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Climate sensitivity is a measurement of how much warming is expected, both in the near-term and long-term for a given increase in CO₂ concentration. More specifically, it is the globally averaged surface temperature change in response to the changes in radiative forcing, the difference between incoming and outgoing energy on Earth. It is a key measure in climate science and its magnitude is not very well known. If climate sensitivity turns out to be on the high side of what scientists estimate, it will likely be impossible to achieve the Paris Agreement goal of limiting global warming to below 2.0 °C.

In the context of global warming, different measures of climate sensitivity are used. The equilibrium climate sensitivity (ECS) is the temperature increase that would result from sustained doubling of the concentration of carbon dioxide in Earth's atmosphere, after the Earth's energy budget and the climate system reach radiative equilibrium. The magnitude is likely between 1.5 and 4.5 °C. The long timescales involved with ECS makes it arguably a less relevant measure for policy decisions around climate change. The transient climate response (TCR) is the amount of temperature increase that might occur at the time when CO₂ doubles, having increased gradually by 1% each year. This is useful as a gauge for what temperature change can be expected over the current century. The ECS is higher than TCR, since the ocean functions as a buffer for temperature rise in the short term.

Climate sensitivity is typically estimated in three ways; by using direct observations of temperature and levels of greenhouse gases taken during the industrial age, by using indirectly estimated temperature and other data from the Earth's more distant past and by modelling the various aspects of the climate system with computers.

Background

Carbon dioxide levels have risen from 280 ppm in the eighteenth century to over 400 ppm today. As CO₂ is a greenhouse gas, its increase generates an energy inbalance between the incoming energy of sunlight and the outgoing energy from the Earth. This energy imbalance is called the radiative forcing and the the radiative forcing by CO₂ is now over half of the forcing that would occurs if CO₂ concentrations had doubled. Over the same period, the Earth's temperature has risen one degree Celsius. Climate sensitivity links this forcing by CO₂ and other greenhouse gases to temperature change, in the short term and longer term.

Because the economics of climate change mitigation depend a lot on how quickly carbon neutrality needs to be achieved, climate sensitivity is very important economically: one study suggests that halving the uncertainty of the transient climate response could save trillions of dollars. It has been argued that uncertainty in the value of climate sensitivity implies that it becomes more prudent to take climate action as there will be higher tail risks. If climate sensitivity turns out to be on the high side of what scientists estimate, it will be impossible to achieve the Paris Agreement goal of limiting global warming to well below 2 °C. One study estimated that emissions cannot be reduced fast enough for the 2 °C goal if equilibrium climate sensitivity is higher than 3.4 °C.

Radiative forcing

Radiative forcing is the imbalance between incoming and outgoing radiation at the top of the atmosphere, resulting from a change in atmospheric composition or other changes in radiation budget, prior to long-term changes in global temperature due to the forcing. Its unit is Watt per square meter (W/m), or the energy inbalance per second per square meter. A number of inputs can give rise to radiative forcing: the extra downwelling radiation due to the greenhouse effect, solar radiation variability due to orbital changes, changes in solar irradiance, direct aerosol effects (for example changes in albedo due to cloud cover), indirect aerosol effects, and changes in land use.

Radiative forcing by greenhouse gases is well understood but, as of 2013, large uncertainties remain for aerosols. In time-dependent estimates of climate sensitivity, the concept of the effective radiative forcing, which includes rapid adjustments in the stratosphere and the troposphere to the instantaneous radiative forcing, is usually used.

Different forms of climate sensitivity

A component of climate sensitivity is directly due to radiative forcing, for instance by CO₂, and a further contribution arises from climate feedback, both positive and negative. The radiative forcing due to doubling CO₂ from the pre-industrial 280 ppm is approximately 3.7 Watt per square meter (W/m). This energy inbalance would eventually result in roughly 1 °C ofglobal warming in the absence of feedbacks. This is easy to calculate and undisputed. The uncertainty is due entirely to feedbacks in the system: the water vapor feedback, the ice-albedo feedback, the cloud feedback, and the lapse rate feedback. Due to climate inertia, the climate sensitivity depends upon the timescale. The transient response is defined by scientists as the temperature response over human time scales of around 70 years, the equilibrium climate sensitivity over centuries, and finally the Earth system sensitivity after multiple millennia.

Although the term climate sensitivity is usually used in the context of radiative forcing by CO₂, it is thought of as a general property of the climate system: the change in surface air temperature following a unit change in radiative forcing, and the climate sensitivity parameter is therefore expressed in units of °C/(W/m). The measure is approximately independent of the nature of the forcing (e.g. from greenhouse gases or solar variation). When climate sensitivity is expressed for a doubling of CO₂, its units are degrees Celsius (°C).

Equilibrium climate sensitivity

The equilibrium climate sensitivity (ECS) refers to the equilibrium change in global mean near-surface air temperature that would result from a sustained doubling of the atmospheric equivalent CO₂ concentration (ΔT_2×). A comprehensive model estimate of equilibrium sensitivity requires a very long model integration; fully equilibrating ocean temperatures requires the integration of thousands of model years, although it is possible to produce an estimate more quickly using the method of Gregory et al. (2004). As estimated by the IPCC Fifth Assessment Report (AR5), "there is high confidence that ECS is extremely unlikely to be less than 1°C and medium confidence that the ECS is likely between 1.5°C and 4.5°C and very unlikely greater than 6°C".

Effective climate sensitivity

The effective climate sensitivity is an estimate of equilibrium climate sensitivity using data from a climate system, either in a model or real-world observations, that is not yet in equilibrium. Estimation is done by using the assumption that the net effect of feedbacks as measured after a period of warming remains constant afterwards. This is not necessarily true, as feedbacks can change with time, or with the particular starting state or forcing history of the climate system.

