Abstract

Atmospheric radiative cooling is a fundamental aspect of Earth’s greenhouse effect, and is intrinsically connected to atmospheric motions. At the same time, basic aspects of longwave radiative cooling, such as its characteristic value of 2 K day−1, its sharp decline (or “kink”) in the upper troposphere, and the large values of CO2 cooling in the stratosphere, are difficult to understand intuitively or estimate with pencil and paper. Here we pursue such understanding by building simple spectral (rather than gray) models for clear-sky radiative cooling. We construct these models by combining the cooling-to-space approximation with simplified greenhouse gas spectroscopy and analytical expressions for optical depth, and we validate these simple models with line-by-line calculations. We find that cooling rates can be expressed as a product of the Planck function, a vertical emissivity gradient, and a characteristic spectral width derived from our simplified spectroscopy. This expression allows for a pencil-and-paper estimate of the 2 K day−1 tropospheric cooling rate, as well as an explanation of enhanced CO2 cooling rates in the stratosphere. We also link the upper-tropospheric kink in radiative cooling to the distribution of H2O absorption coefficients, and from this derive an analytical expression for the kink temperature Tkink ≈ 220 K. A further, ancillary result is that gray models fail to reproduce basic features of atmospheric radiative cooling.

1. Introduction

Atmospheric radiative cooling is a fundamental aspect of Earth’s greenhouse effect, and is intrinsically connected to atmospheric motions. Although radiative equilibrium states with no atmospheric cooling are possible in principle, on Earth such states are unstable; this leads to a turbulent troposphere with deep convection in the tropics and baroclinic eddies in midlatitudes, both of which lead to non-radiative-equilibrium temperature profiles, which exhibit radiative cooling.

This tight coupling between radiative cooling, turbulence, and the hydrological cycle has many consequences. Perhaps foremost, radiative cooling characterizes the large-scale circulation by governing the clear-sky subsidence velocity (e.g., Mapes 2001). In particular, the clear-sky subsidence velocity turns out to be remarkably uniform across the globe, because radiative cooling is; indeed, a latitude–height distribution of the clear-sky, longwave only heating H (Fig. 1a, taken from ECMWF reanalysis) shows that throughout most of the troposphere, H=2±0.5Kday1. Despite the robustness of this value, however, we currently lack the tools to estimate H from first principles:1 gray models are insufficient for this task (as discussed below), leaving only numerical radiative transfer schemes as a means for calculating H.

Fig. 1.

(a) Zonal mean clear-sky longwave heating rates H from ERA-Interim (Dee et al. 2011) for June–August 2001, calculated with RRTM (Morcrette et al. 1998; Mlawer et al. 1997). White contours are for H=1.5and2.5Kday1, and show that this range encompasses most of the troposphere. (b) Vertical heating rate profiles as calculated by globally averaging the ECMWF H (solid black line) and from our BASE atmosphere as calculated by RFM (dashed black line) and a gray model (dotted gray line) tuned to have the same column-integrated cooling as RFM (i.e., same area under the curve). Gray star denotes the τ = 1 level for the gray model. The 1D RFM calculation emulates the tropospheric ECMWF global mean, but the 1D gray model fails dramatically.

Fig. 1.

(a) Zonal mean clear-sky longwave heating rates H from ERA-Interim (Dee et al. 2011) for June–August 2001, calculated with RRTM (Morcrette et al. 1998; Mlawer et al. 1997). White contours are for H=1.5and2.5Kday1, and show that this range encompasses most of the troposphere. (b) Vertical heating rate profiles as calculated by globally averaging the ECMWF H (solid black line) and from our BASE atmosphere as calculated by RFM (dashed black line) and a gray model (dotted gray line) tuned to have the same column-integrated cooling as RFM (i.e., same area under the curve). Gray star denotes the τ = 1 level for the gray model. The 1D RFM calculation emulates the tropospheric ECMWF global mean, but the 1D gray model fails dramatically.

Another consequence of the coupling between radiative cooling and turbulence is that wherever radiative cooling declines in the upper troposphere, latent heating by convection and baroclinic eddies must follow suit. To get a feel for this, we collapse the ECMWF cooling distribution in Fig. 1a onto a global radiative cooling profile by taking a meridional average (Fig. 1b, solid line). This profile has a characteristic value of −2 K day−1 through most of the troposphere, and also exhibits the well-known “kink” around 200 hPa. This kink is significant, as it has been argued to constrain the altitude of clouds associated with both tropical convection [i.e., the fixed anvil temperature (FAT) hypothesis; Hartmann and Larson 2002; Hartmann et al. 2001] as well as extratropical baroclinic eddies (Thompson et al. 2017). In particular, the kink occurs at a characteristic temperature of roughly Tkink ≈ 220 K, and the FAT hypothesis argues that this should constrain convective anvil cloud fraction to also peak near Tkink, a prediction borne out broadly by cloud-resolving and general circulation models, as well as observations (Singh and O’Gorman 2015; Li et al. 2012; Harrop and Hartmann 2012; Eitzen et al. 2009; Kuang and Hartmann 2007; Kubar et al. 2007; Xu et al. 2007; Hartmann and Larson 2002).2 Despite its importance, however, the kink has so far only been attributed qualitatively to a Clausius–Clapeyron (CC)-driven decline in “water vapor emissivity.” In particular, we lack a quantitative theory for why Tkink ≈ 220 K, or for how this temperature scale might arise from CC scaling, which itself is exponential and thus is scale invariant.

Finally, another conspicuous feature of Fig. 1b is that above the kink one sees H declining toward zero, but then rebounding sharply in the stratosphere. It is well known that this strong stratospheric cooling emanates primarily from CO2 molecules rather than H2O (e.g., Zhu et al. 1992; Manabe et al. 1964), but we lack a simple explanation for how CO2 cooling rates can be many times larger in the stratosphere than the troposphere, despite comparable temperature ranges in these two regions.

There are thus three basic questions we can ask about the H profile shown in Fig. 1b:

  1. Why does it take on a characteristic value of −2 K day−1, and why is this value relatively robust across the troposphere?

  2. What causes the upper-tropospheric kink in H, and why is Tkink constrained to be roughly 220 K?

  3. Why is CO2 cooling enhanced in the stratosphere relative to the troposphere?3

The goal of this paper is to shed light on these questions. To do so, we will need to bridge the gap between the complex radiative transfer calculations used in Fig. 1 and simple gray models, which are still used in atmospheric modeling (see section 8) but are too simple in the sense that they do not reproduce the phenomena of interest. To bring this into focus, we first emulate the ECMWF H profile in Fig. 1b with the H profile of an idealized atmospheric column with H2O only and a constant lapse rate and constant relative humidity, as calculated with the comprehensive line-by-line Reference Forward Model (RFM; dashed line of Fig. 1b; further details of this BASE case in section 2a). This profile emulates the tropospheric global average ECMWF profile quite well. Despite the relative simplicity of our single atmospheric column, however, this RFM calculation is still too complex to provide much understanding, as it still convolves intricate greenhouse gas spectroscopy with radiative transfer.

If we instead turn to a gray radiation model tuned to yield the same column-integrated cooling as the RFM profile,4 we find instead that the gray model cannot emulate the RFM and ECMWF profiles: the gray radiative cooling profile has far too much vertical structure, and its upper-tropospheric heating rates err by a factor of 3 or more [Fig. 1b, gray line; see also Fig. S1 of Seeley et al. (2019b)]. There is thus indeed a gap between our simulation and understanding of radiative cooling, and answering the questions posed above will require bridging this gap.

To accomplish this, we will consider spectral radiation, but will simplify greenhouse gas spectroscopy by approximating the absorption spectrum of our greenhouse gases as piecewise exponentials, following Wilson and Gea-Banacloche (2012). We will also simplify the radiative transfer by invoking the cooling-to-space approximation (e.g., Jeevanjee and Fueglistaler 2020; Petty 2006; Green 1967; Rodgers and Walshaw 1966). These simplifications lead to a succession of simple spectral models, the two-dimensional simple spectral model (SSM2D) and one-dimensional simple spectral model (SSM1D), constructed in section 3. These models exhibit the phenomena of interest in questions 1–3 above but are analytically tractable, and are thus used to address those questions in sections 47. We conclude in section 8.

2. Preliminaries

Before building models of radiative cooling we must first lay some groundwork. In this section we detail the line-by-line calculations that we use as a benchmark, then present a qualitative picture of spectrally resolved radiative cooling, and finally review the physics of the cooling-to-space approximation.

a. RFM calculations

All line-by-line calculations in this paper are performed with the RFM (Dudhia 2017). We use High-Resolution Transmission (HITRAN) 2016 spectroscopic data for the most common isotopologue of H2O and CO2 from 10 to 1500 and from 500 to 850 cm−1, respectively. For our BASE case we consider an idealized atmosphere with H2O as the only greenhouse gas, surface temperature Ts = 300 K, a constant lapse rate of Γ = 7 K km−1 up to an isothermal stratosphere at Tstrat = 200 K, a tropospheric relative humidity (RH) of 0.75, and a stratospheric H2O concentration of 23 ppmv (corresponding to an RH of 0.75 at the tropopause, and relatively large due to the 200-K tropopause). We run RFM at a spectral resolution of 0.1 cm−1 and a uniform vertical resolution of 100 m up to model top at 50 km. We output optical depth, fluxes, heating rates, and absorption coefficients, all as a function of wavenumber and height. For simplicity in comparing to our analytical model below, RFM optical depth is calculated along a vertical path (zenith angle of zero), and fluxes and heating rates are computed using a two-stream (rather than RFM’s default four-stream) approximation with a diffusivity factor of D = 1.5 [as specified in Dudhia (2017), following Clough et al. (1992)].5 In BASE we omit the water vapor continuum (Shine et al. 2012), not because it is negligible but simply for analytic tractability. The effects of the water vapor continuum, as parameterized in RFM (using the MT_CKD continuum; Mlawer et al. 2012) are considered in  appendix A.

b. The shape of Hν˜

The object of interest in this paper is the familiar clear-sky longwave radiative heating H. But H is the spectral integral of the less familiar spectrally resolved heating:

 
Hν˜gCppFν˜(Ks1cm),
(1)

where ν˜ denotes wavenumber (following the notation of Petty 2006; Thomas and Stamnes 2002; Houghton 2002) and Fν˜ denotes spectrally resolved net upward LW flux (W m−2 cm−1). Thus, any understanding of H must stem from an understanding of Hν˜. Before pursuing a quantitative model for Hν˜ (as in the next section), we should first develop a qualitative picture of the physics of Hν˜, and understand how it leads to the familiar H profiles of Fig. 1b. We do this now, following the treatment of, for example, Harries et al. (2008) and Clough et al. (1992).

