J. Meteor. Res.  2019, Vol. 33 Issue (1): 80-88   PDF    
http://dx.doi.org/10.1007/s13351-018-8092-8
The Chinese Meteorological Society
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Article Information

HUA, Lijuan, Lin CHEN, Xinyao RONG, et al., 2019.
An Assessment of ENSO Stability in CAMS Climate System Model Simulations. 2019.
J. Meteor. Res., 33(1): 80-88
http://dx.doi.org/10.1007/s13351-018-8092-8

Article History

Received July 4, 2018
in final form September 20, 2018
An Assessment of ENSO Stability in CAMS Climate System Model Simulations
Lijuan HUA1, Lin CHEN2,3, Xinyao RONG1, Jian LI1, Guo ZHANG1, Lu WANG2,3     
1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081;
2. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;
3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
ABSTRACT: We present an overview of the El Niño–Southern Oscillation (ENSO) stability simulation using the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). The ENSO stability was quantified based on the Bjerknes (BJ) stability index. Generally speaking, CAMS-CSM has the capacity of reasonably representing the BJ index and ENSO-related air–sea feedback processes. The major simulation biases exist in the underestimated thermodynamic damping and thermocline feedbacks. Further diagnostic analysis reveals that the underestimated thermodynamic feedback is due to the underestimation of the shortwave radiation feedback, which arises from the cold bias in mean sea surface temperature (SST) over central–eastern equatorial Pacific (CEEP). The underestimated thermocline feedback is attributed to the weakened mean upwelling and weakened wind–SST feedback (μa) in the model simulation compared to observation. We found that the weakened μa is also due to the cold mean SST over the CEEP. The study highlights the essential role of reasonably representing the climatological mean state in ENSO simulations.
Key words: coupled general circulation model (CGCM)     Bjerknes (BJ) stability index     air–sea feedback    
1 Introduction

El Niño–Southern Oscillation (ENSO) is known to play a vital role in affecting the weather and climate globally (e.g.,Duan et al., 2004; McPhaden et al., 2006; Zhou et al., 2014; Ding et al., 2015; Zheng et al., 2015; Wang and Chen, 2016, 2017a, b; Park et al., 2017). Moreover, the ENSO impact on one region differs from that on another region (Larkin and Harrison, 2005). Improving our understanding regarding ENSO dynamics has profound socio-economic consequences.

The coupled general circulation model (CGCM) has gradually become a useful tool for Pacific climate research (Dong et al., 2014) and ENSO prediction over a long period of time (Guilyardi et al., 2009). Continuous efforts have been made to simulate ENSO phenomena more accurately. Unfortunately, CGCMs are still unable to reasonably reproduce ENSO behaviors, although a few “superficial” features of ENSO (e.g., ENSO amplitude) are represented reasonably well. For instance, as Bellenger et al. (2014) pointed out, the diversity in ENSO amplitude in the third phase of the Coupled Model Intercomparison Project (CMIP3) is reduced by a factor of two in phase 5 of the Coupled Model Intercomparison Project (CMIP5); however, some essential air–sea feedbacks associated with ENSO, such as the wind–SST feedback and the shortwave–SST feedback, are still obviously biased. It is suggested that the improvement in the “superficial” features of ENSO (e.g., ENSO amplitude) may arise from model error compensation. Moreover, most CGCMs show poor performance in representing observed ENSO period as well as the asymmetry of ENSO amplitude (Sun et al., 2016; Hua and Chen, 2019), which is also linked to the bias of air–sea physical processes (Im et al., 2015). Thus, investigating the biases of the internal physical processes that determine ENSO behaviors is essential.

