Using Industrial Age (1750–present) data Climate sensitivity can be estimated using the observed temperature increase, the observed
ocean heat uptake, and the modelled or observed radiative forcing. The data are linked through a simple energy-balance model to calculate climate sensitivity. Radiative forcing is often modelled because
Earth observation satellites measuring it has existed during only part of the Industrial Age (only since the late 1950s). Estimates of climate sensitivity calculated by using these global energy constraints have consistently been lower than those calculated by using other methods, around or lower. Estimates of transient climate response (TCR) that have been calculated from models and observational data can be reconciled if it is taken into account that fewer temperature measurements are taken in the polar regions, which
warm more quickly than the Earth as a whole. If only regions for which measurements are available are used in evaluating the model, the differences in TCR estimates are negligible. A very simple climate model could estimate climate sensitivity from Industrial Age data if the effective
heat capacity of the climate system is known, and the timescale is estimated using
autocorrelation of the measured temperature, an estimate of climate sensitivity can be derived. In practice, however, the simultaneous determination of the time scale and heat capacity is difficult. Attempts have been made to use the 11-year
solar cycle to constrain the transient climate response.
Solar irradiance is about 0.9 W/m2 higher during a
solar maximum than during a
solar minimum, and those effect can be observed in measured average global temperatures from 1959 to 2004. Unfortunately, the solar minima in the period coincided with volcanic eruptions, which
have a cooling effect on the global temperature. Because the eruptions caused a larger and less well-quantified decrease in radiative forcing than the reduced solar irradiance, it is questionable whether useful quantitative conclusions can be derived from the observed temperature variations. Observations of volcanic eruptions have also been used to try to estimate climate sensitivity, but as the aerosols from a single eruption last at most a couple of years in the atmosphere, the climate system can never come close to equilibrium, and there is less cooling than there would be if the aerosols stayed in the atmosphere for longer. Therefore, volcanic eruptions give information only about a
lower bound on transient climate sensitivity.
Using data from Earth's past Historical climate sensitivity can be estimated by using
reconstructions of Earth's past temperatures and levels.
Paleoclimatologists have studied different geological periods, such as the warm
Pliocene (5.3 to 2.6 million years ago) and the colder
Pleistocene (2.6 million to 11,700 years ago), and sought periods that are in some way analogous to or informative about current climate change. Climates further back in Earth's history are more difficult to study because fewer data are available about them. For instance, past concentrations can be
derived from air trapped in ice cores, but , the oldest continuous ice core is less than one million years old. Recent periods, such as the
Last Glacial Maximum (LGM) (about 21,000 years ago) and the
Mid-Holocene (about 6,000 years ago), are often studied, especially when more information about them becomes available. A 2007 estimate of sensitivity made using data from the most recent 420 million years is consistent with sensitivities of current climate models and with other determinations. The
Paleocene–Eocene Thermal Maximum (about 55.5 million years ago), a 20,000-year period during which massive amount of carbon entered the atmosphere and average global temperatures increased by approximately , also provides a good opportunity to study the climate system when it was in a warm state. Studies of the last 800,000 years have concluded that climate sensitivity was greater in
glacial periods than in interglacial periods. As the name suggests, the Last Glacial Maximum was much colder than today, and good data on atmospheric concentrations and radiative forcing from that period are available. The period's
orbital forcing was different from today's but had little direct effect on mean annual temperatures. Estimating climate sensitivity from the Last Glacial Maximum can be done by several different ways. and such feedback differences across climate states must be accounted for when inferring today's climate sensitivity from paleoclimate evidence. In a different approach, a model of intermediate complexity is used to simulate conditions during the period. Several versions of this single model are run, with different values chosen for uncertain parameters, such that each version has a different ECS. Outcomes that best simulate the LGM's observed cooling probably produce the most realistic ECS values.
Using climate models of equilibrium climate sensitivity based on simulations of the doubling of . Each model simulation has different estimates for processes which scientists do not sufficiently understand. Few of the simulations result in less than of warming or significantly more than . suggests that if carbon dioxide concentrations double, the probability of large or very large increases in temperature is greater than the probability of small increases. A lower
model resolution (large model cells and long time steps) takes less computing power but cannot simulate the atmosphere in as much detail. A model cannot simulate processes smaller than the model cells or shorter-term than a single time step. The effects of the smaller-scale and shorter-term processes must therefore be estimated by using other methods. Physical laws contained in the models may also be simplified to speed up calculations. The
biosphere must be included in climate models. The effects of the biosphere are estimated by using data on the average behaviour of the average plant assemblage of an area under the modelled conditions. Climate sensitivity is therefore an
emergent property of these models; it is not prescribed, but it follows from the interaction of all the modelled processes. A model is tested using observations, paleoclimate data, or both to see if it replicates them accurately. If it does not, inaccuracies in the physical model and parametrizations are sought, and the model is modified. For models used to estimate climate sensitivity, specific test metrics that are directly and physically linked to climate sensitivity are sought. Examples of such metrics are the global patterns of warming, the ability of a model to reproduce observed relative humidity in the tropics and subtropics, patterns of heat radiation, and the variability of temperature around long-term historical warming. Ensemble climate models developed at different institutions tend to produce constrained estimates of ECS that are slightly higher than . The models with ECS slightly above simulate the above situations better than models with a lower climate sensitivity. Many projects and groups exist to compare and to analyse the results of multiple models. For instance, the
Coupled Model Intercomparison Project (CMIP) has been running since the 1990s.