Figure 2c shows Hν˜ from BASE as computed by RFM. There is a strong band of cooling for ν˜<800cm1, and a much weaker band for ν˜>1200cm1. To understand these structures, we consider the optical depth τν˜, measured from p = 0 and given by

 
τν˜(p)0pκ(ν˜,T,p)m2kg1qυdpgkgm2.
(2)

Here qυ is water vapor specific humidity (kg kg−1) and the mass absorption coefficient κ (m2 kg−1) depends not only on wavenumber ν˜ but also on T and p, due to temperature scaling and pressure broadening (Pierrehumbert 2010).6 All other symbols have their usual meaning. Note that since κ is an effective area per unit mass, and since the rest of the integrand in Eq. (2) is just the mass per unit area of absorber above level p (i.e., the pathlength), τν˜ can be interpreted as the effective area of absorbers above level p per unit geometric area (see also Jeevanjee 2018).

Fig. 2.

Comparison between RFM output and the SSM2D, as follows: (a),(d) H2O absorption spectrum κref at (Tref, pref) = (260 K, 500 hPa) from RFM and our linear fit Eq. (10), respectively. (b),(e) Logarithm of diffusion coefficient D times optical depth τν˜ from RFM and Eq. (12), respectively, along with τν˜=1 contours. (c),(f) Spectrally resolved heating Hν˜ from RFM and the SSM2D [Eqs. (5) and (12)]. All RFM plots show averages over 10-cm−1 bins, with log averaging in (a) and linear averaging in (b) and (c). The plots in(a)–(c) show that the spectrally resolved cooling Hν˜ can be understood as emission from τν˜=1 levels, where the height of these levels is determined by κref(ν˜). The plots in (d)–(f) show that the SSM2D captures this physics.

Fig. 2.

Comparison between RFM output and the SSM2D, as follows: (a),(d) H2O absorption spectrum κref at (Tref, pref) = (260 K, 500 hPa) from RFM and our linear fit Eq. (10), respectively. (b),(e) Logarithm of diffusion coefficient D times optical depth τν˜ from RFM and Eq. (12), respectively, along with τν˜=1 contours. (c),(f) Spectrally resolved heating Hν˜ from RFM and the SSM2D [Eqs. (5) and (12)]. All RFM plots show averages over 10-cm−1 bins, with log averaging in (a) and linear averaging in (b) and (c). The plots in(a)–(c) show that the spectrally resolved cooling Hν˜ can be understood as emission from τν˜=1 levels, where the height of these levels is determined by κref(ν˜). The plots in (d)–(f) show that the SSM2D captures this physics.

The spectral distribution of optical depth as output from RFM is shown in Fig. 2b, which also plots the τν˜=1 levels for each ν˜. The two diagonal bands in the Hν˜ plot correspond to two diagonal τν˜=1 bands, which is where we expect emission to space at a given ν˜ to maximize (Jeevanjee and Fueglistaler 2020; Wallace and Hobbs 2006; Petty 2006; see also gray line in Fig. 1b, and section 2c below). The shape of these τν˜=1 bands in the ν˜p plane can themselves be understood in terms of reference absorption coefficients:

 
κref(ν˜)κ(ν˜,Tref,pref),
(3)

where we take

 
(Tref,pref)=(260K,500hPa),
(4)

roughly in the middle of the BASE troposphere. These κref coefficients, also output from RFM and coarse-grained over 10 cm−1 bins, are shown in Fig. 2a. The two τν˜=1 bands in Fig. 2b correspond to the two absorption bands evident in the κref plot: the pure rotation band (ν˜<1000cm1), and the vibration-rotation band (1000<ν˜<1450cm1). By Eq. (2), where κref is relatively large then τν˜=1 occurs at relatively high altitudes, and vice versa, so that plots of κref and τν˜=1 levels necessarily have the same shape. This shape also manifests in the Hν˜ field. These multiple manifestations of H2O spectroscopy can all be seen in the top row of Fig. 2, and understanding these interrelations between κref, τν˜, and Hν˜ is critical for what follows.

Note that the physics described here also explains why the gray model fails so dramatically in emulating the RFM H profile in Fig. 1b: a gray model has a single κref value and can thus only reach τ = 1 at one height, around which H will peak. Real greenhouse gas spectroscopy, on the other hand, yields a distribution of κref values and hence a distribution of τ = 1 heights (Figs. 2a,b), each of which contribute to H and hence yield a much more uniform H profile.

c. The cooling-to-space approximation and the transmissivity gradient

Strictly speaking, the τ = 1 law invoked above applies only to the emission of OLR (or “cooling to space”), not radiative heating rates per se. This is because heating rates include not just cooling to space, but also radiative exchange between atmospheric layers as well as the surface (Green 1967). However, these “exchange terms” are often negligible (Jeevanjee and Fueglistaler 2020; Clough et al. 1992; Rodgers and Walshaw 1966), so radiative cooling may indeed be approximated solely by the cooling-to-space term. This yields the cooling-to-space approximation:

 
pFν˜πB(ν˜,T)Tν˜p.
(5)

Here Tν˜exp(τν˜) is the transmission function and πB(k,T)(Tν˜/p) is the cooling-to-space (CTS) term in pressure coordinates. This is the differential contribution of an atmospheric layer to the OLR at wavenumber ν˜, and is given by Planck emission πB(ν˜,T) times the transmissivity gradient pTν˜. A key feature of the CTS approximation Eq. (5) is that the CTS term can be computed without solving the radiative transfer equations for the net flux Fν˜; this fact greatly simplifies our formalism, and has even been leveraged in the past in numerical radiative transfer schemes (Dufresne et al. 2005; Joseph and Bursztyn 1976; Fels and Schwarzkopf 1975).

How should one think about and evaluate the transmissivity gradient in Eq. (5)? For this purpose, it is convenient to define the optical depth exponent β,

 
βlnτν˜lnp.
(6)

We will see below that β is constant in the vertical for CO2, and roughly constant for H2O.7 We can use β to write the transmissivity gradient as

 
Tkp=βpτν˜eτν˜.
(7)

The factor β/p=plnτν˜ is an inverse “scale pressure” for optical depth, and measures the rate at which τν˜ increases with pressure by one e-folding. The other factor τν˜eτν˜ is a kind of “weighting function” that peaks at τν˜=1, thus giving rise to the τ = 1 law.8 Thus, the peak magnitude of Eq. (7) can be estimated by evaluation at τν˜=1:

 
Tkp|τν˜=1=1eβp.
(8)

We thus have the key result that β/p governs the peak magnitude of the transmissivity gradient. As such, this quantity will appear frequently throughout this paper.9 In particular, the variations in β/p between CO2 and H2O, and between troposphere and stratosphere, will play a significant role in answering question 3 in section 7.

As a caveat, note that even though the CTS approximation and the accompanying τ = 1 law appear to hold quite well for Earth’s atmosphere, these rules are not entirely general. The CTS approximation fails in the textbook case of pure gray radiative equilibrium (which has zero radiative cooling everywhere; Pierrehumbert 2010), and turns out to also depend on the choice of vertical coordinate. These issues, as well as more general criteria for the CTS approximation (and the τ = 1 law) to hold, are the subject of the companion paper Jeevanjee and Fueglistaler (2020).

3. Simple spectral models for H2O cooling

a. SSM2D: A model for spectrally resolved cooling Hν˜

Having laid the necessary groundwork, we now turn to building a simple model for spectrally and vertically resolved heating rates Hν˜, focusing for the moment on H2O. Since the model has two resolved dimensions (ν˜ and p), we will refer to it as our SSM2D.

The first step in building the SSM2D for H2O is to simplify H2O spectroscopy, that is, the functional form of H2O mass absorption coefficients κref(ν˜) (Fig. 2a). We follow Wilson and Gea-Banacloche (2012) and first coarse grain lnκref(ν˜) over spectral intervals of 10 cm−1 (this is already what is plotted in Fig. 2a). We then delineate the H2O rotation band (rot) and vibration–rotation band (v-r) as10

 
rot:ν˜rot150<ν˜<1000cm1v-r:1000<ν˜<1450cm1ν˜v-r.
(9)

We then apply a linear fit to lnκref(ν˜) within each band to obtain a piecewise exponential approximation for κref(ν˜):

 
κH2O(ν˜){κrotexp(ν˜ν˜rotlrot)forν˜in rotκv-rexp(ν˜v-rν˜lv-r)forν˜in v-r.
(10)

The parameters lnκrot and lnκv-r are obtained as the maxima of the corresponding straight line fits, and lrot and lv-r as the corresponding slopes. These parameter values are tabulated in Table 1, which also includes other symbol definitions. The l parameters have units of cm−1, and describe how fast κref exponentially declines with ν˜ away from the band maximum. These parameters, and their analogs for CO2, will turn out to play a crucial role in what follows.

Table 1.

Definition of important symbols used throughout the paper (top section). Spectroscopic parameters used in the SSM2D and SSM1D (bottom section). The ν˜rot and ν˜v-r parameters are chosen by inspection of Fig. 2a, and ν˜Q is chosen to be proximate to the central 667.66-cm−1 Q branch line. The κ and l parameters, on the other hand, are not externally specified but result from fits to RFM output. See text for details.

Definition of important symbols used throughout the paper (top section). Spectroscopic parameters used in the SSM2D and SSM1D (bottom section). The ν˜rot and ν˜v-r parameters are chosen by inspection of Fig. 2a, and ν˜Q is chosen to be proximate to the central 667.66-cm−1 Q branch line. The κ and l parameters, on the other hand, are not externally specified but result from fits to RFM output. See text for details.
Definition of important symbols used throughout the paper (top section). Spectroscopic parameters used in the SSM2D and SSM1D (bottom section). The ν˜rot and ν˜v-r parameters are chosen by inspection of Fig. 2a, and ν˜Q is chosen to be proximate to the central 667.66-cm−1 Q branch line. The κ and l parameters, on the other hand, are not externally specified but result from fits to RFM output. See text for details.

With the simplified absorption spectra Eq. (10) in hand we can now construct simplified expressions for H2O optical depth τν˜. This is done using the analytical expression for vertically resolved water vapor path (WVP; kg m−2) from SI Eq. (2) of Koll and Cronin (2018), except we consider an unsaturated atmosphere, replace the T factor out front with an average tropospheric temperature Tav = (Ts + Tstrat)/2 (results are not sensitive to this approximation), and consider an arbitrary lapse rate Γ. This yields

 
WVP=TavRHpυΓLWVP0exp(LRυT),
(11)

where WVP0 is a constant with units of WVP, pυ=2.5×1011Pa is a reference value for the saturation vapor pressure pυ* where pυ*(T)=pυexp(L/RυT), and all other symbols have their usual meaning.

