How a Stable Greenhouse Effect on Earth Is Maintained Under Global Warming

Feng, Jing; Paynter, David; Menzel, Raymond

doi:10.1029/2022jd038124

Cited by 12 publications

(16 citation statements)

References 54 publications

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“…Interestingly, these positive non-cloud biases tend to be compensated by cloud biases of an opposite sign, which in some models results in a seemingly good all-sky radiation budget (see Table 1 and Table S1 in Supporting Information S1) as noted above. In addition, it is also interesting to notice that the surface temperature caused radiation biases are mostly noticeable in the Polar region, in agreement with the recognition that the atmosphere is the most transparent to surface emission in these regions (e.g., Feng et al, 2023;H. Huang & Huang, 2022).…”

Section: Tablesupporting

confidence: 76%

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

Huang

2023

Geophysical Research Letters

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Section: Tablesupporting

confidence: 76%

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

Huang

2023

Geophysical Research Letters

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“…When compared to the observed clearsky OLR-T s slope, however, Tcs δB(T s ) is significantly lower and decreases further with increasing T s . The magnitude of Tcs δB(T s ) is highly consistent with the line-by-line calculations conducted in [19] and the clear-sky surface temperature kernel available in publicly accessible datasets [10,20].…”

Section: The Olr-t S Relationship In the Present-day Earthsupporting

confidence: 82%

“…The surface contribution, TδB(T s ), represents the change in OLR resulting from a one-sided partial radiative perturbation in T s . When δT s = 1 K, this term is known as the 'surface Planck feedback' [2,18,19] or the surface temperature kernel [10,20,21] at local grid points. The atmospheric contribution, B(T s + δT s )δ T + δE, accounts for changes in δOLR due to perturbations in atmospheric transmittance and emission.…”

Section: The Olr-t S Relationship In the Present-day Earthmentioning

confidence: 99%

How atmospheric humidity drives the outgoing longwave radiation–surface temperature relationship and inter-model spread

Feng,

Paynter,

Wang

et al. 2023

Environ. Res. Lett.

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The Earth's global radiation budget depends critically on the relationship between outgoing longwave radiation (OLR) and surface temperature ($T_s$). Using the ERA5 reanalysis dataset, we find that although OLR appears to be linearly dependent on $T_s$ over a wide range, there are significant deviations from the linearity in the OLR-$T_s$ relationship for regions warmer than 270 K $T_s$, which covers 89\% of the surface of Earth. While the AMIP runs of CMIP6 models largely capture the overall OLR-$T_s$ relationship, considerable discrepancies are found in clear-sky OLR at given $T_s$ ranges.In this study, we investigate physical mechanisms that control the clear-sky OLR-$T_s$ relationship seen in reanalysis and CMIP6 models by using accurate radiative transfer calculations. Our study identifies three key mechanisms to explain both the linearity and departure from linearity of the clear-sky OLR-$T_s$ relationship. The first is a surface contribution, controlled by the thermal emission of the surface and the infrared opacity of the atmosphere, accounting for 60\% of the observed clear-sky OLR-$T_s$ linear slope. The second is changes in atmospheric emission induced by a foreign pressure effect on water vapor and other greenhouse gases, which accounts for 30\% of the linear slope in a clear-sky condition. The third is changes in atmospheric emission induced by variations in relative humidity, particularly in the mid-troposphere (250 to 750 hPa), which determines the non-linearity in the clear-sky OLR-$T_s$ relationship and adds to the remaining 10\% of the slope. The inter-model spread in mid-tropospheric relative humidity explains a large fraction of the differences in clear-sky OLR across CMIP6 models at given surface temperatures. Furthermore, the three key mechanisms outlined here apply to the OLR-$T_s$ relationship in all-sky conditions: clouds disguise the surface contribution but increase the atmospheric contribution, retaining a similar linear slope to the clear-sky situation while amplifying the non-linear curvature.

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“…More recently, Jeevanjee et al. (2021) explicitly showed that Ingram's result is naturally captured when computing climate feedbacks with relative humidity held fixed, though other studies have emphasized that the outgoing flux is not perfectly constant with surface warming in water vapor bands due to narrowing of water vapor lines and foreign broadening effects (Feng et al., 2023; Raghuraman et al., 2019). Accounting for stratospheric masking slightly revises Ingram's rule: any spectral regions that are not optically thick either to water vapor or in the stratosphere will to first‐order show a “Planckian” increase in flux with warming.…”

Section: Discussionmentioning

confidence: 99%

“…Ingram (2010), highlighting the seminal work of Simpson (1928), clarified that spectral regions where water vapor makes the atmosphere optically thick show nearly zero change in outgoing infrared flux with warming (at constant relative humidity), while all other wavenumbers will show a "Planckian" increase in flux with warming (following 𝐴𝐴 𝐴𝐴𝐴𝐴 𝜈𝜈 ( 𝑇𝑇 𝜈𝜈 𝑏𝑏 ) ∕𝐴𝐴𝑇𝑇 ). More recently, Jeevanjee et al (2021) explicitly showed that Ingram's result is naturally captured when computing climate feedbacks with relative humidity held fixed, though other studies have emphasized that the outgoing flux is not perfectly constant with surface warming in water vapor bands due to narrowing of water vapor lines and foreign broadening effects (Feng et al, 2023;Raghuraman et al, 2019). Accounting for stratospheric masking slightly revises Ingram's rule: any spectral regions that are not optically thick either to water vapor or in the stratosphere will to first-order show a "Planckian" increase in flux with warming.…”

Section: Discussionmentioning

confidence: 99%

How Well do We Understand the Planck Feedback?

Cronin¹

2023

J Adv Model Earth Syst

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A reference or “no‐feedback” radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where trueTe‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the “Planck feedback,” averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming.

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How a Stable Greenhouse Effect on Earth Is Maintained Under Global Warming

Cited by 12 publications

References 54 publications

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

How atmospheric humidity drives the outgoing longwave radiation–surface temperature relationship and inter-model spread

How Well do We Understand the Planck Feedback?

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