Bright green environmentalism

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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.

Negative feedback

From Wikipedia, the free encyclopedia

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

A simple negative feedback system is descriptive, for example, of some electronic amplifiers. The feedback is negative if the loop gain AB is negative.

Negative feedback (or balancing feedback) occurs when some function of the output of a system, process, or mechanism is fed back in a manner that tends to reduce the fluctuations in the output, whether caused by changes in the input or by other disturbances.

Whereas positive feedback tends to instability via exponential growth, oscillation or chaotic behavior, negative feedback generally promotes stability. Negative feedback tends to promote a settling to equilibrium, and reduces the effects of perturbations. Negative feedback loops in which just the right amount of correction is applied with optimum timing, can be very stable, accurate, and responsive.

Negative feedback is widely used in mechanical and electronic engineering, and it is observed in many other fields including biology, chemistry and economics. General negative feedback systems are studied in control systems engineering.

Negative feedback loops also play an integral role in maintaining the atmospheric balance in various climate systems on Earth. One such feedback system is the interaction between solar radiation, cloud cover, and planet temperature.

Blood glucose levels are maintained at a constant level in the body by a negative feedback mechanism. When the blood glucose level is too high, the pancreas secretes insulin and when the level is too low, the pancreas then secretes glucagon. The flat line shown represents the homeostatic set point. The sinusoidal line represents the blood glucose level.

General description

Feedback loops in the human body

In many physical and biological systems, qualitatively different influences can oppose each other. For example, in biochemistry, one set of chemicals drives the system in a given direction, whereas another set of chemicals drives it in an opposing direction. If one or both of these opposing influences are non-linear, equilibrium point(s) result.

In biology, this process (in general, biochemical) is often referred to as homeostasis; whereas in mechanics, the more common term is equilibrium.

In engineering, mathematics and the physical, and biological sciences, common terms for the points around which the system gravitates include: attractors, stable states, eigenstates/eigenfunctions, equilibrium points, and setpoints.

In control theory, negative refers to the sign of the multiplier in mathematical models for feedback. In delta notation, −Δoutput is added to or mixed into the input. In multivariate systems, vectors help to illustrate how several influences can both partially complement and partially oppose each other.

Some authors, in particular with respect to modelling business systems, use negative to refer to the reduction in difference between the desired and actual behavior of a system. In a psychology context, on the other hand, negative refers to the valence of the feedback – attractive versus aversive, or praise versus criticism.

In contrast, positive feedback is feedback in which the system responds so as to increase the magnitude of any particular perturbation, resulting in amplification of the original signal instead of stabilization. Any system in which there is positive feedback together with a gain greater than one will result in a runaway situation. Both positive and negative feedback require a feedback loop to operate.

However, negative feedback systems can still be subject to oscillations. This is caused by a phase shift around any loop. Due to these phase shifts the feedback signal of some frequencies can ultimately become in phase with the input signal and thus turn into positive feedback, creating a runaway condition. Even before the point where the phase shift becomes 180 degrees, stability of the negative feedback loop will become compromised, leading to increasing under- and overshoot following a disturbance. This problem is often dealt with by attenuating or changing the phase of the problematic frequencies in a design step called compensation. Unless the system naturally has sufficient damping, many negative feedback systems have low pass filters or dampers fitted.

Examples

• Mercury thermostats (circa 1600) using expansion and contraction of columns of mercury in response to temperature changes were used in negative feedback systems to control vents in furnaces, maintaining a steady internal temperature.
• In the invisible hand of the market metaphor of economic theory (1776), reactions to price movements provide a feedback mechanism to match supply and demand.
• In centrifugal governors (1788), negative feedback is used to maintain a near-constant speed of an engine, irrespective of the load or fuel-supply conditions.
• In a steering engine (1866), power assistance is applied to the rudder with a feedback loop, to maintain the direction set by the steersman.
• In servomechanisms, the speed or position of an output, as determined by a sensor, is compared to a set value, and any error is reduced by negative feedback to the input.
• In audio amplifiers, negative feedback flattens frequency response, reduces distortion, minimises the effect of manufacturing variations in component parameters, and compensates for changes in characteristics due to temperature change.
• In analog computing, feedback around operational amplifiers is used to generate mathematical functions such as addition, subtraction, integration, differentiation, logarithm, and antilog functions.
• In delta-sigma analog-to-digital and digital-to-analog converters (particularly for high quality audio), a negative feedback loop is used to repeatedly correct accumulated quantization error during conversion.
• In a phase locked loop (1932), feedback is used to maintain a generated alternating waveform in a constant phase to a reference signal. In many implementations the generated waveform is the output, but when used as a demodulator in an FM radio receiver, the error feedback voltage serves as the demodulated output signal. If there is a frequency divider between the generated waveform and the phase comparator, the device acts as a frequency multiplier.
• In organisms, feedback enables various measures (e.g. body temperature, or blood sugar level) to be maintained within a desired range by homeostatic processes.

