development and evaluation of a regional ocean-atmosphere coupled model with focus on the western...

SCIENCE CHINA Earth Sciences

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*Corresponding author (email: [email protected])

• RESEARCH PAPER • May 2012 Vol.55 No.5: 802–815

doi: 10.1007/s11430-011-4281-3

Development and evaluation of a regional ocean-atmosphere cou-pled model with focus on the western North Pacific summer mon-

soon simulation: Impacts of different atmospheric components

ZOU LiWei1,2 & ZHOU TianJun1*

1 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Received April 28, 2011; accepted July 13, 2011; published online October 19, 2011

A regional ocean atmosphere coupled model (ROAM) is developed through coupler OASIS3, and is composed of regional climate model RegCM3 and CREM (Climate version of Regional Eta Model) as its atmospheric component and of a revised Princeton ocean model (POM2000) as its oceanic component. The performance of the ROAM over the western North Pacific summer monsoon region is assessed by the case simulation of warm season in 1998. Impacts of different atmospheric model components on the performance of ROAM are investigated. Compared with stand-alone simulation, CREM (RegCM3) pro-duces more (or less) rainfall over ocean area with inclusion of the air-sea coupling. Different biases of rainfall are caused by the different biases of SST derived from the coupled simulation. Warm (or cold) SST bias simulated by CREM_CPL (RegCM3_CPL) increases (or decreases) the evaporation at sea surface, then increases (or decreases) the rainfall over ocean. The analyses suggest that the biases of vertical profile of temperature and specific humidity in stand-alone simulations may be responsible for the SST biases in regional coupled simulations. Compared with reanalysis data, the warmer (or colder) and moister (or dryer) lower troposphere simulated in CREM (RegCM3) produces less (or more) sea surface latent heat flux. Meanwhile, the more unstable (or stable) lower troposphere produces less (or more) cloudiness at low-level, which increases (or decreases) the solar radiation reaching on the sea surface. CREM (RegCM3) forced by observed SST overestimates (or underestimates) the sea surface net heat flux, implying a potential warm (or cold) heat source. After coupling with POM2000, the warm (or cold) heat source would further increase (or decrease) the SST. The biases of vertical profile of temperature and specific humidity may be ascribed to the different representation of cumulus convection in atmospheric models.

inter-comparison of regional coupling, different atmospheric component, western North Pacific summer monsoon, model biases

Citation: Zou L W, Zhou T J. Development and evaluation of a regional ocean-atmosphere coupled model with focus on the western North Pacific summer monsoon simulation: Impacts of different atmospheric components. Sci China Earth Sci, 2012, 55: 802–815, doi: 10.1007/s11430-011-4281-3

Global climate models are useful tools in understanding the mechanisms involved in climate variability and changes and projecting the future climate [1–5]. But current global cli-mate models with coarse spatial resolution generally show large biases at regional scales. With higher spatial resolu-tions, regional climate models (RCMs) have been widely

used as a downscaling tool in regional climate studies and future climate projection [6–13].

In previous studies, the RCM is usually forced by either observed or predicted sea surface temperature (SST). In this strategy, the model atmosphere is considered to be passively responded to the underlying SST as a slave. While this strategy is reasonable in many regions, limitation is found over the Asian-Australian monsoon (hereafter AAM) region.

Zou L W, et al. Sci China Earth Sci May (2012) Vol.55 No.5 803

Recent studies found that the observed seasonal rainfall and SST anomalies are negatively correlated over the domain, indicating that the SST is forced primarily by the atmos-phere in boreal summer [14]. The SST-rainfall correlations yielded by AGCMs are at odds with the observations, be-cause the model atmosphere is forced to respond passively to the SSTs. Many previous studies based on the compari-sons between AGCM simulations and observation also suggest the importance of regional air-sea interactions in a reasonable simulation of the variability of rainfall and at-mospheric circulation over AAM [2, 3, 15].

To improve the simulation of AAM, it is desirable to de-velop regional ocean-atmosphere coupled models. In recent years, great effort has been devoted to the development of ROAMs with focus on South China Sea monsoon [16], East Asian monsoon [17–23], Indian monsoon [24], and Mari-time continent region [25]. These studies have compared the performance of coupled and uncoupled simulations and found that the ROAMs generally exhibit improvements in the simulation of rainfall, sea surface heat budgets and cir-culation. However, the degree of improvement is different. For example, the improvements from the ROAM simula-tions over East Asia, which employ the same regional at-mospheric model, are quite different [19, 20]. But these comparisons were not based on the same model framework. The biases of ROAM come from atmospheric component and oceanic component, as well as the physical processes involved in the component models. Impacts of regional at-mospheric models and oceanic models on the simulation of ROAM should be based on the same model framework. In this paper, the impacts of different atmospheric models on the performance of ROAM are investigated, since the sea surface fluxes that result in the biases of ROAM are deter-mined mainly by atmospheric model.

The western North Pacific summer monsoon (WNPSM) is an excellent platform for assessing the performance of ROAMs. WNPSM, as a component of the broad AAM sys-tem [26, 27], has important impacts on the regional climate variability [28, 29]. The ability of AGCMs in simulating the WNPSM has been evaluated [2]1), while that of RCMs is

less well known. The regional air-sea interaction should be taken into account for a reasonable simulation of WNPSM since the ocean is partly forced by the atmosphere aloft [14]. The climate anomalies are quite unique in the summer of 1998 over AAM, such as the severe flood along the middle and lower reaches of the Yangtze River valley [30] and the westward shift and unprecedented strength of western North Pacific subtropical high [31], due to the unprecedented 1997/98 El Niño event. This case of 1998 has been selected for the evaluation of AGCMs on AAM simulation [32] and of RCMs on East Asia summer monsoon simulation [33–35]. How well ROAM reproduces the WNPSM of 1998 has not been investigated before.

In this paper, a regional ocean atmosphere coupled model, which is composed of two regional atmospheric models and POM2000, is developed through coupler OASIS3. The im-pacts of different regional atmospheric models on the per-formance of ROAM in simulating the WNPSM of 1998 are investigated. We attempt to address the following questions: (1) What are the differences in simulated rainfall between the coupled and uncoupled simulations? (2) What are the possible reasons that cause these differences?

