Role of the ocean mixed layer processes in the response of the North Pacific winter SST and MLD to global warming in CGCMs

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1 Clim Dyn DOI /s Role of the ocean mixed layer processes in the response of the North Pacific winter SST and MLD to global warming in CGCMs Bo Young Yim Yign Noh Sang-Wook Yeh Received: 8 October 2010 / Accepted: 7 June 2011 Ó Springer-Verlag 2011 Abstract It is investigated how the changes of winter sea surface temperature (SST) and mixed layer depth (MLD) under climate change projections are predicted differently in the North Pacific depending on the coupled general circulation models (CGCMs), and how they are related to the dynamical property of the simulated ocean mixed layer. For this purpose the dataset from eleven CGCMs reported to IPCC s AR4 are used, while detailed analysis is given to the MRI and MIROC models. Analysis of the CGCM data reveals that the increase of SST and the decrease of MLD in response to global warming tend to be smaller for the CGCM in which the ratio of ocean heat transport (OHT) to surface heat flux (SHF), R (= OHT/SHF ), is larger in the heat budget of the mixed layer. The negative correlation is found between the changes of OHT and SHF under global warming, which may weaken the response to global warming in the CGCM with larger R. It is also found that the models with low horizontal resolution tend to give broader western boundary currents, larger R, and the smaller changes of SST and MLD under global warming. Keywords Sea surface temperature Mixed layer depth Coupled general circulation models Climate change projections Heat budget of the mixed layer B. Y. Yim Y. Noh (&) Department of Atmospheric Sciences, Global Environmental Laboratory, Yonsei University, Seoul , South Korea noh@yonsei.ac.kr S.-W. Yeh Department of Environmental Marine Science, Hanyang University, Ansan , South Korea 1 Introduction Many studies have been conducted so far to understand how climate system responds to global warming by analyzing the Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset of Intergovernmental Panel on Climate Change (IPCC) s Fourth Assessment Report (AR4) (Arzel et al. 2006; Collins et al. 2006; Li et al. 2006; Merryfield 2006; Meehl et al. 2007; An et al. 2008; Vecchi et al. 2008; Andrews 2009; DiNezio et al. 2009; Guilyardi et al. 2009). Many of these studies focused on the changes in the tropical Pacific and in El Niño and Southern Oscillation (ENSO) statistics (Merryfield 2006; An et al. 2008; Vecchi et al. 2008; DiNezio et al. 2009; Guilyardi et al. 2009; Yeh et al. 2009), but only a few studies focused on the changes in the North Pacific under global warming (Overland and Wang 2007; Luo et al. 2009a, b). Recently, Overland and Wang (2007) found that the winter mean sea surface temperature (SST) increases in the range of 1.2 C 1.8 C across the North Pacific in relative to and the largest warming is found in the western North Pacific. Meanwhile, Luo et al. (2009a, b) found that the mean mixed layer depth (MLD) decreases in most of the North Pacific, although its spatial structure does not change much. These works are based on the multi-model ensemble mean climate predicted by coupled general circulation models (CGCMs). However, it should be reminded that there is a considerable variance in the predicted values, depending on CGCMs. For example, the predicted SST from the individual CGCMs differs by more than 2 C by mid-2000s in the North Pacific (Overland and Wang 2007). Nonetheless, less attention has been paid to why such a large variability under climate change projections occurs among CGCMs, and how it is related to the specific feature

