Climate sensitivity

Climate sensitivity is the globally averaged temperature change in response to changes in radiative forcing, which can occur, for instance, due to increased levels of carbon dioxide (CO
).[3] 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[note 1] is therefore expressed in units of °C/(W/m2). The measure is approximately independent of the nature of the forcing (e.g. from greenhouse gases or solar variation).[4] When climate sensitivity is expressed for a doubling of CO2, its units are degrees Celsius (°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.[5] 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. The earth system sensitivity (ESS) includes the effects of very-long-term Earth system feedback loops, such as changes in ice sheets or changes in the distribution of vegetative cover.[6]

Climate sensitivity is typically estimated in three ways; by using observations taken during the industrial age, by using temperature and other data from the Earth's past and by modelling the climate system in computers.[6] For coupled atmosphere-ocean global climate models the climate sensitivity is an emergent property; rather than being a model parameter it is a result of a combination of model physics and parameters. By contrast, simpler energy-balance models may have climate sensitivity as an explicit parameter.

Different forms of climate sensitivity

A component of climate sensitivity is directly due to radiative forcing, for instance by CO
, and a further contribution arises from climate feedback, both positive and negative.[7] Without feedbacks the radiative forcing of approximately 3.7 W/m2, due to doubling CO
from the pre-industrial 280 ppm, would eventually result in roughly 1 °C global warming. This is easy to calculate[note 2][8] and undisputed.[9] 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.[9] 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.[10]

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 CO
concentration (ΔT2×). 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).[11] As estimated by the IPCC Fifth Assessment Report (AR5), "there is high confidence that ECS is extremely unlikely 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".[12]

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.[13] Estimation is done by using the assumption that the net effect of feedbacks as measured after a period of warming remains constant afterwards.[14] This is not necessarily true, as feedbacks can change with time, or with the particular starting state or forcing history of the climate system.[15][13]

Transient climate response

The transient climate response (TCR) is defined as the average temperature response over a twenty-year period centered at CO
doubling in a transient simulation with CO
increasing at 1% per year (compounded), i.e., 60 to 80 years following initiation of the increase in CO
.[16] 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.[17] The IPCC literature assessment estimates that TCR likely lies between 1 °C and 2.5 °C.[18] A related concept is the transient climate response to cumulative carbon emissions, which is the globally averaged surface temperature change per unit of CO

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.[20] ESS includes the entire system except the carbon cycle.[21] Changes in albedo as a result of vegetation changes are included.[22]

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.[23] 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.[24]

Radiative forcing by greenhouse gases is well understood but, as of 2013, large uncertainties remain for aerosols.[25] 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.[26]

Sensitivity to nature of the forcing

Radiative forcing from sources other than CO
can cause a higher or lower surface warming than a similar radiative forcing due to CO
; 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 CO
at changing global temperatures while volcanic forcing is less effective.[27] Ignoring these factors causes lower estimates of climate sensitivity when using radiative forcing and temperature records from the historical period.[28]

State dependence

While climate sensitivity is defined as the sensitivity to any doubling of CO
, 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.[29] Thus the climate system may warm by a different amount after a second doubling of CO
than after the first doubling. The effect of this is small or negligible in the first century after CO
is released into the atmosphere.[29] 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.[30] 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.[31][32]

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.[33] The sensitivity of temperature to atmospheric gasses, most notably CO
, 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 CO
. 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 CO
was doubled. In later work he revised this estimate to 4 °C (7.2 °F).[34] 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 CO
. To account for water vapour feedback he assumed relative humidity would stay the same under global warming.[35][36]

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.[37] 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"[38] and "the most influential study of climate of all time."[39]

A committee on anthropogenic global warming, convened in 1979 by the United States National Academy of Sciences and chaired by Jule Charney,[40] 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 ..."[41]

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. Current state-of-the-art climate models span a range of 2.6 to 4.1 °C (4.7 to 7.4 °F), most clustering around 3 °C (5.4 °F)."[9]

