PU Ye， TANG Yn-Lind LI Li-Jun

aState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG）， Institute of Atmospheric Physics，Chinese Academy of Sciences， Beijing， China；bCollege of Earth Science， University of Chinese Academy of Sciences， Beijing， China

Effects of the coupling process on shortwave radiative feedback during ENSO in FGOALS-g

PU Yea，b， TANG Yan-Liaand LI Li-Juana

aState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics （LASG）， Institute of Atmospheric Physics，Chinese Academy of Sciences， Beijing， China；bCollege of Earth Science， University of Chinese Academy of Sciences， Beijing， China

Satisfactory simulation of negative shortwave （SW） radiative feedback during ENSO in the equatorial Pacifc remains a challenging issue for climate models. Previous studies have focused on specifc physical processes in the atmospheric and/or oceanic model， but the coupling process in coupled models has not received much attention. To investigate the coupling efect on SW feedback， two versions of an AGCM and their corresponding coupled models are analyzed. Results indicate that the efects of the coupling process in the two versions lead to weakening and enhancement of the negative feedback in the earlier and new versions， respectively， mainly due to their diferent changes in cloud fraction feedback and dynamical feedback. Further examination of the nonlinearity of the feedback reveals that the opposite coupling efects in the two versions originate from their diferent responses to El Niño and to La Niña.

ARTICLE HISTORY

Revised 7 April 2016

Accepted 19 April 2016

Shortwave radiative feedback； ENSO； coupling efect； GAMIL； FGOALS-g

气候模式对 ENSO 期间负的短波辐射反馈的模拟依然有挑战性，以往的研究主要侧重于大气或者海洋模式中具体物理过程的影响。为了探究耦合作用对其负反馈的效用，本研究分析对比了两个大气环流模式 （GAMIL1 和 GAMIL2） 以及相应的耦合模式 （FGOALS-g1 和 FGOALS-g2） 对ENSO 的模拟，结果显示新旧两个版本的耦合作用对短波反馈的影响是不一样的，主要来源于云量反馈和动力学反馈的差异；通过对反馈的非线性过程的分析进一步揭示了新旧两个版本相反的耦合作用来自于它们对于厄尔尼诺和拉尼娜的不同相应。

1. Introduction

ENSO， the strongest interannual signal in global climate variability， is characterized by large-scale SST anomalies in the eastern equatorial Pacifc Ocean， and afects the atmospheric circulation on a global scale （Magnusson，Alonso-Balmaseda， and Molteni 2013； Bellenger et al. 2014）. As a complex climate phenomenon， ENSO is amplifed by positive Bjerknes feedback and damped by negative heat fux feedback， and the role of atmospheric feedback has increasingly been emphasized （Guilyardi et al. 2004； Lloyd et al. 2009； Lloyd， Guilyardi， and Weller 2011， 2012； Chen， Yu， and Sun 2013）. In particular， the simulation of shortwave （SW） radiative feedback （the dominant component of heat fux feedback） has become a key indicator for ENSO simulation. However， the simulation of SW feedback remains a challenging task in most coupled and uncoupled models， with problems such as underestimated negative feedback in the equatorial Pacifc（Bellenger et al. 2014； Kim et al. 2014）， and even incorrect positive feedback in the Niño3 region （5°S-5°N， 150-90°W）in the models participating in CMIP5.

The biases in SW feedback have been investigated in many studies， and are mainly attributed to the physical processes in specifc model components. For example， in the atmospheric component， the convective parameterization scheme （Neale， Richter， and Jochum 2008； Guilyardi et al. 2009）， nonconvective condensation processes （Li，Wang， and Zhang 2014， 2015）， and the coupling of cloud radiative efects to atmospheric circulation （Rädel et al. 2016）， may play an important role in SW feedback. The climatological mean state of some variables in the oceanic model， such as an excessive equatorial SST cold tongue， may also have an efect （Sun， Yu， and Zhang 2009）. However， the efect of the coupling process on SW feedback in coupled models has not received sufcient attention. To investigate this efect， simulations of twoversions of the Grid-point Atmospheric Model of the LASG，IAP （GAMIL1 and GAMIL2） and the corresponding coupled models， FGOALS-g1 and FGOALS-g2， are analyzed in the present study.