Transient climate response

The transient climate response (TCR) is defined as the average temperature response over a twenty-year period centred at CO₂ doubling in a transient simulation with CO₂ increasing at 1% per year (compounded), i.e., 60 to 80 years following initiation of the increase in CO₂. The transient response is lower than the equilibrium sensitivity because the deep ocean, which takes many centuries to reach a new steady state after a perturbation, continues to serve as a sink for heat from the upper ocean. The IPCC literature assessment estimates that TCR likely lies between 1 °C and 2.5 °C. A related concept is the transient climate response to cumulative carbon emissions, which is the globally averaged surface temperature change per unit of CO₂ emitted.

Earth system sensitivity

The Earth system sensitivity (ESS) includes the effects of slower feedback loops, such as the change in Earth's albedo from the melting of large ice sheets that covered much of the northern hemisphere during the last glacial maximum. These extra feedback loops make the ESS larger than the ECS – possibly twice as large. Data from Earth's history is used to estimate ESS, but climatic conditions were quite different which makes it difficult to infer information for future ESS. ESS includes the entire system except the carbon cycle. Changes in albedo as a result of vegetation changes are included.

Sensitivity to nature of the forcing

Radiative forcing from sources other than CO₂ can cause a higher or lower surface warming than a similar radiative forcing due to CO₂; the amount of feedback varies, mainly because these forcings are not uniformly distributed over the globe. Forcings that initially warm the northern hemisphere, land, or polar regions more strongly; are systematically more effective at changing temperatures than an equivalent amount of CO₂ whose forcing is more uniformly distributed over the globe. Several studies indicate that aerosols are more effective than CO₂ at changing global temperatures while volcanic forcing is less effective. Ignoring these factors causes lower estimates of climate sensitivity when using radiative forcing and temperature records from the historical period.

State dependence

While climate sensitivity is defined as the sensitivity to any doubling of CO₂, there is evidence that the sensitivity of the climate system is not always constant. Until the world's ice has melted, for instance, a positive ice-albedo feedback loop makes the system more sensitive overall. Thus the climate system may warm by a different amount after a second doubling of CO₂ than after the first doubling. The effect of this is small or negligible in the first century after CO₂ is released into the atmosphere. Furthermore, the climate may become more sensitive if tipping points are crossed. It is unlikely that climate sensitivity increases instantly; rather, it changes at the time scale of the subsystem that is undergoing the tipping point. The more sensitive a climate system is to increased greenhouse gases, the more likely it is to have decades when temperatures are much higher or much lower than the longer-term average.

Estimating climate sensitivity

Climate sensitivity is often evaluated in terms of the change in equilibrium temperature due to radiative forcing caused by the greenhouse effect. The radiative forcing, and hence the change in temperature, is proportional to the logarithm of the concentration of infrared-absorbing ("greenhouse") gases in the atmosphere, as quantified by Svante Arrhenius in the 19th century. The sensitivity of temperature to atmospheric gasses, most notably CO₂, is often expressed in terms of the change in temperature per doubling of the concentration of the gas.

Historical estimates

Arrhenius was the first person to quantify global warming as a consequence of a doubling 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 this estimate to 4 °C (7.2 °F). Arrhenius used the observations of radiation emitted by the full moon made by the astronomer Samuel Pierpont Langley to estimate the amount of radiation that was absorbed by water vapour and CO₂. To account for water vapour feedback he assumed relative humidity would stay the same under global warming.

The first calculation of climate sensitivity using detailed measurements of absorption spectra, and the first to use a computer to numerically integrate the radiative transfer through the atmosphere, was by Manabe and Wetherald in 1967. For constant humidity they computed a climate sensitivity of 2.3 °C per doubling of CO₂ (which they rounded to 2, the value most often quoted from their work, in the abstract of the paper). This 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 climate sensitivity to be 3 °C (5.4 °F), give or take 1.5 °C (2.7 °F). Apart from the Manabe and Wetherald model, with a climate sensitivity of 2 °C (3.6 °F), the only other available was from James E. Hansen, with 4 °C (7.2 °F). According to Manabe, "Charney chose 0.5 °C (0.90 °F) 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 wrote, regarding the Charney report's original range of uncertainty; "At that time, this range was on very shaky ground. Since then, many vastly improved models have been developed by a number of climate research centers around the world."

Intergovernmental Panel on Climate Change

After the publication of the Charney report, despite considerable progress in the understanding of the climate system, further assessments reported a similar range in climate sensitivity. The 1990 IPCC First Assessment Report 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). This report used models that had simplified representations of ocean dynamics. The IPCC supplementary report, 1992, which used full-ocean circulation models, saw "no compelling reason to warrant changing" from this estimate; and the IPCC Second Assessment Report said, "No strong reasons have emerged to change" these estimates. In these reports, much of the uncertainty was attributed to cloud processes. The 2001 IPCC TAR also retained this likely range.

Authors of the IPCC Fourth Assessment Report stated that confidence in estimates of equilibrium climate sensitivity had increased substantially since the Third Annual Report. IPCC authors concluded 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 (4 to 8.1 °F), with a most likely value of about 3 °C (5 °F). For fundamental physical reasons and data limitations, the IPCC stated a climate sensitivity higher than 4.5 °C (8.1 °F) could not be ruled out, but that agreement for these values with observations and "proxy" climate data is generally worse compared with values within the likely range.

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

Using industrial-age data

Climate sensitivity can be estimated using observed temperature rise, observed ocean heat uptake, and modelled or observed radiative forcing. These data are linked though a simple energy-balance model to calculate climate sensitivity. Radiative forcing is often modelled, because Earth observation satellites that measure it have not existed for the entire period. Estimates of climate sensitivity calculated from these global energy constraints have consistently been lower than those calculated using other methods; estimates calculated using this method have been around 2 °C (3.6 °F) or lower (e.g.).

