logic, the concepts of theorems and proofs have been formalized in order to allow mathematical reasoning about them. In this context, statements become well-formed formulas of some formal language. A theory consists of some basis statements called axioms, and some deducing rules (sometimes included in the axioms). The theorems of the theory are the statements that can be derived from the axioms by using the deducing rules. This formalization led to proof theory, which allows proving general theorems about theorems and proofs. In particular, Gödel's incompleteness theorems show that every consistent theory containing the natural numbers has true statements on natural numbers that are not theorems of the theory (that is they cannot be proved inside the theory).

As the axioms are often abstractions of properties of the physical world, theorems may be considered as expressing some truth, but in contrast to the notion of a scientific law, which is experimental, the justification of the truth of a theorem is purely deductive. A conjecture is a tentative proposition that may evolve to become a theorem if proven true.

Theoremhood and truth

Until the end of the 19th century and the foundational crisis of mathematics, all mathematical theories were built from a few basic properties that were considered as self-evident; for example, the facts that every natural number has a successor, and that there is exactly one line that passes through two given distinct points. These basic properties that were considered as absolutely evident were called postulates or axioms; for example Euclid's postulates. All theorems were proved by using implicitly or explicitly these basic properties, and, because of the evidence of these basic properties, a proved theorem was considered as a definitive truth, unless there was an error in the proof. For example, the sum of the interior angles of a triangle equals 180°, and this was considered as an unquestionable fact.

One aspect of the foundational crisis of mathematics was the discovery of non-Euclidean geometries that do not lead to any contradiction, although, in such geometries, the sum of the angles of a triangle is different from 180°. So, the property "the sum of the angles of a triangle equals 180°" is either true or false, depending whether Euclid's fifth postulate is assumed or denied. Similarly, the use of "evident" basic properties of sets leads to the contradiction of Russell's paradox. This has been resolved by elaborating the rules that are allowed for manipulating sets.

This crisis has been resolved by revisiting the foundations of mathematics to make them more rigorous. In these new foundations, a theorem is a well-formed formula of a mathematical theory that can be proved from the axioms and inference rules of the theory. So, the above theorem on the sum of the angles of a triangle becomes: Under the axioms and inference rules of Euclidean geometry, the sum of the interior angles of a triangle equals 180°. Similarly, Russell's paradox disappears because, in an axiomatized set theory, the set of all sets cannot be expressed with a well-formed formula. More precisely, if the set of all sets can be expressed with a well-formed formula, this implies that the theory is inconsistent, and every well-formed assertion, as well as its negation, is a theorem.

In this context, the validity of a theorem depends only on the correctness of its proof. It is independent from the truth, or even the significance of the axioms. This does not mean that the significance of the axioms is uninteresting, but only that the validity of a theorem is independent from the significance of the axioms. This independence may be useful by allowing the use of results of some area of mathematics in apparently unrelated areas.

An important consequence of this way of thinking about mathematics is that it allows defining mathematical theories and theorems as mathematical objects, and to prove theorems about them. Examples are Gödel's incompleteness theorems. In particular, there are well-formed assertions than can be proved to not be a theorem of the ambient theory, although they can be proved in a wider theory. An example is Goodstein's theorem, which can be stated in Peano arithmetic, but is proved to be not provable in Peano arithmetic. However, it is provable in some more general theories, such as Zermelo–Fraenkel set theory.

Epistemological considerations

Many mathematical theorems are conditional statements, whose proofs deduce conclusions from conditions known as hypotheses or premises. In light of the interpretation of proof as justification of truth, the conclusion is often viewed as a necessary consequence of the hypotheses. Namely, that the conclusion is true in case the hypotheses are true—without any further assumptions. However, the conditional could also be interpreted differently in certain deductive systems, depending on the meanings assigned to the derivation rules and the conditional symbol (e.g., non-classical logic).

Although theorems can be written in a completely symbolic form (e.g., as propositions in propositional calculus), they are often expressed informally in a natural language such as English for better readability. The same is true of proofs, which are often expressed as logically organized and clearly worded informal arguments, intended to convince readers of the truth of the statement of the theorem beyond any doubt, and from which a formal symbolic proof can in principle be constructed.

In addition to the better readability, informal arguments are typically easier to check than purely symbolic ones—indeed, many mathematicians would express a preference for a proof that not only demonstrates the validity of a theorem, but also explains in some way why it is obviously true. In some cases, one might even be able to substantiate a theorem by using a picture as its proof.

Because theorems lie at the core of mathematics, they are also central to its aesthetics. Theorems are often described as being "trivial", or "difficult", or "deep", or even "beautiful". These subjective judgments vary not only from person to person, but also with time and culture: for example, as a proof is obtained, simplified or better understood, a theorem that was once difficult may become trivial. On the other hand, a deep theorem may be stated simply, but its proof may involve surprising and subtle connections between disparate areas of mathematics. Fermat's Last Theorem is a particularly well-known example of such a theorem.

Informal account of theorems

Logically, many theorems are of the form of an indicative conditional: If A, then B. Such a theorem does not assert B — only that B is a necessary consequence of A. In this case, A is called the hypothesis of the theorem ("hypothesis" here means something very different from a conjecture), and B the conclusion of the theorem. The two together (without the proof) are called the proposition or statement of the theorem (e.g. "If A, then B" is the proposition). Alternatively, A and B can be also termed the antecedent and the consequent, respectively. The theorem "If n is an even natural number, then n/2 is a natural number" is a typical example in which the hypothesis is "n is an even natural number", and the conclusion is "n/2 is also a natural number".

In order for a theorem to be proved, it must be in principle expressible as a precise, formal statement. However, theorems are usually expressed in natural language rather than in a completely symbolic form—with the presumption that a formal statement can be derived from the informal one.

It is common in mathematics to choose a number of hypotheses within a given language and declare that the theory consists of all statements provable from these hypotheses. These hypotheses form the foundational basis of the theory and are called axioms or postulates. The field of mathematics known as proof theory studies formal languages, axioms and the structure of proofs.

A planar map with five colors such that no two regions with the same color meet. It can actually be colored in this way with only four colors. The four color theorem states that such colorings are possible for any planar map, but every known proof involves a computational search that is too long to check by hand.

Some theorems are "trivial", in the sense that they follow from definitions, axioms, and other theorems in obvious ways and do not contain any surprising insights. Some, on the other hand, may be called "deep", because their proofs may be long and difficult, involve areas of mathematics superficially distinct from the statement of the theorem itself, or show surprising connections between disparate areas of mathematics. A theorem might be simple to state and yet be deep. An excellent example is Fermat's Last Theorem, and there are many other examples of simple yet deep theorems in number theory and combinatorics, among other areas.

Other theorems have a known proof that cannot easily be written down. The most prominent examples are the four color theorem and the Kepler conjecture. Both of these theorems are only known to be true by reducing them to a computational search that is then verified by a computer program. Initially, many mathematicians did not accept this form of proof, but it has become more widely accepted. The mathematician Doron Zeilberger has even gone so far as to claim that these are possibly the only nontrivial results that mathematicians have ever proved. Many mathematical theorems can be reduced to more straightforward computation, including polynomial identities, trigonometric identities and hypergeometric identities.