Historical estimates Svante Arrhenius in the 19th century was the first person to quantify global warming as a consequence of a doubling of the concentration of . In his first paper on the matter, he estimated that global temperature would rise by around if the quantity of was doubled. In later work, he revised that estimate to . Arrhenius used
Samuel Pierpont Langley's observations of radiation emitted by the full moon to estimate the amount of radiation that was absorbed by
water vapour and by . To account for water vapour feedback, he assumed that
relative humidity would stay the same under global warming. The first calculation of climate sensitivity that used detailed measurements of
absorption spectra, as well as the first calculation to use a
computer for
numerical integration of the radiative transfer through the atmosphere, was performed by
Syukuro Manabe and Richard Wetherald in 1967. Assuming constant humidity, they computed an equilibrium climate sensitivity of 2.3 °C per doubling of , which they rounded to 2 °C, the value most often quoted from their work, in the abstract of the paper. The work has been called "arguably the greatest climate-science paper of all time" and "the most influential study of climate of all time." A committee on anthropogenic
global warming, convened in 1979 by the
United States National Academy of Sciences and chaired by
Jule Charney, estimated equilibrium climate sensitivity to be , plus or minus . The Manabe and Wetherald estimate (),
James E. Hansen's estimate of , and Charney's model were the only models available in 1979. According to Manabe, speaking in 2004, "Charney chose 0.5 °C as a reasonable margin of error, subtracted it from Manabe's number, and added it to Hansen's, giving rise to the range of likely climate sensitivity that has appeared in every greenhouse assessment since ...." In 2008, climatologist
Stefan Rahmstorf said: "At that time [it was published], the [Charney report estimate's] range [of uncertainty] was on very shaky ground. Since then, many vastly improved models have been developed by a number of climate research centers around the world." Despite considerable progress in the understanding of Earth's
climate system, assessments continued to report similar uncertainty ranges for climate sensitivity for some time after the 1979 Charney report. The
First Assessment Report of the
Intergovernmental Panel on Climate Change (IPCC), published in 1990, estimated that equilibrium climate sensitivity to a doubling of lay between , with a "best guess in the light of current knowledge" of . The report used models with simplified representations of
ocean dynamics. The
IPCC supplementary report, 1992, which used full-ocean
circulation models, saw "no compelling reason to warrant changing" the 1990 estimate; and the
IPCC Second Assessment Report stated, "No strong reasons have emerged to change [these estimates]," In the reports, much of the uncertainty around climate sensitivity was attributed to insufficient knowledge of cloud processes. The 2001
IPCC Third Assessment Report also retained this likely range. Authors of the 2007
IPCC Fourth Assessment Report stated that confidence in estimates of equilibrium climate sensitivity had increased substantially since the Third Annual Report. The IPCC authors concluded that ECS is very likely to be greater than and likely to lie in the range , with a most likely value of about . The IPCC stated that fundamental physical reasons and data limitations prevent a climate sensitivity higher than from being ruled out, but the climate sensitivity estimates in the likely range agreed better with observations and the
proxy climate data. The report also stated that ECS is extremely unlikely to be less than (high confidence), and it is very unlikely to be greater than (medium confidence). Those values were estimated by combining the available data with expert judgement. Across 27
global climate models, estimates of a higher climate sensitivity were produced. The values spanned and exceeded in 10 of them. The estimates for equilibrium climate sensitivity changed from 3.2 °C to 3.7 °C and the estimates for the
transient climate response from 1.8 °C, to 2.0 °C. The cause of the increased ECS lies mainly in improved modelling of clouds. Temperature rises are now believed to cause sharper decreases in the number of low clouds, and fewer low clouds means more sunlight is absorbed by the planet and less reflected to space. Remaining deficiencies in the simulation of clouds may have led to overestimates, A fifth of the models began to 'run hot', predicting that global warming would produce significantly higher temperatures than is considered plausible. According to these models, known as
hot models, average global temperatures in the
worst-case scenario would rise by more than 5°C above preindustrial levels by 2100, with a "catastrophic" impact on human society. In comparison, empirical observations combined with physics models indicate that the "very likely" range is between 2.3 and 4.7°C. Models with a very high climate sensitivity are also known to be poor at reproducing known historical climate trends, such as warming over the 20th century or cooling during the
last ice age. == See also ==