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  • View in gallery

    Response of surface shortwave cloud radiative forcing (SWCF) to the Niño-3 index for (a) ISCCP, (b) GAMIL1, and (c) GAMIL2 as measured linear regression coefficients (W m−2 K−1). The Niño-3 index is defined as the Niño-3 sea surface temperature anomaly. Bold red and black rectangles represent the Niño-4 and Niño-3 regions, respectively.

  • View in gallery

    As in Fig. 1, but for (a)–(d) total, (e),(f) convective, and (g),(h) stratiform rainfall (mm day−1 K−1).

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    As in Fig. 1, but for total liquid water path (g m−2 K−1).

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    As in Fig. 1, but for 500-hPa vertical velocity (hPa s−1 K−1).

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    As in Fig. 1, but for (a)–(c) total, (d)–(f) high, (g)–(i) middle, and (j)–(l) low clouds (% K−1).

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    Vertical cross section of cloud fraction response to El Niño (color) and climatological mean in-cloud liquid water path (contour) for (a) GAMIL1 and (b) GAMIL2 and in-cloud liquid water path response to El Niño (color) and climatological mean cloud fraction (contour) for (c) GAMIL1 and (d) GAMIL2 averaged over the equatorial Pacific (5°S–5°N) (units: percent for cloud fraction and g m−2 for ICLWP).

  • View in gallery

    Linear regression coefficients between 700-hPa temperature anomalies and Niño-3 SST anomalies averaged over (5°S–5°N) as functions of longitude from NCEP, ERA-Interim, GAMIL1, and GAMIL2.

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    Vertical cross section of heating rate response to El Niño warming in deep convective, stratiform, and total moist processes from (top) GAMIL1, (middle) GAMIL2, and (bottom) their differences (K day−1 K−1).

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    Hovmöller plots of total liquid water path anomalies from January 1979 to December 2002 for (a) SSM/I, (b) ISCCP, (c) GAMIL1, and (d) GAMIL2 across the equatorial (5°S–5°N) Pacific. The anomalies are calculated with respect to each data mean (g m−2).

  • View in gallery

    Vertical cross sections of multiyear annual mean specific humidity (shaded) and vertical velocity (contour) averaged over (5°S–5°N) from (a) NCEP, (b) ERA-Interim, (c) GAMIL1, and (d) GAMIL2 and the differences (e) between GAMIL1 and NCEP and (f) between GAMIL2 and GAMIL1.

  • View in gallery

    As in Fig. 10, but for relative humidity (shaded) and temperature (contour).

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    Geographic distributions of annual mean (a)–(c) total, (d)–(f) high, (g)–(i) middle, and (j)–(l) low clouds from ISCCP, GAMIL1, and GAMIL2 (%).

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    As in Fig. 10, but for moistening rate (g kg−1 day−1; shaded) and heating rate (K day−1; contour) in deep convective, stratiform, and total moist processes.

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The Role of Nonconvective Condensation Processes in Response of Surface Shortwave Cloud Radiative Forcing to El Niño Warming

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  • 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 2 Ministry of Education Key Laboratory for Earth System Modeling, Center of Earth System Science (CESS), Tsinghua University, and State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 3 Ministry of Education Key Laboratory for Earth System Modeling, Center of Earth System Science (CESS), Tsinghua University, Beijing, China, and Scripps Institution of Oceanography, La Jolla, California
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Abstract

The weak response of surface shortwave cloud radiative forcing (SWCF) to El Niño over the equatorial Pacific remains a common problem in many contemporary climate models. This study shows that two versions of the Grid-Point Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) (GAMIL) produce distinctly different surface SWCF response to El Niño. The earlier version, GAMIL1, underestimates this response, whereas the latest version, GAMIL2, simulates it well. To understand the causes for the different SWCF responses between the two simulations, the authors analyze the underlying physical mechanisms. Results indicate the enhanced stratiform condensation and evaporation in GAMIL2 play a key role in improving the simulations of multiyear annual mean water vapor (or relative humidity), cloud fraction, and in-cloud liquid water path (ICLWP) and hence in reducing the biases of SWCF and rainfall responses to El Niño due to all of the improved dynamical (vertical velocity at 500 hPa), cloud amount, and liquid water path (LWP) responses. The largest contribution to the SWCF response improvement in GAMIL2 is from LWP in the Niño-4 region and from low-cloud cover and LWP in the Niño-3 region. Furthermore, as a crucial factor in the low-cloud response, the atmospheric stability change in the lower layers is significantly influenced by the nonconvective heating variation during La Niña.