1 Regional ocean-atmosphere coupled model, experimental design

1.1 Regional atmospheric model

The atmospheric components of regional ocean-atmosphere coupled model have two options: one is Climate version of Regional Eta Model (CREM) developed at LASG/IAP, and another is Regional Climate Model version 3 (RegCM3) developed at ICTP in Italy. Brief descriptions of CREM and RegCM3 are given in Table 1.

CREM is an extension of numerical forecast model AREM (Advanced Regional Eta-coordinate Model) [36] developed at LASG/IAP with improvements in physical packages [37]. The model shows reasonable performance in simulating the East China summer monsoon during 1995– 2004, especially for the variability of precipitation and temperature over the Yangtze River Valley [37]. The dy-

Table 1 Model descriptions

Model CREM RegCM3

Dynamic core AREM2.3 [36] MM5

Resolution About 37 km (101×161) 45 km (113×136)

Convective parameterization Betts-Miller [39] Grell FC closure [47]

Large-scale precipitation scheme Explicit prognostic cold cloud [40] SUBEX [46]

Radiation scheme UKMO [41, 42] CCM3 [49]

Planetary boundary layer scheme Non-local boundary scheme [43] Non-local boundary scheme [43]

Land surface scheme BATS1e [44] BATS1e [44]

Ocean flux scheme Bulk formula [45] Bulk formula [45]

1) Liu X J, Zhou T, Zhang L X, et al. The western North Pacific summer monsoon simulated by GAMIL 1.0: Influences of the parameterization of wind

gustiness (in Chinese). Chin J Atmos Sci, 2011, in press

804 Zou L W, et al. Sci China Earth Sci May (2012) Vol.55 No.5

namic framework of CREM, based on the two layer atmos-pheric general circulation model developed at IAP, employs hydrostatic primitive equations in spherical coordinate and the mathematical formulation which can perfectly guarantee energy and mass conservations. The model employs co-ordinate in the vertical that reduces the pressure gradient force error at steep topographic area in the coordinate [38].

The sub-grid precipitation processes are parameterized by Betts-Miller cumulus convection scheme [39], while the large scale precipitation is computed by explicit prognostic cold cloud scheme [40]. Prognostic variables in the scheme include mixing ratio of water vapor, cloud water, rain, and cloud ice. The radiation package is an advanced version [42] based on the scheme of Edwards and Slingo [41]. The non-local atmospheric boundary layer scheme [43] and land surface scheme (BATS 1e) [44] are adopted. The sea sur-face latent heat flux, sensible heat flux, and turbulence mo-mentum fluxes are computed by bulk formula [45].

RegCM3 [46], which is an extension version of RegCM2 [6, 7], is based on numerical forecast model MM5. The Grell cumulus parameterization scheme [47] with the Fritsch and Chappell assumption closure is used to compute the convective rainfall. This scheme was also employed in regional climate studies [12, 34] and regional ocean- atmosphere model studies [23, 24] over East Asia. The Subgrid Explicit Moisture Scheme (SUBEX) [48] is used to treat non-convective cloud and precipitation processes. The National Center for Atmospheric Research (NCAR) Com-munity Climate Model version 3 (CCM3) radiative transfer package [49] is employed to represent radiation processes. RegCM3 employs the same planetary boundary layer scheme, land surface scheme, and ocean surface flux scheme as those in CREM.

1.2 Regional oceanic model

The oceanic component of ROAM is Princeton Ocean Model version 2000 (POM2000) [50], which was developed in Princeton University and further improved by Chu and Chang [51] and Qian et al. [52] for a better simulation of East Asian coastal regions. POM is a sigma-coordinate, free-surface, primitive equation oceanic model.

1.3 Coupler and ocean-atmosphere coupling

The regional atmospheric models and POM2000 are cou-pled through the Ocean Atmosphere Sea Ice Soil 3.0 (OASIS3.0) coupler [53]. OASIS has been widely employed for the development of global ocean-atmosphere coupled model and earth system model [54]. During the process of coupling, the atmospheric model provides sea surface latent heat flux, sensible heat flux, shortwave radiation flux, longwave radiation flux, and wind stress to POM2000, while the POM2000 supplies the SST field to atmospheric

model as the surface condition. The exchange frequency is three hours. Due to different resolutions of atmospheric and oceanic models, the coupling fields from source grid to tar-get grid are interpolated by using “mosaic” method [53] inside the coupler. The framework of regional ocean- at-mosphere couple model is shown in Figure 1.

1.4 Experimental design

The atmospheric model domain and topography in this study are shown in Figure 2. The domain covers 0°–40°N, 105°–160°E. The horizontal resolution of CREM (RegCM3) is 37 km (45 km) with uneven 32 (18) levels in the vertical. The model top of CREM (RegCM3) is 10 (50) hPa. The buffer zones are located across 10 grid points along all four domain edges. The initial and lateral boundary conditions of the atmosphere are derived from National Center for Envi-ronmental Prediction/Department of Energy (NCEP/DOE) reanalysis 2 (R2) [55] and updated every six hours.

The horizontal resolution of POM2000 is 0.5°×0.5° and there are 16 levels in the vertical. The POM2000 domain covers 0°–40°N, 105°–160°E. The lateral temperature and salinity boundary conditions of POM2000 were derived from the Levitus climatological monthly mean data [56]. A non-gradient extrapolation method is adopted to treat the open boundary condition [57].

Before fully coupling, the ocean model was driven by climatological monthly mean surface wind and heat fluxes derived from R2 averaged from 1979–2007 for one year, and then the model was continually driven by daily surface R2 data interpolated from the monthly mean data from Jan-uary 1 to April 25 of 1998 to obtain an initial ocean condi-tion for next step’s fully-coupled run.

The fully-coupled simulation period is from April 25 to August 31 of 1998. The stand-alone regional atmospheric model simulations forced by weekly OISST2 (Optimal In-terpolation Sea Surface Temperature V2) SST [58] are car-ried out to investigate the role of regional ocean-atmosphere coupling. The stand-alone simulations are termed “control experiments” (CREM_CTRL and RegCM3_CTRL), while

Figure 1 The framework of regional ocean-atmosphere coupled model.


Figure 2 Model domain and topographic height (m) in CREM (a) and RegCM3 (b).

the ocean-atmosphere coupling simulations are termed CREM_CPL and RegCM3_CTRL in the following discus-sion. The period from April 25 to April 30 is regarded as “spin-up” time of simulations and is excluded in the fol-lowing analysis.

