Large-eddy simulation of subtropical cloud-topped boundary layers: 2. Cloud response to climate change
How subtropical marine boundary layer (MBL) clouds respond to warming is investigated using large‐eddy simulations (LES) of a wide range of warmer climates, with CO_2 concentrations elevated by factors 2–16. In LES coupled to a slab ocean with interactive sea surface temperatures (SST), the surface latent heat flux (LHF) is constrained by the surface energy balance and only strengthens modestly under warming. Consequently, the MBL in warmer climates is shallower than in corresponding fixed‐SST LES, in which LHF strengthens excessively and the MBL typically deepens. The inferred shortwave (SW) cloud feedback with a closed energy balance is weakly positive for cumulus clouds. It is more strongly positive for stratocumulus clouds, with a magnitude that increases with warming. Stratocumulus clouds generally break up above 6 K to 9 K warming, or above a four to eightfold increase in CO_2 concentrations. This occurs because the MBL mixing driven by cloud‐top longwave (LW) cooling weakens as the LW opacity of the free troposphere increases. The stratocumulus breakup triggers an abrupt and large SST increase and MBL deepening, which cannot occur in fixed‐SST experiments. SW cloud radiative effects generally weaken while the lower‐tropospheric stability increases under warming—the reverse of their empirical relation in the present climate. The MBL is deeper and stratocumulus persists into warmer climates if large‐scale subsidence decreases as the climate warms. The contrasts between experiments with interactive SST and fixed SST highlight the importance of a closed surface energy balance for obtaining realizable responses of MBL clouds to warming.
© 2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. Received 12 SEP 2016; Accepted 18 NOV 2016; Accepted article online 20 DEC 2016; Published online 20 JAN 2017. This work was supported by the U.S. National Science Foundation (grant CCF‐1048575), by Caltech's Terrestrial Hazard Observation and Reporting (THOR) Center, and by the Swiss National Science Foundation. The numerical simulations are performed on the Euler Cluster operated by the high performance computing (HPC) team at ETH Zürich. We also thank Chris Bretherton and Peter Blossey for helpful discussions about the experimental design and interpretation of results. The PyCLES code and the configurations for the idealized climate change experiments are available online at climate-dynamics.org/software.
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