On the Factors That Determine Boundary Layer Albedo

Rampal, Neelesh; Davies, Roger ✉

Angol nyelvű Tudományos Szakcikk (Folyóiratcikk)
Megjelent: JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES 2169-897X 2169-8996 0747-7309 125 (15) Paper: e2019JD032244 , 12 p. 2020
  • SJR Scopus - Aquatic Science: D1
Azonosítók
Szakterületek:
    This study investigates the factors that control marine boundary layer cloud albedo measured by the Multiangle Imaging SpectroRadiometer (MISR) over domains of (200 km)(2). We use three key metrics to investigate domain albedo: cloud fraction, cloud heterogeneity, and cloud morphology. Cloud heterogeneity is quantified at the domain level with a unified heterogeneity index. Cloud morphology is determined from a cloud classification algorithm using an Artificial Neural Network (ANN) to classify each domain into one of four categories: (i) closed-cell Mesoscale Cellular Convection (MCC); (ii) open-cell MCC; (iii) disorganized MCC; and (iv) No MCC. These different types of MCC are usefully defined as low clouds of different morphologies. Classifications from the ANN are also combined with the satellite observations of MISR to develop relationships between cloud morphology, domain albedo, cloud fraction, and cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction-albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low cloud behavior and improving their representation in General Circulation Models.Plain Language Summary Boundary-layer marine clouds are an essential component of the global radiation budget, as they strongly reflect incoming sunlight and reduce the amount of radiation absorbed at the surface. Climate models are unable to simulate/reproduce the albedo of boundary-layer marine clouds, particularly over the Southern Ocean, resulting in large uncertainties in future climate model predictions. Therefore, by improving the model's ability to reconstruct the albedo of boundary layer clouds, we can reduce the margin of error in future climate model predictions. Our study investigates how changes in three important physical variables within the domain affect the overall domain albedo. These variables are the amount of cloud present within the domain (domain cloud fraction); the amount of variability/heterogeneity that exists within the cloud (domain heterogeneity); and the type of cloud that is present within the domain, obtained using an Artificial Neural Network (pattern-recognizing algorithm). For the same amount of cloud cover, domains that have more variability or larger fluctuations in reflectivity within the cloud have a lower albedo. Therefore, within a climate model, failing to correctly represent domain heterogeneity would result in a biased estimate of marine boundary layer albedo. Similarly, for the same amount of cloud cover, closed-cell domains have a larger albedo than open-cell domains. This difference in albedo is attributed to open-cell domains having a stronger presence of heterogeneity or larger fluctuations in reflectivity within the cloud. Incorporating information such as cloud heterogeneity and cloud type into climate models could help reduce uncertainties and improve future climate predictions.
    Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
    2021-05-15 18:59