The following satellite and reanalysis datasets are used for the validation of model results: (1) the daily rainfall with a resolution of 0.25°×0.25° derived from TRMM 3B42 [59]; (2) sea surface temperature derived from weekly Optimally Interpolated Sea Surface Temperature (OISST) data with 1°×1° resolution [58]; (3) the sea surface latent and sensible heat fluxes derived from the objectively analyzed air-sea heat fluxes (OAFlux) version 3 [60]; (4) surface radiation fluxes derived from the International Satellite Cloud Cli-matology Project (ISCCP) [61]; (5) the three dimension air

temperature and specific humidity data from NCEP/NCAR Reanalysis-2 [56].

2 Results

In this section, the performances of different regional atmospheric models coupled with POM2000 are compared in terms of rainfall and SST to investigate the role of regional air-sea coupling.

2.1 Rainfall

The spatial distribution of rainfall averaged from May to August of 1998 over 5°–35°N, 110°–155°E is shown in Figure 3. The analyzed domain is smaller than the simulated domain, since the buffer zones are excluded. In observation (Figure 3(a)), the major rainbands (larger than 10 mm/d) are located over south of 10°N, west to Philippine islands, southeast of China and “Meiyu” front region. The open ocean east to 120°E dominated by subtropical high is rain-less region (less than 6 mm/d). The CREM_CTRL exhibits reasonable performance in simulating the spatial distribu-tion of rainfall, having a spatial pattern correlation coeffi-cient (root mean square error) of 0.47 (4.02 mm/d) with TRMM3B42 (Table 2). The main deficiencies of CREM_ CTRL are the underestimation (or overestimation) of the rainfall intensity over region dominated by subtropical high (major rainbands). Compared with CREM_CTRL, the CREM_CPL produces much rainfall over ocean and shows a northeast-southwestern rainband with intensity larger than 17 mm/d over the northwest of simulated domain (Figure 3(c)). The SCC (RMSE) of CREM_CPL is 0.37 (7.31 mm/d) with TRMM3B42 (Table 2), indicating that the role of re-gional air-sea coupling on the mean rainfall pattern in CREM is negative.

There are obvious biases in the major rainbands simulat-ed by RegCM3_CTRL (Figure 3(d)). A spurious north-east-southwestern rainband with the intensity larger than 17 mm/d is found over the open ocean south to Japan. The SCC (RMSE) of RegCM3_CTRL is 0.20 (5.25 mm/d) with TRMM342. Compared with RegCM3_CTRL, RegCM3_ CPL reduces the rainfall over the open ocean (Figure 3(e)). The spurious rainband in RegCM3_CTRL is eliminated, while a strong rainband is found east to 140°E. The SCC (RMSE) of RegCM3_CPL is 0.19 (3.19 mm/d) with TRMM3B42 (Table 2). Although the spatial pattern of rainfall does not show obvious improvement, the RMSE is

Table 2 Spatial correlation coefficient (SCC) and root mean square error (RMSE) of averaged rainfall from May to August of 1998 between TRMM3B42 and simulations as shown in Figure 3

CREM_CTRL CREM_CPL RegCM3_CTRL RegCM3_CPL

SCC 0.47 0.37 0.20 0.19

RMSE (mm/d) 4.02 7.31 5.25 3.19


Figure 3 Spatial patterns of rainfall averaged from May to August of 1998 for TRMM3B42 (a), CREM_CTRL (b), CREM_CPL (c), RegCM3_CTRL (d) and RegCM3_CPL (e).

significantly reduced, indicating the positive role of regional air-sea coupling in RegCM3.

The major rainbands of western North Pacific summer moon shows obvious northward shifts [28]. The latitude- time cross sections of rainfall averaged between 120°E and 140°E are shown in Figure 4. Three northwards shifts dur-ing May to August of 1998 are evident in TRMM3B42, i.e. late May to late June, early July to late July, and early Au-gust to late August. CREM_CPL reasonably reproduces the first northward shift of rainband, while overestimates the rainfall south of 10°N and hardly reproduces the second and third northward shift (Figure 4(b)). Compared with CREM_CTRL, CREM_CPL increases the rainfall frequen-cy and rainfall intensity over 20°–30°N, displaying the im-provement of northward shift of rainband (Figure 4(c)). On the other hand, RegCM3_CTRL hardly reproduces the northward shift of rainbands and produces excessive rainfall

over 20°–30°N (Figure 4(d)). RegCM3_CPL with inclusion of the regional air-sea coupling shows reasonable perfor-mance in simulating the first two northward shifts of rain-bands, despite the weaker rainfall intensity than that in TRMM3B42.

How well the rainfall variability is reproduced is the im-portant test for model. Variations of daily rainfall rates av-eraged over 10°–25°N, 120°–150°E are shown in Figure 5. Table 3 gives the statistics of correlation coefficients and RMSE. There are four rainfall events during the simulated period in TRMM3B42, while the strongest rainfall event (14 mm/d) is found in late May. The averaged rainfall in-tensity is 4.21 mm/d in TRMM3B42. The averaged rainfall intensity in CREM_CTRL is 3.30 mm/d, which is weaker than that in TRMM3B42, while the correlation coefficient (RMSE) of rainfall series is 0.62 (2.77 mm/d) with TRMM3B42. CREM_CPL with the inclusion of regional


Figure 4 The latitude-time cross sections of rainfall (mm/d) averaged from 120° to 150°E for TRMM3B42 (a), CREM_CTRL (b), CREM_CPL (c), RegCM3_CTRL (d) and RegCM3_CPL (e).

Figure 5 Time series of rainfall (mm/d) averaged over 10°–25°N, 120°–150°E.

Table 3 Correlation coefficients (CC) and RMSE of simulated daily rainfall rates shown in Figure 5 with TRMM3B42

CREM_CTRL CREM_CPL RegCM3_CTRL RegCM3_CPL

CC 0.62 0.60 0.28 0.39

RMSE (mm/d) 2.77 5.72 5.07 3.22

air-sea coupling increases the rainfall intensity. The aver-aged rainfall intensity is 8.73 mm/d. The correlation coeffi-cient (RMSE) of rainfall series is 0.60 (5.72 mm/d).

