Long-term Behaviors of Two Versions of FGOALS2 in Preindustrial Control Simulations with Implications for 20th Century Simulations

ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 30, NO. 3, 2013, 577 592 Long-term Behaviors of Two Versions of FGOALS2 in Preindustrial Control Simulations with Implications for 20th Century Simulations LIN Pengfei ( + ), LIU Hailong (4 9), YU Yongqiang ( [r), and ZHOU Tianjun ( U ) State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 (Received 30 July 2012; revised 27 January 2013) ABSTRACT Climate drift in preindustrial control (PICTL) simulations can lead to spurious climate trends and large uncertainties in historical and future climate simulations in coupled models. This study examined the longterm behaviors and stabilities of the PICTL simulations in the two versions of FGOALS2 (the Flexible Global Ocean-Atmosphere-Land System model Version 2), which have been submitted to the Coupled Model Intercomparison Project Phase 5 (CMIP5). As verified by examining time series of thermal fields and their linear trends, the PICTL simulations showed stable long-term integration behaviors and no obvious climate drift [the magnitudes of linear trends of SST were both less than 0.04 C (100 yr) 1 ] over multiple centuries. The changed SSTs in a century (that corresponded to the linear trends) were less than the standard deviations of annual mean values, which implied the internal variability was not affected. These trend values were less than 10% of those of global averaged SST from observations and historical runs during the periods of slow and rapid warming. Such stable long-term integration behaviors reduced the uncertainty of the estimation of global warming rates in the historical and future climate projections in the two versions of FGOALS2. Compared with the trends in the Northern Hemisphere, larger trends existed in the SST and sea ice extents at the middle to high latitudes of the Southern Hemisphere (SH). To estimate the historical and future climate trends in the SH or at some specific regions in FGOALS2, corrections needed to be carried out. The similar long-term behaviors in the two versions of FGOALS2 may be attributed to proper physical processes in the ocean model. Key words: FGOALS, climate drift, preindustrial run, global warming Citation: Lin, P. F., H. L. Liu, Y. Q. Yu, and T. J. Zhou, 2013: Long-term behaviors of two versions of FGOALS2 in preindustrial control simulations with implications for 20th century simulations. Adv. Atmos. Sci., 30(3), 577 592, doi: 10.1007/s00376-013-2186-0. 1. Introduction According to observations, the global averaged surface temperature has increased by approximately 0.8 C from 1900 to 2011. Present coupled models have a certain level of ability and have been used to reproduce historical warming from observation data and to predict future warming (Meehl et al., 2007). However, the amplitudes or rates of global warming in different coupled models vary (i.e. there are large uncertainties). The large climate drift (trend) in preindustrial control (PICTL) simulations is one of the reasons for these uncertainties, as the PICTL simulations are used for the initial values of historical runs (e.g. Cai and Gordon, 1999; Taylor et al., 2011; Sen Gupta et al., 2012). To overcome this climate drift, most previous coupled models (Coupled Model Inter-comparison Project Phase 2 or 2+, i.e. CMIP2 or CMIP2+) employed flux adjustments (Sausen et al., 1988). However, climate drift also existed in CMIP2+, although to a lesser extent (Covey et al., 2006). With the developments of coupled models, most of the CMIP3 models did not employ flux adjustments. However, climate drift still existed in these coupled models, con- Corresponding author: LIU Hailong, lhl@lasg.iap.ac.cn China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2013

578 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 tinuing to affect 20th century historical climate warming (Sen Gupta et al., 2012). To reduce uncertainties, the requirement for PICTL simulations in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) experiment design is to provide >500 yr of simulations without obvious climate drift (Taylor et al., 2012). Thus, the degree of climate drift in the PICTL simulations will affect the estimation of the rate of global warming in historical runs and future projections. To examine climate drift, the PICTL simulations were submitted to CMIP5 in the Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) (Li et al., 2013) and Spectral Version 2 (FGOALS-s2) (Bao et al., 2013). The previous version of FGOALS-g2 was FGOALS-g1.0 (Yu et al., 2004, 2007, 2011; Lin et al., 2011), which was used in CMIP3. Among the simulations submitted to CMIP5, there was another coupled model named FGOALS-s2 (Bao et al., 2013), which shares the same ocean component (LICOM2) as FGOALS-g2. FGOALS-g2 and FGOALS-s2 are non-flux-corrected coupled climate models, and were mainly developed by scientists at LASG/IAP (State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics). By comparing the long-term simulation behaviors of the PICTL runs in the two coupled models, the role of the ocean component can be clarified and the differences between the models noted. As stated above, the main purpose of the study reported in the present paper was to examine climate stabilities and to measure reliability based on time series of thermal fields and their linear trends in the PICTL simulations in FGOALS-g2 and FGOALSs2. By comparing the trends in PICTL simulations with those from Twentieth-Century Climate in Coupled Models (20C3M) simulations and observations, we examine whether the coupled models show climate drift and whether this can affect the estimation of global warming. The role of the ocean component in the long-term integration behaviors is also discussed by comparing the two coupled models using the same ocean component. The remainder of the paper is organized as follows. Section 2 provides an overview of FGOALS and the experiments used for the analysis. The stabilities in the PICTL simulations and their corresponding comparisons of key thermal variables between the two coupled versions of FGOALS2 and observations are presented in section 3. Section 4 provides concluding remarks. 2. Models and experiments 2.1 Models In CMIP3, the atmospheric and oceanic components of FGOALS-g1.0 were the Grid-point Atmospheric Model of IAP LASG, version 1 (GAMIL1) and thelasg/iapclimatesystem Ocean Model version 1 (LICOM1) (Liu et al., 2004a, b), respectively. In this study, the atmospheric components of FGOALSg2 and FGOALS-s2 are GAMIL2 and SAMIL2 which were developed from GAMIL1 (Wang et al., 2004) and the Spectral Atmospheric Model of IAP LASG, version 1 (SAMIL1) (Bao et al., 2010), respectively. The oceanic component in FGOALS-g2 and FGOALS-s2 is LICOM2, which is the updated version of LICOM1. The basic framework of their components, as well as the performances of FGOALS-g2 and FGOALS-s2, are introduced by Li et al. (2013) and Bao et al. (2013), respectively. The stand-alone ocean component (LI- COM2) of FGOALS2 and its performance are introduced by Liu et al. (2012). The performances from an oceanic view in the two coupled models are introduced in detail by Lin et al. (2013a, b). 2.2 Experiments To examine the model stability, the PICTL simulations in the two versions of FGOALS2 were used. In the simulations, FGOALS-g2 was integrated for 900 model years initializing from the 500-yr spinup simulations in stand-alone LICOM2, which was startedfromanannualmeanobservationalseatemperature and salinity from the World Ocean Atlas 2005 (WOA05) (Antonov et al., 2006; Locarnini et al., 2006). The PICTL simulations were forced by the well-mixed preindustrial CO 2 (fixed at 284 ppm) and other greenhouse gas concentrations (CH 4 = 790 ppbv; N 2 O = 275 ppbv; F11 = 0; F12 = 0; ppbv = parts per billion by volume), constant solar radiation (1365 W m 2 ), well-mixed tropospheric and stratospheric ozone and climatological monthly mean threedimensional distribution of aerosols (sulphate, black and organic carbon, dust, and sea salt) in the preindustrial period. Simulations in the PICTL runs from model year 1-900 are used for analysis in the present paper. The four initial conditions of historical runs forced by the 20C3M scenarios (the simulations are also called 20C3M runs) were chosen arbitrarily from 400 to 600 model years in the PICTL runs. The 20C3M runs in FGOALS-g2 were forced by annually resolved total solar irradiation (TSI) (Lean, 2009), greenhouse gas concentrations from http:// www.iiasa.ac.at/webapps/tnt/rcpdb/dsd?action=htmlpage&page=welcome, monthly interpolated ozone data from separate stratospheric and tropospheric data sources (Cionni et al., 2011), and aerosols (except those released due to volcanism). The forcing data were mainly those recommended by CMIP5. Also recommended by CMIP5, the forcing data used

