Idealized atmospheric GCM experiments
The response of clouds to CO2 increase and associated global warming in coupled ocean-atmosphere experiments may result from the interaction of a myriad of physical and dynamical processes, purely atmospheric and/or involving coupled interactions between the ocean and the atmosphere. To simplify the analysis and identify the dominant processes, we now analyze the response of clouds to a range of prescribed perturbations in model experiments run with exactly the same physical package but using different configurations.
One configuration consists in atmosphere-only experiments following the protocol of CMIP5 experiment #3.3.Footnote 2 In this experiment, commonly referred to as Atmospheric Model Intercomparison Project (AMIP) experiment (Gates 1992), the atmospheric component of the coupled ocean-atmosphere model is used in isolation using Sea Surface Temperatures (SST) prescribed from observations over the period 1979–2008. To distinguish the relative role of CO2 increase and global warming in cloud changes, additional atmospheric experiments forced either by a globally uniform 4 K increase in SST (CFMIP2/CMIP5 experiment #6.8 referred to as AMIP4K) or by a prescribed quadrupling of the atmospheric CO2 concentration (CFMIP2/CMIP5 experiments #6.5 referred to as AMIP4xCO2) are also performed.Footnote 3
Aqua-planet experiments are also performed, in which the atmospheric model is run in perpetual equinox conditions using a specified, time-invariant distribution of SST zonally-uniform and symmetrical to the equator [the so-called “QOBS” distribution proposed by Neale and Hoskins (2000)]. These experiments run without any season nor land-atmosphere or ocean-atmosphere interactions, allow us to examine the response of clouds in a highly idealized framework and thus to assess the robustness of some predicted features. Aquaplanet experiments in which CO2 is quadrupled ("Aqua4xCO2”) or in which the SST is uniformly increased by 4 K (“Aqua4K”) are also performed. These experiments correspond to the CFMIP2/CMIP5 experiments #6.7a, #6.7b and #6.7c, respectively.
The tropically-averaged change in CRF associated with the different experiments is given in Table 2. As in the OAGCM experiment, the change in NET CRF is dominated by the change in SW CRF. In all experiments, the change in SW CRF is also dominated by the thermodynamic component, which is itself dominated by the change in cloudiness that occurs in weak subsidence regimes (not shown). The vertical profile of cloud fraction simulated by the model in weak subsidence regimes in AMIP and aqua-planet configurations (Fig. 4) resemble very much that predicted in the OAGCM (Fig. 3), with however a slightly smaller cloud fraction at 950 hPa (about 13 vs. 20%), and a slightly larger cloudiness around 800 hPa in the aqua-planet configuration than in the more realistic AMIP or OAGCM configurations. The change in cloudiness between +4K and control experiments in AMIP and aqua-planet configurations are of same order as those found in OAGCM experiments (once normalized by the temperature change, which is roughly twice as large in +4K experiments than in the 1% CO2 experiment at the time of CO2 doubling), and occur at the same level. Note that these absolute changes are relative to their current climatological cloud profiles, which are slightly different in the three model configurations. In all configurations, the relative humidity decreases within the cloud layer and increases at the top of the boundary layer and above (Fig. 5), in association with an enhanced shallow convective activity. The response of clouds to the CO2 radiative effect largely differs from the response to temperature change: both in AMIP and aqua-planet experiments, the cloud fraction changes little with CO2, and exhibits only a weak increase around 950 hPa and a weak decrease around 800 hPa (Fig. 4).
Three main conclusions arise from this series of experiments: (1) the response of low-level clouds to temperature and CO2 is similar in AMIP and aqua-planet experiments, suggesting that it is controlled by robust physical processes independent on their exact geographical distribution, and independent on land-surface processes at first order; (2) low-level clouds exhibit opposite responses to surface ocean warming and CO2 radiative forcing: the former induces a decrease of low-level clouds and a weakening of their radiative effects while the latter induces an increase of low-level clouds and an enhanced cooling effect of clouds on climate (3) the response of clouds to climate change experiments performed with ocean-atmosphere coupling and associated with both surface warming and CO2 increase is qualitatively and quantitatively much more consistent with the response of clouds to SST change, than to the response to CO2 increase. It suggests that in the IPSL model, and contrary to some other models (Gregory and Webb 2008), the tropospheric adjustment to CO2 radiative forcing exerts a much weaker impact on boundary-layer clouds than surface temperature changes. The sensitivity of low-level clouds to SST changes may stem from local and/or remote influences. To examine how much local processes may be responsible for this sensitivity, we now go one step further in the model hierarchy by considering Single Column Model (SCM) simulations forced by large-scale forcings representative of weak subsidence conditions. These simulations are run with exactly the same physical parameterizations as GCM experiments previously discussed.