Estimates of transient climate response (TCR) 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 average. If only regions for which measurements are available are used in evaluating the model, differences in TCR estimates almost disappear.

A very simple climate model can be used to estimate climate sensitivity from industrial-age data. If we could wait for the climate system to get into equilibrium and recorded a warming of ΔT_eq (°C), we would be able to compute equilibrium climate sensitivity S (°C) by using the climate forcing ΔF (W/m) and this temperature rise. The forcing for a doubling of CO₂, F_{2${\displaystyle \times }$CO2}, is relatively well known at about 3.7 W/m. Combining this information:

${\displaystyle S=\Delta T_{eq}\times F_{2\times CO_{2}}/\Delta F}$.

However, the climate system is not in equilibrium. The actual warming lags the equilibrium warming because the oceans take up heat. To estimate climate sensitivity from industrial-age data, we must therefore make an adjustment to the equation above. The actual forcing felt by the atmosphere is the radiative forcing minus the ocean heat uptake H (W/m), so that climate sensitivity can be estimated by:

${\displaystyle S=\Delta T\times F_{2\times CO_{2}}/(\Delta F-H).}$

The global temperature increase between the beginning of the industrial period (taken as 1750) and 2011 was about 0.85 °C (1.53 °F). In 2011, the radiative forcing due to CO₂ and other long-lived greenhouse gases – mainly methane, nitrous oxide, and chlorofluorocarbons – emitted since the eighteenth century was roughly 2.8 W/m. The climate forcing ΔF also contains contributions from solar activity (+0.05 W/m), aerosols (−0.9 W/m), ozone (0.35 W/m), and other smaller influences, bringing the total forcing over the industrial period to 2.2 W/m according to the best estimate of the IPCC AR5, with substantial uncertainty. The ocean heat uptake estimated by the IPCC AR5 as 0.42 W/m, 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 timescale of the climate system, and thus climate sensitivity. If the effective heat capacity of the climate system is known and the timescale estimated by using the autocorrelation of the measured temperature, an estimate of climate sensitivity can be derived. However, in practice determination of both the timescale and heat capacity is difficult.

Attempts to use the 11-year solar cycle to constrain the transient climate response have been made. Solar irradiance is about 0.9 W/m brighter during solar maximum than during solar minimum, which correlated in measured average global temperature over the period 1959-2004. The solar minima in this period coincided with volcanic eruptions, which have a cooling effect on the global temperature. Because this causes a larger radiative forcing than the solar variations, it is questionable whether much information can be derived from the temperature variations.

Volcanic eruptions have also been used to try to estimate climate sensitivity, but as the aerosols from a single volcanic eruption only last a couple of years in the atmosphere so the climate system's response can never come close to equilibrium. As such, there is less cooling compared to a case where the aerosols stayed in the atmosphere for longer. Therefore, volcanic eruption only give information about a lower bound on the transient climate sensitivity.

Using data from Earth's past

Climate sensitivity can be estimated by using reconstructions of Earth's past temperatures and CO₂ levels. Different geological periods, for instance the warm Pliocene and the colder Pleistocene, have been studied. Scientist seek periods that are in some sense analogous or informative to current climate change. As more information about them becomes available; recent periods, such as the Mid-holocene that occurred about 6,000 years ago, and the Last Glacial Maximum (LGM) that took place about 21,000 years ago, are often chosen.

As the name suggests, the LGM was a lot colder than today; also scientists have a good idea of the CO₂ concentration and radiative forcing during that period. While orbital forcing was different from the present, this had little effect on mean annual temperatures. Different approaches to the task of estimating climate sensitivity from the LGM are taken. One way is to use estimates of global radiative forcing and temperature directly. The set of feedbacks active during the LGM, however, may be different than the feedbacks due to doubling CO₂, introducing additional uncertainty. In a different approach, a single model of intermediate complexity is run using a set of parameters so that each version has a different ECS. Those model versions that can best simulate the cooling during the LGM are thought to have the best ECS values. Various other researchers use an ensemble of different models.

Over the last 800,000 years, climate sensitivity has been found to be greater in cold periods than in warm periods. Although climates further back in Earth's history are also used; an additional difficulty is that CO₂ concentrations cannot be readily obtained from ice cores so they must be estimated less directly. An estimate of sensitivity made using data from a major part of the Phanerozoic is consistent with sensitivities of current climate models and with other determinations. The Paleocene–Eocene Thermal Maximum provides a good opportunity to study the climate system when it is in a warm state.

Using climate models

Climate models of earth, for example the Coupled model intercomparison project (CMIP), are used to simulate the quantity of warming that will occur with rising CO₂ concentrations. The models are based on physical laws and represent the biosphere. Because of limited computer power, the physical laws have to be approximated, which leads to a wide range of estimates of climate sensitivity. Climate sensitivity is an emergent property of these models.

In preparation for the 2021 6th IPCC report, a new generation of climate models have been developed by scientific groups around the world. The average climate sensitivity has increased in Coupled Model Intercomparison Project phase 6 (CMIP6) compared to the previous generation, with values spanning 1.8–5.6 °C (3.2–10.1 °F) across 27 global climate models and exceeding 4.5 °C (8.1 °F) in 10 of them. The cause of the increased sensitivity lies mainly in improved modelling of clouds so that low clouds decrease more strongly when temperature rise causing enhanced planetary absorption of sunlight. Models with the highest sensitivities are however not consistent with observed warming.