NO. 3 LIN ET AL. 579 in FGOALS-s2 included annual mean TSI (Fröhlich and Lean, 2004), annual mean greenhouse gas concentrations (Buhaug et al., 2009), monthly mean ozone (Cionni et al., 2011), and monthly aerosols (decadal originally, sulfate, black carbon, organic carbon, sea salt, and dust) (Lamarque et al., 2010). However, the land use was kept unchanged in the preindustrial and historical periods. Detailed forcing information for FGOALS-g2 and FGOALS-s2 can be found in Li et al. (2013) and He et al. (2013). To examine the model stability and possible drift, time series of different variables in the PICTL and 20C3M simulations were used. The variables included global averaged SST, averaged SST at different latitude bands, sea ice extents (SIEs) in the Northern Hemisphere (NH) and Southern Hemisphere (SH), global (Atlantic) meridional heat transport at different latitudes, and heat fluxes including net heat fluxes, shortwave and longwave radiation, latent and sensible heat fluxes, and heat due to sea ice melting. 3. Results 3.1 SST Figure 1 shows time series of global averaged SST, global volume averaged temperature and net heat flux at the sea surface. After experiencing the first 50-yr spin-up integration, the global averaged SST reached a quasi-equilibrium state in FGOALS-g2. Over multicentury integrations, the linear trends of the global averaged SST were negative, with the values being approximately 0.02 C (100 yr) 1 and 0.04 C (100 yr) 1 in FGOALS-g2 and FGOALS-s2, respectively (Table 1). These trends were significant due to small standard errors (SEs; <5% of trends) of the linear trends (Table 1). The changed SST values in a century were less than their standard deviations (STDs) of annual mean SST in FGOALS-g2 and FGOALS-s2 (Table 2), which imply the long-term trend may not affect the internal variability. The time-mean SSTs were approximately 17.5 C and 17.3 C (Table 2), colder than the Fig. 1. Time series of globally averaged SST ( C) (a), ocean volume temperature ( C) (b) and surface net heat fluxes (W m 2 ) (c) in the PICTL runs in FGOALS-g2 and FGOALS-s2. The thin lines are annual mean values and the thick lines are their 9-yr running mean values. The dashed line in (a) is the observed value from ERSST v3b (Xue et al., 2003; Smith et al., 2008).