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.[44] The 1990 IPCC First Assessment Report estimated that equilibrium climate sensitivity to a doubling of CO
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).[45] 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;[46] and the IPCC Second Assessment Report said, "No strong reasons have emerged to change" these estimates.[47] In these reports, much of the uncertainty was attributed to cloud processes. The 2001 IPCC TAR also retained this likely range.[48]

Authors of the IPCC Fourth Assessment Report[42] stated that confidence in estimates of equilibrium climate sensitivity had increased substantially since the Third Annual Report.[49] 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.[49]

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.[6] 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.[43]

Using industrial-age data

Climate sensitivity can be estimated using observed temperature rise, observed ocean heat uptake, and modeled or observed radiative forcing. These data are linked though a simple energy-balance model to calculate climate sensitivity.[50] Radiative forcing is often modeled, 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;[51] estimates calculated using this method have been around 2 °C (3.6 °F) or lower (e.g.[50][52][53][54]).

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.[6][55]

Rahmstorf (2008)[9] provides an informal example of the estimation of climate sensitivity using observations made since the pre-industrial era, from which the following is modified. Denote the sensitivity, i.e. the equilibrium increase in global mean temperature including the effects of feedbacks due to a sustained forcing by doubled CO
(F2CO2; taken as 3.7 W/m2), as S (°C). If Earth was to experience an equilibrium temperature change of ΔT (°C) due to a sustained forcing of ΔF (W/m2), then:


The global temperature increase since the beginning of the industrial period (taken as 1750) is about 0.8 °C (1.4 °F), and the radiative forcing due to CO
and other long-lived greenhouse gases  mainly methane, nitrous oxide, and chlorofluorocarbons  emitted since that time is about 2.6 W/m2. Neglecting other forcings and considering the temperature increase to be an equilibrium increase would lead to a sensitivity of about 1.1 °C (2.0 °F). However, ΔF also contains contributions from solar activity (+0.3 W/m2), aerosols (−1.0 W/m2), ozone (0.3 W/m2), and other smaller influences, bringing the total forcing over the industrial period to 1.6 W/m2 according to the best estimate of the IPCC AR4, with substantial uncertainty. The absence of equilibrium of the climate system must be accounted for by subtracting the planetary heat uptake rate H from the forcing; i.e.,

Taking planetary heat uptake rate as the rate of ocean heat uptake estimated by the IPCC AR4 as 0.2 W/m2, yields a value for S of 2.1 °C (3.8 °F).

Other strategies

In theory, industrial-age temperatures could also be used to determine a timescale of the climate system, and thus climate sensitivity.[56] 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.[57][58][59]

Attempts to use the 11-year solar cycle to constrain the transient climate response have been made.[60] Solar irradiance is about 0.9 W/m2 brighter during solar maximum than during solar minimum, which correlated in measured average global temperature over the period 1959-2004.[61] 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.[62]

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 and the climate system's response to radiative forcing has inertia, only a lower bound on the transient climate sensitivity can be found.[63]

Using data from Earth's past

Climate sensitivity can be estimated by using reconstructions of Earth's past temperatures and CO
levels. Different geological periods, for instance the warm Pliocene and the colder Pleistocene, are studied.[64] 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.[65]

As the name suggests, the LGM was a lot colder than today; also scientists have a good idea of the CO
concentration and radiative forcing during that period.[66] While orbital forcing was different from the present, this had little effect on mean annual temperatures.[67] Different approaches to the task of estimating climate sensitivity from the LGM are taken.[66] 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 CO
, introducing additional uncertainty.[67][68] 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.[69] Other researchers use an ensemble of different models.[70][66]

Over the last 800,000 years, climate sensitivity has been found to be greater in cold periods than in warm periods.[71] Although climates further back in Earth's history are also used; an additional difficulty is that CO
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.[72] The Paleocene–Eocene Thermal Maximum provides a good opportunity to study the climate system when it is in a warm state.[73]

