Assessing the Impact of Meteorological History on Subtropical Cloud Fraction

Abstract

This study presents findings from the application of a new Lagrangian method used to evaluate the meteorological sensitivities of subtropical clouds in the northeast Atlantic. Parcel back trajectories are used to account for the influence of previous meteorological conditions on cloud properties, whereas forward trajectories highlight the continued evolution of cloud state. Satellite retrievals from Moderate Resolution Imaging Spectroradiometer (MODIS), Clouds and the Earth's Radiant Energy System (CERES), Quick Scatterometer (QuikSCAT), and Special Sensor Microwave Imager (SSM/I) provide measurements of cloud properties as well as atmospheric state. These are complemented by meteorological fields from the ECMWF operational analysis model. Observations are composited by cloud fraction, and mean trajectories are used to evaluate differences between each composite.Systematic differences in meteorological conditions are found to extend through the full 144-h trajectories, confirming the need to account for cloud history in assessing impacts on cloud properties. Most striking among these is the observation that strong synoptic-scale divergence is associated with reduced cloud fraction 0-12 h later. Consistent with prior work, the authors find that cloud cover variations correlate best with variations in lower-tropospheric stability (LTS) and SST that are 36 h upwind. In addition, the authors find that freetropospheric humidity, along-trajectory SST gradient, and surface fluxes all correlate best at lags ranging from 0 to 12 h. Overall, cloud cover appears to be most strongly impacted by variations in surface divergence over short time scales (,12 h) and by factors influencing boundary layer stratification over longer time scales (12-48 h).Notably, in the early part of the trajectories several of the above associations are reversed. In particular, when trajectories computed for small cloud fraction scenes are traced back 72 h, they are found to originate in conditions of weaker surface divergence and stronger surface fluxes relative to those computed for large cloud fraction scenes. Coupled with a drier boundary layer and warmer SSTs, this suggests that a decoupling of the boundary layer precedes cloud dissipation. The authors develop an approximation for the stratification of the boundary layer and find further evidence that stratification plays a role in differentiating between developing and dissipating clouds.

On the transitions in marine boundary layer cloudiness

“…This suggests that the pace of the transition depends on the absolute value of the SST/LTS rather than on its along-trajectory gradient. This finding is consistent with previous work linking LTS and the low-level cloud cover (Klein and Hartmann, 1993;Pincus et al, 1997;Mauger and Norris, 2010). The line of argument behind this idea is that a stronger inversion (hence a stronger LTS) limits the entrainment velocity at cloud top, which results in a reduced deepening and less drying of the boundary layer.…”

“…Marked changes in the large-scale divergence are only observed after the transition in cloud fraction, which suggests that such changes play a relatively minor role in the evolution of the cloud layer. The transition time scale seems to be mostly related to the strength of the LTS (which is governed by the SST) during the first part of the trajectories or prior to their starting time (Klein and Hartmann, 1993;Pincus et al, 1997;Mauger and Norris, 2010), but also (even if to a lesser extent) to the gradual humidification of the free-troposphere.…”

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

“…For example, relative humidity in the boundary layer or the state of cloud organization would be questionable candidates for a controlling factor and is not listed in Table 1 for this reason. For the cloud-controlling factors listed in Table 1, substantial observational evidence exists that cloud properties are best correlated to upwind (Klein et al 1995;Klein 1997;Mauger andNorris 2010) or earlier (deSzoeke et al 2016) sampling of the factors. These lines of evidence reinforce the notion that these quantities are external and largescale characteristics of the atmosphere or ocean which influence the boundary layer and its clouds, rather than the other way around.…”

“…Modeling studies demonstrate that a positive radiative feedback from tropical low clouds can amplify low frequency (multi-year and decadal) SST variability (Bellomo et al 2014(Bellomo et al , 2015, so there is no doubt that clouds affect SST. Nonetheless, it is also clear from large-eddy simulations ) and observational evidence (Klein et al 1995;Klein 1997;Mauger and Norris 2010) that clouds respond to SST over just a few days. For an ocean mixed-layer depth of 50 m, it takes about 300 days to produce an SST anomaly in response to cloudradiative anomalies that is consistent with the observed value of oC oSST (deSzoeke et al 2016); covariations of cloud with SST at time-scales shorter than 300 days would therefore reflect the influence of SST on cloud, and not the other way around.…”

Satellite observations of cloud regime development: the role of aerosol processes

“…We also use this technique to ensure the high and low AI populations have the same distributions of divergence (which can be approximated by 500 hPa vertical velocity), 10 m windspeed (Engstrom and Ekman, 2010) and LTSS (Mauger and Norris, 2010), as they have all been suggested to affect the AI-CF relationship.…”