CALIPSO dataset and its application in AGCM with McICA scheme

Atmospheric Research 170 (2016) 52–65

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Cloud overlapping parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM with McICA scheme Xianwen Jing a,b, Hua Zhang a,b,⁎, Jie Peng c, Jiangnan Li d, Howard W. Barker e a

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China Shanghai Meteorological Bureau, Shanghai, China d Canadian Center for Climate Modeling and Analysis, University of Victoria, Victoria, BC, Canada e Cloud Physics Research Division, Environment Canada, Downsview, ON, Canada b c

a r t i c l e

i n f o

Article history: Received 20 July 2015 Received in revised form 6 October 2015 Accepted 23 November 2015 Available online 2 December 2015 Keywords: Cloud overlap Decorrelation length CloudSat/CALIPSO AGCM

a b s t r a c t Vertical decorrelation length (Lcf) as used to determine overlap of cloudy layers in GCMs was obtained from CloudSat/CALIPSO measurements, made between 2007 and 2010, and analyzed in terms of monthly means. Global distributions of Lcf were produced for several cross-sectional lengths. Results show that: Lcf over the tropical convective regions typically exceeds 2 km and shift meridionally with season; the smallest Lcf (b1 km) tends to occur in regions dominated by marine stratiform clouds; Lcf for mid-to-high latitude continents of the Northern Hemisphere (NH) ranges from 5–6 km during winter to 2–3 km during summer; and there are marked differences between continental and oceanic values of Lcf in the mid-latitudes of the NH. These monthly-gridded, observationally-based values of Lcf data were then used by the Monte Carlo Independent Column Approximation (McICA) radiation routines within the Beijing Climate Center's GCM (BCC_AGCM2.0.1). Additionally, the GCM was run with two other descriptions of Lcf: one varied with latitude only, and the other was simply 2 km everywhere all the time. It is shown that using the observationally-based Lcf in the GCM led to local and seasonal changes in total cloud fraction and shortwave (longwave) cloud radiative effects that serve mostly to reduce model biases. This indicates that usage of Lcf that vary according to location and time has the potential to improve climate simulations. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The horizontal resolutions of atmospheric global climate models (AGCMs) are usually dozens to hundreds of kilometers. At these scales, microphysical properties of clouds are parameterized (Lu et al., 2013) and clouds usually cover just a portion of the grid-cell. Thus the vertical alignment, or overlap, of fractional clouds has to be specified in order to compute radiative flux profiles (Tian and Curry, 1989; Hogan and Illingworth, 2000; Willén et al., 2005). The manner in which clouds overlap influences both the verticallyintegrated cloud fraction and the horizontal distribution of cloud water path, and these consequently modulate shortwave and longwave reflectance and transmittance (e.g., Barker, 2008a; Barker et al., 1999, 2003; Li et al., 2005). In the past, AGCMs treated cloud overlap with simple descriptions such as the maximum-random-overlap (MRO) assumption (Morcrette and Fouquart, 1986; Tian and Curry, 1989). Such treatments are, however, unable to represent realistic features of cloud overlap as ⁎ Corresponding author at: Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China. E-mail address: [email protected] (H. Zhang).

http://dx.doi.org/10.1016/j.atmosres.2015.11.007 0169-8095/© 2015 Elsevier B.V. All rights reserved.

seen in ground-based radar observations (Hogan and Illingworth, 2000; Mace and Benson-Troth, 2002) and depend much on the arbitrary vertical layering of the host model (Bergman and Rasch, 2002). Hogan and Illingworth (2000) and Mace and Benson-Troth (2002) proposed a more general cloud overlap algorithm, referred to hereinafter as “generalized overlap” (GenO), in which the degree of overlap grades from maximum to random exponentially as the vertical geometric distance between cloud layers increases. The exponential decay that describes the probability of cloud overlap is modulated by the decorrelation length Lcf, which is the distance at which the relationship of two cloud layers slackens to virtually random overlap (see Section 2.2.1). The decreasing–overlap with increasing–distance relationship was also reported from cloud resolving model simulations (Oreopoulos and Khairoutdinov, 2003). However, while GenO is capable in generating quite realistic vertical alignments of cloud, Lcf depends on cloud type, thereby making it a challenge to apply in AGCMs. Since then, a variety of attempts have been made to derive global and seasonal information about Lcf and to extract simplified expressions to represent it in AGCMs (Di Giuseppe, 2005; Barker, 2008b; Kato et al., 2010; Shonk et al., 2010; Oreopoulos et al., 2012; Zhang et al., 2013b; Peng et al., 2013 and others). One simplistic implementation of GenO