Combining this expression for water vapor path with an approximate pressure-broadening factor p/pref, as well as a diffusivity factor D = 1.5 to account for the two-stream approximation, we obtain the following approximation to Eq. (2) for H2O:

 
τν˜=DκH2O(ν˜)pprefWVP0exp(LRυT).
(12)

Substituting Eq. (12) into the CTS approximation Eq. (5) then yields an approximation for Hν˜. This Hν˜, along with the fitted κH2O of Eq. (10) and optical depth τν˜ of Eq. (12), constitute the SSM2D. These fields are shown in the bottom row of Fig. 2, directly underneath their RFM counterparts for easy comparison. For all three fields, the SSM2D captures the gross behavior of the RFM calculation.

At the same time, of course, the SSM2D neglects all finescale spectral structure. One drawback of this is that the SSM2D overestimates the peak values of Hν˜ (Figs. 2c,f). This occurs because the finescale structure in the RFM calculation, when combined with a coarse graining into 10-cm−1 bins as we have done here, yields an Hν˜ field that is “smeared out” in the ν˜p plane, such that the cooling around a given (ν˜,p) occurs over a larger (ν˜,p) range in RFM than in the SSM2D, and must thus have a smaller magnitude in RFM.

These errors are analyzed further in  appendix B, where it is shown that they are not fundamental and indeed largely cancel upon spectral or vertical integration. The SSM2D thus produces reasonable values for the spectrally integrated cooling H, as we will see below. Given this, the SSM2D could be used at this point to try and answer questions 1–3 posed above. However, further insight can be gained by making additional approximations and analytically integrating the Hν˜ field over wavenumber space. We turn to this next.

b. SSM1D: A model for spectrally integrated cooling H

We now construct an SSM1D for spectrally integrated cooling H. Our starting point will be the SSM2D for Hν˜, which itself is still too complicated to be analytically integrated over wavenumber space, so further approximations will be required. (The SSM1D is thus not equivalent to simply integrating the SSM2D numerically over wavenumber space, though we will employ this latter quantity later on as well.) To proceed we need two more derived quantities. The first is an expression for β for H2O, which we obtain by substituting our expression (12) for τν˜ into the definition (6) of β:

 
βH2O=1+LRυTΓRdg.
(13)

Typical tropospheric values11 for Γ = 7 K km−1 are βH2O=5.5±1.

The second (and more important) derived quantity is the wavenumber profile ν˜1,j(p), which parameterizes the τν˜=1 contour in the ν˜p plane for band j and hence gives the corresponding locus of cooling in the ν˜p plane [here j denotes either the rot or v-r bands from Eq. (9)]. We obtain analytical expressions for ν˜1,j by substituting κH2O from Eq. (10) into the τν˜ formula in (12), setting τν˜=1, and solving for ν˜ in each band, yielding

 
ν˜1,rot=ν˜rot+lrot[ln(DκrotWVP0)+ln(p/pref)LRυT],ν˜1,v-r=ν˜v-rlv-r[ln(Dκv-rWVP0)+ln(p/pref)LRυT].
(14)

These τ = 1 contours are overlaid over the simple τν˜ distribution in Fig. 2e, and capture the overall shape and x and y intercepts of the noisier τ = 1 contours diagnosed from RFM output (Fig. 2b). Note that the exponentials in κH2O(k) and exp(−L/RυT) cancel out when solving Eq. (12) for ν˜1,j, so that ν˜1,j has a relatively strong 1/T temperature dependence and only a logarithmic dependence on other variables. This will be of significance in section 5.

With these ingredients in place we now obtain an analytical approximation for the spectrally integrated heating in band j, denoted Hj. We begin by integrating Eq. (1) over band j at a given p, assuming the limits of the spectral integral are implicitly given by the appropriate wavenumber range from Eq. (9). We also invoke the CTS approximation Eq. (5) as well as Eq. (7). This yields

 
Hj=gCpβpdν˜πB(ν˜,T)τν˜exp(τν˜).
(15)

To evaluate this integral, recall that the function τν˜exp(τν˜) peaks at τν˜=1 (with corresponding wavenumber ν˜1,j), and that its integral 0dτν˜τν˜exp(τν˜)=1. These properties are shared by the Dirac delta function δ(τν˜1), so we might approximate τν˜exp(τν˜) by δ(τν˜1). This approximation can be applied to Eq. (15) once we convert δ(τν˜1) to a delta function in ν˜ coordinates, using the appropriate chain rule (e.g., Gasiorowicz 2003) as well as Eqs. (12) and (10):

 
δ(τν˜1)=|τν˜ν˜(ν˜1,j)|1δ(ν˜ν˜1,j)=ljδ(ν˜ν˜1,j).

Note the appearance of the lj parameter here, which we comment on further below. Plugging this last equation into Eq. (15) and performing the now trivial spectral integration yields finally our desired result, an analytic expression for bandwise integrated radiative cooling:

 
HjgCpπB(ν˜1,j,T)βplj.
(16)

Note that B(ν˜1,j,T) gives the Planck emission as a function of height only, since ν˜1,j [cf. Eq. (14)] is a function of height, which gives the wavenumber that cools to space at a given height. Equation (16), along with its inputs from Eqs. (13) and (14), constitute the SSM1D.

4. Interpretation and estimation

a. Interpretation

The SSM1D Eq. (16) is a central result of this paper. How should we interpret it? From Eq. (8) we know that the β/p factor in Eq. (16) is closely related to the transmissivity gradient pTν˜ evaluated at τν˜=1. Furthermore, pTν˜ can be interpreted as the “emissivity to space” gradient: for an atmospheric layer of thickness Δp we can interpret ΔppTν˜=Δp(dτν˜/dp)Tν˜ as its “emissivity to space,” since Δp(dτν˜/dp) gives the absolute emissivity of the layer and Tν˜ is the fraction of emitted radiation that escapes to space. Thus pTν˜ is the emissivity-to-space gradient (in pressure coordinates). This suggests that we rewrite Eq. (16) as

 
Hj=gCpπB(ν˜1,j,T)Planckemission(Wm2cm1)(1eβp)emissivitygradient(1/Pa)(lje)spectralwidthΔν˜(cm1).
(17)

The factor lje in Eq. (17) can be interpreted as an “effective emitting width” Δν˜, that is, as the width of the spectral region at any given height that is cooling to space. For the H2O rot band we find Δν˜=lrote165cm1, in rough eyeball agreement with the width of the active cooling regions in Figs. 2c and 2f. That lj simultaneously gives the inverse decay rate of κ(ν˜) [Eq. (10)] as well as the effective emitting width Δν˜ may seem mysterious at first, but is consistent in that a larger decay rate for κ(ν˜) implies a narrower peak of τν˜eτν˜ in ν˜ coordinates in Eq. (15), and hence a smaller Δν˜. This quantitative connection between the spectroscopy of a greenhouse gas and its effective emitting width (and hence Hj) is one of the major insights provided by the SSM1D.

In summary, then, Eq. (17) says that radiative heating at a given height may be interpreted as spectral Planck emission, times a spectral width, times an appropriate measure of emissivity, all evaluated at ν˜1,j where τν˜1,j=1. This yields a flux divergence (which in pressure coordinates has units of W m−2 Pa−1), which we multiply by g/Cp to get a heating rate in K day−1.

b. A back-of-the-envelope estimate of H

A primary motivation for developing the SSM1D is to address question 1 from the introduction and make a back-of-the-envelope estimate of H, which we now do. We use our BASE atmosphere and consider the reference level given in Eq. (4), which corresponds to ν˜1,rot=500cm1 and ν˜1,v-r=1350cm1. Evaluating πB(ν˜1,j,T) there yields

 
πB(ν˜1,rot,260K)0.3Wm2cm,
 
πB(ν˜1,v-r,260K)0.05Wm2cm.

Thus v-r Planck emission is roughly 1/6 of that for rot, which explains why the cooling in the v-r band in Figs. 2c and 2f is much smaller than that in the rot band. We thus neglect Hv-r for this estimate, and also take βH2O5. We then have

 
HgCpπB(ν˜1,rot,T)lrotβH2Op(104Ks1Wm2hPa1)(20Wm2)(5500hPa)=2×105Ks12Kday1.
(18)

Thus the SSM1D indeed allows us to quickly estimate the characteristic value H2Kday1, using only fundamental constants, the atmospheric lapse rate (used in β), a typical value of the Planck function, and the RFM-derived parameter lrot, which characterizes water vapor spectroscopy.

5. Validation and parameter sensitivities

We now seek to evaluate the SSM2D and SSM1D H profiles against the RFM benchmark, as well as gain insight into why the −2 K day−1 value seems relatively robust across the troposphere (Fig. 1a and question 1 above). To this end, Fig. 3 shows Hrot+Hv-r as calculated via RFM, SSM2D, and SSM1D. The SSM profiles (1D and 2D) track each other closely but underestimate cooling near the surface, an error due to the CTS approximation (Jeevanjee and Fueglistaler 2020). The SSM profiles also overestimate cooling in the upper troposphere and underestimate cooling in the stratosphere. Despite these errors and approximations, however, the tropospheric SSM profiles nonetheless lie in the characteristic ~2 ± 0.5 K day−1 range produced by comprehensive radiation calculations. The SSM profiles also both seem to reproduce the upper-tropospheric kink, which was the subject of question 2 above, and to which we return in section 6 below.

Fig. 3.

Heating rate profiles calculated for our BASE case, using RMF, the SSM2D, and the SSM1D. Despite errors, the SSM2D and SSM1D capture the characteristic −2 ± 0.5 K day−1 magnitude of H from water vapor. See text for further discussion. Note that the water vapor continuum is turned off in the RFM BASE run.

Fig. 3.

Heating rate profiles calculated for our BASE case, using RMF, the SSM2D, and the SSM1D. Despite errors, the SSM2D and SSM1D capture the characteristic −2 ± 0.5 K day−1 magnitude of H from water vapor. See text for further discussion. Note that the water vapor continuum is turned off in the RFM BASE run.

