Improving Representation of Tropical Cloud (4)

The better representation of Lcf is primarily expected to be capable of generating a realistic Ctot for given cloud fraction profiles. We evaluate here the generated Ctot by using the Lcf parameterized from Eq. (3) (denoted as PARA). Two other cloud overlap treatments are also included to compare with PARA: the traditional MRO assumption, which has been widely used not only in GCMs(R?is?nen, 1998; Collins, 2001), but also in model assessment tools such as the CFMIP Observation Simulator Package (Bodas-Salcedo et al., 2011); and the simplified GenO using a constant Lcf (2 km) (denoted as G2KM), which applies the framework of GenO, but with a constant Lcf suggested as a global mean value (Barker,2008). These cloud overlap treatments are used to generate sub-grid cloud fields from the cloud fraction profiles of the CRM and the values of Ctot of the generated fields are then compared with those of the original CRM fields.

Figure 7 shows the 7-day mean biases of Ctot generated from the three cloud overlap representations relative to the reference value. It can be seen that the widely used MRO assumption remarkably underestimates Ctot in most regions. This is because there are few clear layers to separate the cloud layers in the vertical columns and therefore there is a maximum overlap of clouds in most demonstrates one drawback of the MRO technique:it depends greatly on the vertical resolution of the host model and is therefore usually non-equivalent among models.

The use of GenO significantly reduces the negative biases compared with the MRO assumption, even when a constant Lcf of 2 km is used (Fig. 7b). However, G2KM leads to notable Ctot biases in the ITCZ, especially in the western Pacific and Amazon regions, where clouds are generally more vertically organized because of systematic large-scale upward motion. When the dynamic representation of Lcf for regions of ascent is used, the cloud fraction errors in the ITCZ are remarkably reduced (Fig. 7c).

3.3 Evaluation in terms of radiation effects

Radiation calculations are performed for the generated cloud fields and the original CRM cloud fields in the evaluation using the BCC-RAD correlated-k distribution radiation model (Zhang et al., 2003, 2006a, b ),which has been implemented in the GCM of the Beijing Climate Center (BCC_) (Zhang et al., 2014).For both the generated and original cloud fields, the cloud water/ice content in each GCM grid is obtained as a grid mean value to eliminate the effect of the horizontal distributions of cloud water/ice. Because only the cloud and dynamic output from NICAM are stored (due to the large amount of data), we apply in the radiation calculation the atmospheric pressures, temperatures, and gas concentrations from the tropical atmosphere of the US Air Force Geophysics Laboratory atmospheric models(Anderson et al., 1986) and the broadband surface albedos for the ocean (0.08, Jin et al., 2004) and solid surfaces (0.28, Liang, 2001), respectively.

Figure 8 shows the biases in the generated net longwave (LWTOA) and shortwave (SWTOA) radiative fluxes at the top of the atmosphere relative to the CRM results. The largest radiation biases occur in the ITCZ,where the largest Ctot biases are also seen. The MRO assumption shows significant negative biases for LWTOA and positive biases for SWTOA (mostly ＞ 10 and ＞ 25 W m-2, respectively) (Figs. 8a, b) due to the underestimation of Ctot. Figures 8c and 8d show that the use of GenO with a constant Lcf of 2 km reduces the biases in subtropical regions, but also introduces notable biases in the ITCZ with opposite signs to the biases of the MRO assumption. The dynamic representation of Lcf largely reduces the negative biases in the ITCZ (Figs. 8e, f), with considerably fewer regions having absolute biases ＞ 10 and ＞ 25 W m-2 for LWTOA and SWTOA, respectively.

Figure 9 shows the biases in the net longwave (LWSFC) and shortwave (SWSFC) radiative fluxes at the surface. By comparing Fig. 9 with Fig. 8, it is seen that the main features of the biases at the surface are similar to those at the TOA, except that G2KM and PARA have much smaller (larger) biases in LWSFC over (outside)the ITCZ. PARA has the smallest errors among the three treatments of cloud overlap over the ITCZ for both LWSFC and SWSFC. It is therefore evident that the dynamic representation of Lcf yields the best spatial patterns of the radiation fields.

Fig. 7. Biases of generated Ctot using (a) the MRO assumption, (b)GenO with universal Lcf = 2 km (G2KM), and (c) GenO with dynamic representation of Lcf (PARA) relative to the true Ctot from the CRM(REF). Contour lines are shown for .

Fig. 8. Biases of generated LWTOA (left-hand panels) and SWTOA (right-hand panels) using (a, b) the MRO assumption, (c, d) GenO with Lcf= 2 km universally, and (e, f) GenO with dynamic representation of Lcf, relative to those calculated directly from the CRM fields. The downward direction is defined as positive. The contour lines for ±10 and ±25 W m-2 are shown for LWTOA and SWTOA, respectively.

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