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  • Zhou, T., , and Z. Li, 2002: Simulation of the East Asian summer monsoon using a variable resolution atmospheric GCM. Climate Dyn., 19, 167180, doi:10.1007/s00382-001-0214-8.

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  • Zhou, T., , B. Wu, , and B. Wang, 2009: How well do atmospheric general circulation models capture the leading modes of the interannual variability of the Asian–Australian monsoon? J. Climate, 22, 11591173, doi:10.1175/2008JCLI2245.1.

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  • Zhou, T., , B. Wu, , and L. Dong, 2014: Advances in research of ENSO changes and the associated impacts on Asian–Pacific climate. Asia-Pac. J. Atmos. Sci., 50, 405422, doi:10.1007/s13143-014-0043-4.

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  • Zou, L. W., , Y. Qian, , T. J. Zhou, , and B. Yang, 2014: Parameter tuning and calibration of RegCM3 with MIT–Emanuel cumulus parameterization scheme over CORDEX East Asia domain. J. Climate, 27, 76877701, doi:10.1175/JCLI-D-14-00229.1.

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    Spatial distributions of JJA mean (2001–08) precipitation (shaded, mm day−1) and 850-hPa wind (vector, m s−1) in (a) observation, (b) ensemble mean of simulations, and (c) their difference, respectively. (d) The spatial distribution of the intermodel standard deviation of the simulated precipitation.

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    Responses of model performances [i.e., EM; Eq. (1)] to the eight parameters (normalized to [0, 1]) based on GPCP precipitation over the (a) global and (b) Asian regions, respectively. The fitting values are represented by red lines. The fitting values based on TRMM data are denoted by green lines.

  • View in gallery

    Responses (fitting values) of model performances to different convective parameters over East Asia, South Asia, the Maritime Continent, and the intertropical convergence zone, respectively. Model skill is normalized by the mean and standard deviation of the 100 simulations.

  • View in gallery

    Responses (fitting values) of model performances to different convective parameters over the Asian region. Model skill scores are based on magnitude [EMMag; Eq. (2)], spatial pattern [EMR; Eq. (3)], and mean-square error [EMMSE; Eq. (4)], respectively. Model skill is normalized by the mean and standard deviation of the 100 simulations.

  • View in gallery

    Spatial distributions of JJA (2001–08) precipitation (shaded, mm day−1) and 850-hPa wind (vector, m s−1) in (a) low-skill and (b) high-skill simulations subtracted by the total ensemble mean fields in Fig. 1b.

  • View in gallery

    Latitude–time sections of climatological (2001–08) pentad precipitation (mm day−1) over East Asia (110°–130°E) from April to October in (a) observation, (b) low-skill simulations, and (d) high-skill simulations, respectively. Also shown are the differences between (c) low-skill simulations and observation and (e) high- and low-skill simulations.

  • View in gallery

    Spatial distributions of sensitivities of JJA mean precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) to different parameters. Model sensitivities are calculated as the regression coefficients between output variables and input parameters from the 100 simulations (see section 2d). The dotted areas indicate that the precipitation responses are statistically significant at 90% confidence level.

  • View in gallery

    Vertical profiles of sensitivities of liquid and ice cloud water contents to different parameters over the Asian region in JJA. Model sensitivities are calculated as in Fig. 7.

  • View in gallery

    Spatial distributions of sensitivities of JJA convective (red lines) and stratiform (blue lines) precipitation to different convective parameters. Model sensitivities are calculated as in Fig. 7. Contour values at −4, −2, 2, and 4 mm day−1 are shown with negative values indicated by dashed lines.

  • View in gallery

    Vertical cross sections (pressure–latitude) of sensitivities of condensation heating to different convective parameters in JJA over East Asia (averaged over 105°–140°E). Model sensitivities are calculated as in Fig. 7.

  • View in gallery

    Observed spatial distributions of (a),(b) SSTA (°C) and (c),(d) anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during (left) El Niño–developing summer and (right) El Niño–decaying summer, respectively.

  • View in gallery

    Spatial distributions of sensitivities of anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during El Niño–decaying summer to different parameters. Model sensitivities are calculated as in Fig. 7.

  • View in gallery

    Scatter diagrams of model skill in simulating the ENSO related precipitation anomaly (y axis) vs that in simulating the climatology (x axis). The skill scores for ENSO related anomaly are based on (a) a Taylor diagram and (b) mean-square error, respectively. The skill scores for climatology are normalized to [0, 1].

  • View in gallery

    Spatial distributions of anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during El Niño–decaying summer in the (a) low- and (b) high-skill simulations, respectively.

  • View in gallery

    (a) Distributions of climatological JJA rain rate (mm day−1, fitting value) as a function of SST (°C) over the Asia–Pacific Ocean area (30°S–30°N, 60°E–180°) from observation and low- and high-skill simulations. The total number of model grid points at each SST interval is shown as bars. (b) As in (a), but for simulations with low and high values of the parameter C0_deep.

  • View in gallery

    (left) Zonal (averaged over 0°–15°N) distributions of (a) precipitation climatology and (d) precipitation anomaly during El Niño–decaying summer in observation, low- and high-skill simulations, respectively. (center),(right) As in (left), but for meridional distributions over the Indian summer monsoon region (averaged over 75°–105°E) and the East Asian summer monsoon region (averaged over 105°–140°E), respectively.

  • View in gallery

    (left) Spatial distributions of climatological (1979–2008) precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) in (a) observation and simulations with mean parameter values (see Table 2) in (c) low- and (e) high-skill simulations, respectively. (right) As in (left), but for anomalies during El Niño–decaying summer.

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Parametric Sensitivity Analysis for the Asian Summer Monsoon Precipitation Simulation in the Beijing Climate Center AGCM, Version 2.1

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  • 1 School of Atmospheric Sciences, Nanjing University, and Jiangsu Collaborative Innovation Center for Climate Change, Nanjing, China
  • | 2 Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
  • | 3 Beijing Climate Center, National Climate Center, China Meteorological Administration, Beijing, China
  • | 4 School of Atmospheric Sciences, Nanjing University, and Jiangsu Collaborative Innovation Center for Climate Change, Nanjing, China
  • | 5 Beijing Climate Center, National Climate Center, China Meteorological Administration, Beijing, China
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Abstract

In this study, the authors apply an efficient sampling approach and conduct a large number of simulations to explore the sensitivity of the simulated Asian summer monsoon (ASM) precipitation, including the climatological state and interannual variability, to eight parameters related to the cloud and precipitation processes in the Beijing Climate Center AGCM, version 2.1 (BCC_AGCM2.1). The results herein show that BCC_AGCM2.1 has large biases in simulating the ASM precipitation. The precipitation efficiency and evaporation coefficient for deep convection are the most sensitive parameters in simulating the ASM precipitation. With optimal parameter values, the simulated precipitation climatology could be remarkably improved, including increased precipitation over the equatorial Indian Ocean, suppressed precipitation over the Philippine Sea, and more realistic mei-yu distribution over eastern China. The ASM precipitation interannual variability is further analyzed, with a focus on the ENSO impacts. It is shown that simulations with better ASM precipitation climatology can also produce more realistic precipitation anomalies during El Niño–decaying summer. In the low-skill experiments for precipitation climatology, the ENSO-induced precipitation anomalies are most significant over continents (vs over ocean in observations) in the South Asian monsoon region. More realistic results are derived from the higher-skill experiments with stronger anomalies over the Indian Ocean and weaker anomalies over India and the western Pacific Ocean, favoring more evident easterly anomalies forced by the tropical Indian Ocean warming and stronger Indian Ocean–western Pacific teleconnection as observed. The model results reveal a strong connection between the simulated ASM precipitation climatological state and interannual variability in BCC_AGCM2.1 when key parameters are perturbed.