The paper is organized as follows: Section 2 gives a brief description of the four models， the observational data and the method used for decomposing the SW feedback. In Section 3， the simulations of SW feedback are compared in the four models， and in particular the coupling efects on SW feedback are investigated in the two versions of FGOALS-g. Finally， a summary and discussion are given in Section 4.

2. Models， data， and decomposition method

2.1. Models and observational data

In this study， we select AMIP runs （AMIP years 1979-2008）of the two versions of GAMIL （GAMIL1 and GAMIL2） （Li and Wang 2010； Li et al. 2012， Li， Wang， et al. 2013）， and CMIP runs （the frst ensemble member of their historical runs （years 1951-2005） （Taylor， Stoufer， and Meehl 2012））of their corresponding coupled models （FGOALS-g1 and FGOALS-g2） （Yan et al. 2009， 2010； Yu and Sun 2009； Yu et al. 2011； Li， Lin， et al. 2013）. GAMIL1 and GAMIL2 are the atmospheric components of FGOALS-g1 and FGOALS-g2，which have participated in the CMIP3 and CMIP5， respectively. All these models have been developed by the LASG， IAP， Chinese Academy of Sciences. After a series of improvements， GAMIL2 still shares the same dynamical core and grids as GAMIL1， but the physical processes have been improved considerably in GAMIL2， including the adoption of many upgraded cloud-related processes and retuning some parameters in convection， cloud macro/microphysical and boundary layer schemes. In particular， the convective rainfall is reduced and stratiform rainfall is enhanced in the new deep convective parameterizations； for nonconvective cloud processes， the onemoment cloud microphysical scheme （Rasch and Kristjánsson 1998） is replaced by the two-moment scheme（Morrison and Gettelman 2008）. In addition， the new oceanic component in FGOALS-g2 has increased the grid resolution， introduced the two-step shape-preserving advection scheme advection scheme （Xiao 2006）， and improved some physical processes （Liu et al. 2012）， as compared with that in FOGLAS-g1， the detail of which can be found in Li， Lin， et al. （2013）， and Li， Wang， and Zhang 2014）.

For comparison， the following reference datasets are used in this study: SW fux， cloud cover， and liquid water path（LWP） datasets from the ISCCP （Rossow and Schifer 1999） for the period July 1983 to December 2008； and vertical velocity from NCEP Reanalysis-2 （Kanamitsu et al. 2002）； SST from HadISST （Rayner et al. 2003）. All these reference datasets are bilinearly interpolated onto a uniform 1.875° × 1.875° grid.

2.2. Decomposition method

Following the previous study of Li， Wang， and Zhang（2015）， the feedback of an atmospheric variable F during ENSO is defned by the linear regression coefcient α:

where the angle brackets indicate averaging over the Niño3 region （5°S-5°N， 150-90°W）， and FA （SSTA） is the anomaly of F （SST） after removing the annual cycle. To calculate the nonlinearity of a variable with respect to the El Niño and La Niña conditions， the feedback at each grid point is frst computed separately for SST ＞ 0 and SST ＜ 0 before averaging over the Niño3 region （Lloyd， Guilyardi， and Weller 2012）.

Based on the highly idealized decomposition method of SW feedback developed by Lloyd， Guilyardi， and Weller（2012）， Li， Wang， and Zhang （2014） developed a more accurate method that can be written as

3. Results

3.1. SW feedback behavior

First， we compare the SW feedback of the four simulations against the observations， as shown in Figure 1. In ISCCP，the negative SW feedback mainly occurs in the equatorial Pacifc， east of 150°E， with a maximum near the dateline where convection is enhanced， resulting in an increased convective cloud amount and decreased SW fux reaching the surface during El Niño warming. On average， the negative feedback in the Niño4 region （5°S-5°N， 160°E-150°W），-13.6 W m-2K-1， is much larger than in the Niño3 region（5°S-5°N， 150-90°W）， -6.4 W m-2K-1. Compared with the observations， both the two earlier versions （GAMIL1 and FGOALS-g1） exhibit much weaker negative feedback in the above two regions， with the value in the Niño3 region even turning positive. The simulations in the two new versions（GAMIL2 and FGOALS-g2） are more reasonable， although the negative feedback is a little too strong. Moreover， the efect of the coupling process on the negative strength，which indicates that the SW feedback in an AGCM may be weakened or enhanced in coupled models because of its interactions with other components， is found to beopposite in the earlier and new versions: weakening in the earlier versions （coupling GAMIL1 to FGOALS-g1） and enhancement in the new versions （coupling GAMIL2 to FGOALS-g2）.