2 of individual CGCMs. Recently, there have been attempts to understand the relation between the mean state of the tropical ocean simulated by the CGCM and the modification of ENSO variability under climate change projections (Collins 2000; Yeh et al. 2009). Little works has been reported so far for the North Pacific, however. Among various factors that determine the SST response to climate change, the ocean mixed layer is probably the most critical element, because it controls the exchange of heat and momentum between the atmosphere and the ocean. Therefore, it is useful to investigate the dynamical process of the mixed layer in association with the diverse response of SST to climate change in CGCMs. The mixed layer process is affected by various factors, such as surface heat and freshwater fluxes, horizontal advection, and the ocean structure below the mixed layer in the North Pacific (e.g., Tully 1964; Hanawa and Toba 1981). Qiu and Kelly (1993) and Vivier et al. (2002) showed that both surface heat flux and horizontal advection contribute to the heat content of the upper ocean in the Kuroshio Extension (KE) region. Furthermore, Kang et al. (2010) suggested that the MLD growth during winter in the North Pacific is largely determined by the heat budget of the mixed layer in which both surface heat flux and ocean heat transport are included. The importance of ocean heat transport in the heat budget has been also documented in various modeling and observation studies (Huang and Liu 2001; Alexander et al. 2002; Tomita et al. 2002; Vivier et al. 2002; Kelly 2004; Barnett et al. 2005). In the present paper, we investigate how the changes in SST and MLD under global warming are predicted in the North Pacific by various CGCMs, and how they are different depending on CGCMs. We also investigate how the mixed layer process differs depending on CGCMs, focusing on the heat budget of the mixed layer, and attempt to understand the relation between the dynamical process of the ocean mixed layer and the predicted SST and MLD changes in CGCMs. 2 Models and methodology The CGCMs used in this study are part of the World Climate Research Programme s (WCRP s) CMIP3 multimodel dataset and were performed for the IPCC s AR4. These are made available by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on the website (e.g., Meehl et al. 2007). The eleven CGCMs used here are listed in Table 1. The models are selected based on the availability of output data necessary to calculate the heat budget of the mixed layer, while outdated model versions are excluded. For the cases of CGCM3.1 and MIROC3.2, both model results with two different horizontal resolutions are included with an aim to understand the sensitivity to the resolution. Detailed analysis is carried out for two CGCMs; the MRI and MIROC models, which represent the typical cases with the weak and strong responses of SST and MLD to climate change projections, respectively. Two sets of data are used here. One corresponds to the climate of the twentieth century experiment (20C3M) as a control experiment, which was initialized from a point early enough in the pre-industrial control run fixed at the atmospheric CO 2 of 280 ppm level. The only forcing in this simulation is the history of greenhouse gases and solar activity of the late nineteenth and twentieth century. The other corresponds to the 720 ppm stabilization experiment (SRESA1B), representing the hypothetical future climate. The SRESA1B experiment was run starting from the final year of the 20C3M simulation, in which CO 2 concentration increases linearly until 2100, and was fixed thereafter. For each model, only one member run out of several ensemble Table 1 The CGCMs used in this study (They are referred by the names in parentheses) Model Resolution Reference Atmosphere Ocean MRI-CGCM2.3.2 (MRI) T42, L (Lon.) (Lat.), L23 Yukimoto et al. (2001) GFDL-CM2.0 (GFDL) , L , L50 Delworth et al. (2006) CGCM3.1_T63 (CGCM_T63) T63, L , L29 Flato and Boer (2001) CGCM3.1_T47 (CGCM_T47) T47, L , L29 CNRM-CM3 (CNRM) T63, L , L31 Salas-Melia et al. (2005) ECHO-G (ECHO) T30, L , L20 Legutke and Voss (1999) MIROC3.2_hires (MIROC_H) T106, L , L47 Hasumi and Emori (2004) MIROC3.2_medres (MIROC) T42, L , L43 IPSL-CM4 (IPSL) , L , L31 Goosse and Fichefet (1999) UKMO-HadCM3 (UKMO) , L , L20 Gordon et al. (2000) ECHAM5/MPI-OM (ECHAM) T63, L , L40 Marsland et al. (2003)

