Journal of Advances in Modeling Earth Systems

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1 RESEARCH ARTICLE Key Points: A new hybrid coupled model developed to represent the coupling among atmosphere and ocean physics-biogeochemistry in the tropical Pacific An ocean biogeochemistry-induced negative feedback illustrated on ENSO, which can be opposed by change in vertical mixing and ML processes The induced negative feedback is mainly driven by more solar penetrating out of the ML during El Nino and less penetrating during La Nina Correspondence to: R.-H. Zhang, rzhang@qdio.ac.cn Citation: Zhang, R.-H., Tian, F., & Wang, X. (2018). A new hybrid coupled model of atmosphere, ocean physics, and ocean biogeochemistry to represent biogeophysical feedback effects in the tropical Pacific. Journal of Advances in Modeling Earth Systems, 10, Received 28 NOV 2017 Accepted 7 MAY 2018 Accepted article online 10 MAY 2018 Published online 10 AUG 2018 VC The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. A New Hybrid Coupled Model of Atmosphere, Ocean Physics, and Ocean Biogeochemistry to Represent Biogeophysical Feedback Effects in the Tropical Pacific Rong-Hua Zhang 1,2,3, Feng Tian 1,3, and Xiujun Wang 4 1 Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China, 2 Qingdao National Laboratory for Marine Science and Technology, Qingdao, China, 3 University of Chinese Academy of Sciences, Beijing, China, 4 College of Global Change and Earth System Science, Joint Center for Global Change Studies, Beijing Normal University, Beijing, China Abstract Multiple processes are involved in modulating the El Ni~no-Southern Oscillation (ENSO) in the tropical Pacific, and these processes are neither well-represented nor well-understood in climate models. A new hybrid coupled model (HCM) of atmosphere, ocean physics, and ocean biogeochemistry (AOPB) is developed to represent the feedback from ocean biogeochemistry onto ocean physics via modulating the penetration of shortwave radiation in the upper ocean. An ocean biogeochemistry model is coupled with a simplified ocean-atmosphere system consisting of an ocean general circulation model (OGCM) and a statistical atmospheric model for interannual anomalies of wind stress (s). The HCM AOPB serves as a simple Earth system for the tropical Pacific to represent the coupling among the atmospheric and physical and biogeochemical ocean components. Model experiments are performed to illustrate this new model s ability to depict the mean ocean state and interannual variability associated with the ENSO. The relationships among anomaly fields are analyzed to illustrate the ocean biogeochemistry-induced heating feedback and its modulating effects on the ENSO, which is characterized by a negative feedback. The underlying processes and mechanisms are analyzed and can be attributed to dominant modulation of the penetrative solar radiation through the base of the mixed layer (ML). It is demonstrated that the ocean biogeochemistry-induced negative feedback is mainly driven by more solar radiation penetrating out of the ML during El Nino and less penetrating during La Nina. Further model applications to studies on these processes and biogeochemicalphysical interactions are discussed. 1. Introduction The tropical Pacific is the region where the El Ni~no-Southern Oscillation (ENSO) emerges as the strongest interannual anomaly that affects weather and climate worldwide (e.g., Bjerknes, 1969; Zhang & Gao, 2016). Additionally, multiple processes coexist that can modulate the ENSO in the region on various space-time scales, including ocean biogeochemical cycling and hydrological cycles. As interactions between ocean biogeochemistry and climate system are very complicated in the region, numerical models provide a powerful tool to study the related feedback and coupling. In the past, various coupled atmosphere-ocean models with varying levels of complexity (e.g., hybrid coupled models (HCM) and general circulation models (GCMs)) have been developed to investigate the interactions between different forcing and feedback processes in the tropical Pacific, including freshwater flux and biogeochemistry-induced heating (e.g., Zhang & Busalacchi, 2009; Zhang et al., 2012, 2015). For example, recent modeling studies indicate that the ENSO can be affected by ocean biogeochemistry-induced radiative heating in the tropical Pacific (e. g., Anderson et al. 2009; Ballabrera-Poy et al., 2007; Jochum et al., 2010; Lengaigne et al., 2007; Park et al., 2014). Therefore, ocean biogeochemistry-induced heating effects and their interactions with the climate system need to be adequately represented in climate models. At present, however, large uncertainties exist in representing and understanding biogeophysical feedbacks, which can be strongly model dependent and very sensitive to the way the ocean biogeochemical processes are represented in models. In our previous studies, we adopted a statistical modeling approach to representing the effects of ocean biogeochemistry-induced heating on the climate system of the tropical Pacific (Zhang, 2015; Zhang et al., ZHANG ET AL. 1901

