Shortwave Cloud Radiative Forcing on Major Stratus Cloud Regions in AMIP-type Simulations of CMIP3 and CMIP5 Models

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1 ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 30, NO. 3, 2013, Shortwave Cloud Radiative Forcing on Major Stratus Cloud Regions in AMIP-type Simulations of CMIP3 and CMIP5 Models ZHANG Yi 1,2 ( ) and LI Jian 3 ( ) 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing University of the Chinese Academy of Sciences, Beijing State key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing (Received 20 July 2012; revised 16 December 2012) ABSTRACT Cloud and its radiative eæects are major sources of uncertainty that lead to simulation discrepancies in climate models. In this study, shortwave cloud radiative forcing (SWCF) over major stratus regions is evaluated for Atmospheric Models Intercomparison Project (AMIP)-type simulations of models involved in the third and fifth phases of the Coupled Models Intercomparison Project (CMIP3 and CMIP5). Over stratus regions, large deviations in both climatological mean and seasonal cycle of SWCF are found among the models. An ambient field sorted by dynamic (vertical motion) and thermodynamic (inversion strength or stability) regimes is constructed and used to measure the response of SWCF to large-scale controls. In marine boundary layer regions, despite both CMIP3 and CMIP5 models being able to capture well the center and range of occurrence frequency for the ambient field, most of the models fail to simulate the dependence of SWCF on boundary layer inversion and the insensitivity of SWCF to vertical motion. For eastern China, there are large diæerences even in the simulated ambient fields. Moreover, almost no model can reproduce intense SWCF in rising motion and high stability regimes. It is also found that models with a finer grid resolution have no evident superiority than their lower resolution versions. The uncertainties relating to SWCF in state-of-the-art models may limit their performance in IPCC experiments. Key words: shortwave cloud radiative forcing, CMIP, stratus clouds, IPCC Citation: Zhang, Y., and J. Li, 2013: Shortwave cloud radiative forcing on major stratus cloud regions in AMIP-type simulations of CMIP3 and CMIP5 models. Adv. Atmos. Sci., 30(3), , doi: /s Introduction The spread of the sensitivity in climate models has long been attributed to the uncertainty in depicting the role of cloud (e.g. Cess et al., 1990; Senior and Mitchell, 1993; Colman, 2003). The Fourth Assessment Report (AR4) of the IPCC pointed out that there remains large inter-model diæerences in cloud feedback (IPCC, 2007). It also concluded that these diæerences are mostly attributable to the shortwave cloud feedback component. The shortwave radiative eæect of cloud (shortwave cloud radiative forcing; SWCF) is largely generated by stratus clouds (Zuidema and Hartmann, 1995). An evaluation of simulated SWCF associated with stratus clouds will be valuable for inspecting the simulation capability and particular model biases. However, considering current model outputs, it is not possible to extract the radiative forcing of stratus clouds alone. Therefore, we focus on the SWCF over stratus regions and presume that it is mainly generated by stratus clouds. Klein and Hartmann (1993) documented several major stratus regions across the globe. Regions between 60 ± S and 60 ± N are classified into three cate- Corresponding author: LI Jian, lijian@cams.cma.gov.cn China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2013

2 NO. 3 ZHANG AND LI 885 gories: (sub)tropical ocean; mid-latitude ocean; and eastern continental China. We focus only on the (sub)tropical ocean and eastern continental China. It will be shown that these regions have one of the largest discrepancies in simulated SWCF. The (sub)tropical oceans have large numbers of marine boundary layer (MBL) clouds, which have long been regarded as a di±cult task for climate models (Bachiochi and Krishnamurti, 2000). The associated SWCF is also found to be the most uncertain in coupled model simulations (Bony and Dufresne, 2005; Wyant et al., 2006). Observational evidence has revealed the lower tropospheric temperature structure (e.g. stability) can explain well the seasonal variation and interannual response of clouds and SWCF over the MBL, because it captures many co-varying factors that are favorable to the formation of stratus (Klein and Hartmann, 1993; Wood, 2012). Besides, previous studies also showed that cloud and SWCF in these subsiding motion regimes are not very sensitive to the variation of vertical velocity (e.g. Bony et al., 2004; Stowasser and Hamilton, 2006; Eitzen et al., 2011). DiÆerent from MBL regions, stratus clouds over eastern China (EC) (27 ± 32 ± N, 103 ± 118 ± E) are more distinctive. Mid-level nimbostratus and altostratus dominate stratus amounts and they mainly form in cold seasons (Yu et al., 2001). Stratus clouds here are generated due to the large divergence diæerence between middle and low levels downstream of the Tibetan Plateau (TP), which leads to low-level largescale lifting. Along with high stability in winter, they create a favorable environment for stratiform cloud formation (Yu et al., 2004; Li and Gu, 2006). Thus, the variation of stratus amount in cold seasons can be explained by the changes in both the circulation and temperature. The purpose of this paper is to evaluate the simulated SWCF in the third and fifth phases of Coupled Models Intercomparison Project (CMIP3 and CMIP5) models. The SWCF performance over stratus regions and the sources of SWCF spread will be illustrated. The role of increased resolution in the simulated SWCF is also discussed. The remainder of the paper is organized as follows. Section 2 briefly describes the CMIP models, observational data and analysis methods used in the study. Section 3 presents a global view of the model discrepancies. The simulation of SWCF over stratus regions is quantified in section 4. Section 5 inspects the response of SWCF to large-scale controls. A discussion and conclusions are presented in section Model, data and analysis method 2.1 Model and data The Atmospheric Models Intercomparison Project (AMIP) experiment outputs of 12 CMIP3 and 16 CMIP5 models (Table 1) are analyzed in this paper. The atmospheric components of climate models are forced by observational SST for simulating the present observed climate (Phillips, 1996). The CMIP5 models generally have higher resolutions than CMIP3. There are two models [Geophysical Fluid Dynamics Laboatory HIgh Resolution Model Cube 360 (GFDL HIRAM C360) and Meteorological Research Institute-Atmospheric General Ciculation Model 2s (MRI AGCM3 2s)] that both have high (finer than 0.5 ± ) and medium resolution outputs, enabling us to inspect whether the resolution alone can improve the simulation. Table 1. The 12 CMIP3 and 16 CMIP5 models and their resolutions (lat lon; units: ± ). CMIP3 models Resolution CMIP5 models Resolution NCAR CCSM BCC CSM CNRM CM CNRM CM MPI ECHAM MPI ESM LR GFDL CM GFDL HIRAM C GISS E R GISS E2 R IAP FGOALS g IAP FGOALS g INMCM INMCM MIROC3.2 hires MIROC MIROC3.2 medres MRI AGCM3 2hs MRI CGCM2.3.2a MRI CGCM UKMO HadGEM HadGEM2 A IPSL CM NorESM1 M IAP FGOALS s CanAM MRI AGCM3 2S GFDL HIRAM C