The Bjerknes (BJ) stability index (Jin et al., 2006) provides an effective tool with which to assess overall ENSO stability and ENSO-related feedback processes, including three positive feedbacks—the thermocline feedback (TH), zonal advection feedback (ZA), and Ekman pumping feedback (EK), and two negative feedbacks—the thermodynamic feedback (TD) and the mean advection feedback (MA). The above ENSO-related feedbacks play a crucial role in ENSO dynamics, for example, Ren and Jin (2013) pointed out that the TH term plays a dominant role in contributing to the growth and phase transitions of both ENSO types (Ren and Jin, 2011), whereas the ZA term plays an important role chiefly in their phase transitions. A detailed description of the BJ index can be found in the Appendix. The index has been widely applied in numerous studies (Im et al., 2015; Chen et al., 2016; Hua et al., 2019) to examine the ENSO-related feedback processes. For example, Kim and Jin (2011b) pointed out that the good ENSO statistics in CMIP3 models may be due to an incorrect reason based on BJ index analysis. Although ENSO amplitude and ENSO stability in CMIP5 models are close to those of observation, systematic biases of underestimated ENSO-related feedbacks still exist (Kim et al., 2014). The aforementioned studies are based on statistical multi-model ensemble results.

Moreover, ENSO has the highest prediction skill in all of the interannual climate variabilities. However, it is becoming more complicated than before (Timmermann et al., 2018), and its changed nature and associated dynamics need to be further investigated, particularly using the CGCM. For example, some recent studies investigated the simulations of ENSO-related evaporative damping process (Ferrett et al., 2017) and shortwave feedback process (Ferrett et al., 2018) with innovative approaches. Motivated by this, in the present study, we intend to gauge model errors by quantifying the simulated ENSO-related air–sea feedbacks in the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM) using the BJ index.

The remainder of this paper is organized as follows. In Section 2, the model and data are briefly presented. We provide the overall ENSO-related feedback processes simulated by CAMS-CSM in Section 3. We analyze the bias of the ENSO-related thermodynamic process simulated by CAMS-CSM in Section 4, and the bias of the ENSO-related dynamic process simulated by CAMS-CSM in Section 5. Meanwhile, our conclusions are given in the end.

2 Model and data 2.1 Model

The CAMS-CSM has a modified atmospheric GCM known as ECHAM5 (v5.4), which was developed at the Max Planck Institute for Meteorology (MPI-Met) (Roeckner et al., 2003). In addition, the CAMS-CSM has the MOM4 (Griffies et al., 2004) as its oceanic component, which was developed at the Geophysical Fluid Dynamics Laboratory (GFDL). The sea ice component is the GFDL Sea Ice Simulator (SIS) (Winton, 2000) and the land component is the Common Land Model (CoLM) (Dai et al., 2003). The CAMS-CSM uses the GFDL Flexible Modeling System (FMS) coupler for flux/state calculations and interpolations between component models. The model includes some refinements based on the version used in Hua et al. (2018), and the difference mainly lies in the atmospheric component. For a detailed description, refer to Rong et al. (2018). The last 50 years’ output from the pre-industrial control (piControl) run is used for analysis in this study. We chose the 50 years’ output to be consistent with the observational period, and the results from longer years’ output would not change the main conclusions of the present study.

2.2 Data

The observation data and reanalysis products include the following: (1) SST data from the Hadley Center Sea Ice and Sea Surface Temperature dataset (HadISST1; Rayner et al., 2003) during 1958–2009, the horizontal grid resolution of which is 1° × 1°; (2) oceanic variables from version 2.2.4 of Simple Ocean Data Assimilation Reanalysis (SODA2.2.4; Giese and Ray, 2011) during 1958–2001, the horizontal grid resolution of which is 0.5° × 0.5°; (3) monthly precipitation data from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) during 1979–2008, the horizontal grid resolution of which is 2.5° × 2.5°; (4) total cloud amount from the International Satellite Cloud Climatology Project (ISCCP) dataset (Zhang et al., 2004) during 1984–2009, the horizontal grid resolution of which is 2.5° × 2.5°; (5) surface heat fluxes and wind stress from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40; Uppala et al., 2005) during 1958–2001, the horizontal grid resolution of which is 2.5° × 2.5°; and (6) surface heat fluxes from the Objectively Analyzed Air–Sea Fluxes (OAFlux; Yu et al., 2008) during 1958–2001, the horizontal grid resolution of which is 1° × 1°. These datasets show the reliability in the former studies (An and Choi, 2013; Chen et al., 2016; Hua et al., 2018).