Corresponding author address: Yaocun Zhang, Ph.D., School of Atmospheric Sciences, Nanjing University, 163 Xianlin Ave., Nanjing 210023, China. E-mail: yczhang@nju.edu.cn

Abstract

In this study, the authors apply an efficient sampling approach and conduct a large number of simulations to explore the sensitivity of the simulated Asian summer monsoon (ASM) precipitation, including the climatological state and interannual variability, to eight parameters related to the cloud and precipitation processes in the Beijing Climate Center AGCM, version 2.1 (BCC_AGCM2.1). The results herein show that BCC_AGCM2.1 has large biases in simulating the ASM precipitation. The precipitation efficiency and evaporation coefficient for deep convection are the most sensitive parameters in simulating the ASM precipitation. With optimal parameter values, the simulated precipitation climatology could be remarkably improved, including increased precipitation over the equatorial Indian Ocean, suppressed precipitation over the Philippine Sea, and more realistic mei-yu distribution over eastern China. The ASM precipitation interannual variability is further analyzed, with a focus on the ENSO impacts. It is shown that simulations with better ASM precipitation climatology can also produce more realistic precipitation anomalies during El Niño–decaying summer. In the low-skill experiments for precipitation climatology, the ENSO-induced precipitation anomalies are most significant over continents (vs over ocean in observations) in the South Asian monsoon region. More realistic results are derived from the higher-skill experiments with stronger anomalies over the Indian Ocean and weaker anomalies over India and the western Pacific Ocean, favoring more evident easterly anomalies forced by the tropical Indian Ocean warming and stronger Indian Ocean–western Pacific teleconnection as observed. The model results reveal a strong connection between the simulated ASM precipitation climatological state and interannual variability in BCC_AGCM2.1 when key parameters are perturbed.

Corresponding author address: Yaocun Zhang, Ph.D., School of Atmospheric Sciences, Nanjing University, 163 Xianlin Ave., Nanjing 210023, China. E-mail: yczhang@nju.edu.cn

1. Introduction

The Asian summer monsoon (ASM) is one of the most complex climate systems in the world (Wang and Fan 1999; Lau et al. 2000; Wang et al. 2001, 2003, 2008; Zhang 2001; Liu et al. 2002; Enomoto et al. 2003; Ding and Chan 2005; Qian and Leung 2007; Kosaka et al. 2011; Chang et al. 2013; Hsu et al. 2014). Because of the distinct dynamics associated with remarkable land–sea contrast and complex orography, it remains a challenge for current atmospheric general circulation models (AGCMs) to reliably and reasonably simulate the large-scale ASM system and associated precipitation in terms of both climatological mean state and interannual variability (e.g., Liang et al. 2001; Kang et al. 2002a,b; Zhou and Li 2002; Arai and Kimoto 2008; Zhang et al. 2008; Zhou et al. 2009; Chen et al. 2010; Wei et al. 2011; Zhang et al. 2012; D.-Q. Huang et al. 2013; Song and Zhou 2014).

In the past decades, climate models have been developed to employ more sophisticated physical parameterizations with increasing levels of complexity, which inevitably introduces more parameters with large uncertainties and compensating errors among them (Gilmore et al. 2004; Mölders 2005; Murphy et al. 2007). The perturbed parameter ensembles (PPE) approach using the same climate model but different parameter combinations has been applied to assess the uncertainties of parameters and model results (Murphy et al. 2004; Jackson et al. 2003, 2008; Collins et al. 2011). The parametric sensitivity analyses based on the PPE results can also help investigate the impacts of process-level parameters on the model simulation and explore the potential for improving a physical scheme at a given structure (e.g., Yang et al. 2012; Zhao et al. 2013; Guo et al. 2014).

A large portion of the precipitation biases in AGCMs could be attributed to the cloud and convection parameterization schemes that usually contain many uncertain parameters (Cess et al. 1996; Colman 2003; Webb et al. 2006; Jackson et al. 2008; Yang et al. 2012; Yan et al. 2014; Qian et al. 2015). Zhao et al. (2013) used a quasi–Monte Carlo method to effectively explore the 16-dimensional parameter space related to cloud microphysics and aerosols in the Community Atmosphere Model, version 5 (CAM5; Neale et al. 2012). Their generalized linear model results constructed based on hundreds of CAM5 simulations revealed that model internal physical parameters are much more important than external factors such as anthropogenic and natural emissions for the global mean radiation flux at the top of the atmosphere. By perturbing five key parameters with a stochastic importance-sampling algorithm, Yang et al. (2015) calibrated the Kain–Fritsch convection scheme (Kain 2004) in the Weather Research and Forecasting Model (Skamarock et al. 2008) over the East Asian summer monsoon (EASM) region, showing that convective parameters could have evident impacts on the simulated precipitation and atmospheric vertical profiles. When applying the calibrated parameters, the simulated EASM precipitation was significantly improved in terms of magnitude, south–north distribution, and seasonal migration.

Previous studies about parametric sensitivities mainly focused on one extreme case or the climatological mean state (e.g., Yang et al. 2012; Zhao et al. 2013; Zou et al. 2014). However, it is not clear how the interannual variability of the ASM will be captured when key parameters are perturbed toward producing better climatological state over the monsoon regions. Early studies (e.g., Sperber and Palmer 1996; Kang et al. 2002b) showed that AGCMs with better performance in simulating the rainfall climatology could generally simulate better the interannual variability of precipitation over the monsoon regions. Zhang et al. (2012) also suggested a strong connection between model performances in simulating the climatological state and interannual variability of the northwestern Pacific (NWP) rainfall, highlighting the key roles of mean convective diabatic heating in the model’s ability to reproduce the ASM interannual variability. Wu and Zhou (2013) found that in the Flexible Global Ocean–Atmosphere–Land System Model (Bao et al. 2013) the precipitation anomaly over East Asia in June following El Niño is much weaker than that in observations owing to the biases in the simulated mean state. Therefore, it is important to explore whether some linkages exist between the simulated climatology and interannual variability of the ASM precipitation when key parameters are perturbed in the same model. These are important issues for the development of parameterizations and identification of model limitations in simulating the ASM system.

In this study, we apply an efficient sampling approach and conduct a large number of simulations to study the sensitivity of the simulated climatological state and interannual variability of the ASM precipitation to eight parameters related to the cloud and precipitation processes in the Beijing Climate Center AGCM, version 2.1 (BCC_AGCM2.1; T. W. Wu et al. 2010). Our goals are 1) to explore the potential of improving the climatological mean state of the ASM precipitation by parameter tuning, 2) to quantify the impacts of different parameters on the simulated climatology and interannual variability of the ASM precipitation, and 3) to investigate whether the simulated interannual variability of the ASM precipitation is dependent on the model’s climatological background or not. A total of 100 eight-year simulations from 2001 to 2008 are conducted because of computational resource limitations. Since eight years is too short a period for interannual-scale analyses, as a first step we mainly focus on the ASM interannual variability induced by El Niño–Southern Oscillation (ENSO), which is the most dominant interannual mode over the tropics.