3 886 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 The temperature, humidity and vertical velocity fields are taken from ERA-Interim reanalysis as the evaluation standard. The observed shortwave (SW) flux at top of atmosphere (TOA) are taken from (1) Earth Radiation Budget Experiment (ERBE) data during to match the period of the CMIP3 models, and (2) Clouds and the Earth s Radiant Energy System (CERES)-Energy Balanced and Filled (EBAF) data during for the CMIP5 models. We also use total cloud fraction data obtained from the International Satellite Cloud Climatology Project (ISCCP) with 2.5 ± 2.5 ± resolution. All data are monthly-averaged. 2.2 Method SWCF is calculated by taking the diæerence between the clear sky and total sky outgoing SW flux at TOA. To reveal the discrepancies, standard deviation (STDDV) and root mean square error (RMSE) are respectively calculated for December January February (DJF) and June July August (JJA) climatological mean as follows: v u STDDV = t 1 nx [x(i) x e ] n 1 2, (1) v u RMSE = t 1 n 1 i=1 nx [x(i) o] 2. (2) i=1 Here, x(i), x e, o and n respectively denote: the DJF or JJA climatological mean of the individual model; the ensemble mean of the model group (CMIP3 or CMIP5); observation; and sampled model numbers. STDDV represents the inconsistency among the models, while RMSE shows the bias between models and observation. Additionally, the model and satellite data are interpolated to the same 2.5 ± 2.5 ± mesh by bilinear interpolation. Six regions (Table 2) were chosen as major stratus regions. The five (sub)tropical marine regions were first selected by Klein and Hartmann (1993), and then adopted in some subsequent studies (e.g. Wood and Bretherton, 2006; Yue et al., 2011). The EC region is defined downstream of the TP as (27 ± 32 ± N, 103 ± 118 ± E), as in Yu et al. (2004), which is slightly diæerent from the (20 ± 30 ± N, 105 ± 120 ± E) region used by Klein and Hartmann (1993). This is because the dominant stratus types are diæerent within the eastern continental China. South China (to the south of 25 ± N) is dominated by low-level stratocumulus (Wood, 2012), while the part downstream of the TP is mainly covered by more distinctive mid-level nimbostratus and altostratus (Yu et al., 2004). The SWCF is binned together with vertical velocity and stability to measure its response to environmental variations, following some previous studies (e.g. Williams et al., 2003; Norris and Iacobellis, 2005; Eitzen et al., 2011). We use bilinear interpolation to regrid the observed SWCF data into the resolution of the reanalysis data. For MBL regions, we take vertical velocity at 500 hpa (! 500 ) and estimated inversion strength (EIS) as two proxies.! 500 represents the vertical motion in the free troposphere as a whole (Weaver and Ramanathan, 1997), and EIS is proposed as a better predictor than the conventional lower troposphere stability (LTS; the potential temperature difference between 700 hpa and 1000 hpa) for its correlation with stratus amount (Wood and Bretherton, 2006), especially in MBL regions. Calculation of EIS is as follows: EIS = µ 700 µ 0 m,850 (Z 700 Z). (3) Here, µ 700 and µ 0 are potential temperature at 700 hpa and 1000 hpa, respectively. m,850 is the moistadiabatic potential temperature gradient at 850 hpa. Z 700 is geopotential height at 700 hpa, and Z is 1000 hpa based lifting condensation level, respectively. For EC, stratus cloud is generated due to the midlevel divergence and low-level convergence, both of which lead to the compensational low-level large-scale lifting, quite diæerent from the subsidence environment over MBL regions. So, we take the vertical velocity at 700 hpa (! 700 ) instead of! 500. Additionally, EIS is replaced by LTS, which is defined as the potential temperature diæerence between 500 hpa and 850 hpa, as the stability proxy. This usage can be found in many previous studies (e.g. Yu et al., 2004; Li et al., Table 2. The six selected stratus regions, which are also represented by boxes in Fig. 1. Region Symbol Location Type Peruvian coast P 10 ± 20 ± S, 80 ± 90 ± W Tropical ocean Namibian coast N 10 ± 20 ± S, 0 ± 10 ± E Tropical ocean Californian coast C 20 ± 30 ± N, 120 ± 130 ± W Subtropical ocean Australian coast A 25 ± 35 ± S, 95 ± 105 ± E Subtropical ocean Canarian coast CA 15 ± 25 ± N, 25 ± 35 ± W Subtropical ocean Eastern China EC 27 ± 32 ± N, 103 ± 118 ± E Subtropical continent