The averaged rainfall intensity of rainfall series simulat-ed by RegCM3_CTRL is 7.59 mm/d, while the correlation coefficient of rainfall series is 0.28 with TRMM3B42, which is weaker than that simulated by CREM. The RMSE

of rainfall series simulated by RegCM3_CTRL is 5.07 mm/d. RegCM3_CPL with inclusion of the regional air-sea coupling reduces the rainfall intensity with the average be-ing 3.21 mm/d. The correlation coefficient (RMSE) of rain-fall series simulated by RegCM3_CPL is 0.39 (3.22 mm/d) with TRMM3B42.


2.2 Sea surface temperature

The analysis above indicates that two regional atmospheric models exhibit distinct performance in simulating rainfall when coupling with the same regional ocean model. Com-pared with control experiments, CREM_CPL increases the rainfall intensity over ocean, and RMSE does not show ob-vious improvement in spatial distribution of mean rainfall, while RegCM3_CPL reduces the RMSE of rainfall and im-proves the simulation of daily rainfall variability. Since rainfall is affected by the sea surface temperature (SST), we investigate the responses of SST to regional air-sea coupling in this subsection.

Figure 6 shows the spatial distributions of observed and simulated SST averaged from May to August of 1998. Sea surface water warmer than 29°C is found over south of 24°N in OISST (Figure 6(a)). SST warmer than 30°C is located over South China Sea. The SST simulated by CREM_CPL is warmer than that in observation (Figure 6(b)), especially over the South China Sea and the open ocean east to Philippine islands. The evolution of SST av-eraged over 10°–25°N, 120°–155°E indicates that the simu-lated SST reaches a steady state after 20 days (Figure 6(d),

red line). However, the cold bias of SST simulated by RegCM3_CPL is evident (Figure 6(c)), especially over north of South China Sea and the ocean south to Japan. This pattern is also evident in some previous ROAMs [20–23]. The evolution of SST averaged over 10°–25°N, 120°–155°E indicates that the initial SST cold bias is less than 1°C, and then amplifies with time.

SST shown in Figure 6 is the direct products of air-sea coupling. The distinct SST bias can explain the different response of rainfall simulated by different regional atmos-pheric model coupled with the same regional ocean model. The warm SST bias simulated by CREM_CPL increases the sea surface evaporation, and then supplies more water vapor. The increase of water vapor, on one hand, will increase the atmospheric instability energy, which favors more rainfall events. On the other hand, the increase of water vapor will result in stronger rainfall intensity under the same circula-tion background since the regional atmospheric models are forced by the same lateral boundary. Both will produce more rainfall over ocean (Figure 3(c)). However, the cold SST bias simulated by RegCM3_CPL reduces the sea sur-face evaporation and then decreases the simulated rainfall over ocean.

Figure 6 Spatial patterns of SST averaged from May to August of 1998 for OISST (a). Spatial maps for the difference of averaged SST between CREM_CPL (b), RegCM3_CPL (c) and OISST. (d) Time series of SST averaged over (10°–30°N, 120°–155°E) (black: OISST; red: CREM_CPL; blue: RegCM3_CPL).


3 Sea surface heat budget analysis

Sea surface heat fluxes are the key variables that connect SST with rainfall. Previous studies show that compared with stand-alone regional atmospheric model simulation, the regional ocean-atmosphere coupled model improves the simulation of sea surface heat fluxes [23, 24]. In this section, we compare the changes of sea surface heat fluxes between the simulations with and without regional air-sea coupling to understand the possible reasons behind the distinct SST biases.

Shown in Figure 7 are time series of sea surface latent heat flux (positive upward), sensible heat flux (positive up-ward), shortwave radiation flux (negative upward), longwave radiation flux (positive upward) and net heat flux

(positive upward) averaged over (10°N–30°N, 120°E– 155°E). The values averaged from May to August of 1998 are given in Table 4. The averaged sea surface latent flux derived from OAFlux is 103.7 W/m2. The averaged latent heat flux simulated by CREM_CTRL is 47.8 W/m2, which is less than that of OAFlux about 60 W/m2, indicating that the latent flux is significantly underestimated by CREM_CTRL. On the contrary, RegCM3_CTRL signifi-cantly overestimates the latent heat flux. The averaged val-ue is 165.3 W/m2, which is larger than that of OAFlux about 60 W/m2. CREM_CPL (RegCM3_CPL) with inclusion of the regional air-sea coupling increases (or decreases) the latent flux, showing improvements in latent heat flux.

Shortwave radiation flux is one of the major components of heat budget over this region (Figure 7(c)). The averaged shortwave radiation flux derived from ISCCP is 252.9

Table 4 May to August of 1998 averaged sea surface latent heat flux, sensible heat flux, shortwave radiation flux, longwave radiation flux, and net heat flux averaged over 10°–30°N, 120°–155°E

OAFlux/ISCCP CREM_CTRL CREM_CPL RegCM3_CTRL RegCM3_CPL

Latent heat (W/m2) 103.7 47.8 102.1 165.3 108.4

Sensible heat (W/m2) 4.9 1.7 0.7 23.9 18.6

Shortwave radiation (W/m2) 252.9 240.5 205.7 200.4 190.6

Longwave radiation (W/m2) 43.1 27.5 24.0 45.9 38.8

Net heat (W/m2) 101.2 166.9 78.9 34.7 24.8

Figure 7 Time series of sea surface latent heat flux (a), sensible heat flux (b), shortwave radiation flux (c), longwave radiation flux (d), and net heat flux (e) averaged over 10°–30°N, 120°–155°E during May to August of 1998. Units are W/m2.


W/m2. The averaged shortwave radiation flux derived from CREM_CTRL (RegCM3_CTRL) is 240.5 W/m2 (200.4 W/m2). Compared with field measurements, the ISCCP data overestimate shortwave radiation flux by about 10%-15% [62]. So the “real” value of averaged shortwave radiation flux may be between the values simulated by CREM_CTRL and RegCM3_CTRL. Compared with control experiments, the simulations with inclusion of the regional air-sea cou-pling reduce the shortwave radiation flux. The averaged shortwave radiation flux derived from CREM_CPL (RegCM3_CPL) is 205.7 W/m2 (190.6 W/m2). The warm SST bias simulated by CREM_CPL supplies more water vapor, and then increases the clouds, which reduces the so-lar radiation reaching at sea surface. The cold SST bias simulated by RegCM3_CPL increases the static stability of lower troposphere atmosphere, and then increases the clouds at low level, which reduces the solar radiation reaching at sea surface.