580 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Table 1. Linear trends [units: (100 yr) 1 ] of globally averaged SST, averaged SST at different latitude bands, SIE and meridional heat transport (MHT) at different latitudes (calculated from Figs. 1 4) and their standard errors (the numbers following the ± sign) in the preindustrial control (PICTL) runs, ensemble mean of 20C3M runs and observation. The values in/not in parentheses are for linear trends during the period 1900 2005 (except 1900 2010 for observed) and 1979 2005, respectively. The areas in the middle and high latitudes of the NH and SH are defined by the areas between 30 Nand90 Nand30 Sand90 S, respectively. FGOALS-g2 FGOALS-s2 FGOALS-g2 FGOALS-s2 PICTL PICTL 20C3M- 20C3Mrun run Ensemble Ensemble Data Variables OBS mean mean Global SST 1.13±0.13 0.02±0.001 0.04±0.001 1.23±0.06 2.07±0.20 [K (100 yr) 1 ] (0.65±0.03) (0.46±0.02) (0.99±0.03) Global net het fuxes 0.001±0.005 0.03±0.007 1.53±0.86 5.14±1.53 [W m 2 (100 yr) 1 ] (0.72±0.06) (1.02±0.12) Global shortwave 0.06±0.004 0.05±0.008 0.19±0.51 2.04±1.63 radiation ( 0.92±0.04) ( 1.70±0.13) [W m 2 (100 yr) 1 ] Global Longwave 0.03±0.003 0.01±0.006 3.67±0.45 5.65±1.36 radiation (1.45±0.05) (3.10±0.12) [W m 2 (100 yr) 1 ] Global latent fluxes 0.06±0.005 0.12±0.006 3.75±0.82 4.64±1.28 [W m 2 (100 yr) 1 ] ( 0.29±0.07) ( 1.02±0.09) Global sensible fluxes 0.02±0.001 0.02±0.002 1.27±0.17 1.83±0.34 [W m 2 (100 yr) 1 ] (0.46±0.02) (0.57±0.03) Heat due to melting 0.01±0.003 0.02±0.001 0.14±0.14 0.25±0.17 [W m 2 (100 yr) 1 ] (0.01±0.01) (0.07±0.01) Tropical SST 1.17±0.20 0.01±0.001 0.02±0.002 1.50±0.12 1.81±0.27 [K (100 yr) 1 ] (0.68±0.04) (0.55±0.03) (1.04±0.04) SST north of 30 N 2.23±0.17 0.01±0.002 0.04±0.002 1.26±0.10 2.37±0.20 [K (100 yr) 1 ] (0.44±0.05) (0.28±0.03) (1.12±0.05) SST south of 30 S 0.41±0.09 0.05±0.002 0.06±0.002 0.73±0.03 2.37±0.21 [K (100 yr) 1 ] (0.70±0.02) (0.35±0.01) (0.83±0.04) NH SIE in September 8.47±0.96 0.05±0.007 0.09±0.01 3.77±0.20 6.83±0.68 [10 6 km 2 (100 yr) 1 ] ( 0.31±0.07) ( 3.02±0.11) NH SIE in March 4.31±0.49 0.07±0.005 0.05±0.004 1.12±0.13 1.96±0.19 [10 6 km 2 (100 yr) 1 ] ( 0.21±0.03) ( 1.13±0.04) SH SIE in September 1.16±0.92 0.46±0.02 0.47±0.02 1.26 ± 0.31 9.39±1.62 [10 6 km 2 (100 yr) 1 ] ( 0.92±0.06) ( 1.92±0.22) SH SIE in March 1.38±0.59 0.13±0.008 0.11±0.01 1.55±0.48 3.23±1.18 [10 6 km 2 (100 yr) 1 ] ( 0.38±0.07) ( 1.19±0.14) Global MHT at 30 N 0.005±0.001 0.003±0.002 0.19±0.16 0.11±0.19 (0.03±0.01) ( 0.02±0.01) Global MHT at 30 S 0.008±0.002 0.006±0.003 0.20±0.17 0.42±0.39 (0.07±0.01) (0.05±0.03) Atlantic MHT at 30 N 0.004±0.0008 0.002±0.001 0.12±0.06 0.07±0.08 (0.01±0.005) ( 0.02±0.01) NH, Northern Hemisphere; SH, Southern Hemisphere; SIE, sea ice extent. SST from ERSST (Extended Reconstructed Sea Surface Temperature) dataset; SIEs from National Snow and Ice Data Center (NSIDC). observed value ( 18 C) from 1854 to 1859 from extended reconstructed SST v3b (ERSST v3b). Meanwhile, the global volume-averaged temperatures also decreased (i.e. negative trends) as the time of integration increased in FGOALS-g2 and FGOALS-s2. The negative SST and temperature trends were due to the loss of heat ( 0.23 W m 2 and 0.31 W m 2 for FGOALS-g2 and FGOALS-s2, respectively; Table 2) at the sea surface. To test the long-term behavior further, linear SST trends were calculated during the first 100-yr period and the last 100-yr period. For the global averaged SST in FGOALS-g2 and FGOALSs2, the linear trends became one order of magnitude smaller in the last 100-yr period [ 0.04 C (100 yr) 1 and 0.04 C (100 yr) 1, respectively] than in the first 100-yr period [ 0.26 C (100 yr) 1 and 0.23 C (100 yr) 1, respectively]. The evolutions of the global averaged SST indicated that FGOALS-g2 and FGOALS-s2 gradually became more stable as the time of integration increased under the preindustrial forcing (greenhouse gas concentration and solar radiation). Before the PICTL run was performed, FGOALSs2 was integrated for approximately 1000 model years (Lin et al., 2013a). More importantly, the loss of heat was larger at the sea surface in the PICTL simulations in FGOALS-s2 than that in FGOALS-g2. Thus, the global averaged SSTs and volume temperatures were lower in FGOALS-s2 than those in FGOALS-g2 (Figs. 1a, b and c). The linear trends of the net heat fluxes were 0.01 W m 2 (100 yr) 1 and 0.03 W m 2 (100 yr) 1 in FGOALS-g2 and FGOALS-s2, respectively. The

NO. 3 LIN ET AL. 581 Table 2. Multi-year mean values and STDs of global averaged SST, averaged SST at different latitude bands, SIEs and MHT at different latitudes (calculated from Figs. 1 4) in the PICTL runs and observation. The areas in the middle and high latitudes of the NH and SH are defined by the areas between 30 Nand90 Nand30 Sand90 S, respectively. The MHT values from SODA Reanalysis are used (Zheng and Giese, 2009). Data Variables OBS/Reanalysis FGOALS-g2 PICTL run FGOALS-s2 PICTL run Global SST ( C) 18.0 17.5±0.1 17.3±0.1 Tropical SST ( C) 25.7 25.4±0.1 25.0±0.1 SST north of 30 N( C) 9.9 9.5±0.1 10.1±0.1 SST south of 30 S( C) 8.4 7.6±0.2 7.5±0.2 NH SIE in September (10 6 km 2 ) 7.8±0.5 8.1±0.4 NH SIE in March (10 6 km 2 ) 12.2±0.4 13.5±0.2 SH SIE in September (10 6 km 2 ) 6.4±0.7 7.5±0.6 SH SIE in March (10 6 km 2 ) 20.4±1.8 22.9±1.3 Net heat fluxes (W m 2 ) 0.23±0.42 0.31±0.45 Shortwave radiation (W m 2 ) 177.0±0.4 185.6±0.6 Longwave radiation (W m 2 ) 59.4±0.3 64.1±0.4 Latent fluxes (W m 2 ) 99.9±0.4 114.1±0.4 Sensible fluxes (W m 2 ) 16.9±0.1 6.3±0.1 Heat due to sea ice melting (W m 2 ) 1.1±0.2 1.5±0.1 Global MHT at 30 N(PW) 1.27±0.13 1.59±0.13 1.65±0.16 Global MHT at 30 S(PW) 0.34±0.12 0.27±0.16 0.41±0.22 Atlantic MHT at 30 N(PW) 0.95±0.14 1.15±0.07 0.98±0.07 trends had relatively large SEs (>20% of trends), and these trend values in a century were only 1% of their STDs of annual mean. Each constituent component of the net heat fluxes (i.e. shortwave, longwave, latent, sensible, and heat due to sea ice melting) presented very small linear trends with small SEs (10%), except relatively large SEs for the heat due to sea ice melting (Fig. 2, Table 1). These changed flux values in a century were smaller than their mean values and STDs (Tables 1 and 2). The shortwave radiation was mainly balanced by longwave radiation and latent fluxes (Fig. 2). The absorbed shortwave radiation in FGOALS-s2 was 8.6 W m 2 larger than that in FGOALS-g2. The sea surface released more latent heat (14.2 W m 2 ) and more longwave radiation (4.7 Wm 2 ) to the atmosphere in FGOALS-s2 than those in FGOALS-g2. However, the sea surface released less sensible heat ( 10.6 W m 2 )infgoals-s2thanin FGOALS-g2. The differences between latent fluxes and sensible fluxes in these two versions of FGOALS2 were mainly caused by the specific humidity and not by wind speeds. Although the heat due to sea ice melting was lower than the other components of the net heat flux, it was important because it affected the net heat fluxes (Fig. 2). Because the spatial difference of trends may affect the trends of global averaged SST, the time series and their trends of averaged SST at different latitude bands were investigated, and the results are presented in Fig. 3 and Table 1. These time series of averaged SST in FGOALS-g2 and FGOALS-s2 underwent stablebehaviorsasthetimeofintegration increased. The magnitudes of trends at different latitude bands were not larger than 0.06 C (100 yr) 1 in the two coupled models. Meanwhile, the trends were significant due to small SEs (<15% of linear trends), except averaged SST in the middle and high latitudes of the NH in FGOALS-g2. By comparison, the magnitudes of the trends between 30 Sand30 N (tropical regions) were the smallest and the magnitudes of trends in the middle and high latitudes of the SH were the largest in the two coupled models. Although the averaged SST trends were relatively large and significant (SE of only 5% of trends) in the middle and high latitudes of the SH, they were less than 30% of the STDs of the spatialaveraged SST (Fig. 3; Tables 1 and 2). This indicates that the internal variability was significant in the middle and high latitudes of the SH. Meanwhile, the trends of the meridional heat transports (MHTs) with large SEs (>20% of trends) were also less than their own STDs of annual mean values (Figs. 3 and 4; Tables 1 and 2). Therefore, the MHTs by ocean meridional ocean circulations nearly reached a stable balance, although it was a relatively slow process that needed to be integrated over several hundred years. As observed, the ocean gained heat in the tropics and released heat in the extra-tropics (Figs. 3d f) in FGOALS-g2 and FGOALS-s2. In the tropics, the averaged SST was higher in FGOALS-g2 than in FGOALSs2. However, the averaged net heat flux obtained by the ocean was larger in FGOALS-s2thaninFGOALSg2, which means the difference of net heat flux cannot

