Using A-Train Observations to Evaluate Cloud Occurrence and Radiative Effects in the Community Atmosphere Model during the Southeast Asia Summer Monsoon

Abstract

The distribution of clouds and their radiative effects in the Community Atmosphere Model, version 5 (CAM5), are compared to A-Train satellite data in Southeast Asia during the summer monsoon. Cloud radiative kernels are created based on populations of observed and modeled clouds separately in order to compare the sensitivity of the TOA radiation to changes in cloud fraction. There is generally good agreement between the observation- and model-derived cloud radiative kernels for most cloud types, meaning that the clouds in the model are heating and cooling like clouds in nature. Cloud radiative effects are assessed by multiplying the cloud radiative kernel by the cloud fraction histogram. For ice clouds in particular, there is good agreement between the model and observations, with optically thin cirrus producing a moderate warming effect and cirrostratus producing a slight cooling effect, on average. Consistent with observations, the model also shows that the median value of the ice water path (IWP) distribution, rather than the mean, is a more representative measure of the ice clouds that are responsible for heating. In addition, in both observations and the model, it is cirrus clouds with an IWP of 20 g m−2 that have the largest warming effect in this region, given their radiative heating and frequency of occurrence.

Southern Ocean Cloud Properties Derived From CAPRICORN and MARCUS Data

“…Following the method described in Berry et al. (2019, 2020), we calculate the downwelling solar flux at the surface using the two‐stream radiative transfer model described by Toon et al. (1989) as modified by Kato et al.…”

“…We assume that retrieval biases would show up as overall biases in the solar flux comparison. Following the method described in Berry et al (2019Berry et al ( , 2020, we calculate the downwelling solar flux at the surface using the two-stream radiative transfer model described by Toon et al (1989) as modified by Kato et al (2001). For solar zenith angles less than 80° with no higher cloud layers, we compare the downwelling solar fluxes measured on the ships with those calculated using the retrieved microphysical quantities.…”

The Top‐of‐Atmosphere, Surface and Atmospheric Cloud Radiative Kernels Based on ISCCP‐H Datasets: Method and Evaluation

“…Since Z2012, there have appeared a number of literatures (Berry et al., 2019; Yue et al., 2016; Zhou et al., 2013) using Z2012's method to produce CRKs, with which to estimate cloud feedback. The CRKs may be GCM based, observation based or their combination.…”

Cloud phase characteristics over Southeast Asia from A-Train satellite observations

“…While cloud overlap or vertical heterogeneity is important in the radiative transfer, so is cloud horizontal heterogeneity (Marshak and Davis, 2005). It has long been demonstrated that the neglect of cloud horizontal heterogeneity with the plane-parallel assumption in radiative transfer can cause significant biases in computing irradiances and atmospheric heating rates (e.g., O'Hirok and Gautier, 2005), photolysis rates (e.g., Bouet et al, 2006), the emerging spectral and angular distribution of outgoing radiation field (Loeb and Davies, 1997;Song et al, 2016), and in retrieving cloud microphysical properties from passive sensors (Loeb and Davies, 1996;Marshak et al, 2006). Many studies have since examined, at least in part, the global nature of these biases by successfully associating them with measured local spatial heterogeneity in SW radiance (Di Girolamo et al, 2010;Ham et al, 2015;Liang et al, 2015).…”