Detailed implementations

Error-controlled regulation

Basic error-controlled regulator loop

See also: Control engineering, Homeostasis, and Allostasis

A regulator R adjusts the input to a system T so the monitored essential variables E are held to set-point values S that result in the desired system output despite disturbances D.

One use of feedback is to make a system (say T) self-regulating to minimize the effect of a disturbance (say D). Using a negative feedback loop, a measurement of some variable (for example, a process variable, say E) is subtracted from a required value (the 'set point') to estimate an operational error in system status, which is then used by a regulator (say R) to reduce the gap between the measurement and the required value. The regulator modifies the input to the system T according to its interpretation of the error in the status of the system. This error may be introduced by a variety of possible disturbances or 'upsets', some slow and some rapid. The regulation in such systems can range from a simple 'on-off' control to a more complex processing of the error signal.

In this framework, the physical form of a signal may undergo multiple transformations. For example, a change in weather may cause a disturbance to the heat input to a house (as an example of the system T) that is monitored by a thermometer as a change in temperature (as an example of an 'essential variable' E). This quantity, then, is converted by the thermostat (a 'comparator') into an electrical error in status compared to the 'set point' S, and subsequently used by the regulator (containing a 'controller' that commands gas control valves and an ignitor) ultimately to change the heat provided by a furnace (an 'effector') to counter the initial weather-related disturbance in heat input to the house.

Error controlled regulation is typically carried out using a Proportional-Integral-Derivative Controller (PID controller). The regulator signal is derived from a weighted sum of the error signal, integral of the error signal, and derivative of the error signal. The weights of the respective components depend on the application.

Mathematically, the regulator signal is given by:

${\displaystyle \mathrm{M}\mathrm{V}(\mathrm{t})=K_p\left(e(t)+\frac{1}{T_i}{\int }_0^{t}e(\tau )d\tau +T_d\frac{d}{dt}e(t)\right)}$

where

${\displaystyle T_i}$ is the integral time

${\displaystyle T_d}$ is the derivative time

Negative feedback amplifier

Main article: Negative feedback amplifier

The negative feedback amplifier was invented by Harold Stephen Black at Bell Laboratories in 1927, and granted a patent in 1937 (US Patent 2,102,671) "a continuation of application Serial No. 298,155, filed August 8, 1928 ...").

"The patent is 52 pages long plus 35 pages of figures. The first 43 pages amount to a small treatise on feedback amplifiers!"

There are many advantages to feedback in amplifiers. In design, the type of feedback and amount of feedback are carefully selected to weigh and optimize these various benefits.

Advantages of amplifier negative voltage feedback

Negative voltage feedback in amplifiers has the following advantages; it

1. reduces non-linear distortion, i.e., produces higher fidelity;
2. increases circuit stability: i.e., gains remain stable over variations in ambient temperature, frequency, and signal amplitude;
3. slightly increases bandwidth;
4. modifies input and output impedances;
5. considerably reduces harmonic, phase, amplitude, and frequency distortions; and
6. considerably reduces noise.

Though negative feedback has many advantages, amplifiers with feedback can oscillate (see Step response of feedback amplifiers), and they may exhibit instability. Harry Nyquist of Bell Laboratories proposed the a stability criterion and a plot to identify stable feedback systems, including amplifiers and control systems.

Negative feedback amplifier with external disturbance. The feedback is negative if βA >0.

The figure shows a simplified block diagram of a negative feedback amplifier.

The feedback sets the overall (closed-loop) amplifier gain at a value:

${\displaystyle \frac{O}{I}=\frac{A}{1+\beta A}\approx \frac{1}{\beta },}$

where the approximate value assumes βA >> 1. This expression shows that a gain greater than one requires β < 1. Because the approximate gain 1/β is independent of the open-loop gain A, the feedback is said to 'desensitize' the closed-loop gain to variations in A (for example, due to manufacturing variations between units, or temperature effects upon components), provided only that the gain A is sufficiently large. In this context, the factor (1+βA) is often called the 'desensitivity factor', and in the broader context of feedback effects that include other matters like electrical impedance and bandwidth, the 'improvement factor'.

If the disturbance D is included, the amplifier output becomes:

${\displaystyle O=\frac{AI}{1+\beta A}+\frac{D}{1+\beta A},}$

which shows that the feedback reduces the effect of the disturbance by the 'improvement factor' (1+β A). The disturbance D might arise from fluctuations in the amplifier output due to noise and nonlinearity (distortion) within this amplifier, or from other noise sources such as power supplies.