This paper is organized as follows. Section 2 describes the BCC_AGCM2.1 model, selected parameters, methodology, and observations. The sensitivities of the simulated ASM precipitation climatology and precipitation anomalies induced by ENSO to different parameters, as well as the possible relationships between them, are analyzed in section 3. Conclusions and discussion are given in section 4.

2. Model, methodology, and data

a. Model and parameterizations

The model used in this study is BCC_AGCM2.1, the atmospheric component in the Beijing Climate Center Climate System Model (Wu et al. 2013). The dynamical core of BCC_AGCM2.1 was originally adopted from the Eulerian dynamic framework in the Community Atmosphere Model, version 3 (CAM3; Collins et al. 2004, 2006), but the governing equations were modified with introduced reference surface pressure and stratified atmospheric temperature by Wu et al. (2008). Most of the physical schemes in BCC_AGCM2.1 are the same as in CAM3. An updated deep cumulus convection scheme (Wu 2012) and different parameterizations for dry adiabatic adjustment and latent heat and sensible heat fluxes over ocean (Zeng et al. 1998; Collins et al. 2006) are applied. Land surface processes are represented by the Community Land Model, version 3 (Oleson et al. 2004), with a modified coefficient for snow cover fraction (T. W. Wu et al. 2010). Here BCC_AGCM2.1 is configured at a horizontal T106 spectral resolution (approximately 1° × 1°) (A. Huang et al. 2013) and a 40-level terrain-following hybrid vertical coordinate with the sea surface temperature (SST) and sea ice conditions prescribed.

Detailed descriptions about the physical parameterizations in BCC_AGCM2.1 can be found in Collins et al. (2004), T. W. Wu et al. (2010), and Wu (2012). Here we briefly introduce the key parameterizations relevant to this study. The BCC_AGCM2.1 deep convection scheme is a simple mass flux cumulus convection scheme essentially based on the bulk cloud model idea of Yanai et al. (1973), Tiedtke (1989), and Zhang and McFarlane (1995). The main differences from that in CAM3 (Wu 2012) are the following: 1) the entrainment of environmental air into updraft below the cloud base is considered; 2) entrainment/detrainment of convective updrafts are not prescribed but calculated as the mass changes of the updraft flux with altitude, which are determined based on the properties of the updraft and environmental air with energy conservation constraint; 3) saturated convective downdraft is assumed and its mass flux is specified as a simple function of updraft mass flux and environmental relative humidity; and 4) the updraft mass flux at the cloud base is derived following the closure scheme suggested by Zhang (2002) in which the change of convective available potential energy induced by convection in the free troposphere approximately balances that resulting from large-scale processes.

The parameterizations for shallow convection, macro- and microphysics, and cloud fractions in BCC_AGCM2.1 resemble those in CAM3. The Hack (1994) shallow convection scheme is used to characterize the shallow and midlevel convective activities that are not processed by the deep convection scheme. The parameterization of stratiform cloud processes in CAM3 was originally formulated by Rasch and Kristjánsson (1998) and revised by Zhang et al. (2003) to include more realistic treatments of the condensation and evaporation. This parameterization includes two components related to macrophysics and microphysics, respectively. The macrophysical component controls the mass exchanges between the water vapor and cloud condensate, as well as the temperature change associated with the phase change (Zhang et al. 2003), while the microphysical component describes the conversion from condensate to precipitate and the evaporation process (Rasch and Kristjánsson 1998).

b. Selected parameters

In this study, we focus on eight tunable parameters related to the cloud and precipitation processes. Descriptions and perturbed ranges for these parameters are given in Table 1. The selection of parameters and their uncertain ranges are based on the suggestions of model developers or previous studies (e.g., Collins et al. 2004; Jackson et al. 2008; Yang et al. 2012, 2013; Zhao et al. 2013; G. Zhang 2012, personal communication; T. Wu 2014, personal communication). Because some aspects of the climate system are not exactly known, the lower and upper bounds of parameters, specified by parameterization developers or based on former experiences, could to a large degree represent the uncertainty of parameters. Among the eight parameters, Qic and Ke_strat are for the stratiform microphysical process. Previous studies have shown that the autoconversion size of cloud ice to snow (Qic) is one of the most effective tuning parameters in CAM5 for the cloud properties and radiative budgets. The range of Qic was given by H. Morrison and used by Zhao et al. (2013). The parameters RH_low and RH_high are cloud fraction parameters that are closely related to the stratiform processes and radiation calculations. The ranges of RH_low and RH_high were given by Jackson et al. (2008). The parameter C0_shal is the efficiency coefficient for shallow convective precipitation, which is tunable in CAM3 and BCC_AGCM2.1 and may use different values at different resolutions (Collins et al. 2004; T. Wu 2014, personal communication). The last three parameters of C0_deep, Ke_conv, and β are from the new deep convection scheme. The ranges of these convective parameters are mainly based on Jackson et al. (2008), G. Zhang (2012, personal communication), and T. Wu (2014, personal communication).

Table 1.

Default values, perturbed ranges, and descriptions of the eight selected parameters in BCC_AGCM2.1.

Table 1.

c. Experimental design

To explore the parameter sensitivities, the Latin hypercube sampling (LHS) method (McKay et al. 1979; Stein 1987; Beachkofski and Grandhi 2002) is used to efficiently sample points within the eight-dimensional distribution. The LHS method divides the range of each parameter into n equal bins (n is the total number of parameter sets), and then each bin will be sampled exactly once at an optimal even distance within the multidimensional space. These constrains ensure that the sampled points are spread out as evenly as possible to represent the entire parameter space. In practice, the number of points needed in the LHS is at least 10 times the number of parameters investigated (Loeppky et al. 2009). In this study, a total of 100 parameter sets are sampled for eight parameters.

All the sampled parameter sets are applied to conduct the BCC_AGCM2.1 experiments from 2000 to 2008, and the results of 2001–08 are used for the analyses in the present study. To further confirm the parameter sensitivity, two additional 30-yr (1979–2008) simulations are conducted by applying the low-skill and high-skill parameter sets derived from the 8-yr results.

d. Analysis method

In this study, the model performance is evaluated by a skill score following Taylor (2001), which is defined as
e1
where σobs and σmod denote the spatial standard deviation of observations and simulations, respectively. Also, R is the pattern correlation coefficient between the simulations and observations, and R0 is the maximum possible pattern correlation (set to 1 here); k is an adjustable coefficient that determines the relative contributions of spatial pattern and magnitude (i.e., intensity) to the skill score. Here we specify k = 4 to assign a larger weight to the spatial pattern, as the observed spatial pattern is expected to be more reliable than the exact magnitude for some observational products (e.g., satellite-retrieved precipitation). This kind of skill score has been applied and proved to be an effective metric for model evaluations (e.g., Yang et al. 2013; Zou et al. 2014).
To investigate how the selection of the metric could affect the analysis, we calculate the model skill score based on three different metrics:
e2
e3
e4

Among them, EMMag and EMR represent the similarities between observations and simulations in terms of the exact magnitude and spatial pattern [i.e., subterms of EM in Eq. (1)], respectively. Note that EMMSE is calculated based on the mean-square error (MSE) of simulations against observations, where eij denotes the model bias at point (i, j) and I and J are the maximum numbers of grid points in zonal and meridional directions, respectively. Here, a higher value means a better model performance for all the metrics in Eqs. (1)(4).