Constrained models

Modelling of the climate system can lead to a wide range of outcomes. Models are often run using different plausible parameters in their approximation of physical laws and the behaviour of the biosphere; a so-called perturbed physics ensemble. Alternatively, structurally different models developed at different institutions are put together, creating an ensemble. By selecting only those simulations that can simulate some part of the historical climate well, a constrained estimate of climate sensitivity can be made. One strategy is the placing of more trust in climate models that perform well in general.

Alternatively, specific metrics that are directly and physically linked to climate sensitivity are sought; examples of this are the global patterns of warming, the ability of the models to reproduce observed relative humidity in the tropics and sub-tropics, patterns of radiation, and the variability of temperature about long term historical warming. When using ensemble climate models developed in different institutions, many of these constrained estimates of ECS are slightly higher than 3 °C (5.4 °F); as the models with ECS slightly above 3 °C (5.4 °F) perform better in these metrics than models with a low climate sensitivity.

References

1. "What is 'climate sensitivity'?". Met Office. Retrieved 2020-02-14.
2. PALAEOSENS Project Members (November 2012). "Making sense of palaeoclimate sensitivity" (PDF). Nature. 491 (7426): 683–91. Bibcode:2012Natur.491..683P. doi:10.1038/nature11574. hdl:2078.1/118863. PMID 23192145.
3. "Climate sensitivity: fact sheet" (PDF). Australian government. Department of the Environment.{{cite web}}: CS1 maint: url-status (link)
4. ^ Tanaka, Katsumasa; O'Neill, Brian C. (2018). "The Paris Agreement zero-emissions goal is not always consistent with the 1.5 °C and 2 °C temperature targets". Nature Climate Change. 8 (4): 319–324. doi:10.1038/s41558-018-0097-x. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
5. Held, Isaac and Winton, Mike. Transient and Equilibrium Climate Sensitivity, Geophysical Fluid Dynamics Laboratory, U.S. National Oceanic & Atmospheric Administration. Retrieved 2019-06-19.
6. ^ "Explainer: How scientists estimate climate sensitivity". Carbon Brief. 2018-06-19. Retrieved 2019-03-14.
7. Myhre, Gunnar; Myhre, Cathrine Lund; Forster, Piers M.; Shine, Keith P. (2017). "Halfway to doubling of CO2 radiative forcing" (PDF). Nature Geoscience. 10 (10): 710–711. doi:10.1038/ngeo3036. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
8. Watts, Jonathan (2018-10-08). "We have 12 years to limit climate change catastrophe, warns UN". The Guardian. ISSN 0261-3077. Retrieved 2020-02-13. {{cite news}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)CS1 maint: url-status (link)
9. Hope C (November 2015). "The $10 trillion value of better information about the transient climate response". Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 373 (2054): 20140429. Bibcode:2015RSPTA.37340429H. doi:10.1098/rsta.2014.0429. PMID 26438286.
10. Freeman MC, Wagner G, Zeckhauser RJ (November 2015). "Climate sensitivity uncertainty: when is good news bad?". Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 373 (2055): 20150092. Bibcode:2015RSPTA.37350092F. doi:10.1098/rsta.2015.0092. PMID 26460117.
11. "Explained: Radiative forcing". MIT News. Retrieved 2019-03-30.
12. Climate Change: The IPCC Scientific Assessment (1990), Report prepared for Intergovernmental Panel on Climate Change by Working Group I, J. T. Houghton, G. J. Jenkins and J. J. Ephraums (eds.), chapter 2, Radiative Forcing of Climate Archived 2018-08-08 at the Wayback Machine, pp. 41–68
13. Myhre et al. 2013
14. Gregory JM, Andrews T (2016). "Variation in climate sensitivity and feedback parameters during the historical period" (PDF). Geophysical Research Letters. 43 (8): 3911–3920. Bibcode:2016GeoRL..43.3911G. doi:10.1002/2016GL068406.
15. Lenton TM, Rockström J, Gaffney O, Rahmstorf S, Richardson K, Steffen W, Schellnhuber HJ (November 2019). "Climate tipping points - too risky to bet against". Nature. 575 (7784): 592–595. Bibcode:2019Natur.575..592L. doi:10.1038/d41586-019-03595-0. PMID 31776487.
16. Roe, Gerard (2009). "Feedbacks, Timescales, and Seeing Red". Annual Review of Earth and Planetary Sciences. 37 (1): 93–115. Bibcode:2009AREPS..37...93R. doi:10.1146/annurev.earth.061008.134734.
17. ^ Rahmstorf, Stefan (2008). "Anthropogenic Climate Change: Revisiting the Facts". In Zedillo, Ernesto (ed.). Global Warming: Looking Beyond Kyoto (PDF). Brookings Institution Press. pp. 34–53.
18. Knutti, Reto; Rugenstein, Maria A. A.; Knutti, Reto (2017). "Beyond equilibrium climate sensitivity". Nature Geoscience. 10 (10): 727–736. Bibcode:2017NatGe..10..727K. doi:10.1038/ngeo3017. hdl:20.500.11850/197761. ISSN 1752-0908. {{cite journal}}: Cite has empty unknown parameter: |1= (help); Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
19. Modak, Angshuman; Bala, Govindasamy; Cao, Long; Caldeira, Ken (2016). "Why must a solar forcing be larger than a CO2forcing to cause the same global mean surface temperature change?". Environmental Research Letters. 11 (4): 044013. Bibcode:2016ERL....11d4013M. doi:10.1088/1748-9326/11/4/044013. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
20. Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe RB, Lowe JA, Johns TC, Williams KD (2004). "A new method for diagnosing radiative forcing and climate sensitivity". Geophysical Research Letters. 31 (3): L03205. Bibcode:2004GeoRL..31.3205G. doi:10.1029/2003GL018747.