Corresponding author address: Dr. Lijuan Li, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, No. 40, Hua Yan Li, Beijing 100029, China. E-mail: ljli@mail.iap.ac.cn

Abstract

The weak response of surface shortwave cloud radiative forcing (SWCF) to El Niño over the equatorial Pacific remains a common problem in many contemporary climate models. This study shows that two versions of the Grid-Point Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG) (GAMIL) produce distinctly different surface SWCF response to El Niño. The earlier version, GAMIL1, underestimates this response, whereas the latest version, GAMIL2, simulates it well. To understand the causes for the different SWCF responses between the two simulations, the authors analyze the underlying physical mechanisms. Results indicate the enhanced stratiform condensation and evaporation in GAMIL2 play a key role in improving the simulations of multiyear annual mean water vapor (or relative humidity), cloud fraction, and in-cloud liquid water path (ICLWP) and hence in reducing the biases of SWCF and rainfall responses to El Niño due to all of the improved dynamical (vertical velocity at 500 hPa), cloud amount, and liquid water path (LWP) responses. The largest contribution to the SWCF response improvement in GAMIL2 is from LWP in the Niño-4 region and from low-cloud cover and LWP in the Niño-3 region. Furthermore, as a crucial factor in the low-cloud response, the atmospheric stability change in the lower layers is significantly influenced by the nonconvective heating variation during La Niña.

Corresponding author address: Dr. Lijuan Li, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, No. 40, Hua Yan Li, Beijing 100029, China. E-mail: ljli@mail.iap.ac.cn

1. Introduction

The shortwave cloud radiative forcing (SWCF) is the difference between the shortwave radiative energy fluxes under clear-sky and all-sky conditions; it has a large impact on the energy budget at the top of the atmosphere (TOA), at the surface, and in the atmosphere (e.g., Ramanathan et al. 1995; Tian and Ramanathan 2002). The annually averaged global mean SWCF at the TOA is from about −47 W m−2 (Bacmeister et al. 2014) based on Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) to −54 W m−2 (Li and Zhang 2008, hereafter LZ08) based on the Earth Radiation Budget Experiment (ERBE), and thus it has a general cooling effect on the planet because of higher albedo of clouds than that of the underlying surface. Moreover, the SWCF feedback [defined as the regression coefficient between surface SWCF and sea surface temperature (SST) anomalies in Niño-3 ] in the equatorial Pacific is one of the dominant components of negative heat flux feedbacks that drives the El Niño–Southern Oscillation (ENSO) evolution (Zebiak and Cane 1987; Guilyardi et al. 2009a; Lloyd et al. 2011, 2012; Bellenger et al. 2013). However, it is poorly reproduced by most of the models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), with a weak amplitude and even the wrong sign (Sun et al. 2003, 2006; Zhang and Sun 2006; Lloyd et al. 2011, 2012; Bellenger et al. 2013; Chen et al. 2013; Kim et al. 2014). Improving the model simulation of the SWCF feedback is a very important but challenging task. Recently, a notable improvement on the SWCF feedback simulation has been achieved by the Grid-Point Atmospheric Model of the Institute of Atmospheric Physics (IAP)/State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), version 2 (GAMIL2) (Chen et al. 2013).

In nature, the shortwave feedback tends to be negative for El Niño warming and positive for La Niña cooling. This is because, under unstable conditions, a sufficient increase in SST leads to an increase of convective cloud, which decreases the shortwave (SW) flux reaching the surface, and hence a negative feedback. On the other hand, during La Niña the atmosphere is stable. Under such conditions, a negative SST anomaly increases the stability of the atmospheric boundary layers and the amount of marine stratiform clouds, which decrease the surface SW flux and thus provide a positive feedback (e.g., Philander et al. 1996; Xie 2005; Bellenger et al. 2013). However, many GCMs fail to capture this nonlinear relationship between surface SW heat flux and SST anomalies, resulting in biases in SWCF feedback . To elucidate these biases, Lloyd et al. (2012) developed a highly idealized method to decompose the SWCF feedback into three individual, local responses,
e1
where represents shortwave flux response to total cloud cover, represents total cloud cover response to atmospheric dynamics, and represents the response of atmospheric dynamics to SST. They found that the underestimation of the dynamical response to SST contributed to the underestimated in the coupled models while the biases from the two cloud-related responses were the main source of errors in AGCMs (Lloyd et al. 2011). It should be pointed out that Eq. (1) implicitly assumes that SWCF depends on total cloud cover only, total cloud cover depends on dynamics only, and dynamics depends on local SST only. In nature, SWCF is a function of liquid water path (LWP; i.e., column amount of liquid water in the cloud) and cloud cover. In the same vein, cloud cover can depend on atmospheric dynamics as well as the underlying SST. Thus, we can rewrite Eq. (1) as
e2
where , , and are cloud fraction, atmospheric dynamics, and LWP responses to SST, respectively.