2 2009). More specifically, penetration depth (H p ) is introduced to represent the ocean biogeochemistryinduced heating effects (Zhang et al., 2011), serving as a link between the ocean biogeochemistry and physics. A statistical model for interannual H p variability is derived to quantify the effects on the penetrative incoming solar radiation. This statistical H p model, together with prescribed climatological H p field, can be used to represent ocean biogeochemistry-climate coupled effects on the transfer of shortwave radiation into heating in the upper ocean. Because historical data are used to derive the statistical relationship between interannual SST and H p anomalies and to describe the climatological H p field, this approach has limited applications. Although the bulk biogeophysical effect on the climate system can be taken into account, detailed biogeochemical processes are not represented in the statistical model for H p. In addition, this type of statistical model is derived from historical data during one specific period and thus may not be suitable for use in other periods because these relationships are not stationary but can change with time under global warming. Additionally, the climatological H p field specified in ocean biogeochemistry-climate coupled modeling studies may change from decades to decades. A biogeochemical processes-based modeling approach is clearly needed to determine H p and the related heating effects on the ocean. The primary aim of this study is the further advancement of our previously existent HCM. Specifically, a new hybrid coupled model (HCM) is developed for the atmosphere, ocean physics and ocean biogeochemistry (AOPB) in the tropical Pacific. For the atmosphere-ocean component of the model, a hybrid coupled modeling approach is taken: an oceanic GCM (OGCM) is coupled to a simple statistical model for interannual wind stress variability (s inter ) derived from a singular value decomposition (SVD) analysis, and this component is thus called a hybrid coupled model (HCM). For the biogeochemistry component, an ocean biogeochemistry model is adopted in which detailed biogeochemical processes are considered to represent biogeochemical heating effects on the climate system (Wang et al., 2015). Such an HCM framework can offer an extremely efficient modeling tool for ocean physics-biogeochemistry systems and the coupling thereof with the atmosphere in the tropical Pacific, allowing a large number of experiments to be performed feasibly and affordably. In this paper, we demonstrate the ability of the newly developed HCM AOPB to depict the ocean mean state and interannual variability of the tropical Pacific. As an application, the model is further used to show and explain a negative feedback which is acting between ocean biogeochemistry and ENSO modulations. Such scientific questions of how ocean biogeochemistry affects ENSO dynamics has already been addressed by using our previous HCM, in which SST-chlorophyll relationship is prescribed from satellite observations (Zhang, 2015; Zhang et al., 2015). Hence, the novelty of this study is that now an interactive biogeochemistry is used instead in the HCM. Thus, this new model offers a modeling tool that can be used to perform comparison studies with other model simulations on the topics. The paper is organized as follows. Section 2 describes the models and some observational data used. The results are presented in section 3 to demonstrate the HCM s performance in simulating mean climatology and interannual variability, a mechanism for modulating effects on the ENSO and a supporting sensitivity experiment. Discussion and concluding remarks are given in sections 4 and 5, respectively. 2. Models and Data Based on our previous modeling efforts, a new hybrid coupled model (HCM) is developed for the tropical Pacific to represent the feedback from ocean biogeochemistry on ocean physics via absorption of shortwave radiation in the upper ocean. Figure 1 illustrates a schematic diagram for the coupling among the atmosphere and ocean physics-biogeochemistry (AOPB) in the tropical Pacific, consisting of a simple atmospheric model for wind stress (s), an OGCM, and the ocean biogeochemistry model. These are briefly described in the following section A Hybrid Coupled Model (HCM) of Atmosphere and Ocean Physics and Biogeochemistry (AOPB) Classified as an HCM (the right side of Figure 1), a simple statistical model for interannual wind anomalies (s inter ) is coupled with an OGCM. The total wind stress (s) can be written as s 5 s clim 1 s inter, where s clim is the climatological component (prescribed from observations) and s inter is the interannual component. The empirical model for s inter adopted in this work is a statistical one specifically relating s inter to SST anomalies and is written as s inter 5 a inter F inter (SST inter ), where SST inter represents interannual SST anomalies, F inter ZHANG ET AL. 1902

3 Figure 1. A schematic diagram illustrating a hybrid coupled model (HCM) for the atmosphere and ocean physics and biogeochemistry (AOPB) in the tropical Pacific. The HCM AOPB consists of a statistical atmospheric model for interannual wind stress (s) anomalies (s inter ), an OGCM, and an ocean biogeochemistry model. The total wind stress (s) is separated into its climatological part (s clim ) and its interannual anomaly part (s inter ): s 5 s clim 1 a s s inter. The statistical model for s inter is constructed using a singular value decomposition (SVD) analysis technique between interannual anomalies of SST and s, and a scalar coefficient (a s ) is introduced to represent the strength of the corresponding wind forcing. Furthermore, the climate system is coupled with an ocean biogeochemistry model that determines chlorophyll (Chl), which affects the penetration of solar radiation in the upper ocean; correspondingly, a penetration depth (H p ) is introduced to represent the ocean biogeochemistry-induced heating effects on heating terms that affect ocean thermodynamics. Interactions between the ocean physics and biogeochemistry are represented by H p, a field that directly appears in the ocean biogeochemistry-induced heating terms. In this simplified coupled system, a few climatological fields are prescribed to be seasonally varying (e.g., s clim and SST clim ), and interannual anomalies are determined by departures from climatological fields (e.g., SST inter 5 SST SST clim, in which SST clim is climatological SST). Interactions between the ocean and atmosphere are realized by SST and wind stress/heat flux/freshwater flux, and those between the ocean physics and biogeochemistry are represented by the related heating terms that are modulated by H p, respectively. represents the relationships between interannual variations in s and SST, and a inter is the so-called relative coupling coefficient, a scalar parameter introduced to represent the strength of interannual wind forcing on the ocean. The s inter model has been successfully used for the ENSO-related modeling studies (e.g., Zhang et al., 2003). This statistical approach can be justified with the fact that the ENSO is a dominant driving force in the coupled ocean-atmosphere system in the tropical Pacific, producing large-scale interannual SST anomalies that induce quick surface wind responses. Thus, the coherent relationships between interannual variations in SSTs and surface winds can be used to construct an empirical model. In addition, two other atmosphere forcing fields to the ocean are also included: freshwater flux (FWF) and heat flux. The total FWF, represented by precipitation (P) minus evaporation (E), (P-E), is also represented by its climatological component ((P-E) clim) plus the interannual anomaly component (FWF inter ). The heat flux (HF) is interactively determined using an advective atmospheric mixed layer (AML) model (Seager et al., 1995). Various climatological fields are specified for use in the calculation of wind stress, heat flux, and FWF. ZHANG ET AL. 1903