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is to use a constant value of Lcf everywhere and throughout the simulation. Barker (2008a,b) suggested that if this approach were to be taken, a value of Lcf = 2 km would suffice as it is the median derived from global observations of cloud profiles inferred from measurements made by the CloudSat and CALIPSO satellites (Stephens et al., 2008). More informed approaches attempt to depict latitudinal (Shonk et al., 2010) and interseasonal variations of Lcf (Oreopoulos et al., 2012). These parametrizations, however, fail to address variations in the longitudinal direction. On the other hand, Zhang et al. (2014b) proposed a cloudtype-dependent function, which yields similar zonal mean Lcf to the observation based results. But their approach inevitably depends on highly uncertain cloud classification schemes that would have to be used in the AGCM. It is interesting to ask how important it is to include accurate descriptions of Lcf in climate model simulations. Such assessments using different, and simple, Lcf schemes have been performed in previous studies (Oreopoulos et al., 2012; Zhang et al., 2014b). What is lacking, however, is a comparison using spatially and temporally resolved descriptions of Lcf. Hence, the purpose of the present study is to extend these comparisons using a globally-gridded view of Lcf based on four years of CloudSat/CALIPSO observations. A 12month global climatological dataset of Lcf was obtained and used to constrain the description of cloud overlap in the McICA radiation algorithm in the updated BCC_AGCM2.0.1 global model (Zhang et al., 2014b). The impacts on simulated cloud cover and radiation were contrasted against two traditional simple and popular methods: i) a global constant of Lcf = 2 km; and ii) the latitude-dependent method of Shonk et al. (2010). This is the first time that a time- and spacedependent description of Lcf has been used in an AGCM with the McICA scheme. This paper is arranged as follows: Section 2 explains how Lcf was obtained from satellite data and the setups of AGCM experiments. Section 3 shows seasonal global distributions of the obtained Lcf and compares how the BCC_AGCM2.0.1 responded to use of the three descriptions of Lcf mentioned above. Finally, concluding remarks are made in Section 4.

criterion (Barker, 2008b) was used here to identify a cloudy volume (regardless of cloud phase): 8 < CPR Cloud mask ≥20 CloudFraction ≥99% : Z ≥ −30 dBZ

2.2. Methodology 2.2.1. Introduction of GenO Suppose there are two cloud layers k and l, with fractional cloud amounts of Ck and Cl, respectively. In GenO, the vertically projected cloud fraction of the two layers Ck,l is the linear combination of total ran cloud fractions derived from maximum C max k,l and random overlap C k,l assumptions and defined as   ran C k;l ¼ α k;l C max k;l þ 1−α k;l C k;l ;

ð2Þ

where C max k;l ¼ maxðC k ; C l Þ;

ð3Þ

C ran k;l ¼ C k þ C l −C k C l :

ð4Þ

The overlap parameter αk,l is prescribed via an exponential decay function of the vertical separation between cloud layers as 2

α k;l

ZZ l 6 ¼ exp4− Zk

The CloudSat/CALIPSO satellites were launched in 2006 with the objective of inferring global distributions of the vertical structure of clouds. These members of the A-Train constellation have overlapping views of the atmosphere that are separated by only 15 s. CloudSat carries a 94-GHz nadir-pointing cloud-profiling radar (CPR) which is capable of detecting most warm clouds at altitudes above ~1 km (Stephens et al., 2002; Chen et al., 2011) and very light precipitation within 80°S–80°N (Behrangi et al., 2015). It has difficulties, however, detecting clouds whose particles are small, such as thin cirrus (Sassen and Wang, 2008). But its deficiencies are greatly covered by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on CALIPSO, for it is sensitive to optically thin clouds that contain small particles (Winker et al., 2003; Weisz et al., 2007), for instance highly-located cirrus clouds (Iwasaki et al., 2015). Hence, the combination of the two is often adopted to obtain a more complete view of clouds (Hagihara et al., 2009; Mace et al., 2009). In this work, the 2B-GEOPROF and 2B-GEOPROF-LIDAR products during 2007–2010 were used. Both products are at 1.1 km along-track and divided into layers of about 240 m thick. The width of granules is 1.4 km across-track. In 2B-GEOPROF, the quantity CPR_Cloud_mask assigns a number between 0 and 40 to a CPR volume (i.e., a pixel within a profile of satellite data); the larger the number the greater the probability that a volume contained hydrometeors. In 2B-GEOPROF-LIDAR, the quantity CloudFraction indicates the percentage of a CPR volume identified as cloud by CALIOP. Based on these quantities, the following

ð1Þ

where Z is radar reflectivity in 2B-GEOPROF. The lowest three volumes near surface are eliminated due to ground-clutter contamination. In addition, because radiation calculations in AGCMs rarely consider the effect of precipitation, the simple screening sequence proposed by Barker (2008b) is also performed here to every profile in order to approximately remove precipitation approaching the surface: first, locate the surface bin (Jsrf); next, when there is precipitation near surface (CPR_Cloud_mask ≥ 20) find the layer with maximum Z (Jmax); then set layers from Jsrf to Jmax as cloudless.