The cloud-related responses are directly associated with the moist process schemes in AGCMs: for example, convective and nonconvective condensation schemes. Several recent studies have focused on convective parameterization scheme and its individual processes, such as closure assumption and momentum transport (e.g., Wu et al. 2007; Kim et al. 2008; LZ08; Neale et al. 2008; Guilyardi et al. 2009b). The roles of tuning parameters in AGCMs and model resolutions have also been discussed (Toniazzo et al. 2008; Zhang and Sun 2008; Philip et al. 2010; Kim et al. 2011; Watanabe et al. 2011). However, the importance of nonconvective condensation processes (i.e., large-scale condensation processes including cloud macro and microphysical processes; e.g., Sundqvist 1978; Rotstayn 1997; Mahfouf 1999; Zhang et al. 2003) has not received much attention. This paper will demonstrate their key roles in shortwave cloud responses to El Niño and the climatological mean states using two versions of the GAMIL model, GAMIL1 and GAMIL2.

The paper is organized as follows: Section 2 describes the difference between the moist processes including the tuning parameters in the two models, the observational data for model validation, and the method used to estimate the responses. The detailed response analysis and climatological mean state are shown in section 3. Section 4 gives a summary and discussion.

2. Models, data, and analysis method

a. Models

Two versions of the GAMIL, GAMIL1 (the CMIP3 version) and GAMIL2 (the CMIP5 version), are used in this study (Li and Wang 2010; Li et al. 2012, 2013a,b). Both use a finite difference dynamical core that conserves the total mass and effective energy under the standard stratification approximation while solving the primitive hydrostatic equations of baroclinic atmosphere (Wang et al. 2004) and a two-step shape-preserving advection scheme (TSPAS) for the moisture equation (Yu 1994), with the same horizontal (128 × 60) and vertical (26σ) resolutions. Both estimate three types of clouds using the diagnostic Slingo-type scheme (Slingo 1987): namely, convective cloud, low-level marine stratus, and layered cloud (Rasch and Kristjánsson 1998).

The major differences between the two model versions are the cloud-related process schemes including the retuning of 14 uncertain parameters (for details see Tables 1 and 2 in Li et al. 2013b). The deep convective parameterization, convective cloud fraction, and microphysical scheme are all changed or heavily modified from GAMIL1 to GAMIL2. For the deep convective scheme, the free-tropospheric quasi-equilibrium closure of Zhang (2002) replaces the convective available potential energy (CAPE) adjustment closure in Zhang and McFarlane (1995). In addition, a relative humidity (RH) threshold at the parcel’s lifting level is used for convection trigger to suppress convection when the boundary layer air is too dry and set to 80% in Zhang and Mu (2005). Four tuning parameters in the new scheme (i.e., rainwater autoconversion coefficient, evaporation efficiency, and RH and CAPE threshold) are recalibrated in GAMIL2 and its coupled model to reduce convective rainfall and enhance stratiform rainfall as well as the performance of other model aspects (Li et al. 2013a,b). For nonconvective cloud processes, a two-moment cloud microphysical scheme (Morrison and Gettelman 2008) is used in GAMIL2 whereas the one-moment scheme (Rasch and Kristjánsson 1998) is used in GAMIL1, with both sharing the same cloud macrophysics scheme of Zhang et al. (2003). In the one-moment scheme, only a single variable, the sum of liquid and ice phase condensate mass mixing ratio, is predicted and partitioned into two phases according to temperature, while in the two-moment scheme both the droplet mass mixing ratio and number concentration of liquid and ice phases are predicted.

Two sets of numerical experiments using GAMIL1 and GAMIL2 are performed following the standard setting for phase II of the Atmospheric Model Intercomparison Project (AMIP-II) with the same forcing recommended by the CMIP5 project, such as the solar constant, greenhouse gases, and Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). Each experiment runs for 28 yr from January 1975 to December 2002, and the last 24 yr of the model output (1979–2002) are used for analyses and comparisons.

b. Observational data

The cloud radiation fluxes at the surface, cloud cover, and LWP from the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer 1999) for the period of July 1983–December 2002 are used in this study. For comparison, LWP from the Special Sensor Microwave Imager (SSM/I; July 1987–December 2002) is also included (Weng et al. 1997). The precipitation dataset are from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) (Xie and Arkin 1997) and the Global Precipitation Climatology Project (GPCP) (Adler et al. 2003). The specific humidity, temperature, and vertical velocity are from the National Centers for Environmental Prediction (NCEP) Reanalysis-2 (Kanamitsu et al. 2002) and the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim; Simmons et al. 2006). All of the above observational and reanalysis data are bilinearly interpolated into the model grid.

