↩︎Ĭarbon Brief explainer: How scientists estimate climate sensitivity ↩︎Ī more detailed discussion of the concept of climate sensitivity, including different time scales wikipedia: Measures of Climate Sensitivity ↩︎ “Beyond Equilibrium Climate Sensitivity.”, Nature Geoscience 10.10, str. Skeptical Science: How could global warming accelerate if CO₂ is ‘logarithmic’? ↩︎ More precisely: radiative forcing is directly proportional to the logarithm of concentration - and warming is directly proportional to radiative forcing for more see wikipedia: Radiative Forcing ↩︎ The first calculations were published by Svante Arrhenius in 1886, and his estimates of climate sensitivity have been confirmed and refined by subsequent studies. The link between global warming and atmospheric carbon dioxide concentration is one of the most crucial and longest-studied relationships in the study of climate change. The uncertainty band is shown between S = 2.0 ☌ and S = 3.1 ☌, which corresponds to the uncertainty profile in both TCR and ECS and partially accounts for the effect of climate inertia in stabilizing CO 2 concentrations. Therefore, we take the TCR and ECS values obtained from the simulations as indicative only and fit the value of S to show the dependence (S = 2.37 ☌).
However, in the data shown, global warming is not only due to an increase in CO 2 concentrations but also due to increasing concentrations of other greenhouse gases. This theoretical relationship is used in idealized conditions of simulations – either as a relation for warming after equilibrium, where S corresponds to ECS ( Equilibrium Climate Sensitivity), or for the transient warming when the CO 2 concentration increases by 1% each year, where S corresponds to TCR ( Transient Climate Response). Δ T ( c ) = S ⋅ log 2 ( c 0 + Δ c c 0 ) ≈ S c 0 ln 2 ⋅ Δ c \Delta T(c) = S \cdot \log_2 \left(\frac) Δ T ( c ) = S ⋅ lo g 2 ( c 0 c ), where c c c is the concentration, c 0 c_0 c 0 is the initial concentration, and S S S is the continuous climate sensitivity parameter. 1 2 However, for a small range of concentrations, we can approximate this relationship reasonably well with a linear relationship, as shown by the measured data. The theoretically derived relationship for the dependence of global warming on greenhouse gas concentration is logarithmic. Is it correct to state that every time the CO 2 concentration rises by 10 ppm also leads to a rise in temperature of about 0.1 ☌? The following paragraphs show how this relationship is only an approximation. On the other hand, many other factors also influence global warming: other greenhouse gases, currents in the atmosphere and ocean that distribute heat around the planet, as well as aerosols and cloud formation (the shading effect). The dominant effect of carbon dioxide on global warming is well established, so the graph shows causation, not just random correlation. How is the relationship between CO 2 concentration and global warming inaccurate? On the right-hand side of the graph, we show the expected global warming for higher CO 2 concentrations if emissions continue at the current rate. The points show that the increase in CO 2 concentrations has been accelerating in recent years, corresponding to increasing annual CO 2 emissions. The dots in the graph are color-coded per individual year (grouped by 20 years each).
The text below describes this relationship in more detail. The graph shows that the relationship is approximately proportional, with each 10 ppm increase in CO 2 concentration leading to a temperature increase of about 0.1 ☌. The location of the point corresponds to the CO 2 concentration values (on the horizontal axis) and the temperature anomaly values for that year (on the vertical axis).
The points on the left side of the graph show the individual years from 1884-2020.
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