Figure 1.SW fux feedback （units: W m-2K-1） during ENSO in the tropical Pacifc， based on （a） ISCCP， （b） GAMIL1， （c） GAMIL2，（d） FGOALS-g1， and （e） FGOALS-g2， and measured as the linear SW regression against the Niño3 index. The Niño3 index is defned as the Niño3 SST anomaly. Red and black rectangles represent the Niño4 and Niño3 regions， respectively.

Figure 2.Average monthly （a） SW feedback （units: W m-2K-1）， （b） total cloud fraction feedback （units: mm d-1K-1）， （c） total LWP feedback（units: g m-2K-1）， and （d） dynamical feedback （units: hPa s-1K-1）， based on observations （black）， GAMIL1 （red）， GAMIL2 （dark blue），FGOALS-g1 （purple）， and FGOALS-g2 （light blue）.

The seasonal variations of the SW feedback in the observations and the four simulations in the Niño3 region are then investigated （Figure 2）. The two earlier versions exhibit weaker negative SW feedback throughout the whole year， and the new versions exhibit a stronger negative value than the observations， except in spring. For the coupling process， the weakening of the feedback after coupling in the earlier versions mainly occurs in spring and may have little infuence on ENSO， while the strengthening of the coupling dominates in late summer， autumn， and winter in the new versions， which may play an important role in the evolution of ENSO.

3.2. Effect of coupling

To explain the underlying mechanism for the efect of coupling on SW feedback， the SW feedback is decomposed into cloud fraction feedback， LWP feedback， andcloud fraction feedback containing dynamical （vertical velocity at 500 hPa） feedback， according to Equation （2）（Lloyd， Guilyardi， and Weller 2012； Li， Wang， and Zhang 2014）. These three components in the Niño3 region in the AMIP and CMIP runs are shown in Table 1. In the earlier versions， both the weakening of the cloud fraction feedback and the dynamical feedback contribute to the weaker SW feedback， while the LWP feedback operates in the opposite way. In the new versions， both the strengthening of the cloud fraction feedback and the dynamical feedback contribute to the stronger SW feedback， while the LWP feedback weakens. Therefore， the efects of coupling on SW feedback are infuenced by the cloud fraction feedback and the dynamical feedback in both FGOALS-g versions.

Table 1.Coefcients of linear regression against SST of surface SW feedback （units: W m-2K-1）， total LWP （units: g m-2K-1）， 500-hPa vertical velocity （units: hPa s-1K-1）， and total-cloud fraction （units: % K-1）， based on observations and the four models over the Niño4 and Niño3 regions.

Table 2.Coefcients of linear regression against SST of surface SW feedback （units: W m-2K-1）， total LWP （units: g m-2K-1）， 500-hPa vertical velocity （units: hPa s-1K-1）， and total-cloud fraction （units: % K-1）， based on observations and the four models over the Niño3 region for positive and negative SST anomalies， separately.