c. Method

For an atmospheric variable F (e.g., SWCF, cloud fraction, and LWP), its response to SST is measured by the linear regression coefficient ,
e3
where FA is the anomaly of F after removing the annual cycle and is the SST anomaly averaged over the Niño-3 (5°S–5°N, 150°–90°W) region from the HadISST. To take into account the nonlinearity of with respect to El Niño and La Niña conditions, as in Lloyd et al. (2012), the linear regression of FA against SSTA at each point is computed separately for SSTA > 0 and SSTA < 0 and then averaged over the Niño-3 region (Table 2).

3. Results

a. Response analysis

1) Strength

The observed and simulated responses of surface SWCF to the Niño-3 index, the SST anomalies (SSTA) averaged over the 5°S–5°N, 150°–90°W region based on the HadISST, are shown in Fig. 1. In ISCCP, the negative responses mainly occur in the equatorial Pacific with a maximum near the date line where convection is enhanced, resulting in increased convective cloud amount and decreased shortwave flux reaching the surface during El Niño warming. On average, the response amplitude in the Niño-4 (5°S–5°N, 160°E–150°W) region is −13.6 W m−2 K−1 and much larger than that (−6.4 W m−2 K−1) in the Niño-3 region (Table 1). The positive response appears in the off-equatorial Pacific including the west coast of the Americas, which is considered as a local response to SST in Philander et al. (1996) and Xie (2005) but as a remote response to the equatorial SST in Bony and Dufresne (2005). In GAMIL1, although the spatial negative/positive response patterns as seen in ISCCP are generally reproduced, the amplitudes are much weaker over both the Niño-4 (−5.2 W m−2 K−1) and Niño-3 (−0.41 W m−2 K−1) regions. In particular, there is a positive response over the eastern equatorial Pacific, opposite to the observations. By contrast, GAMIL2 produces a much more reasonable negative/positive response. The average magnitudes of the SWCF response in GAMIL2 are −10.3 and −7.1 W m−2 K−1 in Niño-4 and Niño-3, respectively, much closer to the observed values.

Fig. 1.
Fig. 1.

Response of surface shortwave cloud radiative forcing (SWCF) to the Niño-3 index for (a) ISCCP, (b) GAMIL1, and (c) GAMIL2 as measured linear regression coefficients (W m−2 K−1). The Niño-3 index is defined as the Niño-3 sea surface temperature anomaly. Bold red and black rectangles represent the Niño-4 and Niño-3 regions, respectively.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Table 1.

Coefficients of linear regression against SST of surface SWCF (W m−2 K−1); total, convective, and stratiform precipitation (mm day−1 K−1); total liquid water path (g m−2 K−1); 500-hPa vertical velocity (hPa s−1 K−1); and total-, high-, middle-, and low-cloud fraction (% K−1) in observations and two models over the Niño-4 and Niño-3 regions.

Table 1.

For rainfall response to El Niño (), the positive responses coincide with the negative SWCF response over the equatorial regions, with the maximum around the date line in the two observations and simulations (Fig. 2). The regressed coefficients from GAMIL2 are 1.96 and 1.3 mm day−1 K−1 in the Niño-4 and Niño-3 regions, respectively, which are comparable to the values from GPCP (1.7 and 1.3 mm day−1 K−1) and CMAP (1.9 and 1.2 mm day−1 K−1) and are significantly larger than those (0.98 and 0.75 mm day−1 K−1) from GAMIL1 (Table 1). The significantly different regression values between the two models are due to contributions from both convective and stratiform precipitation (Table 1 and Fig. 2). Particularly, GAMIL1 predicts a very small (nearly zero) stratiform/grid-scale precipitation response in the equatorial Pacific.

Fig. 2.
Fig. 2.

As in Fig. 1, but for (a)–(d) total, (e),(f) convective, and (g),(h) stratiform rainfall (mm day−1 K−1).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