For each parameter, we have 100 simulations and 100 parameter values. The regression coefficients between simulation results and parameter values (normalized to [0, 1]) are calculated at all model grid points for each parameter, so as to produce a regression pattern that helps illustrate the parameter sensitivities on the simulated spatial distribution.

The ENSO index is defined as the averaged SST anomaly (SSTA) over the eastern equatorial Pacific (Niño-3.4: 5°S–5°N, 120°–170°W) in December–February (DJF). Based on this index, three years of 2002, 2004, and 2006 are identified as El Niño years (SSTA > 0.5°C) from 2001 to 2008 as recorded by the National Oceanic and Atmospheric Administration Climate Prediction Center. The El Niño–induced anomalies are defined as the differences between the mean during El Niño years and the 2001–08 average in this study.

e. Observational dataset

The simulated precipitation is evaluated against the pentad and monthly mean precipitation from the Global Precipitation Climatology Project (GPCP; 1° × 1° resolution) (Huffman et al. 2001, 2012). The Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly rainfall datasets (0.25° × 0.25° resolution) are also used for comparison (Huffman et al. 2007, 2013). Monthly wind products from the National Centers for Environmental Prediction reanalysis at 2.5° (provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, from http://www.esrl.noaa.gov/psd/; Kalnay et al. 1996) are applied for the ASM feature analyses and model evaluations.

3. Results

Previous studies (e.g., Yang et al. 2012, 2013) have shown that process-level parameters can significantly affect the simulated climate, and the model performance could be significantly improved by parameter calibration. In this study, the calibrated parameter values are not given since our main purposes are to investigate the model sensitivities. In the rest of this section, the parameter impacts on the simulated ASM precipitation climatology and anomalies induced by ENSO are investigated. The possible relationships between them are also explored.

a. Parameter sensitivity on precipitation climatology

Figure 1 presents the spatial distributions of the observed and simulated (ensemble mean of the 100 simulations) climatological June–August (JJA) 2001–08 precipitation and 850-hPa wind, as well as the model–observation differences, over the Asia–Pacific region. In observations (Fig. 1a), rainfall centers can be found over the west coastal areas of India, the Bay of Bengal (BOB), and the Philippine Sea within the warm pool. A clear rain belt is detected from eastern China to Japan, corresponding to the mei-yu in China and baiu in Japan. At 850 hPa, strong southerly cross-equatorial jets over the Indian Ocean and western Pacific introduce intense rainfall to India and the NWP. Abundant water vapor is transported to the mei-yu region along the northwest edge of the western Pacific subtropical high, which is a distinct feature of the EASM.

Fig. 1.
Fig. 1.

Spatial distributions of JJA mean (2001–08) precipitation (shaded, mm day−1) and 850-hPa wind (vector, m s−1) in (a) observation, (b) ensemble mean of simulations, and (c) their difference, respectively. (d) The spatial distribution of the intermodel standard deviation of the simulated precipitation.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Obvious model biases over the abroad ASM region are seen in the model ensemble (Fig. 1c). For example, in the Indian summer monsoon (ISM) region, the precipitation is evidently overestimated over Indochina and southern India and adjacent seas but underestimated over the equatorial Indian Ocean. In the NWP region, the simulated precipitation over the Philippine Sea is much stronger than observed. In contrast, weaker precipitation compared with observations is produced over the Maritime Continent (MC) and mei-yu region to the south and north of the Philippine Sea rainfall center, respectively. Thus, the model bias in AGCM is not a local issue as the precipitation simulations over different regions are closely related (Liang et al. 2001; Kang et al. 2002a). The under- or overestimated precipitation is usually accompanied by circulation biases at 850 hPa. For example, there is a westerly bias over the Philippine Sea and an easterly bias over the EASM region, which cuts off the moist transport to eastern China and causes a much weaker mei-yu in the model.

Figure 1d gives the spatial distribution of the intermodel standard deviation derived from the JJA precipitation of the 100 simulations, which indicates the locations where the simulated precipitation is sensitive to the selected parameters. It shows that the intermodel standard deviation pattern is well correlated to the model bias pattern, implying that the simulated precipitation could potentially be improved by parameter optimization (e.g., Yang et al. 2015).

The responses of model skill [i.e., EM in Eq. (1)] as a function of different parameters, based on the GPCP precipitation over the global region are given in Fig. 2. The results based on the TRMM data are also shown. It is apparent that some parameters can induce larger impacts than others. For the global precipitation, the parameters of C0_deep and Ke_conv are the most important parameters. Better model results can be derived with smaller values for the parameters of C0_deep or Ke_conv (i.e., smaller precipitation efficiency or evaporation coefficient for deep convection). By comparing the GPCP and TRMM results, we can find the responses of model skill based on different precipitation measurements to each parameter are similar except for a systematic difference. Therefore, only the GPCP precipitation data are used for the evaluation for the rest of this paper.

Fig. 2.
Fig. 2.

Responses of model performances [i.e., EM; Eq. (1)] to the eight parameters (normalized to [0, 1]) based on GPCP precipitation over the (a) global and (b) Asian regions, respectively. The fitting values are represented by red lines. The fitting values based on TRMM data are denoted by green lines.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

The responses of model skill over the Asian region (15°S–50°N, 60°E–180°) are also given in Fig. 2. It is found the responses of model performances induced by parameter tuning are similar over Asia and over the globe. Similar model responses can also be found if the evaluation area is confined within different regions, such as East Asia, South Asia, the MC, or the intertropical convergence zone (Fig. 3). This is probably because the optimal parameters identified from one region could be transferable to other regions (Yang et al. 2012; Yan et al. 2014) or because the precipitation simulations over different regions are closely related and improved precipitation in some region could cause better simulations in other areas (Gadgil and Sajani 1998; Liang et al. 2001; Kang et al. 2002a).

Fig. 3.
Fig. 3.

Responses (fitting values) of model performances to different convective parameters over East Asia, South Asia, the Maritime Continent, and the intertropical convergence zone, respectively. Model skill is normalized by the mean and standard deviation of the 100 simulations.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Figure 4 presents the responses of model skill to convective parameters based on different metrics [Eqs. (2)(4)] for the Asian region. It shows that the responses of model performances to parameters in simulating the precipitation spatial pattern and in simulating the magnitude are similar. Similar responses can also be found if the model skill is calculated based on the MSE metric which is mainly determined by the magnitude.

Fig. 4.
Fig. 4.

