Ice water content vertical profiles of high-level clouds: classification and impact on radiative fluxes

Abstract

Abstract. In this article, we discuss the shape of ice water content (IWC) vertical profiles in high ice clouds and its effect on their radiative properties, both in short-and in long-wave bands (SW and LW). Based on the analysis of collocated satellite data, we propose a minimal set of primitive shapes (rectangular, isosceles trapezoid, lower and upper triangle), which represents the IWC profiles sufficiently well. About 75 % of all high-level ice clouds (P < 440 hPa) have an ice water path (IWP) smaller than 100 g m −2 , with a 10 % smaller contribution from single layer clouds. Most IWC profiles (80 %) can be represented by a rectangular or isosceles trapezoid shape. However, with increasing IWP, the number of lower triangle profiles (IWC rises towards cloud base) increases, reaching up to 40 % for IWP values greater than 300 g m −2 . The number of upper triangle profiles (IWC rises towards cloud top) is in general small and decreases with IWP, with the maximum occurrence of 15 % in cases of IWP less than 10 g m −2 . We propose a statistical classification of the IWC shapes using IWP as a single parameter. We have estimated the radiative effects of clouds with the same IWP and with different IWC profile shapes for five typical atmospheric scenarios and over a broad range of IWP, cloud height, cloud vertical extent, and effective ice crystal diameter (De). We explain changes in outgoing LW fluxes at the top of the atmosphere (TOA) by the cloud thermal radiance while differences in TOA SW fluxes relate to the De vertical profile within the cloud. Absolute differences in net TOA and surface fluxes associated with these parameterized IWC profiles instead of assuming constant IWC profiles are in general of the order of 1-2 W m −2 : they are negligible for clouds with IWP < 30 g m −2 , but may reach 2 W m −2 for clouds with IWP > 300 W m −2 .

Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications

“…z COD0.5 is required to be located within the corresponding GEOPROF cloud layer. z COD0.5 is deduced from the CALIPSO L2 COD, assuming a constant increase of COD from cloud top towards cloud base, except for high-level clouds, for which the shape of the ice water content profile as a function of cloud emissivity is taken into account (Feofilov et al, 2015b). As the COD of CALIPSO might be slightly underestimated , especially for larger COD, we reduce the ratio 0.5 / COD to 0.4 / COD, used in the estimation of z COD0.5 .…”

“…We use the same collocation procedure as in Feofilov et al (2015b): first, each AIRS footprint is collocated with NASA CALIPSO L2 cloud data averaged over 5 km (version 3;Winker et al, 2009) in such a way that for each AIRS golf ball, three CALIPSO samples are matched to the centres of three AIRS footprints. These data are then collocated with the NASA L2 CloudSat-lidar geometrical profiling (GEOPROF) data (version R04; Mace and Zhang, 2014).…”

Combining In Situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment

“…Specifically, the vertical variability of cloud ice and liquid phase partitioning in regions where supercooled liquid water droplets and ice particles coexist strongly impacts representation of cloud and precipitation in models (Grabowski et al, 2019; Korolev et al, 2017; McCoy et al, 2016). For satellite remote sensing and in situ observations, the vertical structures of hydrometeors continue to serve as a key component in constructing a priori and developing retrieval algorithms (e.g., Feofilov et al, 2015; Hashino et al, 2013; Jiang et al, 2019; Reinhart et al, 2014; Seo & Biggerstaff, 2006; Wang et al, 2019; Zhang et al, 2010).…”

“…The vertically inhomogeneous structures of clouds are important considerations for theoretical cloud modeling, satellite remote sensing, and in situ observations (e.g., Feofilov et al, 2015; Grabowski et al, 2019; Hashino et al, 2013; Korolev et al, 2017; Reinhart et al, 2014; Seo & Biggerstaff, 2006). Often, the inhomogeneous structures of clouds are quantitatively represented by the vertical variability of microphysical properties for different hydrometeor species in the atmosphere, which often vary over certain range and covary with each other.…”

Vertical Profiles of Ice Cloud Microphysical Properties and Their Impacts on Cloud Retrieval Using Thermal Infrared Measurements

“…Maestri and Holz (2009) emphasized the necessity of incorporating cloud vertical inhomogeneity in passive remote sensing of ice clouds using TIR wavelengths. Other studies have also discussed the importance of cloud vertical inhomogeneity for accurate calculations of radiative irradiance/flux within the atmosphere (e.g., Feofilov et al, 2015;Ham & Sohn, 2012;Maestri et al, 2005).…”