3 ENSO-related feedback processes simulated by CAMS-CSM

Figure 1 presents the SST anomaly (SSTA) averaged in the Niño 3 region (5°S–5°N, 150°W–90°W) derived from the observation and the CAMS-CSM simulation. The results show that CAMS-CSM could capture the obvious observed interannual variability, but it seems more regular. In addition, the simulated standard deviation (STD) is larger than that observed. Further analysis demonstrates that CAMS-CSM successfully captures the observed feature of seasonal ENSO phase-locking (Bellenger et al., 2014), with the most significant STD of SSTA in the boreal winter (figure omitted). Although the seasonal variability simulated by CAMS-CSM resembles that of observation, the simulated ENSO amplitude is larger (Fig. 1). To explore the relative roles of ENSO-related feedback processes in producing the overestimated ENSO amplitude, here we employ the BJ index to investigate the corresponding physical causes.

Figure 1 Time series of Niño 3 index (°C) derived from (a) observation and (b) model simulation. The x-axis denotes time in month. (c) Standard deviations (STD; °C) of Niño 3 (blue) and Niño 3.4 indices (red).

Figure 2 shows the total BJ index and the associated five components. We can see that the BJ index in the observation is negative, indicating that the coupled system is damped. The CAMS-CSM well represents the stable system just as in observation, but the value is larger in the simulation compared with observation. The deviation in such values is consistent with that in ENSO amplitude (i.e., larger ENSO magnitude in CAMS-CSM than in observation, as shown in Fig. 1). By comparing the differences between simulation and observation in the five contributing terms (green bar in Fig. 2), we find that the major simulation biases lie in the underestimation of the thermodynamic feedback (TD) and thermocline feedback (TH) terms. It is noted that the biases of MA (magnitude underestimation) and EK (magnitude overestimation) also contribute to the total bias. However, even though biases are exhibited in the terms of MA and EK, the largest biases refer to effects of TD and TH. Thus, we will mainly investigate the biases of TD and TH in the present study. In addition, the sum of five feedback terms is not equal to the total BJ index; the damping rate of ocean adjustment also involves the calculation of BJ index, but the five feedback terms represent the key ENSO-related feedback processes (Kim et al., 2011a).

Figure 2 Total BJ index and its associated contributing terms (units: yr–1), derived from observation (black) and model simulation (red). Difference between the model output and observation is indicated by green bar.

The weakened TD term is directly caused by the underestimated ${\alpha _s}$ in the CAMS-CSM, as shown in Table 1. Since the TH term is a product of three important regression coefficients and the mean vertical upwelling, we also present the strength of each corresponding coefficient and the mean state in Table 1. It is found that the underestimated TH term is due to the underestimation in both the mean vertical upwelling and μa, while the parameters βh and αh simulated by the CAMS-CSM are generally similar to those observed (Table 1). Therefore, we focus on the physical mechanism for the difference in these two feedbacks (namely, ${\alpha _s}$ and μa) in the following two sections.

Table 1 Important regression coefficients in TD and TH terms, derived from observation and model simulation
αs (s–1) μa (W m–2K–1) βh [m (N m–2)–1] ah (K m–1) $\overline W $ (m s–1)
Observation 8.2 × 10–8 0.0040 7.9 28.1 6.1 × 10–6
Model 4.5 × 10–8 0.0030 7.2 29.1 4.5 × 10–6
4 Bias of ENSO-related thermodynamic process simulated by CAMS-CSM

Figure 3 presents the net surface heat flux (Qnet) feedback and the associated components, since the TD term ( ${\alpha _s} = \dfrac{{Q_{\rm net}}}{{\rho {C_p}{H_1}}}$ ) has the following components: the shortwave radiation feedback (SWF), the latent heat flux feedback (LHF), the longwave radiation feedback (LWF), and the sensible heat flux feedback (SHF). Clearly, such underestimated Qnet feedback (green bar in Fig. 3) is mainly due to the underestimation in the SWF. It is noted that the underestimation of the SWF in the CAMS-CSM also exists in most CGCMs (e.g., Sun et al., 2009; Lloyd et al., 2012; Li et al., 2014, 2015; Chen et al., 2018a,b).