Responses (fitting values) of model performances to different convective parameters over the Asian region. Model skill scores are based on magnitude [EMMag; Eq. (2)], spatial pattern [EMR; Eq. (3)], and mean-square error [EMMSE; Eq. (4)], respectively. Model skill is normalized by the mean and standard deviation of the 100 simulations.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

To investigate how the simulated precipitation pattern can be improved, in Fig. 5 we present the spatial distributions of the JJA precipitation averaged from the low-skill simulations (LSS) and high-skill simulations (HSS) subtracted by the total ensemble mean fields in Fig. 1b. Here HSS (LSS) represents the first (last) 25 simulations ranked based on the model skill over the Asian region (Fig. 2). The average parameter values used in LSS and HSS are given in Table 2, respectively. As mentioned above, the ensemble mean of the simulated precipitation shows large biases over East and South Asia. From Fig. 5a we can find such model biases are more significant in LSS. In contrast, the results in HSS show more agreement with observations (Fig. 5b), such as the increased precipitation over the equatorial Indian Ocean, suppressed precipitation over the Philippine Sea, and stronger mei-yu over eastern China. In the EASM region, the improved mei-yu can be attributed to a more realistic 850-hPa wind comparing with that produced by LSS, in which the EASM region is dominated by easterlies (not shown). From Fig. 5b, it is clear that the spatial pattern of the HSS-mean differences is highly correlated to the reversed pattern of the ensemble mean biases (Fig. 1c), except for over the Arabian Sea. Thus, the simulation over southern India and the Arabian Sea cannot be simultaneously improved, which is different from the results indicated by Yang et al. (2013). This is probably because some important model processes are not governed by the selected parameters or systematic structure errors exist within the parameterization schemes in BCC_AGCM2.1.

Fig. 5.
Fig. 5.

Spatial distributions of JJA (2001–08) precipitation (shaded, mm day−1) and 850-hPa wind (vector, m s−1) in (a) low-skill and (b) high-skill simulations subtracted by the total ensemble mean fields in Fig. 1b.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Table 2.

Mean parameter values used in the low-skill and high-skill simulations. Units are as in Table 1.

Table 2.

The seasonal advances of the EASM rain belt are distinct features over eastern China (Ding and Chan 2005). Figure 6 depicts the climatological (2001–08) seasonal variation (from April to October) of the precipitation at time–latitude sections (110°–130°E), based on observations, LSS, and HSS, respectively. The differences between LSS and observations, as well as between HSS and LSS, are also given. In observations (Fig. 6a), the main rain belt is located over southern China to the south of 25°N before June. Then the rain belt gradually advances northward to the Yangtze River basin (YRB) region (i.e., mei-yu region). The rainfall season in northern China starts at around the end of July. In LSS (Fig. 6b), the precipitation north of 25°N is much weaker than observed in the entire summer. This is closely related to the overestimated precipitation to the south. Relative to the results in LSS, more agreement with observations can be found in HSS (Fig. 6d). Figure 6e shows a systematic difference between the HSS and LSS results, with less (more) precipitation to the south (north) of 25°N in HSS. However, the seasonal migration of the rain belt is not improved in HSS. For example, the rain belt is located at around 28°N before May in the observations (i.e., the spring persistent rains over southeastern China; Wan and Wu 2008) but at 32°N in the simulations. In addition, the simulated southern China rainfall in May is weaker and advances too early to the YRB region compared with observations.

Fig. 6.
Fig. 6.

Latitude–time sections of climatological (2001–08) pentad precipitation (mm day−1) over East Asia (110°–130°E) from April to October in (a) observation, (b) low-skill simulations, and (d) high-skill simulations, respectively. Also shown are the differences between (c) low-skill simulations and observation and (e) high- and low-skill simulations.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Figure 2 indicates that the parameters of C0_deep and Ke_conv are the most important parameters for the model performance. To investigate the impacts of each parameter over different regions, in Fig. 7 we present the horizontal distributions of sensitivities of JJA precipitation and 850-hPa wind to different parameters (i.e., regression coefficient between variables and parameters; see section 2d). It is clear that the parameters of C0_deep and Ke_conv can significantly affect the precipitation and 850-hPa circulation patterns. With larger precipitation efficiency for deep convection (i.e., larger C0_deep), the simulated precipitation increases over southern India, Indochina, and the northern Philippine Sea and decreases over the Arabian Sea, BOB, MC, and mei-yu regions. Larger C0_deep also induces anomalous westerlies over the NWP and easterlies over eastern China. Thus, a larger C0_deep can aggravate the model biases except for over the Arabian Sea, and the improved precipitation and 850-hPa circulation over the ASM region in HSS (Fig. 5) could be attributed to the perturbation of C0_deep to a large degree. As shown in Tables 1 and 2, the value of C0_deep in the default parameter set is larger than that used in HSS, indicating that the standard BCC_AGCM2.1 may overestimate the precipitation efficiency for deep convection. A similar impact of the parameter Ke_conv can be found with that of C0_deep, in terms of both magnitude and pattern, although the direct responses of precipitation to increasing precipitation efficiency (C0_deep) and evaporation efficiency (Ke_conv) are opposite; that is, larger C0_deep (Ke_conv) can cause more (less) deep convective precipitation. It is considered that the parameter impacts are highly dependent on the climate regimes. For example, the impact of C0_deep could be more significant over regions with strong convection (e.g., the Philippine Sea), while Ke_conv could be more important over regions with relatively dry condition that favors the evaporation of rainwater. Since the global total precipitation is strongly controlled by the underlying SST, different responses of convective precipitation at different regions may cause redistribution of diabatic heating in the spatial and subsequently affect the precipitation pattern. It is possible that increasing C0_deep may cause more precipitation over the Philippine Sea but less precipitation over the MC. In contrast, the increase of Ke_conv may significantly reduce the precipitation over the MC but shift precipitation to the wettest regions, such as the Philippine Sea.

Fig. 7.
Fig. 7.

Spatial distributions of sensitivities of JJA mean precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) to different parameters. Model sensitivities are calculated as the regression coefficients between output variables and input parameters from the 100 simulations (see section 2d). The dotted areas indicate that the precipitation responses are statistically significant at 90% confidence level.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Although the responses of precipitation and wind to β (Fig. 7h) are weaker compared with those to C0_deep and Ke_conv, it is still worth noting that increasing β (i.e., stronger downdraft) could enhance the precipitation over the Arabian Sea, Philippine Sea, and Indochina at the same time, suggesting that the model’s precipitation pattern could be significantly improved with smaller β. Other parameters can also affect the model results; for example, larger Ke_strat can result in more precipitation over the tropical area but less over the midlatitude regions. A larger value for RH_low may cause less precipitation over oceans but more over continents, inducing a possible stronger ISM and EASM with more precipitation penetrating to the north. The parameter of C0_shal can play important role over the Indian Ocean, and a larger C0_shal leads to more precipitation over the equatorial Indian Ocean but less over the northern Indian Ocean and India.

Comparing with precipitation, cloud water is more directly affected by the parameters selected in this study. The responses of vertical profiles of liquid/ice cloud water (LCW/ICW) contents to each parameter over the Asian region are presented in Fig. 8, respectively. With larger conversion size for ice (i.e., larger Qic), the conversion from ice to snow will be less efficient. As a result, more ICW and LCW are found between 500 and 100 hPa. In contrast, LCW at around 600 hPa is slightly decreased probably because of the reduced melting associated with less snow. Larger Ke_strat (stronger evaporation for stratiform precipitation) will cause more LCW below 500 hPa but less LCW and ICW above. The increase of RH_low can significantly reduce the low-level (between 900 and 500 hPa) LCW but slightly increase the midlevel (between 500 and 300 hPa) LCW. Differently, larger RH_high could cause less LCW between 800 and 300 hPa and less ICW above 500 hPa. The convective parameters can evidently affect the LCW but show relatively weak influences on the ICW. For example, larger C0_shal can accelerate the conversion from cloud water to shallow convective precipitation, and cause less LCW above 800 hPa but more below 800 hPa, probably due to the increased precipitation evaporation. Larger C0_deep can cause less LCW throughout the troposphere. Increased evaporation efficiency (i.e., larger Ke_conv) or downdraft flux (i.e., larger β) in the deep convection scheme could cause more precipitation evaporation and thus more LCW.