2. Data and methodology 2.1. Data

53

3 dz 7 5 Lc f ðzÞ

ð5Þ

in which Zk and Zl are the altitudes of the midpoints of layers k and layer l, respectively. Eq. (2) is applied to both continuous and discontinuous clouds as in Mace and Benson-Troth (2002). The rate of the decay of αk,l is controlled by the “decorrelation length” Lcf. In Eq. (5) for given Zk and Zl, αk,l increases with increasing Lcf, thus larger Lcf results in smaller Ck,l (closer to maximum overlap) and smaller Lcf results in larger Ck,l (closer to random overlap). Lcf is related to cloud type and atmospheric dynamics (Naud et al., 2008) and has a global median value of ~2 km (Barker, 2008b). Geographic and seasonal variations of Lcf will be discussed below. 2.2.2. Calculation of Lcf First, divide the globe into 2.8° × 2.8° cells. This is resolution corresponds to a T42 spectral truncation. Each time the satellites cross a cell they provide a sample of the instantaneous meteorology of that grid. Then, from that cross-section, calculate the cloud fraction profile Ci and total vertically-projected cloud fraction Ctot. As shown in Fig. 1, scan-length across a cell can vary from track-to-track. Following Barker (2008a), the stochastic cloud generator (SCG) of Räisänen et al. (2004) is then applied to each cross-section's Ci. Beginning at a hypothesized decorrelation length Lcf′ = 0.1 km, and stepping Lcf′ forward in increments of 0.1 km, we generate 10,000 subcolumns per value of Lcf′ until the generated total cloud fraction (Ctot′) equals the observed Ctot calculated from satellite data (or until Lcf′ = 20 km is

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B2

L1

B1

A1 L2

BN LN

.. .

A2 AN

Fig. 1. Schematic diagram showing the sampling of satellite cross-sections across a gridcell. The portions of cross-sections falling in the grid (A1–B1, A2–B2, …, AN–BN), denoted as L1, L2, …, LN, are the samples on which estimates of Lcf are based.

reached). When this condition is met, the Lcf′ is adopted as the “real” value (or set to 20 km). The upper limit of 20 km is assigned because of the very rare occurrence of Lcf over 20 km (essentially maximum overlap). Though SCG suffers from random noise, its impact on results is negligible when large numbers of subcolumns are used (Barker, 2008b). It has been demonstrated (Barker, 2008a; Zhang et al., 2014b) that the SCG generally yields consistent subgrid cloud structures with the reference field. Other studies have used direct fitting of the exponential function to calculate Lcf (Mace and Benson-Troth, 2002; Naud et al., 2008). It should be noted we didn't calculate vertically-varying Lcf here as in Zhang et al. (2014b). This allows for direct comparison with the global constant Lcf and latitude-dependent methods. Moreover, for each 2.8° × 2.8° cell there are typically ~100,000 samples per month; though due to the A-Train's orbit, the number of samples per cell increases from equator to pole. The samples not appropriate for calculation of Lcf are discarded from the original collection; these include cases with either max(Ci) N 0.99 or Ctot b 0.01. This is because the generated Ctot are insensitive to Lcf. In addition, those that lack clouds entirely or with only a single cloudy layer are discarded because overlap is not an issue. Moreover, considering that the grid-sizes of present GCMs rarely fall below dozens of kilometers, our analysis was limited to samples with scan lengths larger than 30 km (i.e., samples with more than 28 profiles) thus allowing for calculation of meaningful Lcf. Consequently, about 60% of the original segments were removed. Then, for each month of a year, the typical Lcf for each grid is obtained using all samples through 2007–2010, which results in 12 monthly values of Lcf for each grid-cell. One important point is that the sample scan lengths are diverse rather than constant, even for a particular grid, which could impact the statistics of Lcf. However, Barker (2008b) found that the median Lcf is only mildly sensitive to scan length, although the interquartile range is generally wider for smaller segments. It is shown in Section 3 that scan length is not a sensitive factor for analysis of Lcf, implying that the above approach is credible. 2.2.3. AGCM experiments To test the impact of using the observationally-based Lcf from satellite observations on AGCM simulations, the BCC_AGCM2.0.1 model was used. This model was developed by the Beijing Climate Center (BCC) at the China Meteorological Administration (CMA) and runs at T42 spectral horizontal resolution (2.8° × 2.8°) with a terrain-following hybrid vertical coordinate with 26 levels (Wu et al., 2010). Radiative transfer for