The magnitude of sensible heat flux and longwave radia-tion flux are smaller than that of latent heat flux and shortwave radiation flux over this region during the simula-tion period. The averaged sensible heat flux derived from OAFlux is 4.9 W/m2, while CREM_CTRL (RegCM3_ CTRL) underestimates (or overestimates) the sensible heat flux. The difference of coupled and uncoupled simulation in the variable is less than 6 W/m2, and the coupled experi-ments improve the simulation of sensible heat flux. The averaged longwave radiation flux derived from ISCCP is 43.1 W/m2. CREM_CTRL and CREM_CPL underestimate the longwave radiation flux by about 20 W/m2 and the dif-ference between them is less than 5 W/m2. RegCM3_CTRL and RegCM3_CPL exhibit reasonable performance in sim-ulating the longwave radiation flux and the difference be-tween them is less than 8 W/m2.

The averaged net heat flux derived from observation is 101.2 W/m2. The averaged net heat flux derived from CREM_CTRL is 166.9 W/m2, which is significantly over-estimated. The averaged net heat flux derived from CREM_CPL is 78.9 W/m2, which is close to the observa-tion, indicating regional air-sea coupling improves the sim-ulation of net heat flux. On the contrary, the underestima-tion of net heat flux derived from RegCM3_CTRL is evi-dent. The averaged value is 34.7 W/m2, signifying that ocean is a heat source. Compared with RegCM3_CTRL, the averaged net heat flux derived from RegCM3_CPL is in-

creased by 60 W/m2, close to the observation. The analysis above suggests that the simulation with re-

gional air-sea coupling improves the simulation of net heat flux over ocean, especially for latent heat flux, despite the cold or warm SST bias. These results are consistent with previous studies [23, 24]. We also notice that the differ-ences of latent heat flux and shortwave radiation flux be-tween coupled simulation and uncoupled simulation are much larger than those of sensible heat flux and longwave radiation flux, indicating that the performance of coupled model is dominated by latent heat flux and shortwave radia-tion flux.

The turbulent heat flux formula show sea surface sensi-ble heat and latent heat flux are related to the wind speed at 10 m, the difference of temperature and humidity between sea surface and surface air. Time series of wind speed at 10 m, air temperature and humidity at 2 m, sea surface temper-ature and sea surface saturated humidity averaged over 10°–30°N, 120°–155°E are shown in Figure 8. The values averaged from May to August of 1998 are given in Table 5. The improvement of sensible heat flux simulated by CREM_CPL is contributed by both the increase of wind speed at 10m and the increase of difference of temperature between sea surface and surface air. The sea surface tem-perature and surface air temperature simulated by CREM_CPL are warmer than those in CREM_CTRL, while the increase of sea surface temperature is more significantly. The reduction of sensible heat flux simulated by RegCM3_CPL is resulted from the decrease of wind speed at 10m. The increase (or decrease) of latent heat flux simu-lated by CREM_CPL (RegCM3_CPL) is due to the increase (or decrease) of wind speed at 10 m and the increase (or decrease) of sea surface saturated humidity induced by the warm (or cold) SST bias.

4 Discussion on SST biases

The improvements of sea surface latent heat flux and net heat flux by inclusion of regional air-sea coupling are closely associated to the SST biases, indicating that the im-provements are at the expense of SST. Since the same re-gional ocean model is employed, the biases from regional atmospheric model should take primary responsibility for the SST bias.

Table 5 May to August of 1998 averaged wind speed at 10 m, air temperature at 2 m, surface temperature, humidity at 2 m, surface saturated humidity averaged over 10°–30°N, 120°–155°E

OAFlux CREM_CTRL CREM_CPL RegCM3_CTRL RegCM3_CPL

Wind speed at 10 m (m/s) 5.6 3.4 4.2 6.2 4.7

Air temperature at 2 m (°C) 28.9 29.0 30.1 26.6 25.1

Surface temperature (°C) 29.0 29.0 30.6 29.0 27.5

Humidity at 2 m (g/kg) 19.4 21.2 21.7 19.0 17.7

Surface saturated humidity (g/kg) 22.6 22.6 24.6 22.6 20.8


Investigating the performance of CREM (RegCM3) forced by OISST2 will help us understand why there are distinct SST biases in the coupled simulations. Since the sea surface net heat flux is significantly overestimated (or un-derestimated) in CREM_CTRL (RegCM3_CTRL), signify-ing a potential warm (or cold) heat source. After coupling with POM2000, the warm (or cold) heat source will lead to warm (or cold) of SST bias. As shown in Figure 6(d), this initial SST biases will persist and will be amplified.

The sea surface heat budget analysis (Figure 7) suggests that the potential warm (or cold) heat source in CREM_CTRL (RegCM3_CTRL) is primary due to the un-derestimation (or overestimation) of sea surface latent heat flux and the overestimation (or underestimation) of solar radiation reaching at sea surface. Then, why is there a po-tential warm (or cold) heat source in CREM_CTRL (RegCM3_CTRL)?

The vertical profiles of bias of temperature and humidity simulated by CREM_CTRL (RegCM3_CTRL) are shown in Figure 9. The temperature of troposphere simulated by CREM_CTRL is warmer than that in observation (Figure 9(a)). The largest bias is 2°C located at 900 hPa, indicating that the stratification of troposphere simulated by CREM_ CTRL is more unstable than that in observation. Warmer lower troposphere leads to less sea surface sensible heat flux in CREM_CTRL (Figure 7(b)). The lower (middle)

Figure 9 Vertical profiles of the biases of temperature (°C) and humidity (g/kg) averaged over 10°–30°N, 120°–155°E for CREM_CTRL and RegCM3_CTRL.

Figure 8 Time series of surface wind speed at 10 m, humidity at 2 m, air temperature at 2 m, sea surface tem-perature, sea surface saturated humidity averaged over 10°–30°N, 120°–155°E during May to August of 1998. Sine control experiments are forced by observed SST, the SST and surface saturated humidity derived from control experiments in (d) and (e) are nearly the same with the observation.


troposphere is colder (or warmer) in RegCM3_CTRL than that in observation, indicating that the stratification of trop-osphere simulated by RegCM3_CTRL is more stable than that in observation. Colder lower troposphere of RegCM3_CTRL leads to the overestimation of sensible heat flux (Figure 7(b)). Cold bias of land surface air temperature in RegCM3 has been reported in previous RegCM3 simula-tions over East Asia [63].