21. Bindoff, Nathaniel L.; Stott, Peter A. (2013). "10.8.2 Constraints on Long-Term Climate Change and the Equilibrium Climate Sensitivity". Climate Change 2013: The Physical Science Basis - IPCC Working Group I Contribution to AR5. Geneva, Switzerland: Intergovernmental Panel on Climate Change. {{cite book}}: External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help); Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
22. ^ Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, eds. (2007). "Glossary A-D, Climate sensitivity". Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge University Press. {{cite book}}: External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help)
23. Bitz CM, Shell KM, Gent PR, Bailey DA, Danabasoglu G, Armour KC, et al. (2011). "Climate Sensitivity of the Community Climate System Model, Version 4". Journal of Climate. 25 (9): 3053–3070. doi:10.1175/JCLI-D-11-00290.1. ISSN 0894-8755.
24. Prentice IC, et al. (2001). "9.2.1 Climate Forcing and Climate Response, in chapter 9. Projections of Future Climate Change". In Houghton, J. T., et al. (eds.). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. ISBN 9780521807678. {{cite book}}: External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help)
25. Randall DA, et al. (2007). "8.6.2 Interpreting the Range of Climate Sensitivity Estimates Among General Circulation Models, In: Climate Models and Their Evaluation.". In Solomon SD, et al. (eds.). Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. {{cite book}}: External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help)
26. Hansen, James; Sato, Makiko; Kharecha, Pushker; von Schuckmann, Karina (2011). "Earth's energy imbalance and implications". Atmospheric Chemistry and Physics. 11 (24): 13, 421–49. arXiv:1105.1140. Bibcode:2011ACP....1113421H. doi:10.5194/acp-11-13421-2011. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)CS1 maint: unflagged free DOI (link)
27. Collins et al. 2013, Executive Summary; p.1033
28. Matthews HD, Gillett NP, Stott PA, Zickfeld K (June 2009). "The proportionality of global warming to cumulative carbon emissions". Nature. 459 (7248): 829–32. Bibcode:2009Natur.459..829M. doi:10.1038/nature08047. PMID 19516338.
29. "Target CO₂". RealClimate. 7 April 2008. Archived from the original on 24 August 2017.
30. "On sensitivity: Part I". RealClimate.org.
31. Previdi M, Liepert BG, Peteet D, Hansen J, Beerling DJ, Broccoli AJ, et al. (2013). "Climate sensitivity in the Anthropocene". Quarterly Journal of the Royal Meteorological Society. 139 (674): 1121–31. Bibcode:2013QJRMS.139.1121P. CiteSeerX 10.1.1.434.854. doi:10.1002/qj.2165.
32. Marvel, Kate; Schmidt, Gavin A.; Miller, Ron L.; Nazarenko, Larissa S. (2016). "Implications for climate sensitivity from the response to individual forcings". Nature Climate Change. 6 (4): 386–389. Bibcode:2016NatCC...6..386M. doi:10.1038/nclimate2888. hdl:2060/20160012693. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
33. Pincus, Robert; Mauritsen, Thorsten (2017). "Committed warming inferred from observations". Nature Climate Change. 7 (9): 652–655. Bibcode:2017NatCC...7..652M. doi:10.1038/nclimate3357. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
34. Hansen J, Sato M, Russell G, Kharecha P (October 2013). "Climate sensitivity, sea level and atmospheric carbon dioxide". Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 371 (2001): 20120294. doi:10.1098/rsta.2012.0294. PMC 3785813. PMID 24043864.
35. ^ Pfister, Patrik L.; Stocker, Thomas F. (2017). "State-Dependence of the Climate Sensitivity in Earth System Models of Intermediate Complexity". Geophysical Research Letters. 44 (20): 10, 643–10, 653. Bibcode:2017GeoRL..4410643P. doi:10.1002/2017GL075457. ISSN 1944-8007. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
36. Lontzek, Thomas S.; Lenton, Timothy M.; Cai, Yongyang (2016). "Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction". Nature Climate Change. 6 (5): 520–525. Bibcode:2016NatCC...6..520C. doi:10.1038/nclimate2964. hdl:10871/20598. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
37. "Opinion: Europe is burning just as scientists offer a chilling truth about climate change". The Independent. 2019-07-24. Retrieved 2019-07-26.
38. Nijsse, Femke J. M. M.; Cox, Peter M.; Huntingford, Chris; Williamson, Mark S. (2019). "Decadal global temperature variability increases strongly with climate sensitivity". Nature Climate Change. 9 (8): 598–601. Bibcode:2019NatCC...9..598N. doi:10.1038/s41558-019-0527-4. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
39. Walter, Martin E. (2010). "Earthquakes and Weatherquakes: Mathematics and Climate Change" (PDF). Notices of the American Mathematical Society. 57 (10): 1278–84. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
40. Lapenis, Andrei G. (1998). "Arrhenius and the Intergovernmental Panel on Climate Change". Eos, Transactions American Geophysical Union. 79 (23): 271. Bibcode:1998EOSTr..79..271L. doi:10.1029/98EO00206. ISSN 2324-9250.
41. Sample, Ian (2005-06-30). "The father of climate change". The Guardian. ISSN 0261-3077. Retrieved 2019-03-18.
42. Anderson TR, Hawkins E, Jones PD (September 2016). "2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today's Earth System Models". Endeavour. 40 (3): 178–187. doi:10.1016/j.endeavour.2016.07.002. PMID 27469427.
43. Manabe S, Wetherald RT (May 1967). "Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity". Journal of the Atmospheric Sciences. 24 (3): 241–259. Bibcode:1967JAtS...24..241M. doi:10.1175/1520-0469(1967)024<0241:teotaw>2.0.co;2.
44. Forster P (May 2017). "In Retrospect: Half a century of robust climate models" (PDF). Nature. 545 (7654): 296–297. Bibcode:2017Natur.545..296F. doi:10.1038/545296a. PMID 28516918. Retrieved 2019-10-19.
45. Pidcock, Roz (July 6, 2015) "The most influential climate change papers of all time", CarbonBrief. Retrieved 2019-10-19.
