Journal of Oceanology and Limnology   2023, Vol. 41 issue(2): 409-417     PDF       
http://dx.doi.org/10.1007/s00343-022-1433-6
Institute of Oceanology, Chinese Academy of Sciences
0

Article Information

LIANG Yun, DU Yan, XIE Shang-Ping
SST effect on the pre-monsoon intraseasonal oscillation over the South China Sea based on atmospheric-coupled GCM comparison
Journal of Oceanology and Limnology, 41(2): 409-417
http://dx.doi.org/10.1007/s00343-022-1433-6

Article History

Received Dec. 20, 2021
accepted in principle Jan. 26, 2022
accepted for publication Feb. 25, 2022
SST effect on the pre-monsoon intraseasonal oscillation over the South China Sea based on atmospheric-coupled GCM comparison
Yun LIANG1,2,3, Yan DU1,2,4, Shang-Ping XIE5     
1 State Key Laboratory of Tropical Oceanography & Key Laboratory of Science and Technology on Operational Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences & Innovation Academy of South China Sea Ecology and Environmental Engineering, Guangzhou 510301, China;
2 College of Marine Science, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Peking University Chongqing Research Institute of Big Data, Chongqing 401332, China;
4 Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou 510301, China;
5 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, USA
Abstract: The role of sea surface temperature (SST) variability in the pre-monsoonal (April to July) intraseasonal oscillation (ISO) over the South China Sea (SCS) is investigated using the Community Earth System Model Version 2 (CESM2). An Atmospheric Model Intercomparison Project (AMIP) simulation forced by daily sea surface temperatures (SSTs) derived from a parallel coupled general circulation model (CGCM) run was compared with observations and the mother coupled simulation. In the coupled model, the SST warming leads the peak convection about 1/4 period as in observations. The paralell uncoupled model fails to simulate this phase relationship, implying the importance of air-sea coupling in reproducing realistic ISO. Due to the near-quadrature phase relationship between SST and precipitation ISOs during the ISO events, it is difficult to distinguish the active/passive role of SST from observations alone. Significant correlation in intraseasonal precipitation between the daily SST-forced AMIP and mother CGCM runs indicates that SST plays a role in driving the atmospheric ISO.
Keywords: intraseasonal variability    South China Sea    Community Earth System Model Version 2 (CESM2)    sea surface temperature    
1 INTRODUCTION

The South China Sea (SCS) is where the Indian and East Asian monsoons interact. The SCS summer monsoon (SCSSM) produces heavy rains and affects ecosystems, fisheries, and human activities over East Asia and adjacent marginal seas. The SCSSM onset marks the beginning of the rainy season over East Asia (Wang et al., 2004). The SCSSM onset is consistent with the phase transition from depressed to active of the SCS intraseasonal oscillation (ISO) (Shao et al., 2015; Wang et al., 2018). Therefore, understanding ISO in SCS during the SCSSM onset season (April–July) is critical for integrating the study on the Asian summer monsoon system.

The importance of air-sea coupling in monsoon oscillation has been well established in regions like the Bay of Bengal and Arabian Sea (Li et al., 2016, 2018). The underlying sea surface temperature (SST) has been suggested to play a role in the atmospheric ISO cycle (Xie et al., 2007; Roxy and Tanimoto, 2012; Ye and Wu, 2015). Xie et al. (2007) showed oceanic feedback onto the atmosphere on the intraseasonal timescale over the SCS when investigating the development of the wind jet and cold filament. The intraseasonal oceanic feedback on the atmosphere over the SCS mainly occurs over off-equatorial regions (Ye and Wu, 2015). The ocean-to-atmosphere effect is mainly analyzed by the lag-lead correlation between observed SST and precipitation. The positive SST form a favorable condition for convection activity by destabilizing the atmosphere in the boundary layer (Roxy and Tanimoto, 2012). A positive correlation coefficient is often used as a proxy that SST forces deep convection. However, on the intraseasonal timescale, SST and precipitation have a near-quadrature phase relationship. This phase relationship makes it difficult to distinguish the active/passive role of SST during an ISO event by conducting conventional regression or composite on the observation and reanalysis dataset. For example, the positive (negative) SST anomaly could be a driving force to the latter active (depressed) convection, and it could also be a response to the former depressed (active) convection. The comparison between the coupled model and the SST-forced atmospheric model provides a solution to this problem (Fu and Wang, 2004; Pegion and Kirtman, 2008; Wang et al., 2009; Sharmila et al., 2013).