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 CO
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.[6]

In preparation for the 2021 6th IPCC report, a new generation of climate models are being developed:[74] some show climate sensitivity around 5 °C (9.0 °F), meaning temperature can rise by 6.5 - 7 degree by 2100 in the worst socio-economic scenario ("SSP5 8.5 – rapid economic growth driven by fossil fuels without mitigation"). However the CMIP6 models have yet to be thoroughly independently analysed and researchers do not yet fully understand why some show this higher sensitivity.[75][76][77]

Constrained models

Bottom-up 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.[78]

Alternatively, specific metrics that are directly and physically linked to climate sensitivity are sought; examples of this are the global patterns of warming,[79] the ability of the models to reproduce observed relative humidity in the tropics and sub-tropics,[80] patterns of radiation,[81] and the variability of temperature about long term historical warming.[82][83][84] 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.[85]

Socio-economic implications

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.[86] 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.[87]



  1. 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/m2)/°C.
  2. This calculation goes as follows. In equilibrium, the energy of incoming and outgoing radiation have to balance. The outgoing radiation is given by the Stefan-Boltzmann law: . When incoming radiation increases, the outgoing radiation, and therefore temperature has to increase as well. The temperature rise for the additional radiative forcing due to doubling of CO2 is then given by
    Given an effective temperature of 255 K, a constant lapse rate, the value of the Stefan-Boltzmann constant of 5.67 W/m2 K-4 and around 4 W/m2, this gives a climate sensitivity of a world without feedbacks of approximately 1 K.