The lower troposphere is moister (or dryer) in CREM_CTRL (RegCM3_CTRL) than that in observation (Figure 9(b)), resulting in the underestimation (overestima-tion) of latent heat flux shown in Figure 7(a).

Cloudiness is an important factor that affects the solar radiation reaching at sea surface. The spatial patterns of cloud cover averaged between 950–100 hPa simulated by CREM_CTRL and RegCM3_CTRL are shown in Figure 10 (left panel). The cloud cover simulated by CREM_ CTRL is much less than that simulated by RegCM3_CTRL. Less (or more) cloud cover induces more (or less) solar radiation reaching at sea surface (Figure 7(b)). The western Pacific is covered primarily by high cloud in boreal summer, while the low cloud also accounts for 20%–30% [64]. The low cloud fraction is related to the static stability of lower trop-osphere [65]. The warmer (or colder) and moister (or dryer) lower t roposphere s imula ted by CREM_CTRL (RegCM3_CREL) decreases (or increases) the low level

static stability, favoring (or suppressing) the formation of low cloud. Since the ISCCP cloud simulator [66] has not been implemented in RegCM3, the data that separate clouds into high and low clouds are not available. The cloud covers averaged between 950–700 hPa simulated by the control experiments are shown in Figure 10 (Right Panel). The low-level cloudiness in RegCM3_CTRL is much more than that in CREM_CTRL.

The analysis above indicates that the biases of vertical distribution of temperature and humidity are important fac-tors that result in the biases of sea surface latent heat flux and solar radiation in control experiments. Since the com-mon land surface model, sea surface flux parameterization and planetary boundary layer scheme are employed in RegCM3 and CREM, the different biases of vertical distri-bution of temperature and humidity between RegCM3_ CTRL and CREM_CTRL may be related to the different cumulus convection parameterization schemes. The spatial distribution of atmosphere convective available potential energy (CAPE) averaged from May to August of 1998 over ocean are shown in Figure 11. Large CAPE is found over South China Sea and the ocean east to Philippine islands in observation (Figure 11(a)). CREM_CRL significantly over-estimate the CAPE, while RegCM3_CTRL significantly underestimate the CAPE over the ocean east to Philippine islands. Therefore, we hypothesize that the convection fre-

Figure 10 Spatial distributions of cloudiness averaged between 950－100 hPa (left panel) and 950–700 hPa (right panel) for CREM_CTRL ((a), (b)) and RegCM3_CTRL ((c), (d)).


Figure 11 Spatial distributions of ocean convective available potential energy (CAPE) averaged from May to August of 1998 for NCEP2 (a), CREM_CTRL (b), and RegCM3_CTRL (c).

quency of CREM (RegCM3) may be underestimated (or overestimated), which keeps (or consumes) more CAPE in the atmosphere. This hypothesis can be confirmed by the convection percentages of rainfall (defined as the percent of total rainfall that is convective rainfall) (Figure not shown). The convective percentages are 90% larger in RegCM3_CTRL, while CREM_CTRL is dominated by large scale precipitation.

5 Summary and concluding remarks

In this paper, the development of a regional ocean-atmosphere coupled model through OASIS3.0 coupler is de-

scribed. The atmospheric component of the regional ocean- atmosphere coupled model has two options: one is CREM and the other is RegCM3. The oceanic component is POM2000. The impacts of different atmospheric compo-nents on the performance of regional coupled model in sim-ulating the WNPSM in 1998 are investigated. The model biases are also discussed. The major conclusions are sum-marized below.

(1) Compared with the uncoupled simulations, CREM_CPL (RegCM3_CPL) increases (or decreases) the rainfall intensity and RMSE over the ocean domain. The variability of daily rainfall rates is also improved in RegCM3_CPL.

(2) The different responses of rainfall in two regional ocean-atmosphere coupled simulations with the same re-gional oceanic model are related to the distinct SST bias. The warm SST biases simulated by CREM_CPL are evident over the South China Sea and the ocean east to Philippine islands. The largest warm SST bias is 2.5°C. The cold SST biases simulated by RegCM3_CPL are evident over the South China Sea and the ocean east to Japan. The largest SST cold bias is 2.5°C. Warmer (or colder) SST supplies more (or less) water vapor, which increases (or decreases) the rainfall over ocean.

(3) The simulated sea surface latent heat flux is improved by inclusion of regional air-sea coupling. The underestima-tion (or overestimation) of sea surface latent heat flux and the overestimation (or underestimation) of shortwave radia-tion flux in CREM_CTRL (RegCM3_CTRL) imply a po-tential warm (or cold) heat source. After coupled with POM2000, this warm (or cold) heat source causes warm (or cold) SST bias.

(4) The biases of vertical profiles of temperature and humidity are important factors that impact the SST bias. The moister (or dryer) and warmer (or colder) atmosphere at lower troposphere simulated by CREM_CTRL (RegCM3_CTRL), on one hand, underestimates (or overes-timates) the sea surface latent heat flux, and on the other hand, decreases (or increases) the low level static stability, suppressing (or favoring) the formation of low cloud. Less (or more) clouds induce more (or less) solar radiation to reach the sea surface.

(5) The different biases of vertical profiles of tempera-ture and humidity may be due to different cumulus convec-tion parameterization schemes. CREM (RegCM3) domi-nated by large scale (convective) rainfall, overestimates (or underestimates) the CAPE over ocean, favoring the moister (or dryer) and warmer (or colder) atmosphere at lower trop-osphere.

In this paper, the distinct SST biases are ascribed to the different cumulus convection parameterization schemes. But the differences in other physics processes are also evi-dent (Table 1). Those differences may also contribute to the different biases of vertical profile of temperature and hu-midity. The impacts of other physical processes on the sim-


ulation of vertical profile of temperature and humidity war-rant further study. In addition, the performance of regional ocean-atmosphere coupled model on the simulation of “rainfall-SST” relationship also deserves further study. This issue will be addressed in an ongoing study of multi-year consecutive simulation.

