Linking large-scale circulation patterns to low-cloud properties

Abstract

Abstract. The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850-hPa synoptic-scale circulation patterns. This approach, which yields the frequency at which these regimes occur, reveals two distinguishable patterns – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of regimes. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration and cloud droplet effective radius) on 850-hPa circulation patterns over the northeast Pacific Ocean. Moreover, we find that shortwave cloud radiative forcing ranges from − 144.0 to − 117.5 W/m2, indicating that the range of MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite observations to help clarify the relationship between synoptic-scale dynamics and cloud microphysics.

Demystifying the complex nature of stratocumulus clouds with machine learning

“…However, it is also a demonstration that machine learning tools are invaluable for studying the behaviour of these cloud systems. Other machine learning techniques, such as neural networks, are excellent for applications like classification of cloud structures since we have large quantities of satellite data (Denby, 2020; Juliano and Lebo, 2020). But in modelling studies where the quantity of data may be limited by computational resource, a method like statistical emulation is more appropriate.…”