cloudy columns is treated by the Monte Carlo Independent Column Approximation (McICA) method (Pincus et al., 2003) and the SCG with the radiation code of BCC-RAD (Zhang et al., 2003, 2006a,b). This code has been evaluated by Zhang et al. (2014b) and includes a four-stream radiative transfer parameterization (Zhang and Li, 2013; Zhang et al., 2013a). The AGCM experiments conducted in this study are listed in Table 1. Two reference experiments were done, both using GenO overlap but with simple Lcf specifications: one (denoted as EXP1) used a global mean Lcf of 2 km (Barker, 2008b). The other (denoted as EXP2) represented Lcf by a linear function of latitude (Shonk et al., 2010). The simulation that used the gridded climatology of Lcf developed here is termed EXP3. First, a 5-year diagnostic run was conducted to illustrate the instantaneous effect of using the observationally-based Lcf (i.e., EXP3). In order to diagnose instantaneous impact, radiation calculations were performed two additional times at each model integration time step: using EXP1's and EXP2's description of Lcf. Differences in radiation between the two calculations and those of EXP3 reflect the effects due to different Lcf. In the model integrations, the model fields are only updated using the radiative result from the first calculation, and therefore there is no climate feedback from the diagnostic calculations. Interactive runs for all three definitions of Lcf listed in Table 1 were then conducted for 31 years with prescribed SSTs and sea-ice of the AMIP dataset (Hurrell et al., 2008) from 1970 to 2000. Results for the last 30 years were analyzed to show impacts once feedbacks are allowed. 3. Results 3.1. Gridded Lcf from CloudSat/CALIPSO observations Since sample scan length can influence estimation of Lcf, samples were partitioned into lengths 30–80 km, 80–200 km, and 200–500 km and statistics of Lcf compiled for each range. Fig. 2 shows zonal mean Lcf for each group and the all groups together for January and July during 2007–2010. The curves for the three groups show very similar latitudinal distributions with larger values (~ 2.5 km) in the summer tropics and winter high latitudes, and smaller values (typically 1.2–1.5 km) in mid-latitudes. The outstandingly large Lcf near the South Pole may stem from the large amount of vertically extensive clouds (Bromwich et al., 2012), which are usually a single vertically continuous cloud regime when viewed from scales as small as the grid-division in this study (grid-cells become smaller with increasing latitude). It can be seen in Fig. 2 that when viewed from larger scales, Lcf over the Antarctic is sharply reduced. The values for different groups in Fig. 2 are generally close to each other, especially in the mid-latitudes. Nevertheless, the group 30– 80 km shows relatively large deviation from the others in the tropics and high latitude. This is because, viewing from this small scale, it is less likely to include various cloud blocks, and thus there is larger probability that clouds in vertical layers are closely related to each other and give rise to enormous Lcf, which broadens the tails in the distribution of Lcf. That a small segment size tends to yield larger Lcf than those using broader sizes was also demonstrated by Barker (2008b). Though Lcf depends somewhat on scan-length, given the very small percentage of the 30–80 km group in the tropics (less than 5%), the all-together Lcf is not noticeably influenced by including the 30–80 km group: the allTable 1 Descriptions of Lcf used in the AGCM. ϕ is the absolute latitude in degrees. Experiments

Lcf specifications

EXP1 EXP2 EXP3

Global invariant Lcf (2 km) Lcf = 2.899–0.02759ϕ Observed gridded Lcf

X. Jing et al. / Atmospheric Research 170 (2016) 52–65

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Fig. 2. The zonal mean Lcf calculated for the three groups of different scan-lengths as well as for all groups together for (a) January and (b) July during the period 2007–2010.