4 NO. 3 ZHANG AND LI 887 Fig. 1. Standard deviation and root mean square deviation of SWCF in CMIP3 (left column) and CMIP5 (right column) models in DJF and JJA. The six boxes in (a) show the locations of the selected stratus regions. Units: W m ; Li and Gu, 2006), because the inversion layer is higher and thicker downstream of the TP in cold seasons. 3. Global SWCF deviation in CMIP3 and CMIP5 models Figure 1 displays the STDDV and RMSE of SWCF in CMIP3 and CMIP5 between 60± S and 60± N. The model-observation biases are overall larger than the inter-model diæerences. In most regions that have distinctive clouds, large RMSE and STDDV (higher than 30 W m 2 ) generally coexist. For example, the Indian monsoon region (10± 25± N, 70± 90± E) has a high coverage of high clouds and intense SWCF in JJA (Yu et al., 2001). The maxima centers of STDDV and RMSE there show the di±culty in reproducing the cloud radiative properties. It is also found that the averaged STDDV (RMSE) in the Indian Monsoon region decreases from 36.2 W m 2 (40 W m 2 ) to 29 W m 2 (32 W m 2 ) from CMIP3 to CMIP5, indicating a certain amount of improvement in high cloud regions, which are usually associated with the cumulus parameterizations in models (Ichikawa et al., 2011).

5 888 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 Another major source of error stems from stratus regions, e.g. the Californian west coast, Peruvian west coast, Australian west coast, Canarian west coast, Namibian west coast and EC. DiÆerent from high cloud regions, there is even enlarged RMSE or STDDV in some of these regions from CMIP3 to CMIP5 (e.g. RMSE for the west coasts of California and Peru). In DJF, the Peruvian coast has large STDDVs in CMIP3 and CMIP5. The maxima center of STDDV over the Namibian coast enlarges from CMIP3 to CMIP5. The other four regions show no evident maxima centers of STDDV in this season. RMSEs over the Namibian coast, Australian coast and Peruvian coast increase from CMIP3 to CMIP5. There are some regions covered by large RMSE but accompanied by much smaller STDDV (e.g. EC), demonstrating most models may unanimously overestimate or underestimate SWCF in these regions. From DJF to JJA, the maxima centers of STDDV emerge over the Californian and Canarian coasts and still exist over the Namibian and Peruvian coasts. RMSE over the Californian, Namibian and Peruvian coasts all enlarge from CMIP3 to CMIP5. It seems there is no evident improvement for most stratus regions (even worse in some regions). In the following section, the simulation of SWCF in selected regions will be quantified. 4. SWCF over the six stratus cloud regions 4.1 Regional mean Figure 2 shows the regional averaged SWCF (CMIP3) in six selected regions during (including DJF and JJA). We also calculated the STDDV for all averaged samples within the space time range. If one model s mean lies outside a STDDV range of observation, this model will be regarded as having no simulation capability in this region and is therefore marked by a cross. The ensemble mean for all models was also calculated to estimate the overall performance. In DJF, the average and variability are all largely dispersed among the models. Interestingly, the ensemble means in the P and N regions are very close to the observation. Both underestimation and overestimation of SWCF are found in the P, N, C and CA regions. For the A region, eight underestimation cases are evident, while the other four models have roughly the same values as the observation. Worse still, all models unanimously underestimate SWCF in EC and there are nine crosses. In winter, EC is covered by optically-thick mid-level stratus (Yu et al., 2001). The unanimous underestimation may result from the incapability of reproducing the adequate stratus cloud properties. Since we are not focusing on comparing cloud properties, a discussion about this will be given in section 6. In JJA, there are still seven crosses in EC. However, the reason may lie in the simulation of optically-thick high clouds, because these clouds dominate total cloud amount in summer (Yu et al., 2001). In MBL regions, a large spread can still be seen. The ensemble means in the N and A regions agree with the observation. There is no single model whose reproducibility reaches the standard in all regions for DJF and JJA; only Meteorological Research Institute-Coupled General Circulation Model 2 3 2a (MRI CGCM2 3 2a) meets the standard at all regions in DJF. With regard to CMIP5 (Fig. 3), large diæerences in the variability and mean still exist. Among all models, Hadley Center Global Environment Model2 Atmosphere (HadGEM2 A) performs the best with only one cross (CA region in JJA). There are increased diæerences in the P region (DJF and JJA) and the C region (JJA). Although RMSE in EC for DJF decreases (Fig. 1f), there are still 12 of the 16 models failing to reach the standard. All CMIP5 models still unanimously underestimate SWCF for EC in winter. 4.2 Seasonal cycle As stratus amount is strongly coupled with the lower troposphere temperature structure in selected regions (Klein and Hartmann, 1993; Yu et al., 2004), the annual cycles of stability (EIS for MBL or LTS for EC) and SWCF (the opposite number) in the CMIP3 models over six regions are shown in Fig. 4. The patterns and amplitudes of EIS annual cycles are more consistent than those of SWCF. The models barely agree with the observational feature that the SWCF annual cycle corresponds well with EIS. For example, in the P region, the observed EIS and SWCF reach their maxima in September October November (SON). National Center for Atmospheric Research Community Climate System Model3 0 (NCAR CCSM3 0), Centre National de Recherches Meteorologiques Climate Model3 (CNRM CM3), Flexible Global Ocean Atmosphere Land System1 0 g (FGOALS1 0 g) and United Kingdom Met O±ce HadGEM1 (UKMO HadGEM1) can reproduce a close EIS peak during this time, while only UKMO HadGEM1 simulates the corresponding SWCF peak. Meanwhile, although some models have relatively weaker EISs than in the observation, they reproduce similar seasonal variation patterns. For example, EISs in Geophysical Fluid Dynamics Laboratory Climate Model2 1 (GFDL CM2 1), Institute of Numerical Mathematic Climate Model3 (INMCM3) and Max-Planck Institut European Center at Hamburg At-