46. Ad Hoc Study Group on Carbon Dioxide and Climate (1979). Carbon Dioxide and Climate: A Scientific Assessment (PDF). National Academy of Sciences. doi:10.17226/12181. ISBN 978-0-309-11910-8. Archived from the original (PDF) on 13 August 2011.
47. Kerr RA (August 2004). "Climate change. Three degrees of consensus". Science. 305 (5686): 932–4. doi:10.1126/science.305.5686.932. PMID 15310873.
48. ^ Meehl GA, et al., "Ch. 10: Global Climate Projections; Box 10.2: Equilibrium Climate Sensitivity", IPCC Fourth Assessment Report WG1 2007
49. ^ Solomon S, et al., "Technical summary", Climate Change 2007: Working Group I: The Physical Science Basis, Box TS.1: Treatment of Uncertanties in the Working Group I Assessment, in IPCC AR4 WG1 2007
50. Forster PM (2016). "Inference of Climate Sensitivity from Analysis of Earth's Energy Budget". Annual Review of Earth and Planetary Sciences. 44 (1): 85–106. Bibcode:2016AREPS..44...85F. doi:10.1146/annurev-earth-060614-105156.
51. Climate Change: The IPCC Scientific Assessment (1990), Report prepared for Intergovernmental Panel on Climate Change by Working Group I, J. T. Houghton, G. J. Jenkins and J. J. Ephraums (eds.), chapter 5, Equilibrium Climate Change — and its Implications for the Future Archived 2018-04-13 at the Wayback Machine, pp. 138–9
52. IPCC '92 p118 section B3.5
53. IPCC SAR p 34, technical summary section D.2
54. Albritton DL, et al. (2001). "Technical Summary: F.3 Projections of Future Changes in Temperature". In Houghton J. T., et al. (eds.). Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. {{cite book}}: |archive-url= requires |url= (help); External link in |chapterurl= (help); Unknown parameter |chapterurl= ignored (|chapter-url= suggested) (help)
55. ^  This article incorporates public domain material from US EPA (7 December 2009), "Ch. 6: Projected Future Greenhouse Gas Concentrations and Climate Change: Box 6.3: Climate sensitivity" (PDF), Technical Support Document for Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act, Washington, DC, USA: Climate Change Division, Office of Atmospheric Programs, US EPA, p.66 (78 of PDF file)
56. ^ Skeie RB, Berntsen T, Aldrin M, Holden M, Myhre G (2014). "A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series". Earth System Dynamics. 5 (1): 139–75. Bibcode:2014ESD.....5..139S. doi:10.5194/esd-5-139-2014.{{cite journal}}: CS1 maint: unflagged free DOI (link)
57. Armour, Kyle C. (2017). "Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks". Nature Climate Change. 7 (5): 331–335. Bibcode:2017NatCC...7..331A. doi:10.1038/nclimate3278. ISSN 1758-6798.
58. Forster PM, Gregory JM (2006). "The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data". Journal of Climate. 19 (1): 39–52. Bibcode:2006JCli...19...39F. doi:10.1175/JCLI3611.1.
59. Lewis, Nicholas; Curry, Judith A. (2014). "The implications for climate sensitivity of AR5 forcing and heat uptake estimates". Climate Dynamics. 45 (3–4): 1009–23. Bibcode:2015ClDy...45.1009L. doi:10.1007/s00382-014-2342-y. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
60. Otto A, Otto FE, Boucher O, Church J, Hegerl G, Forster PM, et al. (2013). "Energy budget constraints on climate response" (PDF). Nature Geoscience. 6 (6): 415–416. Bibcode:2013NatGe...6..415O. doi:10.1038/ngeo1836. ISSN 1752-0908.
61. Stolpe, Martin B.; Ed Hawkins; Cowtan, Kevin; Richardson, Mark (2016). "Reconciled climate response estimates from climate models and the energy budget of Earth" (PDF). Nature Climate Change. 6 (10): 931–935. Bibcode:2016NatCC...6..931R. doi:10.1038/nclimate3066. ISSN 1758-6798. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
62. IPCC AR5 WG1 Technical Summary 2013, p. 53-56.
63. IPCC AR5 WG1 Technical Summary 2013, p. 39.
64. Schwartz, Stephen E. (2007). "Heat capacity, time constant, and sensitivity of Earth's climate system". Journal of Geophysical Research: Atmospheres. 112 (D24): D24S05. Bibcode:2007JGRD..11224S05S. CiteSeerX 10.1.1.482.4066. doi:10.1029/2007JD008746.
65. Knutti R, Kraehenmann S, Frame DJ, Allen MR (2008). "Comment on "Heat capacity, time constant, and sensitivity of Earth's climate system" by SE Schwartz". Journal of Geophysical Research: Atmospheres. 113 (D15): D15103. Bibcode:2008JGRD..11315103K. doi:10.1029/2007JD009473.
66. Foster G, Annan JD, Schmidt GA, Mann ME (2008). "Comment on "Heat capacity, time constant, and sensitivity of Earth's climate system" by SE Schwartz". Journal of Geophysical Research: Atmospheres. 113 (D15): D15102. Bibcode:2008JGRD..11315102F. doi:10.1029/2007JD009373.
67. Scafetta N (2008). "Comment on "Heat capacity, time constant, and sensitivity of Earth's climate system" by SE Schwartz". Journal of Geophysical Research: Atmospheres. 113 (D15): D15104. Bibcode:2008JGRD..11315104S. doi:10.1029/2007JD009586.
68. Tung KK, Zhou J, Camp CD (2008). "Constraining model transient climate response using independent observations of solar-cycle forcing and response" (PDF). Geophysical Research Letters. 35 (17): L17707. Bibcode:2008GeoRL..3517707T. doi:10.1029/2008GL034240.
69. Camp CD, Tung KK (2007). "Surface warming by the solar cycle as revealed by the composite mean difference projection" (PDF). Geophysical Research Letters. 34 (14): L14703. Bibcode:2007GeoRL..3414703C. doi:10.1029/2007GL030207. Archived from the original (PDF) on 13 January 2012. Retrieved 20 January 2012.
70. Rypdal, K. (2012). "Global temperature response to radiative forcing: Solar cycle versus volcanic eruptions". Journal of Geophysical Research: Atmospheres. 117 (D6): n/a. Bibcode:2012JGRD..117.6115R. doi:10.1029/2011JD017283. ISSN 2156-2202.
71. Merlis TM, Held IM, Stenchikov GL, Zeng F, Horowitz LW (2014). "Constraining Transient Climate Sensitivity Using Coupled Climate Model Simulations of Volcanic Eruptions". Journal of Climate. 27 (20): 7781–7795. Bibcode:2014JCli...27.7781M. doi:10.1175/JCLI-D-14-00214.1. hdl:10754/347010. ISSN 0894-8755.