together curves in low-mid latitudes are closer to those from the other two groups. On the other hand, in polar regions, the all-together Lcf is close to that from the 30–80 km group which represents the typical grid size at these latitudes. Therefore, for both lower and higher latitudes, the all-together Lcf are representative of the corresponding cloud overlap climatology. Hereinafter, results of all-together are used as the observationally-based Lcf applied to climate simulations. The global annual-mean value of all-together Lcf is 1.88 km, very close to the previously suggested 2 km. Fig. 3 shows the global distribution of the seasonal mean gridded Lcf during winter (DJF) and summer (JJA) seasons. To better demonstrate the geographical differences in Lcf, Fig. 3a shows four domains whose domain-averaged annual cycles are shown in Fig. 4. Lcf over region A shows remarkable seasonal variation with values during DJF up to 5–6 km and during JJA of 2–3 km at the most. This corresponds to Mace and Benson-Troth's (2002) and Oreopoulos and Norris's (2011) results for ground-based radar observations. The relative unstable atmosphere and regional convection over NH continents in summer results in relative small cloud bodies (e.g. altocumulus Li et al., 2015; Zhang et al., 2014a) and increases the complexity of the clouds within a GCM grid, leading to the smaller Lcf. On the contrary, during boreal winter, the atmosphere over NH continents are more stable and clouds

are more frequently related to large scale lifting such as what occurs during a synoptic-scale front (e.g. altostratus and nimbostratus; Yang and Zou, 2013). These clouds typically stretch hundreds of kilometers and have considerable thickness with good vertical organization, leading to the larger Lcf in DJF. Fig. 3 shows that, in region B, Lcf is usually N 2 km. This is due to the frequent occurrence of deep convective systems in which clouds can extend from lower level to above the tropopause (Wang et al, 1998). The meridional shift of Lcf with convective center from season to season is also clearly captured in Figs. 3 and 4. Lcf over region C are usually small with only modest seasonal variation. Sea surface temperatures in this region are fairly low and low-level stratiform clouds often prevail (Wood, 2012). Such clouds are often irregular in shape and weak in vertical organization. The smallest Lcf occur over region D, which is on the descending side of the Walker cell. To understand the variation in distribution of observed Lcf from year to year, global distributions of standard deviation of Lcf are shown in Fig. 5. It is seen that the standard deviation of Lcf is generally close to zero over most ocean areas. However, over tropical convective regions and high-latitude continents it becomes larger, implying that the multi-year mean observed Lcf is less representative.

Fig. 3. Global distributions of seasonally-averaged Lcf (km) during (a) DJF and (b) JJA. The four areas designated by red squares (A: (30–70°N, 30–130°E); B: (15°S–15°N, 60–170°E); C: (35°S–65°S, 60°E–90°W); D: (30°S–0, 120–75°W)) represent regions with distinct cloud overlap characteristics.

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X. Jing et al. / Atmospheric Research 170 (2016) 52–65 Table 2 Global annual-mean total cloud fractions (Ctot) and cloud radiative forcings (CRFS and CRFL represent shortwave and longwave cloud radiative forcing, respectively). CRFN is net cloud radiative forcing (CRFS + CRFL).

EXP1 EXP2 EXP3 CERES

Fig. 4. Annual cycle of domain-averaged Lcf (2007–2010 mean) for the four regions designated in Fig. 3a.

3.2. AGCM experiments AGCM simulations with the observationally-based Lcf and two other descriptions, as listed in Table 1, are now compared in terms of cloud cover, cloud radiative forcing, and radiative heating rate. Table 2 shows global annual-mean total cloud fractions and cloud radiative forcings for the three experiments as well as from the CERES SYN1deg-Month Ed3A (for cloud cover) and CERES_EBAF (for radiative forcing) datasets during 2001–2013 (available at http://ceres.larc.nasa. gov/order_data.php, Minnis et al., 2011a,b). Differences among the AGCM experiments are small: at most ~ 0.24 W m− 2 for shortwave cloud forcing (CRFS) and ~ 0.13 W m−2 for longwave cloud forcing (CRFL) which compare well to the respective differences of 0.3 and 0.1 W m−2 reported by Oreopoulos et al. (2012) for two different setups of Lcf. The global-mean has included offsets among different regions, and the following analysis will show that there are notable and statistically significant differences in seasonal and regional scales.

3.2.1. Impacts on cloud cover Fig. 6 shows DJF and JJA mean Ctot from the CERES SYN1deg-Month Ed3A dataset, as well as differences between the three interactive simulations and the observational dataset. Although the overall patterns

Ctot

CRFS (W m−2)

CRFL (W m−2)

CRFN (W m−2)