Fig. 8.
Fig. 8.

Vertical profiles of sensitivities of liquid and ice cloud water contents to different parameters over the Asian region in JJA. Model sensitivities are calculated as in Fig. 7.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

To better illustrate the parameter impacts on different types of precipitation, the horizontal distributions of sensitivities of convective and stratiform precipitation to the convective parameters are presented in Fig. 9, respectively. We see that C0_shal, C0_deep, and Ke_conv can induce large responses of both convective and stratiform precipitation. It is clear that for the parameter C0_deep the response distributions of convective and stratiform precipitation are highly correlated. Similar features can also be found for Ke_conv. Generally, the precipitation efficiency (i.e., C0_deep) only affects the ratio of cloud water to precipitation, having no direct impact on the condensation heating. However, the cloud water not being converted to precipitation will detrain to the stratiform cloud region. The detrained cloud may stay, evaporate, or convert to stratiform precipitation, which are all expected to produce different condensation heating profiles from those falling directly as convective precipitation. The modified heating profiles (Fig. 10) may further influence the large-scale circulation (Figs. 7f,g) and thus change the background condition for stratiform processes. In contrast to those two deep convective parameters, larger C0_shal can cause more convective but less stratiform precipitation. Thus, C0_shal can induce large impacts on the partitioning between convective and stratiform precipitation but less impact on the total precipitation and circulation in BCC_AGCM2.1 (Fig. 7e).

Fig. 9.
Fig. 9.

Spatial distributions of sensitivities of JJA convective (red lines) and stratiform (blue lines) precipitation to different convective parameters. Model sensitivities are calculated as in Fig. 7. Contour values at −4, −2, 2, and 4 mm day−1 are shown with negative values indicated by dashed lines.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Fig. 10.
Fig. 10.

Vertical cross sections (pressure–latitude) of sensitivities of condensation heating to different convective parameters in JJA over East Asia (averaged over 105°–140°E). Model sensitivities are calculated as in Fig. 7.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

The parameters related to the stratiform processes are less important for the simulations of different precipitation types when compared with the convective parameters. It is found (not shown) that the responses of convective and stratiform precipitation to the parameter RH_low are highly correlated with comparable magnitudes. Alternatively, the increase of Ke_strat can result in less stratiform but more convective precipitation. Despite the weaker impacts on the simulated precipitation, these stratiform parameters could induce large responses of the clouds (Fig. 8) and radiative fluxes (e.g., Zhao et al. 2013).

b. Parameter sensitivity on monsoon interannual variability

Previous studies (e.g., Sperber and Palmer 1996; Kang et al. 2002b; Zhang et al. 2012) have revealed a strong connection between the simulated climatological state and interannual variability of the monsoon precipitation. Therefore, a main motivation of this study is to explore whether or not the simulated interannual variability of the ASM precipitation is dependent on the model’s climatological background when key parameters are perturbed in BCC_AGCM2.1.

ENSO is the most dominant interannual mode in the tropics and has been found to play important roles in the interannual variability of the ASM (e.g., Huang and Wu 1989; Wang et al. 2000, 2001; Wu et al. 2003; Liu et al. 2008; B. Wu et al. 2010; Ye and Lu 2011; Zhou et al. 2014). During the integration period from 2001 to 2008, the three years of 2002, 2004, and 2006 are identified as El Niño years (section 2d). Figure 11 presents the observed spatial distributions of SSTA and associated anomalies of precipitation and 850-hPa wind during El Niño–developing and El Niño–decaying summer, respectively. As stated by Wu and Zhou (2013), the warm SSTA has established in the equatorial central-eastern Pacific during El Niño–developing summer, which enhances the local convection and induces an anomalous cyclone over the NWP (Figs. 11a,c). Because of the anomalous NWP cyclone, the EASM region is dominated by easterly anomalies and below-normal precipitation. During El Niño–decaying summer (Figs. 11b,d), the tropical Indian Ocean (TIO) warms up (Klein et al. 1999; Huang and Kinter 2002; Xie et al. 2002; Tokinaga and Tanimoto 2004; Schott et al. 2009), causing enhanced local deep convection, suppressed convection and anomalous anticyclones over the NWP region, and increased mei-yu–baiu precipitation over East Asia (Xie et al. 2009).

Fig. 11.
Fig. 11.

Observed spatial distributions of (a),(b) SSTA (°C) and (c),(d) anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during (left) El Niño–developing summer and (right) El Niño–decaying summer, respectively.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Since the anomaly patterns are highly anticorrelated between El Niño–developing summer and El Niño–decaying summer, hereafter we mainly focus on the results for El Niño–decaying summer. Figure 12 presents the spatial distributions of sensitivities of anomalous precipitation and 850-hPa wind during El Niño–decaying summer to different convective parameters. Of the four parameters, C0_deep is the most important for the simulated anomalies. Larger C0_deep may cause a northward shift of the positive precipitation anomalies to the continents over the ISM region. Over the Pacific, stronger above-normal precipitation over the tropical western Pacific (TWP) associated with enhanced westerly anomalies can be found when C0_deep is larger, implying increased responses of precipitation and wind to the perturbed SST over that region (Fig. 11b). However, the parameter impacts are mainly confined to the east of 140°E, with the mei-yu region less affected. The impacts of Ke_conv are similar to that of C0_deep, especially over the ISM region. Over the western Pacific, a stronger positive precipitation anomaly can be found over the Philippine Sea with larger Ke_conv, which may be partially due to the stronger precipitation climatology there (Fig. 7g). The response map of precipitation anomaly is also highly correlated with that of precipitation climatology for the parameter β (cf. Figs. 12d and 7h).

Fig. 12.
Fig. 12.

Spatial distributions of sensitivities of anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during El Niño–decaying summer to different parameters. Model sensitivities are calculated as in Fig. 7.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Comparing Figs. 7 and 12 shows that the simulated precipitation anomalies induced by ENSO are dependent on the model’s climatological background to some extent. One may ask whether or not the improved ASM climatology will also lead to better simulation of the monsoon interannual variability. In Fig. 13 we present scatter diagrams of model skill in simulating the ENSO related anomaly versus that in simulating the climatology. As is shown, when using the MSE-based metric (Fig. 13b) the simulated anomaly associated with ENSO could be better simulated with improved climatology. However, when using the Taylor diagram–based metric, the relationship of model performances in simulating the climatology and anomaly is not clear. This indicates that with improved climatological simulation, the intensity of the anomaly associated with ENSO could be generally better simulated, while the improvement in the spatial pattern of anomaly is not significant in the broad ASM region (Kang et al. 2002b).

Fig. 13.
Fig. 13.

Scatter diagrams of model skill in simulating the ENSO related precipitation anomaly (y axis) vs that in simulating the climatology (x axis). The skill scores for ENSO related anomaly are based on (a) a Taylor diagram and (b) mean-square error, respectively. The skill scores for climatology are normalized to [0, 1].