Figure 3 Qnet feedback (W m–2 K–1) and its four contributing terms, derived from observation (black) and model simulation (red). Difference between the model output and observation is indicated by green bar.

Previous studies (Chen et al., 2013; Chen and Yu, 2014; Chen et al., 2016, 2018a) pointed out that the bias of the underestimated SWF originates in the bias of the underestimated mean SST over the equatorial central-eastern Pacific (hereafter CEEP: 5°S–5°N, 180°–80°W); this bias is known as the cold bias in CGCMs. As clearly presented in Fig. 4, CAMS-CSM has the cold bias in the CEEP. As a previous study pointed out, the deep convection tends to occur where SST is higher than a threshold of approximately 28°C (Graham and Barnett, 1987). This means that, owing to the fact that the climatological mean SST is cold in the CEEP, the total SST in the CEEP cannot exceed the convection threshold triggering the convection; hence, the induced convection and the corresponding anomalous SW shifts to the relatively warmer western region (Chen et al., 2013).

Figure 4 Climatological SST (°C) along the equator (averaged between 5°S and 5°N), derived from observation (black) and CAMS-CSM simulation (red).

To further test this hypothesis, we plot the spatial pattern of the corresponding SWF. As shown in Fig. 5, the SWF magnitude in the CEEP in the CAMS-CSM is smaller than that observed. Moreover, the spatial distribution of the SWF in the CAMS-CSM shifts westward, e.g., the peak of the SWF is near 160°E simulated by CAMS-CSM, whereas in the observation, the peak is located around the dateline (Fig. 5a). Furthermore, the SWF bias coincides with the bias in the simulated precipitation feedback (PRF: the response of precipitation anomalies to SSTA) (see Fig. 5b) and the corresponding total cloud cover feedback (TCCF: the response of total cloud cover anomalies to SSTA) (see Fig. 5c). Specifically, the PRF and the TCCF simulated by the CAMS-CSM also show the biases in magnitude (underestimation in the eastern equatorial Pacific) and spatial distribution (westward shift), which resemble the biases in the simulated SWF.

Figure 5 (a) SWF (W m–2 K–1) along the equator (averaged between 5°S and 5°N), (b) PRF (mm day–1 K–1), and (c) TCCF (% K–1), derived from observation (black) and CAMS-CSM simulation (red).
5 Bias of ENSO-related dynamic process simulated by CAMS-CSM

As discussed above, another obvious bias in the CAMS-CSM simulation is the ENSO-related dynamic air–sea feedback process (the TH term), ${\rm TH} = {\mu _a}\cdot$ $ {\beta _h}\cdot$ ${\left\langle {\dfrac{{\overline W}}{{{H_1}}}} \right\rangle _{\rm E}}\cdot{a_h}$ . The value of each component of the TH term is listed in Table 1. Clearly, both items in the TH including the mean upwelling ( $\overline W $ ) and the air–sea feedback ofμa deviate from observation, whereas there is only a slight difference in the parameters βh and ah. This indicates that the bias of the TH term is mainly due to the bias of the following two factors, i.e., $\overline W $ and μa. Furthermore, it is necessary to distinguish the respective roles of $\overline W $ and μa. We use the following equation to separate their relative contributions: d(AB) = d(A)B + Ad(B) + d(A)d(B), where A denotes the calculated value of μa and B the calculated value of $\overline W $ . Meanwhile, the operator d(AB) denotes the difference of (μa× $\overline W $ ) (model results minus the observations), d(A) shows the difference of μa (model results minus the observations), and d(B) shows the corresponding difference of $\overline W $ (model results minus the observations). Calculated results show that the relative contributions of μa and $\overline W $ are nearly identical (almost 50% from each). Thus, we need to explore the physical cause for the biases of these two factors.