In recent decades, general circulation models (GCMs) sensitivity experiments have been used to study intraseasonal ocean-atmosphere interactions. Although coupled GCMs (CGCMs) show more realistic simulated ISOs than atmosphere-only models (e.g., Jiang et al., 2015), two problems exist in CGCM simulations. First, the upper ocean response to the ISO forcing is poorly simulated (DeMott et al., 2015). Second, mean states in CGCM, especially tropical SST and circulations, have large systematic errors (Klingaman and Woolnough, 2014). The impact of air-sea coupling is mostly quantified by Atmospheric Model Intercomparison Project (AMIP) simulations forced by SST variations from observation or different models (e.g., Liess et al., 2004; DeMott et al., 2014). The present study compares AMIP and CGCM simulations with an identical atmospheric model and identical daily SST variations. This ensures that the difference between AMIP and CGCM simulations can be attributed to the air-sea interaction process. Pegion and Kirtman (2008) compared the coupled model and uncoupled model forced by daily SST from the coupled model. They suggested that the intraseasonal SST is critical for intensifying and propagating the eastward propagating ISO during the boreal winter. This study focuses on the northward propagating ISO over the SCS. Besides the Community Earth System Model Version 2 (CESM2), the simulation we used in this study significantly improves the earlier models in simulating the northward propagating ISO in the South Asian Monsoon (Meehl et al., 2020).

2 DATA AND METHOD 2.1 Observational and reanalysis data

We use the daily Optimum Interpolation Sea Surface Temperature version 2 (OISST V2) data (Reynolds et al., 2002) from National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis daily wind at 850 hPa (Kalnay et al., 1996) and sea level pressure (SLP), and Global Precipitation Climatology Project (GPCP) daily precipitation data. The 0.25°×0.25° OISST is regridded to 1°×1°, and it is consistent with the precipitation data. The analysis period is 1997–2020.

The presented study focuses on variability on intraseasonal time scales. After removing the linear trend, daily anomalies are derived relative to the climatological mean over the whole period (1997– 2020). The intraseasonal variabilities are obtained by a 20–100-days band-passed filter. Our analysis focuses on the variations from April to July.

2.2 CESM2 simulation

We use two simulations from the Community Earth System Model version 2 (CESM2) at a nominal 1° (1.25° in longitude and 0.9° in latitude) horizontal resolution in all model variables. The CESM2 is the latest generation of the coupled/ Earth system models that contain atmosphere-ocean-land-sea ice component models. A full description of the CESM2 can be seen in Danabasoglu (2020). The CGCM simulation we use is the multi-century 1850 control simulation with constant preindustrial forcing. Daily SST and sea ice for the AMIP simulation are derived from the above CGCM simulation starting at the year 1000. The AMIP simulation is performed using the atmospheric component of the CGCM, so the radiation forcing of the pair of simulations is the same. We use a 50-years period from the model year 1001 to 1050, to assess the effects of ocean-atmosphere coupling. The comparison between the AMIP simulations forced with SST variations from observation or different models is commonly used to investigate ocean-atmosphere interactions, but it is affected by model biases. This study compares the pair of the simulations that share the same atmospheric model and identical daily SST, to evaluate the SST impacts.