To further test the SSM1D, as well as understand the sensitivity of H to humidity and temperature variations, we perturb our atmospheric column. In one perturbation calculation we change RH from 0.75 to 0.3, and in another we change the lapse rate Γ from 7 to 5 K km−1. We run RFM and also evaluate the SSM1D on these two perturbed atmospheres, with the results shown in Figs. 4a and 4b (we cut off the RFM profiles at the same height that the SSM1D profiles go to zero, for clarity). Both the RFM and SSM1D profiles show a marked and perhaps surprising insensitivity to RH. Both models also show a reduction in cooling in the middle and lower troposphere of roughly 30% with the reduction in Γ (in the upper troposphere this signal becomes convolved with that from the differing upper-tropospheric temperatures). These relatively small sensitivities, which are consistent with the relative uniformity of clear-sky H across the globe (Fig. 1), can be understood using Eq. (16) as well as Eq. (14). These show that the change in RH only affects ν˜1,j, decreasing WVP0 by roughly a factor of 2 and hence changing the ν˜1,j by only ljln2 ≈ 35 cm−1, not enough to significantly change the Planck emission B(ν˜1,j,T). Changing Γ yields similarly small changes in ν˜1,j but also has the additional effect of decreasing β; this is not negligible but is still a small change of roughly 30% [cf. Eq. (13)], leading to a similar reduction in H (physically, reducing the vertical temperature gradient lowers water vapor emissivity gradients because of Clausius–Clapeyron, which lowers the heating rate). Taken together, then, neither these RH or Γ variations (which are typical of such variations across the globe) significantly change the characteristic value of H, because they are unable to significantly change Planck emission or vertical emissivity gradients (as encapsulated in β). This provides some insight into question 1 above. At the same time, of course, these perturbations are highly idealized, a point we return to in section 8. Also note that while our Γ perturbation does not change the characteristic value of H, it does perturb the vertical structure and make H less vertically uniform, a point we return to below.

Fig. 4.

(a) Profiles of H as output from RFM applied to BASE (black line), as well as atmospheric columns with RH = 0.3 (blue line) and Γ = 5 K km−1 (red line). (b) As in (a), but computed from the SSM1D in Eq. (16). (c) As in (a), but for atmospheres with parameters as in BASE but with varying Ts. (d) As in (c), but computed from the SSM1D. All RFM profiles are cut off where the corresponding SSM1D profile cuts off for clarity of comparison. The characteristic value of H does not change significantly with these typical RH and Γ perturbations, but there is a systematic variation with Ts. These effects are exhibited by both RFM and the SSM1D.

Fig. 4.

(a) Profiles of H as output from RFM applied to BASE (black line), as well as atmospheric columns with RH = 0.3 (blue line) and Γ = 5 K km−1 (red line). (b) As in (a), but computed from the SSM1D in Eq. (16). (c) As in (a), but for atmospheres with parameters as in BASE but with varying Ts. (d) As in (c), but computed from the SSM1D. All RFM profiles are cut off where the corresponding SSM1D profile cuts off for clarity of comparison. The characteristic value of H does not change significantly with these typical RH and Γ perturbations, but there is a systematic variation with Ts. These effects are exhibited by both RFM and the SSM1D.

The final parameter to vary is Ts, which we vary across Ts = (270, 280, 290, 300) K, leaving RH and Γ unchanged from BASE. The comparison of H for these atmospheres is shown in Figs. 4c and 4d. The RFM and the SSM1D H profiles behave similarly, with both weakening from roughly −2 K day−1 at Ts = 300 K to roughly −1 K day−1 at Ts = 270 K. To the extent that the SSM1D captures this for the right reasons, Eq. (16) tells us that this must be due to a weakening Planck function (which, at fixed p, is being evaluated at both lower ν˜ and lower T as Ts decreases). Both the RFM and SSM1D profiles also decrease in vertical extent with decreasing Ts, due to shoaling of the troposphere. These behaviors are also evident in the extratropical ECMWF H distribution in Fig. 1a, particularly in the Southern (winter) Hemisphere.

The foregoing explains to some degree the magnitude of latitudinal variations in H, but does not explain the vertical uniformity of H profiles evident in Fig. 1. Some insight into this can also be gained from the SSM1D in Eq. (16), which tells us that this uniformity is largely coincidental, and arises from a cancellation between increasing transmissivity gradients (as encapsulated in β/p) and a declining Planck function πB(ν˜1,j,T) with height. This suggests there is no fundamental constraint that H be uniform in the vertical; indeed, the SSM1D profile in Fig. 3 is noticeably less uniform than the RFM profile. Furthermore, even simply changing the lapse rate Γ is sufficient to make H notably less uniform, in both RFM and the SSM1D (Fig. 4). At the same time, of course, all of these profiles are much more vertically uniform than that from a gray model, as discussed in section 2b.

6. The upper-tropospheric kink

We now turn to question 2 above, about the origin of the upper-tropospheric kink in H and why Tkink ≈ 220 K. By Eq. (5), the two places to look for the origin of the kink are the Planck function B(ν˜,T) and the transmissivity gradient pTν˜. Postponing discussion of B(ν˜,T) for the moment, we begin by plotting pTν˜ for the rot band for BASE in Fig. 5a. From this plot it is not obvious why H exhibits a kink at roughly 250 hPa, with a sharp decrease above; to the contrary, the characteristic value of pTν˜ actually increases with height, in accordance with Eq. (8).

Fig. 5.

(a) Transmissivity gradient pTν˜ for BASE as computed by RFM for the rot band only, averaged over 10-cm−1 bins. (b) Spectrally integrated transmissivity gradient pTν˜¯ as defined in Eq. (19), computed via RFM and the SSM2D for the rot band and BASE atmosphere. Also shown is the SSM1D approximation to this quantity, given by lrotβ/p. The RFM and SSM2D profiles exhibit upper-tropospheric kinks coincident with their kinks in H (Fig. 3). (c) Effective emitting width Δν˜ as computed via Eq. (20). Both RFM and the SSM2D exhibit a kink in Δν˜ corresponding to their respective kinks in (b). (d) Density distribution of κref values for the rot band from RFM. This shows a kink at κrefkink40m2kg1, above which the occurrence of more strongly absorbing wavenumbers declines. The density distribution is computed with respect to lnκref, in which the bins are equally spaced. (e) Effective emitting width Δν˜ plotted as a function of D(p/pref)WVP, where the vertical axis is exactly inverted relative to that in (d). When plotted this way, the effective emitting width profile mimics the κref density, as expected. The gray dotted line in all panels is the tropopause, which lies roughly 100 hPa, ~3.5 km, above the RFM kink.

Fig. 5.

(a) Transmissivity gradient pTν˜ for BASE as computed by RFM for the rot band only, averaged over 10-cm−1 bins. (b) Spectrally integrated transmissivity gradient pTν˜¯ as defined in Eq. (19), computed via RFM and the SSM2D for the rot band and BASE atmosphere. Also shown is the SSM1D approximation to this quantity, given by lrotβ/p. The RFM and SSM2D profiles exhibit upper-tropospheric kinks coincident with their kinks in H (Fig. 3). (c) Effective emitting width Δν˜ as computed via Eq. (20). Both RFM and the SSM2D exhibit a kink in Δν˜ corresponding to their respective kinks in (b). (d) Density distribution of κref values for the rot band from RFM. This shows a kink at κrefkink40m2kg1, above which the occurrence of more strongly absorbing wavenumbers declines. The density distribution is computed with respect to lnκref, in which the bins are equally spaced. (e) Effective emitting width Δν˜ plotted as a function of D(p/pref)WVP, where the vertical axis is exactly inverted relative to that in (d). When plotted this way, the effective emitting width profile mimics the κref density, as expected. The gray dotted line in all panels is the tropopause, which lies roughly 100 hPa, ~3.5 km, above the RFM kink.

Of course, H involves a spectral integral, so we consider instead the integrated transmissivity gradient:

 
pTν˜¯10cm11000cm1dν˜pTν˜.
(19)

Figure 5b confirms that this quantity does indeed exhibit a kink at the right height, and that this feature is reproduced by the SSM2D. Since transmissivity gradients can also be interpreted as emissivity-to-space gradients (section 4), this upper-tropospheric kink in pTν˜¯ can also be thought of as a precise quantification of the “declining water vapor emissivity” referred to in the FAT literature (Hartmann and Larson 2002; Hartmann et al. 2001). Also, the kink in pTν˜¯ tells us that additional variations in B(ν˜,T) are not required to produce the kink in Hrot: if B(ν˜,T) were a constant function, Hrot would be proportional to pTν˜¯ [by Eq. (5)] and hence would still exhibit a kink, without variations in B(ν˜,T).

We may thus conclude that the kink in H stems from the kink in pTν˜¯. But what, then, causes the kink in pTν˜¯? Since the integrand in Eq. (19) is not decreasing with height, a plausible alternative hypothesis is that the kink is due to changes in the spectral width over which pTν˜ is significant, that is, changes in the effective emitting width Δν˜. We quantified this earlier in section 4 as a constant Δν˜=lje for the SSM1D, but now generalize it for application to RFM and the SSM2D as simply the range of wavenumbers for which ee/2<τν˜<ee/2:

 
Δν˜10cm11000cm1dν˜H(τν˜ee/2)H(ee/2τν˜).
(20)

Here H is the Heaviside step function, and the range of τν˜ values is chosen such that Δν˜ indeed yields the SSM1D value of lrote ≈ 165 cm−1 almost everywhere when calculated for the SSM2D using Eq. (12).12 Profiles of Δν˜ for RFM, SSM2D, and SSM1D are shown in Fig. 5c. Both RFM and SSM2D exhibit a kink in their Δν˜ profiles at the same height at which their respective H kinks occur. This is consistent with the hypothesis that the kink is caused by a decline in integrated transmissivity gradient pTν˜¯, which itself is caused by a decline in effective emitting width Δν˜.13

What about the kink in Fig. 3 in the SSM1D H profile? In the SSM1D [Eq. (17)], pTν˜¯=βH2Olrot/p and Δν˜=lrote, both of which match the SSM2D in the lower troposphere but neither of which features a kink (Figs. 5b and 5c, solid red line). According to Eq. (16), then, the kink in the SSM1D H profile in Fig. 3 must then stem from an upper-tropospheric decline in πB(ν˜1,rot,T), which as we argued above is not necessary for a kink in the SSM2D or in RFM. This suggests that the SSM1D, by not allowing Δν˜ to vary, is too simple to accurately model the kink in H. Note also that gray models, for which Δν˜ is not even defined, are also too simple to accurately model the kink in H.

Although the Δν˜ diagnostic is intuitive, it is derived from the τν˜ field and thus convolves spectroscopy with thermodynamic profiles. Is it possible to think of the Hν˜ kink in purely spectroscopic terms? The distribution of κref values from RFM for the rot band is shown in Fig. 5d, and indeed one finds a kink there at

 
κrefkink40m2kg1
(21)

[note that the value of κrefkink depends on the pref at which absorption coefficients are evaluated; cf. Eq. (3)]. Above this preferred value, there is a sharp decline in the occurrence of more strongly absorbing wavenumbers. This is also roughly consistent with Figs. 2a and 2d, in which the (bin averaged and parameterized, respectively) spectral κref distributions peak at roughly 100 m2 kg−1.