582 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Fig. 2. Time series of globally averaged (a) shortwave radiation, (b) longwave radiation, (c) latent fluxes, (d) sensible fluxes and (e) heat due to sea ice melting in the PICTL runs for FGOALS-g2 and FGOALS-s2 (W m 2 ). The thin lines are annual mean values and the thick lines are their 9-yr running mean values. explain the difference in SST between FGOALS-s2 and FGOALS-g2 in the tropics (Fig. 3f). Actually, more heat was transported to the middle and high latitudes, as shown in Fig. 4 and Table 2. In FGOALS-s2, there was more heat to be transported poleward across 30 N and 30 S than in FGOALS-g2. In the NH, the larger MHT across 30 N caused a higher SST in FGOALS-s2 and a larger release of net heat fluxes at the middle and high latitudes (Figs. 4a and d). Actually, across 30 N in the North Atlantic, more MHTs were transported northward (Fig. 4f), resulting in higher SSTs north of 40 N in FGOALS-g2 than in FGOALS-s2 (Lin et al., 2013a, b). This pattern also caused large cold biases in the North Pacific in FGOALS-g2 (Lin et al., 2013b). Although a larger MHT was transported to the middle and high latitudes in the SH (Fig. 4b), the SST was lower in FGOALS-s2 than in FGOALS-g2 due to the larger release of heat (Figs. 3b and e). 3.2 Sea ice The radiation balance of the whole earth system is greatly affected by sea ice because of its large albedo. The long-term change of sea ice is an important indicator for the heat budget in the whole climate system. Under global warming, the changes of SIEs in the NH were more significant than those in the SH. The time series of SIEs in the NH in March and September (Figs. 5a and b) were used to examine their behaviors. For FGOALS-g2, during the 900-model-year period, the SIE trends were 0.07 10 6 km 2 (100 yr) 1 and 0.05 10 6 km 2 (100 yr) 1 in March and in September in the NH, respectively. For FGOALS-s2, the SIE trends were 0.05 10 6 km 2 (100 yr) 1 and 0.09 10 6 km 2 (100 yr) 1 in March and in September in the NH during the whole 600-model-year period, respectively. Their SEs of trends fell between 7% and 13% (Table 1). The SIEs in the NH in March and September had no obvious climate drift in FGOALS-s2 or FGOALSg2. Moreover, the changed SIEs in a century were smaller than their STDs (Table 2), which implied internal variability dominated. In contrast to the NH, the SIE trends were significant (Table 1) and larger in the SH (Figs. 6c and d), especially in September [ 0.46 10 6 km 2 (100 yr) 1 ]. The large trends corresponded to the large trends in averaged SST in the middle and high latitudes of the SH. The changed SIEs

NO. 3 LIN ET AL. 583 Fig. 3. Time series of averaged SSTs ( C)(leftpanel)andnetheat fluxes (W m 2 ) (right panel) at different latitude bands: (a, c) in the middle and high latitudes of the NH north of 30 N; (b, e) in the middle and high latitudes of the SH south of 30 S; and (c, f) in the tropical region between 30 Sand30 N in the PICTL runs for FGOALS-g2 and FGOALS-s2. The thin lines are annual mean values and the thick lines are their 9-yr running mean values. The dashed lines are the corresponding observed values from ERSST v3b (Xue et al., 2003; Smith et al., 2008).