3 RESULT 3.1 ISOs in observations and CESM2 simulations

Before analyzing the ocean-atmosphere coupling in the models, it is important to examine how well the model simulates the mean state and intraseasonal variabilities. Figure 1 shows the mean state and intraseasonal variabilities of precipitation during April–July in the observation and the simulations. The mean rainfall pattern is generally reproduced, while those in simulations are stronger. Maximum mean precipitation exists over the China-Indochina Peninsula and the Kalimantan Island. In AMIP and CGCM simulations, mean rainfall over the China-Indochina Peninsula extends eastward, and the counterpart over the Kalimantan Island spread along the equator. In observation, significant intraseasonal precipitation anomalies appear in the northern SCS, which is the target region of this study. Wang et al. (2018) investigated the dynamics of the boreal summer ISO over this region. They compared atmospheric and oceanic effects on the ISO by analyzing low-level moisture budget and revealed that the ocean plays a more important role in front (north) of the deep convection related to ISO. Compared with the CGCM simulation, which fails to capture the maximum intraseasonal precipitation anomalies west of Luzon Island, the AMIP simulation is closer to observation. However, the intraseasonal variability in SST prescribed AMIP is spuriously stronger. In the coupled model, the reduced solar radiation and enhanced wind lead to SST cooling through wind-evaporation-SST (WES) feedback (Xie and Philander, 1994). The SST cooling then decreases convection in return. However, this negative ocean feedback on the atmosphere is missed so that the intraseasonal convective variability would be spuriously intensified.

Fig.1 Pre-monsoonal (April–July) mean and standard deviations of 20–100-day bandpass filtered daily precipitation anomalies (shaded) in different schemes a. observation from 1997 to 2020; b. CGCM simulation from model year 1001 to 1050; c. AMIP simulation from model year 1001 to 1050. The black dashed box (10°N–25°N, 105°E–120°E) indicates the target region in this study with larger precipitation variance over the northern SCS. The interval of green contours is 1 mm/day starting at 7 mm/day.

Wind's mean state and intraseasonal variability at 850 hPa are also examined (Fig. 2). Mean winds are well simulated. The maximum intraseasonal zonal wind anomaly exists around 10°N and from 105°E to 120°E. In the CGCM simulation, the maximum signal appears inappropriately farther northwestward (Fig. 2b). The AMIP simulation produces the right spatial pattern with a larger amplitude (Fig. 2c).

Fig.2 Pre-monsoonal (April–July) mean wind at 850 hPa (black vectors) and standard deviations of 20–100-day bandpass filtered daily zonal wind anomalies at 850 hPa (shaded) in different schemes a. observation from 1997 to 2020; b. CGCM simulation from model year 1001 to 1050; c. AMIP simulation from model year 1001 to 1050.

In observations, large intraseasonal SST anomalies distribute over the northern SCS (Fig. 3a). The AMIP and CGCM simulations share SST fields (Fig. 3b). Intraseasonal SST anomalies are artificially damped in the CGCM simulation compared to the observation. The strongly damped SST anomalies are applied to compensate for systematic errors caused by insufficient resolution (Ham et al., 2014; DeMott et al., 2015). This discrepancy leads to underestimated effects of ocean-atmosphere interactions on the ISO, a recognized model deficiency in previous studies (DeMott et al., 2015 and references therein).

Fig.3 Pre-monsoonal (April–July) mean SST (red contours, interval is 0.5 ℃ starting at 28 ℃, thick contours for 28 ℃) and standard deviations of 20–100-day bandpass filtered daily SST anomalies (shaded) in different schemes a. observation from 1997 to 2020; b. CGCM simulation from model year 1001 to 1050.
3.2 Local SST-precipitation correlation

To clarify the intraseasonal SST-precipitation relationship in the observations and the simulations, the lead-lag correlations of the precipitation for the SST anomalies on the intraseasonal timescale averaged over the northern SCS (10°N–25°N, 105°E–120°E) are estimated from -60 day (SST lags precipitation 60 days) to +60 days (SST leads precipitation 60 days), as shown in Fig. 4. SST leads (lags) the precipitation anomalies with positive (negative) correlations, which is identified as an oceanic (atmospheric) effect on the atmospheric (oceanic) ISO (Roxy and Tanimoto, 2007; Wu, 2010; Roxy et al., 2013). The magnitude of the correlation means the intensity of the driving force, and the corresponding lead-time (positive day) denotes how quickly the atmosphere responds to the SST anomalies.

Fig.4 Pre-monsoonal (April–July) lag-lead correlation of intraseasonal SST anomalies with respect to precipitation anomalies averaged over the northern SCS (10°N–25°N, 105°E–120°E) in different schemes a. observations from 1997 to 2020; b. CGCM simulation from model year 1001 to 1050; c. its parallel AMIP simulation. SST leads (lags) precipitation at positive (negative) lag-lead days. The dashed lines indicate > 95% confidence level based on the t-test.

