3 Results

a. Mean circulation and precipitation

Figure 1 shows the zonal mean meridional overturning circulation in the Control and ClimRad runs. Overall the difference between the two simulations is very small, indicating that the mean circulation remains almost unchanged without radiative interactions. Also, we note that suppressing radiative interactions has little impact on the magnitude of the global-mean precipitation because the overwriting approach applied in the ClimRad run does not change the magnitude of the global-mean radiative cooling of the atmosphere. Overall the atmospheric energy budget remains nearly unaffected in the ClimRad run.

b. Convective aggregation, cloud and relative humidity

Although the mean circulation is essentially the same between the Control and ClimRad runs, suppressing radiative interactions significantly changes features related to synoptic-scale convection. Figure 2 shows probability density functions (PDFs) of daily subsidence fraction in the Control and ClimRad runs computed over the entire tropics (30°S­–30°N). Small (large) values of daily subsidence fraction in the ClimRad run are more (less) frequent than those in the Control run, indicating that convection becomes less aggregated when radiative interactions are suppressed. These results are in line with previous numerical simulations with an aquaplanet configuration (Coppin & Bony, 2015) and in convection-resolving models (Muller & Bony, 2015; Muller & Held, 2012; Yang, 2018).
Using satellite observations, Bony et al. (2020) found that the spatial organization of deep convection can modulate high-level clouds and relative humidity in the free troposphere, which further impacts the tropical radiation budget. Based on an ensemble of radiative-convective equilibrium simulations, Wing et al. (2020) showed similar results in which the occurrence of convective self-aggregation reduces high cloud coverage and dries the mid-troposphere. Here, we investigate how clouds and relative humidity respond to suppressed radiative interactions (Figure 3). Negative values are found in the upper troposphere over the tropics for the Control run minus the ClimRad run, indicating that a more aggregated state is associated with fewer high-level clouds. Also, we find that the free troposphere is in general drier in the Control run than that in the ClimRad run (Figure 4). The reduction in high-level clouds and mid-tropospheric relative humidity in the Control run is qualitatively consistent with observations (Bony et al., 2020; Holloway et al., 2017; Stein et al., 2017; Tobin et al., 2013; Tobin et al., 2012) and other model simulations (Bretherton et al., 2005; Wing et al., 2017; Wing & Emanuel, 2014; Wing et al., 2020). This shows that, even when the large-scale circulations are nearly identical, differences in the spatial organization of convection can alter the mean state of the free troposphere.
However, the responses in cloud fraction and relative humidity are not vertically uniform. We find that the Control run has an overall increase in cloud fraction and relative humidity in the boundary layer, which may not be directly linked with the degree of aggregation. Previous results on the relationship between convective aggregation and low cloud fraction are inconclusive. While an increase in low-level clouds with aggregation is found in Tobin et al. (2013) and Stein et al. (2017), Tobin et al. (2012) found the opposite result. Recently, Wing et al. (2020) found that most radiative-convective equilibrium simulations agree on an increase in low-level clouds with convective self-aggregation, although such increase is less robust in magnitude. They proposed that the difference in horizontal grid spacing, rather than the occurrence of self-aggregation itself, may contribute to the increase in low-level clouds. In addition, lower tropospheric stability may also play