  1. 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
  2. Roe, Gerald H.; Baker, Marcia B. (2007). "Why Is Climate Sensitivity So Unpredictable?". Science. 318 (5850): 629–632. Bibcode:2007Sci...318..629R. doi:10.1126/science.1144735. ISSN 0036-8075. PMID 17962560.
  3. PALAEOSENS Project Members (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.
  4. 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. ISSN 1748-9326.
  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. Lenton, Timothy M.; Rockström, Johan; Gaffney, Owen; Rahmstorf, Stefan; Richardson, Katherine; Steffen, Will; Schellnhuber, Hans Joachim (2019-11-27). "Climate tipping points — too risky to bet against". Nature. 575 (7784): 592–595. doi:10.1038/d41586-019-03595-0. PMID 31776487.
  8. 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/
  9. 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.
  10. 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.
  11. Gregory, J. M.; et al. (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.
  12. Bindoff, Nathaniel L.; Stott, Peter A. (2013). "10.8.2 Constraints on Long-Term Climate Change and the Equilibrium Climate Sensitivity" (PDF). Climate Change 2013: The Physical Science Basis - IPCC Working Group I Contribution to AR5. Geneva, Switzerland: Intergovernmental Panel on Climate Change.
  13. Solomon, S. D.; et al., eds. (2007). "Glossary A-D, Climate sensitivity" (PDF). Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge University Press.
  14. Bitz, C. M.; Shell, K. M.; Gent, P. R.; Bailey, D. A.; Danabasoglu, G.; Armour, K. C.; Holland, M. M.; Kiehl, J. T. (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.
  15. Prentice, I. C.; et al. (2001). "9.2.1 Climate Forcing and Climate Response, in chapter 9. Projections of Future Climate Change" (PDF). 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.
  16. Randall, D. A.; 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, S. D.; 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.
  17. 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.
  18. Collins et al. 2013, Executive Summary; p.1033
  19. Matthews, H. D.; Gillett, N. P.; Stott, P. A.; Zickfeld, K. (2009). "The proportionality of global warming to cumulative carbon emissions". Nature. 459 (7248): 829–832. Bibcode:2009Natur.459..829M. doi:10.1038/nature08047. PMID 19516338.
  20. "Target CO
    . RealClimate. 7 April 2008. Archived from the original on 24 August 2017.
  21. "On sensitivity: Part I".
  22. Previdi, M.; et al. (2013). "Climate sensitivity in the Anthropocene". Quarterly Journal of the Royal Meteorological Society. 139 (674): 1121–31. Bibcode:2013QJRMS.139.1121P. CiteSeerX doi:10.1002/qj.2165.
  23. "Explained: Radiative forcing". MIT News. Retrieved 2019-03-30.
  24. 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
  25. Myhre et al. 2013
  26. Gregory, J. M.; 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.
  27. 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.
  28. Robert Pincus; 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.
  29. 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.
  30. 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.
  31. "Opinion: Europe is burning just as scientists offer a chilling truth about climate change". The Independent. 2019-07-24. Retrieved 2019-07-26.
  32. 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.
  33. Walter, Martin E. (2010). "Earthquakes and Weatherquakes: Mathematics and Climate Change" (PDF). Notices of the American Mathematical Society. 57 (10): 1278–84.
  34. 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.
  35. Sample, Ian (2005-06-30). "The father of climate change". The Guardian. ISSN 0261-3077. Retrieved 2019-03-18.
  36. Anderson, Thomas R.; Hawkins, Ed; Jones, Philip D. (2016). "CO
    , 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.
  37. Manabe S.; Wetherald R. T. (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.
  38. Forster, Piers (18 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.
  39. Pidcock, Roz (July 6, 2015) "The most influential climate change papers of all time", CarbonBrief. Retrieved 2019-10-19.
  40. 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.
  41. Kerr, Richard A. (2004). "Three Degrees of Consensus". Science. 305 (5686): 932–4. doi:10.1126/science.305.5686.932. PMID 15310873.
  42. Meehl, G. A.; et al., "Ch. 10: Global Climate Projections; Box 10.2: Equilibrium Climate Sensitivity", IPCC Fourth Assessment Report WG1 2007
  43. 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
  44. Forster, Piers M. (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.
  45. 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
  46. IPCC '92 p118 section B3.5
  47. IPCC SAR p 34, technical summary section D.2
  48. Albritton, D. L.; 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. Archived from the original on 12 January 2012.
  49.  This article incorporates public domain material from the US Environmental Protection Agency (US EPA) document: 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)
  50. Skeie, R. B.; Bernsten, 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.
  51. 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.
  52. Forster, Piers M. de F.; Gregory, Jonathan M. (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.