The non-simultaneous phase relationships between local SST-precipitation are consistent in the observation and CGCM simulation (Fig. 4ab), which means the ocean-atmosphere coupling works in the CGCM. However, the intraseasonal SST variance is underestimated. As a result of intensified sensitivity of the atmospheric response to SST in the CGCM, the maximum correlation between SST and precipitation is even overestimated in the CGCM simulation (R=0.5), compared with observations (R=0.4). Maximum positive correlation occurs when SST leads precipitation by about 10 days, indicating enhanced precipitation as a response to positive SST anomalies on the intraseasonal timescale. Maximum negative correlation occurs when precipitation leads SST by 10 days, suggesting SST cooling (warming) after an active (suppressed) convection activity. Due to the absence of the atmosphere-to-ocean effect, the AMIP simulation produces a spurious coincident phase relationship (Fig. 4c).

Figure 5 shows the local pointwise correlation between intraseasonal precipitation anomalies and SST anomalies at 10 days lead for April–July calculated by observed and simulated datasets. Positive local SST-precipitation correlations significant at 95% confidence level exist in most of the SCS for observation and the CGCM simulation (Fig. 5). The most pronounced correlations are found over the northern SCS, to the southwest of Luzon Island. The positive local SST-precipitation is often used as a metric of oceanic forcing on the atmosphere, suggesting the warm (cold) SST anomalies enhance (reduce) convection activities. During an ISO event, SST and precipitation have a near-quadrature phase relationship. The alternation of positive and negative anomalies obscures the ocean and atmosphere's active and passive role. For example, SST anomaly is positively correlated with precipitation anomaly with 10 days lag and negatively correlated with precipitation anomaly with 10 days lead. One could hardly tell whether the SST variation response to the leading convective activity or the driving force of the following atmospheric variation. This is the motivation for conducting an SST-forced AMIP experiment. Without ocean coupling, the correlations in the AMIP simulation are relatively lower than correlations calculated based on observations and its parallel CGCM simulation. The pattern of correlation distribution is well simulated.

Fig.5 Pre-monsoonal (April–July) correlation coefficients between intraseasonal SST and local precipitation anomalies (shaded) in different schemes a. observations from 1997-2020; b. CGCM simulation from model year 1001 to 1050; c. its parallel AMIP simulation. According to Fig. 4, SST leads precipitation 10 days in (a) and (b), and (c) shows correlation of consistent SST and precipitation. The dotted indicates > 95% confidence level based on the t-test.

Latitude-time plots of intraseasonal SST (shaded), sea level pressure (red contours), and precipitation (gray contours) anomalies over the SCS (105°E–120°E) are shown in Fig. 6. They are expressed as correlation coefficients for intraseasonal SST anomalies based on the observation (Fig. 6a) and the simulations (Fig. 6bc). In the observations, precipitation, sea surface pressure, and wind at 850-hPa anomalies propagate northward. While intraseasonal SST variability shows no significant northward-propagating signals over the target region, SST is critical for the northward propagation of atmospheric variables. At the 10 days lead, easterly wind anomalies prevail the SCS from the 10°N to 18°N, and the northeasterly wind anomalies prevail the SCS from the equator to 10°N, leading the negative SLP and positive precipitation anomalies. South of the negative SLP and positive precipitation anomalies is dominated by westerly/southwesterly wind anomalies (as can be seen at 10 day in Fig. 6a). The wind anomalies to the north of the convection center presented by positive precipitation anomalies offset part of the southwesterly background wind. The reduced upward latent heat flux warms the SST. Positive SST anomalies tend to destabilize the boundary layer and thus leading to the convection growth to the north of the primary deep convection center. The detailed dynamic mechanism has been proposed in previous studies (Roxy and Tanimoto, 2012; Wang et al., 2018).