Is this kink in the κref distribution at κrefkink causing the kink in Δν˜ (and hence pTν˜¯ and Hν˜)? If so, then by setting τν˜=1 in Eq. (12) we would expect the kink in Δν˜ to occur where

 
DpprefWVP1κrefkink.
(22)

Substituting p=pref(T/Tref)g/RdΓ into Eq. (22) and employing the Lambert W function [which satisfies W(xex) = x] allows us to solve Eq. (22) for the kink temperature Tkink:

 
Tkink=T*W[T*Tref(DWVP0κrefkink)RdΓ/g]=214K,
(23)

where T* ≡ (LRdΓ)/(gRυ). This agrees reasonably well with the values for Tkink given in the literature, solidifying the connection between the kink in the κref distribution and that in Δν˜. Moreover, Eq. (23) helps answer question 2 from the introduction by providing a quantitative, theoretical expression for Tkink in terms of only fundamental constants, basic atmospheric parameters (such as RH and Γ), and the preferred value κrefkink of H2O absorption coefficient.

To further check the picture that led to Eq. (23), we plot Δν˜ versus D(p/pref)WVP in Fig. 5e. This profile indeed exhibits a kink near D(p/pref)WVP=1/κrefkink=0.025kgm2, as expected from Eq. (22). Furthermore, the whole shape of the Δν˜ profile when plotted this way mimics the shape of the κref distribution in Fig. 5d, as it should: κref values that occur more frequently should give rise to corresponding peaks in Δν˜, and this correspondence is made plain by plotting Δν˜ using D(p/pref)WVP as the vertical coordinate.

To summarize, the kink in the heating rate H is due to a kink in pTν˜¯, which stems from a kink in Δν˜. The latter arises from a kink in the κref distribution. Thus, the H kink ultimately has a spectroscopic origin. By Eq. (22), however, the kink in the κref distribution implies that the kink in H must occur at a preferred value of D(p/pref)WVP, yielding the characteristic kink temperature Eq. (23). This kink temperature is strongly influenced by the CC scaling of WVP [Eq. (11)], roughly consistent with the broader FAT hypothesis that radiative cooling declines at a fixed temperature due to the CC scaling of water vapor pressure. However, Eq. (22) shows that other physics is also relevant, in particular the preferred value κrefkink of H2O absorption coefficient (which is required for CC scaling to determine a preferred temperature), along with a minor modification from pressure broadening.

7. CO2

Having addressed questions 1 and 2 from the introduction, we now turn to question 3 concerning the stratospheric enhancement of CO2 cooling rates. Addressing this question requires applying our formalism to CO2, which we do now.

For CO2 cooling we run RFM just as described in section 2a, except our ν˜ range is now 500–850 cm−1, we use a preindustrial CO2 concentration of 280 ppmv, and RFM’s χ factor (from Cousin et al. 1985) is used to suppress far-wing absorption of CO2. In analogy to Eq. (9), we begin by defining bands for CO2:

 
CO2Pband:500<ν˜<ν˜Q667.5cm1CO2Rband:ν˜Q<ν˜<850cm1,
(24)

(here ν˜Q denotes the spectral location of the main CO2 Q branch, which lies between its associated P and R branches; Coakley and Yang 2014).14 This band structure can be seen in κref(ν˜) from RFM (Fig. 6a). We then again coarse grain lnκref(ν˜) over spectral intervals of 10 cm−1 and apply a linear fit to lnκref(ν˜) within each band. These two linear fits give very similar slopes and maxima, so we combine the two fits into a single expression for simplified reference absorption coefficients:

 
κCO2(ν˜)κQexp(|ν˜ν˜Q|lQ)forν˜inPorR,
(25)

where lnκQ and lQ are averages of the maxima and slopes from the P and R bands. These parameter values are also tabulated in Table 1. Note that CO2’s lQ parameter is much smaller than that for H2O, a point to which we return below.

Fig. 6.

As in Fig. 2, but for the 500–850-cm−1 CO2 band. (c),(f) The SSM2D again emulates the behavior of RFM, and in particular reproduces the stratospheric enhancement of Hν˜.

Fig. 6.

As in Fig. 2, but for the 500–850-cm−1 CO2 band. (c),(f) The SSM2D again emulates the behavior of RFM, and in particular reproduces the stratospheric enhancement of Hν˜.

Next, we obtain expressions for CO2 optical depth. Evaluating Eq. (2) with pressure broadening (but no temperature scaling) and constant CO2 specific concentration q (rather than variable qυ) yields

 
τν˜=κCO2(ν˜)qp22gpref.
(26)

Note that Eqs. (26) and (6) imply

 
βCO2=2,

a value 2–3 times smaller than that for H2O, because CO2 is well mixed while H2O exhibits Clausius–Clapeyron scaling [cf. Eq. (13)].

We can now substitute Eq. (26) into Eq. (5) to obtain a spectrally simplified Hν˜. This, along with the expressions (25) and (26), constitute the SSM2D for CO2. These fields, along with their RFM counterparts, are shown in Fig. 6. As with H2O, the SSM2D captures the broad characteristics of the Hν˜ produced by RFM, though again without finescale structure.

In particular, the SSM2D reproduces the stratospheric enhancement of Hν˜ exhibited by RFM (Figs. 6c,f). From Eqs. (5) and (7) with βCO2=2 we see that this high-altitude enhancement of Hν˜ stems from the high-altitude enhancement of β/p. But, what is the meaning of this β/p factor? We know that β/p=(dτν˜/dp)|τν˜=1, but why is this optical depth gradient enhanced at low p? To understand this, note that by Eq. (2) we have (dτν˜/dp)=κ(ν˜,T,p)q/g. If we set τν˜=1 in Eq. (26), and solve for the (absolute, not reference) absorption coefficient κ1 emitting at a given p, we find

 
κ1=2gqp.
(27)

This is again an inverse relationship between pathlength and absorption coefficient [similar to Eq. (22) for H2O], and says that the “effective absorption coefficient” κ1 scales as 1/p. This 1/p scaling is confirmed in Fig. 7, which plots Eq. (27) as well as κ1 diagnosed directly from RFM by averaging the absolute absorption coefficients at each height over the same wavenumbers contributing to Δν˜ in Eq. (20). Physically, Hν˜|τν˜=1 is enhanced at low p because wavenumbers with such strong κ can cool a unit mass of air at a much higher rate than the more weakly absorbing wavenumbers that reach τν˜=1 at higher p. This explanation differs from some previously proposed, such as those based on decreased pressure broadening (Petty 2006, p. 317) or H2O–CO2 overlap (Zhu et al. 1992).

Fig. 7.

Profiles of the effective absorption coefficient κ1 for CO2, as predicted by Eq. (27) (red) as well as diagnosed from RFM (black) by linearly averaging κ(ν˜,p) over those ν˜ that also contribute to Δν˜ in Eq. (20). The good agreement confirms the 1/p scaling for κ1, which underlies the stratospheric enhancement of Hν˜ (Fig. 6).

Fig. 7.

Profiles of the effective absorption coefficient κ1 for CO2, as predicted by Eq. (27) (red) as well as diagnosed from RFM (black) by linearly averaging κ(ν˜,p) over those ν˜ that also contribute to Δν˜ in Eq. (20). The good agreement confirms the 1/p scaling for κ1, which underlies the stratospheric enhancement of Hν˜ (Fig. 6).

We are now also in a position to explain why tropospheric CO2 cooling rates are negligible compared to H2O. We noted above that βCO2 is 2–3 times smaller than βH2O, which by Eqs. (5)(7) tells us that tropospheric Hν˜ will also be 2–3 times smaller for CO2 (cf. Figs. 2 and 6). In other words, because CO2 optical depth does not increase with pressure as fast as H2O, its cooling at any given wavenumber is more spread out in the vertical and is thus smaller.15 This decrease in Hν˜ is compounded by CO2’s smaller effective emitting width Δν˜, which is roughly lQe ≈ 35 cm−1, approximately 1/5 that of H2O (cf. Figs. 2 and 6). Together, these two effects imply that spectrally integrated radiative cooling H from CO2 will only be a fraction of that from H2O.16 Due to its relative insignificance in the troposphere, then, we resist the temptation to construct an SSM1D for CO2, though it is straightforward from here.

8. Summary and discussion

Our main results can be summarized as follows:

  • The characteristic H value of −2 ± 0.5 K day−1 can be obtained as the product of the Planck function, a vertical emissivity gradient, and an effective emitting width, all of which can be estimated via the SSM1D [Eq. (18)]. This characteristic value is relatively insensitive to typical RH and Γ variations, but is sensitive to Ts (Fig. 4).

  • The upper-tropospheric kink in H ultimately stems from a kink in the distribution of absorption coefficients κref in the H2O rot band (Fig. 5). The value of κref at this kink determines Tkink via Eq. (23).

  • The stratospheric enhancement of CO2 cooling is due to the 1/p factor in the transmissivity gradient in Eq. (7). This 1/p factor itself can be traced to the strength of the effective absorption coefficients that emit from a given height [Eq. (27) and Fig. 7].

This work could be generalized and extended in various ways. One extension would be to use the SSM1D, which by providing τν˜=1 contours identifies an emission height for each wavenumber ν˜, to generate an estimate for spectrally resolved OLR. A first attempt at this for H2O only, without the continuum, is given in  appendix C and Fig. C1. Further work could incorporate idealized models of H2O–CO2 overlap, as well as the H2O continuum, for estimation of both H and OLR. One could also formulate a simplified spectroscopy akin to Eqs. (10) and (25) for other important greenhouse gases such as methane and ozone, and thus incorporate those gases into the SSMs.

Another direction for future work would be to investigate the radiative cooling profiles of less idealized atmospheres, and in particular atmospheres with nonuniform RH profiles, as nonuniform RH can significantly affect radiative cooling. For example, Seeley et al. (2019b) found that the kink disappeared in cloud-resolving radiative–convective equilibrium simulations at Ts of 270 K and colder, but further investigation showed that the kink reappeared when the simulated RH profiles were replaced with a uniform RH profile (not shown). Observations of nonuniform RH profiles are discussed in Stevens et al. (2017), who focused on strong vertical RH gradients observed in the subtropics and emphasized the implications of these RH gradients for radiative cooling profiles and the associated circulations. Similar effects have also been studied in the context of radiative instabilities and self-aggregation of convection (Beucler et al. 2018; Beucler and Cronin 2016; Emanuel et al. 2014).

While the primary goal of this work was to shed light on questions 1–3 posed in the introduction, an ancillary benefit was the development of the SSMs, which might be thought of as filling in the intermediate rungs of a “radiation hierarchy,” with gray models on the bottom rung and line-by-line codes like RFM at the top. Moving between these rungs to generate and test hypotheses exemplifies the “hierarchical” approach to climate science (Maher et al. 2019; Jeevanjee et al. 2017; Polvani et al. 2017; Held 2005; Hoskins 1983; Schneider and Dickinson 1974).