72. McSweeney, Robert (2015-02-04). "What a three-million year fossil record tells us about climate sensitivity". Carbon Brief. Retrieved 2019-03-20. {{cite web}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
73. Hargreaves, Julia C.; Annan, James D. (2009). "On the importance of paleoclimate modelling for improving predictions of future climate change" (PDF). Climate of the Past. 5. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
74. ^ Masson-Delmotte et al. 2013
75. ^ Hopcroft, Peter O.; Valdes, Paul J. (2015). "How well do simulated last glacial maximum tropical temperatures constrain equilibrium climate sensitivity?: CMIP5 LGM TROPICS AND CLIMATE SENSITIVITY". Geophysical Research Letters. 42 (13): 5533–5539. doi:10.1002/2015GL064903. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
76. Ganopolski, Andrey; von Deimling, Thomas Schneider (2008). "Comment on "Aerosol radiative forcing and climate sensitivity deduced from the Last Glacial Maximum to Holocene transition" by Petr Chylek and Ulrike Lohmann". Geophysical Research Letters. 35 (23): L23703. Bibcode:2008GeoRL..3523703G. doi:10.1029/2008GL033888. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
77. Schmittner A, Urban NM, Shakun JD, Mahowald NM, Clark PU, Bartlein PJ, et al. (December 2011). "Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum". Science. 334 (6061): 1385–8. Bibcode:2011Sci...334.1385S. CiteSeerX 10.1.1.419.8341. doi:10.1126/science.1203513. PMID 22116027.
78. Hargreaves JC, Annan JD, Yoshimori M, Abe-Ouchi A (2012). "Can the Last Glacial Maximum constrain climate sensitivity?". Geophysical Research Letters. 39 (24): L24702. Bibcode:2012GeoRL..3924702H. doi:10.1029/2012GL053872. ISSN 1944-8007.
79. von der Heydt AS, Köhler P, van de Wal RS, Dijkstra HA (2014). "On the state dependency of fast feedback processes in (paleo) climate sensitivity". Geophysical Research Letters. 41 (18): 6484–6492. arXiv:1403.5391. doi:10.1002/2014GL061121. ISSN 1944-8007.
80. Royer DL, Berner RA, Park J (March 2007). "Climate sensitivity constrained by CO2 concentrations over the past 420 million years". Nature. 446 (7135): 530–2. Bibcode:2007Natur.446..530R. doi:10.1038/nature05699. PMID 17392784.
81. Kiehl JT, Shields CA (October 2013). "Sensitivity of the Palaeocene-Eocene Thermal Maximum climate to cloud properties". Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. 371 (2001): 20130093. Bibcode:2013RSPTA.37130093K. doi:10.1098/rsta.2013.0093. PMID 24043867.
82. ^ Edited quote from public-domain source: Lindsey, Rebecca (3 August 2010), What if global warming isn't as severe as predicted? : Climate Q&A : Blogs, NASA Earth Observatory, part of the EOS Project Science Office, located at NASA Goddard Space Flight Center
83. Roe GH, Baker MB (October 2007). "Why is climate sensitivity so unpredictable?". Science. 318 (5850): 629–32. Bibcode:2007Sci...318..629R. doi:10.1126/science.1144735. PMID 17962560.
84. "The CMIP6 landscape". Nature Climate Change. 9 (10): 727. 2019-09-25. Bibcode:2019NatCC...9..727.. doi:10.1038/s41558-019-0599-1. ISSN 1758-6798.
85. "New climate models suggest Paris goals may be out of reach". France 24. 2020-01-14. Retrieved 2020-01-18.
86. ^ Zelinka MD, Myers TA, McCoy DT, Po-Chedley S, Caldwell PM, Ceppi P, Klein SA, Taylor KE (2020). "Causes of Higher Climate Sensitivity in CMIP6 Models". Geophysical Research Letters. 47 (1): e2019GL085782. doi:10.1029/2019GL085782. ISSN 1944-8007.
87. Bender, Maddie (2020-02-07). "Climate Change Predictions Have Suddenly Gone Catastrophic. This Is Why". Vice. Retrieved 2020-02-09.
88. Sanderson, Benjamin M.; Knutti, Reto; Caldwell, Peter (2015). "Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties". Journal of Climate. 28 (13): 5150–5170. Bibcode:2015JCli...28.5150S. doi:10.1175/JCLI-D-14-00361.1. ISSN 0894-8755. {{cite journal}}: Unknown parameter |name-list-format= ignored (|name-list-style= suggested) (help)
89. Forest CE, Stone PH, Sokolov AP, Allen MR, Webster MD (January 2002). "Quantifying uncertainties in climate system properties with the use of recent climate observations" (PDF). Science. 295 (5552): 113–7. Bibcode:2002Sci...295..113F. CiteSeerX 10.1.1.297.1145. doi:10.1126/science.1064419. PMID 11778044.
90. Fasullo JT, Trenberth KE (2012). "A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity". Science. 338 (6108): 792–94. Bibcode:2012Sci...338..792F. doi:10.1126/science.1227465. PMID 23139331.. Referred to by: ScienceDaily (8 November 2012). "Future warming likely to be on high side of climate projections, analysis finds". ScienceDaily.
91. Brown PT, Caldeira K (December 2017). "Greater future global warming inferred from Earth's recent energy budget". Nature. 552 (7683): 45–50. Bibcode:2017Natur.552...45B. doi:10.1038/nature24672. PMID 29219964.
92. Cox PM, Huntingford C, Williamson MS (January 2018). "Emergent constraint on equilibrium climate sensitivity from global temperature variability" (PDF). Nature. 553 (7688): 319–322. Bibcode:2018Natur.553..319C. doi:10.1038/nature25450. PMID 29345639.
93. Brown PT, Stolpe MB, Caldeira K (November 2018). "Assumptions for emergent constraints". Nature. 563 (7729): E1 – E3. Bibcode:2018Natur.563E...1B. doi:10.1038/s41586-018-0638-5. PMID 30382203.
94. Cox PM, Williamson MS, Nijsse FJ, Huntingford C (November 2018). "Cox et al. reply". Nature. 563 (7729): E10 – E15. Bibcode:2018Natur.563E..10C. doi:10.1038/s41586-018-0641-x. PMID 30382204.
95. Caldwell PM, Zelinka MD, Klein SA (2018). "Evaluating Emergent Constraints on Equilibrium Climate Sensitivity". Journal of Climate. 31 (10): 3921–3942. Bibcode:2018JCli...31.3921C. doi:10.1175/JCLI-D-17-0631.1. ISSN 0894-8755.