584 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Fig. 4. Time series of global meridonal heat transports (MHT) (1PW=10 15 W) at different latitudes: (a) 30 N; (b) 30 S; and (c) Atlantic MHT at 30 N.The thin lines are annual mean values and the thick lines are their 9-yr running mean values. The dashed lines are the mean observed values from Ganachaud and Wunsch (2003). in a century in the SH compared with the STDs of annual mean values, especially in September (Tables 1 and 2). 3.3 Spatial distribution of temperature trend To determine where the long-term trends in the PICTL runs affect the climate trend due to global warming, the spatial distributions of the long-term trend of near-surface air temperature (equal to SST in the ocean) in the two versions of FGOALS2 are presented in Fig. 6. The trends were large in some regions at the middle to high latitudes in FGOALS-g2 and FGOALS-s2. In the tropics, the long-term trends were generally less than 0.1 K (100 yr) 1. Some common long-term trend features existed in FGOALS-g2 and FGOAL-s2. In the North Atlantic, the slightly positive trends [<0.1 K (100 yr) 1 ] surrounded an obvious negative trend [ 0.3 K (100 yr) 1 ]. In the southern Ocean, the obvious negative trends dominated the bands between 65 Sand55 S in the Pacific sector in the two versions of FGOALS2. However, there were obvious differences in behavior between FGOALS-g2 and FGOALS-s2. The trend was negative south of the Bering Strait only in FGOALS-g2, while an obvious negative trend was located in the Japan Sea, around the Kuroshio extension northeast of Japan and in the Arctic Ocean only infgoals-s2. In the middle to high latitudes of the NH, the linear trends of SST were relatively large in some specific regions (Fig. 6). The signs of linear trends of SST were almost opposite for specific regions in FGOALSg2 and FGOALS-s2 (Fig. 6). However, the correlations between SST tendency and net heat fluxes were positive in most regions in the two versions of FGOALS2, which indicated the increase (decrease) of SST was partly due to an increase (decrease) of net heat fluxes (Figs. 7a and b). The correlations between SST tendency and net heat fluxes were basically due to those between SST tendency and latent heat fluxes in the two versions of FGOALS2 (Figs. 7e and f). The increases in shortwave and longwave radiation possibly led to a decrease and increase in SST in the middle to high latitudes of the NH, respectively (Figs. 7c, d, g, and h). The changed SST by sensible heat fluxes had distinct spatial and model differences (Figs. 7i and j). In the regions covered by sea ice (north of 60 N;

NO. 3 LIN ET AL. 585 Fig. 5. Time series of sea ice extents ( 10 6 km 2 ) in the NH in March (a) and September (b); and in the SH in March (c) and September (d). The black and blue lines represent FGOALS-g2 and FGOALS-s2, respectively. The thin lines are annual mean values and the thick lines are their 9-yr running mean values. The dashed lines are the observed values from Cavalieri and Parkinson (2003). Figs. 7k and l), the increase of heat due to sea ice melting mainly led to an increase in SST. However, at the border of sea ice, the increase of heat due to sea ice melting possibly led to a decrease in SST. In the later stage of simulation, SSTs were decreasing south of 55 S due to the negative trends. The decrease (increase) of SST was due to the decrease (increase) of the net heat flux because there were positive correlations between SST tendency and net heat fluxes locally, apart from negative correlations in some specific regions (Figs. 7a and b). The positive correlations between SST tendency and net heat flux were mainly due to the positive contributions between longwave radiation, latent and sensible heat fluxes, and SST tendency (Figs. 7e j). The changed SSTs were not due to the shortwave radiation and heat due to sea ice melting because their negative correlations south of 55 S (Figs. 7c, d, k and l). The above analysis implies that the decrease in SST can be attributable to the release of more turbulent heat fluxes or longwave fluxes from ocean. Owing to small correlations between SST tendency and each heat flux component, other processes such as oceanic advections (including meridional and zonal heat advections) may play an important role in the negative linear trend of SST in the SH. This process needs to be clarified in the future. Between 30 Sand30 N, the changed SST can be partly explained by the changed net heat flux due to

586 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Fig. 6. Horizontal distribution of linear trends of near surface air temperature (SST in the ocean; shaded) [K (100 yr) 1 ] during the last 500 years: (a) FGOALS-g2; (b) FGOALS-s2. the positive correlations between SST tendency and net heat fluxes. The changed net heat fluxes were mainly due to the changed latent fluxes in the two versions of FGOALS2. In FGOALS-g2, the decrease (increase) in SST could be partly due to the decrease (increase) in shortwave radiation and sensible fluxes because of positive correlations (Figs. 7a, c, e and i). In FGOALS-s2, the increases in shortwave radiation and sensible fluxes could lead to the increase of SST being limited only to the tropical Indian Ocean, and the western and central tropical Pacific (Figs. 7b, d, f and j). In the eastern Pacific, the correlations between SST tendency and shortwave radiation, and sensible heat fluxes, were both negative in FGOALS-s2. The different roles of shortwave radiation in the SST tendency implies cloud processes were different in the eastern tropical Pacific in the two versions of FGOALS2. The correlations between SST tendency and longwave radiation were negative in the tropics in the two versions of FGOALS2 (Figs. 7g and h). The trends of zonal mean temperature and salinity are displayed in Fig. 8. The most obvious negative trends [ 0.09 K (100 yr) 1 ] were located at a depth of 1200 m between 10 N and 30 N in FGOALS-g2 (Fig. 8a). At this depth, the obvious negative trends were located north of the Indian Ocean, at approximately 30 N in the North Atlantic and east of 180 E and between 20 Sand20 N in the Pacific (Fig. 9a). Another obvious negative trend was located at the ocean bottom around a center of 22 N, and its values were also approximately 0.09 K (100 yr) 1 in FGOALS-g2 (Fig. 8a). In addition, there was an obvious negative trend at the sea surface at approximately 55 S, which was connected with the depth of 2000 3000 m at approximately 40 S. Compared with the negative trend, obvious positive temperature trends [>0.15 K (100 yr) 1 ] were mainly located north of 60 N in FGOALS-g2. In FGOALS-s2, the obvious positive trends were located at the tilted depth band of 1500 5000 m between 45 Sand60 N, from the surface to 500 m at approximately 55 S, and from the surface to 1000 m between 40 Nand60 N. The temperature at a depth of 3000 m was chosen for analysis in FGOALS-s2 (Fig. 9b). At this depth, there were obvious negative trends in the Argentine Basin and east of the North Atlantic. North of 60 N, there was a positive trend in FGOALS-s2. The positive trend was smaller in FGOALS-s2 than in FGOALS-g2 due to the much longer integration time in FGOALS-s2 than in FGOAL-g2 as the PICTL run was started. Long-term trends also existed in the zonal mean air temperature (Fig. 10). The large magnitudes of the negative trends of air temperature were mainly at the high latitudes in the two versions of FGOALS2. The maximal negative trends were located at a low level, approximately 60 S and with values of approximately 0.09 K (100 yr) 1. Although decreasing trends existed at high latitudes in the NH [ 0.06 K (100 yr) 1 ], they are smaller than those in the SH. The trends of zonal mean salinity increased in the two versions of FGOALS2 (Figs. 8c and d). In FGOALS-g2, the trends were small (close to zero) in