6 NO. 3 ZHANG AND LI 889 CMIP3 MODELS Fig. 2. Regional averaged SWCF of ERBE and CMIP3 models in DJF and JJA for the six stratus regions. The error bars indicates one standard deviation; gray lines and black line shows the range of one standard deviation and the mean of the observation. Models whose mean lies outside of one standard deviation of the observation is marked by a cross. Units: W m 2.

7 890 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP5 MODELS Fig. 3. The same as Fig. 2, but for the CMIP5 models.

8 NO. 3 ZHANG AND LI 891 Fig. 4. Seasonal cycle of stability (EIS for MBL and LTS for EC) (left column) and SWCF ( 1) (right column) for the six stratus regions. Units: K for EIS and LTS; W m 2 for SWCF.

9 892 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 mospheric Model5 (MPI ECHAM5) peak during SON and reach their minima during February March April (FMA), just as in the observation, while the strongest and weakest SWCFs do not occur at the corresponding times. Similar problems are also evident in other MBL regions. Among all regions, the A region, where the observational stratus amount remains almost constant, has the least SWCF inconsistency. MPI ECHAM5 has the weakest SWCF in all MBL regions except the A region. UKMO HadGEM1 performs the best in all MBL regions. For the EC region, the patterns and strengths of the annual cycle of LTS diæer a lot among models. Most of the models underestimate the peak strength of LTS during January February March (JFM). As for SWCF, the peak time during March to June can be captured. The problems lie in that models have large discrepancies in the peak strength and a unanimous underestimation during November December January February (NDJF). Figure 5 shows the same comparisons for CMIP5. From CMIP3 to CMIP5, the seasonal cycles of EIS become more consistent and closer to the observation in five MBL regions. However, this improvement does not contribute to the simulation of SWCF. Still taking the P region as an example, although some models capture the EIS peak during SON, only HadGEM2 A and Beijing Climate Center Climate System Model 1.1 (BCC CSM 1.1) reach their maxima of SWCF during this time. Some models have an SWCF peak in December [e.g. FGOALS g2, MRI CGCM3 and Canada Atmospheric Model4 (CanAM4)] or underestimate SWCF all year (e.g. GFDL HIRAM C180, GFDL HIRAM C360, MRI AGCM 2h, MRI AGCM 2s and CNRM CM5). The A region still has the most consistent patterns of SWCF in all MBL regions. Among all the models, CNRM CM5 has the weakest SWCFs in the P, N, C and CA regions. In EC, although the CMIP5 models still have large diæerences in their simulated stability, SWCFs tend to become more consistent than in CMIP3. The major improvement is the better representation during the peak time (March to June). Beginning in March, stratus amount decreases and high cloud amount increases. Consequently, the total cloud amount peaks and leads to the most intense SWCF during March to June (Yu et al., 2001). The better simulated SWCF annual cycle in EC may result from the improved representation of high clouds. This is consistent with the reduced RMSE and STDDV in the Indian monsoon region (section 3), which is another representative high cloud region. Nevertheless, the underestimation in NDJF is still evident in most of the models. We focus on NDJF because the stratus amount during this time dominates total cloud amount and is the maximum in the year. In the following section, we try to find some reasons from the large-scale aspects to understand the poorly simulated SWCF in CMIP3 and CMIP5 models. 5. The response of SWCF to large-scale controls 5.1 Marine boundary layer Large-scale environmental variations have significant influences on cloud fraction and cloud radiative forcing (Bony et al., 2004; Norris and Iacobellis, 2005; Yuan et al., 2008). Looking specifically at MBL low clouds under subsidence, Eitzen et al. (2011) showed that the interannual variations of low clouds and SWCF there tend to be dominated by EIS anomalies rather than vertical velocity anomalies. Our observational metrics are similar except that we used the monthly mean values instead of monthly anomalies. Figure 6 shows the occurrence frequencies of EIS and! 500 bins for ERA-Interim and CMIP3 models (ECHAM5 is excluded due to the lack of vertical velocity data). The center of occurrence frequency in the reanalysis (Fig. 6a) lies at 30 hpa d 1 (! 500 ) and 4 10 K (EIS). The! 500 and EIS bins range approximately from 30 hpa d 1 to 100 hpa d 1 and 1 K to 12 K. As for model results, albeit with certain deviation, the center and range of the occurrence frequencies are overall reasonable. No model shows extreme vertical motion or EIS occurrence. The result for CMIP5 (Fig. 7) is similar. Most models can simulate the distribution of occurrence frequency. The ambient fields in CMIP5 are generally consistent despite the various resolutions, from 3 ± (lat) 2.8 ± (lon) (FGOALS g2) to 0.2 ± (lat) 0.2 ± (lon) (MRI AGCM3 2s). The above analysis suggests that most CMIP3 and CMIP5 models can reasonably reproduce the ambient fields. We further measure the response of SWCF to these two proxies. Figure 8 shows the results for CMIP3. In the reanalysis and ERBE composite, the variation of SWCF is dominated by EIS rather than! 500. SWCF ranges between 90 W m 2 and 100 W m 2 when EIS is 12 K, and varies from 40 W m 2 to 100 W m 2 at the center of occurrence frequency of the ambient field (from 4 K to 10 K at 30 hpa d 1 ). Although SWCF is insensitive to the vertical velocity, it is relatively more intense in the weak sinking motion regime (lower than 30 hpa d 1 ), confirming the discussion in Weaver and Ramanathan (1997) that too strong subsidence may lead to the dissipation of the stratiform cloud deck. The model-observation biases and inter-model dif-