Relation with scientific theories

Theorems in mathematics and theories in science are fundamentally different in their epistemology. A scientific theory cannot be proved; its key attribute is that it is falsifiable, that is, it makes predictions about the natural world that are testable by experiments. Any disagreement between prediction and experiment demonstrates the incorrectness of the scientific theory, or at least limits its accuracy or domain of validity. Mathematical theorems, on the other hand, are purely abstract formal statements: the proof of a theorem cannot involve experiments or other empirical evidence in the same way such evidence is used to support scientific theories.

The Collatz conjecture: one way to illustrate its complexity is to extend the iteration from the natural numbers to the complex numbers. The result is a fractal, which (in accordance with universality) resembles the Mandelbrot set.

Nonetheless, there is some degree of empiricism and data collection involved in the discovery of mathematical theorems. By establishing a pattern, sometimes with the use of a powerful computer, mathematicians may have an idea of what to prove, and in some cases even a plan for how to set about doing the proof. It is also possible to find a single counter-example and so establish the impossibility of a proof for the proposition as-stated, and possibly suggest restricted forms of the original proposition that might have feasible proofs.

For example, both the Collatz conjecture and the Riemann hypothesis are well-known unsolved problems; they have been extensively studied through empirical checks, but remain unproven. The Collatz conjecture has been verified for start values up to about 2.88 × 10¹⁸. The Riemann hypothesis has been verified to hold for the first 10 trillion non-trivial zeroes of the zeta function. Although most mathematicians can tolerate supposing that the conjecture and the hypothesis are true, neither of these propositions is considered proved.

Such evidence does not constitute proof. For example, the Mertens conjecture is a statement about natural numbers that is now known to be false, but no explicit counterexample (i.e., a natural number n for which the Mertens function M(n) equals or exceeds the square root of n) is known: all numbers less than 10¹⁴ have the Mertens property, and the smallest number that does not have this property is only known to be less than the exponential of 1.59 × 10⁴⁰, which is approximately 10 to the power 4.3 × 10³⁹. Since the number of particles in the universe is generally considered less than 10 to the power 100 (a googol), there is no hope to find an explicit counterexample by exhaustive search.

The word "theory" also exists in mathematics, to denote a body of mathematical axioms, definitions and theorems, as in, for example, group theory (see mathematical theory). There are also "theorems" in science, particularly physics, and in engineering, but they often have statements and proofs in which physical assumptions and intuition play an important role; the physical axioms on which such "theorems" are based are themselves falsifiable.

Terminology

A number of different terms for mathematical statements exist; these terms indicate the role statements play in a particular subject. The distinction between different terms is sometimes rather arbitrary, and the usage of some terms has evolved over time.

• An axiom or postulate is a fundamental assumption regarding the object of study, that is accepted without proof. A related concept is that of a definition, which gives the meaning of a word or a phrase in terms of known concepts. Classical geometry discerns between axioms, which are general statements; and postulates, which are statements about geometrical objects. Historically, axioms were regarded as "self-evident"; today they are merely assumed to be true.
• A conjecture is an unproved statement that is believed to be true. Conjectures are usually made in public, and named after their maker (for example, Goldbach's conjecture and Collatz conjecture). The term hypothesis is also used in this sense (for example, Riemann hypothesis), which should not be confused with "hypothesis" as the premise of a proof. Other terms are also used on occasion, for example problem when people are not sure whether the statement should be believed to be true.
  • Sometimes the name of a problem in common use does not match what would be technically most correct. Fermat's Last Theorem was historically called a theorem, although, for centuries, it was only a conjecture. Conversely, the Poincaré conjecture is still generally referred to as a conjecture, despite having been proved in 2002.
• A theorem is a statement that has been proven to be true based on axioms and other theorems.
• A proposition is a theorem of lesser importance, or one that is considered so elementary or immediately obvious, that it may be stated without proof. This should not be confused with "proposition" as used in propositional logic. In classical geometry the term "proposition" was used differently: in Euclid's Elements (c. 300 BCE), all theorems and geometric constructions were called "propositions" regardless of their importance.
• A lemma is an "accessory proposition" - a proposition with little applicability outside its use in a particular proof. Over time a lemma may gain in importance and be considered a theorem, though the term "lemma" is usually kept as part of its name (e.g. Gauss's lemma, Zorn's lemma, and the fundamental lemma).
• A corollary is a proposition that follows immediately from another theorem or axiom, with little or no required proof. A corollary may also be a restatement of a theorem in a simpler form, or for a special case: for example, the theorem "all internal angles in a rectangle are right angles" has a corollary that "all internal angles in a square are right angles" - a square being a special case of a rectangle.
• A generalization of a theorem is a theorem with a similar statement but a broader scope, from which the original theorem can be deduced as a special case (a corollary).

Other terms may also be used for historical or customary reasons, for example:

• An identity is a theorem stating an equality between two expressions, that holds for any value within its domain (e.g. Bézout's identity and Vandermonde's identity).
• A rule is a theorem that establishes a useful formula (e.g. Bayes' rule and Cramer's rule).
• A law or principle is a theorem with wide applicability (e.g. the law of large numbers, law of cosines, Kolmogorov's zero–one law, Harnack's principle, the least-upper-bound principle, and the pigeonhole principle).

A few well-known theorems have even more idiosyncratic names, for example, the division algorithm, Euler's formula, and the Banach–Tarski paradox.

Layout

A theorem and its proof are typically laid out as follows:

Theorem (name of the person who proved it, along with year of discovery or publication of the proof)

Statement of theorem (sometimes called the proposition)

Proof

Description of proof

End

The end of the proof may be signaled by the letters Q.E.D. (quod erat demonstrandum) or by one of the tombstone marks, such as "□" or "∎", meaning "end of proof", introduced by Paul Halmos following their use in magazines to mark the end of an article.

The exact style depends on the author or publication. Many publications provide instructions or macros for typesetting in the house style.

It is common for a theorem to be preceded by definitions describing the exact meaning of the terms used in the theorem. It is also common for a theorem to be preceded by a number of propositions or lemmas which are then used in the proof. However, lemmas are sometimes embedded in the proof of a theorem, either with nested proofs, or with their proofs presented after the proof of the theorem.

Corollaries to a theorem are either presented between the theorem and the proof, or directly after the proof. Sometimes, corollaries have proofs of their own that explain why they follow from the theorem.

Lore

It has been estimated that over a quarter of a million theorems are proved every year.

The well-known aphorism, "A mathematician is a device for turning coffee into theorems", is probably due to Alfréd Rényi, although it is often attributed to Rényi's colleague Paul Erdős (and Rényi may have been thinking of Erdős), who was famous for the many theorems he produced, the number of his collaborations, and his coffee drinking.