4 The ocean model used is the reduced gravity model; originally constructed by Gent and Cane (1989). In the vertical, the first layer is treated as a bulk mixed layer the ocean model is divided into a mixed layer and a number of layers below that is separated based upon a sigma coordinate (and is thus referred to as a layer model). The depth of the mixed layer (top layer) and thickness of the last sigma layer (bottom layer) are determined prognostically, while the thicknesses of the internal layers are estimated in such a way that the ratio of each sigma layer to the total depth is held to its prescribed value. Additionally, several related efforts have been made in the past which have improved this ocean model significantly (e. g., Zhang et al., 2009). The OGCM covers the tropical Pacific domain from 308Sto308N and from 1208Eto768W. The horizontal resolutions vary: the zonal resolution is 18 in the central basin and is gradually increases to 0.48 at the western and eastern boundaries; the meridional resolution ranges from 0.38 to 0.68 between 158S and 158N and decreases to 28 at the northern and southern boundaries. The temperature, salinity, nitrate, and other variables are gradually relaxed to their climatological fields obtained from WOA98 (Levitus et al., 2005) in the sponge layer within the 108 domain near the northern and southern boundaries. In the vertical direction, the OGCM has 20 layers, as indexed by k. As detailed in Chen et al. (1994), the mixed layer is always the depth of the first model layer; the model layers below are divided into 19 layers, with the 2nd to 11th model layers having about 5 10 m thickness An Ocean Biogeochemistry Model Previously, an empirical model for interannual variability H p (which is introduced as the penetration depth of shortwave radiation in the upper ocean) was derived from remotely sensed ocean color data to represent the biogeophysical effect on the climate system (Zhang et al., 2011). In this work, an ocean biogeochemistry model is adopted to represent ocean biogeochemical processes. Figure 1 displays a schematic diagram illustrating the ocean biogeochemistry model (the left side of Figure 1), which was detailed in Christian et al. (2002) and Wang et al. (2006, 2008, 2015). The model consists of 12 components, including six nutrients (nitrate, silicon, dissolved inorganic carbon (DIC), ammonium, dissolved oxygen, and dissolved iron) and six biogeochemical components (large and small phytoplankton (Pl and Ps), large and small zooplankton (Zl and Zs), and large and small detritus (Dl and Ds)). The trend for each biogeochemical component (N) is related to advection, mixing, sources, and other factors (Wang et al., 2015). The ocean biogeochemical fields include 12 prognostic variables that are calculated from their time-dependent equations with consistent units of mol N m 23. Additionally, some diagnostic variables were introduced. For example, phytoplankton carbon biomass (mol Nm 23 ) is converted to mol C m 23 according to the Redfield ratio (C:N ), and chlorophyll concentration is then calculated using the Redfield ratio between phytoplankton biomass and chlorophyll called the C:Chl ratio, which varies according to light and nutrient limitation (Wang et al., 2009). The biogeochemical model can also produce the detritus field in association with phytoplankton and zooplankton mortality and zooplankton excretion. Further, detritus can be grazed upon by small zooplankton and decomposed by bacteria Coupling of the Biogeochemical and Physical Ocean Models The penetration of the solar shortwave radiation in the upper ocean follows the Beer-Lambert Law, which indicates that shortwave radiation decays exponentially with depth according to attenuation coefficients. For example, chlorophyll and detritus are considered to have attenuating effects on shortwave radiation. The total attenuation coefficient is computed as follows (Wang et al., 2008): K A ðkþ5k W 1K C ChlðkÞ (1) where K W m 21 and K C m 21 (mg Chl m 23 ) 21 represent the attenuation coefficients for pure water and chlorophyll and Chl(k) is the chlorophyll concentration as calculated by the biogeochemical model. In this study, we considered only the Chl effect on K A because Chl plays a dominant role in ocean biogeochemistry-induced heating effects. The penetration depth (H p ) of the shortwave radiation is defined as the inverse of K A. Then, Chl serves as a linkage between the ocean biogeochemistry and physics. Accordingly, H p is introduced to simply represent the attenuation depth of solar radiation in the upper ocean, which can be ZHANG ET AL. 1904