To understand the causes for the different responses of surface SWCF and rainfall to SST in the two models, we analyze the underlying physical mechanisms [Eq. (2)]. First, to investigate the SWCF response, we examine the response of dynamics (as measured by vertical velocity at 500 hPa ), cloud fraction (including total, high, middle, and low cloud), and LWP to Niño-3 SST anomalies (Figs. 35 and Table 1). The high, low, and middle clouds are defined as those clouds with tops above 400 hPa, below 700 hPa, and in between in the models, which is very close to the ISCCP definition of less than 440 hPa for high clouds, greater than 680 hPa for low clouds, and in between for middle clouds, respectively. In the Niño-4 region, all of the above factors contribute to the weak SWCF response in GAMIL1 when compared to GAMIL2. The largest contribution to the difference is from LWP, changing by more than a factor of 4, from 3.1 g m−2 K−1 in GAMIL1 to 12.9 g m−2 K−1 in GAMIL2. The latter is much closer to 11.8 g m−2 K−1 in ISCCP. The LWP response estimated from SSM/I is much larger (29.5 g m−2 K−1) than those from the two models and ISCCP (Fig. 3). The reason for such a large response with the SSM/I data is unknown. The dynamical and cloud (except for low cloud) amount response also plays a role, albeit to a lesser extent. The 500-hPa vertical velocity response in GAMIL1 is about half of the counterpart in GAMIL2 (Fig. 4). For cloud amount, both models underestimate the total- and high-cloud response and overestimate the low-cloud response compared to ISCCP (Fig. 5). The underestimated response of total cloud is from both high and middle cloud in GAMIL1 and from high cloud in GAMIL2. The overestimated middle- and low-cloud responses in GAMIL2 may be not so serious because of the possible shielding effect of high cloud in ISCCP. In the Niño-3 region, the low-cloud cover and LWP responses contribute more to the discrepancies between simulations than other factors. The Niño-3 average values of and are opposite in sign between the two simulations, −1.4% K−1 and −3.3 g m2 K−1 in GAMIL1 and 2.0% K−1 and 10.8 g m2 K−1 in GAMIL2, respectively. In the eastern part of Niño-3 (about east of 120°W), the negative and are mainly responsible for the positive in GAMIL1: all of which generally reverse in GAMIL2 and ISCCP. In the western part of Niño-3, the weak low-cloud and LWP responses, together with the weak dynamical and middle-cloud responses, contribute to the weak SWCF response in GAMIL1 relative to GAMIL2 and ISCCP.

Fig. 3.
Fig. 3.

As in Fig. 1, but for total liquid water path (g m−2 K−1).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Fig. 4.
Fig. 4.

As in Fig. 1, but for 500-hPa vertical velocity (hPa s−1 K−1).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Fig. 5.
Fig. 5.

As in Fig. 1, but for (a)–(c) total, (d)–(f) high, (g)–(i) middle, and (j)–(l) low clouds (% K−1).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Although GAMIL2 has significantly improved the surface SWCF and its relevant response simulations over the equatorial Pacific, it still has nonnegligible biases in the responses of precipitation, , and middle and high clouds in the western Pacific region east of the Philippines. The precipitation bias there is mainly from the convective component, along with the and middle- and high-cloud biases, indicating that these biases are closely associated with the convective scheme.

From the above analyses, the LWP is the dominant factor contributing to the large differences in SWCF response between GAMIL1 and GAMIL2. Since LWP is a vertical integral of cloud liquid water, which is a product of cloud fraction and in-cloud LWP (ICLWP), can be roughly divided into two parts,
e4
where reflects the combined contribution from cloud fraction response () and climatological mean ICLWP to and reflects the combined effect of ICLWP response () and mean cloud fraction. Note that, on the right-hand side (rhs) of Eq. (4), the term related to the response of column-integrated pressure is neglected. For the weak LWP response in GAMIL1, the vertically mismatched distribution of responses and mean states plays a key role. From the vertical distributions of ICLWP, cloud amount, and their response to SST in Fig. 6, there are two distinct features of this vertical mismatch. First, large (small) ICLWP response approximately coincides with small (large) mean cloud amount values. Similarly, large (small) cloud response approximately coincides with small (large) ICLWP. Both contribute to small integrands on the rhs of Eq. (4). Second, the positive/negative cloud amount and ICLWP anomalies above/in the boundary layer offset each other in the eastern equatorial Pacific (Figs. 6a,c). On the other hand, the large positive ICLWP and cloud anomalies match well with their climatological mean distributions in GAMIL2. Thus, when vertically integrated the product of cloud amount response (mean state) and ICLWP mean state (response) is much smaller in GAMIL1 than in GAMIL2.
Fig. 6.
Fig. 6.