Fig.6 Hovmöller diagrams of intraseasonal SST (shaded), sea level pressure (red contours), and precipitation (gray contours) anomalies over the SCS (105°E–120°E) in different schemes a. observations from 1997 to 2020; b. CGCM simulation from model year 1001 to 1050; c. its parallel AMIP simulation. SST leads (lags) precipitation at positive (negative) lag-lead days. Numbers are expressed in correlation coefficients for intraseasonal SST anomalies averaged between 105°E–120°E and maximum at day=0. The contours in (a) and (b) starts from 0.2 (-0.2) with a 0.05 interval and contours in (c) starts from 0.12 (-0.12) with a 0.02 interval. All the contours shown are beyond 95% confidence level. The dotted indicates the > 95% confidence level based on the t-test. The solid lines indicate positive correlation coefficients and the dashed lines indicate negative correlation coefficients.

The CGCM generally produces realistic northward propagation of the air-sea coupled system and the quadrature-phase relationship between SST and precipitation. A minor difference exists between the observation and the CGCM simulation. In the observations, SLP anomalies occur 1–2 days before the precipitation anomalies (Fig. 6a), while in the CGCM simulation, the SLP and precipitation anomalies are almost consistent. SLP leading precipitation is an indicator of SST forcing on the atmosphere. The meridional SST gradient enhances the south-north surface pressure gradient, strengthening the northerlies and thus increasing the surface convergence to the north of the convection center. Lindzen and Nigam (1987) first proposed this boundary layer model. The contribution of SST to the atmospheric ISO is underestimated in the CGCM.

The Hovmöller diagram of intraseasonal SST (shaded), sea level pressure (red contours), and precipitation (gray contours) anomalies based on AMIP simulation manifests SST forcing on the atmosphere (Fig. 6c). Although the prescribed SST minimizes differences between the AMIP and CGCM, the AMIP simulation fails to reproduce the quadrature SST-precipitation relationship. The precipitation maximum in the Hovmöller diagram occurs around +2 days, indicating a 2-day response time of convective activities to SST forcing. The minimum SLP leads precipitation 1 day, indicating the precedent SLP response to SST. No northerly to the north of the convection center is produced in the AMIP simulation. In the observation and the CGCM run, warm SST anomalies lead precipitation and SLP anomalies in the north. In the AMIP run, warm SST is almost located slightly southward to the maximum of the precipitation anomalies. The displacement is attributed to the deficiency of the atmosphere-to-ocean effect. These results imply an important contribution of atmosphere impact on the ocean to the north of the convection center. Meanwhile, the SST-forced southerly, a proxy of SST anomaly gradient, mainly appears around and to the south of the convection center. Diagnosis of Wang et al. (2018) based on the momentum equation of the slab boundary layer model (Wang and Li, 1993) suggested that the meridional SST anomaly gradient reduces the low-level convergence anomalies over the active convection center and promotes the negative low-level convergence anomalies to the south of the active convection center. The simulation result confirms the above oceanic impact.

3.3 AMIP-CGCM comparison

Considering that, precipitation variability can also be caused by remote SST variability via the atmospheric bridge mechanism (Klein et al., 1999; Alexander et al., 2002; Zhou et al., 2018), the local SST-precipitation correlation may not entirely reflect the SST impact on the atmosphere. Inspired by Zhou et al. (2018), we use the local correlation of pre-monsoonal intraseasonal precipitation as a direct metric for AMIP skill in reproducing intraseasonal precipitation variations in the parallel CGCM simulation. In the observation and CGCM run, intraseasonal SST variability contains the active and passive components. The active component indicates SST forcing on the atmosphere and the passive component means SST variation as a response to atmospheric forcing at lead days. In the AMIP run, SST is treated as 100% forcing component. The correlation of local precipitation from the AMIP forced by prescribed SST and CGCM denotes the SST impact on convective activities.

Figure 7 shows the pointwise correlation between intraseasonal precipitation anomalies in AMIP and the parallel coupled CGCM for pre-monsoonal (April–July). Given that SST-precipitation relationship is synchronous in the AMIP simulation and near-quadrature in the CGCM simulation, we conduct the correlation with AMIP precipitation leading CGCM precipitation by 10 days. Over the SCS, the maximum correlation coefficient exists in 8°N–10°N, 110°E–106°E and the significant correlation is northeastward oriented (Fig. 7). The correlation distribution of intraseasonal precipitation from the AMIP and the CGCM is quite different from the local SST-precipitation correlation, implying remote SST forcing also plays a role in the atmospheric ISO events. Besides, overestimated background SST over SCS in the model may lead to exaggerated SST forcing on the atmosphere on the intraseasonal timescale. This is why the intraseasonal precipitation variability in the AMIP runs spuriously large while the intraseasonal SST variability is relatively subtle.