The difference signal I–βO at the amplifier input is sometimes called the "error signal". According to the diagram, the error signal is:

${\displaystyle \text{Error signal}=I-\beta O=I\left(1-\beta \frac{O}{I}\right)=\frac{I}{1+\beta A}-\frac{\beta D}{1+\beta A}.}$

From this expression, it can be seen that a large 'improvement factor' (or a large loop gain βA) tends to keep this error signal small.

Although the diagram illustrates the principles of the negative feedback amplifier, modeling a real amplifier as a unilateral forward amplification block and a unilateral feedback block has significant limitations. For methods of analysis that do not make these idealizations, see the article Negative feedback amplifier.

Operational amplifier circuits

Main article: Operational amplifier applications

A feedback voltage amplifier using an op amp with finite gain but infinite input impedances and zero output impedance.

The operational amplifier was originally developed as a building block for the construction of analog computers, but is now used almost universally in all kinds of applications including audio equipment and control systems.

Operational amplifier circuits typically employ negative feedback to get a predictable transfer function. Since the open-loop gain of an op-amp is extremely large, a small differential input signal would drive the output of the amplifier to one rail or the other in the absence of negative feedback. A simple example of the use of feedback is the op-amp voltage amplifier shown in the figure.

The idealized model of an operational amplifier assumes that the gain is infinite, the input impedance is infinite, output resistance is zero, and input offset currents and voltages are zero. Such an ideal amplifier draws no current from the resistor divider. Ignoring dynamics (transient effects and propagation delay), the infinite gain of the ideal op-amp means this feedback circuit drives the voltage difference between the two op-amp inputs to zero. Consequently, the voltage gain of the circuit in the diagram, assuming an ideal op amp, is the reciprocal of feedback voltage division ratio β:

${\displaystyle V_{\text{out}}=\frac{R_{\text{1}}+R_{\text{2}}}{R_{\text{1}}}{V}_{\text{in}}=\frac{1}{\beta }{V}_{\text{in}}}$.

A real op-amp has a high but finite gain A at low frequencies, decreasing gradually at higher frequencies. In addition, it exhibits a finite input impedance and a non-zero output impedance. Although practical op-amps are not ideal, the model of an ideal op-amp often suffices to understand circuit operation at low enough frequencies. As discussed in the previous section, the feedback circuit stabilizes the closed-loop gain and desensitizes the output to fluctuations generated inside the amplifier itself.

Areas of application

Mechanical engineering

See also: Control systems and Control engineering

The ballcock or float valve uses negative feedback to control the water level in a cistern.

An example of the use of negative feedback control is the ballcock control of water level (see diagram), or a pressure regulator. In modern engineering, negative feedback loops are found in engine governors, fuel injection systems and carburettors. Similar control mechanisms are used in heating and cooling systems, such as those involving air conditioners, refrigerators, or freezers.

Biology

See also: Counterregulatory hormone and Homeostasis

Control of endocrine hormones by negative feedback.

Some biological systems exhibit negative feedback such as the baroreflex in blood pressure regulation and erythropoiesis. Many biological processes (e.g., in the human anatomy) use negative feedback. Examples of this are numerous, from the regulating of body temperature, to the regulating of blood glucose levels. The disruption of feedback loops can lead to undesirable results: in the case of blood glucose levels, if negative feedback fails, the glucose levels in the blood may begin to rise dramatically, thus resulting in diabetes.

For hormone secretion regulated by the negative feedback loop: when gland X releases hormone X, this stimulates target cells to release hormone Y. When there is an excess of hormone Y, gland X "senses" this and inhibits its release of hormone X. As shown in the figure, most endocrine hormones are controlled by a physiologic negative feedback inhibition loop, such as the glucocorticoids secreted by the adrenal cortex. The hypothalamus secretes corticotropin-releasing hormone (CRH), which directs the anterior pituitary gland to secrete adrenocorticotropic hormone (ACTH). In turn, ACTH directs the adrenal cortex to secrete glucocorticoids, such as cortisol. Glucocorticoids not only perform their respective functions throughout the body but also negatively affect the release of further stimulating secretions of both the hypothalamus and the pituitary gland, effectively reducing the output of glucocorticoids once a sufficient amount has been released.

Chemistry

Closed systems containing substances undergoing a reversible chemical reaction can also exhibit negative feedback in accordance with Le Chatelier's principle which shift the chemical equilibrium to the opposite side of the reaction in order to reduce a stress. For example, in the reaction

N₂ + 3 H₂ ⇌ 2 NH₃ + 92 kJ/mol

If a mixture of the reactants and products exists at equilibrium in a sealed container and nitrogen gas is added to this system, then the equilibrium will shift toward the product side in response. If the temperature is raised, then the equilibrium will shift toward the reactant side which, since the reverse reaction is endothermic, will partially reduce the temperature.