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

In Fig. 14, we depict the spatial distributions of anomalies of precipitation and 850-hPa wind during El Niño–decaying summer in LSS and HSS, respectively. Generally, both LSS and HSS can successfully reproduce the main features of the precipitation and circulation responses to the SSTA during El Niño–decaying summer (e.g., the enhanced convection over the ISM region, anomalous anticyclone over the NWP, and increased precipitation over the mei-yu region). However, large biases still exist in the model for all the experiments. For example, the TWP east of MC features a strong positive precipitation anomaly in the model but a negative one in the observations. This is because the positive SSTA there always causes enhanced precipitation in the model, whereas in observations the warmer SST is usually induced by the suppressed local convection. Such model bias may be partially related to the missing of atmosphere–ocean interaction in AGCM experiments (Wang et al. 2005), and partially because of the unrealistic simulation of the Indian Ocean–western Pacific teleconnection as indicated by Zhang et al. (2012) and Song and Zhou (2014). For example, the easterly anomalies over the western Pacific induced by the TIO warming are much weaker than in observations probably due to a weak precipitation anomaly over the TIO and a strong one over the western Pacific in the model. It is considered that, besides the case with observations (Fig. 11d), the increased precipitation over Indochina, as well as the enhanced (suppressed) precipitation over the TWP (NWP) [i.e., the East Asia–Pacific or Pacific–Japan pattern (Nitta 1987; Huang and Lu 1989)] may also lead to stronger mei-yu in the model, especially in LSS. Despite the model deficiency, it is still worth noting the differences between HSS and LSS. In LSS, the precipitation responses over the ISM region are most significant over subcontinents (e.g., India and Indochina), which is very different from the observed results that show larger positive responses over ocean areas. In contrast, more realistic responses over the ISM region are found in HSS with stronger precipitation anomalies over the northern and equatorial Indian Ocean and weaker ones over India and Indochina. We also find that with improved precipitation climatology, the unrealistic positive precipitation anomalies over the TWP and Philippine Sea are slightly reduced and the easterlies over the western Pacific, especially over the South China Sea, that are forced by the TIO warming are more evident, all of which exhibits more agreement with the observations. Such improvements are mainly contributed by the combining effects of lower C0_deep and Ke_conv (Fig. 12). Although the analysis period (i.e., 8 years with 3 El Niño events) is too short for composite analysis, significant responses of the ASM precipitation can still be detected in observations during El Niño–decaying summer (Fig. 11). Besides that, our sensitivity analyses are mainly based on the model ensemble results, indicating the robust impacts of model parameters on the simulated ASM interannual variability induced by ENSO in BCC_AGCM2.1.

Fig. 14.
Fig. 14.

Spatial distributions of anomalous precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) during El Niño–decaying summer in the (a) low- and (b) high-skill simulations, respectively.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

In general, precipitation is mainly controlled by the underlying SST. To better understand the parameter impacts on the simulated precipitation, here we assume a simple point-to-point relationship between precipitation and SST (Zhang 1993) as
e5
where P and T represent precipitation and SST, respectively, i and j denote the horizontal location, and a and b are model parameters such as C0_deep, Ke_conv, and so on. If there is an anomalous SST ΔT from the climatological SST T0, the anomalous precipitation will be (location indices i and j are omitted)
e6
If ΔT is much smaller than T0, ΔP could be expressed as
e7
where P0 represents precipitation at T0. It indicates that both ΔT (i.e., SSTA) and P0 (equal to precipitation climatology when ΔT is much smaller than T0) may affect the precipitation anomaly. When a significant nonlinear relationship exists between P and T (e.g., b > 1) in the spatial, the anomalous precipitation will depend on the precipitation climatology to a large degree; for example, a higher mean rainfall can contribute to an amplification of SST-induced response via circulation–heating interactions (Wu and Kirtman 2005).
If the relationship between P and T is mainly linear (e.g., b = 1), Eq. (7) could be simplified as
e8
Then only the SSTA will have impacts on the precipitation anomaly. However, it still indicates that the precipitation anomaly will be more sensitive to the SSTA if a stronger relationship between the precipitation climatology and SST exists (i.e., larger parameter a).

To illustrate the relationship between the precipitation climatology and SST, in Fig. 15 we present the distributions of climatological JJA precipitation rate as a function of SST (at each 0.5°C interval) over the Asia–Pacific Ocean area (30°S–30°N, 60°E–180°) from observations and simulations with different model skill or different values for parameter C0_deep, respectively. The total number of model grid points at each SST interval is also included in the plot, showing that in the analysis region the SSTs over most ocean areas are in the range from 28° to 30°C. We find that the model always overestimates the precipitation rate over oceans with different SSTs. In LSS with low model skill, the sensitivity of precipitation to SST variation from 28° to 30°C is much stronger than that in observations, causing larger model bias at warmer oceans. Compared with LSS, less (more) precipitation is produced over oceans with SST above (below) 29°C in HSS, implying a weaker response of precipitation to SST variation in the spatial, especially for regions with SST from 28° to 30°C. Among all the parameters, C0_deep has the largest impacts on the precipitation–SST relationship (Fig. 15b). Impacts from Ke_conv are similar to those of C0_deep (not shown).

Fig. 15.
Fig. 15.

(a) Distributions of climatological JJA rain rate (mm day−1, fitting value) as a function of SST (°C) over the Asia–Pacific Ocean area (30°S–30°N, 60°E–180°) from observation and low- and high-skill simulations. The total number of model grid points at each SST interval is shown as bars. (b) As in (a), but for simulations with low and high values of the parameter C0_deep.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

Figure 12b shows that larger C0_deep can induce a stronger positive precipitation anomaly associated with enhanced westerly anomaly over the TWP region, which implies larger responses of precipitation and wind to the perturbed SST there as discussed with Eq. (8). Further analyses (not shown) indicate that when using larger C0_deep, the relationship between anomalous precipitation and SSTA during El Niño–decaying summer is more evident than when using smaller C0_deep.

Figure 16 (left) depicts the zonal (averaged over 0°–15°N) distributions of precipitation climatology and precipitation anomaly during El Niño–decaying summer in observations, LSS, and HSS. The meridional distributions over the ISM (averaged over 75°–105°E) and EASM (averaged over 105°–140°E) regions are also shown. From the climatological distributions (Fig. 16, top), we can find more agreement with observations when the skill score is higher, such as the reduced precipitation over the Philippine Sea (10°–20°N in Fig. 16c) and over India and Indochina (10°–20°N in Fig. 16b), as well as increased precipitation over the TIO (10°S–5°N in Fig. 16b), MC (10°S–10°N in Fig. 16c), and mei-yu regions (25°–40°N in Fig. 16c). Corresponding to the simulated precipitation climatology, the distributions of precipitation anomalies are also better reproduced in HSS. For example in Fig. 16d, the unrealistic positive anomaly over the TWP (140°E–180°) is less evident in HSS, which is partially related to the reduced climatological precipitation (Fig. 16a) and partially related to the weaker relationship between anomalous precipitation and SSTA there (Fig. 12b). Over the ISM region (Fig. 16e), the positive precipitation anomalies are mainly distributed over the Indian Ocean (10°S–10°N) in observations but over India and Indochina (10°–20°N) in simulations. In HSS, better results can be derived but large model biases are still found because of the overestimated (underestimated) precipitation climatology over India and Indochina (the Indian Ocean). In fact, we find that the bias in precipitation anomaly does not correlate well with that in precipitation climatology over the ISM region (Fig. 16b vs Fig. 16e), which is probably because the point-to-point relationship cannot fully reflect the relationship between the precipitation climatology and anomaly. For all the simulations, the responses over the western Pacific (140°E–180° in Fig. 16d) induced by the TIO warming are weaker when compared with observations (also seen in Fig. 14), mainly due to both the underestimated precipitation climatology over the TIO and overestimated mean precipitation over the western Pacific (Fig. 1c), causing an overestimated local precipitation–SST relationship over the western Pacific but an underestimated Indian Ocean–western Pacific teleconnection.