Vertical cross section of cloud fraction response to El Niño (color) and climatological mean in-cloud liquid water path (contour) for (a) GAMIL1 and (b) GAMIL2 and in-cloud liquid water path response to El Niño (color) and climatological mean cloud fraction (contour) for (c) GAMIL1 and (d) GAMIL2 averaged over the equatorial Pacific (5°S–5°N) (units: percent for cloud fraction and g m−2 for ICLWP).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Interestingly, LWP and SWCF responses similar to those in the two GAMIL models were also found in two T42 spectral resolution Community Atmosphere Model, version 3 (CAM3) simulations, one with the Zhang and McFarlane (1995) deep convection scheme as in GAMIL1 and the other with the revised convective closure as used in Zhang and Mu (2005) and here in GAMIL2 (LZ08). They show that the SWCF and LWP responses to SST are much weaker when the original Zhang–McFarlane scheme is used while the responses become much stronger when the revised scheme is used. They attribute the difference to the contribution of LWP in the lower troposphere associated with low-level clouds through the interaction between deep and shallow convections. In other words, it corresponds to the second mismatch in GAMIL1. In this study, the mismatch of high and middle clouds and their ICLWP appears to play a more important role. This, together with the dynamical and cloud amount responses, contributes to the weak SWCF response in GAMIL1.

2) Nonlinearity

To determine whether the errors in shortwave cloud feedback in GAMIL1 are from El Niño or La Niña, Table 2 lists the averaged regression coefficients in Niño-3 under SSTA > 0 and SSTA < 0 conditions separately. In general, the responses of the atmospheric variables to El Niño are much stronger than those to La Niña in both observations and simulations since the atmosphere is probably more stable to the smaller SST changes associated with La Niña compared to the large changes of El Niño. For GAMIL1, the small is from both underestimation of the negative response to El Niño and overestimation of the positive response to La Niña. Furthermore, the underestimated negative stems from errors in LWP, dynamical and cloud cover responses during El Niño, whereas the overestimated positive derives from the overestimation of during La Niña, in accordance with earlier analysis.

Table 2.

Coefficients of linear regression against SST of surface SWCF (W m−2 K−1); total, convective, and stratiform precipitation (mm day−1 K−1); total liquid water path (g m−2 K−1); 500-hPa vertical velocity (hPa s−1 K−1); and total, high, middle, and low cloud fraction (% K−1) in observations and two models over the Niño-3 region for positive and negative SST anomalies separately.

Table 2.

The excessive is directly connected to the enhanced atmospheric stability under cold SST, which is measured by the differences in potential temperature between 700 hPa () and the surface (Klein and Hartmann 1993). Given the surface temperature, the lower-tropospheric stability is governed by changes in (or temperature at 700 hPa ), which is affected by latent heat release/uptake from condensation/evaporation in moist processes. In comparison with the NCEP and ECMWF reanalyses, GAMIL1 has less sensitivity of to SST (Fig. 7), especially over the Niño-3 region. This is closely associated with the lack of strong sensitivity at 700 hPa of heating rate from the total moist process to El Niño warming (Fig. 8c). Thus, the lower troposphere is more stable under colder surface conditions, resulting in more boundary layer clouds and excessive in Niño-3. By comparison, GAMIL2 displays a clear heating rate response at 700 hPa, mostly from the stratiform cloud processes (Figs. 8e,f), and hence the temperature at 700 hPa and the lower stability are more sensitive to surface temperature. Additionally, the differing stratiform heating responses in two GAMIL versions conform to their rainfall features (Fig. 2). Therefore, the different responses of the stratiform heating to SST in the two models are one of the most important causes of their different SWCF and rainfall responses.

Fig. 7.
Fig. 7.

Linear regression coefficients between 700-hPa temperature anomalies and Niño-3 SST anomalies averaged over (5°S–5°N) as functions of longitude from NCEP, ERA-Interim, GAMIL1, and GAMIL2.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Fig. 8.
Fig. 8.

Vertical cross section of heating rate response to El Niño warming in deep convective, stratiform, and total moist processes from (top) GAMIL1, (middle) GAMIL2, and (bottom) their differences (K day−1 K−1).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

The nonlinearity in El Niño and La Niña is also observed in the Hovmöller plots of LWP anomalies (Fig. 9). The SSM/I starts in January 1988 and has an 18-month gap from July 1990 to December 1991 because of instrument failure (Weng et al. 1997). In both SSM/I and ISCCP, the magnitude of positive LWP anomalies during strong El Niño (e.g., 1986, 1991, 1997) is larger than that of negative LWP anomalies during strong La Niña (1988/89 and 1999/2000). The weak El Niño (1993 and 1994/95) and La Niña signals (1983/84 and 1995/96) are also identifiable around the date line in SSM/I and ISCCP but not in the eastern Pacific. The insignificant signals in the eastern Pacific during weak ENSO suggest that the nonlinearity between weak and strong ENSO events exists there. GAMIL1 does not show the systematic LWP changes in both El Niño and La Niña. Even in 1983 and 1997 El Niño, GAMIL1 shows negative LWP anomalies over the eastern Pacific, opposite to the SSM/I and ISCCP. In contrast, GAMIL2 simulates the reasonable LWP evolutions including the nonlinearity between El Niño and La Niña and the potential nonlinear behavior in the eastern Pacific between weak and strong ENSO events. The amplitude of LWP variations in GAMIL2 is within the range of two observations, agreeing with the regression results of Fig. 3.