Self-organization

Main articles: Self-organization and Emergence

Self-organization is the capability of certain systems "of organizing their own behavior or structure". There are many possible factors contributing to this capacity, and most often positive feedback is identified as a possible contributor. However, negative feedback also can play a role.

Economics

In economics, automatic stabilisers are government programs that are intended to work as negative feedback to dampen fluctuations in real GDP.

Mainstream economics asserts that the market pricing mechanism operates to match supply and demand, because mismatches between them feed back into the decision-making of suppliers and demanders of goods, altering prices and thereby reducing any discrepancy. However Norbert Wiener wrote in 1948:

"There is a belief current in many countries and elevated to the rank of an official article of faith in the United States that free competition is itself a homeostatic process... Unfortunately the evidence, such as it is, is against this simple-minded theory."

The notion of economic equilibrium being maintained in this fashion by market forces has also been questioned by numerous heterodox economists such as financier George Soros and leading ecological economist and steady-state theorist Herman Daly, who was with the World Bank in 1988–1994.

Environmental Science

Some effects of climate change can either enhance (positive feedbacks) or weaken (negative feedbacks) global warming.

A basic and common example of a negative feedback system in the environment is the interaction among cloud cover, plant growth, solar radiation, and planet temperature. As incoming solar radiation increases, planet temperature increases. As the temperature increases, the amount of plant life that can grow increases. This plant life can then make products such as sulfur which produce more cloud cover. An increase in cloud cover leads to higher albedo, or surface reflectivity, of the Earth. As albedo increases, however, the amount of solar radiation decreases. This, in turn, affects the rest of the cycle.

Cloud cover, and in turn planet albedo and temperature, is also influenced by the hydrological cycle. As planet temperature increases, more water vapor is produced, creating more clouds. The clouds then block incoming solar radiation, lowering the temperature of the planet. This interaction produces less water vapor and therefore less cloud cover. The cycle then repeats in a negative feedback loop. In this way, negative feedback loops in the environment have a stabilizing effect.

History

Negative feedback as a control technique may be seen in the refinements of the water clock introduced by Ktesibios of Alexandria in the 3rd century BCE. Self-regulating mechanisms have existed since antiquity, and were used to maintain a constant level in the reservoirs of water clocks as early as 200 BCE.

The fly-ball governor, an early example of negative feedback

Negative feedback was implemented in the 17th century. Cornelius Drebbel had built thermostatically controlled incubators and ovens in the early 1600s, and centrifugal governors were used to regulate the distance and pressure between millstones in windmills. James Watt patented a form of governor in 1788 to control the speed of his steam engine, and James Clerk Maxwell in 1868 described "component motions" associated with these governors that lead to a decrease in a disturbance or the amplitude of an oscillation.

The term "feedback" was well established by the 1920s, in reference to a means of boosting the gain of an electronic amplifier. Friis and Jensen described this action as "positive feedback" and made passing mention of a contrasting "negative feed-back action" in 1924. Harold Stephen Black came up with the idea of using negative feedback in electronic amplifiers in 1927, submitted a patent application in 1928, and detailed its use in his paper of 1934, where he defined negative feedback as a type of coupling that reduced the gain of the amplifier, in the process greatly increasing its stability and bandwidth.

Karl Küpfmüller published papers on a negative-feedback-based automatic gain control system and a feedback system stability criterion in 1928.

Nyquist and Bode built on Black's work to develop a theory of amplifier stability.

Early researchers in the area of cybernetics subsequently generalized the idea of negative feedback to cover any goal-seeking or purposeful behavior.

All purposeful behavior may be considered to require negative feed-back. If a goal is to be attained, some signals from the goal are necessary at some time to direct the behavior.

Cybernetics pioneer Norbert Wiener helped to formalize the concepts of feedback control, defining feedback in general as "the chain of the transmission and return of information", and negative feedback as the case when:

The information fed back to the control center tends to oppose the departure of the controlled from the controlling quantity...^: 97

While the view of feedback as any "circularity of action" helped to keep the theory simple and consistent, Ashby pointed out that, while it may clash with definitions that require a "materially evident" connection, "the exact definition of feedback is nowhere important". Ashby pointed out the limitations of the concept of "feedback":

The concept of 'feedback', so simple and natural in certain elementary cases, becomes artificial and of little use when the interconnections between the parts become more complex...Such complex systems cannot be treated as an interlaced set of more or less independent feedback circuits, but only as a whole. For understanding the general principles of dynamic systems, therefore, the concept of feedback is inadequate in itself. What is important is that complex systems, richly cross-connected internally, have complex behaviors, and that these behaviors can be goal-seeking in complex patterns.^: 54

To reduce confusion, later authors have suggested alternative terms such as degenerative, self-correcting, balancing, or discrepancy-reducing in place of "negative".