10 NO. 3 ZHANG AND LI 893 CMIP5 MODELS Fig. 5. The same as Fig. 4, but for the CMIP5 models.

11 894 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP3_MBL Fig. 6. Occurrence frequency of EIS (horizontal axis) and! 500 (vertical axis) bins over MBL regions in the CMIP3 models. Bin intervals: 1 K for EIS and 10 hpa d 1 for! 500. EIS axis ranges from 7 K to 17 K;! 500 axis ranges from 40 hpa d 1 to 120 hpa d 1. All the samples are annual monthly means from 1985 to ferences are both very large. Institut Pierre Simon Laplace Climate Model4 (IPSL CM4), GFDL CM2.1, Goddard Institute for Space Studies E R (GISS E R), INMCM3 and Model for Interdisciplinary Research On Climate3 2 high resolution version (MIROC3 2 hires) fail to simulate intense SWCF in the strong stability regime. CCSM3 and HadGEM1 perform relatively better overall, while they tend to produce more intense SWCF in the weak sinking motion regime. FGOALS g1 also exhibits similar SWCF variation with EIS regime. However, its problem lies in the excessively concentrated ambient field bins (Fig. 7e). HadGEM1 seems to show the best performance among all the CMIP3 models according to this metric. Figure 9 shows the same result for CMIP5. The latest CERES data ( ) show a very similar observational pattern as ERBE ( ). Several models have extremely low SWCFs in all bins, e.g. CNRM CM5, MRI AGCM3 2h and MRI AGCM3 2s weaker than 30 W m 2. GFDL-HIRAM-C180,

12 NO. 3 ZHANG AND LI 895 CMIP5_MBL f) G g) G Fig. 7. The same as Fig. 6, but for the CMIP5 models, and all the samples are annual monthly means from 2001 to 2008.

13 896 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP3_MBL Fig. 8. SWCF binned by EIS (horizontal axis) and! 500 (vertical axis) over MBL regions in the CMIP3 models. Bin intervals and axis ranges are all the same as Fig. 6. All the samples are annual monthly means from 1985 to Units: W m 2. GFDL HIRAM C360 and MIROC5 also have weak SWCFs in all bins and show no evident diæerences as EIS varies. GISS-E2-R produces relatively stronger SWCF in the lower EIS regime. CanAM4, FGOALS2 s and Max-Planck Institut Earth System Model Low Resolution version (MPI ESM LR) capture the dependence of SWCF on EIS, while they have weaker SWCFs in the high stability regime. BCC CSM 1.1, FGOALS g2 and HadGEM2 A agree with the observation the most. The atmospheric component of BCC CSM 1.1 is an improved Community Atmospheric Model3 (CAM3) (Wu et al., 2008), and its pattern resembles that of CCSM3 (CAM3) in CMIP3. The problem of concentrated ambient field bins in FGOALS1 0 g was improved in FGOALS g2. Meanwhile, its good reproducibility for SWCF in the high stability regime and the insensitivity of SWCF to vertical velocity remain. HadGEM2 A still performs as well as its predecessor in CMIP3 (UKMO HadGEM1).

14 NO. 3 ZHANG AND LI 897 CMIP5_MBL f) G g) G Fig. 9. The same as Fig. 8, but for the CMIP5 models, and all the samples are annual monthly means from 2001 to 2008.

