Explicit Boundary Layer

Here the CRM has a 250m horizontal and 20m vertical resolution in the inversion mandating a 1 second time-step size or smaller.

This model is a computational grand challenge and requires a Peta-scale platform to operate on. I think this sort of convection-permitting models will be standard practice in the near future. Today, if you have a state-of-the-art supercomputer in your backyard and looking for some fun, 15,000 cores in 1 hour can give you a 1-day global simulation in Ultra-parameterized framework :).

Ultra-parameterization captures various types of clouds

Stratocumulus (Sc), shallow cumulus (Cu) and Cu-under-Sc clouds are realistically captured in ultra-parameterized framework. In the next plot, simulated height-time evolution of Sc clouds is shown off the coasts of California and Peru, along with shallow Cu clouds in Barbados and also Cu-under-Sc in South/Central Pacific; all of which are validated against NASA C3M satellite data (article submission imminent). The 125 level vertical cloud-resolving grid is shown on the left.

Why low clouds are so important?

Climate scientists talk about low clouds all the time. The reason is that the low clouds are the most important yet uncertain elements on the planet to regulate the global temperature by reflecting the solar radiation back to space. You have seen the top of the low clouds when flying in an airplane. They are so bright (just like the Sun itself) that you will find it hard to look at them. Enormous areas of such low clouds exist off the coasts of California, Peru and Namibia (marked red in the plot bellow). These are almost permanent type of clouds and they sit there and hang around for a long time. For instance, the snapshot on the left is how Peruvian low clouds look like from NASA Earth Observatory.

Indeed IPCC-AR5 shows that the majority of uncertainty in climate feedback is caused by the huge uncertainty of low-clouds in the GCMs. The next plot shows the bias of the short-wave absorbed radiation between the NASA satellite observation and the model. The three red boxes are the areas in which the current GCMs are not able to simulate the low-clouds and therefor a huge bias exists in absorbed radiation in those areas. This bias could be ~100 Watt per square meter or larger. Bottomline, the long-standing question is how and to what extent the low-cloud feedback is important to the climate?