Fig. 9.
Fig. 9.

Hovmöller plots of total liquid water path anomalies from January 1979 to December 2002 for (a) SSM/I, (b) ISCCP, (c) GAMIL1, and (d) GAMIL2 across the equatorial (5°S–5°N) Pacific. The anomalies are calculated with respect to each data mean (g m−2).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

b. Climatological mean state

The errors in the response/feedback simulation can often be traced to errors in the mean state (Guilyardi et al. 2009b). Thus in this section we examine how errors in the simulation of climatological state may contribute to errors in the feedback simulations in GAMIL1. The profiles of multiyear averaged annual mean specific and relative humidity, temperature, and vertical velocity averaged over the equator (5°S–5°N) are given in Figs. 10 and 11 . In general, compared to the NCEP reanalysis, GAMIL1 has a wet bias (above 0.5 g kg−1) in the ascending branch of the Walker circulation and a dry (below −0.2 g kg−1) bias in the descending branch, and both branches have much stronger vertical motion (Fig. 10e). Comparing with the moisture bias, the RH bias is amplified by temperature biases (Fig. 11c), resulting in very high RH above 400 hPa in the entire Pacific and relative low RH between 700 and 400 hPa in the east Pacific. GAMIL2 has significantly reduced the wet and dry biases in the west and east equatorial Pacific relative to GAMIL1, with less moisture (about 0.5 g kg−1) in the wet bias region and more moisture (0.2 g kg−1) in the dry bias region. The change of vertical velocity in GAMIL2 is somewhat complicated, with stronger ascending motion between 150° and 170°E, weaker descending motion in the upper troposphere over the eastern Pacific, and weaker ascending motion west of 150°E. The cold bias in GAMIL1 is exacerbated in GAMIL2 over the entire equatorial Pacific, which may be related to the inclusion of aerosol indirect effects (Li et al. 2013b). Although the too low RH in the eastern Pacific in GAMIL1 is increased so much that GAMIL2 shows more positive RH bias (further amplified by the cold bias in GAMIL2) there, the RH in GAMIL2 is still improved, especially in the middle troposphere.

Fig. 10.
Fig. 10.

Vertical cross sections of multiyear annual mean specific humidity (shaded) and vertical velocity (contour) averaged over (5°S–5°N) from (a) NCEP, (b) ERA-Interim, (c) GAMIL1, and (d) GAMIL2 and the differences (e) between GAMIL1 and NCEP and (f) between GAMIL2 and GAMIL1.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Fig. 11.
Fig. 11.

As in Fig. 10, but for relative humidity (shaded) and temperature (contour).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Because of the diagnostic relationship between layered cloud fraction and RH in the models, the RH bias directly affects the cloud simulation. For instance, in GAMIL1 the too high RH particularly above 400 hPa leads to the formation of too much high cloud in the equatorial Pacific and the low RH between 700 and 400 hPa, the middle cloud layers, produces less middle cloud in the eastern Pacific (Fig. 12e). In GAMIL2, the higher RH below 500 hPa induces more middle and low clouds than those in GAMIL1 as well as ISCCP, though the observed lower clouds may be affected by shielding from high clouds. These cloud biases in the two models resemble the errors in their responses to El Niño, suggesting that the former is one of the main sources of the latter.

Fig. 12.
Fig. 12.

Geographic distributions of annual mean (a)–(c) total, (d)–(f) high, (g)–(i) middle, and (j)–(l) low clouds from ISCCP, GAMIL1, and GAMIL2 (%).

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

Another apparent deficiency in GAMIL1 is the vertical distribution of ICLWP that is concentrated below 700 hPa, with a much larger magnitude than that in GAMIL2 (Figs. 6a,b). Moreover, the vertically mismatched distribution of cloud and ICLWP in GAMIL1 (i.e., too much high clouds with less ICLWP above 400 hPa and too few low clouds with overabundant ICLWP below 700 hPa) resembles their response to El Niño, again indicating the response errors largely come from the errors in climatological mean state.

