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Improving Representation of Tropical Cloud (2)

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摘要：

The CRM data and the method used to derive Lcf are described in Section 2. Section 3 presents the analysis of Lcf versus atmospheric convection and evaluates the established representation of Lcf. The discussion and conclusions are presented in Section 4.

2. Data and methods

2.1 CRM data

The global CRM data are from a simulation of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)developed at Japan Agency for Marine-Earth Science and Technology and the University of Tokyo (Tomita and Satoh, 2004; Satoh et al., 2008, 2014). The cloud microphysics scheme of Grabowski (1998) is adopted and no convective scheme is used. This scheme is simpler than other cloud microphysics schemes, but convective circulation is explicitly calculated so that the associations between convection and large-scale atmospheric states are consistently represented. The boundary layer scheme with moist processes is implemented (Nakanishi and Niino, 2006). The vertical resolution of the model is 40 levels with Lorenz grids (Satoh et al., 2008, 2014)stretching from the surface up to about 40 km. The vertical interval of the half-level (ΔZ) increases with height from about 170 m at the bottom to about 3 km near the top (ΔZ ＜ 1 km below 10 km).

NICAM can be run using different horizontal resolutions depending on the grid division level used. In this study, data from a simulation using a grid division level of 11 (corresponding to a grid size of about 3.5 km)(Miura et al., 2007) are adopted; the cloud characteristics of this dataset have been extensively analyzed by Inoue et al. (2008, 2010), Masunaga et al. (2008), Sato et al. (2009), and Satoh et al. (2010). The simulation was started from 0000 UTC 25 December 2006 and integrated for 7 days. The results were stored as instantaneous snapshots at 0000 UTC on each day. Figures 1 and 2 show the simulated total cloud fraction and the zonal mean vertical cloud fraction profile, respectively, compared with those from the 2B-GEOPROF product of CloudSat observations (Marchand et al., 2008) during the same period. The NICAM simulation captures both the geographical distribution of cloud systems in the deep convective regions (Fig. 1) and the vertical cloud profiles in the tropics (Fig. 2). The cloud top height of NICAM in Fig. 2 is slightly higher than the CloudSat observations,which is because CloudSat does not detect optically thin clouds in the topmost layers (Stephens et al., 2008).

The variables used in this study are the mass mixing ratios of liquid and ice water condensates (qc and qi, respectively), in-cloud precipitation (qr) and snow (qs), and vertical velocity (w).

2.2 Derivation of Lcf

To obtain Lcf at the resolution of the GCM and to establish the GCM-oriented relationship between Lcf and atmospheric convection, the original NICAM output is averaged based on a 2.8° × 2.8° (latitude × longitude)grid division, which is close to a T42 GCM grid mean fields are then used to obtain Lcf using a stochastic cloud generator (R?is?nen et al., 2004) as follows.

1) Diagnose the occurrence of cloud from qc, qi, qr,and qs. If qc + qi + qr + qs ＞ 0.01 g kg-1 in a CRM grid cell, then this grid cell is regarded as cloudy (i.e., cloud fraction = 1), otherwise the grid cell is regarded as clear(i.e., cloud fraction = 0). This criterion was also used in Grabowski (1998).

Fig. 1. Comparison of total cloud fraction (Ctot) between (a) CloudSat observations and (b) the NICAM simulation during the 7-day period starting from 0000 UTC 25 December 2006.

2) Average the original CRM fields to the 2.8° × 2.8°grid division. The cloud fraction and the vertical velocity at 500 hPa (w500) are averaged within each GCM grid. These yield vertical distributions of cloud fractions and w500 for each GCM grid. In addition, the vertically projected cloud fraction (i.e., Ctot) for each GCM grid is derived by dividing the number of cloudy CRM columns by the total number of CRM columns within the grid.

3) Obtain Lcf. The vertical cloud fraction profile and Ctot in each GCM grid are supplied to the stochastic cloud generator with GenO incorporated to obtain Lcf. Lcf is defined as the value that gives the same Ctot as the original CRM cloud field when used in Eqs. (1) and (2). To demonstrate the effectiveness of using this procedure to capture the cloud overlap characteristics, a GCM grid with a typical cloud profile often seen in the tropical deep convective region is chosen as an example and the generated and original cloud structures are properties examined include the vertical cloud fraction profile, the downward cumulative cloud fraction,and the cloud fraction exposed to space at different heights (Fig. 3). It is suggested that, by applying the achieved Lcf in GenO, the generated cloud structures(dotted lines) resemble those of the original CRM field(solid lines). The characteristics of clouds shown in Fig. 3 are important to both solar reflectance and upward longwave emissivity. Therefore, the cloud structures generated by GenO with an accurate value of Lcf potentially facilitate the computation of radiation fields.

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