The classification of finite simple groups is regarded by some to be the longest proof of a theorem. It comprises tens of thousands of pages in 500 journal articles by some 100 authors. These papers are together believed to give a complete proof, and several ongoing projects hope to shorten and simplify this proof. Another theorem of this type is the four color theorem whose computer generated proof is too long for a human to read.

Theorems in logic

In mathematical logic, a formal theory is a set of sentences within a formal language. A sentence is a well-formed formula with no free variables. A sentence that is a member of a theory is one of its theorems, and the theory is the set of its theorems. Usually a theory is understood to be closed under the relation of logical consequence. Some accounts define a theory to be closed under the semantic consequence relation (${\displaystyle \models }$), while others define it to be closed under the syntactic consequence, or derivability relation (${\displaystyle ⊢}$).

This diagram shows the syntactic entities that can be constructed from formal languages. The symbols and strings of symbols may be broadly divided into nonsense and well-formed formulas. A formal language can be thought of as identical to the set of its well-formed formulas. The set of well-formed formulas may be broadly divided into theorems and non-theorems.

For a theory to be closed under a derivability relation, it must be associated with a deductive system that specifies how the theorems are derived. The deductive system may be stated explicitly, or it may be clear from the context. The closure of the empty set under the relation of logical consequence yields the set that contains just those sentences that are the theorems of the deductive system.

In the broad sense in which the term is used within logic, a theorem does not have to be true, since the theory that contains it may be unsound relative to a given semantics, or relative to the standard interpretation of the underlying language. A theory that is inconsistent has all sentences as theorems.

The definition of theorems as sentences of a formal language is useful within proof theory, which is a branch of mathematics that studies the structure of formal proofs and the structure of provable formulas. It is also important in model theory, which is concerned with the relationship between formal theories and structures that are able to provide a semantics for them through interpretation.

Although theorems may be uninterpreted sentences, in practice mathematicians are more interested in the meanings of the sentences, i.e. in the propositions they express. What makes formal theorems useful and interesting is that they may be interpreted as true propositions and their derivations may be interpreted as a proof of their truth. A theorem whose interpretation is a true statement about a formal system (as opposed to within a formal system) is called a metatheorem.

Some important theorems in mathematical logic are:

• Compactness of first-order logic
• Completeness of first-order logic
• Gödel's incompleteness theorems of first-order arithmetic
• Consistency of first-order arithmetic
• Tarski's undefinability theorem
• Church-Turing theorem of undecidability
• Löb's theorem
• Löwenheim–Skolem theorem
• Lindström's theorem
• Craig's theorem
• Cut-elimination theorem

Syntax and semantics

Main articles: Syntax (logic) and Formal semantics (logic)

The concept of a formal theorem is fundamentally syntactic, in contrast to the notion of a true proposition, which introduces semantics. Different deductive systems can yield other interpretations, depending on the presumptions of the derivation rules (i.e. belief, justification or other modalities). The soundness of a formal system depends on whether or not all of its theorems are also validities. A validity is a formula that is true under any possible interpretation (for example, in classical propositional logic, validities are tautologies). A formal system is considered semantically complete when all of its theorems are also tautologies.

Interpretation of a formal theorem

Main article: Interpretation (logic)

Theorems and theories

Main articles: Theory and Theory (mathematical logic)

Technogaianism

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Technogaianism 

Technogaianism (a portmanteau word combining "techno-" for technology and "gaian" for Gaia philosophy) is a bright green environmentalist stance of active support for the research, development and use of emerging and future technologies to help restore Earth's environment. Technogaianists argue that developing safe, clean, alternative technology should be an important goal of environmentalists and environmentalism.

Philosophy

Typically, radical environmentalists hold the view that all technology necessarily degrades the environment, and that environmental restoration can therefore occur only with reduced reliance on technology. By contrast, technogaianists argue that technology gets cleaner and more efficient with time, not necessarily progressing to the detriment of the environment. One example used is hydrogen fuel cells. More directly, they argue that such things as nanotechnology and biotechnology can directly reverse environmental degradation. Molecular nanotechnology, for example, could convert garbage in landfills into useful materials and products, while biotechnology could lead to novel microbes that devour hazardous waste.

While many environmentalists still contend that most technology is detrimental to the environment, technogaianists argue that it has been in humanity's best interests to exploit the environment mercilessly until fairly recently, following accurately to current understandings of evolutionary systems; when new factors (such as foreign species or mutant subspecies) are introduced into an ecosystem, they tend to maximize their own resource consumption until either, a) they reach an equilibrium beyond which they cannot continue unmitigated growth, or b) they become extinct. In these models, it is impossible for such a factor to totally destroy its host environment, though they may precipitate major ecological transformation before their ultimate eradication.

Technogaianists believe humanity has currently reached just such a threshold, and that the only way for human civilization to continue advancing is to accept the tenets of technogaianism and limit future exploitive exhaustion of natural resources and minimize further unsustainable development or face the widespread, ongoing mass extinction of species. The destructive effects of modern civilization are to be mitigated by technological solutions, such as using nuclear power. Furthermore, technogaianists argue that only science and technology can help humanity be aware of, and possibly develop counter-measures for, risks to civilization, humans and planet Earth such as a possible impact event.

Sociologist James Hughes mentions Walter Truett Anderson, author of To Govern Evolution: Further Adventures of the Political Animal, as an example of a technogaian political philosopher; argues that technogaianism applied to environmental management is found in the reconciliation ecology writings such as Michael Rosenzweig's Win-Win Ecology: How The Earth's Species Can Survive In The Midst of Human Enterprise; and considers Bruce Sterling's Viridian design movement to be an exemplary technogaian initiative.

The theories of English writer Fraser Clark may be broadly categorized as technogaian.  Clark advocated "balancing the hippie right brain with the techno left brain". The idea of combining technology and ecology was extrapolated at length by a South African eco-anarchist project in the 1990s. The Kagenna Magazine project aimed to combine technology, art, and ecology in an emerging movement that could restore the balance between humans and nature.

George Dvorsky suggests the sentiment of technogaianism is to heal the Earth, use sustainable technology, and create ecologically diverse environments. Dvorsky argues that defensive counter measures could be designed to counter the harmful effects of asteroid impacts, earthquakes, and volcanic eruptions. Dvorksky also suggest that genetic engineering could be used to reduce the environmental impact humans have on the earth.

Methods

Environmental monitoring

The Delta II rocket with climate research satellites, CloudSat and CALIPSO, on Launch Pad SLC-2W, VAFB

See also: Earth observation satellite and Environmental monitoring

Technology facilities the sampling, testing, and monitoring of various environments and ecosystems. NASA uses space-based observations to conduct research on solar activity, sea level rise, the temperature of the atmosphere and the oceans, the state of the ozone layer, air pollution, and changes in sea ice and land ice.

Geoengineering

See also: Climate engineering

Climate engineering is a technogaian method that uses two categories of technologies- carbon dioxide removal and solar radiation management. Carbon dioxide removal addresses a cause of climate change by removing one of the greenhouse gases from the atmosphere. Solar radiation management attempts to offset the effects of greenhouse gases by causing the Earth to absorb less solar radiation.

