The Effects of Explicit Atmospheric Convection at High CO2

Abstract:

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 pre-industrial and quadrupled CO$_2$ simulations between a conventional GCM in which convection is parameterized and a ``super-parameterized'' 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, winter-time Arctic convection, and enhanced MJO activity. Super-parameterization 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 Madden-Julian oscillation.

The representation of clouds and convection has an enormous impact on simulation of the climate system. This study addresses concerns that conventional parameterizations may bias the response of climate models to increased greenhouse gases. The broadly similar response of two models with parameterized and non-parameterized convection and clouds suggests that state-of-the-art predictions, based on parameterized climate models, may not necessarily be strongly biased in either direction (too strong or too weak warming). At the same time, large differences in simulated tropical variability and Arctic sea ice area suggest that improvement in convection and cloud representations remain essential.