Effects of explicit atmospheric convection at high CO2

The effect of clouds on climate remains the largest uncertainty in climate change predictions, due to the inability of global climate models (GCMs) to resolve essential small-scale cloud and convection processes. We compare preindustrial and quadrupled CO₂ simulations between a conventional GCM in which convection is parameterized and a “superparameterized” model in which convection is explicitly simulated with a cloud-permitting model in each grid cell. We find that the global responses of the two models to increased CO₂ are broadly similar: both simulate ice-free Arctic summers, wintertime Arctic convection, and enhanced Madden–Julian oscillation (MJO) activity. Superparameterization produces significant differences at both CO₂ levels, including greater Arctic cloud cover, further reduced sea ice area at high CO₂, and a stronger increase with CO₂ of the MJO.Clouds play an important role in the climate system by reflecting incoming shortwave solar radiation (cooling), intercepting outgoing longwave radiation from the surface (warming), and influencing temperature and circulation. Their net radiative impact at the surface is about −20 W/m² cooling in the global mean, and regional impacts can approach ∼40 W/m². Understanding how clouds will respond to rising CO₂ concentrations is thus a critical issue in climate science. Progress has been complicated by the hundred-kilometer horizontal grid spacing of most global circulation models (GCMs), which remain unable to directly resolve the much smaller-scale turbulent motions involved in atmospheric moist convection, the corresponding cloud-formation processes, and their radiative effects (1, 2).Current treatment of convection in global climate models relies on parameterizations and therefore suffers significant uncertainties, particularly relating to the representation of convection and clouds in a changing climate. Model results are sensitive to formulation and parameter choices in parameterized convection schemes. As a result, the magnitude of cloud feedbacks remains uncertain and inconsistently predicted by different models (2). An alternative approach, “superparameterization,” attempts to reduce the uncertainties of parameterization by running a higher resolution cloud-permitting model in a small domain within each grid cell of the atmospheric GCM, simulating the convection and cloud motions more explicitly (3, 4). Superparameterized GCMs have been shown to have a more realistic representation of convective variability, including the diurnal cycle (5) and intraseasonal variability such as the Madden–Julian oscillation (MJO) (6) and the Australian and Indian monsoons. They are beginning to be used to project future climate changes (7), although such work has been limited due to computational costs of about 100 times that of a standard GCM.Here we present the results of running a global coupled ocean–atmosphere model [the Community Earth System Model (CESM; ref. 8)], and its superparameterized variant (SP-CESM; refs. 3, 4, 9) at a preindustrial CO₂ concentration, as well as at 4 times higher concentration. We run CESM to near steady state for both preindustrial CO₂ concentration and 4 times this value (×1CO₂ and ×4CO₂), and then run shorter simulations of SP-CESM starting from these steady states (Materials and Methods). We choose to examine a rather significant (although not necessarily unrealistic) ×4CO₂ increase scenario because the equilibrium climate sensitivity of CESM to CO₂ doubling is on the low side of the warming range of 2.1–4.7 K seen in a recent model intercomparison (10), and to maximize the signal-to-noise ratio in the model response to superparameterization.CESM and SP-CESM are nearly identical except for their convection and cloud representation and related physics (Materials and Methods), but they show significant differences in their simulations at ×1 and ×4CO₂. Concerns have been raised that convection and cloud parameterizations may lead to either artificial amplification or weakening of the response to CO₂ increase. We find the global climate responses of CESM and SP-CESM to be broadly similar, a reassuring result in terms of present projections that are based on parameterized models. However, we find significant regional differences for Arctic sea ice and the tropical Madden–Julian oscillation on which we focus in this paper. Specifically, we find that SP-CESM shows (i) significantly less sea ice at ×1CO₂ and a larger area reduction at ×4CO₂, and (ii) a stronger MJO at ×1CO₂ and a larger increase at ×4CO₂. We analyze these differences and discuss the implications for uncertainties in climate change projections.The Arctic, and Arctic sea ice melting in particular, is strongly affected by the presence of low clouds that reduce solar heating in summer and by high clouds that induce warming in winter. Arctic sea ice has undergone rapid recent changes (11, 12), and is believed to have played a major role in past abrupt climate changes (13). Sea ice has a major impact on climate due to its high albedo and ability to insulate the atmosphere from the warmer ocean. Arctic sea ice change impacts local ecosystems (14), modulates extreme weather events in the sub-Arctic and midlatitudes (15), and has implications for shipping routes (16).Our focus on the MJO is motivated in part by numerous studies showing that present-day MJO simulations with SP-CESM are significantly improved relative to results from conventional GCMs, which have historically struggled to simulate it realistically. The MJO is characterized by an envelope of convective anomalies with a 30–70-day timescale that forms episodically over the Indian Ocean, propagates slowly eastward at around 5 m/s, and dissipates over the central Pacific (17, 18). The MJO affects the monsoons and Atlantic tropical cyclogenesis, modulates westerly wind bursts that can help trigger El Niño events, dramatically impacts tropical rainfall, and contributes to extreme precipitation events globally (18, 19). There is observational (20–23) and model (24–27) evidence of enhanced MJO activity with warming, although not all models agree on the sign of MJO change (28), and the change may be sensitive to the spatial pattern of warming (29).