5 estimated from Chl and directly affects the penetration of solar radiation in the upper ocean. The ocean biogeochemistry-induced heating (OBH) is represented in the ocean physical model; its effect on the climate system can be taken into account. More specifically, the penetrative solar radiation in each layer can be calculated as follows (Paulson & Simpson, 1977): Q pen ðkþ5cq pen ðk21þexp ð2hðkþ=h p ðkþþ (2) where c represents the visible part of solar radiation that is used in the model, h (k) is the layer thickness of the model, Q pen is the solar radiation part that penetrates at the z layer (when k 5 1, Q pen (0) represents the total solar radiation arriving at the ocean surface; when k 5 2, Q pen (1) represents the solar radiation part that penetrates to the bottom of the mixed layer). Note that we are only taking the blue/ green part of the solar radiation spectrum into account and that is why c is taken so small (the total visible part of the spectrum has rather a value of 0.6), and c is only the fraction that has the potential to penetrate to greater depths (i.e., the blue/green part.) Then, the solar radiation absorbed in each layer is calculated by Q abs ðk21þ5qðk21þ2q pen ðkþ (3) Finally, the ocean biogeochemistry-induced heating effects are represented in the temperature equation, representing the coupling between the ocean biogeochemistry and physics The Experimental Designs The coupling among these components (Figure 1) is implemented as follows. At each time step, the OGCM calculates SST fields, whereas interannual anomalies are obtained relative to the OGCM s uncoupled climatology (SST clim, which is predetermined from the OGCM-only run forced by observed atmospheric climatological fields). The resulting interannual SST anomaly field is then used to calculate s inter using the SVDbased model in which five SVD modes are retained. The interannual anomalies of s and FWF are then added onto their prescribed climatological fields to force the OGCM. At the same time, the ocean biogeochemistry model determines ocean biogeochemical fields including Chl concentration, which is used to calculate H p, acting to directly have effect on the absorption of radiation as described in equation (2). So, an interactively coupled system between the atmosphere, ocean physics, and biogeochemistry is in place. In addition, coupled behaviors sensitively depend on the so-called relative coupling coefficient (a inter ); several tuning experiments are performed with different values of a inter to examine ways the coupled interannual variability can be sustained in the HCM. It is found that taking a inter can produce a sustainable interannual variability in the HCM. The physical and biogeochemical ocean model is initiated from temperature, salinity, and biogeochemical fields from the World Ocean Atlas (WOA98) (Levitus et al., 2005) and, as a spin-up, the model is integrated for more than 50 years using prescribed atmospheric climatological forcing fields, including wind stress from the NCEP-NCAR reanalysis products averaged over the period of (the OGCM spin-up). Based on this ocean spin-up, the coupled simulation using this HCM is then started with imposing westerly wind anomaly for 8 months. The evolution of anomalous conditions thereafter is determined solely by coupled atmosphere and ocean physics-biogeochemistry interactions within the system. The HCM is then integrated for 100 years, and the end year is arbitrarily denoted as model year Then, the model is integrated from model year 2301 to 2500 (200 years). This run accounts for the effects of interannual Chl variations, which is denoted as Chl inter. As will be seen below, the Chl inter run can successfully simulate the mean state and interannual variability associated with the ENSO. In particular, the ENSO is accompanied by large and coherent interannual anomalies in physical and biogeochemical fields, including Chl. The interannual anomalies of Chl modulate the penetration of solar radiation in the upper ocean, which in turn can feed back on ENSO (e.g., Zhang et al., 2009). To clearly demonstrate the modulating effects of ocean biogeochemistry-induced heating feedback on the ENSO, another experiment is conducted (referred to as Chl clim ), in which Chl is prescribed as its seasonally varying climatological field and thus the interannually varying Chl effect is excluded. Then, this experiment is started from the model year 2301 and is integrated for 50 years. Comparisons between ZHANG ET AL. 1905

6 Figure 2. Longitude-time sections along the equator for total fields of (a) SST and (b) Chl simulated from the HCM A-OPB. The contour interval is 18C in Figure 2a and 0.05 mg m 23 in Figure 2b. the Chl inter and Chl clim runs are made to illustrate the biogeophysical effects on the climate system in the tropical Pacific Data Sets Used Various observational and model-based data sets are used to specify climatological fields, to construct the empirical model for s inter (Zhang et al., 2003), and to validate model simulations. For example, in this simplified hybrid coupled modeling system, long-term climatological fields are prescribed in the HCM, including monthly mean wind stresses (s clim ) from the NCEP-NCAR reanalysis (Kalnay et al., 1996), and climatological precipitation data are from GPCP products. The ocean color data from satellites provide a chlorophyll (Chl) concentration field, which can be used to depict interannual variability in Chl and to validate model simulations. Surface chlorophyll data sets are obtained from the GlobColour project from 1998 to 2016, which supplied continuous data sets for merged level-3 ocean color products (including the SeaWIFS, MODIS, MERIS, and VIIRS sensors; see details at Maritorena et al., 2010). Then, monthly CHL- 1 data (chlorophyll concentration (mg/m 3 ) for case 1 water) are interpolated from grids to our model grids. 3. Results 3.1. The Simulated Ocean Physical and Biogeochemical Fields Figure 2 shows examples for the simulated SST and Chl fields along the equator. The model is seen to depict the annual mean, seasonal variations, and interannual variability of physical and biogeochemical fields in the tropical Pacific very well. For instance, the simulated annual mean SST field and seasonal cycle bear a strong resemblance to the corresponding observations, with the cold tongue in the eastern equatorial Pacific and the warm pool in the west (figures not shown). Additionally, pronounced interannual ZHANG ET AL. 1906

7 Figure 3. Longitude-time sections along the equator for interannual anomalies of (a) SST and Chl simulated from the HCM. The contour interval is 0.58C in Figure 3a and 0.05 mg m 23 in Figure 3b. variability is present in the model, which is dominated by ENSO cycles. As has been extensively studied before, interannual variability in the tropical Pacific is dominated by ENSO events, which are determined by the coupling among SST, surface winds and the thermocline (e.g., Bjerknes, 1969; Gao & Zhang, 2017). The HCM can capture interannual oscillations associated with El Ni~no and La Ni~na events. For example, the SST fields (Figure 2a) clearly display large zonal displacements of the warm pool in the western equatorial Pacific and cold tongue in the east during the evolution of the ENSO. During La Ni~na, the cold tongue in the east develops strongly and expands westward along the equator, whereas the warm pool in the west retreats to the west, with the 258C isotherm of SST being located west of 1508W. During El Ni~no, the cold tongue shrinks in the eastern equatorial Pacific, with warm waters in the west expanding eastward along the equator (e.g., the 268C isotherm of SST is located east of 1208W). Interannual features can be more clearly seen in their anomaly fields for SST (Figure 3a), and wind stress (Figure 4a) and mixed layer depth (Figure 4b). The overall time scales of simulated interannual SST variability and the space-time evolution of and coherent phase relationships among various atmospheric and oceanic anomalies are consistent with the observations, which have been extensively described before (e.g., Zhang & Levitus, 1996). The largest SST anomalies occur in the central and eastern equatorial Pacific (Figure 3a), whereas the largest wind variability is located near the date line (Figure 4a). During El Ni~no, for example, warm SST anomalies are located in the eastern equatorial Pacific and are accompanied with westerly wind anomalies to the west. During La Ni~na, an opposite pattern is seen, with cold SST anomalies being associated with easterly wind anomalies. Interannual variations in SST and surface wind do not exhibit clear propagation at the equator: these variations are almost in phase in time, with a zonally westward shift in space. The commonly adopted Ni~no3.4 index can be used to quantify the dominant time scales of interannual variability (Figure 5). For example, a wavelet analysis (Figure 6) indicates that interannual oscillations in the HCM have a dominant peak at approximately 4 years (the corresponding observed one is dominated by ZHANG ET AL. 1907