The intermediate complexity SSMs could also be used to augment or replace gray models where they are still used for research purposes, as the distortions of the gray approximation evident in Fig. 1b suggest that inferences drawn from gray radiation models, as well as fluid-dynamical models coupled to them, may not be reliable (e.g., Tan et al. 2019). As another example of inaccuracies of the gray approximation, the gray model in Fig. 1b, which was tuned to exhibit the same 170 W m−2 column-integrated cooling as RFM, also yields an OLR of 170 W m−2, which is a serious underestimate17 of RFM’s OLR value of 325 W m−2. Despite such errors, however, gray models are still in use in both astronomy (e.g., Parmentier and Guillot 2014; Rauscher and Menou 2012; Robinson and Catling 2012; Heng et al. 2011) as well as terrestrial atmospheric sciences, both for understanding (Hu and Vallis 2019; Goessling and Bathiany 2016; Vallis et al. 2015) and also as radiation schemes for idealized aquaplanet models (e.g., Frierson et al. 2006; see Maher et al. 2019) and Jeevanjee et al. (2017) for extensive further references]. The SSMs could prove useful as alternative, cheap, clear-sky radiation schemes that still only depend on a few parameters (cf. Table 1) but are nonetheless spectral and avoid the distortions of the gray approximation.

Acknowledgments

We thank ECMWF for providing the ERA-Interim data. This research was supported by NSF Grants AGS-1417659 and AGS-1660538, and NJ was supported by a Hess Fellowship from the Princeton Geosciences department. NJ thanks David Romps for guidance in early stages of this work and for suggesting the delta function approach to integrating Eq. (15). NJ also thanks Jacob Seeley and Robert Pincus for discussions, feedback, and encouragement, as well as Dennis Hartmann and two anonymous reviewers for very helpful and detailed reviews. RFM output and R scripts used in producing this manuscript are available (at https://github.com/jeevanje/17rad_cooling2.git).

APPENDIX A

Sensitivity to H2O Continuum and CO2 Overlap

The RFM calculations in the main text neglected the water vapor continuum as well as overlap effects between H2O and CO2, both of which are known to affect radiative cooling and OLR. While we do not pursue simple models of these effects, this appendix investigates the errors induced by neglecting them, and discusses why our 1D RFM calculation that neglects these effects nonetheless resembles the ECMWF profile in Fig. 1b, which includes them.

We first consider the H2O continuum. Figure A1a shows Hν˜ from an RFM calculation identical to BASE, but with the continuum turned on (RFM uses the MT_CKD continuum; Mlawer et al. 2012). The spectrally integrated H profile from this case is shown in Fig. A1c. The continuum increases H throughout the troposphere, especially at lower levels. When we then add on the effects of CO2 overlap (Figs. A1b,c), however, we find that much of this increase is cancelled due to the presence of CO2. From Fig. A1c we see that the only real contrast between our base case and the more realistic case with both continuum and CO2 contributions is for pressures greater than 850 hPa or so, corresponding to temperatures of 290 K or above (thinking here in temperature coordinates). Such temperatures may not necessarily make a strong contribution to the globally averaged H profile shown in Fig. 1, which is averaged on pressure levels and thus conflates different temperatures.

Fig. A1.

Spectrally resolved radiative cooling Hν˜ as output from RFM for the BASE atmosphere with (a) the H2O continuum included and (b) H2O continuum plus CO2 absorption. (c) The spectrally integrated cooling rates H for these cases plus BASE. Continuum emission enhances H, particularly in the lower troposphere, but this effect is largely canceled out by CO2 absorption.

Fig. A1.

Spectrally resolved radiative cooling Hν˜ as output from RFM for the BASE atmosphere with (a) the H2O continuum included and (b) H2O continuum plus CO2 absorption. (c) The spectrally integrated cooling rates H for these cases plus BASE. Continuum emission enhances H, particularly in the lower troposphere, but this effect is largely canceled out by CO2 absorption.

APPENDIX B

Further Validation of the SSM2D

Toward the end of section 3a we discussed a discrepancy between coarse-grained RFM Hν˜ and that produced by the SSM2D, namely that the RFM Hν˜ field appears “smeared out” relative to the SSM2D Hν˜ field (Figs. 2c,f). The purpose of this appendix is to demonstrate that this error does not occur at individual wavenumbers, but occurs as a result of finescale spectral structure and the resulting coarse graining we impose for clarity.

Figure B1 shows profiles of Hν˜ from both RFM and SSM2D at ν˜ = 508.6 and ν˜ = 505 cm−1, respectively, where both models have κref = 0.125 m2 kg−1. Apart from a small offset due to our neglect of temperature scaling, as well as a lack of cooling near the surface due to the CTS approximation (Jeevanjee and Fueglistaler 2020), the SSM2D captures the shape, amplitude, and position of the RFM Hν˜ profile quite well. In Fig. 2c, however, what is plotted are Hν˜ profiles averaged across 10-cm−1 bins. Due to the marked finescale structure in κref(ν˜), within each bin we are thus averaging Hν˜ profiles with a wide variety of κref values and thus a wide variety of heights at which they peak. The resulting Hν˜ profile for the relevant RFM bin is shown in the dashed curve of Fig. B1, and is indeed smeared out relative to the RFM and SSM2D Hν˜ profiles at a single wavenumber. This averaging should not degrade the column integrals of Hν˜, however, and calculating those indeed yields agreement, to within 5% (not shown).

Fig. B1.

Spectrally resolved radiative cooling profiles Hν˜(p) for BASE from both RFM (black line) and SSM2D (red line) at ν˜=508.6 and ν˜=505cm1, respectively, where both models have κref = 0.125 m2 kg−1. Despite small errors, the SSM2D profile captures the shape, amplitude, and position of the RFM profile, validating the SSM2D. The dashed line shows the average of such RFM profiles across a 10-cm−1-wide bin centered on 508.6 cm−1. This coarse graining yields a “smeared out” profile that is broader and has smaller amplitude, consistent with Fig. 2c. The vertical integrals of all three profiles in this plot agree to within 5%.

Fig. B1.

Spectrally resolved radiative cooling profiles Hν˜(p) for BASE from both RFM (black line) and SSM2D (red line) at ν˜=508.6 and ν˜=505cm1, respectively, where both models have κref = 0.125 m2 kg−1. Despite small errors, the SSM2D profile captures the shape, amplitude, and position of the RFM profile, validating the SSM2D. The dashed line shows the average of such RFM profiles across a 10-cm−1-wide bin centered on 508.6 cm−1. This coarse graining yields a “smeared out” profile that is broader and has smaller amplitude, consistent with Fig. 2c. The vertical integrals of all three profiles in this plot agree to within 5%.

What about Hν˜ as a function of ν˜, rather than pressure? These are plotted for both RFM and SSM2D in Fig. B2. Here, since we are plotting Hν˜ as a function of wavenumber, we apply our coarse graining for clarity, and thus the RFM profiles are again smeared out relative to the SSM profile, analogous to what is seen in Fig. B1. Again, however, the integrals (now spectral rather than column) largely agree, though at times with larger errors, up to 25%. These errors seem to be related to the fact that our narrowly peaked Hν˜(ν˜) profiles sample the Planck function B(ν˜,T) over a narrow spectral range, whereas the RFM Hν˜ profiles sample B(ν˜,T) over a significantly larger range, over which nonlinearity of the Planck function becomes significant.

Fig. B2.

Spectrally resolved cooling distribution Hν˜(ν˜) for BASE at p = 250, 500, and 750 hPa, from RFM (black solid lines) and SSM2D (red dashed line), where the RFM output is averaged over bins of width 10 cm−1. As for the vertical profiles in Fig. B1, the coarse graining of RFM output yields a cooling distribution that is broader and of smaller amplitude than the corresponding SSM2D distribution. Again, however, the spectral integrals are comparable, though with errors somewhat larger than for the vertical integrals in Fig. B1. See text for discussion.

Fig. B2.

Spectrally resolved cooling distribution Hν˜(ν˜) for BASE at p = 250, 500, and 750 hPa, from RFM (black solid lines) and SSM2D (red dashed line), where the RFM output is averaged over bins of width 10 cm−1. As for the vertical profiles in Fig. B1, the coarse graining of RFM output yields a cooling distribution that is broader and of smaller amplitude than the corresponding SSM2D distribution. Again, however, the spectral integrals are comparable, though with errors somewhat larger than for the vertical integrals in Fig. B1. See text for discussion.

APPENDIX C

SSM OLR

The formalism developed here can also be applied to estimate the spectrally resolved outgoing longwave radiation OLRν˜Fν˜(p=0), where we estimate this as simply the Planck function evaluated at an effective emission temperature T1(ν˜). We estimate this by setting τν˜=1 in Eq. (12) and solving for T; this is essentially the same calculation we performed to obtain Eq. (23), except in this case we leave κref(ν˜) unspecified, yielding

 
T1(ν˜)=T*W{T*Tref[DWVP0κref(ν˜)]RdΓ/g}.
(C1)

For some ν˜, however, T1(ν˜) will be undefined because τν˜<1 even at the surface; this is the water vapor “window” region ν˜1,rot(Ts)<ν˜<ν˜1,vr(Ts), for which we set OLRν˜ equal to surface Planck emission. Mathematically, our estimate for OLRν˜ is then

 
OLRν˜={πB[ν˜,T1(ν˜)]whereT1(ν˜)is definedπB(ν˜,Ts)whereν˜1,rot(Ts)<ν˜<ν˜1,v-r(Ts)(window region).
(C2)

We refer to Eqs. (C1) and (C2) as the simple spectral model for OLR (SSM OLR).

Figure C1 shows the SSM OLR for our BASE atmosphere, along with OLRν˜ as output directly from RFM. The SSM OLR, while crude, quantitatively captures the gross spectral shape of RFM’s OLRν˜ quite well. Furthermore, it gives us some insight into this shape, as follows. Both the RFM and SSM OLR curves peak at the beginning of the window region, ν˜1,rot(Ts)750cm1, and the SSM OLR curve in particular has a cusp. This is because beyond ν˜1,rot(Ts), the emission temperature is no longer increasing with ν˜ but rather becomes constant at Ts, allowing the explicit ν˜ dependence of B(ν˜,T) to take over and cause an immediate and sharp decline in OLRν˜. Thus the peak in OLRν˜ is caused by a temperature-driven increase for ν˜<ν˜1,rot(Ts) and a ν˜-driven decrease for ν˜>ν˜1,rot(Ts). Adding the H2O continuum to our RFM calculation does not change this picture (Fig. C1, dotted line). This peak is typically obscured in more realistic calculations by the strong 667 cm−1 CO2 absorption feature, but understanding the H2O-only case seems like a prerequisite for understanding these more realistic cases, which could be pursued along similar lines.