0.654 0.653 0.658 0.610

−50.88 −50.86 −51.10 −47.37

28.29 28.33 28.42 26.26

−22.59 −22.53 −22.68 −21.11

of simulations are fairly similar to those of observations, there are systematic positive biases in the tropics and high latitudes, and negative biases in the mid-latitude oceans. A general overestimation of Ctot in DJF and underestimation of Ctot in JJA is seen over NH continents. Fig. 7 shows diagnostic differences in Ctot between EXP3 and EXP1 and between EXP3 and EXP2 for DJF and JJA. This demonstrates the impacts of choice of Lcf on Ctot given identical cloud fields. Compared with the global constant Lcf (Fig. 7a and c), Ctot generated by the observationally-based Lcf is usually smaller in the tropics and larger in mid-latitude oceans. This is because observed Lcf is larger than 2 km in the tropical convective regions and smaller than 2 km over midlatitude oceans. Differences between EXP3 and EXP2 are notably smaller, for most areas, than those between EXP3 and EXP1, as Lcf calculated by EXP2 captures some of the observed latitudinal variations in Lcf; an exception is over tropical eastern oceans where EXP2 yields excessively small Ctot. Fig. 8 shows differences in Ctot among the experiments for interactive runs. The patterns of the differences resemble well those in Fig. 7, but the values are notably larger. This implies that the impact of changing Lcf is amplified by feedbacks. Positive differences in Ctot for EXP3–EXP1 (EXP2) over subtropical to mid-latitude regions act mostly to reduce systematic model biases considering the negative systematic simulation biases shown in Fig. 6; vice versa over the convective centers (e.g., tropical western Pacific) and polar regions. That the simulated cloud cover in most regions shift in the “correct” direction implies that the cloud-overlap-deduced uncertainty in cloud fraction could be reduced by specifying the geographical and temporal details of Lcf, although the changes in Ctot by altering Lcf settings (about ±0.05) are quantitatively about one order of magnitude smaller than the simulation biases against observations (about ±0.5). Fig. 9 shows changes in the annual cycle of total cloud fraction when monthly variations in Lcf are used in EXP3. For region A, which shows the strongest seasonality of Lcf, EXP3 generally gives smaller (larger) cloud fractions (~± 0.01) during the first (second) half of year than EXP1 and EXP2. For the tropical convective region B, cloud fractions of EXP3 are smaller than that of EXP1 in all months, but become similar to those of EXP2. In regions C and D, both of which feature small

Fig. 5. Global distributions of standard deviation of observed Lcf (km) for DJF and JJA during 2007–2010.

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Fig. 6. CERES observed (a) DJF and (b) JJA mean total cloud fraction, as well as the differences between simulations (c, d) EXP1, (e, f) EXP2 & (g, h) EXP3 and CERES observations for the two seasons.

observationally-based Lcf, EXP3 yields larger cloud fractions than EXP1 and EXP2 with maximum differences reaching ~0.029 (EXP3–EXP1 in region C). Because EXP2 captures the variation of Lcf from tropical

convective zones to high-latitude marine surfaces, differences between EXP3 and EXP2 are generally smaller than those between EXP3 and EXP1. However, since EXP2 assigns Lcf independent of longitude, values

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Fig. 7. Differences in simulated cloud fractions between (a, c) EXP3 and EXP1 and (b, d) EXP3 and EXP2 for DJF and JJA. The GCM ran with EXP3 and values for EXP1 and EXP2 were computed diagnostically.

Fig. 8. Differences in simulated cloud fraction between (a, c) EXP3 and EXP1 and (b, d) EXP3 and EXP2 for DJF and JJA. Values correspond to the GCM having run interactively with each description of Lcf (see Table 1). Black dots indicate differences that are significant at the 95% confidence level.

X. Jing et al. / Atmospheric Research 170 (2016) 52–65

Fig. 9. Differences in the annual cycle of domain-mean cloud fraction for the four regions designated in Fig. 3a.

Fig. 10. The same as in Fig. 6c–h, but for shortwave cloud forcing (W m−2).

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of Lcf by EXP2 are strongly overestimated in region D (i.e., the descending branch of the Walker cell). Therefore, in region D differences in cloud fraction between EXP3 and EXP2 become relatively large, especially in the summer. The case of region D shows the importance of zonal variations in Lcf. 3.2.2. Impacts on cloud radiative forcing The more clouds overlap the smaller the area covered by clouds. This tends to reduce shortwave flux and increase longwave flux to the space, due to increased exposure to space of the relatively warm surface. This is referred to, hereinafter, as the cloud-fraction effect. On the contrary, increased cloud overlap increases cloud mean optical depth which increases shortwave albedo and reduces longwave emission for the cloudy part of the grid. This is referred to, hereinafter, as cloudoptical-depth effect. The net outcome of cloud overlap is the compromise of these two effects. The impact of using the observationally-based Lcf on CRFS (CRFL) is displayed in Figs. 10 and 11 (Figs. 12 and 13), by comparing simulations with CERES observations and comparing EXP3 with the other experiments, respectively. To be concise, we show only the results of the interactive runs; the diagnostic results are similar to those shown in Figs. 11 and 13, but slightly smaller. It is shown in Fig. 10 that, during DJF, the simulations primarily overestimate CRFS (i.e. negative biases) in tropical convective regions and underestimate CRFS (i.e. positive biases) in mid-latitude oceans, especially for the SH. Compared to EXP1 and EXP2 (Fig. 11a and b), EXP3 reduces CRFS in tropical convective regions and enhances CRFS in mid-latitude oceans, indicating noticeable improvements of CRFS in these regions. During JJA, the most notable errors in the simulations are the negative biases in the tropical convective regions (especially over South Asia and central-eastern subtropical Pacific) and highlatitude NH continents around 60°N (see Fig. 10), with regional errors over − 50 W m− 2. Again, EXP3 shows obvious reductions in these errors in comparison with EXP1 and EXP2 (see Fig. 11b and d), with