8 Figure 4. Longitude-time sections along the equator for interannual anomalies of (a) zonal wind stress, (b) the mixed layer depth (H m ), and (c) the penetration depth (H p ) simulated from the HCM. The contour interval is 0.1 dyn cm 22 in Figure 4a, 4 m in Figure 4b, and 1 m in Figure 4c. approximately 3.87 years when using SST data during in Figure 6). Quantitatively, the amplitude of SST variability is captured well in the tropical Pacific. For example, the standard deviations of the Ni~no1 1 2, Ni~no-3, Ni~no-3.4, and Ni~no-4 SST anomalies are 0.398C, 0.508C, 0.788C, and 0.898C, respectively. The corresponding observations are 0.888C, 0.758C, 0.768C, and 0.618C, respectively. The mixed layer depth (H m ) simulated from the HCM (Figure 4b) is in good agreement with the corresponding observed values. For example, pronounced interannual H m anomalies are also seen across the tropical Pacific in association with the evolution of the ENSO (Figure 4b). Strikingly evident interannual H m variability is characterized by a see-saw pattern along the equator, with the zero line crossing approximately 1608W. During La Ni~na events, for example, the ML is anomalously deep in the western-central equatorial basin (Figure 4b), whereas the ML was anomalously shallow to the east. The opposite situation is observed during El Ni~no events. Note that H m is treated as a prognostic variable in the model and is explicitly computed using a bulk mixed layer model (Chen et al., 1994). The HCM can capture the structure and variability of temperature, salinity, and current in the tropical Pacific reasonably well (figures not shown). However, some model discrepancies are also evident. For example, the model tends to depict the ENSO as too regular; this result can be attributed to the fact that stochastic atmospheric forcing is not included (e.g., Zhang et al., 2009). In addition, the maximum SST anomalies simulated tend to occur approximately in the region 1208W 1808E, which is more westward than the observed ones, leading to simulated Ni~no1 1 2 SST anomalies that are weaker than the observed values. In addition to the physical fields, the model can produce biogeochemical fields from its ocean biogeochemistry submodel very well. A pronounced basin-scale pattern of interannual Chl variability is seen across the tropical Pacific (Figure 2b) with large Chl anomaly regions located in the western-central equatorial Pacific ZHANG ET AL. 1908

9 (a) (b) Figure 5. (a) Time series of the Ni~no3.4 SST anomalies during the periods 2,301 2,350 and (b) the longitudinal distributions of standard deviation (std) for interannual SST variability averaged over 58S 58N. Shown are simulations from the HCM AOPB with the interannually varying biogeophysical feedback included or not. (Figure 3b). The interannual variability of Chl is clearly dominated by ENSO signals. In the western equatorial Pacific, for example, a low Chl concentration is observed during El Ni~no and a high Chl concentration during La Ni~na, respectively. The Chl field is then used to estimate the penetration depth of solar radiation in the upper ocean (H p ), a field representing the effect of ocean biogeochemistry-induced heating on the climate system. Interannual H p anomalies are shown in Figure 4c. Compared with ocean color data from satellite, the structure and variability of simulated Chl (Figures 2b and 3b) are in good agreement with the corresponding observed values (figures not shown). The HCM can capture essential features of interannual H p variability as derived from satellite measurements (Zhang et al., 2011). For instance, large interannual H p anomalies are seen in the western-central equatorial Pacific in association with the evolution of the ENSO. During La Ni~na events, a negative H p anomaly is located in the western-central and eastern equatorial Pacific with a positive H p anomaly in the far western region. An opposite anomaly pattern is seen during El Ni~no events. Furthermore, interannual variations in H p have coherent relationships with physical fields over the tropical Pacific, including SST and H m. For example, interannual variations in H p and H m tend to be out of phase in the westerncentral equatorial Pacific during ENSO cycles. During El Ni~no events, the ML is anomalously shallow in the western-central regions, and this feature is accompanied with an increase in H p. During La Ni~na events, the ML is anomalously deep in the western-central equatorial Pacific, accompanied by a decrease in H p. In addition, the amplitude of the interannual variations in H p over the western-central basin is comparable to those in H m during El Ni~no and La Ni~na events. In the eastern equatorial basin, however, the amplitude of the interannual variability of H p is smaller than that of H m. ZHANG ET AL. 1909