In addition, the TRMM3B42 data are used to validate the rainfall simulated by regional ocean-atmosphere coupled model. Since this data are retrieved from satellite measure-ment, the uncertainties should also be acknowledged.

We thank Prof. Yaocun Zhang and Yongjie Fang from Nanjing University for their help on implementing the POM2000 model. Prof. Laurent Li from Laboratoire de Météorologie Dynamique at Paris is also appreciated for his assistance on utilizing the OASIS coupler. The comments from two anonymous reviewers are also appreciated. This work was supported by the Ocean Projects of Public Science and Technology Research Funds (Grant No. 201105019-3).

1 Zhou T J, Yu R C. Twentieth century surface air temperature over China and the globe simulated by coupled climate models. J Clim, 2006, 19: 5843–5858

2 Zhou T J, Wu B, Wang B. How well do atmospheric general circula-tion models capture the leading modes of the interannual variability of the Asian-Australian monsoon? J Clim, 2009, 22: 1159–1173

3 Zhou T J, Wu B, Scaife A A, et al. The CLIVAR C20C Project: Which components of the Asian-Australian Monsoon circulation variations are forced and reproducible? Clim Dyn, 2009, 33: 1051 –1068

4 Chen H M, Zhou T J, Neale R B, et al. Performance of the new NCAR CAM3.5 in East Asian summer monsoon simulations: Sensi-tivity to modifications of the convection scheme. J Clim, 2010, 23: 3657–3675

5 Li H, Dai A, Zhou T, et al. Responses of East Asian summer mon-soon to historical SST and atmospheric forcing during 1950–2000. Clim Dyn, 2010, 34: 501–514

6 Giorgi F, Marinucci M R, Bates G T. Development of a second- generation regional climate model (RegCM2). Part I: Boundary-layer and radiative transfer processes. Mon Weather Rev, 1993, 121: 2794– 2813

7 Giorgi F, Marinucci M R, Bates G T, et al. Development of a second- generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions. Mon Weather Rev, 1993, 121: 2814–2832

8 Giorgi F, Mearns L O. Introduction to special section: Regional cli-mate modeling revisited. J Geophys Res, 1999, 104: 6335–6352

9 Leung L R, Mearns L O, Giorgi F, et al. Regional climate research. Bull Amer Meteorol Soc, 2003, 84: 89–95

10 Wang, Y, Leung L R, McGregor J L, et al. Regional climate model-ing: Progress, challenges, and prospects. J Meteorol Soc Jpn, 2004, 82: 1599–1628

11 Fu C B, Wang S Y, Xiong Z, et al. Regional climate model inter-comparison project for Asia (RMIP). Bull Amer Meteorol Soc, 2005, 86: 257–266

12 Gao X, Xu Y, Zhao Z, et al. On the role of resolution and topography in the simulation of East Asia precipitation. Theor Appl Climatol, 2006, 86: 173–185

13 Gao X, Shi Y, Song R, et al. Reduction of future monsoon precipita-tion over China: Comparison between a high resolution RCM simula-tion and the driving GCM. Meteorol Atmos Phys, 2008, 100: 73–86

14 Wang B, Ding Q, Fu X, et al. Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys Res Lett, 2005, 32: L15711

15 Wu R, Kirtman B P. Roles of Indian and Pacific Ocean air-sea cou-pling in tropical atmospheric variability. Clim Dyn, 2005, 25: 155– 170

16 Lu S H, Chen Y C, Zhu B C. A coupled ocean model and atmosphere model and simulation experiment in the South China Sea area (in Chinese). Plateau Meteorol, 2005, 19: 415–426

17 Ren X J, Qian Y F. Numerical simulation of oceanic elements at East Asian coastal oceans from May to August in 1998 by using a coupled regional ocean-atmosphere model (in Chinese). Clim Environ Res, 2000, 5: 480–485

18 Ren X, Qian Y. A coupled regional air-sea model, its performance and climate drift in simulation of the East Asian summer monsoon in 1998. Int J Climatol, 2005, 25: 679–692

19 Yao S X, Zhang Y C. Regional coupled air-sea model simulation of China summer precipitation (in Chinese). Acta Meteorol Sin, 2008, 66: 131–142

20 Li T, Zhou G Q. Preliminary results of a regional air-sea coupled model over East Asia. Chin Sci Bull, 2010, 55: doi: 10.1007/ s11434-010-0071-0

21 Fang Y, Zhang Y, Tang J, et al. A regional air-sea coupled model and its application over East Asia in the summer of 2000. Adv Atmos Sci, 2010, 27: 583–593

22 Kim E J, Hong S Y. Impact of air-sea interaction on East Asian summer monsoon climate in WRF. J Geophys Res, 2010, 115: D19118, doi: 10.1029/2009JD013253

23 Fang Y J, and Zhang Y C. Impacts of regional air-sea coupling on the simulation of precipitation over eastern Chins in the RIEMS model (in Chinese). Chin J Atmos Sci, 2011, 35: 16–28

24 Ratnam J V, Giorgi F, Kaginalkar A, et al. Simulation of the Indian monsoon using the RegCM3_ROMS regional coupled model. Clim Dyn, 2009, 33: 119–139

25 Aldrian E, Sein D, Jacob D, et al. Modelling Indonesian rainfall with a coupled regional model. Clim Dyn, 2005, 25: 1–17

26 Wang B, Lin H. Rainy season of the Asian-Pacific summer monsoon. J Clim, 2002, 15: 386–398

27 Murakami T, Matsumoto J. Summer monsson over the Asian conti-nent and western North Pacific. J Meteorol Soc Jpn, 1994, 72: 719 –745

28 Li T, Wang B. A review on the western North Pacific monsoon: Synoptic to interannual variabilities. Terr Atmos Ocean Sci, 2005, 16: 285–314

29 Lu R Y, Fu Y H. Summer climate variability in East Asia and the western North Pacific and its mechanisms (in Chinese). Adv Earth Sci, 2009, 24: 123–131

30 Yan H. Analysis on the weather and climate features and cause of the extraordinary flood disaster over China in 1998 and the relevant me-teorological prediction services (in Chinese). Clim Environ Res, 1998, 3: 323–334

31 Sun S Q, Ma S J. The study on the relationship between the anomaly of subtropical high over the western Pacific and the heavy flooding in Yangtze River valley in 1998 (in Chinese). Acta Meteorol Sin, 2001, 59: 719–729