maximum reductions at ~8 W m−2 (such as in central-eastern subtropical Pacific against EXP1 and in NH continents against EXP2). These suggest the importance of supplying regionally-dependent Lcf values in GCM simulations to predict local radiation budgets and cloud feedbacks more realistically. Similar improvements can also be seen in CRFL. For instance, compared with Fig. 12, Fig. 13 shows that EXP3 reduces the overestimation (underestimation) of CRFL in western and central SH tropical Pacific (SH mid-latitude Atlantic) in DJF, and also reduces the overestimation of CRFL in central-eastern subtropical Pacific in JJA. The magnitudes of these reductions of biases are within ± 5 W m−2, about one order smaller than the differences between simulations and observations shown in Fig. 12. The improvements in CRFS and CRFL correspond to the improvements in the simulated cloud fraction as shown in Fig. 8, indicating that the cloud-fraction effect, rather than the cloud-optical-depth effect, dominates cloud overlap dependent changes in SW and LW cloud forcing. Although the changes are about an order of magnitude smaller than the biases, the improvements by EXP3 in many regions suggest that part of the GCM's radiation budget biases can be attributed to cloud overlap treatment, and that the incorporation of observational information into the treatment of cloud overlap will mitigate them. Fig. 14 shows changes in CRFS in the four regions designated in Fig. 3a. EXP3 shows notable changes in CRFS in regions A and C relative to both EXP1 and EXP2. They stem from changes in cloud fraction (see Fig. 9) with weakened CRFS in region A during boreal summer and strengthened in region C during boreal winter (to a maximum of ~− 3.5 W m−2). Differences between EXP3 and EXP1 in region B are also notable and clearly related to changes in cloud fraction. It should be noted that, though there are notable differences in cloud fraction in region D, corresponding changes in CRFS are small and relatively less sensitive to the changes to total cloud fraction than in other regions. In the large-scale descending region of D, cloud fractions are small, and so too are changes to cloud forcing.

Fig. 11. The same as in Fig. 8, but for shortwave cloud forcing (W m−2).

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Fig. 12. The same as in Fig. 6c–h, but for longwave cloud forcing (W m−2).

Fig. 15 shows that differences in CRFL also reflect the seasonality of differences in cloud fraction. As expected, however, these differences are smaller (within ~1.0 W m−2) than those seen in the SW. 3.2.3. Impact on heating rate Use of the observationally-based cloud overlap treatment also alters atmospheric heating rate via realignment of cloud occurrence in the vertical (cf. Barker, 2008b). Fig. 16 shows that there is enhanced heating for EXP3 in the tropical lower layers which can extend from the surface to about 500 hPa over 15–20°N in JJA. In Fig. 3 observed values of Lcf in this region are generally N2 km and the result of meridional dependencies result in larger Lcf leading to less cloud fraction. Therefore solar radiation has a greater chance of penetrating to, and heating, the lower atmosphere. At the same time, heating rate differences between EXP3 and EXP1 (EXP2) become negative above the region of the enhanced heating due to less solar irradiance onto upper-level clouds. The same argument applies to 30–40°N and 40–50°S where Lcf is generally b2 km, especially for DJF. Clouds tend to be more randomly overlapped and so more solar energy is absorbed by the upper-level clouds.

Fig. 17 shows differences in longwave cooling rate, which mirror, to some extent, those shown in Fig. 16. A region of enhanced solar heating rate generally corresponds to enhanced longwave cooling rate. As discussed above, larger Lcf leads to less cloud fraction and solar radiation has a larger chance to penetrate to the lower atmosphere and thus enhance heating rates. At the same time, the lower cloud has a larger chance of being exposed to space, which enhances longwave cooling. This kind of cancelation reduces the net change in radiation heating rate.