10 Figure 6. The wavelet power spectra for the Ni~no3.4 SST anomalies simulated from the HCM A-OPB during the periods 2,301 2,350 with the interannually varying biogeophysical feedback included or not. The observed SST data used for the corresponding calculation are from Reynolds et al. (2002). The dotdashed line is the 95% significance level for these runs, assuming a white noise process The Relationships in the Atmospheric Winds, SST, and Chl As analyzed before (e.g., Zhang et al., 2018), Chl anomalies act to modulate SST, which affects the atmosphere, which in turn force the ocean to change, including biogeochemistry and SST. As shown in Figure 3, a negative relationship exists between interannual anomalies of SST and Chl, indicating that Chl anomalies tend to damp SST anomalies (Zhang et al., 2018). In order to have a better sense of how the model actually responds to SST changes in the feedback loop, we show two more figures one describing the atmospheric forcing and the other the ocean response to the forcing. In terms of the atmospheric forcing, Figure 7 shows the spatial patterns of wind stress regressed against Ni~no3.4 SST (the solar radiation is prescribed as seasonally varying climatology). There is well-defined spatial structure of the wind stress forcing and response fields during ENSO evolution. Furthermore, Figure 8 displays the regression of interannual Chl anomalies on Ni~no3.4 SSTA in the model and observations. It is seen that the HCM depicts well the observed interannual variability of physical and biogeochemical fields and their relationships. Quantitatively, the correlation between interannual anomalies of SST and Chl is shown in Figure 9, which serves as a model validation. Putting these figures together clearly reveals coherent relationships among these physical and biogeochemical anomalies that can be used to help trace the processes involved in the ocean biochemistry-related feedback loop The Mechanism for the Biogeophysical Effects on the Climate System As well understood, the ENSO is a dominant driver of interannual variability in physical and biogeochemical fields over the tropical Pacific. Large Chl and H p anomalies act as biogeophysical feedbacks on the climate system. As analyzed by previous studies (e.g., Zhang, 2015), such a biogeochemistry-climate linkage is realized through the H p -induced effects on several heating terms, including the penetrative solar radiation flux through the base of the ML (Q pen ) and the absorbed part within the ML (Q abs ), which are written as Q abs ðh m ; H p Þ5Q sr ½12c exp ð2h m =H p ÞŠ (4) Q pen ðh m ; H p Þ5Q sr ½c exp ð2h m =H p ÞŠ (5) where Q sr is the incoming solar radiation flux at the sea surface, H m is the depth of the ML, H p is the penetration depth, and c is a constant (50.33) denoting the fraction of the radiation available to penetrate to depths beyond the first few centimeters of the sea surface. The net differences between Q sr and Q pen determine the Q abs part, which leads to the rate of the ML temperature change due to the direct heating effect of solar radiation (R sr ), written as R sr ðh m; H p Þ5Q sr ½12cexp ð2h m =H p ÞŠ=ðq 0 c p H m Þ (6) where q 0 is the density of sea water and C p is the heat capacity. These heating terms are directly related to H p and H m, both of which affect the penetration of solar radiation in the upper ocean. As seen in Figures 4b and 4c, coherent relationships exist between these H m and H p fields in the tropical Pacific during ENSO cycles. Therefore, these two anomaly fields and their relative contributions to the related heating terms are further analyzed to illustrate the mechanism for the biogeophysical effects and the underlying processes involved. As demonstrated by Zhang (2015), who used statistical H p modeling approaches in the HCM, Q pen can be significantly modulated by interannual H p variability in the western-central equatorial Pacific, whereas R sr is not. This finding is true in this ocean biogeochemical processes-based modeling study, which uses an ocean biogeochemistry model to represent the biogeophysical effects. These results will be analyzed in this subsection. Note that the signs of interannual variabilities of the Q pen and Q abs fields are out of phase as defined above. Therefore, the analyses below will be shown for Q pen only, and those for Q abs are the same as Q pen but with the opposite sign. ZHANG ET AL. 1910

11 Figure 7. The spatial patterns of wind stress regressed against Ni~no3.4 SSTAs. Figure 10 displays interannual anomalies for Q pen along the equator. The large interannual variability for Q pen is closely associated with ENSO cycles. In the western-central equatorial regions, Q pen has high values during El Ni~no events but low values during La Ni~na events. As the interannual variability of Q pen (Figure 10a) is mirrored by that of H m (Figure 4b), H m is a major factor that determines the structure and variability of Q pen. Additionally, large H p anomalies exist during ENSO cycles; in the western-central basin, the amplitude of interannual variability of H p is comparable to that of H m ; their anomalies can thus equally contribute Figure 8. The regressed spatial patterns of interannual Chl anomalies on Ni~no3.4 SSTA for (a) observations and (b) the model simulation. The contour interval is 0.02 mg m 23 8C 21. ZHANG ET AL. 1911