a role in modulating low-level clouds (Bony et al., 2020). When lower-tropospheric stability increases, more moisture is trapped in the boundary layer, which promotes the formation of low-level clouds (Wood & Bretherton, 2006). However, the impact of lower tropospheric stability on low-level clouds is thought to be independent of the degree of aggregation as noted by Bony et al. (2020). Here we use estimated inversion strength (EIS ), defined as\(EIS=LTS-\Gamma_{m}^{850}\left(z_{700}-LCL\right)\), to represent the stability in the boundary layer. LTS stands for lower tropospheric stability and is computed as\(LTS=\theta_{700}-\theta_{1000}\) where \(\theta_{700}\) and\(\theta_{1000}\) are potential temperatures at 700 hPa and 1000 hPa respectively (Klein & Hartmann, 1993); \(\Gamma_{m}^{850}\) is
Among different factors contributing to the low cloud fraction, radiative interactions with boundary layer clouds could make a difference. In the boundary layer, the coupling between clouds, radiation, turbulence and entrainment was first documented by Lilly (1968). Strong longwave radiative cooling at the cloud top promotes vertical mixing and drives turbulent eddies, which transports moisture from the sea surface upward and maintains the cloud amount (Bretherton et al., 2004b; Wood, 2012). Additionally, strong radiative cooling at the cloud top increases relative humidity in the boundary layer. Higher relative humidity further promotes the formation of low-level clouds (Brient & Bony, 2012). In the ClimRad run, the coupling between radiation and low-level clouds is disabled, which may explain the reduction in both relative humidity and cloud fraction in the boundary layer.
To explore the sensitivity of clouds and relative humidity to the vertical structure of radiative coupling, we conduct two other simulations: one is referred to as ClimRadFT, in which the overwriting procedure is only applied to the free troposphere, whereas radiation in the boundary layer is fully interactive; the other is referred to as ClimRadBL, in which only radiation in the boundary layer is fixed while that in the free troposphere is interactive (see Table 1 for more details). Compared to the Control run, we find that changes in low-level clouds and lower-tropospheric relative humidity in the ClimRadBL run are similar to those in the ClimRad runs (Figure 3 and Figure 4). In contrast, the differences in tropical low-level clouds and relative humidity in the boundary layer are reduced between the Control and ClimRadFT runs (Figure 3 and Figure 4). However, we note that the ClimRadBL and ClimRadFT runs exhibit similar changes in lower tropospheric stability compared to the Control run (Figure S1). This suggests that it is the direct effects of radiative coupling in the boundary layer that is primarily responsible for the changes in low-level clouds and relative humidity in the ClimRad run, rather than the influence of radiative processes in the free troposphere on lower tropospheric stability.
In addition, the changes in low-level clouds and relative humidity are found to be, at least from a qualitative perspective, independent of variations in the degree of aggregation. A comparison of the PDFs of daily subsidence fraction from the Control, ClimRad, ClimRadFT and ClimRadBL runs is shown in Figure 5. Compared to the Control run, the other three simulations exhibit an overall reduction in the degree of aggregation although the magnitude of such reduction varies among them. The qualitatively consistent change in the degree of aggregation cannot explain the differing responses in cloud fraction and relative humidity, indicating that variations in the degree of aggregation may not be a leading factor in modulating the distribution of cloud and humidity. Here, the coupling between radiation, cloud and humidity plays a more important role in maintaining the model’s mean state.