Fig. 16.
Fig. 16.

(left) Zonal (averaged over 0°–15°N) distributions of (a) precipitation climatology and (d) precipitation anomaly during El Niño–decaying summer in observation, low- and high-skill simulations, respectively. (center),(right) As in (left), but for meridional distributions over the Indian summer monsoon region (averaged over 75°–105°E) and the East Asian summer monsoon region (averaged over 105°–140°E), respectively.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

To further confirm the relationship between the simulated climatology and ENSO-related anomaly, we conducted two additional 30-yr (1979–2008) simulations with the mean parameter values used in LSS and HSS (see Table 2), respectively. The observed and simulated results, for both climatology and anomaly during El Niño–decaying summer, are presented in Fig. 17. As in the 8-yr results, the simulated precipitation and wind climatology are significantly improved by using the parameter values in HSS. The parameter impacts on the simulated anomaly during El Niño–decaying summer are also similar to those derived from the 8-yr results in many aspects. For example, the positive anomalies of precipitation over the TWP, South China Sea, and Philippine Sea are reduced when using the parameter values in HSS. Besides that, over South Asia, the positive anomalies with the LSS parameters are mainly limited over subcontinents (e.g., India and Indochina) but shift to the Arabian Sea and BOB when using the HSS parameters. The easterlies over the South China Sea are also stronger in the simulations with the HSS parameters. In general, the impacts of parameter perturbation on the simulated anomaly related to ENSO in long-term simulations are overall consistent with those in the 8-yr results.

Fig. 17.
Fig. 17.

(left) Spatial distributions of climatological (1979–2008) precipitation (shaded, mm day−1) and 850-hPa wind (vectors, m s−1) in (a) observation and simulations with mean parameter values (see Table 2) in (c) low- and (e) high-skill simulations, respectively. (right) As in (left), but for anomalies during El Niño–decaying summer.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00655.1

4. Conclusions and discussion

It remains a challenge for current AGCMs to reliably simulate the large-scale ASM system and associated precipitation in terms of both climatological mean state and interannual variability. In this study, we apply an efficient sampling approach and conduct a large number of simulations to study the sensitivity of the simulated ASM precipitation, including climatological state and interannual variability, to eight parameters related to the cloud and precipitation processes in BCC_AGCM2.1. A total of 100 eight-year simulations from 2001 to 2008 are conducted. Our goals are to explore the potential of improving the climatological state of the ASM precipitation and to quantify the sensitivity of the simulated ASM precipitation to different parameters. The possible relationships between the simulated ASM precipitation climatology and interannual variability are also investigated.

Our results show that BCC_AGCM2.1 has large biases in simulating the ASM precipitation climatology. The selected cloud and precipitation parameters exhibit large impacts on the precipitation simulations. The precipitation efficiency and evaporation coefficient for deep convection are the most sensitive parameters, and the precipitation efficiency is evidently overestimated in the standard BCC_AGCM2.1. In the high-skill simulations with appropriate parameter values, the simulated precipitation climatology could be remarkably improved, for example with increased precipitation over the equatorial Indian Ocean, suppressed precipitation over the Philippine Sea, and subsequently a stronger mei-yu over eastern China, all showing more agreement with observations.

The simulated ASM precipitation interannual variability is further analyzed, with a focus on the impacts of ENSO. It is shown that simulations with better simulated precipitation climatology can also produce more realistic precipitation anomaly during El Niño–decaying summer. Large model biases of the precipitation anomaly induced by ENSO are found in simulations with lower skill for precipitation climatology. For example, the simulated precipitation responses to ENSO are most significant over India and Indochina (vs over the Indian Ocean in observation) in the South Asian monsoon region. In contrast, more realistic responses induced by ENSO are found in the high-skill simulations, featuring stronger precipitation anomaly over the Indian Ocean and weaker precipitation anomalies over India and the western Pacific, favoring more evident easterly anomalies forced by the TIO warming and thus a stronger Indian Ocean–western Pacific teleconnection as in observations. These results reveal a strong connection between the climatological mean state and interannual variability of the ASM precipitation in BCC_AGCM2.1 when key model parameters are simultaneously perturbed.

Several limitations should be taken into account and deserve future investigation. First, we mainly focus on the impacts of model input parameters in this study. It is considered that the conclusions about the relative importance of each parameter could be changed if different parameter ranges are specified. In addition, parameter sensitivities are simply represented by the regression coefficients between model output variables and input parameters here. Statistical tools such as generalized linear model could be applied to quantitatively measuring the parameter sensitivities in terms of output variances that can be explained by the linear, high-order, and interaction terms of input parameters (e.g., Zhao et al. 2013). Besides that, model biases explained by physical parameters are only a portion of the total bias given the existences of structural errors in parameterization schemes. Thus, parametric sensitivity studies based on different cloud and convection parameterizations (e.g., Song and Zhang 2011; Wang et al. 2011; Bogenschutz et al. 2012) are needed, and comparisons between different parameterizations or different models are also important for further identifying the sources of model biases (e.g., Kang et al. 2002a,b; Zhang et al. 2012).

Second, the simulated precipitation is the main focus in this work. This is not only because precipitation is an important component in the ASM system (but poorly simulated by AGCMs), but also because the model skill scores for monsoon circulations and precipitation are highly correlated (Liang et al. 2001; Song and Zhou 2014). However, precipitation is a complex product with many interacting processes involved. It cannot be guaranteed that the improved precipitation simulation is always accompanied by more realistic representations of the internal physical processes within the model. It may be more beneficial to evaluate the behavior of process-level variables to reduce the possible compensating errors in precipitation simulations of AGCMs.

Last, it deserves further research to explore the parameter sensitivity with coupled models as the simulation biases may result from the neglect of air–sea interactions in AGCMs (Wang et al. 2005). On the other hand, model errors in coupled models could be partially originating from the misrepresented atmospheric processes. Thus, it is necessary to investigate how the improved atmospheric component can affect the ocean dynamics (e.g., ENSO) and the atmosphere–ocean coupled system. These are important questions for future climate predictions and projections.

Acknowledgments

The authors acknowledge the editor and three anonymous reviewers for the careful review and constructive comments, and Xiaoge Xin and Jie Zhang of the Beijing Climate Center for help in model configurations. This work is jointly supported by the National Natural Science Foundation of China (41305084 and 41475092), the Fundamental Research Funds for the Central Universities (20620140049), the Special Program for China Meteorology Trade (GYHY201306020), and the Jiangsu Collaborative Innovation Center for Climate Change. The contribution of Yun Qian in this study is supported by the U.S. Department of Energy’s (DOE) Office of Science as part of the Regional and Global Climate Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under Contract DE-AC05-76RL01830.

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