12 Figure 9. Correlations between interannual anomalies of SST and Chl calculated for (a) observations during and for (b) the model simulation. The contour interval is 0.2. to anomalies of Q pen. Thus, H p is expected to have significant effects on the redistribution of the incoming solar radiation in the upper ocean. An analysis is then performed to assess the contributions of interannual H p anomalies to Q pen. Here, an additional calculation is made for Q pen in which the direct effects of interannual H p anomalies on Q pen are quantified by taking H p as its seasonally varying climatology (denoted as Q pen ðh m ; H p Þ) ; the corresponding Q pen anomalies are illustrated in Figure 10b. Furthermore, the differences between Q pen ðh m ; H p Þ and Q pen ðh m ; H p Þ are shown in Figure 10c. It is evident that the relatively large variability of H p in the westerncentral equatorial Pacific exerts a strong influence on Q pen. The relationships among the interannual variations in H m,h p, and Q pen clearly indicate that the Q pen field can be significantly modulated by H p in the western-central regions because the amplitude of interannual variations in H p (Figure 4c) is comparable to that of H m (Figure 4b). During ENSO cycles, the effects of interannual H p anomalies (Figure 4c) lead to an increased interannual Q pen variability when comparing Figure 10a with Figure 10b. It is notable that the effects of interannual H p anomalies result in a Q pen field that is more negative during La Ni~na and more positive during El Ni~no, respectively. Therefore, the combined effects of H m and H p on the penetrative solar radiation need to be taken into account in the western-central equatorial Pacific. In the eastern equatorial region, however, the amplitude of interannual variations of H p (Figure 4c) is relatively small compared with that of H m (Figure 4b); thus, the modulating effects of the H p variability on Q pen can be neglected in the east. This relationship can be more clearly visualized by the horizontal structure of several related interannual anomaly fields for La Ni~na and El Ni~no conditions, which are shown in Figures 11 and 12. Note that Figures 11 and 12 exemplary depict one specific La Ni~na/El Ni~no event, but the feature is present in all events. These fields exhibit a well-defined spatial-temporal relationship among various fields of interest during ZHANG ET AL. 1912

13 Figure 10. Longitude-time sections along the equator for (a) interannual anomalies of Q pen (the penetrative solar radiation flux out of the bottom of the ML) from the HCM AOPB in which H p is interannually varying determined by the ocean biogeochemistry model (denoted as Q pen ðh m ; H p Þ;H p can be written as H p 5H p 1H p 0, in which H p is climatological field and H p 0 is interannual anomalies); (b) the interannual Q pen anomalies estimated when H p is specified to be its climatology without interannual effect (denoted as Q pen ðh m ; H p Þ), and (c) the difference between Q pen ðh m ; H p Þ and Q pen ðh m ; H p Þ. The contour interval is 2 W m 22 in Figures 10a and 10b, and 0.5 W m 22 in Figures 10c. ENSO cycles. During La Ni~na events (Figure 11), for example, a cold SST anomaly in the central and eastern equatorial Pacific is accompanied with the ML that is anomalously deep in the western-central basin (Figure 11b); a negative Q pen anomaly emerges since less solar radiation reaches the base of the ML (Figure 11d). At this time, interannual variations in H p tend to be out of phase with H m in the westerncentral region, and their effects on Q pen are in phase. That is, H p features a negative anomaly in the western-central region (Figure 11c) that also leads to a negative Q pen perturbation. Therefore, the effect of the negative H p anomaly on Q pen is in the same direction as that of the positive H m anomaly (Figure 11e); the combined effects of the positive H m anomaly and negative H p anomaly on Q pen act to produce a more negative Q pen anomaly in the western-central equatorial Pacific (Figure 11f). As a result, the negative H p anomaly induces a direct cooling at subsurface depths (a negative Q pen anomaly) but a warming in the ML (a positive Q abs anomaly), which increases the vertical contrast of the thermal field and thus stabilizes the stratification and weakens the mixing in the upper ocean, which thereby increases the SST. The negative H p anomaly induced effects during the La Ni~na event thus tend to weaken the original cold SST anomalies. Similar but inverse processes operate during El Ni~no events. During an El Ni~no event (Figure 12), a warm SST anomaly in the central and eastern equatorial Pacific is accompanied with an ML that is anomalously shallow in the western-central basin (Figure 12b); a positive Q pen anomaly appears (more solar radiation penetrating to the base of the ML; Figure 12d). At this time, the positive H p anomaly leads to a positive Q pen anomaly (more penetrated solar radiation to the base of the ML (Figure 12f). The combined effects of the ZHANG ET AL. 1913