Earthquake engineering is a technogaian method concerned with protecting society and the natural and man-made environment from earthquakes by limiting the seismic risk to acceptable levels. Another example of a technogaian practice is an artificial closed ecological system used to test if and how people could live and work in a closed biosphere, while carrying out scientific experiments. It is in some cases used to explore the possible use of closed biospheres in space colonization, and also allows the study and manipulation of a biosphere without harming Earth's. The most advanced technogaian proposal is the "terraforming" of a planet, moon, or other body by deliberately modifying its atmosphere, temperature, or ecology to be similar to those of Earth in order to make it habitable by humans.

Genetic engineering

See also: Genetic engineering and Genetically modified food controversies

S. Matthew Liao, professor of philosophy and bioethics at New York University, claims that the human impact on the environment could be reduced by genetically engineering humans to have, a smaller stature, an intolerance to eating meat, and an increased ability to see in the dark, thereby using less lighting. Liao argues that human engineering is less risky than geoengineering.

Genetically modified foods have reduced the amount of herbicide and insecticide needed for cultivation. The development of glyphosate-resistant (Roundup Ready) plants has changed the herbicide use profile away from more environmentally persistent herbicides with higher toxicity, such as atrazine, metribuzin and alachlor, and reduced the volume and danger of herbicide runoff.

An environmental benefit of Bt-cotton and maize is reduced use of chemical insecticides. A PG Economics study concluded that global pesticide use was reduced by 286,000 tons in 2006, decreasing the environmental impact of herbicides and pesticides by 15%. A survey of small Indian farms between 2002 and 2008 concluded that Bt cotton adoption had led to higher yields and lower pesticide use. Another study concluded insecticide use on cotton and corn during the years 1996 to 2005 fell by 35,600,000 kilograms (78,500,000 lb) of active ingredient, which is roughly equal to the annual amount applied in the EU. A Bt cotton study in six northern Chinese provinces from 1990 to 2010 concluded that it halved the use of pesticides and doubled the level of ladybirds, lacewings and spiders and extended environmental benefits to neighbouring crops of maize, peanuts and soybeans.

Bright green environmentalism

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Bright_green_environmentalism 

Bright green environmentalism is an environmental philosophy and movement that emphasizes the use of advanced technology, social innovation, eco-innovation, and sustainable design to address environmental challenges. This approach contrasts with more traditional forms of environmentalism that may advocate for reduced consumption or a return to simpler lifestyles.

Light green, and dark green environmentalism are yet other sub-movements, respectively distinguished by seeing environmentalism as a lifestyle choice (light greens), and promoting reduction in human numbers and/or a relinquishment of technology (dark greens).

Origin and evolution of bright green thinking

The term bright green, coined in 2003 by writer Alex Steffen, refers to the fast-growing new wing of environmentalism, distinct from traditional forms. Bright green environmentalism aims to provide prosperity in an ecologically sustainable way through the use of new technologies and improved design.

Proponents promote and advocate for green energy, electric vehicles, efficient manufacturing systems, bio and nanotechnologies, ubiquitous computing, dense urban settlements, closed loop materials cycles and sustainable product designs. One-planet living is a commonly used phrase. Their principal focus is on the idea that through a combination of well-built communities, new technologies and sustainable living practices, the quality of life can actually be improved even while ecological footprints shrink.

Around the middle of the century we'll see global population peak at something like 9 billion people, all of whom will want to live with a reasonable amount of prosperity, and many of whom will want, at the very least, a European lifestyle. They will see escaping poverty as their nonnegotiable right, but to deliver that prosperity at our current levels of efficiency and resource use would destroy the planet many times over. We need to invent a new model of prosperity, one that lets billions have the comfort, security, and opportunities they want at the level of impact the planet can afford. We can't do that without embracing technology and better design.

The term bright green has been used with increased frequency due to the promulgation of these ideas through the Internet and coverage by some traditional media.

Dark greens, light greens and bright greens

Alex Steffen describes contemporary environmentalists as being split into three groups, dark, light, and bright greens.

Light green

Light greens see protecting the environment first and foremost as a personal responsibility. They fall into the transformational activist end of the spectrum, but light greens do not emphasize environmentalism as a distinct political ideology, or even seek fundamental political reform. Instead, they often focus on environmentalism as a lifestyle choice. The motto "Green is the new black" sums up this way of thinking, for many. This is different from the term lite green, which some environmentalists use to describe products or practices they believe are greenwashing, those products and practices which pretend to achieve more change than they actually do (if any).

Dark green

In contrast, dark greens believe that environmental problems are an inherent part of industrialized, capitalist civilization, and seek radical political and social and cultural change. Dark greens believe that currently and historically dominant modes of societal organization inevitably lead to consumerism, overconsumption, overproduction, waste, alienation from nature and resource depletion. Dark greens claim this is caused by the emphasis on economic growth that exists within all existing ideologies, a tendency sometimes referred to as growth mania. The dark green brand of environmentalism is associated with ideas of ecocentrism, deep ecology, degrowth, anti-consumerism, post-materialism, holism, the Gaia hypothesis of James Lovelock, and sometimes a support for a reduction in human numbers and/or a relinquishment of technology to reduce humanity's effect on the biosphere.

Dark greens may point to effects like the Jevons paradox to argue limits to the benefits of technological approaches such as advocated by bright greens.

Contrast between light green and dark green

In The Song of the Earth, Jonathan Bate notes that there are typically significant divisions within environmental theory. He identifies one group as “light Greens” or “environmentalists,” who view environmental protection primarily as a personal responsibility. The other group, termed “dark Greens” or “deep ecologists,” believes that environmental issues are fundamentally tied to industrialized civilization and advocate for radical political changes. This distinction can be summarized as “Know Technology” versus “No Technology” (Suresh Frederick in Ecocriticism: Paradigms and Praxis).

Bright green

More recently, bright greens emerged as a group of environmentalists who believe that radical changes are needed in the economic and political operation of society in order to make it sustainable, but that better designs, new technologies and more widely distributed social innovations are the means to make those changes—and that society can neither stop nor protest its way to sustainability. As Ross Robertson writes,

[B]right green environmentalism is less about the problems and limitations we need to overcome than the "tools, models, and ideas" that already exist for overcoming them. It forgoes the bleakness of protest and dissent for the energizing confidence of constructive solutions.

Some have included open source technology as part of this new approach.

Climate sensitivity

From Wikipedia, the free encyclopedia

Diagram of factors that determine climate sensitivity. After increasing CO₂ levels, there is an initial warming. This warming gets amplified by the net effect of climate feedbacks.

Climate sensitivity is a key measure in climate science and describes how much Earth's surface will warm in response to a doubling of the atmospheric carbon dioxide (CO₂) concentration. Its formal definition is: "The change in the surface temperature in response to a change in the atmospheric carbon dioxide (CO₂) concentration or other radiative forcing." This concept helps scientists understand the extent and magnitude of the effects of climate change.