c. Response in extreme precipitation

Previous idealized modeling studies showed that extreme daily precipitation becomes weaker when convective aggregation is inhibited (Bao & Sherwood, 2019). To examine the response in extreme precipitation to suppressed radiative interactions, we compute the annual maximum daily precipitation (\(P_{e}\)) at each grid point. While the difference in \(P_{e}\) between the Control and ClimRad runs is small at middle-to-high latitudes, a significant reduction in \(P_{e}\)is found across the tropics in the ClimRad run (Figure 6, left), which indicates that suppressing radiative interactions reduces the strength of extreme daily precipitation. At each grid point, \(P_{e}\) can be estimated by a physical scaling diagnostic (O’Gorman & Schneider, 2009; Pfahl et al., 2017; Sugiyama et al., 2010):
\begin{equation} \begin{matrix}P_{e}\sim-\left\{\omega_{e}\left.\ \frac{dq_{s}}{\text{dp}}\right|_{\theta^{*}}\right\}\#\left(1\right)\\ \end{matrix}\nonumber \\ \end{equation}
where \(\omega_{e}\) is the annual maximum daily vertical pressure velocity, \(q_{s}\) is the saturation specific humidity, \(p\) is the pressure and \(\theta^{*}\) is the saturation equivalent potential temperature. Here \(\left\{\bullet\right\}\) means a mass-weighted vertical integral over the troposphere. We show that the scaling approach reproduces the spatial patterns of \(P_{e}\) in both simulations, leading to a consistent reduction in the scaling when radiative interactions are suppressed (Figure 6, right).
Eq. 1 can be used to decompose changes in extreme precipitation into thermodynamic and dynamic contributions. A thermodynamic scaling is implemented by replacing \(\omega_{e}\) in Eq. 1 with long-term averaged vertical velocity at each grid point, whereas a dynamic scaling is the difference between the full scaling and the thermodynamic scaling (Pfahl et al., 2017). There is little difference in the thermodynamic contribution between the Control and ClimRad runs (Figure S2, left) because both runs are forced by the same SSTs and CO2concentrations. However, the spatial patterns of difference in dynamic contribution (Figure S2, right) largely resemble the spatial patterns of difference in \(P_{e}\) and the scaling, indicating that suppressing radiative interactions primarily reduces the dynamical contribution to extreme precipitation.
To verify the robustness of our results, probability distributions of daily precipitation and updrafts across the tropics (30°S­–30°N) are compared between these two simulations. Figure S3 shows the base-10 logarithm of the probability that daily precipitation and mid-tropospheric updrafts (\(\omega_{500}<0\)) exceed a particular value in the Control and ClimRad runs. We find that both variables exhibit a reduction in the probability of exceedance toward its extreme values in the ClimRad run, indicating that suppressing radiative interactions reduces the frequency of extreme convective events. We note that suppressing radiative interactions also reduces the temporal variance of daily precipitation (Figure S4).
Having demonstrated the impact of suppressing radiative interactions on convective organization, we next explore the physical mechanisms which underlie these changes. To do that we first divide the tropics into\(10\times 10\) regional blocks (Figure 7, top). Within each block, the grid point with the local maximum precipitation is identified, which later becomes the new center of that block. The recentered blocks are then composited based on their domain mean precipitation. Here we show composites of precipitation in the Control run for blocks with domain mean precipitation <5, 5–10, 10–15 and >15 mm day-1 (Figure 7, bottom). Note that composites of precipitation in the ClimRad run show similar results (not shown). However, the number of blocks per year (referred to as \(N_{b}\)) in each bin is different between the Control and ClimRad runs. Boxplots of\(N_{b}\) normalized by the median value in the Control run are shown in Figure 8 (top). In the >15 mm day-1 bin,\(N_{b}\) is reduced in the ClimRad run, which means that blocks with heavy precipitation happen less frequently when radiative interactions are suppressed. Through this block-by-block analysis, we can also compare the degree of aggregation over blocks with similar amplitude of domain mean precipitation. A comparison of PDFs of daily subsidence fraction between the Control and ClimRad runs are shown in Figure 8 (bottom). Higher probabilities of large subsidence fraction are found in the Control run, indicating that suppressing radiative interactions leads to an overall reduction in aggregation across convective systems of different intensities, which is consistent with the results shown in Figure 2.
In idealized models, it is found that the upgradient transport of moist static energy (Neelin & Held, 1987) plays an important role in convective aggregation (Bretherton et al., 2005; Muller & Bony, 2015; Muller & Held, 2012). Here, radiative cooling and circulation are composited over bins as shown in Figure 8. Following Bretherton et al. (2005), we use column relative humidity (CRH), defined as the ratio of water vapor path to the saturation water vapor path of the atmospheric column (Bretherton et al., 2004a; Raymond, 2000), to represent the degree of dryness at each grid point within a block. Next, all grid points in a block are sorted from lowest to highest CRH and the circulation is represented by an effective streamfunction \(\Psi\), which is computed as a horizontal integral over vertical velocity starting with the driest grid point. The streamfunction \(\Psi\) at a certain grid point can be interpreted as an accumulation of vertical mass flux over grid points that are drier than the target grid point. Primarily, the streamfunction is thought to capture the exchange of moist static energy between dry and moist regions (Bretherton et al., 2005).
Figure 9 shows the streamfunction \(\Psi\) and radiative cooling rates in the Control and ClimRad runs. In the Control run, when the domain mean precipitation is small, the circulation is weak and there is little contrast in radiative cooling between dry and moist regions, especially in the lower troposphere. As the domain mean precipitation increases, the circulation gets stronger, with its low-level component below ~850 hPa moving air from dry to moist regions. Although the magnitude of radiative cooling in dry regions does not change much, the radiative cooling reduces significantly in moist regions as domain mean precipitation increases, which is equivalent to adding anomalous radiative heating there. As a result, the horizontal gradient of radiative cooling is enhanced, which promotes the low-level circulation and thus the upgradient transport of energy. In comparison, the enhanced horizontal gradient of radiative cooling shown in the Control run is missing in the ClimRad run, indicating that suppressing radiative interactions inhibits the horizontal gradient of radiative cooling from increasing, which explains why the degree of aggregation and extreme precipitation events are reduced in the ClimRad run.