Idealized Single-Column Simulations
To investigate the response of tropical low-clouds to climate change, we use the CFMIP-GCSS Intercomparison of Large Eddy Models and Single Column Models (CGILS) framework: the aim of this community project is to evaluate subtropical marine boundary layer cloud feedback processes in GCMs and in high-resolution process models using a set of idealized large-scale dynamical conditions.Footnote 4 CGILS focuses on three cases of boundary-layer clouds occurring along a transect ranging from California to Hawaii (Teixeira et al. 2011) and representative of stratus, stratocumulus and shallow cumulus cloud types (Karlsson et al. 2010). For each case, idealized large-scale conditions representative of the present-day climate are derived from European Centre for Medium-Range Weather Forecasts (ECMWF) analysis, and idealized large-scale forcings representative of global warming conditions are derived by prescribing a +2 K SST increase and by assuming that the tropical temperature profile follows a moist adiabat, that the relative humidity remains constant, that profiles of horizontal heat and moisture advection are unchanged, and that large-scale subsidence is changed so as to balance the radiative cooling above the boundary layer (Zhang and Bretherton 2008). Climate change conditions are thus associated with a warmer, more stable atmosphere and a weakened vertical motion.
In this study, we focus on the so-called “S6” CGILS case, which corresponds to large-scale conditions very similar to those of the ω = 20 hPa/day dynamical regime (especially in terms of SST and vertical velocity profile). SCM simulations are performed for an SST of 298.8 K, a surface pressure of 1,014 hPa and a mean solar irradiance of 448.1 W/m2, and they are initialized by specified temperature, humidity and wind conditions. As recommended by CGILS, a relaxation towards a specified temperature profile is applied to the predicted temperature profile between 600 hPa and the top of the atmosphere. The simulations are run for 200 days but a steady state is reached after about 20 days.
The time evolution of the vertical profile of cloud fraction predicted by the IPSL SCM is shown in Fig. 6a, together with the mean profile for present-day condition and its change under idealized climate warming. The SCM simulation exhibits a maximum cloud fraction (of about 25%) around 850 hPa with a secondary maximum around 950 hPa, and the cloud response to SST increase consists in a decrease of both cloud layers by a few percent. Although corresponding to similar large-scale conditions on the monthly time scale, these results thus differ considerably from the robust GCM characteristics associated with weak subsidence regimes (Fig. 4). How to interpret this difference?
The examination of the time evolution of aquaplanet simulations in single geographical points belonging to the weak subsidence regime (monthly ω = 20 hPa/day) reveals a large high-frequency variability, with an alternance of shallow (and sometimes even deep) convection and suppressed conditions (Fig. 7). This variability, related to some internal synoptic variability of the atmosphere such as tropical waves, induces an alternance of cloud layers between 1,000 and 750 hPa, with a maximum occurence and amount at 950 hPa. The high-frequency variance of the GCM large-scale vertical velocity in regimes of weak subsidence is maximum in the upper troposphere, in agreement with NCEP2 meteorological reanalyses (Fig. 8). To investigate the influence that this high-frequency variability might have on the mean state, and also to reduce the proneness of the model to “grid-locking” the simulated cloud layers at particular vertical levels, especially near the trade inversion, we apply at each time step a stochastic forcing on the prescribed CGILS vertical velocity profile. For this purpose, we impose a white noise (of zero mean) that has the same variance as the vertical velocity profile of aqua-planet simulations in weak subsidence regimes, and we assume that stochastic fluctuations of the large-scale vertical velocity are vertically coherent (Fig. 8).
SCM simulations with a time-varying large-scale forcing (Fig. 6b) differ considerably from those with a stationary forcing (Fig. 6a), and the time-averaged cloud fraction obtained with transient forcing is much more consistent with GCM simulations (Fig. 4) than that obtained with stationary forcing. In particular, with time-varying forcing the maximum cloud fraction occurs at 950 hPa as in present-day GCM experiments, while it occurs at 800 hPa with stationary forcing. Idealized climate change experiments associated with a prescribed +2K and performed by applying a stochastic forcing on the perturbed vertical velocity profile (assuming that the variance at each vertical level remains similar) predict time-averaged changes in cloud fraction that qualitatively resemble those predicted in GCM experiments (Fig. 4), with however a larger magnitude. An additional SCM experiment with stochastic forcing in which the atmospheric CO2 concentration is deliberately quadrupled (all other large-scale forcings remaining to their Control values) predicts a slight increase of the low-level cloud fraction and hence a negative cloud-radiative response (Table 3, experiment N) consistent with three-dimensional AMIP and aqua-planet 4xCO2 experiments (Fig. 4).
These results show that SCM simulations forced by CGILS large-scale forcings together with a white stochastic forcing qualitatively reproduce main features of the vertical cloud distribution predicted by the GCM, both under present-day conditions and climate change. In the rest of this study, we thus use stochastically-forced SCM simulations to further interpret the physical mechanisms that control the low-cloud response to external perturbations in the IPSL model.