The bias of mean vertical upwelling is attributed to the climatological mean state. μa shows a response of anomalous zonal wind stress ( $\tau _x'$ ) to anomalous SST. We first examine the relationship of $\tau _x'$ and SSTA, and find that the linear relationship is obvious both in observation and model simulation (Fig. 6), but the regression coefficient in the model simulation is smaller than that observed (Fig. 6), which can also be clearly seen in Table 1. However, the bias in the response of the wind stress anomaly is usually linked to the bias in the response of the precipitation anomaly. Further analysis shows that the PRF (color) peak in the CAMS-CSM simulation shifts westward compared to the observation from the view of the spatial pattern map, and the corresponding wind stress feedback (vector) shows the similar bias (Fig. 7). In order to compare the difference in detail, Fig. 7 also presents the map of the difference in the PRF between model and observation (color), as well as the corresponding difference of the wind stress feedback (vector). From the view of the difference map, we can see that the PRF shifts toward the western equatorial Pacific (WEP), showing a positive bias of PRF in the WEP. The spatial pattern of the wind stress feedback matches the PRF well. In particular, the positive PRF coincides with the convergence of the surface wind stress anomaly over the WEP. Such convergence of the surface wind stress anomaly coincides with the westward $\tau _x'$ over the CEEP. Therefore, the $\tau _x'$ in response to SSTA (i.e., μa) is weakened in the model. Recalling that the positive bias of the PRF occurred in the WEP is due to the cold bias over the CEEP, we thus conclude that the underestimated μa is also linked to the cold mean SST. In all, our analysis implies that the biases in the climatological mean state including the SST and vertical velocity fields are determinative factors in the bias of the TH term. This indicates that improving the simulation of climatological mean state would effectively reduce the error sources of the ENSO-related air–sea feedback process.

Figure 6 Scatter diagrams of zonal wind stress anomaly (N m–2) plotted against SSTA (°C) derived from (a) observation and (b) CAMS-CSM simulation.
Figure 7 Horizontal pattern of PRF (color shaded; mm day–1 K–1) superimposed by horizontal structure of response of anomalous wind stress to SSTA (vector; N m–2 K–1) derived from (a) observation, (b) CAMS-CSM simulation, and (c) difference between the simulation and observation.
6 Conclusions

In this paper, we presented an overview of ENSO stability through a CGCM study. The CGCM is the CAMS-CSM. By investigating the important ENSO-related feedbacks, we found that the CAMS-CSM can represent the stability of the coupled system (damped) just as in observation, but some biases still exist in the thermodynamic (TD) and the thermocline (TH) feedbacks. The physical causes responsible for the differences are as follows.

(1) The CAMS-CSM underestimates the TD feedback over the CEEP. The bias in the thermodynamic process mainly arises from the bias in the simulation of the SWF. In addition, the bias in the SWF originates from the cold climatological mean state over the CEEP.

(2) Both $\overline W $ and μa are the primary contributors to the bias in the TH feedback. Meanwhile, the relative contributions of $\overline W $ and μa are nearly identical. The bias of $\overline W $ is attributed to the climatological mean state. In addition, the surface wind stress anomaly converges around the maximum center of the PRF, which is located in the WEP. Owing to the convergence of surface wind, the $\tau _x'$ in response to SSTA (i.e., μa) is weakened over the CEEP in the CAMS-CSM, suggesting that the unreasonable wind stress feedback matches the unreasonable PRF. Considering that the cold mean SST in the CEEP is the original cause of the precipitation bias, it may contribute to the bias of wind stress anomaly.

Such a realistic simulation of the climatological mean state including both temperature and vertical velocity fields, especially in the CEEP, is essential to a reasonable simulation of the ENSO-associated feedback processes. The realistic background mean state plays a key role in reasonably representing ENSO features in the CGCM. In addition to the background mean state, we also find out that the “regular” interannual variability in the model simulation merits more attention in improving the CGCM, which is linked to the ENSO phase transition (Lu et al., 2018). Moreover, the biases of MA (magnitude underestimation) and EK (magnitude overestimation) also contribute to the total bias, and even though the biases are relatively slight, they require further research for improving the CGCM. The bias of μa is an important error source in the thermocline feedback; thus, the role of the individual atmospheric component of CGCM should also be an object of focus in the future. In addition, the bias of SWF, TCCF, and PRF may be also attributed to other factors, such as the convection parameterization schemes and cloud microphysical schemes in the atmospheric component (Li et al., 2014, 2015). Our planned work aims to continuously improve CAMS-CSM simulation quality to better understand climate change and predict future weather and climate.