32 Wang B, Kang I S, Lee J Y. Ensemble simulations of Asian－Australian monsoon variability by 11 AGCMs. J Clim, 2004, 17: 803–818

33 Wang Y Q, Sen O L, Wang B. A highly resolved regional climate model (IPRC-RegCM) and its simulation of the 1998 severe precipi-tation event over China. Part I: Model description and verification of simulation. J Clim, 2003, 16: 1721–1738

34 Lee D K, Cha D Y, Kang H S. Regional climate simulation of 1998 summer flood over East Asia. J Meteorol Soc Jpn, 2004, 82: 1735–1753

35 Cha D H, Lee D K, Hong S Y. Impact of boundary layer processes on seasonal simulation of the East Asian summer monsoon using a Re-gional Climate Model. Meteorol Atmos Phys, 2008, 100: 53–72

36 Yu R C, Xue J S, Xu Y P. AREMS Mesoscale Heavy Rainfall Nu-merical Prediction Model System (in Chinese). Beijing: Meteorolog-ical Press, 2004. 232


37 Shi H, Yu R, Li J, et al. Development of a Regional Climate Model (CREM) and evaluation on its simulation of summer climate over Eastern China. J Meteorol Soc Jpn, 2009, 87: 381–401

38 Mesinger F. A blocking technique for representation of mountains in atmospheric models. Riv Meteorol Aeronaut, 1984, 44: 195–202

39 Betts A K, Miller M J. A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATES and arctic air-mass data sets. Q J R Meteorol Soc, 1986, 112: 693–709

40 Xu Y P, Xia D Q, Qian Y Y. The water-bearing numerical model and its operitional forecasting experiments. Part II: The operational fore-casting experiments. Adv Atmos Sci, 1998, 15: 321–336

41 Edwards J M, Slingo A. Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Q J R Meteorol Soc, 1996, 122: 689–719

42 Sun Z A, Rikus L. Improved application of exponential sum fitting transmissions to inhomogeneous atmosphere. J Geophys Res, 1999, 104D: 6291–6303

43 Holtslag A A M, Boville B A. Local versus nonlocal boundary-layer diffusion in a global climate model. J Clim, 1993, 6: 1825–1842

44 Dickinson R E, Henderson-Sellers A, Kennedy P J. Biosphere- Atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model, NCAR Tech. Note, NCAR /TN-387+STR, 1993. 72

45 Zeng X B, Zhao M, Dickinson. Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J Clim, 1998, 11: 2628–2644

46 Pal J S, Giorgi F, Bi X Q, et al. Regional climate modeling for the developing world: The ICTP RegCM3 and RegCNET. Bull Amer Meteorol Soc, 2007, 88: 1395–1409

47 Grell G. Prognostic evaluation of assumptions used by cumulus pa-rameterizations. Mon Weather Rev, 1993, 121: 764–787

48 Pal J S, Small E E, Eltahir E A B. Simulation of regional-scale water and energy budgets: Representation of subgrid cloud cloud and pre-cipitation processes within RegCM. J Geophys Res, 2000, 105: 29579–29594

49 Kiehl J T, Hack J J, Bonan G B, et al. Description of the NCAR community climate model (CCM3), Tech. Rep. NCAR/TN-420+ STR, National Center for Atmospheric Research. 1996

50 Mellor G L. Users guide for a three-dimensional, primitive equation, numerical model. 2004, 1–56

51 Chu P C, Chang C P. South China Sea warm pool in boreal spring. Adv Atmos Sci, 1997, 14: 195–206

52 Qian Y F, Zhu C B, wang Q Q. A computational scheme of the hori-zontal pressure gradient in ocean models with coordinate system (in Chinese). J Nanjing Univ (Nat Sci), 1998, 134: 691–700

53 Valcke S. OASIS3 User Guide (prism_2-5). PRISM Support Initiative Report No. 3, CERFACS, Toulouse, France. 2006. 1–64

54 Zhou T, Yu Y Q, Yu R C, et al. Coupled climate system model cou-pler review (in Chinese). Chin J Atmos Sci, 2004, 28: 993–1008

55 Kanamitsu M, Ebisuzaki W, Woollen J, et al. NCEP-DOE AMIP-II Reanalysis (R-2). Bull Amer Meteorol Soc, 2002, 83: 1631–1643

56 Levitus S. Climatological Atlas of the World Ocean. NOAA Prof. Paper 13, and 17 microfiche. 1982. 173

57 Orlanski I. A simple boundary condition for unbounded hyperbolic flows. J Comput Phys, 1976, 21: 251–269

58 Reynolds R W, Rayner N A, Smith T M, et al. An improved in situ and satellite SST analysis for climate. J Clim, 2002, 15: 1609–1625

59 Huffman G J, Adler R F, Bolvin D T, et al. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined- sensor precipitation estimates at fine scales. J Hydrometeor, 2007, 8: 38–55

60 Yu L, Jin X, Weller R. Multidecade global flux datasets from the ob-jectively analyzed air-sea fluxes (OAFlux) project: Latent and sensi-ble heat fluxes, ocean evaporation, and related surface meteorological variables. Tech. Rep. OA-2008-01, Woods Hole Oceanographic In-stitution. 2008. 64

61 Zhang Y, Rossow W B, Lacis A A, et al. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J Geophys Res, 2004, 109: D19105

62 Liu H L, Lin W Y, Zhang M H. Heat budget of the upper ocean in the south-central equatorial Pacific. J Clim, 2010, 23: 1779–1792

63 Zhang D F, Gao X J, Zhao Z C, et al. Simulation of the atmospheric circulation over East Asia and climate in China by RegCM3 (in Chi-nese). J Trop Meteorol, 2007, 23: 444–452

64 Liu Q, Fu Y F, Feng S. Geographical patterns of the cloud amount derived from the ISCCP and their correlation with the NCEP reanal-ysis datasets (in Chinese). Acta Meteorol Sin, 2010, 68: 689–704

65 Klein S A, Hartmann D L. The seasonal cycle of low stratiform clouds. J Clim, 1993, 6: 1587–1606

66 Klein S, Jakob C. Validation and sensitivities of frontal clouds simu-lated by the ECMWF model. Mon Weather Rev, 1999, 127: 2514– 2531

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