The Earth's surface warms as a direct consequence of increased atmospheric CO₂, as well as increased concentrations of other greenhouse gases such as nitrous oxide and methane. The increasing temperatures have secondary effects on the climate system. These secondary effects are called climate feedbacks. Self-reinforcing feedbacks include for example the melting of sunlight-reflecting ice as well as higher evapotranspiration. The latter effect increases average atmospheric water vapour, which is itself a greenhouse gas.

Scientists do not know exactly how strong these climate feedbacks are. Therefore, it is difficult to predict the precise amount of warming that will result from a given increase in greenhouse gas concentrations. If climate sensitivity turns out to be on the high side of scientific estimates, the Paris Agreement goal of limiting global warming to below 2 °C (3.6 °F) will be even more difficult to achieve.

There are two main kinds of climate sensitivity: the transient climate response is the initial rise in global temperature when CO₂ levels double, and the equilibrium climate sensitivity is the larger long-term temperature increase after the planet adjusts to the doubling. Climate sensitivity is estimated by several methods: looking directly at temperature and greenhouse gas concentrations since the Industrial Revolution began around the 1750s, using indirect measurements from the Earth's distant past, and simulating the climate.

Fundamentals

The rate at which energy reaches Earth (as sunlight) and leaves Earth (as heat radiation to space) must balance, or the planet will get warmer or cooler. An imbalance between incoming and outgoing radiation energy is called radiative forcing. A warmer planet radiates heat to space faster and so a new balance is eventually reached, with a higher temperature and stored energy content. However, the warming of the planet also has knock-on effects, which create further warming in an exacerbating feedback loop. Climate sensitivity is a measure of how much temperature change a given amount of radiative forcing will cause.

Radiative forcing

Main articles: Radiative forcing and Earth's energy imbalance

Radiative forcings are generally quantified as Watts per square meter (W/m²) and averaged over Earth's uppermost surface defined as the top of the atmosphere. The magnitude of a forcing is specific to the physical driver and is defined relative to an accompanying time span of interest for its application. In the context of a contribution to long-term climate sensitivity from 1750 to 2020, the 50% increase in atmospheric CO
₂ is characterized by a forcing of about +2.1 W/m². In the context of shorter-term contributions to Earth's energy imbalance (i.e. its heating/cooling rate), time intervals of interest may be as short as the interval between measurement or simulation data samplings, and are thus likely to be accompanied by smaller forcing values. Forcings from such investigations have also been analyzed and reported at decadal time scales.

Radiative forcing leads to long-term changes in global temperature. A number of factors contribute radiative forcing: increased downwelling radiation from the greenhouse effect, variability in solar radiation from changes in planetary orbit, changes in solar irradiance, direct and indirect effects caused by aerosols (for example changes in albedo from cloud cover), and changes in land use (deforestation or the loss of reflective ice cover). In contemporary research, radiative forcing by greenhouse gases is well understood. As of 2019, large uncertainties remain for aerosols.

Key numbers

Further information: Carbon dioxide in Earth's atmosphere and Instrumental temperature record

Carbon dioxide (CO₂) levels rose from 280 parts per million (ppm) in the 18th century, when humans in the Industrial Revolution started burning significant amounts of fossil fuel such as coal, to over 415 ppm by 2020. As CO₂ is a greenhouse gas, it hinders heat energy from leaving the Earth's atmosphere. In 2016, atmospheric CO₂ levels had increased by 45% over preindustrial levels, and radiative forcing caused by increased CO₂ was already more than 50% higher than in pre-industrial times because of non-linear effects. Between the 18th-century start of the Industrial Revolution and the year 2020, the Earth's temperature rose by a little over one degree Celsius (about two degrees Fahrenheit).

Societal importance

Because the economics of climate change mitigation depend greatly on how quickly carbon neutrality needs to be achieved, climate sensitivity estimates can have important economic and policy-making implications. One study suggests that halving the uncertainty of the value for transient climate response (TCR) could save trillions of dollars. A higher climate sensitivity would mean more dramatic increases in temperature, which makes it more prudent to take significant climate action. If climate sensitivity turns out to be on the high end of what scientists estimate, the Paris Agreement goal of limiting global warming to well below 2 °C cannot be achieved, and temperature increases will exceed that limit, at least temporarily. One study estimated that emissions cannot be reduced fast enough to meet the 2 °C goal if equilibrium climate sensitivity (the long-term measure) is higher than 3.4 °C (6.1 °F). The more sensitive the climate system is to changes in greenhouse gas concentrations, the more likely it is to have decades when temperatures are much higher or much lower than the longer-term average.

Factors that determine sensitivity

The radiative forcing caused by a doubling of atmospheric CO₂ levels (from the pre-industrial 280 ppm) is approximately 3.7 watts per square meter (W/m²). In the absence of feedbacks, the energy imbalance would eventually result in roughly 1 °C (1.8 °F) of global warming. That figure is straightforward to calculate by using the Stefan–Boltzmann law and is undisputed.

A further contribution arises from climate feedbacks, both self-reinforcing and balancing. The uncertainty in climate sensitivity estimates is mostly from the feedbacks in the climate system, including water vapour feedback, ice–albedo feedback, cloud feedback, and lapse rate feedback. Balancing feedbacks tend to counteract warming by increasing the rate at which energy is radiated to space from a warmer planet. Self-reinfocing feedbacks increase warming; for example, higher temperatures can cause ice to melt, which reduces the ice area and the amount of sunlight the ice reflects, which in turn results in less heat energy being radiated back into space. The reflectiveness of a surface is called albedo. Climate sensitivity depends on the balance between those feedbacks.

Types

Schematic of how different measures of climate sensitivity relate to one another

Depending on the time scale, there are two main ways to define climate sensitivity: the short-term transient climate response (TCR) and the long-term equilibrium climate sensitivity (ECS), both of which incorporate the warming from exacerbating feedback loops. They are not discrete categories, but they overlap. Sensitivity to atmospheric CO₂ increases is measured in the amount of temperature change for doubling in the atmospheric CO₂ concentration.

Although the term "climate sensitivity" is usually used for the sensitivity to radiative forcing caused by rising atmospheric CO₂, it is a general property of the climate system. Other agents can also cause a radiative imbalance. Climate sensitivity is the change in surface air temperature per unit change in radiative forcing, and the climate sensitivity parameter is therefore expressed in units of °C/(W/m²). Climate sensitivity is approximately the same whatever the reason for the radiative forcing (such as from greenhouse gases or solar variation). When climate sensitivity is expressed as the temperature change for a level of atmospheric CO₂ double the pre-industrial level, its units are degrees Celsius (°C).