d. Meridional width of the tropical rain belt

Recent studies measure the width of tropical ascending regions by the fraction of vertical pressure velocity at 500 hPa less than zero in the tropics (Su et al., 2020; Su et al., 2019). Given the same domain, greater ascending fraction corresponds to smaller subsidence fraction. While in section 3b we show that daily subsidence fraction in the tropics is reduced without radiative interactions. On longer time scales, the mean vertical pressure velocity at 500 hPa exhibits little difference between the Control and ClimRad runs (not shown). However, this definition may not be an appropriate measure of the meridional width of zonal mean Hadley circulation or the width of the intertropical convergence zone (ITCZ), as noted by Su et al. (2020). Therefore, other metrics are required to quantify the width of the tropical rain belt. Based on observations, Popp and Bony (2019) reported a strong link between zonal convective clustering (CC) and the tropical rain belt: when convection becomes more clustered in the zonal direction, the meridional width of tropical rain belt increases and exhibits a double-peak structure. However, it remains unclear how CC is related to the width of ITCZ in climate models (Popp et al., 2020b). In section 3b, we show that suppressing radiative interactions reduces the degree of aggregation across the tropics. Thus, convection should become less clustered in the zonal direction as well without radiative interactions. Here, two metrics are used to characterize zonal CC: i) the precipitation-inferred CC index, which is defined as monthly mean of the meridionally averaged daily zonal standard deviation of precipitation from 6°S to 6°N normalized by the daily mean precipitation over the same region (Popp & Bony, 2019); and ii) the dynamically inferred CC index, which is defined as the monthly average of the daily zonal fraction of positive values of the meridional-mean vertical pressure velocity at 500 hPa between 6°S to 6°N (Popp et al., 2020a). Also, we only consider months during which the tropical precipitation distribution is symmetric about the equator with the tropical precipitation asymmetry index (Hwang & Frierson, 2013; Popp & Bony, 2019) less than 0.4. Another two metrics are used to quantify the ITCZ width: i) the precipitation-inferred ITCZ width, which is defined as the area mean of precipitation from 15°S to 15°N divided by the area mean of precipitation from 6°S to 6°N (Popp & Bony, 2019); and ii) the dynamically inferred ITCZ width, which is defined by the contiguous width in degrees latitude of zonal mean ascent region at 500 hPa around the absolute maximum of zonal mean precipitation (Byrne & Schneider, 2016; Popp & Bony, 2019).
Scatter plots of zonal CC and the ITCZ width in the Control and ClimRad runs are shown in Figure 10. Positive temporal correlations are found between zonal CC and the ITCZ width using either precipitation or dynamically inferred metrics in both simulations, which is consistent with observations (Popp & Bony, 2019). We note that the mean ITCZ width exhibits little difference between the Control and ClimRad runs, which is supported by Figure S5 and Figure S6 based on precipitation minus evaporation. These results indicate that suppressing radiative interactions has little impact on the mean ITCZ width. In comparison, the mean value of zonal CC is reduced in the ClimRad run, which comes as no surprise since the degree of aggregation is also reduced without radiative interactions as illustrated in section 3b. Based on model simulations participating in CMIP5 (Taylor et al., 2012), Popp et al. (2020b) showed that biases in CC cannot explain biases in the ITCZ width and no dominant mechanism could explain the link between the temporal variability of CC and that of the ITCZ width. However, they found a tendency for models with higher spatial resolution to exhibit stronger links between zonal CC and the dynamically inferred ITCZ width. In this study, suppressing radiative interactions has a robust impact on zonal CC but little impact on the mean ITCZ width. One possibility is that while the degree of aggregation/clustering is more sensitive to synoptic-scale radiation-circulation coupling (i.e. the spatial contrast in radiative cooling), the ITCZ width is more dependent on the long-term averaged large-scale circulation in this GCM.