Moreover， to explore the cause of the diferent coupling efects on SW feedback in the two FGOALS-g versions， the SW， total cloud fraction， LWP， and dynamic feedbacks in the Niño3 region under El Niño （SSTA ＞ 0） and La Niña（SSTA ＜ 0） conditions in the AMIP and CMIP runs are shown in Table 2. According to observation， the SW feedback tends to be negative for El Niño warming （-8.9 W m-2K-1）and positive for La Niña cooling （0.9 W m-2K-1）. After coupling GAMIL1 to FGOALS-g1， the negative response of SW feedback to El Niño is weakened （from -2.69 to -0.06 W m-2K-1）， and the positive response to La Niña is strengthened （from 2.60 to 3.86 W m-2K-1）， both contributing to the weakening of the total negative SW feedback. Whereas， after coupling GAMIL2 to FGOALS-g2， although the negative response of SW feedback to El Niño is weakened a little （from -10.6 to -10.27 W m-2K-1）， the incorrect negative response to La Niña is strengthened （from -1.32 to -4.38 W m-2K-1）， which results in the strengthening of the total negative SW feedback. In other words， by coupling GAMIL1 to FGOALS-g1， both the SW feedback for El Niño and La Niña contribute to the weakening of SW feedback； while by coupling GAMIL2 to FGOALS-g2， the strengthening of SW feedback mainly results from the SW feedback to La Niña.

4. Summary and discussion

The ENSO simulations of the earlier version of the atmospheric model， GAMIL1， and the new version， GAMIL2，and the corresponding coupled models （FGOALS-g1 and FGOALS-g2） are compared to investigate the efect of coupling on SW feedback in the equatorial Pacifc. Results indicate that the negative feedback in the earlier versions（GAMIL1 and FGOALS-g1） is much weaker than observed throughout the whole year， while the new versions（GAMIL2 and FGOALS-g2） simulate SW feedback that is much closer to observation， especially in the Niño3 region，albeit the negative feedback is a little stronger. The efect of the coupling process on the negative feedback in the two versions is diferent， with weakening when coupling GAMIL1 to FGOALS-g1， and enhancement when coupling GAMIL2 to FGOALS-g2.

The efects of coupling on the SW feedback are infuenced by the cloud fraction feedback and the dynamical feedback in both versions of FGOALS-g； both feedback components weaken in the earlier version and strengthen in the new version. Further examination reveals that the opposite efects of coupling in the two versions are related to the nonlinearity of the SW feedback in El Niño and La Niña: both the responses to El Niño and La Niña contribute to the weakening of SW feedback when coupling GAMIL1 to FGOALS-g1， while the strengthening of SW feedback mainly comes from the response to La Niña when coupling GAMIL2 to FGOALS-g2.

The study also investigates the coupling efect by decomposing the SW feedback into three related atmospheric feedbacks and also considering its nonlinearity. At a deeper and more practical level， the opposite efect of the coupling process on SW feedback in the two versions may be associated with their diferent physical parameterization schemes in the coupled models； the reduced convective rainfall and stratiform rainfall in the atmospheric component may enhance and weaken the SW feedback through the coupling process. Furthermore， the advection scheme in the oceanic component may also be an important factor. The apparent mechanism will be studied in a follow-up paper. The climatological mean state is another factor determining ENSO behavior （Guilyardi 2006； An and Choi 2013）， so the change in mean state （especially wind stress） associated with the coupling process is also worthy of further research.

Disclosure statement

No potential confict of interest was reported by the authors.

Funding

This work was supported by the National Basic Research Program of China ［973 Program， grant number 2015CB954102］； the National Natural Sciences Foundation of China ［grant number 41205079］； the China Postdoctoral Science Foundation ［grant number 2016M591234］.

Notes on Contributors

PU Ye is a PhD candidate at the Institute of Atmospheric Physics （IAP）， Chinese Academy of Sciences （CAS）. His main research interests are numerical modeling and high-performance computing. His recent publications include papers in Climatic and Environmental Research and other journals. TANG Yan-Li is a postdoctor at the Institute of Atmospheric Physics （IAP）， Chinese Academy of Sciences （CAS）. Her main research interests are coupling and evaluation of the Earth system models. Her recent publications include papers in Atmospheric Science Letters and Science Bulletin.

LI Li-Juan is a professor at the Institute of Atmospheric Physics （IAP）， Chinese Academy of Sciences （CAS）. Her main research interests are developing and evaluation of the climatic system models， ENSO， and the cumulus convection. Her recent publications include papers in Journal of Climate， Atmospheric Science Letters， Advances in Atmospheric Sciences， and Science Bulletin.

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5 March 2016

CONTACT TANG Yan-Li tangyl@lasg.iap.ac.cn

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