Transient climate response

Further information: Transient climate response to cumulative carbon emissions

The transient climate response (TCR) is defined as "the change in the global mean surface temperature, averaged over a 20-year period, centered at the time of atmospheric carbon dioxide doubling, in a climate model simulation" in which the atmospheric CO₂ concentration increases at 1% per year. That estimate is generated by using shorter-term simulations. The transient response is lower than the equilibrium climate sensitivity because slower feedbacks, which exacerbate the temperature increase, take more time to respond in full to an increase in the atmospheric CO₂ concentration. For instance, the deep ocean takes many centuries to reach a new steady state after a perturbation during which it continues to serve as heatsink, which cools the upper ocean. The IPCC literature assessment estimates that the TCR likely lies between 1 °C (1.8 °F) and 2.5 °C (4.5 °F).

A related measure is the transient climate response to cumulative carbon emissions (TCRE), which is the globally averaged surface temperature change after 1000 GtC of CO₂ has been emitted. As such, it includes not only temperature feedbacks to forcing but also the carbon cycle and carbon cycle feedbacks.

Equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) is the long-term temperature rise (equilibrium global mean near-surface air temperature) that is expected to result from a doubling of the atmospheric CO₂ concentration (ΔT_2×). It is a prediction of the new global mean near-surface air temperature once the CO₂ concentration has stopped increasing, and most of the feedbacks have had time to have their full effect. Reaching an equilibrium temperature can take centuries or even millennia after CO₂ has doubled. ECS is higher than TCR because of the oceans' short-term buffering effects. Computer models are used for estimating the ECS. A comprehensive estimate means that modelling the whole time span during which significant feedbacks continue to change global temperatures in the model, such as fully-equilibrating ocean temperatures, requires running a computer model that covers thousands of years. There are, however, less computing-intensive methods.

The IPCC Sixth Assessment Report (AR6) stated that there is high confidence that ECS is within the range of 2.5 °C to 4 °C, with a best estimate of 3 °C.

The long time scales involved with ECS make it arguably a less relevant measure for policy decisions around climate change.

Effective climate sensitivity

A common approximation to ECS is the effective equilibrium climate sensitivity, is an estimate of equilibrium climate sensitivity by using data from a climate system in model or real-world observations that is not yet in equilibrium. Estimates assume that the net amplification effect of feedbacks, as measured after some period of warming, will remain constant afterwards. That is not necessarily true, as feedbacks can change with time. In many climate models, feedbacks become stronger over time and so the effective climate sensitivity is lower than the real ECS.

Earth system sensitivity

By definition, equilibrium climate sensitivity does not include feedbacks that take millennia to emerge, such as long-term changes in Earth's albedo because of changes in ice sheets and vegetation. It also does not include the slow response of the deep oceans' warming, which takes millennia. Earth system sensitivity (ESS) incorporates the effects of these slower feedback loops, such as the change in Earth's albedo from the melting of large continental ice sheets, which covered much of the Northern Hemisphere during the Last Glacial Maximum and still cover Greenland and Antarctica. Changes in albedo as a result of changes in vegetation, as well as changes in ocean circulation, are also included. The longer-term feedback loops make the ESS larger than the ECS, possibly twice as large. Data from the geological history of Earth is used in estimating ESS. Differences between modern and long-ago climatic conditions mean that estimates of the future ESS are highly uncertain. The carbon cycle is not included in the definition of the ESS, but all other elements of the climate system are included.

Sensitivity to nature of forcing

Different forcing agents, such as greenhouse gases and aerosols, can be compared using their radiative forcing, the initial radiative imbalance averaged over the entire globe. Climate sensitivity is the amount of warming per radiative forcing. To a first approximation, the cause of the radiative imbalance does not matter. However, radiative forcing from sources other than CO₂ can cause slightly more or less surface warming than the same averaged forcing from CO₂. The amount of feedback varies mainly because the forcings are not uniformly distributed over the globe. Forcings that initially warm the Northern Hemisphere, land, or polar regions generate more self-reinforcing feedbacks (such as the ice-albedo feedback) than an equivalent forcing from CO₂, which is more uniformly distributed over the globe. This gives rise to more overall warming. Several studies indicate that human-emitted aerosols are more effective than CO₂ at changing global temperatures, and volcanic forcing is less effective. When climate sensitivity to CO₂ forcing is estimated using historical temperature and forcing (caused by a mix of aerosols and greenhouse gases), and that effect is not taken into account, climate sensitivity is underestimated.

State dependence

Artist impression of a Snowball Earth.

Climate sensitivity has been defined as the short- or long-term temperature change resulting from any doubling of CO₂, but there is evidence that the sensitivity of Earth's climate system is not constant. For instance, the planet has polar ice and high-altitude glaciers. Until the world's ice has completely melted, a self-reinforcing ice–albedo feedback loop makes the system more sensitive overall. Throughout Earth's history, multiple periods are thought to have snow and ice cover almost the entire globe. In most models of "Snowball Earth", parts of the tropics were at least intermittently free of ice cover. As the ice advanced or retreated, climate sensitivity must have been very high, as the large changes in area of ice cover would have made for a very strong ice–albedo feedback. Volcanic atmospheric composition changes are thought to have provided the radiative forcing needed to escape the snowball state.

Equilibrium climate sensitivity can change with climate.

Throughout the Quaternary period (the most recent 2.58 million years), climate has oscillated between glacial periods, the most recent one being the Last Glacial Maximum, and interglacial periods, the most recent one being the current Holocene, but the period's climate sensitivity is difficult to determine. The Paleocene–Eocene Thermal Maximum, about 55.5 million years ago, was unusually warm and may have been characterized by above-average climate sensitivity.

Climate sensitivity may further change if tipping points are crossed. It is unlikely that tipping points will cause short-term changes in climate sensitivity. If a tipping point is crossed, climate sensitivity is expected to change at the time scale of the subsystem that hits its tipping point. Especially if there are multiple interacting tipping points, the transition of climate to a new state may be difficult to reverse.

The two most common definitions of climate sensitivity specify the climate state: the ECS and the TCR are defined for a doubling with respect to the CO₂ levels in the pre-industrial era. Because of potential changes in climate sensitivity, the climate system may warm by a different amount after a second doubling of CO₂ from after a first doubling. The effect of any change in climate sensitivity is expected to be small or negligible in the first century after additional CO₂ is released into the atmosphere.

Estimation

Using Industrial Age (1750–present) data

Climate sensitivity can be estimated using the observed temperature increase, the observed ocean heat uptake, and the modelled or observed radiative forcing. The data are linked through a simple energy-balance model to calculate climate sensitivity. Radiative forcing is often modelled because Earth observation satellites measuring it has existed during only part of the Industrial Age (only since the late 1950s). Estimates of climate sensitivity calculated by using these global energy constraints have consistently been lower than those calculated by using other methods, around 2 °C (3.6 °F) or lower.

Estimates of transient climate response (TCR) that have been calculated from models and observational data can be reconciled if it is taken into account that fewer temperature measurements are taken in the polar regions, which warm more quickly than the Earth as a whole. If only regions for which measurements are available are used in evaluating the model, the differences in TCR estimates are negligible.