NO. 3 LIN ET AL. Fig. 7. Correlation coefficients between SST tendency and heat fluxes: (a, b) net heat fluxes; (c, d) shortwave radiation; (e, f) latent heat fluxes; (g, h) longwave radiation; (i, j) sensible heat fluxes; (k, l) heat due to sea ice melting. Before calculating the correlations, the 51-yr running mean was determined for SST and each heat flux component. The left (right) panel represents FGOALS-g2 (FGOALS-s2). 587

588 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Fig. 8. Depth latitude distribution of linear trends of zonal mean temperature [K (100 yr) 1 ]and salinity [Psu (100 yr) 1 ] during the last 500 years: (a) and (c) are temperature and salinity for FGOALS-g2; (b) and (d) are the same but for FGOALS-s2. the upper 500 m with relatively obvious trends [ 0.03 psu (100 yr) 1 ] below 1000 m. In the upper 500 m, the trends of the vertical profile were small (not shown) because the trends in salinity were negative between 40 Sand60 N and positive at other latitudes. Below 500 m, the maximal magnitude of the linear trend was located north of 60 N, and the value exceeded 0.06 psu (100 yr) 1. In FGOALS-s2, the trends in salinity [ 0.01 psu (100 yr) 1 ] were nearly consistent from the sea surface to the bottom. The maximal magnitude of the increasing trends was located below 1500 m north of 60 N, with a value of 0.04 0.06 psu (100 yr) 1. 3.4 Comparison with observations and historical runs To quantify whether the long-term linear trends are small enough without external forcing (i.e. in the PICTL simulations) in FGOALS-g2 and FGOALS-s2, the linear trends from observations and 20C3M simulations are used for comparison. As calculated, the linear trends of the global averaged SST for observation were 0.65 C (100 yr) 1 and 1.13 C (100 yr) 1 for the periods 1900 2005 and 1979 2005 (Table 1), respectively. The linear trends of the global averaged SST of the PICTL simulations in FGOALS-g2 and FGOALSs2 were approximately 2% (3%) and 3% (6%) of the observed values during the period 1979 2005 (1900 2005) (Table 2), respectively. Moreover, relative to the linear trends in the 20C3M runs, the corresponding ratio values in the PICTL runs were 2% (4%) and 2% (4%) during the period 1979 2005 (1900 2005) (Table 3) in FGOALS-g2 and FGOALS-s2, respectively. Affected by initial values from PICTL simulations, the ratios during the period 1850 2005 were slightly larger, at 6% and 8% for FGOALS-g2 and FGOALS-s2, respectively. The above comparisons indicate that the linear trends of global averaged SST were small in the PICTL simulations in FGOALS-g2 and FGOALS-s2 and that their effects on the rates of global warming were limited. Thus, the stable integrations of global averaged SST in the PICTL runs could not lead to the uncertainties in the rate of global warming. To test whether the sea surface warming was due to the change in heat fluxes, the trends of the global averaged heat fluxes were calculated in the 20C3M simulations (Table 1). The large trends of SST corresponded to large positive trends of net heat fluxes. This implies the rapid warming could mainly be due to the gain of more heat at the sea surface. The trends of net heat fluxes were significantly larger during the period of rapid warming (1979 2005) than those during the period of slow warming (1900 2005) in the historical runs. The trends during the two periods in the historical runs were also significantly larger than those in the PICTL simulation. The trends of net heat fluxes were mainly contributed to by the linear

NO. 3 LIN ET AL. 589 Fig. 9. Distributions of linear trends of ocean temperature (a) [K (100 yr) 1 ] at a depth of 1200 m in FGOALS-g2 and temperature (b) [K (100 yr) 1 ]ata depth of 3000 m in FGOALS-s2 during the last 500 years. Fig. 10. Depth latitude distributions of linear trends of air temperature [K (100 yr) 1 ]in FGOALS-g2 (a) and FGOALS-s2 (b) during the last 500 years. trends of longwave radiation and sensible heat fluxes, while the trends of latent fluxes cancelled out most of the positive trends. The trends of shortwave radiation and heat due to sea ice melting made a significant contribution to the positive trends of net heat fluxes only during the rapid warming period in the historical runs. During the slow warming period in the historical runs, the trends of shortwave radiation had negative contributions compared to those of net heat fluxes. During the slow warming period, although the trends of heat due to sea ice melting were positive compared with negative trends in the PICTL runs in the two versions of FGOALS2, the changed values in a century were less than their mean values and STDs (Table 2). By comparing these two periods, one can determine that the rapid melting of sea ice will cause a decrease in albedo and an increase in shortwave radiation absorbed by the ocean during a rapid warming period. In FGOALS-s2, the magnitudes of the trends of each heat flux component were larger than those in FGOALS-g2, which is related to large climate sensitivities in FGOALS-s2. The loss of sea ice in the NH was one of the most dramatic effects of global warming on the polar climate. To examine whether the long-term behaviors of SIEs in the PICTL simulations can affect the estimation of the change in SIEs under global warming, the simulated trends of the SIEs in FGOALS-s2 and FGOAL-g2 were also compared with the observed values during 1979 2011 and the historical runs during 1979 2005 (1900 2005). According to the observed SIEs in the NH, the observed linear trends of the SIEs were 4.31 10 6 km 2 (100 yr) 1 and 8.47 10 6 km 2 (100 yr) 1 in March and September, respectively. Therefore, in FGOALS-g2 (FGOALS-s2), the linear tends of the SIEs in March and September for the PICTL runs were only 1.6% (1.2%) and 0.6% (1.1%) of