4. Conclusion A source of potentially significant uncertainty in large-scale simulations of global climate is the treatment of cloud vertical overlap for radiative transfer calculations. While this problem has been investigated much in prior studies, some critical aspects of cloud overlap, such as its combined variations in space and time, have not been addressed as yet.

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Fig. 13. The same as in Fig. 8 but for longwave cloud forcing (W m−2).

In this study, a 12-month climatology of decorrelation length Lcf, gridded at 2.8° resolution, was obtained from a four-year (2007– 2010) dataset of CloudSat/CALIPSO observations. This enabled, for the first time, a detailed description of Lcf, and subsequent radiative effects, to be introduced into the updated BCC_AGCM2.0.1 GCM. The meridional distribution of Lcf is in good agreement with previous studies, with higher values (more than 2 km) in the tropics and high latitudes and lower values (less than 1.5 km) over mid-latitudes oceans. The smallest Lcf (less than 1 km) exist over subtropical eastern oceans where marine stratiform clouds prevail. The NH mid-to-high latitude

continents see dramatic seasonal variations of Lcf, maximizing in DJF and minimizing in JJA. There are clear contrasts in Lcf between continents and oceans in the NH mid-latitudes, with smaller values over oceans and larger values over land. The spatial and temporal characteristics of Lcf revealed in this study are of practical implication for those interested in structural descriptions or parameterizations of large-scale clouds. The climatic impact of representing the observed climatology of Lcf is explored in the BCC_AGCM2.0.1 model through comparison against simple traditional settings of Lcf that lack temporal, meridional and/or

Fig. 14. The same as in Fig. 9, but for shortwave cloud forcing.

X. Jing et al. / Atmospheric Research 170 (2016) 52–65

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Fig. 15. The same as in Fig. 9, but for longwave cloud forcing.

zonal variations. It is shown that the modeled cloud fraction and cloud radiative forcing are both significantly changed at many locations by introducing the observationally-based Lcf in a McICA scheme, with ±0.05 for total cloud fraction and ±10 (5) W m−2 for shortwave (longwave) cloud radiative forcing, respectively. It is encouraging that these changes generally reduce model errors against corresponding observations, such as the negative (positive) biases of CRFS in the tropical convective regions (subtropical eastern and mid-latitude oceans). However, it

should be noted that errors in simulated cloud profiles and cloud micro-physical properties also contribute much to errors in cloud radiative forcings, and hence improvements to modeled cloud radiative forcing can only be regarded as robust by the co-development in these inherently connected aspects. The changes in atmospheric radiative heating rates show that the use of observationally-based Lcf can trigger substantial alterations in the columnar radiation budget, and that these alterations vary greatly

Fig. 16. Differences in zonal-average shortwave heating rates (K day−1) between EXP3 and EXP1 (left), and between EXP3 and EXP2 (right) for DJF and JJA.

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Fig. 17. The same as Fig. 16, but for longwave heating rate (K day−1).

among regions and cloud systems. LW heating rates are generally more sensitive to the changes in cloud overlap treatment than SW heating rates. The redistribution of energy at different heights in vast regions has the potential to modify the global scale circulation. For these reasons, the results presented here suggest that accounting for spatial and temporal variations of Lcf is important for simulation of global climate. As a final note, GCM sensitivity to changes in cloud overlap treatment will depend on the cloud schemes used in the host model (see Oreopoulos et al., 2012). Nevertheless, the method of treating cloud overlap by obeying an observationally-derived climatology is more physically rigorous than most previous approaches and can be readily applied to other AGCMs. Acknowledgments This work was financially supported by the Public Meteorology Special Foundation of MOST (GYHY201406023), the National Natural Science Foundation of China (grant no. 41375080) and the National Basic Research Program of China (2011CB403405). References Barker, H.W., 2008a. Representing cloud overlap with an effective decorrelation length: an assessment using CloudSat and CALIPSO data. J. Geophys. Res. 113 (D24), D24205. http://dx.doi.org/10.1029/2008JD010391. Barker, H.W., 2008b. Overlap of fractional cloud for radiation calculations in GCMs: a global analysis using CloudSat and CALIPSO data. J. Geophys. Res. 113 (D8), D00A01. http://dx.doi.org/10.1029/2007JD009677. Barker, H.W., Stephens, G.L., Fu, Q., 1999. The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Q. J. R. Meteorol. Soc. 125 (558), 2127–2152. Barker, H.W., Stephens, G.L., Partain, P.T., Bergman, J.W., Bonnel, B., Campana, K., Clothiaux, E.E., Clough, S., Cusack, S., Delamere, J., Edwards, J., Evans, K.F., Fouquart,

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