3 members is used (typically Run1 in the CMIP3 dataset) and Run2 is used for the MIROC model because part of surface heat fluxes is not available in Run1 of 20C3M experiment. All quantities presented in this study are based on monthly data and then averaged over 100 year to produce climatological values. In the SRESA1B experiment, some models were run for 200 year with fixed concentration of greenhouse gases to stabilize, whereas other models were not run for the stabilization at all. Therefore, we use the data of first 100 year ( ) for the SRESA1B experiment ( for the ECHAM5/MPI-OM model). In this study, we analyze only the winter season from October-to-February when MLD is the deepest and the influences of both surface heat flux and ocean heat transport are the most prominent. MLD is determined by the depth where temperature differs from the SST by 0.5 C, as in Levitus (1982) and Monterey and Levitus (1997). We choose a temperature difference criterion because the contribution of salinity to density is insignificant in our analysis domain, namely latitudinal zone of N of the North Pacific (Kara et al. 2000; Kang et al. 2010). 3 Heat budget of the mixed layer The most important factor to affect the winter MLD and SST is the heat budget of the mixed layer, because the growth of MLD during winter by convection and the consequent decrease of SST are mainly controlled by the total buoyancy loss within the mixed layer due to surface heat flux and ocean heat transport. The heat budget of the mixed layer during winter can be represented as (Tomita and Nonaka 2006; Kang et al. 2010) Z h2 0 ½Tðz; t 2 Þ Tðz; t 1 ÞŠdz ¼ 1 qc p Z t2 t 1 Q 0 dt þ Z h2 Z t2 0 t 1 Fdtdzþ Z t2 t 1 Gðz ¼ h 2 Þdt: ð1þ Here t 1 and t 2 represent the start and the end of winter, h 2 is the MLD at t = t 2, q and c p are the density and heat capacity of sea water, Q 0 is the net surface heat flux, F is the horizontal heat flux convergence, and G is the vertical heat flux across h 2, which is usually negligible. Here F can be decomposed into advection by the Ekman velocity and the geostrophic velocity, and diffusion by eddies. Penetration of solar radiation can be neglected, since the winter MLD is sufficiently deep. We, hereafter, refer to the term in the LHS of (1), representing the heat content variation, as HCV. The first term in the RHS representing the contribution from the surface heat flux is referred to as SHF. The second and third terms combined in the RHS, representing the contribution from the ocean heat transport by advection and diffusion, is referred to as OHT. Using these terms, (1) can be rewritten as HCV = SHF? OHT. 4 Results 4.1 Responses of SST and MLD under climate change projections The changes of the winter mean SST and MLD in the North Pacific from the 20C3M to SRESA1B experiments (hereafter, referred to as DSST and DMLD, respectively) from eleven CGCMs are listed in Table 2. The increase of SST and the decrease of MLD are observed under climate change projection in all CGCMs, but large variance appears in the magnitudes of DSST and DMLD, ranging from 1.28 C to 2.40 C indsst and to m in DMLD, as reported by Overland and Wang (2007). The tendency of negative correlation between DSST and DMLD appears, except in the GFDL model, where DSST is less than the ensemble average in spite of the abnormally large DMLD (The correlation coefficient is excluding the GFDL model.). It is consistent with the fact that a stronger warming leads to a more stratified ocean and thus to a shallower MLD. In the present work we investigate the property of the simulated mixed layer focusing MRI and MIROC models, which represent the typical cases with the weak and strong responses of SST and MLD to climate change projection, respectively. Figure 1 shows that the changes of SST and MLD are larger in the MIROC model than in the MRI model over the whole region. Meanwhile, a significant zonal asymmetry of DSST is found in the MRI model with Table 2 DSST and DMLD averaged over the entire analyzed domain in each CGCM Model DSST [ C] DMLD [m] MRI GFDL CGCM_T CGCM_T CNRM ECHO MIROC_H MIROC IPSL UKMO ECHAM Ensemble mean