Notes

1. The CO₂ level in 2016 was 403 ppm which is less than 50% higher than the pre-industrial CO₂ concentration of 278 ppm. However, due to the logarithmic forcing relationship, a halfway to doubling of CO₂, in terms of radiative forcing, has already been reached.
2. This calculation goes as follows. In equilibrium, the energy of incoming and outgoing radiation have to balance. The outgoing radiation ${\displaystyle F}$ is given by the Stefan-Boltzmann law: ${\displaystyle F=-\sigma T^{4}}$. When incoming radiation increases, the outgoing radiation, and therefore temperature has to increase as well. The temperature rise ${\displaystyle \Delta T_{2\times CO_{2}}}$ for the additional radiative forcing ${\displaystyle \Delta F_{2\times CO_{2}}}$ due to doubling of CO₂ is then given by

   ${\displaystyle \Delta F_{2\times CO_{2}}={\frac {dF}{dT}}\Delta T_{2\times CO_{2}}=4\sigma T^{3}\Delta T_{2\times CO_{2}}}$.

   Given an effective temperature of 255 K, a constant lapse rate, the value of the Stefan-Boltzmann constant ${\displaystyle \sigma }$ of 5.67 ${\displaystyle \times 10^{-8}}$W/m K and ${\displaystyle F_{2\times CO_{2}}}$around 4 W/m, this gives a climate sensitivity of a world without feedbacks of approximately 1 K.
3. Here the IPCC definition is used. In some other sources, the climate sensitivity parameter is simply called the climate sensitivity. The inverse of this parameter, is called the climate feedback parameter and is expressed in (W/m)/°C.

Further reading

• Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, eds. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. ISBN 978-0-521-88009-1. (pb: 978-0-521-70596-7)
• IPCC (2013). Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, et al. (eds.). Climate Change 2013: The Physical Science Basis (PDF). Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. ISBN 978-1-107-05799-9. (pb: 978-1-107-66182-0).
• Stocker TF, Qin D, Plattner GK, Alexander LV, Allen SK, Bindoff NL, et al. (2013). "Technical Summary" (PDF). IPCC AR5 WG1 2013. pp. 33–115.
  • Masson-Delmotte V, Schulz M, Abe-Ouchi A, Beer J, Ganopolski A, Rouco JG, et al. (2013). "Chapter 5: Information from Paleoclimate Archives" (PDF). IPCC AR5 WG1 2013. pp. 383–464. {{cite book}}: Invalid |ref=harv (help)
  • Myhre G, Shindell D, Bréon FM, Collins W, Fuglestvedt J, Huang J, et al. (2013). "Chapter 8: Anthropogenic and Natural Radiative Forcing" (PDF). IPCC AR5 WG1 2013. pp. 659–740. {{cite book}}: Invalid |ref=harv (help)
  • Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, et al. (2013). "Chapter 12: Long-term Climate Change: Projections, Commitments and Irreversibility" (PDF). IPCC AR5 WG1 2013. pp. 1029–1136. {{cite book}}: Invalid |ref=harv (help)

External links

• What is ‘climate sensitivity’? Met Office
• How scientists estimate ‘climate sensitivity’ Carbon Brief

• Climate feedbacks
• Paleoclimatology