15 898 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 The above observational metric over MBL regions suggests there exists positive feedback between SST and stratus amount (Eitzen et al., 2011). The increased SST destabilizes the lower troposphere, which results in decreased stratus amounts and a transition from stratocumulus into cumulus. This leads to an increased incoming solar flux to the surface, further increasing SST (Eitzen et al., 2011). Although AMIPtype simulation does not explicitly include feedback between ocean and atmosphere, models should still be able to reproduce an observed climate under prescribed SST forcing (e.g. Williams et al., 2003). The lack of dependence of SWCF on EIS is deemed to result from cloud. Since the radiation transfer part in climate models is generally compared with line-by-line calculations, the distinction between diæerent schemes can be very limited (Shell et al., 2008). For those models with good performance, the associated parameterization schemes are also diæerent. For instance, CCSM3 (CAM3), BCC CSM 1.1 (BCC Atmospheric Model) and FGOALS g (Grid Atmospheric Model of Iap/Lasg (GAMIL)version1 and version 2) all implement an additional stability based MBL cloud fraction (Collins et al., 2004), which is independent of the large-scale stratus amount in the model. DiÆerently, HadGEM models (both in CMIP3 and CMIP5) use a relative humidity based scheme (Smith, 1990; Pope et al., 2007), which relies on local variability of temperature and moisture and treats all stratus clouds uniformly. The vastly diæering intensity and responses in stratus regions can further bring diæerent climate sensitivities among models (Stephens, 2005). The problem associated with MBL clouds in CMIP3 (e.g. Bony and Dufresne, 2005) may still remain in CMIP Eastern China The above synthesis of analysis demonstrates that although most of the CMIP3 and CMIP5 models reasonably reproduce the ambient fields in MBL regions, they fail to capture the response of SWCF to ambient field variations. Nevertheless, the following analysis in EC shows that the models even have large discrepancies in the simulated ambient environment. The observed response of SWCF to the dynamic and thermodynamic proxies can barely be captured in any model. Figure 10 (CMIP3) and Figure 11 (CMIP5) show the occurrence frequency of the ambient field sorted by LTS and! 700 in EC, and only samples in NDJF are selected. The ranges of occurrence frequencies are widely diæerent between models and the reanalysis. The vertical velocity in the reanalysis ranges within ±100 hpa and LTS ranges from 18 K to 35 K. Most models have bins concentrated within small ranges of the LTS axis. This corresponds to the large diæerences in the LTS mean during cold seasons (Figs. 4f and 5f). The ranges of! 700 also diæer vastly among models. In CMIP3, the negative vertical velocity axes of FGOALS1 0 g and GISS E R are extended to 500 hpa d 1 due to the too strong rising motion, which may come from the unreasonable orographic eæects in models. In CMIP5, five models velocity axes are set from 800 hpa d 1 to 600 hpa d 1 because of too much strong subsidence or ascending motion. The three MRI models all have large ranges of vertical velocity. GISS E2 R and FGOALS g2 still show too strong rising motions (reaching 400 hpa d 1 ), as did their predecessors. The above analysis indicates that the distributions of ambient field bins disagree greatly among models. The distinction of EC lies in its geographic location, which is on the lee side of the TP. The models may exhibit quite diæerent orographic eæects, directly influencing the ambient fields. SWCFs binned by two variables for CMIP3 and CMIP5 are described respectively in Figs. 12 and 13. In the observation (Figs. 12a and 13a), intense SWCF mainly occurs in high stability (higher than 25 K) and ascending motion regimes, while low stability and subsiding regimes are associated with weak strength (the CERES data show this feature more clearly because of more samples). This is because low-level large-scale lifting transports more moisture vertically; along with the stable stratification, they jointly form a favorable environment for the maintenance of stratus. Therefore, the coexistence of high stability and low-level rising motion is important in forming strong SWCF. This response pattern helps us to examine whether models can simulate SWCF through a correct process. In both groups, the contribution of rising motion to SWCF is evident. Intense SWCF in the model itself mainly occurs in the rising motion regime. However, the problems can be mainly attributed to three aspects. First, intense SWCF of the model itself occurs in relatively weaker LTS regimes, e.g. UKMO HadGEM1 in CMIP3; BCC CSM 1.1, CanAM4, FGOALS s2, GFDL HIRAM C180, HadGEM2 A, GISS E2 R and MRI AGCM3 2h (s) in CMIP5. For these models, the dependence of intense SWCF to strong LTS is not evident. Another problem is that intense SWCFs in some models (e.g. FGOALS1 0 g and GISS-E-R in CMIP3; GISS E2 R in CMIP5) are generated because of too much strong rising motion and can make the mean SWCF become closer to the observation, which, however, is not a correct process. Lastly, some models can simulate relatively similar response patterns, but the intense SWCF in the model itself is much lower than the observation (e.g. CCSM 3 0 in CMIP3; CNRM CM5, MIROC5, MPI ESM LR and

16 NO. 3 ZHANG AND LI 899 CMIP3_EC Fig. 10. Occurrence frequency of LTS (horizontal axis) and! 700 (vertical axis) bins over EC in the CMIP3 models. Bin intervals: 1 K for LTS and 20 hpa d 1 for! 700. All! 700 axes range from 200 hpa d 1 to 200 hpa d 1, except fgoals1 0 g and giss-e-r (k and l), which range from 500 hpa d 1 to 200 hpa d 1. All LTS axes range from 10 K to 40 K, and all the samples are NDJF monthly means from 1985 to MRI CGCM3 in CMIP5). Overall, there is no model that can reproduce an adequate response in terms of reflecting observational data. The same problem of one model in CMIP3 also occurs in its latest version in CMIP5. For example, FGOALS g1 (g2) and GISS E(2) R all exhibit too strong ascending motions; HadGEM1 and HadGEM2 A both have strong SWCF occurring in relatively lower LTS regimes of themselves. Besides, models with finer simulation over MBL regions (e.g. HadGEM model in CMIP3 and CMIP5; FGOALS g2 in CMIP5; BCC CSM 1.1 in CMIP5) all fail to perform well in EC, reflecting the di±culty for one model to perform well simultaneously in both MBL and EC regions due to the intrinsically diæerent stratus properties. As mentioned before, the simulation uncertainty associated with MBL low clouds has long been recognized. The di±culty in parameterizing MBL clouds lies in a blend of complex physical processes (Brether-

17 900 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP5_EC f) G g) G Fig. 11. Occurrence frequency of LTS (horizontal axis) and! 700 (vertical axis) bins over EC in the CMIP5 models. Bin intervals: (a l) 1 K for LTS and 20 hpa d 1 for! 700; (m q) 1 K for LTS and 70 hpa d 1 for! 700. All! 700 axes range from 200 hpa d 1 to 200 hpa d 1, except the last five models (m q) which range from 800 hpa d 1 to 600 hpa d 1. All LTS axes range from 10 K to 40 K, and all the samples are NDJF monthly means from 2001 to 2008.