4 Fig. 1 Changes of the winter mean a, b SST and c, d MLD from the 20C3M to SRESA1B experiments in the MRI and MIROC models stronger warming in the western part. The largest DSST appearing near 40 o N in both models is attributed to the northward shift of the Kuroshio path. The associated decrease of MLD also appears in this region, as pointed out by Luo et al. (2009b). 4.2 Comparison of the simulated mixed layer dynamics It is possible that the difference in DSST and DMLD in the North Pacific, as shown in the previous section, is related to the dynamical process of the mixed layer in the CGCM. Therefore, we examine the characteristics of the mixed layers simulated by the MRI and MIROC models. Figure 2 shows the distributions of the winter mean SST, MLD, and the surface velocity from the 20C3M experiment by the MRI and MIROC models, in comparison with the Simple Ocean Data Assimilation (SODA) reanalysis data (Carton and Giese 2008). The predicted SST tends to be lower than the SODA data for both models, although the r.m.s. error is smaller for the MIROC model than for the MRI model (1.46 C vs C). The IPCC s AR4 reports that almost every model has the tendency of the cold bias of SST in the mid-lattitudes of the northern hemisphere. Meanwhile, the higher SST along the western boundary current is more prominent in the MI- ROC model. The MLD in the KE region is deeper in both models than in the SODA data. On the other hand, outside the KE region, MLD is shallower in the MRI model and deeper in the MIROC model in comparison with the SODA data. The surface velocity field reveals that the Kuroshio is diffused to the ocean interior in the MRI model, whereas it is stronger and more confined to the western boundary in the MIROC model. It explains the stronger zonal asymmetry of SST observed in the MIROC model (Fig. 2b). The SODA data are closer to the MIROC model results, although they again show the intermediate state between two models. Contrast also appears in the distributions of SHF and OHT (Fig. 3). Here OHT is calculated by HCV SHF in (1). 1 The area with large SHF around the KE region is more spread in the MRI model (Fig. 3a, b). The area with large OHT along the western boundary is also more spread in the MRI model (Fig. 3c, d). It indicates that the larger northward velocity in the ocean interior in the MRI model generates the larger OHT there, and consequently the larger SHF by inducing the larger temperature difference between the atmosphere and ocean. The distributions of SHF and OHT estimated from the SODA data by Kang et al. (2010) are closer to those from the MIROC model. To illustrate the relative importance of OHT versus SHF in two CGCMs in the 20C3M experiment, the ratio of OHT 1 Output data of vertical velocity and eddy diffusivity are not available in the CMIP3 dataset, therefore, we obtain the OHT by subtracting the SHF from the HCV.

5 Fig. 2 Distributions of the winter mean a c SST, d f MLD, and g i the surface velocity field from the 20C3M experiment by the two CGCMs and the SODA data to SHF is shown in Fig. 3e, f. It shows that OHT/SHF in the MRI model is larger than in the MIROC model in most regions except near the western boundary. The domain averaged value of the ratio R (= OHT/SHF ) is given by 0.44 and 0.21 in the MRI and MIROC models, respectively. The distributions of R in the SRESA1B experiment remain similar to Fig. 3e, f (not shown). 4.3 General characteristics from various CGCMs In previous sections the comparison of the results from the MRI and MIROC models reveals that the changes of SST and MLD are smaller when R is larger. The question arising naturally from this result is whether this reflects the general characteristic of CGCMs. In order to examine its generality, we examine the relationship between domain averaged DSST, DMLD and R in the 20C3M experiment in eleven CGCMs, listed in Table 1 (Fig. 4a, b). It reveals the tendency that the magnitudes of DSST and DMLD decrease with R, with the correlation coefficients and Here the models with low horizontal resolution are represented by triangles, and the high resolution version of the same model are represented by crosses (Table 1), on which further discussion will be made on Sect. Effects of horizontal resolution. The separate correlation coefficients between DSST versus OHT and SHF are given by and -0.26, respectively. The correlation between DSST and SHF, albeit weak, is opposite to what is expected from that between DSST versus R. It is due to the fact that SHF tends to increase with OHT, as noticed from Fig. 3 (Fig. 4c). Nonetheless, R increases with OHT as combined. One can also consider that the deeper the MLD is in the control experiment, the smaller the SST increase is under