A very simple climate model could estimate climate sensitivity from Industrial Age data by waiting for the climate system to reach equilibrium and then by measuring the resulting warming, ΔT_eq (°C). Computation of the equilibrium climate sensitivity, S (°C), using the radiative forcing ΔF (W/m²) and the measured temperature rise, would then be possible. The radiative forcing resulting from a doubling of CO₂, F_2×CO2, is relatively well known, at about 3.7 W/m². Combining that information results in this equation:

${\displaystyle S=\mathrm{\Delta }{T}_{eq}\times F_{2\times C{O}_2}/\mathrm{\Delta }F}$.

However, the climate system is not in equilibrium since the actual warming lags the equilibrium warming, largely because the oceans take up heat and will take centuries or millennia to reach equilibrium. Estimating climate sensitivity from Industrial Age data requires an adjustment to the equation above. The actual forcing felt by the atmosphere is the radiative forcing minus the ocean's heat uptake, H (W/m²) and so climate sensitivity can be estimated:

${\displaystyle S=\mathrm{\Delta }T\times F_{2\times C{O}_2}/(\mathrm{\Delta }F-H).}$

The global temperature increase between the beginning of the Industrial Period, which is (taken as 1750, and 2011 was about 0.85 °C (1.53 °F). In 2011, the radiative forcing from CO₂ and other long-lived greenhouse gases (mainly methane, nitrous oxide, and chlorofluorocarbon) that have been emitted since the 18th century was roughly 2.8 W/m². The climate forcing, ΔF, also contains contributions from solar activity (+0.05 W/m²), aerosols (−0.9 W/m²), ozone (+0.35 W/m²), and other smaller influences, which brings the total forcing over the Industrial Period to 2.2 W/m², according to the best estimate of the IPCC Fifth Assessment Report in 2014, with substantial uncertainty. The ocean heat uptake, estimated by the same report to be 0.42 W/m², yields a value for S of 1.8 °C (3.2 °F).

Other strategies

In theory, Industrial Age temperatures could also be used to determine a time scale for the temperature response of the climate system and thus climate sensitivity: if the effective heat capacity of the climate system is known, and the timescale is estimated using autocorrelation of the measured temperature, an estimate of climate sensitivity can be derived. In practice, however, the simultaneous determination of the time scale and heat capacity is difficult.

Attempts have been made to use the 11-year solar cycle to constrain the transient climate response. Solar irradiance is about 0.9 W/m² higher during a solar maximum than during a solar minimum, and those effect can be observed in measured average global temperatures from 1959 to 2004. Unfortunately, the solar minima in the period coincided with volcanic eruptions, which have a cooling effect on the global temperature. Because the eruptions caused a larger and less well-quantified decrease in radiative forcing than the reduced solar irradiance, it is questionable whether useful quantitative conclusions can be derived from the observed temperature variations.

Observations of volcanic eruptions have also been used to try to estimate climate sensitivity, but as the aerosols from a single eruption last at most a couple of years in the atmosphere, the climate system can never come close to equilibrium, and there is less cooling than there would be if the aerosols stayed in the atmosphere for longer. Therefore, volcanic eruptions give information only about a lower bound on transient climate sensitivity.

Using data from Earth's past

Historical climate sensitivity can be estimated by using reconstructions of Earth's past temperatures and CO₂ levels. Paleoclimatologists have studied different geological periods, such as the warm Pliocene (5.3 to 2.6 million years ago) and the colder Pleistocene (2.6 million to 11,700 years ago), and sought periods that are in some way analogous to or informative about current climate change. Climates further back in Earth's history are more difficult to study because fewer data are available about them. For instance, past CO₂ concentrations can be derived from air trapped in ice cores, but as of 2020, the oldest continuous ice core is less than one million years old. Recent periods, such as the Last Glacial Maximum (LGM) (about 21,000 years ago) and the Mid-Holocene (about 6,000 years ago), are often studied, especially when more information about them becomes available.

A 2007 estimate of sensitivity made using data from the most recent 420 million years is consistent with sensitivities of current climate models and with other determinations. The Paleocene–Eocene Thermal Maximum (about 55.5 million years ago), a 20,000-year period during which massive amount of carbon entered the atmosphere and average global temperatures increased by approximately 6 °C (11 °F), also provides a good opportunity to study the climate system when it was in a warm state. Studies of the last 800,000 years have concluded that climate sensitivity was greater in glacial periods than in interglacial periods.

As the name suggests, the Last Glacial Maximum was much colder than today, and good data on atmospheric CO₂ concentrations and radiative forcing from that period are available. The period's orbital forcing was different from today's but had little direct effect on mean annual temperatures. Estimating climate sensitivity from the Last Glacial Maximum can be done by several different ways. One way is to use estimates of global radiative forcing and temperature directly. The set of feedback mechanisms active during the period, however, may be different from the feedbacks caused by a present doubling of CO₂, and such feedback differences across climate states must be accounted for when inferring today's climate sensitivity from paleoclimate evidence. In a different approach, a model of intermediate complexity is used to simulate conditions during the period. Several versions of this single model are run, with different values chosen for uncertain parameters, such that each version has a different ECS. Outcomes that best simulate the LGM's observed cooling probably produce the most realistic ECS values.

Using climate models

Frequency distribution of equilibrium climate sensitivity based on simulations of the doubling of CO₂. Each model simulation has different estimates for processes which scientists do not sufficiently understand. Few of the simulations result in less than 2 °C (3.6 °F) of warming or significantly more than 4 °C (7.2 °F). However, the positive skew, which is also found in other studies, suggests that if carbon dioxide concentrations double, the probability of large or very large increases in temperature is greater than the probability of small increases.

Climate models simulate the CO₂-driven warming of the future as well as the past. They operate on principles similar to those underlying models that predict the weather, but they focus on longer-term processes. Climate models typically begin with a starting state and then apply physical laws and knowledge about biology to predict future states. As with weather modelling, no computer has the power to model the complexity of the entire planet and simplifications are used to reduce that complexity to something manageable. An important simplification divides Earth's atmosphere into model cells. For instance, the atmosphere might be divided into cubes of air ten or one hundred kilometers on each side. Each model cell is treated as if it were homogeneous (uniform). Calculations for model cells are much faster than trying to simulate each molecule of air separately.

A lower model resolution (large model cells and long time steps) takes less computing power but cannot simulate the atmosphere in as much detail. A model cannot simulate processes smaller than the model cells or shorter-term than a single time step. The effects of the smaller-scale and shorter-term processes must therefore be estimated by using other methods. Physical laws contained in the models may also be simplified to speed up calculations. The biosphere must be included in climate models. The effects of the biosphere are estimated by using data on the average behaviour of the average plant assemblage of an area under the modelled conditions. Climate sensitivity is therefore an emergent property of these models; it is not prescribed, but it follows from the interaction of all the modelled processes.

To estimate climate sensitivity, a model is run by using a variety of radiative forcings (doubling quickly, doubling gradually, or following historical emissions) and the temperature results are compared to the forcing applied. Different models give different estimates of climate sensitivity, but they tend to fall within a similar range, as described above.