Fig. C1

. Spectrally resolved outgoing longwave radiation OLRν˜ from H2O only, as computed from RFM (black line) and SSM OLR (red line). The SSM OLR captures the shape of RFM’s OLRν˜, and also shows that the peak in OLRν˜ is due to the onset of the water vapor window. This conclusion is unchanged by comparing to RFM’s OLRν˜ computed with the H2O continuum (dotted line).

Fig. C1

. Spectrally resolved outgoing longwave radiation OLRν˜ from H2O only, as computed from RFM (black line) and SSM OLR (red line). The SSM OLR captures the shape of RFM’s OLRν˜, and also shows that the peak in OLRν˜ is due to the onset of the water vapor window. This conclusion is unchanged by comparing to RFM’s OLRν˜ computed with the H2O continuum (dotted line).

REFERENCES

REFERENCES
Beucler
,
T.
, and
T. W.
Cronin
,
2016
:
Moisture-radiative cooling instability
.
J. Adv. Model. Earth Syst.
,
8
,
1620
1640
, https://doi.org/10.1002/2016ms000763.
Beucler
,
T.
,
T. W.
Cronin
, and
K.
Emanuel
,
2018
:
A linear response framework for radiative-convective instability
.
J. Adv. Model. Earth Syst.
,
10
,
1924
1951
, https://doi.org/10.1029/2018MS001280.
Clough
,
S. A.
,
M. J.
Iacono
, and
J.-l.
Moncet
,
1992
:
Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor
.
J. Geophys. Res.
,
97
,
15 761
15 785
https://doi.org/10.1029/92JD01419.
Coakley
,
J. A.
, Jr.
, and
P.
Yang
,
2014
:
Atmospheric Radiation: A Primer with Illustrative Solutions
.
Wiley
,
239
pp.
Costa
,
S. M. S.
, and
K. P.
Shine
,
2012
:
Outgoing longwave radiation due to directly transmitted surface emission
.
J. Atmos. Sci.
,
69
,
1865
1870
, https://doi.org/10.1175/JAS-D-11-0248.1.
Cousin
,
C.
,
R. L.
Doucen
,
C.
Boulet
, and
A.
Henry
,
1985
:
Temperature dependence of the absorption in the region beyond the 4.3-μm band head of CO2. 2: N2 and O2 broadening
.
Appl. Opt.
,
24
,
3899
3907
, https://doi.org/10.1364/AO.24.003899.
Dee
,
D. P.
, and Coauthors
,
2011
:
The ERA-Interim reanalysis: Configuration and performance of the data assimilation system
.
Quart. J. Roy. Meteor. Soc.
,
137
,
553
597
, https://doi.org/10.1002/qj.828
Dudhia
,
A.
,
2017
:
The Reference Forward Model (RFM)
.
J. Quant. Spectrosc. Radiat. Transfer
,
186
,
243
253
, https://doi.org/10.1016/J.JQSRT.2016.06.018.
Dufresne
,
J. L.
,
R.
Fournier
,
C.
Hourdin
, and
F.
Hourdin
,
2005
:
Net exchange reformulation of radiative transfer in the CO2 15-μm band on Mars
.
J. Atmos. Sci.
,
62
,
3303
3319
, https://doi.org/10.1175/JAS3537.1.
Eitzen
,
Z. A.
,
K. M.
Xu
, and
T.
Wong
,
2009
:
Cloud and radiative characteristics of tropical deep convective systems in extended cloud objects from CERES observations
.
J. Climate
,
22
,
5983
6000
, https://doi.org/10.1175/2009JCLI3038.1.
Emanuel
,
K. A.
,
A. A.
Wing
, and
E. M.
Vincent
,
2014
:
Radiative-convective instability
.
J. Adv. Model. Earth Syst.
,
6
,
75
90
, https://doi.org/10.1002/2013MS000270.
Fels
,
S. B.
, and
M. D.
Schwarzkopf
,
1975
:
The simplified exchange approximation: A new method for radiative transfer calculations
.
J. Atmos. Sci.
,
32
,
1475
1488
, https://doi.org/10.1175/1520-0469(1975)032<1475:tseaan>2.0.co;2.
Frierson
,
D. M. W.
,
I. M.
Held
, and
P.
Zurita-Gotor
,
2006
:
A gray-radiation aquaplanet moist GCM. Part I: Static stability and eddy scale
.
J. Atmos. Sci.
,
63
,
2548
2566
, https://doi.org/10.1175/JAS3753.1.
Gasiorowicz
,
S.
,
2003
:
Quantum Physics
.
Wiley
,
336
pp.
Goessling
,
H. F.
, and
S.
Bathiany
,
2016
:
Why CO2 cools the middle atmosphere—A consolidating model perspective
.
Earth Syst. Dyn.
,
7
,
697
715
, https://doi.org/10.5194/esd-7-697-2016.
Green
,
J. S.
,
1967
:
Division of radiative streams into internal transfer and cooling to space
.
Quart. J. Roy. Meteor. Soc.
,
93
,
371
372
, https://doi.org/10.1002/qj.49709339710.
Harries
,
J.
, and Coauthors
,
2008
:
The far-infrared Earth
.
Rev. Geophys.
,
46
,
RG4004
, https://doi.org/10.1029/2007RG000233.
Harrop
,
B. E.
, and
D. L.
Hartmann
,
2012
:
Testing the role of radiation in determining tropical cloud-top temperature
.
J. Climate
,
25
,
5731
5747
, https://doi.org/10.1175/JCLI-D-11-00445.1.
Hartmann
,
D. L.
, and
K.
Larson
,
2002
:
An important constraint on tropical cloud–climate feedback
.
Geophys. Res. Lett.
,
29
,
1951
, https://doi.org/10.1029/2002GL015835.
Hartmann
,
D. L.
,
J. R.
Holton
, and
Q.
Fu
,
2001
:
The heat balance of the tropical tropopause, cirrus, and stratospheric dehydration
.
Geophys. Res. Lett.
,
28
,
1969
1972
, https://doi.org/10.1029/2000GL012833.
Held
,
I. M.
,
2005
:
The gap between simulation and understanding in climate modeling
.
Bull. Amer. Meteor. Soc.
,
86
,
1609
1614
, https://doi.org/10.1175/BAMS-86-11-1609.
Heng
,
K.
,
D. M. W.
Frierson
, and
P. J.
Phillipps
,
2011
:
Atmospheric circulation of tidally locked exoplanets: II. Dual-band radiative transfer and convective adjustment
.
Mon. Notes Roy. Astron. Soc.
,
418
,
2669
2696
, https://doi.org/10.1111/j.1365-2966.2011.19658.x.
Hoskins
,
B. J.
,
1983
:
Dynamical processes in the atmosphere and the use of models
.
Quart. J. Roy. Meteor. Soc.
,
109
,
1
21
, https://doi.org/10.1002/QJ.49710945902.
Houghton
,
J. T.
,
2002
:
The Physics of Atmospheres
.
Cambridge University Press
,
320
pp.
Hu
,
S.
, and
G. K.
Vallis
,
2019
:
Meridional structure and future changes of tropopause height and temperature
.
Quart. J. Roy. Meteor. Soc.
,
145
,
2698
2717
, https://doi.org/10.1002/QJ.3587.
Jeevanjee
,
N.
,
2018
:
The physics of climate change: Simple models in climate science. arXiv
, http://arxiv.org/abs/1802.02695.
Jeevanjee
,
N.
, and
S.
Fueglistaler
,
2020
:
On the cooling-to-space approximation
.
J. Atmos. Sci.
,
77
,
465
478
, https://doi.org/10.1175/JAS-D-18-0352.1.
Jeevanjee
,
N.
,
P.
Hassanzadeh
,
S.
Hill
, and
A.
Sheshadri
,
2017
:
A perspective on climate model hierarchies
.
J. Adv. Model. Earth Syst.
,
9
,
1760
1771
, https://doi.org/10.1002/2017MS001038.
Joseph
,
J. M.
, and
R.
Bursztyn
,
1976
:
A radiative cooling model in the thermal infrared for application to models of the general circulation
.
J. Appl. Meteor.
,
15
,
319
325
, https://doi.org/10.1175/1520-0450(1976)015<0319:ARCMIT>2.0.CO;2.
Koll
,
D. D.
, and
T. W.
Cronin
,
2018
:
Earth’s outgoing longwave radiation linear due to H2O greenhouse effect
.
Proc. Natl. Acad. Sci. USA
,
115
,
10 293
10 298
, https://doi.org/10.1073/pnas.1809868115.
Kuang
,
Z.
, and
D. L.
Hartmann
,
2007
:
Testing the fixed anvil temperature hypothesis in a cloud-resolving model
.
J. Climate
,
20
,
2051
2057
, https://doi.org/10.1175/JCLI4124.1.
Kubar
,
T. L.
,
D. L.
Hartmann
, and
R.
Wood
,
2007
:
Radiative and convective driving of tropical high clouds
.
J. Climate
,
20
,
5510
5526
, https://doi.org/10.1175/2007JCLI1628.1.
Li
,
Y.
,
P.
Yang
,
G. R.
North
, and
A.
Dessler
,
2012
:
Test of the fixed anvil temperature hypothesis
.
J. Atmos. Sci.
,
69
,
2317
2328
, https://doi.org/10.1175/JAS-D-11-0158.1.
Maher
,
P.
, and Coauthors
,
2019
:
Model hierarchies for understanding atmospheric circulation
.
Rev. Geophys.
,
57
,
250
280
, https://doi.org/10.1029/2018RG000607.
Manabe
,
S.
,
R. F.
Strickler
,
S.
Manabe
, and
R. F.
Strickler
,
1964
:
Thermal equilibrium of the atmosphere with a convective adjustment
.
J. Atmos. Sci.
,
21
,
361
385
, https://doi.org/10.1175/1520-0469(1964)021<0361:TEOTAW>2.0.CO;2.
Mapes
,
B. E.
,
2001
:
Water’s two scale heights: The moist adiabat and the radiative troposphere
.
Quart. J. Roy. Meteor. Soc.
,
127
,
2353
2366
, https://doi.org/10.1002/QJ.49712757708.
Mlawer
,
E. J.
,
S. J.
Taubman
,
P. D.
Brown
,
M. J.
Iacono
, and
S. A.
Clough
,
1997
:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave
.
J. Geophys. Res.
,
102
,
16 663
16 682
, https://doi.org/10.1029/97JD00237.
Mlawer
,
E. J.
,
V. H.
Payne
,
J.-L.
Moncet
,
J. S.
Delamere
,
M. J.
Alvarado
, and
D. C.
Tobin
,
2012
:
Development and recent evaluation of the MT_CKD model of continuum absorption
.
Philos. Trans. Roy. Soc.
,
370A
,
2520
2556
, https://doi.org/10.1098/RSTA.2011.0295.
Morcrette
,
J.-J.
,
S. A.
Clough
,
E. J.
Mlawer
, and
M. J.
Iacono
,
1998
:
Impact of a validated radiative transfer scheme, RRTM, on the ECMWF model climate and 10 day forecasts. ECMWF Tech. Memo. 252, 46 pp.
, https://www.ecmwf.int/node/11252.
Parmentier
,
V.
, and
T.
Guillot
,
2014
:
A non-grey analytical model for irradiated atmospheres
.
Astron. Astrophys.
,
562
,
A133
, https://doi.org/10.1051/0004-6361/201322342.
Petty
,
G. W.
,
2006
:
A First Course in Atmospheric Radiation
. 2nd ed.
Sundog Publishing
,
472
pp.
Pierrehumbert
,
R. T.
,
2010
:
Principles of Planetary Climate
.
Cambridge University Press
,
635
pp.
Polvani
,
L.
,
B.
Medeiros
I.
Simpson
,
A.
Clement
, and
J.
Benedict
,
2017
:
When less is more: Opening the door to simpler climate models
.
Eos, Trans. Amer. Geophys. Union
,
98
, https://doi.org/10.1029/2017EO079417.
Rauscher
,
E.
, and
K.
Menou
,
2012
:
A general circulation model for gaseous exoplanets with double-gray radiative transfer
.
Astrophys. J.
,
750
,
96
, https://doi.org/10.1088/0004-637X/750/2/96.
Robinson
,
T. D.
, and
D. C.
Catling
,
2012
:
An analytic radiative-convective model for planetary atmospheres
.
Astrophys. J.
,
757
,
104
, https://doi.org/10.1088/0004-637X/757/1/104.
Rodgers
,
C. D.
, and
C. D.
Walshaw
,
1966
:
The computation of infra-red cooling rate in planetary atmospheres
.
Quart. J. Roy. Meteor. Soc.
,
92
,
67
92
, https://doi.org/10.1002/QJ.49709239107.
Schneider
,
S. H.
, and
R. E.
Dickinson
,
1974
:
Climate modeling
.
Rev. Geophys.
,
12
,
447
493
, https://doi.org/10.1029/RG012i003p00447.
Seeley
,
J. T.
,
N.
Jeevanjee
,
W.
Langhans
, and
D. M.
Romps
,
2019a
:
Formation of tropical anvil clouds by slow evaporation
.
Geophys. Res. Lett.
,
46
,
492
501
, https://doi.org/10.1029/2018GL080747.
Seeley
,
J. T.
,
N.
Jeevanjee
, and
D. M.
Romps
,
2019b
:
FAT or FiTT: Are anvil clouds or the tropopause temperature-invariant?
Geophys. Res. Lett.
,
46
,
1842
1850
, https://doi.org/10.1029/2018GL080096.
Shine
,
K. P.
,
I. V.
Ptashnik
, and
G.
Rädel
,
2012
:
The water vapour continuum: Brief history and recent developments
.
Surv. Geophys.
,
33
,
535
555
, https://doi.org/10.1007/s10712-011-9170-y.
Singh
,
M. S.
, and
P. A.
O’Gorman
,
2015
:
Increases in moist-convective updraught velocities with warming in radiative-convective equilibrium
.
Quart. J. Roy. Meteor. Soc.
,
141
,
2828
2838
, https://doi.org/10.1002/QJ.2567.
Stevens
,
B.
,
H.
Brogniez
,
C.
Kiemle
,
J. L.
Lacour
,
C.
Crevoisier
, and
J.
Kiliani
,
2017
:
Structure and dynamical influence of water vapor in the lower tropical troposphere
.
Surv. Geophys.
,
38
,
1371
1397
, https://doi.org/10.1007/s10712-017-9420-8.
Tan
,
Z.
,
O.
Lachmy
, and
T. A.
Shaw
,
2019
:
The sensitivity of the jet stream response to climate change to radiative assumptions
.
J. Adv. Model. Earth Syst.
,
11
,
934
956
, https://doi.org/10.1029/2018MS001492.
Thomas
,
G. E.
, and
K.
Stamnes
,
2002
:
Radiative Transfer in the Atmosphere and Ocean
.
Cambridge University Press
,
517
pp.
Thompson
,
D. W. J.
,
S.
Bony
, and
Y.
Li
,
2017
:
Thermodynamic constraint on the depth of the global tropospheric circulation
.
Proc. Natl. Acad. Sci. USA
,
114
,
8181
8186
, https://doi.org/10.1073/pnas.1620493114.
Vallis
,
G. K.
,
P.
Zurita-Gotor
,
C.
Cairns
, and
J.
Kidston
,
2015
:
Response of the large-scale structure of the atmosphere to global warming
.
Quart. J. Roy. Meteor. Soc.
,
141
,
1479
1501
, https://doi.org/10.1002/QJ.2456.
Wallace
,
J. M.
, and
P. V.
Hobbs
,
2006
:
Atmospheric Science: An Introductory Survey
.
Academic Press
,
504
pp.
Weaver
,
C.
, and
V.
Ramanathan
,
1995
:
Deductions from a simple climate model: Factors governing surface temperature and atmospheric thermal structure
.
J. Geophys. Res.
,
100
,
11 585
11 591
, https://doi.org/10.1029/95JD00770.
Wilson
,
D. J.
, and
J.
Gea-Banacloche
,
2012
:
Simple model to estimate the contribution of atmospheric CO2 to the Earth’s greenhouse effect
.
Amer. J. Phys.
,
80
,
306
, https://doi.org/10.1119/1.3681188.
Xu
,
K. M.
,
T.
Wong
,
B. A.
Wielicki
,
L.
Parker
,
B.
Lin
,
Z. A.
Eitzen
, and
M.
Branson
,
2007
:
Statistical analyses of satellite cloud object data from CERES. Part II: Tropical convective cloud objects during 1998 El Niño and evidence for supporting the fixed anvil temperature hypothesis
.
J. Climate
,
20
,
819
842
, https://doi.org/10.1175/JCLI4069.1.
Zhu
,
X.
,
M. E.
Summers
, and
D. F.
Strobel
,
1992
:
Calculation of CO2 15-μm band atmospheric cooling rates by Curtis matrix interpolation of correlated-k coefficients
.
J. Geophys. Res.
,
97
,
12 787
–1
2 797
, https://doi.org/10.1029/92JD01311.