6 Fig. 3 Distributions of a, b SHF, c, d OHT, and e, f the ratios of OHT to SHF from the 20C3M experiment by the two CGCMs climate change projections, because vertical mixing of heat over the larger MLD can help to reduce the change of SST. For example, Yeh et al. (2009) found that the MLD simulated by a CGCM is important to regulate the changes in the tropical Pacific SST under global warming. However, the relation is not clear in the present work (Fig. 4d), although it may explain why DSST is small in spite of very large DMLD in the GFDL model (Table 2). 4.4 The correlation between DSHF and DOHT As an attempt to understand how R affects the responses of SST and MLD to global warming, we examine the changes of SHF and OHT under global warming. Figure 5a d show the distributions of the changes of SHF and OHT from the 20C3M to SRESA1B experiments (hereafter, referred to as DSHF and DOHT, respectively) in the MRI and MIROC models. The most remarkable feature is that DSHF and DOHT tend to be negatively correlated, except in the KE region. For example, one can observe the similar patterns of DSHF and DOHT, but with opposite sign, south of 25 N in the MRI model. This negative correlation is clearly identified in the scatter plots between DSHF and DOHT obtained over the region outside the KE region (27 38 N, E) (Fig. 6a, b). Furthermore, the stronger negative correlation appears in the MRI model (-0.70) than in the MIROC model (-0.56). On the contrary, in the KE region, the correlation between DSHF and DOHT almost disappears in the MRI model (-0.17), while it remains similar in the MIROC model (-0.58) (Fig. 6c, d).

7 (a) ΔSST [ o C] MRI GFDL CGCM_T47 CNRM ECHO MIROC IPSL UKMO ECHAM CGCM_T63 MIROC_H (b) ΔMLD [m] (c) R (= OHT/SHF ) 330 (d) -12 MRI GFDL CGCM_T47 CNRM ECHO MIROC IPSL UKMO ECHAM CGCM_T63 MIROC_H R SHF [ o Cm] MRI IPSL GFDL UKMO CGCM_T47 ECHAM 280 CNRM CGCM_T63 ECHO MIROC_H MIROC OHT [ o Cm] MLD [m] MRI GFDL CGCM_T47 CNRM ECHO MIROC 70 CGCM_T63 MIROC_H ΔSST [ o C] IPSL UKMO ECHAM Fig. 4 Scatter diagrams from eleven CGCMs (Here the models with low resolution are represented by triangles, and the higher resolution versions of the same model are represented by crosses.): a DSST versus R (= OHT/SHF ), b DMLD versus R, c SHF versus OHT, d MLD versus DSST The negative correlation implies that, if SST increases locally by the enhanced SHF, it can induce the decrease of OHT, because the contributions from horizontal advection and diffusion are proportional to the temperature difference between local and surrounding water. It is also expected that, if SST increases locally by the enhanced OHT, it can induce the decrease of SHF, because SHF is proportional to the temperature difference between the atmosphere and the ocean. The larger change of OHT occurs for a given SST change, when the contribution from OHT is larger in the heat budget of the mixed layer, or R is larger, and it contributes to suppress the SST increase more strongly. As a result, the stronger negative correlation appears in the MRI model except in the KE region. On the other hand, the situation is reversed in the KE region, reflecting the stronger western intensification of the Kuroshio in the MIROC model (Fig. 2). Moreover, in the MRI model, in which the surface velocity is directed southward in the KE region, the correlation is weakened by the modification of the Kuroshio and the northward shift of the polar front, which causes the decrease of OHT but the increase of SST in the KE region. This negative correlation between DSHF and DOHT may act as a negative feedback, which suppresses the changes of SST and MLD under climate change projections. Negative feedback of ocean dynamics on the surface heat flux is also found in the tropical Atlantic (Seager et al. 2001; Foltz and McPhaden 2006) and the Gulf Stream region (Dong and Kelly 2004). 4.5 Effects of horizontal resolution One possible reason for the contrast between two models is the difference in the horizontal resolution ( in the MRI model versus in the MIROC model). One can expect that the difficulty to resolve the western boundary current and the overestimation of lateral damping in the low-resolution model may cause too broad