Additionally, in order to examine whether the external forcing during the historical period would impact our conclusions, we also conducted the same analysis of the “historical” simulation. It is found that the simulation biases in the “historical” simulation are similar to those in the piControl simulation, e.g., that similar bias (see online supplementary material) exists in the underestimated SWF (Fig. S1), the underestimated wind-SST feedback (Fig. S2), and the cold bias in the mean SST (Fig. S3). This means that the ENSO-related simulation biases revealed in this study are the common biases in the coupled simulations of the CAMS-CSM.

Appendix: About the BJ index

Based on some assumptions, Jin et al. (2006) and Kim and Jin (2011a, b) simplified the mixed layer heat budget equation. Ultimately, the area-averaged SST tendency equation can be estimated as follows:

$ \dfrac{{\partial {{\left\langle T \right\rangle }_{\rm E}}}}{{\partial t}} = R{\left\langle T \right\rangle _{\rm E}} + F{\left\langle h \right\rangle _{\rm W}}, \tag{A1} $ (A1)

where

$ \begin{align} R = & - \left( {{a_1}\frac{{{{\left\langle {\Delta \overline u } \right\rangle }_{\rm E}}}}{{{L_x}}} + {a_2}\frac{{{{\left\langle {\Delta \overline v } \right\rangle }_{\rm E}}}}{{{L_y}}}} \right) - {\alpha _s} + {\mu _a}{\beta _u}{\left\langle { - \frac{{\partial \overline T }}{{\partial x}}} \right\rangle _{\rm E}} \\ & +{\mu _a}{\beta _w}{\left\langle { - \frac{{\partial \overline T }}{{\partial z}}} \right\rangle _{\rm E}} + {\mu _a}{\beta _h}{\left\langle {\frac{{\overline w }}{{{H_1}}}} \right\rangle _{\rm E}}{a_h}, \end{align}\tag{A2} $ (A2)

and

$ F = {\beta _{uh}}{\left\langle { - \dfrac{{\partial \overline T }}{{\partial x}}} \right\rangle _{\rm E}} + {\left\langle {\dfrac{{\overline w }}{{{H_1}}}} \right\rangle _{\rm E}}{a_h}. \tag{A3} $ (A3)

In the above Eqs. (A1)–(A3),u, v, and w represent the zonal current, meridional current and vertical oceanic movement, respectively; and T represents ocean temperature. < > E and < > W denote volume average quantities in the eastern and western boxes (see the definitions below), respectively, from the surface to the base of the surface mixed layer depth. The overbar represents the climatological seasonal mean. Lx and Ly denote the longitudinal and latitudinal lengths of the eastern box. a1 and a2 are obtained using anomalous SST averaged zonally or meridionally at the boundaries of an area-averaged box and area-averaged SST anomalies (SSTA) in the eastern box. αs denotes the response of the thermodynamic damping to the SSTA; here, the net surface heat flux (Qnet) anomaly divided by (ρCpH 1) represents the thermodynamic damping, with ρ representing the density of seawater, Cp representing the specific heat capacity and H1 representing the mixed layer depth. More detailed description can be found in Kim et al. (Kim and Jin, 2011a, b; Kim et al., 2014).

On the right hand side of Eq. (A2), in the order from left to right, the corresponding terms indicate dynamic damping by mean advection feedback (MA), thermodynamic damping (TD), zonal advection feedback (ZA), Ekman feedback (EK), and thermocline feedback (TH). In this study, we chose a broad eastern box (5°S–5°N, 180°–80°W) and the corresponding western box (5°S–5°N, 120°E–180°) when calculating the BJ index.

Some important parameters in the BJ index and their definitions are provided in Table A1.

Table A1 Definition of the parameters in the BJ index
Parameter Definition
αs Response of the thermodynamic damping to the sea surface
temperature anomalies (SSTA)
μa Response of zonal wind stress anomaly to SSTA
βu Response of anomalous upper-ocean zonal current to wind
βh Anomalous zonal slope of the equatorial thermocline
adjusting to wind
ah Effect of thermocline depth change on ocean subsurface
temperature anomalies
βw Response of ocean upwelling to wind forcing
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