Footnotes

a

Current affiliation: Program in Atmosphere and Ocean Sciences, Princeton University, Princeton, New Jersey.

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JAS-D-18-0352.1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

1

Given the average outgoing longwave radiation (OLR) of 240 W m−2 and a tropospheric mass of ~8000 kg m−2, one can estimate a global average H as H(240Wm2)/(8×103kgm2×1000Jkg1K)=2.5×105Ks1=2.5Kday1. This calculation cannot tell us about the vertical distribution of H, however, and moreover assumes that all OLR emanates from the atmosphere (rather than the surface), an assumption that is reasonable in the global mean but does not hold locally (Costa and Shine 2012).

2

For a recent critique of these claims, however, see Seeley et al. (2019a,b).

3

Note that the strong longwave stratospheric cooling rates are themselves fixed by the need to balance strong shortwave heating by ozone, so this question is really about how CO2 exhibits such strong cooling in a stratosphere that is not significantly warmer than the troposphere.

4

More precisely, we use the same thermodynamic profiles and absorber concentration as the RFM calculation, but use a non-pressure-broadened gray absorption coefficient tuned to yield the same column-integrated radiative cooling (of roughly 170 W m−2) as the RFM calculation.

5

For reproducibility, note also that we run RFM with the BFX flag disabled. This means the Planck function is assumed constant within an RFM vertical grid cell, rather than assuming a subgrid vertical variation of the Planck function that is linear in height.

6

Both temperature scaling and pressure broadening are second-order effects on κ, relative to its wavenumber dependence. At the same time, neglecting pressure broadening entirely leads to results whose inaccuracy we found unacceptable, so we include pressure broadening in our simple models below. Temperature scaling, on the other hand, is omitted.

7

There is also a slight wavenumber dependence of β (standard deviation of ±20% across wavenumber space) that we neglect.

8

Strictly speaking one also needs to worry about the vertical variations in 1/p as well as the Planck function in Eq. (7), but these turn out to compensate each other (Jeevanjee and Fueglistaler 2020).

9

This is one reason why we choose to write Eq. (7) in its particular form, with a mix of p and τν˜ coordinates. Another reason is that the form of Eq. (7) is amenable to the analytical spectral integration we perform in section 3b.

10

These wavenumber ranges were chosen to optimize the piecewise exponential fit in Eq. (10). With these choices, however, the SSM2D neglects wavenumbers less than 150 cm−1. These wavenumbers turn out to make a negligible contribution to heating rates (not shown), presumably due to their low values of the Planck function, but their contribution is nonetheless included in all spectrally integrated RFM calculations in this paper.

11

In the tropics, where Γ is set by the moist adiabat and is roughly 4 K km−1 near the surface and 9 K km−1 in the upper troposphere, the variation in β is larger: βH2O5.5±2.

12

To derive this range, note that over a spectral range of width Δν˜=lrote, κH2O varies by a factor of ee by Eq. (10). This variation in κH2O leads to variation of τν˜ between ee/2 and ee/2, if the variation is centered geometrically around τν˜=1.

13

It should be noted that the agreement in the troposphere between the RFM and SSM2D profiles in Figs. 5b and 5c benefits to some degree from compensation between the SSM2D’s neglect of wavenumbers below ν˜rot=150cm1 and its enhancement of bin-averaged pTν˜ (and hence Hν˜; Figs. 2c and 2f) due to its neglect of finescale structure. These errors do not affect the conclusion, however, that both RFM and the SSM2D exhibit kinks associated with a decline in Δν˜.

14

Spectroscopically speaking, the P and R branches only encompass lines within roughly 30 cm−1 of the Q branch, but for simplicity we use P and R to refer to all wavenumbers lower or higher than ν˜Q throughout the 500–850 cm−1 spectral interval.

15

This also has implications for the validity of the CTS approximation for CO2, as discussed in Jeevanjee and Fueglistaler (2020).

16

This story is of course reversed in the stratosphere, where CO2 cooling dominates over that from H2O (Manabe et al. 1964) even though both gases are well mixed and have β = 2. This is likely due to the relatively low concentration of water vapor in the stratosphere (23 ppmv here vs 280 ppmv of CO2), which yields low-stratospheric WVP values and hence low Δν˜ values (cf. Figs. 5c,e).

17

This error can be addressed by introducing a spectral “window” to the gray model (e.g., Weaver and Ramanathan 1995), but such “window gray” models still suffer from the unrealistic cooling profiles of Fig. 1b.