Testing, comparisons, and climate ensembles

Modelling of the climate system can lead to a wide range of outcomes. Models are often run that use different plausible parameters in their approximation of physical laws and the behaviour of the biosphere, which forms a perturbed physics ensemble, which attempts to model the sensitivity of the climate to different types and amounts of change in each parameter. Alternatively, structurally-different models developed at different institutions are put together, creating an ensemble. By selecting only the simulations that can simulate some part of the historical climate well, a constrained estimate of climate sensitivity can be made. One strategy for obtaining more accurate results is placing more emphasis on climate models that perform well in general.

A model is tested using observations, paleoclimate data, or both to see if it replicates them accurately. If it does not, inaccuracies in the physical model and parametrizations are sought, and the model is modified. For models used to estimate climate sensitivity, specific test metrics that are directly and physically linked to climate sensitivity are sought. Examples of such metrics are the global patterns of warming, the ability of a model to reproduce observed relative humidity in the tropics and subtropics, patterns of heat radiation, and the variability of temperature around long-term historical warming. Ensemble climate models developed at different institutions tend to produce constrained estimates of ECS that are slightly higher than 3 °C (5.4 °F). The models with ECS slightly above 3 °C (5.4 °F) simulate the above situations better than models with a lower climate sensitivity.

Many projects and groups exist to compare and to analyse the results of multiple models. For instance, the Coupled Model Intercomparison Project (CMIP) has been running since the 1990s.

Historical estimates

Svante Arrhenius in the 19th century was the first person to quantify global warming as a consequence of a doubling of the concentration of CO₂. In his first paper on the matter, he estimated that global temperature would rise by around 5 to 6 °C (9.0 to 10.8 °F) if the quantity of CO₂ was doubled. In later work, he revised that estimate to 4 °C (7.2 °F). Arrhenius used Samuel Pierpont Langley's observations of radiation emitted by the full moon to estimate the amount of radiation that was absorbed by water vapour and by CO₂. To account for water vapour feedback, he assumed that relative humidity would stay the same under global warming.

The first calculation of climate sensitivity that used detailed measurements of absorption spectra, as well as the first calculation to use a computer for numerical integration of the radiative transfer through the atmosphere, was performed by Syukuro Manabe and Richard Wetherald in 1967. Assuming constant humidity, they computed an equilibrium climate sensitivity of 2.3 °C per doubling of CO₂, which they rounded to 2 °C, the value most often quoted from their work, in the abstract of the paper. The work has been called "arguably the greatest climate-science paper of all time" and "the most influential study of climate of all time."

A committee on anthropogenic global warming, convened in 1979 by the United States National Academy of Sciences and chaired by Jule Charney, estimated equilibrium climate sensitivity to be 3 °C (5.4 °F), plus or minus 1.5 °C (2.7 °F). The Manabe and Wetherald estimate (2 °C (3.6 °F)), James E. Hansen's estimate of 4 °C (7.2 °F), and Charney's model were the only models available in 1979. According to Manabe, speaking in 2004, "Charney chose 0.5 °C as a reasonable margin of error, subtracted it from Manabe's number, and added it to Hansen's, giving rise to the 1.5 to 4.5 °C (2.7 to 8.1 °F) range of likely climate sensitivity that has appeared in every greenhouse assessment since ...." In 2008, climatologist Stefan Rahmstorf said: "At that time [it was published], the [Charney report estimate's] range [of uncertainty] was on very shaky ground. Since then, many vastly improved models have been developed by a number of climate research centers around the world."

Assessment reports of IPCC

Historical estimates of climate sensitivity from the IPCC assessments. The first three reports gave a qualitative likely range, and the fourth and the fifth assessment report formally quantified the uncertainty. The dark blue range is judged as being more than 66% likely.

Despite considerable progress in the understanding of Earth's climate system, assessments continued to report similar uncertainty ranges for climate sensitivity for some time after the 1979 Charney report. The First Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), published in 1990, estimated that equilibrium climate sensitivity to a doubling of CO₂ lay between 1.5 and 4.5 °C (2.7 and 8.1 °F), with a "best guess in the light of current knowledge" of 2.5 °C (4.5 °F). The report used models with simplified representations of ocean dynamics. The IPCC supplementary report, 1992, which used full-ocean circulation models, saw "no compelling reason to warrant changing" the 1990 estimate; and the IPCC Second Assessment Report stated, "No strong reasons have emerged to change [these estimates]," In the reports, much of the uncertainty around climate sensitivity was attributed to insufficient knowledge of cloud processes. The 2001 IPCC Third Assessment Report also retained this likely range.

Authors of the 2007 IPCC Fourth Assessment Report stated that confidence in estimates of equilibrium climate sensitivity had increased substantially since the Third Annual Report. The IPCC authors concluded that ECS is very likely to be greater than 1.5 °C (2.7 °F) and likely to lie in the range 2 to 4.5 °C (3.6 to 8.1 °F), with a most likely value of about 3 °C (5.4 °F). The IPCC stated that fundamental physical reasons and data limitations prevent a climate sensitivity higher than 4.5 °C (8.1 °F) from being ruled out, but the climate sensitivity estimates in the likely range agreed better with observations and the proxy climate data.

The 2013 IPCC Fifth Assessment Report reverted to the earlier range of 1.5 to 4.5 °C (2.7 to 8.1 °F) (with high confidence), because some estimates using industrial-age data came out low. The report also stated that ECS is extremely unlikely to be less than 1 °C (1.8 °F) (high confidence), and it is very unlikely to be greater than 6 °C (11 °F) (medium confidence). Those values were estimated by combining the available data with expert judgement.

In preparation for the 2021 IPCC Sixth Assessment Report, a new generation of climate models was developed by scientific groups around the world. Across 27 global climate models, estimates of a higher climate sensitivity were produced. The values spanned 1.8 to 5.6 °C (3.2 to 10.1 °F) and exceeded 4.5 °C (8.1 °F) in 10 of them. The estimates for equilibrium climate sensitivity changed from 3.2 °C to 3.7 °C and the estimates for the transient climate response from 1.8 °C, to 2.0 °C. The cause of the increased ECS lies mainly in improved modelling of clouds. Temperature rises are now believed to cause sharper decreases in the number of low clouds, and fewer low clouds means more sunlight is absorbed by the planet and less reflected to space.

Remaining deficiencies in the simulation of clouds may have led to overestimates, as models with the highest ECS values were not consistent with observed warming. A fifth of the models began to 'run hot', predicting that global warming would produce significantly higher temperatures than is considered plausible. According to these models, known as hot models, average global temperatures in the worst-case scenario would rise by more than 5 °C above preindustrial levels by 2100, with a "catastrophic" impact on human society. In comparison, empirical observations combined with physics models indicate that the "very likely" range is between 2.3 and 4.7 °C. Models with a very high climate sensitivity are also known to be poor at reproducing known historical climate trends, such as warming over the 20th century or cooling during the last ice age. For these reasons the predictions of hot models are considered implausible, and have been given less weight by the IPCC in 2022.