590 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Table 3. Ratios (%) of the simulated trend values in the PICTL runs relative to those in observation and ensemble mean of 20C3M runs for global SST, averaged SST at different bands, SIEs and MHT at different latitudes. Global SST (%) Tropical SST (%) SST north of 30 N SST south of 30 S (%) NH SIE in September (%) NH SIE in in March (%) SH SIE in September (%) SH SIE in March (%) Global MHT at 30 N (%) Global MHT at 30 S (%) Atlantic MHT at 30 N (%) FGOALS-g2 PICTL run 2% (3%) 2% (4%) 0.9% (1.5%) 0.7% (1.8%) 0.5% (2.3%) 0.8% (3.6%) 12.2% (7.1%) 6.8% (14.3%) 0.6% 1.3% (16.1%) 1.6% 6.3% (33.3%) 39.7% 36.5% (50%) 9.4% 8.4% (34.2%) 13% (20%) 1% (1%) 6% (71%) FGOALS-s2 PICTL run 3% (6%) 2% (4%) 1.7% (3%) 1.1% (1.9%) 1.8% (9.1%) 1.7% (3.6%) 14.6% (8.6%) 2.5% (7.2%) 1.1% 1.3% (3.0%) 1.2% 2.6% (4.4%) 40.5% 5.0% (24.5%) 8.0% 3.4% (9.2%) 1% (10%) 1% (6%) 1% (14%) the corresponding observed values, respectively. Compared with those in historical runs, the corresponding percentages in March for the SIEs in the NH were 6% (33.3%) and 2.6% (4.4%) in FGOALS-g2 and FGOALS-s2 during the period 1979 2005 (1900 2005), respectively. In September, the corresponding ratios were similar to those in March for the NH (Table 2). In FGOALS-g2, because the rates (trends) of the SIEs in the NH in March and September were small (i.e. relatively weak sensitivities respond to external forcing), their ratios between trends in the PICTL and 20C3M runs were large despite the small trends in the PICTL runs. For the SIEs during the period 1979 2005, however, the ratios between the trends in the PICTL runs and those in the 20C3M runs were smaller than 10%. This result indicates that the long-term trends in the absence of external forcing cannot cause the uncertainties in estimating the reduction of SIEs in the NH during the period of rapid warming (i.e. 1979 2005) in FGOALS-g2 or FGOALS-s2. Because there was an obvious spatial difference in the rate of sea surface warming in the observation and 20C3M runs, we examined what percentage of the long-term trends in the PICTL runs made up the real climate trends for the averaged SST at different latitude bands. Relative to the warming trends from the observation and the two coupled models, the percentages were smaller than 5% in the middle and high latitudes of the NH and in the tropical region (Table 2) during the period 1979 2005. During the period 1900 2005, the percentages were also smaller than 5% except for high-latitude SST in the NH relative to the observation (9%). However, for the SST in the middle high latitudes of the SH, the percentages ranged between approximately 7.1% and 14.6% due to the relatively large long-term trends in the PICTL runs (Table 1) and the small warming trends in the observation and FGOALS-g2. The large percentages in the high latitudes of the SH were associated with the trends and the large percentages for the SIEs in the SH (5% 50%), especially in September. Therefore, the large long-term trends would lead to large uncertainties in SIE and SST estimation in the SH in the historical climate. If the SST trends in some smaller regions or some positions in the PICTL runs are compared with real climate trends, the percentages were much higher (exceeding 30%) in the observation and simulations (FGOALS-g2 and FGOALS-s2), especially at the high latitudes (not shown). This phenomenon also existed in the CMIP3 models (Sen Gupta et al., 2012), which implies there are large uncertainties in estimating the climate trends at specific regions or positions due to large long-term trends or obvious decadal changes in the PICTL runs. FGOALS-g2 and FGOALS-s2 had similar longterm behaviors for globally averaged SST, the SST at different latitude bands, and the SIEs in the NH and SH. The small linear trends were for globally averaged

NO. 3 LIN ET AL. 591 SST, the averaged SST in the tropics and middle and high latitudes of the NH, and the SIEs in the NH. Large linear trends were found for SST in the middle and high latitudes of the SH and the SIEs in the SH, particularly in September. Because the long-term linear trends of meridional overturnning circulation in the two version of FGOALS2 (Lin et al., 2013a, b) were similar and these two coupled versions shared the same OGCM (LICOM2), these similar long-term behaviors imply that the roles of ocean processes were potentially important, which could be mainly attributed to the ocean model. The magnitudes of linear trends of different variables in the PICTL and 20C3M runs between FGOALS-g2 and FGOALS-s2 were different, which may be due to the atmospheric and sea ice processes. 4. Concluding remarks Forced by preindustrial solar radiation and greenhouse gases (i.e. fixed external forcing), FGOALS-g2 and FGOALS-s2 were integrated successfully for multiple centuries. There were no obvious climate drifts in globally averaged SST. During the 900-model-year and 600-model year simulations in the PICTL runs, globally averaged SSTs showed small decreasing trends of 0.02 C (100 yr) 1 and 0.04 C (100 yr) 1 in FGOALSg2 and FGOALS-s2, respectively. These decreasing trends were both due to a loss of heat in the two versions of FGOALS2. The trends in the PICTL runs were less than 10% of the corresponding linear trends in the observation and 20C3M runs during the periods of slow warming (1900 2005) and rapid warming (1979 2005). Meanwhile, the changed values in a century were smaller than their STDs (measurement of the internal variability) of the annual mean values. Therefore, the stable long-term integration behaviors of globally averaged SST cannot affect the estimation of global warming and internal variability in the two versions of FGOALS2. In the tropics, the stable longterm behaviors in the two versions of FGOALS2 cannot lead to the large uncertainties in estimating the real climate trends in observations or historical simulations. The averaged SST and SIEs also showed stable long-term behaviors at the middle and high latitudes of the NH (north of 30 N). However, larger linear trends existed in the SST averaged at the middle to high latitudes of the SH and the SIEs in the SH (especially in September). These large trends of SST can be partly related to the changes of turbulent fluxes and longwave radiation. To estimate the rate of global warming in the present and future estimations, corrections needed to be performed in the middle and high latitudes of the SH. For specific small regions or positions at the middle and high latitudes, the estimation of the warming rate can be affected by the long-term trends (climate drift) in the PICTL simulations. These similar longterm behaviors may be attributable to oceanic physical processes because FGOALS-g2 and FGOALS-s2 share the same OGCM, namely LICOM2. Before estimating global warming in the coupled models, one needs to evaluate the long-term trends (climate drift) in the PICTL simulations and judge whether the climate drift will affect the real climate trends. Acknowledgements. The authors wish to acknowledge the valuable comments made by the reviewers, as well as give many thanks to the support received from the LASG model development group. In particular, the authors wish to thank Prof. LIU Jiping for useful suggestions, Dr. LI Lijuan, BAO Qing and HUANG Wenyu for providing data, and FENG Xiaoli for his kind help. This work was supported by the National Key Program for Developing Basic Sciences (Grant Nos. 2010CB950502 and 2013CB956204), the Strategic Priority Research Program Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences (Grant No. XDA05110302), and the National Natural Science Foundation of China (Grant Nos. 40906012 and 41023002). REFERENCES Antonov,J.I.,R.A.Locarnini,T.P.Boyer,A.V.Mishonov, and H. E. Garcia, 2006: Salinity. Vol. 2, World Ocean Atlas 2005, S. Levitus, Ed., NOAA Atlas NESDIS 62, U.S. Government Printing Office, Washington, D.C., 182pp. Bao, Q., G. X. Wu, Y. M. Liu, J. Yang, Z. Z. Wang, and T. J. Zhou, 2010: An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., 27(5), 1131 1142, doi: 10.1007/s00376-010-9177-1. Bao, Q., and Coauthors, 2013: The flexible global oceanatmosphere-land system model, spectral version 2: FGOALS-s2. Adv. Atmos. Sci., doi: 10.1007/s00376-012-2113-9. Buhaug, Ø., and Coauthors, 2009: Second IMO GHG Study 2009. International maritime organization (IMO). London, UK, 240pp. Cai, W. J., and H. B. Gordon, 1999: Southern highlatitude ocean climate drift in a coupled model. J. Climate, 12, 132 146. Cavalieri, D. J., and C. L. Parkinson, 2003: 30-year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability. Geophys. Res. Lett., 30(18), doi: 10.1029/2003GL018031. Cionni, I., and Coauthors, 2011: Ozone database in support of CMIP5 simulations: Results and corresponding radiative forcing. Atmos. Chem. Phys., 11, 11267 11292, doi: 10.5194/acp-11-11267-2011.