8 Fig. 5 Changes of a, b SHF and c, d OHT from the 20C3M to SRESA1B experiments in the two CGCMs western boundary currents, which lead to the overestimated OHT in the interior ocean. In order to understand its effect, we investigate how R varies with the grid size (Fig. 7). Here X represents the ratio of the averaged area of horizontal grids to that of the grid. Figure 7 shows the tendency of increasing R with X. What is particularly interesting is the fact that R is larger (R [ 0.4) for all three CGCMs with X [ 3 (MRI, ECHO, and CGCM_T47). For these three CGCMs, the changes of SST and MLD under global warming are smaller than the ensemble average (Fig. 4). The larger OHT in the interior ocean, as observed in the MRI model, appears in ECHO and CGCM_T47 models too (not shown). However, Fig. 7 also shows that the increase of horizontal resolution in the same model hardly changes R (CGCM_T63), or even increases it (MIROC_H), and that there exists a large difference of R between the CGCMs with similar X. It implies that R may be affected by various other factors as well, including the parameterization of lateral mixing. 5 Conclusion In the present paper, through the heat budget analysis of the mixed layer, the relation between the dynamical process of the mixed layer and the responses of North Pacific winter SST and MLD under climate change projections is investigated. We analyze the changes of winter mean SST and MLD between the twentieth century experiment (20C3M) and the 720 ppm stabilization experiment (SRESA1B) from eleven CGCMs in the CMIP3 multi-model dataset. The detailed analysis for the MRI and MIROC models reveals that the response to climate change, characterized by increasing SST and decreasing MLD, is stronger in the MIROC model than in the MRI model. On the other hand, the heat budget analysis of the mixed layer shows that the ratio of R (= OHT/SHF ) in the 20C3M experiment is larger in the MRI model than in the MIROC model. The tendency of decreasing DSST and DMLD with increasing R is confirmed from the analysis of eleven CGCMs. It is also found that the models with low horizontal resolution tend to give broader western boundary currents, larger R, and the smaller changes of SST and MLD under global warming. The negative correlation between DSHF and DOHT, which is stronger in the MRI model than in the MIROC model, except in the KE region, suggests that the negative feedback between SHF and OHT may act to suppress the changes of SST and MLD under global warming. In the present paper it is illustrated how the difference in the predicted climate change under global warming by a CGCM is related to the characteristics of the present climate simulated by the CGCM, using the case of SST and

9 Fig. 6 Scatter diagrams between DSHF and DOHT in the two CGCMs: a, b outside the KE region (27 38 N, E) and c, d inside the KE region R MRI GFDL CGCM_T47 CNRM ECHO MIROC IPSL UKMO ECHAM CGCM_T63 MIROC_H X Fig. 7 Scatter diagram of R versus X. Here X represents the ratio of the averaged area of horizontal grids to that of the grid MLD. Further understanding of the relation between climate change prediction and the characteristics of present climate simulated by CGCMs will provide important information for the proper evaluation of climate prediction by CGCMs. Acknowledgments We acknowledge the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, US Department of Energy. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF-2009-C1AAA ). References Alexander MA, Bladé I, Newman M, Lanzante JR, Lau N-C, Scott JD (2002) The atmospheric bridge: the influence of ENSO teleconnections on air-sea interaction over the global oceans. J Clim 15: An S-I, Kug J-S, Ham Y-G, Kang I-S (2008) Successive modulation of ENSO to the future greenhouse warming. J Clim 21:3 21

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