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The ozone layer is often noted to exhibit self-healing, whereby depletion of ozone aloft induces ozone increases below, explained as resulting from enhanced ozone production due to the associated increase in ultraviolet (UV) radiation below. Similarly, ozone enhancement aloft can reduce ozone below (reverse self-healing). This paper considers self-healing and reverse self-healing to manifest a general mechanism we call photochemical adjustment, whereby ozone perturbations lead to a downward cascade of anomalies in UV and ozone. Conventional explanations for self-healing imply that photochemical adjustment is stabilizing, damping perturbations towards the surface. However, photochemical adjustment can be destabilizing if the enhanced UV disproportionately increases the ozone sink, as can occur if the enhanced UV photolyzes ozone to produce atomic oxygen, which speeds up catalytic destruction of ozone. We analyze photochemical adjustment in two linear ozone models (Cariolle v2.9 and LINOZ), finding that (1) photochemical adjustment is destabilizing above 40 km in the tropical stratosphere and (2) self-healing often represents only a small fraction of the total photochemical stabilization. The destabilizing regime above 40 km is reproduced in a much simpler model: the Chapman cycle augmented with destruction of O and O3 by generalized catalytic cycles and transport (the Chapman+2 model). The Chapman+2 model reveals that photochemical destabilization occurs where the ozone sink is more sensitive than the source to perturbations in overhead column ozone, which is found to occur when the window of overlapping absorption by O2 and O3 is optically unsaturated, i.e., when overhead slant column ozone is below approximately 10^18 molec. cm−2. 

The climate simulation frontier of a global storm-resolving model (GSRM; or k-scale model because of its kilometer-scale horizontal resolution) is deployed for climate change simulations. The climate sensitivity, effective radiative forcing, and relative humidity changes are assessed in multiyear atmospheric GSRM simulations with perturbed sea-surface temperatures and/or carbon dioxide concentrations. Our comparisons to conventional climate model results can build confidence in the existing climate models or highlight important areas for additional research. This GSRM’s climate sensitivity is within the range of conventional climate models, although on the lower end as the result of neutral, rather than amplifying, shortwave feedbacks. Its radiative forcing from carbon dioxide is higher than conventional climate models, and this arises from a bias in climatological clouds and an explicitly simulated high-cloud adjustment. Last, the pattern and magnitude of relative humidity changes, simulated with greater fidelity via explicitly resolving convection, are notably similar to conventional climate models. 

Abstract Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known ask-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations. 

Abstract The cooling-to-space (CTS) approximation says that the radiative cooling of an atmospheric layer is dominated by that layer’s emission to space, while radiative exchange with layers above and below largely cancel. Though the CTS approximation has been demonstrated empirically and is thus fairly well accepted, a theoretical justification is lacking. Furthermore, the intuition behind the CTS approximation cannot be universally valid, as the CTS approximation fails in the case of pure radiative equilibrium. Motivated by this, we investigate the CTS approximation in detail. We frame the CTS approximation in terms of a novel decomposition of radiative flux divergence, which better captures the cancellation of exchange terms. We also derive validity criteria for the CTS approximation, using simple analytical theory. We apply these criteria in the context of both gray gas pure radiative equilibrium (PRE) and radiative–convective equilibrium (RCE) to understand how the CTS approximation arises and why it fails in PRE. When applied to realistic gases in RCE, these criteria predict that the CTS approximation should hold well for H2O but less so for CO2, a conclusion we verify with line-by-line radiative transfer calculations. Along the way we also discuss the well-known “τ = 1 law,” and its dependence on the choice of vertical coordinate. 

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 T_kink ~ 220 K. A further, ancillary result is that gray models fail to reproduce basic features of atmospheric radiative cooling. 

This paper addresses issues of statistical misrepresentation of the a priori parameters (henceforth called ancillary parameters) used in geophysical data estimation. Parameterizations using ancillary data are frequently needed to derive geophysical data of interest from remote measurements. Empirical fits to the ancillary data that do not preserve the distribution of such data may induce substantial bias. A semianalytical averaging approach based on Taylor expansion is presented to improve estimated cirrus ice water content and sedimentation flux for a range of volume extinction coefficients retrieved from spaceborne lidar observations by CALIOP combined with the estimated distribution of ancillary data from in situ aircraft measurements of ice particle microphysical parameters and temperature. It is shown that, given an idealized distribution of input parameters, the approach performs well against Monte Carlo benchmark predictions. Using examples with idealized distributions at the mean temperature for the tropics at 15 km, it is estimated that the commonly neglected variance observed in in situ measurements of effective diameters may produce a worst-case estimation bias spanning up to a factor of 2. For ice sedimentation flux, a similar variance in particle size distributions and extinctions produces a worst-case estimation bias of a factor of 9. The value of the bias is found to be mostly set by the correlation coefficient between extinction and ice effective diameter, which in this test ranged between all possible values. Systematic reporting of variances and covariances in the ancillary data and between data and observed quantities would allow for more accurate observational estimates.