18 NO. 3 ZHANG AND LI 901 CMIP3_EC Fig. 12. SWCF binned by LTS (horizontal axis) and! 700 (vertical axis) over EC in the CMIP3 models. Bin intervals and axis ranges are all the same as Fig. 10, and all the samples are NDJF monthly means from 1985 to Units: W m 2. ton et al., 2004). Here, we show that the deep continental stratus clouds downstream of the TP are also di±cult to simulate. In section 4, it was shown that all the models unanimously underestimate SWCFs in NDJF. Figures 12 and 13 further indicate that the underestimation occurs in most LTS and! 700 bins for most models. Additionally, although the models generally simulate the contribution of rising motion to SWCF, some of them with strong enough rising motions (exceeding 100 hpa d 1 ) do not reproduce the observed SWCF. The role of rising motion in contributing to intense SWCF is the vertical transportation of moisture. Without abundant water vapor, SWCF will still be weaker than observed, even if the rising motion is strong. Evident examples are MRI AGCM3 2h, MRI AGCM3 2s, Norway Earth System Model1 M (NorESM1 M) and FGOALS g2, all of which have SWCFs weaker than 50 W m 2 in the rising motion regime with their vertical velocities stronger than 100 hpa d 1. We calculated the regional averaged specific humidity during NDJF between 600 hpa and 850 hpa in EC for these four models. The results were, respectively: 2.3 g kg 1 (MRI AGCM3 2h); 2.5 g kg 1 (MRI AGCM3 2s); 2.3 g kg 1 (NorESM1 M); and 2.0 g kg 1 (FGOALS g2). These results are all lower than found in the reanalysis (3.0 g kg 1 ). Therefore, for reproducing reasonable SWCF during winter, it is important for models

19 902 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP5_EC f) G g) G Fig. 13. SWCF binned by LTS (horizontal axis) and! 700 (vertical axis) over EC in the CMIP5 models. Bin intervals and axis ranges are all the same as Fig. 11, and all the samples are NDJF monthly means from 2001 to Units: W m 2.

20 NO. 3 ZHANG AND LI 903 to simulate a moist environment in EC. 6. Discussion and concluding remarks 6.1 Discussion The SWCF in GCMs is a result of both microand macrophysical processes of clouds. Cloud optical depth, determined by microproperties such as cloud water path and eæective radius, and cloud fraction, are two key factors dominating SWCF and should be responsible for biases in the simulation of SWCF. It has been noticed that current GCMs (CMIP3) tend to underestimate cloud fraction and reproduce more optically-thick clouds (Zhang et al., 2005; Qian et al., 2012). Although the present work has focused mainly on simulated SWCF, it is still necessary to examine cloud properties to find some reasons behind the poorly simulated SWCF. As shown in Fig. 14, the seasonal cycles of total cloud fraction (TCF) and cloud water path (CWP; liquid and ice, grid box mean) are largely dispersed among the models. The model-observation biases of TCF are also very large. Satellite observations for CWP are not provided here. This is because of the intrinsically diæerent definition of cloud. The model cloud depends on hydrometeor concentrations, or an optical depth of the cloud condensate, while satellites use pixel-related cloud detection algorithms to define cloud. Comparisons should rely on model satellite simulators to put them at the same standard (e.g. Zhang et al., 2005; Dong et al., 2012; Kay et al., 2012; Nam and Quaas, 2012). In CMIP5, only a few of the models (BCC CSM 1.1, CanAM4, MIROC5 and MRI CGCM3) currently provide the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) data, placing limitations on a comprehensive evaluation. The bias of SWCF is attributed to the discrepancy in both macro- and microcloud properties. For example, CNRM CM5 has the weakest SWCFs over all MBL regions (especially in the P, N and C regions; Fig. 5). It also correspondingly has the least TCFs and CWPs over these regions (Fig. 14). The underestimation of cloud fraction is still evident in CMIP5. Figure 15 illustrates this point more clearly. Over the MBL region, most models simultaneously underestimate annual climatological mean SWCF and TCF. The failure of simulating the dependence of SWCF to MBL inversion may come from the incapability of reproducing abundant clouds, especially low-level clouds, during a strong inversion period. As stated earlier, high stability actually captures many covarying factors that are favorable to the formation of marine stratus, but the intrinsic physical process is very complex. This is also why several models use an empirical relation between stability and MBL cloud fraction for parameterization. For EC, all the models underestimate NDJF mean SWCF and TCF, also suggesting a poor simulation of cloud fraction is at least an important reason why most GCMs cannot reproduce adequate SWCF. Compared with CMIP3, CMIP5 models generally have higher resolutions (Table 1), which are deemed to be beneficial to the simulation of climate models (e.g. Gent et al., 2010). Nevertheless, we found the contribution of a finer grid to the simulation of SWCF to be limited. For MBL regions, this point of view has been suggested by Richter and Mechoso (2006). They showed that the existence of South American topography is important to Peruvian stratocumulus incidence, which, however, is not sensitive to the level of coarseness or fineness of the topography. In our study, GFDL-HIRAM-C360 and MRI- AGCM3-2s showed similar response patterns in their lower resolution versions for MBL regions, and all of them underestimated SWCF in all dynamic and thermodynamic regimes. Contrary to this, some models with medium or low resolution can show good reproducibility. For example, HadGEM1 and HadGEM2- A were found to be the models with the best performance over MBL regions in both CMIP3 and CMIP5 groups. However, their resolution is in both cases only 1.2 ± (lat) 1.8 ± (lon). FGOALS g2 also shows a certain level of improvement over MBL regions without an increase in resolution. For EC, orographic eæects constitute an important factor in the formation of stratiform clouds downstream of the TP in cold seasons (Yu et al., 2004; Li et al., 2008). Whereas, the two very high-resolution models still failed to capture the response pattern of SWCF, just as their lower resolution versions did. Both of them underestimate the climatological mean of SWCF. It seems that an increase in resolution alone is not enough to simulate the stratus cloud radiative eæect. This is because cloud in GCMs is a result of diæerent combinations of sub-grid-scale physical parameterization, including cloud macro- and microphysics, turbulence, radiation and even surface flux, which can hardly be improved by a finer model grid (Zhang et al., 2010). In Kay et al. (2012), they also showed that cloud fraction and cloud optical depth under diæerent resolutions make a limited diæerence. The increase in resolution to a certain extent can even reduce the scale distinction between diæerent physical processes, which are at quite diæerent scales on a coarse grid (e.g. convective and grid-scale precipitation), requiring more appropriate physical parameterization and enhancing the di±culty.