14 negative H m anomaly and positive H p anomaly on Q pen cause a more positive Q pen anomaly than the case in which the effects of the positive H p anomaly on Q pen are not taken into account (Figure 12e). As a result, the contributions of the positive H p anomaly lead to an enhanced positive Q pen anomaly in the westerncentral equatorial Pacific. As a result, the positive H p anomaly induces direct warming at subsurface depths (a positive Q pen anomaly) and cooling in the ML (a negative Q abs anomaly), which reduces the vertical contrast of the thermal field and thus destabilizes the stratification and enhances the mixing in the upper ocean, which decreases the SST. The positive H p anomaly induced effects thus weaken the original warm SST anomalies during the El Ni~no event. Similar analyses can be made for R sr (the rate of the ML temperature change due to the direct heating effect of solar radiation). Figure 13 shows interannual anomalies for R sr along the equator. The large interannual variability of R sr is closely associated with ENSO cycles. In the western-central equatorial regions, R sr has high values during El Ni~no events but low values during La Ni~na events. Since the interannual variability of R sr (Figure 13a) is mirrored by that of H m (Figure 4b), this indicates that H m is a major factor determining the structure and variability of R sr. One different feature from Q pen (Figure 10) is that R sr is not significantly modulated by H p even in the western-central basin where the amplitude of interannual variability of H p is comparable to that of H m. Further analysis is then performed to assess the contributions of interannual H p anomalies to R sr. The same as Q pen, an additional calculation is made for R sr in which the direct effects of interannual H p anomalies on R sr are quantified by taking H p as its seasonally varying climatology (denoted as R sr ðh m ; H p Þ) ; the corresponding anomalies are illustrated in Figure 13b and the differences between R sr ðh m ; H p Þ and R sr ðh m ; H p Þ are shown in Figure 13c. The relationships among the interannual variations in H m,h p, and R sr indicate that the R sr field is not significantly modulated by H p even in the westerncentral regions where large variability of H p exists. These analyses indicate that R sr is not seen to be affected by H p as significantly as Q pen and Q abs are. Figure 14 further presents the correlations between interannual anomalies of Chl and Q pen, and of Chl and R sr from the model simulation. It is evident that correlation is high between Chl and Q pen (Q abs ), but is substantially low between Chl and R sr. The differences in the correlation between R sr and H p and between Q pen (Q abs ) and H p can be explained by the mathematical expressions in equation (6). R sr is proportional to Q abs (which increases exponentially with H m but decreases exponentially with H p ); also, R sr is additionally inversely proportional to H m (i.e., R sr 5Q abs /(q 0 C p H m ). Therefore, the effect of H p on R sr (implicitly represented through Q abs ) is additionally modulated by H m, which is shown to have the twofold effects on R sr (one being exponential through Q abs and other being directly inverse relationship). The extent to which R sr is less correlated with H p than Q pen and Q abs can be analyzed based on the spatial distributions of these anomaly fields during ENSO cycles (Figures 4, 11, and 12). During an El Ni~no event, for example, H p exhibited a positive anomaly in the western-central regions, which led to a decrease in Q abs ; at the same time, the ML tended to be anomalously shallow (a negative H m anomaly and also correspondingly a decrease in Q abs ; Figures 4 and 12). Because the decreased Q abs field (due to the effect of the positive H p anomaly) now acted on the ML that is anomalously shallow, the change to R sr (5Q abs /(q 0 C p H m )) induced by the reduced Q abs due to H p was compensated by the shoaling effect of the ML. Therefore, R sr was not as significantly affected by the positive H p anomaly as Q abs and Q pen, with the relationship between H p and R sr being not as closely correlated as that between H p and Q pen (Figure 14). These results have implications for revealing the underlying processes that may be responsible for the biogeophysical effects (Zhang, 2015). Because interannual H p anomalies are seen to have large modulating effects on the Q pen and Q abs fields, but less so on R sr, this indicates that the feedback effect is not dominantly realized through direct thermal heating effect on SST. Instead, the modulating effects on Q pen and Q abs induce a differential heating and thermal contrast between the ML and the subsurface layers, further resulting in the modulations of the ocean stratification and vertical mixing in the upper ocean (Zhang, 2015). These processes act to modulate SST in the equatorial Pacific, which affects the atmosphere and in turn feed back onto the ocean. So, through the modulating effects on Q pen, the biogeochemical conditions (as represented by the large interannual H p variability) can affect the interannual climate variability in the tropical Pacific. Furthermore, the oceanic dynamical processes induced by anomalous H p perturbations act in such a way that the ENSO is weakened and the biogeophysical effects thus act as a negative feedback on the ENSO A Regression Analysis The potential influence of biogeophysical feedbacks on ENSO is examined using the newly developed hybrid coupled climate-biogeochemistry model that predicts biogeochemical cycling as well as SST. It ZHANG ET AL. 1914

15 Figure 11. Horizontal patterns of interannual anomaly fields for an La Ni~na condition in Oct 2303: (a) SST, (b) H m, (c) H p, (d) Q pen determined in the HCM AOPB simulation in which H p is interannually varying with its interannual effect included (Q pen ðh m ; H p Þ), (e) Q pen estimated when H p is specified to be its seasonally varying climatology without interannual effect (Q pen ðh m ; H p Þ), and (f) the differences between Q pen ðh m ; H p Þ and Q pen ðh m ; H p Þ. The contour interval is 0.58C in Figure 11a, 5 m in Figure 11b, 1 m in Figure 10c, 2 W m 22 in Figures 11d and 11e, and 1 W m 22 in Figure 10f. argues that the net impact of the ocean biogeochemistry on SST is a negative feedback. In digging more into the mechanisms for the induced heating change, we show the competing impacts on Q pen and R sr due to changes in penetration depth (H p ) and mixed layer depth (H m ) over the tropical Pacific. As shown above, the impact of biogeochemical cycling is a negative feedback, and the actual impact seem relatively small. Note that Gnanadesikan et al. (2017) also found relatively small impacts when including biogeochemical cycling in their GFDL Earth System Model (ESM) series the ENSOs in these bio-coupled results are relatively similar to those runs in uncoupled mode (Dunne et al., 2012; Wittenberg et al., 2006). To explain this, we perform an additional analysis for some dynamical heating terms in the SST budget, including the vertical mixing of heat into the mixed layer (Q vmix ) and the advection of heat through the mixed layer (Q adv ), as well as Q pen. A regression of Q pen,q vmix, and Q adv is made with the Ni~no3.4 SST anomaly which is shown in Figure 15. A well-defined relationship is seen among these terms. One striking feature is that the regression pattern of Q pen is out-of-phase with that of Q vmix in the eastern equatorial Pacific (east of about 1508W), indicating that there is a compensation between the vertical mixing and penetration effects. So, the ocean biochemistry-induced feedback tends to be opposed by changes in the vertical mixing in the eastern equatorial Pacific, which can be one possible reason for the small effect on change in SST due to the biogeophysical feedback. In the western-central equatorial Pacific, on the other hand, the regression pattern of Q pen is in-phase with that of Q vmix ; then, the ocean biochemistry-induced feedback tends to enhance changes in the vertical mixing. Clearly, the ocean biochemistry-induced feedback effects on SST can be different in the east and west. Note that Gnanadesikan et al. (2017) conducted a similar analysis looking at the impact of lateral mixing on El Ni~no. ZHANG ET AL. 1915