Fig.7 Pre-monsoonal (April–July) correlation coefficients between intraseasonal precipitation anomalies in AMIP and the parallel coupled CGCM AMIP leads 10 days. The dotted area indicates the > 95% confidence level based on the t-test.

Figure 8 shows the differences between AMIP and CGCM simulations. The climatology of precipitation shares a similar spatial pattern in AMIP and CGCM simulations (Fig. 1). AMIP overestimates precipitation by 10%–25% to the west of Luzon Island. The intraseasonal precipitation variance is overestimated by 40%–50% in AMIP compared to CGCM (Fig. 8a). The forcing effect of SST is emphasized in the AMIP simulation, leading to a more realistic pattern of intraseasonal precipitation variance. Intraseasonal SST variability is artificially damped to make up for the resolution shortcomings in the models (DeMott et al., 2015). However, too-weak intraseasonal SST variability leads to incorrect pattern of intraseasonal precipitation in the CGCM. The prescribed AMIP simulation reproduces over 50% of SST-precipitation correlations in the middle of SCS (8°N–16°N). This approximately represents the part active role shares of the intraseasonal SST variability.

Fig.8 The fractional difference shown as a percentage relative to the standard deviation of intraseasonal precipitation anomalies (shaded) and mean precipitation (0.1 and 0.2 red contours) in CGCM simulation (a); the fractional variance of SST-precipitation correlations in AMIP and CGCM simulations (b)
4 CONCLUSION

In the SCS, the pre-monsoon ISO is critical to the SCSSM, which signifies the onset of the Asian summer monsoon. It has been recognized that the ISO is an air-sea coupled mode. Understanding the air-sea interaction process is important for integrating the ISO dynamics and improving the forecast of SCSSM-related precipitation. This study investigates the oceanic impact on the atmospheric ISO by comparing a pair of AMIP and CGCM simulations that share the same atmospheric model and daily SST. The primary findings of the study are summarized below.

1) AMIP produces a more realistic spatial distribution of the pre-monsoon precipitation ISO over the SCS. The maximum intraseasonal precipitation variability exists between 10°N –20°N and to the west of the Luzon Island, which is reproduced in the AMIP run while absent in the CGCM run.

2) The significant SST-precipitation correlation and AMIP-CGCM precipitation correlation reveal the SST impacts on precipitation at the intraseasonal timescale. The spatial distribution of the significant SST-precipitation correlation based on the AMIP output signifies where the local SST anomalies affect the rainfall. The AMIP-CGCM comparison further considers the teleconnection between SST and precipitation. Both of the correlations above are significant in the region from southeast of the Indo-China Peninsula to the west of the Luzon Island.

Finally, we discuss implications of our results for the model simulation and forecast of SCS ISOs from an oceanic perspective. From observations, it is challenging to distinguish the dual role of SST variability as an active forcing of and passive response to the atmospheric ISO. AMIP-CGCM comparison shows the active role of SST during the intraseasonal air-sea interaction process. As a caveat, the CGCM underestimate intraseasonal SST variability in the SCS probably because of insufficient resolution for coastal upwelling. Thus, the SST feedback onto atmospheric variabilities is likely to be underestimated as well. Improving the simulation of high-frequency SST variability may lead to a more realistic ISO in the model.

5 DATA AVAILABILITY STATEMENT

The observation and CGCM data analyzed during this study are openly available in a public repository. The AMIP data is available from the corresponding authors upon reasonable request.

6 ACKNOWLEDGMENT

The pair of CESM2 simulations were performed at the National Center for Atmospheric Research as an initiative of the CESM Climate Variability and Change Working Group. The NOAA OI SST V2, NCEP reanalysis data, and GPCP precipitation data were provided by the NOAA, Boulder, Colorado, USA, via their website at https://psl.noaa.gov/data/index.html.

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