21 904 EVALUATION ON STRATUS RADIATIVE EFFECT IN IPCC MODELS VOL. 30 CMIP5 MODELS Fig. 14. Seasonal cycle of total cloud fraction (left column; units: %) and grid-box mean condensed water path (liquid + ice) (right column; units: kg m 2 ) over the six stratus regions. Two models (HadGEM2-A and GISS-E2-R) are excluded owing to lack of data.

22 NO. 3 ZHANG AND LI 905 Fig. 15. Scatter plot of SWCF (units: W m 2 ) and total cloud fraction (units: %) over (a) MBL regions (annual mean) and (b) EC region (NDJF mean). Two models (HadGEM2-A and GISS-E2-R) are excluded owing to lack of data. 6.2 Concluding remarks This paper gives a comprehensive evaluation on simulated SWCF in CMIP3 and CMIP5 models over major stratus regions. The simulation capability and sources of spreads have been analyzed. Large deviations were found in both projects. The major conclusions can be summarized as follows. (1) Large RMSE and STDDV are found in representative stratus regions (MBL regions and EC) for both CMIP3 and CMIP5. CMIP5 models show no improvement in simulated SWCF, and even enlarged deviations for stratus regions. However, they do show certain enhancements in some high cloud regions. (2) Over MBL regions, the seasonal cycles of EIS in both CMIP3 and CMIP5 are more consistent than those of SWCF. The models generally reproduce a reasonable large-scale environment. The center and range of occurrence frequency of the ambient field can be reasonably captured by most of the CMIP3 and CMIP5 models. However, they fail to simulate the dependence of SWCF on boundary layer inversion. The simulated SWCF tends to incorrectly respond too much to vertical motion in some of the models. The feature of strong SWCF under high stability conditions and the insensitivity of SWCF to vertical velocity variation are poorly reproduced. (3) For EC, a notable feature is that all the models in CMIP3 and CMIP5 unanimously underestimate SWCF in winter. Although the RMSE in winter is reduced from CMIP3 to CMIP5, this region still has large discrepancies and is considered to be more di±cult for the models to simulate. The distributions of ambient field bins are widely diæerent between the models and observations. The feature of intense SWCF in high stability and rising motion regimes is barely reproduced in any model. Intense SWCF of each model itself occurs in relatively weaker LTS regimes, or is contributed by too strong rising motion. Some models can simulate relatively similar response patterns, but the intense SWCF of these models is much lower than in observations. This work highlights that SWCF associated with stratus clouds still shows large biases in most state-ofthe-art models. These inconsistencies may further lead to diæerent climate sensitivities in such models. Future work should be carried out to quantify the cloud and climate feedback in CMIP5 experiments. Considering the remaining large discrepancies of SWCF simulation from CMIP3 to CMIP5 as revealed in this study, it can be inferred that cloud feedback will still be the most uncertain error source. REFERENCES Acknowledgements. This research was supported by the Major National Basic Research Program of China (973 Program) on Global Change (Grant No. 2010CB951902), the National Natural Science Foundation of China (Grant No ), and the Basic Scientific Research and Operation Foundation of CAMS (Grant No. 2010Z003). The authors acknowledge the support by World Climate Research Programme s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors thank the Editor and anonymous reviewers for their valuable comments. Bachiochi, D. R., and T. N. Krishnamurti, 2000: Enhanced low-level stratus in the FSU coupled ocean atmosphere model. Mon. Wea. Rev., 128,

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