592 LONG-TERM BEHAVIORS OF FGOALS2 AND ITS IMPLICATIONS VOL. 30 Covey, C., P. J. Gleckler, T. J. Phillips, and D. C. Bader, 2006: Secular trends and climate drift in coupled ocean-atmosphere general circulation models. J. Geophys. Res., 111, D03107, doi: 10.1029/2005JD006009. Fröhlich, C., and J. Lean, 2004: Solar radiative output and its variability: Evidence and mechanisms. Astronomy and Astrophysics Review, 12(4), 273 320. doi: 10.1007/s00159-004-0024-1. Ganachaud, A., and C. Wunsch, 2003: Large-scale ocean heat and freshwater transports during the world ocean circulation experiment. J. Climate, 16, 696 705. He, B., Q. Bao, J. Li, G. Wu, Y. Liu, X. Wang, and Z. Sun, 2013: Influences of external forcing changes on the summer cooling trend over East Asia. Climatic Change, doi: 10.1007/s10584-012-0592-4. Lamarque, F. J., and Coauthors, 2010: Historical (1850 2000) gridded anthropogenic and biomass emissions of reactive gases and aerosols: methodology and application. Atmos. Chem. Phys., 10, 7017 7039. doi: 10.5194/acp-10-7017-2010 Li, L. J., and Coauthors, 2013: The flexible global oceanatmosphere-land system model version: FGOALSg2. Adv. Atmos. Sci., doi: 10.1007/s00376-012-2140-6. Lin, P. F., H. L. Liu, Y. Q. Yu, and X. H. Zhang, 2011: Response of sea surface temperature to chlorophyll-a concentration in the tropical Pacific: Annual mean, seasonal cycle and interannual variability. Adv. Atmos. Sci., 28(3), 492 510. Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013a: Long-term stability and oceanic mean state simulated by the coupled model FGOALS-s2. Adv. Atmos. Sci., 30, 175 192, doi: 10.1007/s00376-012-2042-7. Lin, P. F., Y. Q. Yu, and H. L. Liu, 2013b: Oceanic climatology in the coupled model FGOALS-g2: Improvements and biases. Adv. Atmos. Sci., doi: 10.1007/s00376-012-2137-1. Liu, H. L., X. H. Zhang, W. Li, Y. Q. Yu, and R. C. Yu, 2004a: An eddy-permitting oceanic general circulation model and its preliminary evaluations. Adv. Atmos. Sci., 21, 675 690. Liu, H. L., Y. Q. Yu, W. Li, and X. H. Zhang, 2004b: Manual for LASG/IAP Climate System Ocean Model (LICOM1.0). Science Press, Beijing, 1 128. (in Chinese) Liu, H. L., P. F. Lin, Y. Q. Yu, and X. H. Zhang, 2012: The baseline evaluation of LASG/IAP climate system ocean model (LICOM) version 2.0. Acta Meteorologica Sinica, 26, 318 329. Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, and H. E. Garcia, 2006: Temperature. Vol. 1, World Ocean Atlas 2005, S. Levitus, Ed., NOAA Atlas NESDIS 61, U.S. Government Printing Office, Washington, D.C., 182pp. Meehl, G. A., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, New York, USA, 996pp. Sausen, R., K. Barthel, and K. Hasselmann, 1988: Coupled ocean-atmosphere models with flux correction. Climate Dyn., 2, 145 163. Sen Gupta, A., L. C. Muir, J. N. Brown, S. J. Phipps, P. J. Durack, D. Monselesan, and S. E. Wijffels, 2012: Climate drift in the CMIP3 models. J. Climate, doi: 10.1175/jcli-d-11-00312.1. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA s historical merged land-ocean surface temperature analysis (1880 2006). J. Climate, 21, 2283 2296. Taylor, K. E, R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485 498, doi: http://dx.doi.org/10.1175/bams-d-11-00094.1. Wang, B., H. Wan, Z. Z. Ji, X. H. Zhang, R. C. Yu, Y. Q. Yu, and H. L. Liu, 2004: Design of a new dynamical core for global atmospheric models based on some efficient numerical methods. Science in China (A), 47(Suppl.), 4 21. Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871 2000. J. Climate, 16, 1601 1612. Yu, Y. Q., X. H. Zhang, and Y. F. Guo, 2004: Global coupled ocean-atmosphere general circulation models in LASG/IAP. Adv. Atmos. Sci., 21, 444 455. Yu, Y. Q., W. P. Zheng, X. H. Zhang, and H. L. Liu, 2007: LASG coupled climate system model FGCM- 1.0. Chinese Journal of Geophysics, 50(6), 1677 1687.(in Chinese) Yu, Y. Q., W. P. Zheng, B. Wang, H. L. Liu, and J. P. Liu, 2011: Versions g1.0 and g1.1 of the LASG/IAP flexible global ocean-atmosphere-land system model, Adv. Atmos. Sci., 28(1), 99 117, doi: 10.1007/s00376-010-9112-5. Zheng, Y., and B. S. Giese, 2009: Ocean heat transport in simple ocean data assimilation: Structure and mechanisms, J. Geophys. Res., 114, C11009, doi: 10.1029/2008JC005190.