  53. 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.
  54. Otto, Alexander; Otto, Frederieke, E. L.; Boucher, Olivier; Church, John; Hegerl, Gabi; Gillet, Nathan P.; Gregory, Jonathan; Johnson, Gregory C.; Knutti, Reto (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.
  55. 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.
  56. 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 doi:10.1029/2007JD008746.
  57. Knutti, R.; Kraehenmann, S.; Frame, D. J.; Allen, M. R. (2008). "Comment on "Heat capacity, time constant, and sensitivity of Earth's climate system" by SE Schwartz". Journal of Geophysical Research: Atmospheres, 113(D15. 113 (D15): D15103. Bibcode:2008JGRD..11315103K. doi:10.1029/2007JD009473.
  58. Foster, G.; Annan, J. D.; Schmidt, G. A.; Mann, M. E. (2008). "Comment on "Heat capacity, time constant, and sensitivity of Earth's climate system" by SE Schwartz". Journal of Geophysical Research: Atmospheres, 113(D15). 113 (D15): D15102. Bibcode:2008JGRD..11315102F. doi:10.1029/2007JD009373.
  59. 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). 113 (D15): D15104. Bibcode:2008JGRD..11315104S. doi:10.1029/2007JD009586.
  60. Tung, Ka Kit; Zhou, Jiansong; Camp, Charles D. (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.
  61. Camp, Charles D.; Tung, Ka Kit (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.
  62. 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.
  63. Merlis, Timothy M.; Held, Isaac M.; Stenchikov, Georgiy L.; Zeng, Fanrong; Horowitz, Larry W. (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.
  64. McSweeney, Robert (2015-02-04). "What a three-million year fossil record tells us about climate sensitivity". Carbon Brief. Retrieved 2019-03-20.
  65. 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.
  66. Masson-Delmotte et al. 2013
  67. 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.
  68. 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.
  69. Schmittner, Andreas; Urban, Nathan M.; Shakun, Jeremy D.; Mahowald, Natalie M.; Clark, Peter U.; Bartlein, Patrick J.; Mix, Alan C.; Rosell-Melé, Antoni (2011). "Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum". Science. 334 (6061): 1385–8. Bibcode:2011Sci...334.1385S. CiteSeerX doi:10.1126/science.1203513. PMID 22116027.
  70. Hargreaves, J. C.; Annan, J. D.; 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.
  71. von der Heydt, A. S.; Köhler, P.; van de Wal, R. S. W.; Dijkstra, H. A. (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.
  72. Royer, Dana L.; Berner, Robert A.; Park, Jeffrey (2007). "Climate sensitivity constrained by CO
    concentrations over the past 420 million years". Nature. 446 (7135): 530–2. Bibcode:2007Natur.446..530R. doi:10.1038/nature05699. PMID 17392784.
  73. Kiehl, Jeffrey T.; Shields, Christine A. (2013). "Sensitivity of the Palaeocene–Eocene Thermal Maximum climate to cloud properties". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 371 (2001): 20130093. Bibcode:2013RSPTA.37130093K. doi:10.1098/rsta.2013.0093. PMID 24043867.
  74. "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.
  75. "Two French climate models consistently predict a pronounced global warming". CNRS. Retrieved 19 September 2019.
  76. "New climate models predict a warming surge". American Association for the Advancement of Science. 16 April 2019.
  77. Hood, Marlowe (2019-09-17). "Earth to warm more quickly, new climate models show". Retrieved 2019-09-17.
  78. 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.
  79. Forest, Chris E.; Stone, Peter H.; Sokolov, Andrei P.; Allen, Myles R.; Webster, Mort D. (2002). "Quantifying uncertainties in climate system properties with the use of recent observations" (PDF). Science. 295 (5552): 113–7. Bibcode:2002Sci...295..113F. CiteSeerX doi:10.1126/science.1064419. PMID 11778044.
  80. Fasullo, John T.; Trenberth, Kevin E. (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
  81. Caldeira, Ken; Brown, Patrick T. (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. ISSN 1476-4687. PMID 29219964.
  82. Cox, Peter M.; Huntingford, Chris; Williamson, Mark S. (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. ISSN 0028-0836. PMID 29345639.
  83. Brown, Patrick T.; Stolpe, Martin B.; Caldeira, Ken (2018). "Assumptions for emergent constraints". Nature. 563 (7729): E1–E3. Bibcode:2018Natur.563E...1B. doi:10.1038/s41586-018-0638-5. ISSN 0028-0836. PMID 30382203.
  84. Cox, Peter M.; Williamson, Mark S.; Nijsse, Femke J. M. M.; Huntingford, Chris; et al. (2018). "Cox et al. reply". Nature. 563 (7729): E10–E15. Bibcode:2018Natur.563E..10C. doi:10.1038/s41586-018-0641-x. ISSN 0028-0836. PMID 30382204.
  85. Caldwell, Peter M.; Zelinka, Mark D.; Klein, Stephen A. (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.
  86. Hope, Chris (2015). "The $10 trillion value of better information about the transient climate response". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 373 (2054): 20140429. Bibcode:2015RSPTA.37340429H. doi:10.1098/rsta.2014.0429. ISSN 1364-503X. PMID 26438286.
  87. Freeman, Mark C.; Wagner, Gernot; Zeckhauser, Richard J. (2015-11-28). "Climate sensitivity uncertainty: when is good news bad?". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 373 (2055): 20150092. Bibcode:2015RSPTA.37350092F. doi:10.1098/rsta.2015.0092. PMID 26460117.


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