Assuming that the total cloud water content remains unchanged, if there is less evaporation (including reevaporation) and precipitation, there will be more cloud water in the atmosphere. For the ICLWP in GAMIL1, both the weak evaporation in the stratiform cloud process (Fig. 13c) and the small stratiform precipitation (Li et al. 2013b) lead to too much water content (or LWP) in the lower levels. Meanwhile, the weak condensation process in the upper levels contributes to its wet bias, though this bias is also attributed to the deficiency of too frequent convection occurrence because of a lack of convective inhibition in the Zhang and McFarlane (1995) scheme (Dai and Trenberth 2004; Zhang and Sun 2006). Once again the inactive stratiform (nonconvective) cloud process in GAMIL1 plays a vital role in producing the background state errors (e.g., ICLWP, water vapor/RH, and cloud). The above biases of mean states and feedbacks, especially for ICLWP and cloud, are markedly reduced in GAMIL2 because of a reasonable simulation of nonconvective heating/cooling structure, although some biases (e.g., temperature and middle cloud), which do not change the SWCF and its relevant feedbacks much, are exacerbated in GAMIL2.

Fig. 13.
Fig. 13.

As in Fig. 10, but for moistening rate (g kg−1 day−1; shaded) and heating rate (K day−1; contour) in deep convective, stratiform, and total moist processes.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00632.1

4. Summary and discussion

The important role of nonconvective processes in responses of surface SWCF to El Niño over the equatorial Pacific is emphasized using two GAMIL versions, GAMIL1 and GAMIL2. They differ in the treatment of moist processes: for example, deep convective scheme, convective cloud fraction, and cloud microphysical schemes as well as the retuning of 14 uncertain parameters. The ISCCP observation shows strong negative SWCF responses to El Niño in the equatorial Pacific. The average amplitudes are −13.6 W m−2 K−1 in the Niño-4 region and −6.4 W m−2 K−1 in the Niño-3 region. In the two model simulations, GAMIL1 only simulates weak negative responses or even has the wrong sign in the eastern equatorial Pacific, while GAMIL2 reasonably reproduces the SWCF responses with the magnitudes of −10.3 and −7.1 W m−2 K−1 in Niño-4 and Niño-3. The different SWCF responses are attributed to differences in the dynamical, cloud fraction, and LWP responses between the two simulations, with the largest contribution to the differences from the LWP response in Niño-4 and the low-cloud cover and LWP responses in Niño-3. Two types of vertical mismatch between cloud amount and ICLWP feedbacks and climatological mean states are responsible for the weak LWP response in GAMIL1. The first one is the large/small ICLWP (cloud amount) responses with the small/large annual mean cloud fraction (ICLWP), and the second one is the positive/negative cloud amount and ICLWP anomalies above/in the boundary layers that offset each other in the eastern equatorial Pacific.

The nonlinearity analysis shows that the weak SWCF response in GAMIL1 is from both underestimation of negative response to El Niño and overestimation of positive response to La Niña. The overestimated response to La Niña could be attributed to the insignificant stratiform heating at 700 hPa that overly increases the atmospheric stability under cold SST. Also the stratiform rainfall response is an essential factor in the precipitation response differences by the two models.

Further examination of the climatological mean state simulated by GAMIL1 indicates that the very weak stratiform moistening (heating)/drying (cooling) and the small stratiform precipitation are conducive to the biases of ICLWP, cloud, and moisture, in accordance with their response errors. The similarity of the errors in climatological state and responses (feedbacks) suggests that the former may be the main reason of the latter. On the other hand, GAMIL2 simulates a reasonable stratiform heating, which agrees with the derived latent heating from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) (Schumacher et al. 2004). Thus, the biases of cloud, ICLWP, and moisture are greatly reduced in GAMIL2.

Additionally, as with any model development effort, parameter tuning also played a role in improving the simulation of stratiform rainfall fraction (or response) in GAMIL2. First, to maintain the radiative energy balance at TOA, we artificially amplified LWP with a factor of 1.57 in GAMIL1, which is also the reason for enormous LWP at the lower levels besides the weak stratiform process, while we changed the RH threshold for low-cloud formation in GAMIL2. Second, to suppress the excessive convective precipitation seen in GAMIL1, we decreased the rainwater autoconversion coefficient and increased the threshold of CAPE and RH and evaporation efficiency for deep convection. Third, to increase the stratiform rainfall, the stratiform precipitation evaporation rate and the threshold for autoconversion of cold and warm ice are reduced (Li et al. 2013b). Many recent uncertainty qualification (UQ) studies could shed light on the training of uncertain parameters in models and reducing the biases of the mean state and response in model simulations (e.g., Jackson et al. 2008; Yokohata et al. 2010; Yang et al. 2012).

Acknowledgments

This work was supported by the National “973” Project (Grant 2010CB951904), China Meteorological Administration R &D Special Fund for Public Welfare (meteorology) (Grant GYHY201006014), and National Natural Science Foundation of China (40923002 and 41005053).

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